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Fair Exhibits

The list of Fair exhibits will be posted soon.

Vote for your favorite exhibits! Awards will be given for Outstanding Exhibition of Unified Modeling and for Outstanding Model Visualization. Voting will open during the Fair.

September 10, 2018 - 1:30-5:30PM

Exhibit No:
1
Title:
Vector Autoregressive Spatio-Temporal (VAST) models: a unifying framework for stock, ecosystem, habitat, and climate assessments
Author(s):
James T. Thorson, Fisheries Resource Analysis and Monitoring Division, Northwest Fisheries Science Center, National Marine Fisheries Service, NOAA
Abstract:
Modern spatio-temporal models can simultaneously estimate population density for living resources at 1000s of locations, 100s of time periods, and 10s of species. Spatio-temporal variation in population density for multiple species can serve as a "common currency" within the stock, ecosystem, habitat, and climate vulnerability assessments that are used nation-wide across NOAA. A unified approach to these different assessment activities has many benefits including: shared development costs; similar approaches to inference and peer review; and improved communication among research teams. I therefore review the development and history of the Vector Autoregressive Spatio-Temporal (VAST) package, which has been used in 14 stock assessments and seven sections of ecosystem assessments since 2015. I quickly outline the major features and potential uses for VAST models while highlighting the primary decisions that users must make. I then illustrate three examples in depth: estimating an index of abundance for arrowtooth flounder in the Eastern Bering Sea (i.e., stock assessment); identifying species assemblages and shared demographic trends for eight groundfishes in the Gulf of Alaska (i.e., ecosystem assessment); and forecasting future distribution shifts for five commercially important species in the California Current (i.e., climate vulnerability assessment). I conclude with a "horizon-scan" of potential future developments for multivariate spatio-temporal models including: improved integration with economic modelling models and techniques; linking individual movement to vector-field outputs from regional oceanographic modelling systems (ROMS); and adding stage-structured dynamics for use in stock/ecosystem assessments.
Exhibit No:
2
Title:
Satellite observation modeling using the Community Radiative Transfer and Surface Emissivity Models
Author(s):
Tong Zhu (CIRA/CSU@NOAA/NESDIS); Ming Chen, (ESSIC/UMD@NOAA/NESDIS) and Kevin Garrett (NOAA/NESDIS/STAR)
Abstract:
The Community Radiative Transfer Model (CRTM) is a broadband fast radiative transfer model developed through the coordinated efforts of the U.S. Joint Center for Satellite Data Assimilation partners and external research communities. The CRTM is capable of simulating microwave, infrared and visible radiances observed by passive instruments on board spacecraft and aircraft under all-sky conditions, including in the presence of clouds and aerosols. Coupling with the Community Surface Emissivity Model (CSEM), the CRTM is valid over all surface types in a continuous broad spectrum by utilizing either empirical and physical emissivity models or user-specified emissivity. In addition to the forward simulation, the well-validated tangent-linear, adjoint and K-matrix modules of CRTM allow the computation of radiance sensitivities with respect to the state variables, which is an essential component of satellite data assimilation. The CRTM has broad applications in NOAA, NASA, U.S. Navy operational models and by research community for satellite observation simulation, data assimilation and radiance monitoring. The CRTM is used extensively in the NOAA/NESDIS/STAR for instrument calibration, validation, and monitoring, and also for Level 2 product generation. The CRTM has been playing an important role in supporting the GOES-R and JPSS programs, as well as many NOAA partner satellite missions, for instrument design, retrieval algorithm development, and satellite data impact studies. In the exhibit, we will provide more details about the CRTM and CSEM structures, functions, and the uses of CRTM in satellite data assimilation and modeling systems.
Exhibit No:
3
Title:
Satellite Land Data Products for Environmental Prediction Models
Author(s):
Xiwu Zhan, Yunyue Yu, Felix Kogan, Kevin Gallo, Ivan Csiszar and Kevin Garrett, NESDIS-STAR Environmental Monitoring Branch
Abstract:
Land surface characteristics are critical state variables in many environmental prediction models, including models for weather, climate, and hydrological prediction, ecological simulations, and agricultural forecasts. NOAA has invested in many environmental monitoring satellites and generated dozens of satellite geophysical products to characterize the land surface state. In this exhibit, we provide a general overview of each of the following land data products generated from current NOAA operational satellites (i.e. Suomi NPP, NOAA-20, GOES-16/17 and GCOM-W1): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Green Vegetation Fraction (GVF), Annual Surface Type (AST), Land Surface Temperature (LST), Land Surface Albedo (LSA), Vegetation Health Indices/Drought, Climate and Land Cover Change, and Soil Moisture (SSM). We also highlight the relevant applications of these land products to the current suite of land and hydrology models, and explore future opportunities as both the satellite global observing system architecture and the Unified Forecast System (UFS) evolve over the next 5-10 years.
Exhibit No:
4
Title:
NOAA Satellite Aerosol Products for Air Quality Applications
Author(s):
Shobha Kondragunta
Abstract:
Aerosol products from NOAA operational geostationary and polar-orbiting satellites have been shown to be very useful for air quality monitoring and forecasting applications. The products are continuously evolving due to a new generation of satellite sensors such as Visible Imaging Infrared Radiometer Suite (VIIRS) on Suomi NPP and NOAA-20 satellites and Advanced Baseline Imager (ABI) on GOES-16 and GOES-17 satellites. The user need for high spatial and temporal resolution air quality products is finally met with VIIRS and ABI fire hot spots, aerosol optical depth, and aerosol detection products. Urban, regional, and global views of aerosol products along with example applications of exceptional events and imagery/data access will be presented in this exhibit.
Exhibit No:
5
Title:
Science for an uncertain future: Evaluating climate impacts and management approaches using a coupled modeling framework
Author(s):
Kirstin Holsman 1, Anne Hollowed 1, Alan Haynie 1, Albert Hermann 2, Wei Cheng 2, Kerim Aydin1 , James Ianelli 1, Stephen Kasperski 1, Andre Punt 3, Amanda Faig 3, Jonathan Reum 3, Thomas Wilderbuer 1 and William Stockhausen 1 1 National Oceanic and Atmospheric Administration, Alaska Fisheries Research Center, Seattle, WA, USA E-mail: Alan.Haynie@noaa.gov 2 Joint Institute for the Study of Atmosphere and the Ocean, University of Washington/ National Oceanic and Atmospheric Administration Pacific Marine Environmental Laboratory, Seattle, WA, USA 3 School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, USA
Abstract:
Marine ecosystems face an unknown future. Interacting pressures, including those from climate variability and long-term change, present challenges while nascent resource management approaches and evolving technologies offer sustainable adaptive solutions. Consideration of these combined pressures and adaptive potentials in projection modeling can yield divergent, yet equally plausible future trajectories, which invariably reflect the relative strength of bottom-up, top-down, and density-dependent interactions. We demonstrate the importance of evaluating the sensitivity of projections to these controlling factors using a multi-model integrated framework as part of the Alaska Climate Integrated Modeling (ACLIM) project. This includes projecting the biomass and distribution of groundfish and invertebrate species in the Bering Sea, Alaska (USA) under 11 future climate trajectories with low to high carbon emissions, various assumptions regarding climate-recruitment relationships and trophic interactions, as well as multiple socioeconomic, harvest, and management scenarios. We demonstrate that management performance varies under future climate conditions, and that trophic interactions can both amplify and attenuate climate impacts. Our results reinforce the importance of conducting multiple climate and management projections that can provide the contrast and breadth of scientific support needed to operationalize policy tradeoff evaluations under future climate change.
Exhibit No:
6
Title:
A Cross-timescale Climate-and-Health Forecast System for Aedes-borne diseases in the US
Author(s):
Angel G Munoz (International Research Institute for Climate and Society (IRI) Columbia University) Madeleine C Thomson (International Research Institute for Climate and Society (IRI) Columbia University)
Abstract:
Aedes-borne diseases, such as dengue and chikungunya, are responsible for more than 50-100 million infections worldwide every year, with an overall increase of 30-fold in the last 50 years, mainly due to city population growth and more frequent travels. In the case of United States of America, the vast majority of Aedes-borne infections is imported from endemic regions by US travelers, who can become new sources of transmission once they are back in the country if the mosquitoes and suitable environmental conditions are present. Historical, current and forecasted climate data can be combined with disease models to improve climate-sensitive health planning and targeting of resources. For infectious disease models, the goal has frequently been to explore different interventions scenarios in order to help priority-setting for policy makers (Heesterbeek et al. 2015). However, in recent years there is increasing interest in using models for real-time forecasting (Yang et al. 2014), although there remains a significant gap in the operational readiness of the numerous forecasting systems presented in the literature (Corley et al. 2014). Although the historical seasonal behavior -- and similar diagnostics -- of these diseases is useful (Monaghan et al. 2016), it is not enough, as inter-annual variability (e.g., the role of El Nino-Southern Oscillation) tends to play an important role in the actual variability of Aedes borne diseases. Hence, a formal forecast system and its associated skill assessment is required and -- to the best of our knowledge -- is still nonexistent for the entire US and its transboundary regions.
Exhibit No:
7
Title:
Satellite Oceanography and Climatology Division's (SOCD) OceanWatch Monitor for simultaneous monitoring of multiple parameters: SST, Salinity, Height, Wind and Color
Author(s):
Prasanjit Dash(1,2,3), Paul DiGiacomo(1), Sheekela Baker-Yeboah(1,4), Veronica P. Lance(1,2) (1) NOAA STAR SOCD (2) Global Science and Technology, Inc. (3) Affiliate, Colorado State University, CIRA (4) University of Maryland, CICS prasanjit.dash@noaa.gov, paul.digiacomo@noaa.gov, sheekela.baker-yeboah@noaa.gov, veronica.lance@noaa.gov
Abstract:
An increasing number of satellite-based products, e.g., VIIRS/AVHRR/ABI sea surface temperature (SST), sea surface height (SSH) from multi-sensor data-fusion, VIIRS ocean color (OC) etc. are generated at the Satellite Oceanography and Climatology Division (SOCD) of NOAA by dedicated product teams. These products are made available to the users through various long-term and short-term data dissemination facilities, e.g., the framework of NOAA CoastWatch/OceanWatch/PolarWatch (CW/OW/PW) program and NOAA NCEI. At large, these dedicated teams also perform product monitoring to track progress in product evolution and to remain aware of the state of the processing systems. Examples of such simultaneous development-and-monitoring practices at NOAA and internationally include NOAA SQUAM (https://www.star.nesdis.noaa.gov/sod/sst/squam/) and EUMETSAT METIS (http://metis.eumetsat.int/) systems. While employing monitors specific to an environmental data record is becoming a de facto standard, there are no dedicated systems to routinely and simultaneously monitor satellite-based multiple ocean products that are interrelated, e.g., SSH and SST during an El Nino event, or sea surface wind (SSW) and sea surface salinity (SSS) in the Mediterranean. This likely is a common practice in the modelling community generating multiple oceanic parameters but is not prevalent in the satellite community, partly due to activities led independently by different product teams. A program, such as CW/OW/PW provides an opportunity to monitor, analyze and utilize different datasets simultaneously. The CW/OW/PW program serves as an interface between the users and the developers for multiple products and highlights of CW activities will be presented separately. This presentation/exhibition will focus on routine monitoring and evaluation of products distributed through the CW/OW/PW program. Towards this goal, efforts are being put to develop capabilities for an 'enterprise monitor' that has shown encouraging early potential. The monitor will offer capabilities for both intra-theme monitoring (e.g., only SST) and inter-theme analysis (observe simultaneous changes in two parameters, e.g., SSS and SST). This may potentially help users to gain an overview of the quality of a product of their interest in a convenient way as well as assist subject matter experts to joint-examine different parameters, e.g., whether the anomaly trends in OC and SST are (anti)correlated or independent. The objective is to comprehensively evaluate the products in a routine manner and steps beyond conventional validation approaches in a web-interface useful for users, producers and scientists alike.
Exhibit No:
8
Title:
The important role of international standards in transforming maritime data into usable information for e-Navigation: methods and application
Author(s):
Greg Seroka (NOS/OCS), Erin Nagel (UCAR), Kurt Hess (UCAR), Neil Weston (NOS/OCS), Jason Greenlaw (ERT, Inc.), Joseph Phillips (NWS/NCEP/OPC), Edward Payson Myers III (NOS/OCS), and Julia Powell (NOS/OCS)
Abstract:
e-Navigation—the digitalization and harmonization of data collection, integration, exchange, presentation, and analysis on board and ashore—is an International Maritime Organization initiative that enhances marine navigation and supports safety of life at sea. As e-Navigation and marine activities continue to grow and become more diverse, data providers and users are also becoming more diverse, and the number of data types and sources and amount of data are increasing. Thus, there is an important need to standardize the data into uniform, usable formats so that 1) navigation systems can easily integrate and seamlessly display the data, and 2) mariners can make effective, accurate, and swift navigation decisions based on those data. Effective international standards are developed in consideration of all stakeholder countries' needs, with the flexibility to evolve to cater to new user requirements. Following these principles, the International Hydrographic Organization has developed a framework for standardization of maritime data products (the S-100 Universal Hydrographic Data Model)—a "standard for standards." S-100 serves as the umbrella structure by which all other data products should follow, such as high resolution bathymetry (S-102), water levels (S-104), surface currents (S-111), and weather (S-412). This presentation will provide insight into the development and application of international standards for three maritime data products: S-104, S-111, and S-412. Further, we will provide examples of how a data provider employs the international S-100 standard, via state-of-the-art work at the NOAA/National Ocean Service (NOS)/Office of Coast Survey that is converting in real-time, gridded NOS Operational Forecast System surface current predictions into an S-100/S-111 compliant format. Future work includes developing interoperability standards and testing products to ensure they meet user needs.
Exhibit No:
9
Title:
Bering 10k ROMS Modelling Suite for Ecosystem Based Fisheries Management
Author(s):
Ivonne Ortiz, Joint Institute for the Study of the Atmosphere and Oceans (JISAO), University of Washington (UW)/ Alaska Fisheries Science Center (AFSC), NMFS affiliate; Kerim Aydin, AFSC/NMFS; Al Hermann, JISAO UW/ Pacific Marine Environmental Laboratory (PMEL), OAR affiliate ; Kelly Kearney, JISAO UW - AFSC/NMFS affiliate; Wei Cheng, JISAO UW/ PMEL, OAR affiliate
Abstract:
For the past 10 years we have been developing a climate-to-fish coupled ecosystem models to inform both management and research. We use a regional ocean modelling system (ROMS) for the Bering Sea, coupled with a nutrient-phytoplankton-zooplankton-detritus model, and a fish growth and movement model for a simplified food-web of forage and euphausiid abundance in space and time (FEAST). This modeling suite (named Bering 10K ROMS) was adopted by the NOAA's Alaska Integrated Ecosystem Assessment (IEA) Program for ongoing operational use in ecosystem-based fishery management (EBFM). Here, we show three applications of results from each of the components of the Bering 10k ROMS modeling suite, highlighting its direct use in EBFM. First, oceanographic output from a seasonal (9-month) forecast of the Bering Sea shelf "cold pool" (area of bottom water with temperature <2°C) has been presented to the North Pacific Fishery Management Council (NPFMC) for the past 5 years (currently expanding this effort under MAPP, CPO funding). Second, bottom temperature and zooplankton fall biomass from a hindcast are used in a multispecies stock assessment model as part of the stock assessment and fishery evaluation report for walleye pollock (the largest US fishery with ~1 million tons annual catch). Output from forecasts to 2100 are used in the same multispecies model to evaluate fish stock size under different harvest policies and climate change scenarios (currently expanding to most recent climate scenarios, CMIP6, with funding from NOAA fisheries and NOAA Research). Third, linking pollock availability to spatially explicit northern fur seal bioenergetics, an ongoing collaboration between the University of Washington and Alaska Fisheries Science Center, funded by the Lenfest Ocean Program and NOAA. Results from the modeling suite have been used to inform the design of field observations. Future steps include the use of climate-to-fish coupled ecosystem models for developing policy, for example through the incorporation of modeling into the NPFMC's developing Bering Sea Fisheries Ecosystem Plan.
Exhibit No:
10
Title:
NOAA Coral Reef Watch's Ecoforecasts for Resource Management: Subseasonal-to-Seasonal Coral Bleaching Outlook
Author(s):
Gang Liu (NOAA/NESDIS/STAR Coral Reef Watch) Mingyue Chen (NOAA/NWS/NCEP) C. Mark Eakin (NOAA/NESDIS/STAR Coral Reef Watch) Arun Kumar (NOAA/NWS/NCEP) Jacqueline L. De La Cour (NOAA/NESDIS/STAR Coral Reef Watch) Erick F. Geiger (NOAA/NESDIS/STAR Coral Reef Watch) William J. Skirving (NOAA/NESDIS/STAR Coral Reef Watch) Scott F. Heron (NOAA/NESDIS/STAR Coral Reef Watch) Denise A. Devotta (NOAA/NESDIS/STAR Coral Reef Watch) Andrea M. Gomez (NOAA/NESDIS/STAR Coral Reef Watch) Benjamin L. Marsh (NOAA/NESDIS/STAR Coral Reef Watch) Kyle V. Tirak (NOAA/NESDIS/STAR Coral Reef Watch)
Abstract:
Coral reefs are one of the most diverse ecosystems on Earth and provide significant ecological, economic, and societal benefits valued at about $9.8 trillion U.S. dollars per year. Since 1997, NOAA's Coral Reef Watch (CRW) has used near real-time satellite monitoring to provide ecological nowcasting of the ocean heat stress that can cause mass coral bleaching. While this benefitted coral reef stakeholders, our users desired longer-range forecasts. In 2012, CRW launched its probabilistic, global Four-Month Coral Bleaching Outlook system based on NOAA's operational Climate Forecast System (now CFSv2). The Outlook proved accurate in predicting bleaching in multiple coral reef regions for the next two years. From June 2014-May 2017, the longest, most widespread, and probably most damaging coral bleaching event on record occurred. As this global event greatly threatened all tropical coral reefs, the Outlook system proved critical in helping users worldwide prepare for and respond to bleaching including actions to reduce damage from intense marine heatwaves. This presentation introduces CRW's ecoforecasting tools and focuses on four "use cases" of CRW's Outlook during the 2014-17 global coral bleaching event. In 2015, concern over CRW's bleaching forecasts prompted two actions by the State of Hawaii. First, the "Eyes of the Reef" volunteer network organized numerous training sessions and its first Bleach Watch "Bleachapalooza" event to monitor bleaching state-wide. Second, State scientists collected rare corals to preserve them in onshore nurseries. One species is now locally extinct on Hawaii's reefs, and rescued specimens are being prepared for re-introduction. Next, as CRW predicted bleaching would persist for several months in the Northern Line Islands, NOAA mounted a special cruise to monitor these remote reefs. Record heat stress killed over 98% of the corals at Jarvis Island. Finally, in 2016, prior to peak bleaching, Thailand used CRW's prediction of severe heat stress to close ten heavily used coral reefs to tourism to reduce further reef stress. These actions show the value of ecoforecasts to prepare all coral reef stakeholders for further climate change impacts.
Exhibit No:
11
Title:
NOAA-ESRL/CIRES Coupled Arctic Forecast System
Author(s):
Amy Solomon (NOAA/CIRES) Janet Intrieri (NOAA) Ola Persson (NOAA/CIRES)
Abstract:
In recent decades, Arctic climate has changed rapidly. The most apparent physical manifestation of this change is a decline of Arctic sea ice, which is a key indicator of global climate transitions. Sea ice has significant and immediate consequences for evolving societal and economic interests in the region, such as transportation, resource development, safety, and ecosystem responses. Ultimately, improved sea ice forecasting must be based on improved model representation of coupled system processes that impact the sea ice thermodynamic and dynamic state. Pertinent coupled system processes that remain uncertain include surface energy fluxes, clouds, precipitation, boundary layer structure, momentum transfer and sea-ice dynamics, interactions between large-scale circulation and local processes, among others. The NOAA-ESRL/CIRES Sea Ice Forecasting (SIF) team produces daily quasi-operational 10-day forecasts of the Arctic Ocean with the Coupled Arctic Forecast System (CAFS) and posts real-time animations and model output at https://www.esrl.noaa.gov/psd/forecasts/seaice/. These forecasts are used for model guidance by the NWS Alaska Sea Ice Program, the NOAA Arctic Testbed, the U.S. National Ice Center, and by the U.S. Navy and NOAA for operations during Arctic campaigns. In this presentation, we will present results from on-going studies that validate and study coupled ocean-ice-air processes such as, lower-level atmospheric jets, near surface atmospheric stratification, upper-ocean mixing, the dynamics of Arctic cyclones, cloud formation and impact on boundary layer structure and surface fluxes. We will discuss the role of CAFS in the development and evaluation of the NGGPS.
Exhibit No:
12
Title:
NOAA's National Water Model
Author(s):
Brian Cosgrove1, David Gochis2, Thomas Graziano1, Ed Clark1, Trey Flowers1, Fred Ogden1 1Office of Water Prediction, National Weather Service, NOAA, Silver Spring, MD, USA 2Research Applications Laboratory, NCAR, Boulder, CO, USA
Abstract:
Implemented into National Weather Service (NWS) operations just over two years ago, the National Water Model (NWM) provides seamless 24x7 guidance on streamflow conditions and other water budget fields such as soil moisture, snowpack, runoff and evaporation. The system, based on the community Weather Research and Forecasting (WRF)-Hydro software architecture, has been rapidly upgraded via a partnership between the Office of Water Prediction (OWP), the National Center for Atmospheric Research (NCAR) and the National Centers for Environmental Prediction (NCEP). Version 2.0, scheduled for implementation in early 2019, will build on prior capabilities to provide vital hydrologic guidance to NWS River Forecast Centers (RFCs), Weather Forecast Offices (WFOs), NCEP Centers, the Federal Emergency Management Agency (FEMA) and other Federal, private and academic end users. As with prior versions V2.0 is underpinned by a network of 2.7 million vector-type river reaches for river routing, a 1km land surface grid for land surface modeling, and a 250m grid for surface and subsurface routing of runoff. However, departing from previous versions, the new model will feature notable upgrades including use of NWS River Forecast Center (RFC)-based precipitation in a new extended analysis cycle, the addition of Hawaii to the modeling domain, a new medium range ensemble configuration, and improved forcing downscaling and parameter calibration. One of the other major upgrades initiated in V2.0 centers on improved code modularity. This advance is significant in that it underlies an intensive strategy to establish a broad NWM development community. It is envisioned that the NWM will serve as a common platform for water research and operations. By drawing innovation from across a broad spectrum of interests, the system will support a variety of research and applications beyond its core NWS operational forecasting mission. This community development effort requires both tools to enable collaboration (i.e., code modularity and code management to ensure code accessibility) as well as governance and mechanisms for partnerships (inter-governmental consortiums, partnerships with the private sector and academia, funding calls to enable collaborative work). While it will take time to establish this community, the initial building blocks--such as the IWRSS interagency group and increased code modularity and version control--have already been established. Beyond community development, NWM V2.0 and subsequent versions will provide the foundation needed to support a variety of additional activities within the NWS and broader hydrologic community. These include a machine learning-based enhancement to better capture the impact of stream regulation, a model extension to simulate combined impact of freshwater and coastal flooding, and an improved shallow groundwater model. Hyper-resolution modeling will support the ability to model flooding in areas of urban coverage or high terrain and NWM-based regional flood inundation mapping efforts will expand to the continental scale to support emergency responders across the Nation. This presentation will provide an overview of recent and planned NWM upgrades, along with updates on community development and the other hydrologic activity areas discussed above. Current and planned methods of real-time and retrospective data dissemination will be described as will NWM visualization efforts which are planned for future implementation.
Exhibit No:
13
Title:
Atmospheric Chemistry Modeling at NOAA's Air Resources Laboratory
Author(s):
Rick Saylor, ARL-Atmospheric Turbulence and Diffusion Division; Pius Lee, ARL-HQ; Mark Cohen, ARL-HQ; Daniel Tong, ARL-HQ; Barry Baker, ARL-HQ; Youhua Tang, ARL-HQ
Abstract:
NOAA's Air Resources Laboratory (ARL) conducts atmospheric chemistry research using a variety of modeling systems, ranging from process analysis models, through regional- and continental-scale air quality modeling, to global-scale aerosol modeling. One modeling system being developed in ARL is referred to as the Atmospheric Chemistry and Canopy Exchange Simulation System (ACCESS), which is being developed to provide a more physically, chemically and biologically consistent representation of important processes affecting the exchange of trace species between the Earth's surface and the atmosphere. ARL's Air Quality Forecasting Products Research helps to ensure that air quality forecast models, run operationally by NOAA's National Weather Service (NWS), provide consistently high-quality forecast products and support air quality planners and managers, air quality forecasters, and the research community. To this end, ARL has led the research, configuration and testing of the National Air Quality Forecasting Capability (NAQFC), an integrated modeling system linking the National Weather Service's numerical weather prediction model to the NOAA-EPA developed Community Multiscale Air Quality model. ARL also does research in modeling the atmospheric mercury cycle. A cornerstone of this work is a state-of-the-art modeling system that tracks mercury emission sources and links these emissions to atmospheric transport, transformation, and deposition. ARL is also contributing to the creation of NOAA's Next Generation Global Prediction System (NGGPS) through a project to provide both primary and aerosol precursor emissions to the NGGPS global aerosol model. The exhibit will highlight these modeling activities and provide selected examples of results generated in each effort.
Exhibit No:
14
Title:
Vibrio Predictive Models and Tools for U.S. Coastal Waters
Author(s):
Robert Daniels, IMSG @ NOAA/NCEP/Ocean Prediction Center; Co-authors (not attending): John Jacobs, NOAA/NCCOS/Cooperative Oxford Lab; Rohinee Paranjpye, NOAA/NMFS/Northwest Fisheries Science Center
Abstract:
Through the Ecological Forecasting Roadmap, NOAA has embarked on an effort to harness existing NOAA infrastructure (i.e.; observational platforms, ecosystem models, operational framework) for application to ecological issues. One focus area has been the distribution and concentration of Vibrio bacteria in surface waters and oysters. In the Chesapeake Bay, tools have been developed to predict the probability of occurrence of Vibrio vulnificus (Vv) in surface waters, and to help predict and reduce concentrations of Vibrio parahaemolyticus (Vp) in oysters both pre and post harvest. The Chesapeake Bay Vv model is being transitioned to operations at NOAA, through an intra-agency effort across several NOAA line offices. The model is based on a relationship between Vv and environmental variables developed by the NOAA/NCCOS/Cooperative Oxford Lab, and is being transitioned from NOAA/NWS/NCEP/Ocean Prediction Center to NOAA/NOS/CO-OPS where it will be run operationally. These tools that help predict and reduce Vibrio in oysters have been explored through a strong partnership with FDA and the states, where NOAA is using output of hydrodynamic and atmospheric environmental variables from operational models around the coastal U.S. including the Chesapeake Bay, Long Island Sound, Gulf of Mexico, and the Puget Sound to force FDA algorithms for growth of Vibrio spp. in oyster and post-harvest. Other tools have also been developed to demonstrate doubling time of Vp in oysters, and growing area scale best harvest window calculators. Vp doubling time and best harvest window tools have recently been developed for Delaware Bay.
Exhibit No:
15
Title:
The GNOME Suite for Oil Spill Modeling
Author(s):
Dylan Righi, Chris Barker, Amy MacFadyen, NOAA/NOS/ORR
Abstract:
The National Oceanic and Atmospheric Administration (NOAA) Emergency Response Division (ERD) provides scientific support to the US Coast Guard in the event of oil and chemical spills in the coastal environment. As part of this mission, ERD develops and maintains a set of computer modeling tools, the GNOME Suite for Oil Spill Modeling, for predicting the fate and transport of pollutants spilled in water. In recent years, these tools have been undergoing significant development, resulting in improved algorithms, deployment, and user interface. The major changes visible to the users are the merging of the transport model (GNOME) and the Weathering model (ADIOS), the integration of the Response Options Calculator (ROC), and the development of a new web-based user interface hosted by NOAA. The entire code base is distributed as open source, and for advanced users, the models can scripted for custom use and stochastic analysis. The GNOME Suite can be used and accessed in multiple ways and includes the following features: * Web interface as WebGNOME, including Location Files that no longer need to be downloaded. * Integrated weathering algorithms: updated from the ADIOS2 weathering model. * 3D transport modeling. * An updated version of the ADIOS oil library. * Integration with the TAMOC deep water blowout model. * Integration of the Response Options Calculator (ROC) to assess performance of spill response systems. * Enhanced output for interaction with GIS systems. * A scripting interface (PyGNOME) for automation and batch processing. * An open-source code base to allow customization and extension. This presentation will provide an overview of the tools available and outline the core features and algorithms. We plan to be able to provide a hands-on demonstration of these tools and their use in oil and chemical spill response, focusing on the WebGNOME interface.
Exhibit No:
16
Title:
The NASA Land Information System (LIS): A high-performance terrestrial hydrology modeling and data assimilation software framework
Author(s):
David M. Mocko[1,2], Sujay V. Kumar[1], Christa D. Peters-Lidard[3], Jerry Wegiel[1,2,4], Joseph A. Santanello, Jr.[1], Jack Kain[5], Helin Wei[5,6], Youlong Xia[5,6], and Jiarui Dong[5,6] 1. Hydrological Sciences Laboratory, NASA/GSFC, Greenbelt, MD 2. SAIC (Science Applications International Corporation) 3. Deputy Director for Hydrosphere, Biosphere, and Geophysics, Earth Science Division, NASA/GSFC, Greenbelt, MD 4. 16th Weather Squadron, 557th Weather Wing, Offutt AFB, NE 5. Environmental Modeling Center (EMC), NOAA's National Centers for Environmental Prediction (NCEP), College Park, MD 6. I. M. Systems Group at EMC/NCEP/NOAA, College Park, MD
Abstract:
The Land Information System (LIS) is a software framework for high-performance terrestrial hydrology modeling and data assimilation developed with the goal of integrating satellite and ground-based observational data products and advanced modeling techniques to produce optimal fields of land-surface states and fluxes. LIS is open source, and is developed and maintained at NASA Goddard Space Flight Center's Hydrologic Sciences Laboratory. LIS software is used by numerous agencies, universities, and users for countless hydrologic scientific studies and applications. The LIS software is also used for several Land Data Assimilation Systems (LDAS) to construct the best available spatially/temporally consistent land-based observations and land-surface model outputs to support modeling activities. LIS has been demonstrated for the assimilation of a large suite of land remote sensing observations including soil moisture, snow depth, snow cover, terrestrial water storage, land surface temperature, leaf area index, and albedo, both serially and concurrently. The optimization and uncertainty estimation system in LIS enables the refinement of model parameters based on observational information. Using LIS, the beneficial impact of data assimilation and parameter estimation for improving initial conditions and short-term weather forecasts have been demonstrated. LIS is also being coupled to the WRF-Hydro system, to leverage the assimilation impacts for hydrological modeling and forecasting. Operationally, LIS is used by several groups to provide land initial conditions for atmospheric model forecasts as well as for drought monitoring and food security assessments. The U.S. Air Force's 557th Weather Wing uses a custom version of LIS, which includes operational data assimilation of remotely-sensed land-states. NOAA currently uses LIS as part of the North American LDAS (NLDAS) for drought monitoring, and as part of NOAA's Global (GLDAS) for providing land initial conditions to the NCEP CFSv2 and Reanalysis (CFSR). The LIS software will also be used for the in-development NCEP Unified LDAS (NULDAS), which will be embodied in the NOAA Next General Global Prediction System (NGGPS). NULDAS will be a high-resolution global system to support various public users and provide the capability for global land-surface initialization for weather and climate prediction models. Capabilities and usage instances of the LIS software and various applications, including LDAS outputs, will be presented.
Exhibit No:
17
Title:
Drought Task Force III: Improvements to modeling, data assimilation, prediction, and monitoring capabilities for drought
Author(s):
Christa Peters-Lidard (NASA Goddard Space Flight Center), Pierre Gentine (Columbia University), Michael Barlage (UCAR), Dennis Lettenmaier (University of California Los Angeles), Dan Barrie (NOAA Climate Program Office), Maggie Hurwitz (SSAI/NASA Goddard Space Flight Center)
Abstract:
The Drought Task Force III (DTF3) coordinates the activities of researchers supported through the NOAA Climate Program Office's Modeling, Analysis, Predictions and Projections (MAPP) and National Integrated Drought Information System (NIDIS) joint grant competition titled "Advancing drought understanding, monitoring and prediction". The DTF3 is a three-year effort, which began in September 2017. Scientifically, the DTF3 aims to advance understanding of drought causes as well as advance capabilities for drought modeling, prediction, and monitoring, building on two previous MAPP Drought Task Forces and decades of preceding research on drought, hydroclimate, and related modeling. The core membership of DTF3 is comprised of MAPP/NIDIS-funded investigators from universities, NOAA, and other Federal centers and laboratories. Also, DTF3 includes scientists with expertise in the DTF3's research areas. The Task Force is led by Christa Peters-Lidard (NASA Goddard Space Flight Center), with co-leads Pierre Gentine (Columbia University), Michael Barlage (UCAR) and Dennis Lettenmaier (University of California Los Angeles). Through monthly teleconferences, the Task Force provides a formal mechanism for PIs to communicate, coordinate, and collaborate. Members of the DTF3 share new datasets, methodologies, and research results. The Task Force may choose to synthesize collective efforts through technical reports, review articles, or journal special collections and engage with the rest of the community via workshops and meeting sessions. Also, the DTF3 facilitates collaboration with other relevant activities inside and outside of NOAA, including international drought research.
Exhibit No:
18
Title:
Tsunami Modeling
Author(s):
Diego Arcas, NOAA/Pacific Marine Environmental Lab
Abstract:
The National Oceanic and Atmospheric Administration (NOAA) is operationally responsible for rapidly detecting and evaluating a tsunami hazard in order to issue timely and accurate messages to emergency managers for decision support. Two tsunami warning centers use observations from seismic networks to identify potential tsunamigenic earthquakes and utilize a forecast system that combines measurements of tsunami waves in the deep ocean with hydrodynamic models to forecast tsunami arrival times, flooding, and currents. Tsunami detection, the general modeling approach and capabilities of the Short-term Inundation and Forecast of Tsunami system, and development efforts that are underway to take advantage of technological advances are visually showcased. Capabilities now in transition to operations include the use of Graphics Processing Units (GPU) to significantly speed up numerical model calculations of tsunami propagation and flooding along vulnerable coastlines, effectively replacing the pre-computed database with numerical calculations made on-the-fly as a tsunami event unfolds. A project evaluating the use of seismic ground displacement data recorded by Global Navigation Satellite System (GNSS) ground stations to characterize seismic events significantly faster and more accurately than with seismic stations alone is highlighted. Translation of the GNSS-seismic solution into tsunami initial conditions for automated input into the real-time computation of propagation together with is expected to increase system timeliness, robustness, and warning value.
Exhibit No:
19
Title:
Improving the ecosystem modeling toolbox with an alternative open source version of Ecopath with Ecosim.
Author(s):
Sean M. Lucey and Sarah K. Gaichas Northeast Fisheries Science Center, Woods Hole, MA 02543 Kerim Y. Aydin Alaska Fisheries Science Center, Seattle, WA 98115
Abstract:
As ecosystem-based fisheries management (EBFM) moves from the theoretical stage towards implementation the need for flexible tools will increase. The main tools utilized for EBFM are ecosystem models. Ecosystem models can address many questions not typically covered by more traditional fishery models such as how alternative management policies may impact the ecosystem both directly and indirectly. Among a wide range of ecosystem models, a common approach for fisheries related questions are aggregate or box models, including a popular representation of the ecosystem as a mass balance model. Mass balance models use a series of equations to balance the energy flow through a system ensuring that energy is conserved. Here we present Rpath, an alternative open source implementation of the popular Ecopath (mass balance) with Ecosim (dynamic simulation) (EwE) box model algorithms. The original implementation of EwE has many strengths including its ease of use both in terms of set-up and run time due to a convenient user interface. In addition, EwE is open source which means that there is universal access to the code and the code can be redistributed and subsequently improved by any interested users. This form of open collaboration makes EwE a powerful tool. However, a major draw-back to modifying the EwE code is the Microsoft Visual Basic platform upon which it is built. Many ecologists do not have the skills necessary to modify the code to tailor the model to their needs. Rpath is a complementary product to EwE built on a more familiar software platform, R. R is a rapidly growing open source statistical language that is familiar to many ecologists. Once fully developed, Rpath will allow users to fully customize their models to meet their needs. R has the advantage of allowing fully customizable outputs of publication quality without switching between software packages. Here we demonstrate the similarities and differences between EwE and Rpath using a generic ecosystem, R Ecosystem. Further development of this flexible tool that integrates statistical analysis and visualization tools in one package will be extremely useful in bridging the gap from theory to practice.
Exhibit No:
20
Title:
NOAA's coupled meteorology-chemistry models and their applications from regional to global scales
Author(s):
R. Ahmadov1,2, G. Grell2, E. James1,2, L. Zhang1,2, C. Alexander2, S. Benjamin2, S. McKeen1,2, 1 Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, Boulder, CO, USA 2 Earth System Research Laboratory, NOAA, Boulder, CO, USA
Abstract:
NOAA/ESRL Global Systems Division in collaboration with other labs and research organizations has developed a regional to storm scale coupled meteorology-chemistry model - Weather Research and Forecasting coupled with Chemistry (WRF-Chem), which is widely used around the world. The WRF-Chem model enables conducting simulations of meteorology in conjunction with air quality and atmospheric chemistry in an inline mode (chemistry and meteorology are integrated in lockstep with each other). The model provides many chemistry modules with different levels of complexity that may be used for air quality and weather forecasts and/or to look at processes that are of high importance for global and environmental changes. It includes modules to simulate dust, volcanic ash, fire emissions, numerous gaseous and aerosol species, their chemical and physical transformations, including aqueous chemistry. The model is currently used worldwide for wide range air quality related research and operational applications. For instance, the paper by Grell et al., 2005 describing the original WRF-Chem model was cited 991 times (per Web of Science) in peer-reviewed literature. GSD maintains a WRF-Chem web-page, which provides user documentation, online tutorials and other necessary materials to the modeling community: https://ruc.noaa.gov/wrf/wrf-chem/ One of recent applications of the WRF-Chem model at NOAA/ESRL/GSD is the development of the experimental smoke forecasting system RAP- and HRRR-Smoke: https://rapidrefresh.noaa.gov/hrrr/HRRRsmoke/. The RAP/HRRR-Smoke system provides rapidly updated experimental forecast products of smoke (near surface and aloft), visibility and other related variables in high spatial resolution to forecasters and researchers across the US. The use of a coupled meteorology-smoke modeling framework inherited from WRF-Chem extends our forecasting capabilities further by enabling smoke feedback on meteorological processes, which could help to improve weather forecasting in the future. We are also leveraging on WRF-Chem in global NWP and air quality model development (FV3-Chem). A computationally efficient aerosol parameterization and full gas-aerosol chemistry scheme from WRF-Chem were implemented in FV3-Chem (https://fim.noaa.gov/FV3chem/) This development work will help us in the future to forecast air quality and impact of air pollution on weather from regional to global scales on a unified modeling framework provided by NGGPS.
Exhibit No:
21
Title:
Aerosol Impact on Subseasonal to Seasonal Prediction using FIM-Chem-iHYCOM Coupled Model
Author(s):
Shan Sun, Stuart McKeen, Georg A. Grell and Li Zhang
Abstract:
The aerosol impact on subseasonal to seasonal (s2s) prediction is investigated using a global coupled atmosphere, chemistry and ocean system of FIM-Chem-iHYCOM. The online chemistry includes a simple suite with bulk aerosols only. The sources and sinks for aerosols, fire and anthropogenic emissions are prescribed during the model integration. The resulting aerosol optical depths from the model are shown to be in good agreement with observations. We also compare the model sensitivity with various aerosol emissions at different seasons in a multiyear study. Additional emphasis of this work is on the effect of aerosols on surface radiation, cloudiness and precipitation, to demonstrate the importance of using the correct aerosol optical properties at s2s time scale. Overall, these multiple case studies show that the biggest aerosol impact from online chemistry is on the radiation budget and less so on the cloudiness and precipitation.
Exhibit No:
22
Title:
A Coupled Land-Water-Atmosphere Modeling Approach to Meeting Stakeholder Needs in the Great Lakes
Author(s):
Eric J. Anderson1, Philip Chu1, Drew Gronewold1, Brent Lofgren1, Jia Wang1, Craig Stow1, Greg Lang1, Tim Hunter1, Ayumi Fujisaki-Manome2, James Kessler2, Lindsay Fitzpatrick2, Mark Rowe2, Greg Mann3, John Kelley4, Ed Myers4, Yi Chen4, Aijun Zhang5, Pat Burke5, Carolyn Lindley5, Machuan Peng5, Stan Benjamin6, Curtis Alexander6, Arun Chawla7, Henrique Alves7, Ed Clark8, Trey Flowers8 1NOAA/OAR Great Lakes Environmental Research Laboratory, Ann Arbor, MI 2Cooperative Institute for Great Lakes Research, University of Michigan, Ann Arbor, MI 3NOAA/NWS/WFO-Detroit, White Lake, MI 4NOAA/NOS/OCS/CSDL, Silver Spring, MD 5NOAA/NOS/CO-OPS, Silver Spring, MD 6NOAA/OAR/ESRL, Boulder, CO 7NOAA/NCEP/EMC, College Park, MD 8NOAA/OHD/NWC, Tuscaloosa, AL
Abstract:
The Great Lakes and the surrounding watershed provide a multitude of ecosystem services, including drinking water for 40 million people in the US and Canada, 1.5 million US jobs, a $7 billion fishery, and recreation for 4.3 million registered US boats. In order to support the Great Lakes community and economy, multiple numerical models that span various forecast horizons have been developed for operations within NOAA. However, in most cases, these models are isolated from other parts of the physical system and stand alone as either atmospheric, hydrologic, and hydrodynamic models. This inherently leaves gaps in our ability to forecast some phenomena and limits model skill in others where coupled processes are important. In order to improve forecast skill and provide a broad suite of products to support user requirements, OAR/GLERL is working NOS/CO-OPS, NOS/OCS/CSDL, OAR/ESRL, NWS/NCEP/EMC, NWS/OHC/NWC, and academic partners to develop a coupled modeling approach for the Great Lakes that includes (1) WRF-Hydro across the US and Canadian watersheds, (2) 3D hydrodynamics using the Finite Volume Community Ocean Model (FVCOM), (3) the Los Alamos Sea Ice model (CICE), (4) water quality models for HAB and hypoxia, (4) coupling to Wave Watch III, and (5) linkage to atmospheric models (e.g. WRF). This coordinated approach builds on and expands existing frameworks of the National Water Model (NWM), Great Lakes Operational Forecast System (GLOFS), and High-Resolution Rapid Refresh (HRRR) to serve user needs in flood forecasting, ecological forecasting, lake-effect snow or precipitation, recreational boating or commercial navigation, spill response, and other stakeholder needs.
Exhibit No:
23
Title:
Model Diagnostics Task Force: A Community Pathway to Improving Models
Author(s):
Yi Ming (NOAA GFDL, presenting); Eric Maloney (Colorado State University); Andrew Gettelman (NCAR); J. David Neelin (UCLA); Dan Barrie (NOAA CPO); Annarita Mariotti (NOAA CPO)
Abstract:
Diagnosis of the sources of climate and weather forecasting model error is a critical step in the model development process. Diagnostics have often focused on performance issues -- bias in important fields such as 500 millibar heights in the atmosphere, soil moisture, and sea surface temperatures, for example. These metrics of model performance are useful for benchmarking purposes, but they do not directly lead model developers to insights regarding underlying sources of model bias that are rooted in the parameterizations and formulation of the models. The NOAA MAPP Model Diagnostics Task Force is tackling this issue by engaging communities at NOAA laboratories, Cooperative Institutes, universities, and federal centers such as NCAR and Lawrence Livermore National Laboratory to develop process-oriented diagnostics that can be used to diagnose the sources of model error and inspire relevant development steps to improve those errors. This activity has leveraged data from the Coupled Model Intercomparison Project, and development versions of the NOAA GFDL and NCAR models to build an open-source diagnostics package for use by the model development and model evaluation communities.
Exhibit No:
24
Title:
Unified Weather Forecast Modeling Implementation and Evaluation of Physics Parameterization Schemes in FV3 at a Convection-Allowing Resolution
Author(s):
Chunxi Zhang1, Ming Xue1, 2, Tim A. Supinie1, Fanyou Kong1, Nate Snook1, Kevin W. Thomas1, Keith Brewster, Youngsun Jung1, Lucas M. Harris3, and Shian-Jiann Lin3 1Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma, USA. 2School of Meteorology, University of Oklahoma, Norman, Oklahoma, USA. 3NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA.
Abstract:
The Geophysical Fluid Dynamics Laboratory (GFDL) Finite-Volume Cubed-Sphere Dynamical core version 3 (FV3) was chosen by the National Weather Service (NWS) in 2016 to serve as the dynamic core of the Next-Generation Global Prediction System (NGGPS). The operational Global Forecasting System (GFS) physics suite was implemented within FV3 but they are not necessarily suitable for convective-scale predictions. We implemented a number of more advanced physics schemes include planetary boundary layer (PBL) and microphysics (MP) schemes into FV3 and ran the system for five weeks during the 2018 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiment (SFE), with a convection-allowing ~3 km grid covering the continental United States, two-way interactively nested inside a global grid. Precipitation is evaluated using the neighborhood-based Equitable Threat Score (ETS) and Fractions Skill Score (FSS). Percentiles (as opposed to fixed precipitation values) are used for the thresholds. The results show that among five PBL schemes no single scheme clearly outperforms the others in terms of precipitation forecasts. Between the Thompson and NSSL microphysics schemes, the former slightly outperforms the latter. Overall, FV3 performs similarly as the more established Weather Research and Forecasting model (WRF) run at a similar resolution, establishing its viability to be used for convective-scale forecasting as part of the NWS's vision towards a Unified Forecasting System based on a single dynamic core. The minimum skillful scale increases rapidly with forecast lead-time, and the skillful scale is very large for heavy rainfall, indicating that heavy rainfall forecasts are still very challenging. Other FV3 atmospheric variables, such as temperate, specific humidity, wind and radiation, are also compared against observations. The results show that there are wet biases above near surface layer and large warm biases in upper troposphere.
Exhibit No:
25
Title:
A Blended Snow Analysis for Weather and Hydrologic Prediction Models
Author(s):
Cezar Kongoli, ESSIC, University of Maryland; Thomas Smith, NOAA/NESDIS; Jiarui Dong, NOAA/NWS/NCEP; Helin Wei, NOAA/NWS/NCEP; Ivan Csiszar, NOAA/NESDIS
Abstract:
Assimilation of snow observations into operational Numerical Weather Prediction and hydrologic forecasting models represents a key component that impacts the accuracy of predicted meteorological and hydrological parameters. At NOAA National Centers for Environmental Prediction (NCEP) and other major climate and weather prediction Centers, Snow Cover Extent (SCA) and Snow Depth (SD) observations are routinely assimilated. Additionally, information on Snow Water Equivalent (SWE) distribution is critical for hydrological forecasting models. Here we present the latest developments of an improved global blended analysis method and algorithm for estimating SD and its future expansion to estimate SWE distribution. The new snow analysis blends remotely sensed, modeled and in-situ SD observations using 2-dimensional optimal interpolation (2D-OI). Initial (first guess) SD is computed from an off-line snow accumulation and melt model driven by NCEP's Global Forecast System (GFS)-forecast temperature and precipitation. Next, first guess SD is fused with near real time in-situ (from Global Historical Climatology Network) and satellite (from AMSR2 on board GCOM-W1) SD using 2D-OI. This blended approach marks an advancement in the utilization of snow observations from multiple observing systems. Note that satellite-derived SD is currently not assimilated into operational NWP models. Incorporated into the analysis are improved spatial correlation scale parameters of SD distribution estimated from analysis of multi-year in-situ snow observations over continental US. Assessment of the new analysis shows improved results for the modeled (first guess) and the blended SD with respect to GFS SD. Future enhancement of the method and algorithm to estimate SWE will also be discussed.
Exhibit No:
26
Title:
The West Coast Ocean Forecast System: impact of VIIRS SST and HF radar surface current assimilation, quality control, and visualization
Author(s):
Kurapov, A. L. (NOAA CSDL); J. Xu (NOAA COOPS); E. Myers (NOAA CSDL); E. Bayler (NOAA NESDIS); A. Ignatov (NOAA NESDIS); Z. Burnett (NOAA CSDL)
Abstract:
The West Coast Operational Forecast System (WCOFS) has been developed and tested at NOAA/NOS/OCS/CSDL and transitioned to NOAA/NOS/CO-OPS for semi-operational testing. It is based on the Regional Ocean Modeling System (ROMS), a fully nonlinear, three-dimensional ocean circulation model featuring advanced numerics and terrain-following coordinates that are most convenient for flow description over the continental shelf and slope. The system provides everyday updates of 3-day forecasts of the tidal and non-tidal sea level, currents, temperature, salinity and other oceanic quantities along the entire US West Coast. The forecasts will provide guidance to navigation, environmental hazard response, fisheries, search and rescue, etc. Four-dimensional variational (4DVAR) data assimilation is implemented in WCOFS to correct the initial conditions for the forecasts. The assimilation includes observations of SST (JPSS VIIRS L3U) and surface currents from a network of land-based high-frequency (HF) radars. Our presentation will focus on the assessment of the accuracy of the surface velocity forecasts and SST front geometry. The currents from the NOAA global Real-Time Ocean Forecast System (RTOFS) are used as the benchmark. New visualization tools are explored for pre-assimilation quality control (QC) of the observations and post-assimilation QC of the forecasts
Exhibit No:
27
Title:
Estimates of food limitation experienced by coastal-pelagic fish larvae in the Gulf of Mexico: An interdisciplinary model application between three-dimensional ocean models and fishery assessment models
Author(s):
Taylor Shropshire (Florida State University), Michael Stukel (Florida State University), Steve Morey (Florida State University), Mandy Karnauskas (Southeast Fisheries Science Center), Sang-Ki Lee (Atlantic Oceanographic and Meteorological Laboratory)
Abstract:
NOAA Fisheries conducts stock assessments to evaluate the current state of a given fish population through the use of statistical models. Stock assessment models are based on population dynamics and driven by demographic data of mature fish populations. Historically environmental forcing has largely been neglected in these models. While the mature portion of a stock is indeed driven by population dynamics, individuals in the larval stage are also significantly tied to environmental dynamics being planktonic in nature. Therefore, classic age-structured assessment models in isolation are not well suited to model age-0 individuals. Realistic three-dimensional ocean models offer a potentially valuable tool to include environmental forcing into future assessment models by explicitly representing the impact of environmental conditions on larval fish mortality. In the current modeling project, we configured a coupled physical-biogeochemical ocean model in the Gulf of Mexico and developed a methodology to evaluate larval fish susceptibility to starvation based on model estimated zooplankton concentrations and published metabolic requirements across a wide range of coastal-pelagic species. Starvation is known to be a dominate contributor to total natural mortality for larval fish particularly during the period where individuals switch from feeding on yolk reserves to feeding on plankton. The current modeling project demonstrates a valuable interdisciplinary application for coupled ocean and fishery assessment models. This work is being conducted in collaboration between Florida State University, AOML and SEFSC.
Exhibit No:
28
Title:
From Top to Bottom: An Inventory of NWS and OAR Environmental Modeling
Author(s):
Bradford Johnson, TriVector Services, Inc., NOAA/OAR/Office of Weather and Air Quality; Vijay Tallapragada, NOAA/NWS/NCEP/Environmental Modeling Center
Abstract:
Both the National Weather Service (NWS) and the Office of Oceanic and Atmospheric Research (OAR) develop and operationally run predictive numerical modeling technologies to support the NOAA mission. In 2018 the NWS and OAR jointly developed an inventory of modeling activities across both line offices. NOAA provides products and services derived, in part, from 45+ operational and 40+ research modeling efforts. These modeling suites cover a wide range of phenomena from tropical cyclone development to Arctic sea ice properties to global scale atmospheric transport, just to name a few. The temporal scales ranges from minutes to decades with operational support out to seasonal prediction while scientific capabilities extend to climate-scale projections. The compilation and maintenance of a comprehensive inventory will serve as a foundation to increase intra- and inter-agency collaboration and efficiency. By serving as a reference tool, the NWS and OAR model inventory will enable interested parties to quickly ascertain the scope of NOAA's drive to obtaining a better understanding of our complex earth system environment through an increasingly interconnected modeling approach. The following offices and laboratories were involved in the construction of the OAR portion of the inventory: Office of Weather and Air Quality, Climate Prediction Office, Air Resources Laboratory, Atlantic Oceanographic and Meteorological Laboratory, Earth System Research Laboratory, Geophysical Fluid Dynamics Laboratory, Great Lakes Environmental Research Laboratory, National Severe Storms Laboratory, and Pacific Marine Environmental Laboratory. The following offices and laboratories were involved in the construction of the NWS portion of the inventory: Environmental Modeling Center, National Hurricane Center, Space Weather Prediction Center, Ocean Prediction Center, Storm Prediction Center, Climate Prediction Center, Office of Science and Technology Integration, Office of Central Processing, and Office of Water Prediction.
Exhibit No:
29
Title:
The Gulf of Maine Alexandrium catenella Forecast System: From Experimental to Operational
Author(s):
Yizhen Li, Richard Stumpf, and Wayne Litaker National Centers for Coastal Ocean Science, NOAA
Abstract:
The recurrent Alexandrium catenella blooms have been a major social-economic issue to the New England states, as they cause extensive shellfish bed closures, and pose threats to human health due to Paralytic Shellfish Poisoning. A fully coupled physical-biological modeling system for the Gulf of Maine regional A. catenella bloom developed by NCCOS and its funded partners is currently in transition to operations. After thorough biological parameter re-tuning and skill assessment, the Gulf of Maine A. catenella modeling system is now ready for the next phase of transition. At this moment, NCCOS is conducting the experimental A. catenella forecast, which provides useful guidance for the stakeholders of New England States. Here we briefly present the framework of the modeling system. Relevant modeling products include surface wind, circulation transport, observations of cell concentration and toxicity, model validations, and a real-time dashboard for onshore bloom potential. While NCCOS continues to lead the model development and refinement to better meet user need, future collaborations between NCCOS, CO-OPs and National Weather Service will warrant the smooth transition of the modeling system into operational.
Exhibit No:
30
Title:
Assessing local atmosphere-to-ocean and ocean-to-atmosphere predictability using Granger causality
Author(s):
Eviatar Bach, Safa Motesharrei, Alfredo Ruiz-Barradas, Amir BozorgMagham, and Eugenia Kalnay (University of Maryland, College Park, Department of Atmospheric and Oceanic Science)
Abstract:
The atmosphere and ocean are coupled through complex interactions. Thus, information about the ocean helps to better predict the future of the atmosphere, and information about the atmosphere helps to better predict the ocean. Here, we investigate the spatial and temporal nature of this predictability: where, at what timescales, and for how long does the ocean provide significant predictability to the atmosphere, and vice-versa? We apply Granger causality, a statistical test of predictability, to time-series of sea-surface temperature and atmospheric vorticity at daily, 5-, and 15-day average resolutions. By introducing time delays, we also determine the persistence of the predictability. We find that in the tropics the ocean provides more predictability to the atmosphere than vice-versa, while the opposite is true in the extratropics. The predictability of the ocean due to the atmosphere generally persists for less than five days, except along the western boundary currents where it can persist for 15-20 days. The predictability of the atmosphere due to the ocean lasts for 30 days or longer in the Tropical Pacific. This is consistent with previous work that has shown that in the tropics, the atmospheric state is highly predictable from sea-surface temperature anomalies (e.g. Shukla, 1998). Furthermore, with anomalies averaged over 15 days, only the ocean can provide predictability to the atmosphere. The patterns we observe generally agree with the results of the Kalnay dynamical rule (Ruiz-Barradas et al., 2017), which predicts the directional forcing between the atmosphere and ocean by considering the local phase relationship between simultaneous sea-surface temperature and vorticity anomaly signals.
Exhibit No:
31
Title:
Overview of Modeling and Application Activities at the NOAA Earth System Research Laboratory Physical Sciences Division
Author(s):
Robert S. Webb[1], Mimi Hughes[2] , Janet Intrieri[1] (1-NOAA Earth System Research Laboratory Physical Sciences Division, 325 Broadway, Boulder, CO; 2- University of Colorado, Cooperative Institute for Research in Environmental Sciences at the NOAA Earth System Research Laboratory, Physical Sciences Division, 325 Broadway, Boulder, CO)
Abstract:
The Physical Sciences Division (PSD) of NOAA's Earth System Research Laboratory (ESRL) supports NOAA and the nation through research that advances understanding and predictions of weather, water, and climate extremes and their impacts. PSD strives to transition the resulting research findings into actionable information and services in support of NOAA mission responsibilities to provide early warning and inform preparedness to protect lives and property. Advances in prediction capabilities are driven by PSD's broad array of physical science research and are demonstrated and implemented through partnerships with NOAA's operational centers and affiliated organizations. Examples include advances in coupled modeling, model parameterizations, data assimilation techniques, the representation of uncertainty in forecast models, and the development of non-traditional approaches. PSD advances NOAA's modeling capabilities to forecast and predict hydrologic extremes, near term changes and variation in Arctic sea ice, regional coastal ocean conditions impacting marine resources, air sea fluxes, hurricane sea spray, and both shallow and deep convection. PSD model development improves NOAA's forecasting and prediction capabilities through the increased use of in situ and remotely sensed observation data, improved data assimilation techniques, enriched ensemble techniques, and optimization of stochastic parameterizations. PSD modeling applications range from reanalysis and reforecasts for weather prediction, to 20th century reanalysis to place current atmospheric circulation patterns into a historical perspective, to the use of model-based analog predictions as an alternative predictive system on subseasonal to seasonal and longer timescales, and to an on-line resource for analyzing and visualizing multimodel, large ensemble climate simulations (Facility for Climate Assessments, FACTS) in support of attribution and predictability assessments.
Exhibit No:
32
Title:
Developmental Testbed Center: Status update and outlook
Author(s):
Louisa Nance1, Jeff Beck2,4, Ligia Bernardet2,3, Grant Firl1, Michelle Harrold1, Ming Hu2,3, Tara Jensen1, Evan Kalina2,3, Kathryn Newman1, Dave Turner2, Jamie Wolff1, and Chunhua Zhou1 1. National Center for Atmospheric Research, Boulder, CO 2. NOAA Earth System Research Laboratory/Global Systems Division, Boulder, CO 3. Cooperative Institute for Research in Environmental Science, University of Colorado, Boulder, CO 4. Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO
Abstract:
The primary mission of the Developmental Testbed Center (DTC) is to facilitate the transition of research related to Numerical Weather Prediction (NWP) into operations. To fulfill this mission, the DTC (i) provides user and developer support for community NWP systems in close collaboration with the system developers, (ii) performs testing and evaluation of promising new NWP techniques, as well as the operational systems, to inform the operational implementation process, and (iii) brings together the research and operational NWP communities through workshops, tutorials and the DTC Visitor Program. Currently, the DTC focuses its activities in five task areas: regional ensembles, data assimilation, hurricanes, verification and physics for global applications. This presentation will provide a high level overview of the breadth of current DTC activities, including avenues for the community to engage in key areas of research directed at helping EMC address model performance issues, and an outlook for the future.
Exhibit No:
33
Title:
An overview of GFDL's participation in Phase 6 of the Coupled Model Intercomparison Project (CMIP6)
Author(s):
Jasmin John, NOAA/GFDL GFDL Model Development Teams, NOAA/GFDL
Abstract:
The Coupled Model Intercomparison Project (CMIP) is a collaborative multi-model framework designed to better understand past, present, and future climate change, and the publicly available model data produced through the project supports national and international assessments of climate change. For the sixth phase of CMIP, NOAA/OAR Geophysical Fluid Dynamics Laboratory (GFDL) has developed two new fully-coupled models: a high resolution physical climate model (CM4), and an earth system model focusing on increased comprehensiveness (ESM4). This presentation will give an overview of GFDL's contributions to CMIP6, through utilization of these models to participate in a standard suite of experiments, as well as 13 specialized Model Intercomparison Projects (MIPs). These contributions encompass multiple earth system components and processes and a range of scientific foci and offers opportunities for transdisciplinary and cross-line office collaborations across NOAA.
Exhibit No:
34
Title:
Seasonal Climate Forecasts Applied to Products for Fire Managers in Alaska
Author(s):
Uma Suren Bhatt1, Akila Sampath1, Peter Bieniek2, Robert Ziel3, Ms. Heidi Strader4, Brian Brettschneider5, Alison S York6, Richard Thoman7, Ms. Sharon Alden4, GaBriella Branson8 and Peitao Peng9, (1)University of Alaska Fairbanks, Fairbanks, AK, United States, (2)International Arctic Research Center, Fairbanks, AK, United States, (3)Fuels and Fire Analyst, Dept. Natural Resources, State of Alaska, Falmouth, MA, United States, (4)Predictive Services Fire Weather Program Manager, Fairbanks, AK, United States, (5)Postdoctoral Associate, IARC,UAF, Anchorage, AK, United States, (6)University of Alaska Fairbanks, Alaska Fire Science Consortium, Fairbanks, AK, United States, (7)National Weather Service Alaska Region, Environmental and Scientific Services Division, Fairbanks, AK, United States, (8)Predictive Services Intelligence Coordinator, Bureau of Land Management, Fairbanks, AK, United States, (9)Climate Prediction Center College Park, College Park, MD, United States
Abstract:
Seasonal climate determines the extent of the fire season in Alaska and the need for seasonal outlooks is increasing as budgets tighten and efficient allocation of resources/staff requires advanced planning. In this study, we develop seasonal climate outlooks for fire in Alaska using seasonal forecasts model output. For fuel conditions, the CFFDRS FWI (Canadian Forest Fire Danger Rating System Fire Weather Index), which was developed for the boreal forest, has been used in Alaska since 1992 by Alaska fire managers. The Buildup Index (BUI) provides an integrated measure of fuel conditions and has been constructed for the seasonal forecasts of June-August that are initialized in March and May for the NOAA CFSv2 model. Large values of BUI suggest that conditions are right for fires. Forecast BUI is generally lower than observed, particularly in spring since May-June forecast temperatures are too low and precipitation is too high. A quantile mapping-based bias correction for precipitation and temperature was developed for the CFSv2 to correct for BUI biases. High BUI alone does not portend an extensive fire season which also needs an ignition source. Lightning provides ignition for the summers with the greatest acres burned in Alaska. Alaska has an extensive network of lightning strike data which has been homogenized and is correlated with observed (i.e., ERA-Interim downscaled reanalysis) atmospheric conditions. The same atmospheric variables from seasonal forecasts are only weakly correlated with lightning, limiting success of prognostic lightning forecasts. However, a probabilistic forecast is being explored. Co-production of information is an iterative process that requires good communication between research activities and stakeholders to ensure that products are applicable for decision support.
Exhibit No:
35
Title:
Forecasting seasonal sea levels for the U.S. Coast: opportunities and challenges
Author(s):
Matthew J. Widlansky1, Mark A. Merrifield2, Philip R. Thompson1, H. Annamalai3, Xiaoyu Long1, Arun Kumar4, John J. Marra5, William V. Sweet6, Eric Leuliette7, and Gary T. Mitchum8 1University of Hawaii Sea Level Center, School of Ocean and Earth Science and Technology, University of Hawai'i at M?noa, Honolulu, HI 2Scripps Institution of Oceanography, Center for Climate Change Impacts and Adaptation, La Jolla, CA 3International Pacific Research Center, School of Ocean and Earth Science and Technology, University of Hawai'i at M?noa, Honolulu, HI 4NOAA NCEP CPC Operational Monitoring Branch, College Park MD 5NOAA NESDIS National Centers for Environmental Information, IRC, Honolulu, HI 6NOAA NOS Center for Operational Oceanographic Products and Services, Silver Spring, MD 7NOAA NESDIS Center for Satellite Applications and Research, College Park MD 8College of Marine Science, University of South Florida, St. Petersburg, FL
Abstract:
With higher relative sea levels, minor coastal flooding is occurring more often during periods of large astronomical tides. If combined with above-normal seasonal sea levels, often associated with climate-driven variability in the ocean, coastal flooding becomes more severe. Many coastal communities, ranging from small-island nations to large-urban centers, are experiencing recurrent flooding. Such total high water events expose coastlines to potentially damaging storm-related flooding, yet seasonal prediction of coastal high water is in an early development stage. Advancements in forecasting seasonal climate variability using global coupled ocean-atmosphere models, which have the ability to simulate sea level variability, provides an opportunity to predict future high water events several months in advance. By compiling monthly sea level anomaly predictions from multiple models, which are especially skillful in the tropical Pacific Ocean (out to 6 months) but more challenged along continental coasts, improved future outlooks are perceivable. At the University of Hawaii Sea Level Center, we are exploring the seasonal predictability of U.S. coastal sea level anomalies as part of the Marine Prediction Task Force (NOAA-CPO-MAPP) effort to expand on a real-time forecasting product that is being served online to the Pacific Islands community. The goal is to reduce the residual between predicted tides and observed water levels by forecasting relative sea level changes. Here, an update on the opportunities and challenges in expanding sea level forecasts to the Atlantic, Gulf of Mexico, and Pacific Coasts will be discussed.
Exhibit No:
36
Title:
Development of advanced physics and chemistry parameterizations for the Next Generation Global Prediction System (NGGPS, global and regional versions of FV3)
Author(s):
Georg A. Grell, NOAA/ESRL/GSD; Joseph Olson, NOAA/ESRL/GSD and CIRES; Haiqin Li, NOAA/ESRL/GSD and CIRES; Tanya Smirnova, NOAA/ESRL/GSD and CIRES; Li Zhang, NOAA/ESRL/GSD and CIRES; Mike Toy, NOAA/ESRL/GSD and CIRES; Saulo R. Freitas, NASA/GMAO; Jaymes Kenyon, NOAA/ESRL/GSD and CIRES; Eric James, NOAA/ESRL/GSD and CIRES
Abstract:
The Next Generation Global Prediction System (NGGPS) is currently under development in the US. The high-stake goals of NGGPS require to generate a much-advanced global modeling system with state-of-the-art nonhydrostatic dynamics, physics and data assimilation. The selection of the advanced physics parameterizations may be the most challenging task for 2019. One of the physics packages considered as the advanced physics suite - developed at ESRL - include a unified scale-aware parameterization of subgrid cloudiness feedback to radiation (coupled PBL, microphysics, radiation, shallow convection) and the Grell-Freitas scale and aerosol aware convective parameterization. The microphysics scheme is also aerosol aware and currently already used operationally in the High-Resolution Rapid Refresh (HRRR). Additionally, ESRL has been developing increasingly improved inline chemistry/aerosol techniques that are being applied in both regional and global models. Effective reforecasting has been successfully applied to global models by ESRL from medium range to sub-seasonal time-scales using, in part, advanced physics adapted from the regional scale.
Exhibit No:
76
Title:
Virtual Ecosystem Scenario Viewer
Author(s):
Howard Townsend, Jason Link, Isaac Kaplan; NMFS/S&T/Marine Ecosystems
Abstract:
NOAA's National Marine Fisheries Service (NOAA Fisheries) has long recognized the importance of implementing ecosystem-based fisheries management (EBFM) in order to explicitly account for environmental changes and make trade-off decisions for actions that impact multiple species. The explicit treatment, transparent examination, and analytical exploration among the trade-offs across the many objectives in a region are essential for the execution of EBFM. To support transparent examination of fisheries management, innovative means for visualizing complex ecosystem data, management strategy evaluation and model output needed. The use of social media, interactive graphics, and engaging storytelling has become commonplace and is now almost expected; however, we typically present model results in complex, static graphic format. As technologies and tools continue to develop, the ability to more interactively allow stakeholders to "play" possible fishing, aquaculture, mitigation, or other management scenarios seems warranted, and better captures the truest sense of transparency when making multi-objective decisions. In this presentation, we demonstrate NMFS's Virtual Ecosystem Scenario Viewer (VES-V). VES-V uses computer generated images and a gamming engine to visually illustrate the responses of virtual marine ecosystems to a range of living marine resource management scenarios. Visualizations can help many audiences see the potential for widespread application of models in our work managing marine resources.

September 11, 2018 - 1:30-5:30PM

Exhibit No:
37
Title:
VDatum: Vertical Datum Transformation Tool
Author(s):
Stephen White, Edward Myers, Michael Michalski
Abstract:
The land-water interface in the coastal zone is a function of water level change and land motion in both space and time. To combine or compare coastal elevations (land heights and water depths) from diverse sources, they must be referenced to the same vertical datum. Using inconsistent datums can cause discontinuities that become problematic when producing maps, and assimilating data and advanced model results or simulations at the accuracy needed to perform informed and intelligent coastal zone management decision-making. Since 1999, NOS has supported the development of VDatum, a software tool designed to convert geospatial data among tidal, orthometric and ellipsoidal vertical datums, allowing users to establish a common reference system for mapping and other applications. The need for a single tool to combine multiple geospatial data sets into one common base has long been recognized by the surveying and mapping community, and the availability of initial versions of VDatum models covering coastal areas of the contiguous United States has helped in acquiring hydrographic and shoreline data more accurately and efficiently. As VDatum continues to mature as a result of ongoing technical development and improved source data, building it to a consistent and mature level is essential.
Exhibit No:
38
Title:
NOAA's Ecological Forecasting Roadmap: Insight Into Modeling Partnership Opportunities
Author(s):
National Ocean Service/National Centers for Coastal Ocean Science (NCCOS)
Abstract:
This presentation will provide an overview of NOAA's ecological forecasting portfolio with special emphasis on illustrating modeling needs and user requirements. The NOAA Ecological Forecasting Roadmap (2015-2019) establishes a framework for cross-NOAA coordination to develop ecological forecasts that meet the needs of stakeholders across the US coast and Great Lakes. To date, NOAA's efforts have focused on conducting applied research with an eye on transitioning science into operations or applications in 4 technical areas; Harmful Algal Blooms, Hypoxia, Pathogens, and Habitat. Ecological forecasts are providing stakeholders with products and services used to protect public health, promote US aquaculture, support long-term sustainability of fisheries, and inform land management decisions. Success in these areas relies upon a OneNOAA approach that incorporates the agency's diverse science expertise, extensive ocean observation networks, and ability to engage with key stakeholders. This presentation will explore potential opportunities to integrate NOAA's ecological forecasting portfolio into Unified Modeling efforts with an eye on developing an understanding of how NOAA-wide modeling platforms can support current and future forecasts.
Exhibit No:
39
Title:
Digital Coast Sea Level Rise Viewer
Author(s):
Doug Marcy, NOAA Office for Coastal Management; Josh Murphy, NOAA Office for Coastal Management
Abstract:
Being able to visualize potential impacts from sea level rise (SLR) and coastal flooding is a powerful teaching and planning tool, and the Sea Level Rise Viewer brings this capability to coastal communities. The purpose of this web-based mapping application is to provide coastal communities with a preliminary look at SLR and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. A recent update to the tool provides a visual crosswalk between the 2017 NOAA sea level rise scenarios and the inundation layers within the map viewer. The tool is presented as part of NOAA's Digital Coast platform and can be viewed at the following URL: (www.csc.noaa.gov/slr). This demonstration will provide a description of the tool features as well as a discussion about mapping methods, data access, and current and future map updates.
Exhibit No:
40
Title:
IOOS Model Viewer Demo
Author(s):
Kathleen Bailey (NOAA/IOOS), Kelly Knee (RPS)
Abstract:
The Integrated Ocean Observing System (IOOS) Model Viewer and the Environmental Data Server (EDS) that manages the data feeding the Viewer provides users with a single access point to integrated model output from varied federal and nonfederal sources. RPS, an IOOS private industry partner, developed the EDS as a way to provide operational users like the US Coast Guard with homogenized access to oceanographic and meteorological data in support of their operations (search and rescue, disaster response, etc). The data are collected in disparate formats, converted to a single format (netCDF), and are made available via standard web services. IOOS leveraged this development and implemented a modified version of the RPS visualization - the IOOS Model Viewer. The Model Viewer displays a subset of the output harvested by the EDS via Web Mapping Services (WMS), and also provides users direct access to the netCDF files via an OPeNDAP service provided by EDS' THREDDS server. The IOOS Model Viewer allows users to examine model output in a map-based environment, and do some basic analysis via plot comparisons and an error analysis tool. The Viewer contains model output from IOOS Regional Associations (RA), their partners, and federal groups like NOAA and the Navy. Some observations are included for comparisons purposes from NOAA (water level stations and weather buoys), USACE (wave buoys), and IOOS (HF Radar surface currents plus some RA stations), among others. A demo of this tool will showcase some comparisons of CO-OPS OFS model output to observations and highlight features that provide different ways to view, analyze, and access the output. The IOOS Model Viewer is located at eds.ioos.us.
Exhibit No:
41
Title:
The Virtual Lab (VLab) is a set of services which enables NOAA employees and their partners to share ideas, collaborate, and engage in software development and conduct applied research.
Author(s):
Jason Burks, NOAA/NWS VLab John Schattel, NOAA/NWS VLab Todd Neison, NOAA/NWS VLab
Abstract:
The purpose of the NOAA Virtual Lab (VLab) is to promote the successful and efficient transfer of research and development to operations, and the flow of information to the research community. The VLab users can leverage VLabs' many tools regardless of their location and schedule. VLab provides resources useful to collaborators. Vlab 'Communities', based on the Liferay DXP framework allow users to build wikis, blogs, forms and document libraries. VLab 'Development' services are provided by several open source tools, and provide core capabilities to the developers and project managers. Task management, lifecycle planning, release roadmaps, Agile/Scrum development, and overall project planning with Gantt charting is provided by Redmine. Source code version control is provided by either Git or Subversion depending on your needs. Application code review is provided by Gerrit, and continuous integration by Jenkins. This is a true end to end solution for the developer community. Access to the VLab is provided via NOAA google accounts, and additionally non-NOAA partners can also be granted access.
Exhibit No:
42
Title:
Apply NOAA North American Mulit-model Ensemble seasonal forecasts to support regional water resource planning
Author(s):
Rong Fu, University of California, Los Angeles, Nelun Fernando, Texas Water Development Board, Sudip Chakraborty, University of California, Los Angeles
Abstract:
The NOAA North American Multi-Model Ensemble seasonal forecasts have higher prediction skills for the large-scale circulation and temperature in winter and spring seasons, and lower skills in predicting summer rainfall over the US Great Plains. To utilize the above mentioned strengths and mitigate the weakness of the NMME predictions, we have developed a hybrid NMME-statistical prediction system for seasonal prediction of the summer rainfall over the US Great Plains with 3-5 months leadtime. This system has been adopted by the Texas Water Development Board (TWDB) for supporting water resource decisions since 2015 (http://waterdatafortexas.org/drought/drought-forecast). The TWDB has automated this hybrid NMME-statistical prediction system, and also used in conjunction with Conditional Reliability Modeling (CRM), to derive probabilistic forecasts of whether reservoir storage levels will be above or below prior-defined drought trigger thresholds for three reservoirs on the Brazos River basin in Texas. The CRM is an operational mode of the Water Rights Analysis Package (WRAP), which is a generalized river/reservoir system simulation model that is routinely applied in Texas for regional and statewide planning studies and in the administration of the state's water right permit system. The TWDB and university researchers, Rong Fu and Sudip Chakraborty, are continuing their collaboration to transfer the latest research results to improved prediction skills in part leveraged by NOAA MAPP support. We will report the results of this application of the NOAA modeling and prediction capability to support regional water resource decision.
Exhibit No:
43
Title:
A modeling methodology for quantitative design and evaluation of observing systems in the atmosphere and the oceans
Author(s):
George R. Halliwell, Jr., NOAA/AOML/PhOD; Robert Atlas, NOAA/AOML; Lidia Cucurull, NOAA/AOML/HRD; Villy Kourafalou, RSMAS, University of Miami
Abstract:
The exhibit will showcase AOML's modeling methodology for quantitative design and evaluation of observing systems in the atmosphere and the oceans. OSSE (Observing System Simulation Experiment) systems have been developed at AOML under the NOAA Quantitative Observing System Assessment (QOSAP) Program for both the atmosphere and ocean with the goal of improving initialization of the atmospheric and oceanic components of coupled hurricane prediction models. The design and validation procedures for both the atmospheric and oceanic systems follow strict guidelines to ensure that credible impact assessments are obtained. QOSAP is based at AOML and it coordinates the assessment of the impact of current and new observations across the different NOAA Line Offices. QOSAP uses OSSEs, as well as OSEs (Observing System Experiments), as effective techniques to evaluate the impact of the different observation types. These interdisciplinary studies help NOAA management prioritize mission designs and observing system deployments in a cost-effective way by analyzing tradeoffs in the design of proposed sampling strategies. QOSAP's primary objective is to improve quantitative and objective assessment capabilities to evaluate operational and future observation system impacts and trade-offs to assess, and to prioritize NOAA's observing system architecture. More specifically, QOSAP's main focuses are (1) increase NOAA's capacity to conduct quantitative observing system assessments, (2) develop and use appropriate quantitative assessment methodologies, and (3) inform major decisions on the design and implementation of optimal composite observing systems. AOML has a long history in conducting atmospheric OSEs and OSSEs to quantitatively assess the impact of aircraft and satellite observing systems to improve hurricane prediction. More recently, a research ocean OSE-OSSE system has been developed through collaboration between AOML and the University of Miami (the joint "Ocean Modeling and OSSE Center", OMOC). The ocean system has been applied to quantitatively assess the impact of existing global ocean observing systems, of regional seasonal enhancements to these systems by deploying arrays of underwater gliders and profiling floats, and of rapid-response airborne ocean profile surveys. The methodology will be exhibited, including modeling components, model evaluation tools, product generation and visualization. Specific examples will demonstrate the positive impact of both atmospheric and oceanic observations on improving coupled hurricane forecasts.
Exhibit No:
44
Title:
Storm Surge Modeling at NOS
Author(s):
Sergey Vinogradov (NOS/OCS), Edward Myers (NOS/OCS), Yuji Funakoshi (NOS/OCS, UCAR), Saeed Moghimi (NOS/OCS, UCAR), Jaime Calzada (NOS/OCS, UCAR)
Abstract:
The Coast Survey Development Laboratory (CSDL) of the National Ocean Service (NOS) has established an Extratropical Surge and Tide Operational Forecast System (ESTOFS) and a Hurricane Surge On-Demand Forecast System (HSOFS) for U.S. coastal waters. The ESTOFS-Atlantic and HSOFS-Atlantic covers the Western North Atlantic Ocean including the U.S. East Coast and Gulf of Mexico and has been in operation since 2012 and 2018, respectively. The ESTOFS-Pacific covers the Eastern North Pacific Ocean including the U.S. West Coast, Gulf of Alaska and Hawaiian Islands and has been in operation since 2014. The ESTOFS-Micronesia covers the Western Tropical Pacific including Guam, Federated States of Micronesia, Palau, Marianas Islands, Marshall Islands, and Wake Island and has been in operation since 2018. In collaboration with NOAA's National Weather Service, a major effort is underway to develop total water prediction capability for on-demand and operational applications through coupling of the surge, wave and river modeling components.
Exhibit No:
45
Title:
The NOAA Research Modeling, Analysis, Predictions, and Projections Program
Author(s):
Annarita Mariotti (CPO), Daniel Barrie (CPO), Alison Stevens (CPO), Emily Read (CPO)
Abstract:
The Modeling, Analysis, Predictions, and Projections (MAPP) program is a competitive grants program hosted by the Climate Program Office, which is located in the National Oceanic and Atmospheric Administration's (NOAA) Office of Oceanic and Atmospheric Research (OAR). MAPP's mission is to enhance the Nation's and NOAA's capability to understand, predict, and project variability and long-term changes in Earth's system and mitigate human and economic impacts. Since 2011, MAPP has supported research and development (R&D) projects, providing opportunities for exploratory research to advance NOAA's mission, as well as activities to test and evaluate promising research advances and ultimately transition research into a broad set of applications, with emphasis on NOAA operational use. Several important transitions to NOAA operations and applications have occurred annually because of MAPP's work. MAPP's projects and transition activities address NOAA's modeling, monitoring, prediction and projections needs in support of its stakeholders, while complementing NOAA's internal research capabilities. Spanning from foundational Earth system science research to development of applications with socio-economic benefits, MAPP's activities are integrated with those of other NOAA line offices, CPO programs, and the external community. Through partnerships and as part of third party organizations, the MAPP program provides NOAA broad connectivity and a holistic suite of activities for modeling and prediction across timescales, both national and internationally. This presentation will provide an opportunity to learn about the MAPP program, its key priorities and activities, how to get engaged, and meet with the MAPP program team.
Exhibit No:
46
Title:
Coupled Modeling Framework for Total Water Simulations
Author(s):
Saeed Moghimi (UCAR), Edward Myers (NOS/OCS), Sergey Vinogradov (NOS/OCS), Andre Van der Westhuysen (NWS/NCEP/EMC), Ali Abdolali (NWS/NCEP/EMC), Lei Shi (NOS/OCS), Zaizhong Ma (NWC/NCEP/EMC), Arun Chawla (NWS/NCEP/EMC), Hassan Mashriqui (NWS/OWP), Trey Flowers (NWS/OWP), Nicole Kurkowski (NWS/OSTI)
Abstract:
The unstructured-mesh ADvanced CIRCulation (ADCIRC) model has a wide application both in research and as an operational tide and surge model. Similarly, WAVEWATCH III is a major ocean wave modeling system that is widely applied in both operational and research arenas. Also the National Water Model utilizing WRF-Hydro is the NOAA's operational hydrologic model delivering streamflow forecasts on 2.7 million river reaches as well as gridded analyses of a host of other hydrologic variables across the Nation. Creating a coupled system among these models using a community-based coupling infrastructure is therefore a valuable contribution to a wide range of applications. As the first step, we have developed a fully coupled wave and storm surge models using the National Unified Operational Prediction Capability (NUOPC) framework. The wave-surge coupled sub-system was evaluated for Hurricane Ike, Isabel, Andrew ans Sandy for the whole U.S. Atlantic coasts. ADCIRC component also interacts with NWM by providing water level and current velocities at the NWM coastal boundaries and receiving inland flooding discharge information as river inflow boundary condition in the landward side. The ADCIRC-NWM sub-system is being evaluated for Hurricane Isabel and Sandy for the Delaware Bay region before its final validation of ADCIRC-WW3-NWM for the whole U.S. Atlantic coast.
Exhibit No:
47
Title:
Linking Salmon Life Cycle Models with hydrological models
Author(s):
Rich Zabel, NOAA/NWFSC
Abstract:
Life Cycle Models are an effective tool for exploring alternative management scenarios to restore salmon populations. They translate a suite of actions across life stages into the common currency of Population Viability metrics, such as long-term abundance or probability of extinction. At the NWFSC, we have developed models for several populations of Chinook salmon throughout the Columbia River basin. These models link salmon behavior with environmental factors in several life stages. Freshwater rearing capacity and downstream migration survival is related to flow and temperature, ocean survival is related to sea-surface temperature, and upstream migration survival is related to temperature. To model the impact of alternative hydropower operations on salmon populations, we use the COMPASS model. COMPASS uses inputs from the HYDSIM or RESIM hydrological models to estimate survival and migration timing through the Snake and Columbia Rivers. I will demonstrate how the model is used to assess mitigation actions and to assess potential impacts of climate change.
Exhibit No:
48
Title:
Stock Synthesis: Providing Fishery Management Advice from Fish Population Models with Environmental Inputs
Author(s):
Richard D. Methot, NOAA Senior Scientist for Stock Assessments, Northwest Fisheries Science Center, Seattle, WA
Abstract:
Fish population (aka "stock") assessment models determine the impact of past fishing on the historical and current abundance of the population, evaluate sustainable rates of removals (catch), and project future levels of catch that will implement risk-averse catch rules. These catch rules are codified in regional Fishery Management Plans according to requirements of the Sustainable Fisheries Act. In the U.S., ~500 federally managed fish and shellfish populations are under ~50 Fishery Management Plans. About 200 of these populations are assessed according to a prioritized schedule for their current status each year, but many minor species have never been quantitatively assessed. Although the pace is slower than weather forecasting, fish stock assessments are operational models for fisheries management. Assessment models typically assimilate several decades of annual catch, data on fish abundance from diverse surveys and fishery sources, and biological information regarding fish body size and the proportions at age. A suite of models is available depending on the degree of data availability and unique characteristics of the fish population or its fishery. Where feasible, environmental time series are used as indicators of changes in population or observation processes, especially to improve the accuracy of the projections of abundance and sustainable catch into the future. Such linkages are based principally on correlations given the challenge of conducting field observations on an appropriate scale. The frontier of model development is in the rapid estimation of parameters to include random temporal effects, in the simultaneous modeling of a suite of interacting species, and in the explicit treatment of the spatial distribution of the population. Assessment models are loosely coupled to other models. For example, an ocean-temperature or circulation model or benthic-habitat map may be directly included in the pre-processing of the fish abundance survey. A time series of a derived ocean factor, like the North Atlantic Oscillation, can be included as an indicator of a change in a population process. Output of a multi-decadal time series of derived fish abundance can be an input to ecosystem and economic models to better understand cumulative impacts and benefits. . Stock Synthesis (SS) is an age- and size-structured assessment model in the class of models termed "integrated analysis models." SS has a population sub-model that simulates a stock's growth, movement, and mortality processes; an observation sub-model estimates expected values for various types of data; a statistical sub-model characterizes the data's goodness of fit and obtains best-fitting parameters with associated variance; and a forecast sub-model projects needed management quantities. SS outputs the quantities, with confidence intervals, needed to implement risk-averse fishery control rules. The model is coded in C++ with parameter estimation enabled by automatic differentiation (www.admb-project.org). Windows, Linux, and iOS versions are available. Output processing and associated tools are in R, and a graphical interface is in QT. SS is available from NOAA's VLAB. The rich feature set in SS allows it to be configured for a wide range of situations and it has become the basis for a large fraction of U.S. assessments and many other assessments around the world.
Exhibit No:
49
Title:
Evolution of Model Guidance for National Hurricane Center Forecast Products
Author(s):
Monica L. Bozeman, NCEP/NHC
Abstract:
Development of objective tropical cyclone forecast guidance began shortly after the National Hurricane Center started issuing quantitative forecasts of the 24-hour position of tropical cyclones in 1954. The early models used simple empirical relationships between storm properties, such as initial position and motion, to predict the future positions. Over the next six decades, much more sophisticated models were implemented, and the majority of the improvements in NHC's official track forecasts during this time can be attributed to improvements in available model guidance. This paper provides a brief review and current capabilities of tropical cyclone models used by NHC and their evolution from statistical to dynamical systems. NHC also recognized the need to convey uncertainty information with their forecasts, and has done so for more than 30 years. The Hurricane Strike Probability products first became available with Hurricane Alicia in 1983, and this product provided track forecast uncertainty information using a statistical approach. Model guidance on forecast uncertainty is expected to follow a path similar to that for the deterministic model predictions, where statistical approaches will transition to dynamical model ensemble systems. This paper will provide a vision for the future, where coupled ocean-atmosphere modeling systems can be used as guidance for NHC's deterministic and probabilistic forecast and warning products. The future role of the forecaster in that process will also be discussed.
Exhibit No:
50
Title:
Facilitating the use of NOAA CoastWatch/OceanWatch/PolarWatch Products and Applications
Author(s):
Sheekela Baker-Yeboah(1,2), Veronica Lance(1,2), Paul DiGiacomo(2), Cara Wilson(3), Heng Gu(2,4) (1) University of Maryland/CICS, (2) NOAA/NESDIS/Center for Satellite Applications and Research (STAR)/Satellite Ocean and Climate Division (SOCD), (3) NOAA/NESDIS/Southwest Fisheries Science Center (4) Digital MindTrust Inc.
Abstract:
The NOAA CoastWatch (CW) Program continues to expand with international satellite missions (both NOAA and outside of NOAA missions) collecting remotely sensed and calibration-validation in situ data. The program extends well beyond the World Ocean coastal regions, encompassing also OceanWatch (OW) (https://coastwatch.noaa.gov) and PolarWatch (PW) (https://polarwatch.noaa.gov/) components. Given the variety of NOAA and non-NOAA satellite missions, CW remains at the leading edge of providing NOAA environmental data products and user applications for (1) data exploration, (2) improved understanding of the health of our coastal and global ocean regions, and (3) continuous monitoring and management of our oceans and coasts, including the Arctic and Antarctic regions. CW is also exploring the inclusion of more oceanographic cross-disciplinary model data for the growing user community of government, academia, the general public, and more. This work will give a brief overview of data sets provided through CW/OW/PW and of how some of these can be accessed. All data are freely available.
Exhibit No:
51
Title:
Public and Private Access to NCEP Operational Model Output
Author(s):
Jordan Alpert/NOAA/NCEP/EMC
Abstract:
The NOAA Operational Model Archive Distribution System (NOMADS) is a NOAA official model data archive and real time service that has been operating for a decade. High resolution analysis and model forecast datasets such as sea surface temperate, blended ensemble model output, Wave Watch and Ocean model output, Climate Forecast System model output, Aerosol and high resolution meso-regional and global deterministic and ensemble model data are accessible in real time, virtually all NCEP model output with some archives. Access to the data from a users' own application/language code including server dataset aggregation make NOMADS successful for model data acquisition and time critical product creation. The tutorial will demonstrate how to access operational model data from NOAA servers and create probability forecast products using the aggregation of ensemble forecast data.
Exhibit No:
52
Title:
Then and Now: How HYSPLIT Changed the Course of Transport and Dispersion Modeling and Fundamentally Embedded Itself into Both Research and Operational Decision-Making
Author(s):
Tainfeng Chai (NOAA Air Resources Lab and CICS-MD); Mark Cohen (NOAA Air Resources Lab); Alice Crawford (NOAA Air Resources Lab and CICS-MD); HyunCheol Kim (NOAA Air Resources Lab and CICS-MD); Daniel Lefevre (NOAA Air Resources Lab and ERT); Christopher Loughner (NOAA Air Resources Lab and CICS-MD); Fong Ngan (NOAA Air Resources Lab and CICS-MD); Glenn Rolph (NOAA Air Resources Lab); Ariel Stein (NOAA Air Resources Lab); Barbara Stunder (NOAA Air Resources Lab)
Abstract:
This exhibit, brought to you by the HYSPLIT team at NOAA's Air Resources Laboratory (ARL), will summarily guide you through the 30+ years of development highlights that propelled HYSPLIT into its current position as one of the most extensively used transport and dispersion models in the atmospheric sciences community. See for yourself how HYSPLIT evolved from estimating simplified single trajectories based on radiosonde (balloon) observations to the current system capable of accounting for multiple interacting pollutants transported, dispersed and deposited over local to global scales. Then delve even further, beyond the immediate present, to experience future enhancements already in progress including improved source attribution methods, probabilistic output, improved mixing schemes, new graphical displays, and more. Absorb the intricacies of HYSPLIT's design as ARL scientists demonstrate its ability to support a wide range of simulations related to the atmospheric transport and dispersion of pollutants and hazardous materials, as well as the deposition of these materials. Continual improvements by the HYSPLIT development team ensure that the model is sufficiently robust, yet fast enough for use in time sensitive applications both nationally and internationally. NOAA's National Weather Service is the primary operational user of HYSPLIT, via the National Centers for Environmental Prediction and local Weather Forecast Offices. Examples of core applications include tracking and forecasting the release of hazardous materials such as radionuclides, volcanic ash, smoke from wildfires and prescribed burns, and pollutants from various stationary and mobile emission sources. HYSPLIT boasts an extensive user community comprised of academics, regional/state level regulators, national agencies such as the U.S. forest service, and international organizations such as the World Meteorological Organization.
Exhibit No:
53
Title:
Achieving Accurate Weather Predictions
Author(s):
William Cave & Robert Wassmer, Prediction Systems, Inc. (PSI)
Abstract:
To substantially increase accuracy of weather predictions, various factors must be addressed. Most important, weather prediction is a complex problem and must be placed in a scientific framework where theory is backed by experiment. Measurement and comparison of results must be performed by independent parties working together to achieve accuracy. This abstract lists items that must be understood to achieve success and describes how the problem can be solved - right now. Weather is "forecast." Prediction accuracy of forecasts is not defined mathematically. Weather predictions are inaccurate over mountains, foliated areas, sea coasts and large areas of water. Computer modeling: Run-Time Speed - Accuracy trade-offs have led to transformations that do not accurately represent the physical systems. Current weather models use linear transformations that do not accurately represent the nonlinear momentum properties caused by the earth's surface. Heterogeneous Cell-Sizes: Current models use (x, y) cell scales that are too large by one to two orders of magnitude. Cells numbers can be decreased using heterogeneous cell sizes. Heterogeneous Time-Steps can vary with heterogeneous cell sizes. Use of detailed terrain data to accurately reflect nonlinear changes in momentum represents a difficult problem requiring complex data bases and corresponding mathematical algorithms that operate on heterogeneous cells for speed. The major reason given for inaccurate models is that parallel processor computer hardware is not nearly fast enough to support the cell sizes and nonlinear models needed to produce accuracy. In fact, software is the problem and it has been solved by PSI. Testing the accuracy of a new approach must be done in different regions: mountainous, foliated, etc., where many years of weather data are available.
Exhibit No:
54
Title:
Providing Maps of NWS and NOS Model Forecast Guidance via NOAA's nowCOAST
Author(s):
John G.W. Kelley, Jason Greenlaw, John Evans, and Edward Myers
Abstract:
NOAA/National Ocean Service's nowCOAST (https://nowcoast.noaa.gov) is an operational GIS-based web mapping portal which provides coastal users with situational awareness of present and future environmental conditions. nowCOAST integrates and displays observations, analyses, imagery, warnings, forecasts and model forecast guidance from NWS, NOS, and NESDIS. This includes the latest analyses from NWS' Real-Time Mesoscale Analysis (RTMA) System and forecast model guidance from NWS' Global Real-Time Ocean Forecast System (GRTOFS), NOS' Extratropical Surge and Tide Operational Forecast System (ESTOFS), and NOS' operational oceanographic circulation forecast systems for the coastal ocean, estuaries, and the Great Lakes. Visualization techniques developed at the NOAA-UNH Joint Hydrographic Center are used to display the analyses and forecast guidance. RTMA's surface wind analyses are displayed used wind barbs curved to follow streamlines. The surface water currents from GRTOFS and NOS forecast systems are depicted using streamlets or streaklets placed along streamlines. Maps of analyses and forecast guidance can be obtained from nowCOAST's interactive map viewer or its time-enabled web mapping services via GeoServices REST or OGC-compliant Web Map Service (WMS) protocols. In addition, nowCOAST provides geo-referenced hyperlinks to time-series plots of point forecast guidance displayed on web pages operated by other offices in NWS and NOS. nowCOAST is relied upon by many different user communities including commercial and recreational mariners, seaport operations, coastal emergency management and homeland security, search and rescue operations, risk management, coastal management, and HAZMAT response. nowCOAST is hosted and monitored 24 x 7 in NOAA's Integrated Dissemination Program infrastructure operated by NWS/NCEP Central Operations. Recently, nowCOAST has received over 500 million web hits per month during periods of significant weather.
Exhibit No:
55
Title:
Coastal Operational Forecast System Modeling
Author(s):
Edward Myers 1, Neeraj Saraf 1, Julia Powell 1, Pat Burke 2, Paul Bradley 2, Carolyn Lindley 2, Aijun Zhang 2, Derrick Snowden 3 1) NOAA/National Ocean Service/Office of Coast Survey 2) NOAA/National Ocean Service/Center for Operational Oceanographic Products and Services 3) NOAA/National Ocean Service/Integrated Ocean Observing System
Abstract:
NOAA's Operational Coastal Modeling Program (NOCMP) is a partnership between offices within the National Ocean Service (Office of Coast Survey, Center for Operational Oceanographic Products and Services, Integrated Ocean Observing System) and other programs within NOAA. The primary objective of this program is to develop and operate a national network of Operational Nowcast and Forecast Hydrodynamic Model Systems (called Operational Forecast Systems, or OFS) to support NOAA's mission goals and priorities. An OFS consists of the automated integration of observing system data streams, hydrodynamic model predictions, product dissemination and continuous quality-control monitoring. State-of-the-art numerical hydrodynamic models driven by real-time data and meteorological, oceanographic, and/or river flow rate forecasts form the core of these end-to-end systems. The OFS perform nowcast and short-term forecast predictions of pertinent parameters (e.g., water levels, currents, salinity, temperature, waves) for dissemination to users. NOS is leveraging a unified modeling framework and standards to facilitate the transition of new models and system upgrades. This includes streamlined processes for sharing data sets and model code. Coordinated stakeholder engagement to identify these variable requirements (e.g., navigation services, flooding and inundation, ecological forecasting and water quality, search and rescue, spill response) that can be supported within the same coastal ocean modeling framework will facilitate improved and efficient service delivery of model products. To ensure operational models can evolve to support more mission requirements, NOS is working with the modeling community to evaluate: 1) the transition of mature hydrodynamic models to fill geographic coverage gaps, 2) improving algorithms (mixing, wetting and drying, etc.) and model performance of critical parameters such as salinity, to improve the support of numerous NOS applications, 3) the provision of additional operations needed for data assimilation and validation, and 4) the development of a sustained regional modeling framework that incorporates coupling with NWS' National Water Model.
Exhibit No:
56
Title:
CarbonTracker-NGGPS
Author(s):
Lori Bruhwiler, NOAA ESRL Global Monitoring Division; Sourish Basu, Cooperative Institute for Research in Environmental Sciences (CIRES); Mark Leonard, CIRES; Andy Jacobson, CIRES
Abstract:
CarbonTracker is a greenhouse gas data assimilation system that uses in situ atmospheric greenhouse gas observations from NOAA and other institutions to estimate the atmospheric states and emissions of CO2, CH4 and other greenhouse gases. CarbonTracker-NGGPS (CT-NGGPS) will transition the current CarbonTracker systems to an on-line modeling approach with higher spatial and temporal resolution and improved characterization of transport error through use of NGGPS ensembles. It will also be an initial step towards coupled land surface models that include carbon exchanges. CT-NGGPS will be a fully coupled weather-carbon DA system so that observations of CO2 and CH4 variability will be allowed to constrain the full atmospheric state. It will be used to improve "top-down" flux estimates based on both in situ and remotely sensed GHG data, and these will be used to evaluate "bottom-up" flux estimates from both land surface models and estimates of anthropogenic fossil fuel-CO2 emissions. Used operationally, CarbonTracker-NGGPS could provide useful information about carbon emissions and the status of natural processes, which currently remove from the atmosphere about half of what humans emit. The future evolution of natural carbon sinks as climate changes is currently a large uncertainty in our ability to project and plan for future climate, including the occurrence of extreme weather and global food production. The CarbonTracker-NGGPS project therefore supports NOAA's goal of providing better understanding of climate and climate variability to inform decision. Currently, a large uncertainty in projections of future climate is the role of carbon cycle feedbacks.
Exhibit No:
57
Title:
Developing Ecological Forecasts and Applications Linked to the Great Lakes Operational Forecast System models
Author(s):
Mark D. Rowe, Cooperative Institute for Great Lakes Research Doran Mason, NOAA/GLERL Eric Anderson, NOAA/GLERL Felix Martinez, NOS/NCCOS/Competitive Research Program
Abstract:
Hydrodynamic models of large lakes and oceans provide information on physical processes that are important drivers and modifying influences on ecosystem processes, which in turn can be related to economic benefits, public health risks, and other factors of importance to society. For example, water temperature, mixed layer depth, and transport patterns can influence ecosystem dynamics and productivity, transport patterns of larval fish, habitat suitability for invasive species, and spatial patterns of harmful algal blooms and hypoxia. Hydrodynamic models can be further linked to atmospheric models to consider the influence of meteorology and climate on ecological processes. Our fair exhibit will highlight examples of collaborative efforts between physical scientists and ecologists at the NOAA Great Lakes Environmental Research Laboratory and Cooperative Institute for Great Lakes Research to link hydrodynamic models to ecological forecasts and applications. GLERL is working with NOS/CO-OPS and NOS/OCS/CSDL to upgrade the Great Lakes Operational Forecasting System (GLOFS), based on the Finite Volume Community Ocean Model (FVCOM). With greater spatial resolution and more realistic depiction of shoreline morphology, these models promise more realistic representation of coastal and transport processes than the previous generation of models. The next-generation Lake Erie Operational Forecast System (LEOFS) model was linked to a Lagrangian particle tracking model to provide a daily nowcast and five-day forecast of harmful algal bloom distribution, called the Lake Erie HAB Tracker. The HAB Tracker is initialized using satellite-derived cyanobacterial index, produced by COOPS. The HAB Tracker has provided timely information to drinking water systems, anglers, and recreational boaters on an experimental basis for 2014-2018, and is on a path to transition to operations at NOS/CO-OPS. In a separate project, funded by NCCOS, the LEOFS model was extended to predict dissolved oxygen, and is being used to provide a daily nowcast and five-day forecast of spatial patterns of hypoxia in Lake Erie. The experimental LEOFS-hypoxia model is used to give drinking water systems advance notice of upwelling events that can bring hypoxic water into nearshore water intakes. A project goal is to transition the LEOFS-hypoxia model to operations in 2021. Finally, hydrodynamic models have been linked to foodweb models to consider the potential impacts of invasive Asian carp on economically-important recreational fisheries. In these applications, we have used interfaces within the FVCOM model framework to link Lagrangian particle models and NPZD-type lower food web models. Our fair exhibit will provide an opportunity for discussion of progress and opportunities for development of further linkages and common frameworks among models that could further streamline development of ecological models linked to FVCOM and other hydrodynamic models.
Exhibit No:
58
Title:
Lake Water Temperatures for Regional Models
Author(s):
Eileen Maturi -NESDIS/STAR/SOCD Andy Harris-University of Maryland/CICS
Abstract:
NESDIS has long provided surface temperatures for the Great Lakes region as part of the CoastWatch suite of products. However, there has been no specific attempt to tailor these products to ensure that accuracy and coverage requirements are being met. Furthermore, there are many other lakes in North America for which end users desire accurate surface temperatures, but which have received scant attention. The cumulative effect of these smaller lakes is becoming a significant issue for forecasting. One prime indication of the need to increase work in this area is the User Request put in by NWS to the NESDIS Satellite Products and Services Review Board some years ago that stated a need for accurate lake water temperatures. In this User Request, NWS went on to outline some of the problems that they perceive to exist in current lake surface temperature products, including inadequate cloud masking, biases due to anomalous atmospheric conditions, and water turbidity. Similar issues are highlighted in reports of the Global High Resolution Sea Surface Temperature (GHRSST) Inland Water Working Group. Most of the problems in existing NESDIS satellite-based lake temperature products are due to the processing methodologies for cloud detection and temperature retrieval (e.g. Advanced Clear-Sky Processor for Oceans, ACSPO) have been optimized for the open ocean, where the atmosphere is usually close to equilibrium with the water surface, and the target is far from the disrupting influence of land. Examples are presented to illustrate the issues with current Lake Surface Temperature products, primarily in terms of coverage and accuracy. Possible ways are proposed to improve the product, such as application of tailored QC filters to mitigate the effect of increased retrieval error with a relaxed cloud mask. Such effects are likely to be regional and seasonal. Prospects for reducing error due to anomalous atmospheres (e.g. cool lake surrounded by warm land, or vice versa), emissivity differences, diurnal warming (water turbidity, wind speed, insolation) are also discussed. In this regard, addition of NCEP NAM surface and upper air data will be invaluable. In addition to localized refinement of cloud detection, a deterministic physical temperature methodology (Modified Total Least Squares, MTLS) is considered. MTLS is quite tolerant of noisy data, and does not require error covariance information, since regularization strength is calculated dynamically from the data at solution time. MTLS also offers a mechanism for additional QC filtering, along with a pixel-level uncertainty estimate for the retrieval. Once such improved (in terms of accuracy, coverage, and uncertainty information) lake water temperatures are made available, their impact will be assessed by assimilation into NCEP regional models.
Exhibit No:
59
Title:
Development of Prototype National Water Model Soil Moisture Products for Drought Monitoring
Author(s):
Mimi Hughes 1,2, Darren Jackson 1,2, Robert Zamora 2, Robert Cifelli 2, Mike Hobbins 1,2, Fernando Salas 3,4, Kent Sparrow 3,4, Robert Webb 2, David DeWitt 5, and Peter Colohan 4 1. CIRES, University of Colorado, Boulder 2. NOAA/ESRL/PSD 3. Cooperative Programs for the Advancement of Earth System Science | UCAR/UCP 4. NOAA/NWS National Water Center | Office of Water Prediction 5. NOAA/NWS/CPC
Abstract:
NOAA's new National Water Model (NWM) provides analyses and predictions of hydrologic variables of relevance to drought monitoring and forecasts at fine time and space scales. We present preliminary results exploring the potential for NWM soil moisture nowcasts to inform drought monitoring. Because drought monitoring relies on an accurate representation of climatological soil moisture values to establish anomalies, we begin our analysis focused on comparisons of this climatology in the NWM with soil moisture climatologies from in-situ observations and other gridded datasets currently used to inform the US Drought Monitor, specifically those from the NOAA Climate Prediction Center's soil moisture model and from the North American Land Data Assimilation System (NLDAS). In particular, we discuss three challenges we have had to confront in the project: the NWM's operational schedule and configuration, and what that implies for the stability of our intended product; the limitations of sometimes working from shorter-than-optimal baseline climatologies, again arising from a combination of the NWM's operational timeline and its computational expense; and extremely small ranges of values in soil moisture fields at some times and locations in the CONUS domain. In addition, we use soil moisture observations from NOAA-Physical Science Division's instrumentation network in National Integrated Drought Information System (NIDIS) pilot basins as well as observations from the National Soil Moisture Network to evaluate the NWM's soil moisture anomalies. Differences in the climatologies and anomalies will be discussed in the context of implications for drought monitoring.
Exhibit No:
60
Title:
Facilitating Development of Physical Parameterizations for NOAA's Unified Forecast System
Author(s):
Ligia Bernardet (1,3,4), G. Firl (2,3), D. Heinzeller (1,3,4), L. Carson (2,3), M. Zhang (1,3,4), D. Stark (2,3), J. Schramm (2,3), L. Xue (2,3), J. Dudhia (2,3) 1NOAA ESRL Global Systems Division, Boulder, CO, USA 2National Center for Atmospheric Research, Boulder, CO, USA 3Developmental Testbed Center, Boulder, CO, USA 4University of Colorado Cooperative Institute for Research in Environmental Sciences, Boulder, CO, USA
Abstract:
The National Centers for Environmental Prediction (NCEP) has decided to adopt a modularized system architecture for its new Unified Forecast System (UFS), which will be transitioned gradually to operations within the next few years. Applications of the UFS will range from analysis and nowcasting to weather and subseasonal prediction, encompassing both global and limited-area configurations, in deterministic and ensemble forecast mode. While the dynamical core for the UFS has been selected to be the Finite-Volume Cubed Sphere (FV3) developed by NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), the physics suites for the various applications are currently under review. The Global Model Test Bed (GMTB) within the Developmental Testbed Center was established by NOAA's Next Generation Global Prediction System (NGGPS) program to facilitate the community involvement in the process of developing and testing advanced physics. Much of the GMTB work to date has been devoted to the development of the Common Community Physics Package (CCPP), which encompasses a library of physical parameterizations envisioned to contain operational and developmental parameterizations (the CCPP-Physics), as well as a framework that connects the parameterizations to host models (the CCPP-Framework). The CCPP-Physics and its associated CCPP-Framework are developed as open source codes and freely distributed through GitHub. The first public release of CCPP took place in April 2018. This package included all parameterizations of the current operational GFS, and the ability to connect to the GMTB Single Column Model. Since then, additional parameterizations have been added to the CCPP and the code is undergoing integration with the UFS. In this presentation we will describe the CCPP public release and its progress in transition to operations. Additionally, we will discuss a hierarchical model testing framework that is being put in place to assess the performance of the physics schemes and of the physics suite as a whole using tools ranging from the GMTB SCM to a workflow for fully cycling the UFS with data assimilation.
Exhibit No:
61
Title:
Weather and Interdisciplinary Modeling as part of the Office of Weather and Air Quality
Author(s):
DaNa L. Carlis, Ph.D. and John Cortinas, Ph.D.
Abstract:
The Office of Weather and Air Quality finds, funds, and fosters collaborative research to understand and develop products, tools, and services to improve weather and air quality forecasting and societal outcomes. Our ultimate goal through prioritized research investments is to improve NOAA's operational forecasts that help save lives and reduce property damage. OWAQ funds research through its three major program, U.S. Weather Research Program (USWRP), Earth System Prediction Capability (ESPC), and the Joint Technology Transfer Initiative. Our investments in weather and interdisciplinary modeling efforts span many disciplines and NOAA modeling systems such as the Finite-Volume Cubed (FV3), High Resolution Rapid Refresh (HRRR), National Water Model (NWM), Whole Atmosphere Model (WAM), and a National Coupled Prediction System. During the NOAA Modeling Fair, we will highlight these models through sharing information on the societal benefits of these modeling systems.
Exhibit No:
62
Title:
Merged Observatory Data Files (MODFs) from the International Arctic Systems for Observing the Atmosphere (IASOA) in support of the WMO Year of Polar Prediction (YOPP)
Author(s):
Taneil Uttal (Taneil.Uttal@noaa.gov) and Janet Intrieri (NOAA/OAR/ESRL/PSD, Boulder, CO); Emily Osborne (NOAA/OAR/GOMO/ARP, Silver Springs, MD); Robert Grumbine (NOAA/NWS/EMC/MDAB, College Park MD); Elena Konopleva (Science Technology Corporation, Boulder, CO); Sara Morris, Christopher Cox, Ola Persson, Andrey Grachev, Matthew Shupe, Gijs de Boer, and Amy Solomon (Cooperative Institute for Studies of Environmental Sciences, Boulder CO); Barbara Casati (Environmental and Climate Change Canada, Montreal, Canada); Jonathan Day (European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom); Gunilla Svensson (Stockholm University, Stockholm, Sweden); Øystein Godøy (Norwegian Meteorological Institute, Oslo, Norway); Siri Jodha Khalsa (National Snow Ice and Data Center, Boulder, CO)
Abstract:
The International Arctic Systems for Observing the Atmosphere or "IASOA" (www.iasoa.org) is a consortium of 10 Observatories that encircle the Arctic Ocean and are each supported by the National environmental monitoring and/or university institutions of each respective Arctic Nation. Although often co-located with regular weather stations that broadcast to the GTS, the Observatories are distinguished by the operation of suites of complex instrumentation that collect information on not only the how, but also the why of how the Arctic atmosphere and surface are interacting and evolving. This wealth of research grade data is being used for individual process studies such as cloud-aerosol interactions and surface energy budgets as well as pan-Arctic climatologies of a number of components, for instance black carbon, ozone, methane, aerosol transport and cloud macrophysical properties. The IASOA consortium was initiated by NOAA during the International Polar Year (2007-2009) with a goal of standardizing measurement practices, filling geographical measurement "gaps", and developing standardized data processing and QC procedures to create interoperable datasets across the network. IASOA has also developed a data portal for accessing a wide selection of data through a standardized meta-data and harvesting procedure. https://www.esrl.noaa.gov/psd/iasoa/dataataglance The data portal serves as only a partial inventory, however it documents that there are hundreds of measurements producing information on scores of variables (made by as many instruments) with multiple versions of data being stored separately in a myriad of different files, formats and locations. The portal has proved to be insufficient for comprehensive and uncomplicated data access; many of these data sets data are collected by non-NOAA and non-U.S. organizations with a wide range of archiving, metadata, availability and attribution practices. It is particularly daunting to assemble coordinated sets of these measurements across the network to serve such purposes as system science studies and model or satellite verification that require a consistent network product. The access, usability and significance of the research-grade data from IASOA network will be substantially improved by the creation of federated data files. These files would be standardized, integrated, comprehensive, and inclusive of the totality of measurements at each observatory; in other words Merged Observatory Data Files (MODFs). The files will include variables such as surface meteorology (temperature, pressure, skin temperature, 2m and 10 m temperature, dew point temperature, 2m and 10m specific humidity, 2m and 10 m wind speeds/direction, surface visibility, etc.), surface energy budget terms (incoming and outgoing LW/SW radiation, latent and sensible heat fluxes, etc.) , atmospheric constituents (O3 , CH4 , CO2, etc.), cloud (IWP/LWP, fraction, hydrometeor sizes and concentrations, layer heights) and aerosol properties (absorption, scattering particle sizes and concentrations, etc.), boundary layer properties (depth, stability), integrated water vapor, atmospheric optical properties (cloud opacity and aerosol optical depth), precipitation (type, accumulation, extent), and surface properties (ground flux, albedo, vegetation, surface roughness, soil moisture). This presentation will diagram in particular the development of MODFs that is underway to support the model-observations verification objectives of the Year of Polar Prediction by matching observatory file content and formats as much as possible to the YOPP Special Observing Period (SOP) model data sets. It is expected that these MODF data files will substantially improve access to complex Arctic ground site data sets across a network beyond what can be achieved with just archives, portals and existing data discovery and synthesis tools. In addition, these files will set a precedent for organizing surface observation data to support model-observation research for other integrated special observation data sets such as NOAA ship cruises and special campaigns.
Exhibit No:
63
Title:
GFDL coupled Carbon- Chemistry- Climate Modeling for the 6th Coupled Model Intercomparison Project
Author(s):
John Dunne, Larry Horowitz, Elena Shevliakova, Alistair Adcroft, Paul Ginoux, Robert Hallberg, Isaac Held, Jasmin John, John Krasting, Sergey Maleshev, Vaishali Naik, Fabien Paulot, Charles Stock, Michael Winton, Niki Zadeh, and Ming Zhao
Abstract:
For the 6th phase of the international Coupled Model Intercomparison Project, GFDL has developed an unprecedentedly unified coupled Carbon- Chemistry- Climate Model that merges advances over the last decade in the modeling of physical climate, atmospheric chemistry, and the carbon cycle. This unified Earth System Model will be described in its major features and application to environmental and ecosystem variabity and change, air and water quality, and feedbacks on a continuum of timescales from days to centuries. Key advances in this version include a doubling of Ocean and atmospheric resolution, advanced atmospheric and Ocean physics, comprehensive earth system cycling of carbon and dust interactions, updated atmospheric chemistry and aerosols, and dynamic vegetation canopy, fire and other land interactions.
Exhibit No:
64
Title:
Modeling as an integral part of the NOAA Ocean Acidification Program (OAP)
Author(s):
Fabian A. Gomez, Sang-Ki Lee, Rik Wanninkhof, and Leticia Barbero
Abstract:
Significant advances in our understanding of ocean carbon system dynamics have been made during the last decades, but there is still large uncertainty in the spatiotemporal variability of the ocean carbon chemistry. The ocean acidification efforts require high-resolution ocean biogeochemical models to reduce this uncertainty and better understand and attribute observed ocean acidification patterns. Here we present results from an ocean-biogeochemical model for the Gulf of Mexico (GoM) based on the regional ocean model system (ROMS), and progress in the configuration of a North Atlantic basin model using the Modular Ocean Model (MOM) including the biogeochemistry module TOPAZ (Tracers of Phytoplankton with Allometric Zooplankton). The outputs from our biogeochemical model for the GoM compare reasonably well with observed patterns in dissolved inorganic carbon (DIC), alkalinity, and partial CO2 pressure (pCO2) derived from research cruises (GOMECC, GRIIDC), and a coastal buoy (CenGOOS-01). We find significant trends in DIC, pCO2, and pH at surface during the study period (1984-2014), reflecting ocean acidification due to atmospheric CO2 increase. The model pCO2 variability is mainly modulated by SST excepts in the northern GoM shelf, where large biological productivity significantly reduces surface DIC in spring-summer, thus decreasing pCO2 and increasing pH. The model air-sea CO2 fluxes show important spatial and seasonal variability, with the GoM largely acting as CO2 sink in winter and CO2 source in summer.
Exhibit No:
66
Title:
Developmental Testbed Center (DTC) Support of the Convective Allowing Model (CAM) version of the Finite Volume Cubed Sphere (FV3) and related Unified Forecast System (UFS) evaluation
Author(s):
Jeff Beck1, Jamie Wolff2, Gerard Ketefian3, Julie Schramm2, Lindsay Blank2, Michelle Harrold2, Michael Kavulich2, Laurie Carson2, and Donald Stark2 1Cooperative Institute for Research in the Atmosphere (CIRA) at NOAA/ESRL/GSD and the Developmental Testbed Center (DTC) 2National Center for Atmospheric Research (NCAR) and the Developmental Testbed Center (DTC) 3Cooperative Institute for Research in Environmental Sciences (CIRES) at NOAA/ESRL/GSD and the Developmental Testbed Center (DTC)
Abstract:
In response to recommendations from the UCACN Model Advisory Committee (UMAC), NCEP/NOAA/EMC is moving towards a Unified Forecast System (UFS) that will encompass both regional and global spatial scales, providing short-term to seasonal forecasts. At the center of this plan is unification around the GFDL FV3 dynamical core, including development of the regional, convective-allowing model (CAM) version of the FV3. Effective adoption of the UFS-CAM by a broad sector of the NWP community will require well-defined code management practices, documentation, community access to code and datasets, user support, and testing and evaluation protocols. While the research community needs flexible and simple tools that support basic research to quickly engage and pursue research projects, EMC requires a software infrastructure that minimizes performance impacts and failures. Therefore, it is important to carefully assess the best approaches for supporting a community modeling infrastructure that meets the needs of the research and operational communities. An overview of the initial efforts by the DTC to develop this UFS-CAM support will be provided during this presentation. In addition to the regional version of the FV3, several groups have been working on testing global configurations of the FV3. One such effort, undertaken during the 2018 Hazardous Weather Testbed Spring Forecasting Experiment (HWT-SFE), involved the contribution of several global FV3 member configurations with a CONUS nest to the Community Leveraged Unified Ensemble (CLUE) dataset. Variations in the microphysics, planetary boundary layer (PBL), and cumulus parameterizations (outside of the high-resolution nest) were employed, making up 11 total FV3 members that were included in CLUE. An overview of the analysis underway using this dataset will also be provided.
Exhibit No:
68
Title:
JEDI exposition
Author(s):
Daniel Holdaway, JCSDA
Abstract:
The Joint Effort for Data assimilation Integration (JEDI) - led by the Joint Center for Satellite Data Assimilation (JCSDA) - is an inter-organizational endeavor to develop a common framework for performing data assimilation on model native grids. In this fair exhibit we demonstrate some of the capabilities of the JEDI system and explore the system architecture. We will also provide details of how the FV3GFS system has been interfaced to JEDI and show how different flavors of data assimilation can be exercised for the FV3GFS system. An overview of how new observations are added to the system is also provided. We will discuss the GitHub, Zenhub, GitFlow, WIKI and ReadTheDocs collaborative tools and issue trackers that are used to develop JEDI and detail some of the working practices that are applied.
Exhibit No:
69
Title:
Towards the Implementation of the National Ocean Service's Lake Michigan and Huron Operational Forecast System (LMHOFS)
Author(s):
Machuan Peng1*, Aijun Zhang1, Carolyn Lindley1, John Kelly2, Yi Chen2, Eric Anderson3, and Greg Lang3 1 NOAA/NOS/Center for Operational Oceanographic Products and Services (CO-OPS) 2 NOAA/NOS/Office of Coastal Survey (OCS) 3 NOAA/OAR/Great Lakes Environmental Research Laboratory (GLERL)
Abstract:
A new operational forecast system is being developed for the Lake Michigan and Huron region by Oceanic and Atmospheric Research (OAR) and National Ocean Service (NOS). Lake Michigan and Huron Operational Forecast System (LMHOFS) uses the Finite Volume Community Ocean Model (FVCOM) to provide users with nowcast (analyses of near present) and forecast guidance of water levels, currents, and water temperature out to 120 hours, four times per day. Lake Michigan is connected to Lake Huron via the Straits of Mackinac. The two bodies of water need to be combined in an integrated grid in the model to obtain accurate hydrodynamic results. Otherwise, the water exchange through the Straits will be misrepresented, and the model results may be synoptically wrong. By combining Lake Michigan and Lake Huron into one model grid and invoking advanced model schemes and algorithms, LMHOFS is expected to provide more accurate information than NOAA's existing Lake Michigan OFS (LMOFS) and Lake Huron OFS (LHOFS), which have separate model domains based on the Princeton Ocean Model (POM). The successful implementation and operation of LMHOFS will provide reliable information to help pilots and mariners more safely and efficiently navigate through Lake Michigan and Huron, and also provide high-quality digital support for coastal zone management and hazard mitigation in this Great Lakes region. KEYWORDS: Lake Michigan and Huron, Nowcast/Forecast Systems, FVCOM
Exhibit No:
70
Title:
Analysis of Fog and Smog Events over Lahore
Author(s):
Danyal Bin, Taufiq Tahir, and Muhammad Bilal. Institute of Space Technology, Islamabad. Pakistan.
Abstract:
In a report released in 2017 by World Health Organization, Pakistan was ranked as the 4th most polluted country in the world. Lahore faced an intense smog event in November 2016 and 2017 causing fatal breathing problems and hindering daily life for locals. Fog also occurs in most regions of Punjab in winters and hampers daily life in form of fatalities through road accidents, road blockages and flights delays. Study area we chose for our project is Pakistan's Punjab cities of Lahore and Faisalabad. We have performed Climate Data Operations on Re-Analysis Datasets of European Center for Medium Range Weather Forecast for Fog and Smog Events of 2016 and 2017. Output was generated by developing scripts over Grid Analysis and Display System in form of regional circulations and graphs of meteorological parameters over the study area. Moreover, National Oceanic and Atmospheric Administration's HYSPLIT Model was used to identify the air parcels source over the study region during smog months. The results from our study will help to understand fog and smog events through reanalysis datasets over Lahore and Faisalabad for first time in Pakistan. It will also open new areas of research relating to smog sources, public awareness and timely planning to mitigate effects of such hazards in future, not only in Pakistan but as a joint global challenge.
Exhibit No:
71
Title:
User-Based Skill Assessment Tool for NOS Coastal Ocean Operational Forecast system
Author(s):
Aijun Zhang (NOAA/NOS/CO-OPS)
Abstract:
The National Ocean Service (NOS) is developing and implementing oceanographic nowcast and forecast modeling systems to support navigational and environmental applications in U.S. coastal waters. NOS requires these modeling systems, whether developed within or outside NOS be assessed for skill in adherence to NOS standards (Hess et al., 2003). Skill assessment is an objective measurement of how well the model nowcast or forecast guidance does when compared to observations. Therefore, NOS has developed a software package that computes the scores, such Root-Mean-Square-Errors, Central Frequency, etc., automatically using data files containing observed, nowcast, and forecast variables. The skill assessment results are displayed in tables and figures which can be incorporated into model evaluation reports. Standard statistical variables and NOS skill assessment standards will also be presented. The tool is a stand-alone package and can be run in Unix environment either Linux servers or Linux clusters.
Exhibit No:
72
Title:
Enhanced 30-year global daily snow and ice cover dataset for environmental modeling and climate change studies
Author(s):
Peter Romanov, NOAA-CREST at City University of New York and NESDIS/STAR
Abstract:
Snow and ice cover are major components of the Earth's cryosphere and critical elements of the Earth's climate system. Information on the spatial extent and variations of snow and ice presents the primary input to NWP, hydrological, climate and other environmental models. Current weather satellites provide routine monitoring of the global snow and ice cover at daily time interval and at high, about 1km or even less, spatial resolution which is generally sufficient for most operational applications. In the same time longer-term snow and ice cover data needed for climate modeling and climate change studies are available only at a much coarser spatial and temporal resolution and often have limited area coverage. In particular, a widely used snow cover dataset of Rutgers University produced from NOAA interactive snow and ice cover chart and spanning from 1972 to the present day has the spatial resolution of 180 km, covers only the Northern Hemisphere and is generated at a weekly time step. Since 2006 NOAA NESDIS has been using Global Multisensor Automated Snow and Ice Mapping System (GMASI) to support operational monitoring of the Earth's cryosphere. The GMASI system implements a fully automated snow and ice mapping technique using a synergy of optical observations from the AVHRR sensor onboard NOAA and METOP satellites and observations in the microwave from SSMI and SSMIS sensors onborad DMSP satellites. The primary output of the system is a daily spatially-continuous (gap-free) gridded map of snow and ice cover at the nominal spatial resolution of 4 km. Recently the GMASI algorithm has been applied to consistently reprocess historical satellite data back to 1988. This yielded an over 30-year long daily dataset of high resolution global maps of snow and ice cover. In the presentation we will show sample snow and ice maps comprising the dataset and will demonstrate advantages of the new dataset before other available snow and ice datasets. We will also discuss the long-term trends in the snow cover extent derived from the GMASI-reprocessed data and will present the enhanced global snow and ice cover climatology. The latter includes yearly and multi-year mean snow cover duration, monthly frequency of snow and ice occurrence for all years in the time series as well as daily and monthly mean frequency of snow and ice occurrence.
 

About the Fair

The Fair provides an opportunity for the NOAA modeling community and external partners to connect with one another, and to share knowledge and information on modeling and technological capabilities in support of Unified Modeling and its interdisciplinary applications.

The Fair will be held at the NCWCP Conference Center during the afternoons of September 10 and 11.

Fair Exhibit Abstract Submission

Abstract submission for exhibits is done through the main registration page, and includes registration for the meeting. The deadline for abstract submission is August 8, 2018. Exhibitors will be notified of their acceptance to the Fair and associated technical specifications by August 15, 2018.

Fair abstracts should target main modeling activities and projects at NOAA Laboratories, Centers, Programs, and their partners in the community. Abstracts should be beyond the scope of projects by individuals. Abstracts that specifically address interdisciplinary applications or opportunities are especially encouraged.

Exhibits can be focused on various elements of the end-to-end modeling framework from underlying model development, application and product generation, visualization tools, analysis and verification software, or service delivery tools; exhibits on NOAA modeling frameworks and R&D programs are also relevant. Exhibits should be at a technical level that is accessible to modelers and users across the array of NOAA disciplines.

Abstract language should be at a technical level that is accessible to modelers and users across the array of NOAA disciplines. Abstract title length should not exceed 300 characters. Abstract length should not exceed 3000 characters.

Contact:

For more information, please contact:
Rachel Horoschak