This page lists past seminars and presentations by STAR
scientists and visiting scientists. These seminars include the STAR
Science Forum and similar events. Presentation materials for
seminars will be provided when available.
STAR Science Seminars Note: This seminar will be presented online only.
Presenter(s): Jason Apke,Cooperative Institute for Research in the Atmosphere (CIRA)
Contributions From: Steven Miller (CIRA), Dan Lindsey (NOAA/STAR), Kristopher Bedka (NASA/Langley Research Center), and Eric Olson (CIRA)
Sponsor(s): STAR Science Seminar Series
Abstract: The science of computing brightness motion in imagery pairs and sequences at every image pixel, or so-called Dense Optical Flow (DOF), has advanced considerably in the last four decades to support applications like objective robotic vision, autonomous driving, augmented reality, and motion picture special effects. While seldom explored, DOF derivation is now enabled in visible and infrared satellite imagery by the spatial and temporal resolution of new-generation instruments like the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite (GOES)-R series platform. DOF derivation from satellite imagery would have a variety of unique applications that are beneficial for research,forecasting, and decision-making products currently in development. These applications include atmospheric motion vector retrieval, temporal brightness interpolation, feature tracking, feature nowcasting, image stereoscopy, and semi-Lagrangian cloud-top cooling derivation. This presentation will go into detail on how some of these new DOF techniques are derived and highlight studies at the Cooperative Institute for Research in the Atmosphere to explore and validate novel applications. Demonstrations will also be shown on how improving feature tracking with DOF can complement machine-learning and artificial intelligence efforts for image classification and prognosis tasks. Examples of several DOF satellite imagery applications will be presented along with validation comparisons to state-of-the-art Derived Motion Wind products. Finally, this presentation will highlight current efforts to bring novel DOF applications into relevant operational environments.
Jason Apke is a Research Scientist I at the Cooperative Institute for Research in the Atmosphere. He received his Bachelor of Sciences degree in Meteorology from the University of Northern Colorado in Greeley, CO in 2011, a Master's degree in Earth and Atmospheric Sciences from the University of Nebraska-Lincoln in 2013, and a Ph.D. in Atmospheric Sciences from the University of Alabama-Huntsville in 2018. His dissertation focused on using atmospheric motion vectors to depict flow fields over deep convection observed from super-rapid scan geostationary satellite imagery, and how they could be used to identify signals relevant severe weather forecasting. He currently works on developing and implementing dense-optical flow derivation algorithms for a variety of satellite meteorology-related applications.
STAR Science Seminars Note: This seminar will be presented online only.
Presenter(s): Dr. Chris Collimore, NOAA CESSRST/City College of New York
Sponsor(s): STAR Science Seminar Series
Abstract: The relationship between aerosol concentrations and tropical cyclone (TC) formation is investigated. Sixty-three convective cloud clusters in the tropical Atlantic that developed into TCs (developers) and 98 tropical Atlantic clusters that dissipated before becoming a TC (nondevelopers) were examined. Aerosol content (as measured by satellite-derived aerosol optical depth) near developers was averaged; likewise for nondevelopers. The average aerosol content surrounding developers was much higher than that surrounding nondevelopers. This indicates high aerosol concentrations do not significantly inhibit a cluster's ability to develop into a TC, which is contrary to widespread perception. Several analyses indicate the measured difference between developer and nondeveloper aerosol content is quite robust.
Dr. Chris Collimore obtained his B.A. from Dartmouth College in 1985, majoring in Geography. He then obtained an M.S. in Atmospheric Science from Colorado State University in 1989. The topic of his thesis was the cause of the cessation of El Nino. He then worked as a researcher at the University of Wisconsin-Madison for several years, using satellite data to test different theories related to atmospheric phenomena. Most notably, he investigated seasonal variations of deep convection in the tropics and how to predict them. He then returned to graduate school and earned his doctorate in Atmospheric Science from UCLA in June, 2018. The topic of his dissertation was the role aerosols play in hurricane formation. Dr. Collimore is currently a Postdoc at NOAA CESSRST/City College of New York.
Abstract: The Indian Ocean and the monsoon system are dynamically complex. In the Bay of Bengal (BoB) and southeastern Arabian Sea (SEAS), surface circulation is strongly influenced by the Indian monsoon and notable local eddying that modulates the East India Coastal Current (EICC). In this study, the role of freshwater transported from the BoB into the SEAS in determining both the timing of monsoon onset and the strength of the ensuing monsoon is examined. The scientific value of sea surface salinity (SSS) derived from NASA's Soil Moisture Active Passive (SMAP) has revolutionized the monitoring freshwater transport through the EICC in the BoB and has significant value for monsoon studies.It is found that the long-term decrease in moisture flux and freshwater transport into the SEAS, along with a rise in ocean heat content (OHC) over a15-year duration after 1994 contributed to a lack of strong monsoons in recent years; the prevailing interannual and interdecadal variability in these parameters associated with the Indian Ocean Dipole (IOD) and El Nio Southern Oscillation (ENSO) events favored weaker and normal monsoons after 1994. Further comparisons are made between the strong monsoon in 1994 to the recent strong monsoon in 2019. Intraseasonal oscillations (ISOs) significantly contribute to the variability and strength of rainfall associated with the Indian monsoon. Satellite observations of the atmosphere and ocean are used to monitor the 30-90-day, 10-20-day, and 3-7-day ISOs, and how they influence local dynamics. This research has shown the importance of using blended satellite altimetric observations and satellite-derived salinity for the monitoring of ISOs in the Indian Ocean. While SLA best captures the conditions that lead to genesis of 30-90-day and 10-20-day ISOs, the current altimeter footprints are too small to adequately capture the 3-7-day signal. Given the amplitude of SLA signals in the AS and BoB, it is very difficult for a single altimeter to resolve ISO patterns, and so altimetry missions with wide swaths are needed,such as the upcoming Surface Water and Ocean Topography (SWOT) mission. Additional high resolution, blended ocean color observations are found to be useful for the monitoring of ISOs and the impact of ISOs on ocean basins, particularly as the sensors' wide swaths allow them to capture both the structure and intensity of ISO features. While SSS is more useful for the monitoring of oceanic responses to ISOs rather than their prediction, it more clearly captures the ISO signal due to the wide swath (1000 km for SMAP) and faster repeat cycle. In order to further monitor and understand these ISOs, continuous salinity missions are required. As neither SLA nor SSS experience the cloud interference that SST does, it is important to be able to use these parameters to monitor ISOs in real time.
Heather received her BS in Environmental Geoscience at Boston College where her senior thesis involved the reconstruction of 4500 years of paleorainfall in Puerto Rico. She then went on to earn her MS in Meteorology from Florida State University where she became involved in research with tropical climatology and dynamics using scatterometer winds, which has ultimately resulted in her PhD work at the University of South Carolina, which the use of satellite observations of the ocean and atmosphere to study air-sea interactions and air-sea coupled feedbacks involved with intraseasonal oscillations in the Indian Ocean and how these processes influence the Indian monsoon system. Several of the satellite data-processing techniques developed are both novel and unique in their application to the study of oceanographic and meteorological processes in the Indian Ocean. Heather has published numerous research articles in leading oceanography and remote sensing journals,including Journal of Geophysical Research-Oceans, Deep-Sea Research, Advances in Space Research, Journal of Climate, and Geophysical Research Letters.Recently, the University of South Carolina Research Office awarded her a 2020 Breakthrough Graduate Scholar award and the2020 Marine Science Publication Award. Heather's PhD research work has been partially supported by the 2019-2020 NASA/South Carolina Space Grant Consortium Graduate Research Fellowship.
Abstract: Global climate models represent small-scale processes such as convection using subgrid models known as parameterizations, and these parameterizations contribute substantially to uncertainty in climate projections. Machine learning of new parameterizations from high-resolution model output is a promising approach, but such parameterizations have been prone to issues of instability and climate drift, and their performance for different grid spacings has not yet been investigated. Here we use a random forest to learn a parameterization from output of a three-dimensional high-resolution idealized atmospheric model. The parameterization leads to stable simulations at coarse resolution that replicate the climate of the high-resolution simulation. Retraining for different coarse-graining factors shows the parameterization performs best at smaller horizontal grid spacings. Our results yield insights into parameterization performance across length scales, and they also demonstrate the potential for learning parameterizations from global high-resolution simulations that are now emerging.
Bio(s): Janni Yuval is a post-doctoral fellow at MIT at the department of Earth, Atmospheric and Planetary Sciences. At MIT he works with Paul O'Gorman on machine learning parameterization. He is a diverse person with a wide spectrum of interests and skills. He has a BSc. in theoretical physics, an MSc in theoretical soft matter physics, and a PhD in atmospheric dynamics. Furthermore, after finishing his PhD he worked as an algorithm developer at Mobileye. Later, he worked as a data scientist at Clalit Research Institute, where he used machine learning, and causal inference methods to develop personalized medicine. Nowadays, he is excited about the possibility to use machine learning for reducing the uncertainty in climate projections. The work he will present is accepted to Nature Communications (in press).
Abstract: Since 2017 LST is an essential climate variable (ECV) as specified by the Global Climate Observing System (GCOS). Together with Land Surface Emissivity (LSE),which only recently has become available as an independent and physically retrieved parameter, LST offers a broad range of applications, e.g. the monitoring land degradation, Urban Heat Islands (UHI), and determining the melting of snow and ice. However, for a meaningful scientific use of satellite LST products, reliable information on their uncertainty has to be available. The most accurate and established method to obtain such information is the validation against in situ LST: unfortunately, this approach is complicated by the fact that field measurements cannot be controlled to the same extent as in the laboratory and are often not representative on the - usually considerably larger - spatial scale of satellite measurements. Karlsruhe Institute of Technology (KIT) addresses these issues by operating permanent LST validation stations in naturally homogenous sites, e.g. on the vast gravel plains of the Namib Desert and on Lake Constance (Germany - Switzerland). An overview of KIT's sites and methods used to obtain in-situ LST and LSE is given and highlights from an international field inter-comparison experiment will be shown (ESA FRM4STS project). The presentation concludes with examples of recently available satellite products that retrieve LST and LSE simultaneously or under all-weather conditions. -----------------------------------
Presenter(s): Frank-M. Gttsche received his M.Sc. degree in Physics (1993) and his PhD in Geophysics (1997) from the University of Kiel, Germany. After a year as a scholar at the Department of Earth Sciences, Uppsala University, Sweden, he worked between 1997 and 2003 as research scientist at the Institute of Meteorology and Climate Research (IMK), Forschungszentrum Karlsruhe, Germany. Afterwards Frank joined the United Arab Emirates University, UAE, as Assistant Professor and lectured Physics and Remote Sensing classes while additionally serving as scientific consultant to EUMETSAT's Land Surface Analysis Satellite Application Facility (LSA SAF). In 2007 Frank returned to Karlsruhe Institute of Technology (KIT), Germany, where he continues to work as senior researcher for LSA SAF and is in charge of KITs permanent ground truth stations in Europe and Africa. His research focuses on the in-situ determination of Land Surface Temperature (LST) and its use for validating satellite-derived LST products. Frank serves as focus area lead (Europe) of the CEOS Land Product Validation (LPV) sub group on LST & Emissivity and is involved in the Copernicus LAW project (in-situ validation of LST, aerosol and water vapour) and ESA's LST Climate Change Initiative (CCI). https://orcid.org/0000-0001-5836-5430 -----------------------------------
Abstract: As stated in the recent Decadal Survey for Earth Observations from Space, the climate and weather forecast predictive capability for precipitation intensity is limited by gaps in the understanding of basic cloud-convective processes. This process lacks several observational constraints, one being the difficulty in obtaining the thermodynamic profile (i.e., vertically resolved pressure,temperature, and water vapor structure) in close proximity to convective clouds. The objective of the Radio Occultations and Heavy Precipitation (ROHP) experiment, orbiting onboard the Spanish PAZ satellite since May 2018, is to demonstrate the simultaneous capability to detect heavy precipitation along the same RO ray paths used to estimate the thermodynamic profile. While conventional RO does not directly provide this capability, PRO enhances standard RO by receiving the GNSS signals in two orthogonal polarizations (H and V). Owing to hydrometeor asymmetry, the H- and V-polarized radio signals propagating through heavy precipitation will experience differential phase delays,measurable via the ROHP polarimetric antenna. In this presentation we will discuss the on-orbit calibration and validation of the ROHP data, and present potential applications for these data in weather modeling. The ROHP calibration is performed with an extensive dataset of one year of observations, co-located with independent information from Global Precipitation Mission (GPM) precipitation products and ionospheric activity. The validation demonstrates how the calibrated products can be used as a proxy for heavy precipitation. The PRO signals also exhibit positive differential phase signatures well above the freezing level, indicating possible sensitivity to frozen hydrometeors and the cloud vertical structure. This knowledge of the presence of precipitation associated with the RO observation is useful for the evaluation and diagnosis of NWP forecast models. The use of PRO in data assimilation methods will require an observation operator that can simulate all contributions to the differential phase delay along realistic RO propagation paths, taking into account the cloud structure.
Presenter(s): F.J. "Joe" Turk is a radar scientist at JPL, where he has been since 2009. From 1995-2009, he was a member of the meteorological applications group at the Naval Research Laboratory, Marine Meteorology Division, in Monterey, CA. He received his Ph.D. degree from Colorado State University, and his M.S. and B.S. degrees from Michigan Technological University, all in electrical engineering. His work experience has covered polarimetric weather radar, satellite passive microwave and radar observations and applications, microwave radiative transfer, polarimetric RO, and airborne radar and wind lidar observations. He is a member of NASA's Precipitation Measurement Missions science team.Chi O. Ao is a research technologist at JPL with over 15 years of experience in GNSS radio occultation (RO) receiver tracking and inversion techniques, simulation methods, data analysis, and climate applications. He leads the RO processing and applications team from multiple missions including CHAMP and COSMIC at JPL. He is currently the GNSS-RO Scientist of the Jason-CS/Sentinel-6mission, the Principal Investigator of the NASA Earth Science U.S. Participating Program for the ROHP-PAZ experiment, and a member of the Decadal Survey Incubation Study Team for the Planetary Boundary Layer.
Abstract: A nonlinear ensemble averaging technique is demonstrated using neural networks applied to one year (2017) of Global ocean Wave Ensemble forecast System (GWES) data provided by NCEP. Post-processing algorithms are developed based on multilayer perceptron neural networks (NN) trained with altimeter data to improve the global forecast skill, from nowcast to forecast ranges up to 10 days, including significant wave height (Hs) and wind speed (U10). NNs are applied as an alternative to the typical use of the arithmetic ensemble mean (EM). The novel method shows that one single NN model with 140 neurons is able to improve the error metrics for the whole globe while covering all forecast ranges analyzed. The bias of the widely used EM of GWES that varies from -10% to 10% for Hs compared to altimeters can be reduced to values within 5%. The RMSE of day-10 forecasts from the NN simulations indicated a gain of two days in predictability when compared to the EM, using a reasonably simple post-processing model with low computational cost.
Bio(s): Dr. Ricardo Campos has been studying and working in the fields of Ocean Engineering, Physical Oceanography, and Meteorology for the last 17 years, more specifically with ocean waves. He has recently concluded postdoctoral research at the University of Maryland in collaboration with EMC/NCEP/NOAA, working on neural networks to improve wave forecasts. Ricardo is currently a principal investigator and visiting professor at the Centre for Marine Technology and Ocean Engineering (CENTEC/IST) in Portugal.
STAR Science Seminars Note: This seminar will be presented online only.
Presenter(s): Tapio Schneider, Caltech and NASA/JPL
Sponsor(s): STAR Science Seminar Series Host: Imme Ebert-Uphoff, CIRA
Abstract: While climate change is certain, precisely how climate will change is less clear. But breakthroughs in the accuracy of climate projections and in the quantification of their uncertainties are now within reach, thanks to advances in the computational and data sciences and in the availability of Earth observations from space and from the ground. I will survey the design of a new Earth system model (ESM), under development by the Climate Modeling Alliance (CliMA) of Caltech, MIT, Jet Propulsion Laboratory, and the Naval Postgraduate School. The talk will cover key new concepts in the ESM, including turbulence, convection, and cloud parameterizations and fast and efficient algorithms for assimilating data and quantifying uncertainties through a three-step process involving calibration, emulation, and sampling.
Bio(s): Tapio Schneider is the Theodore Y. Wu Professor of Environmental Science and Engineering at Caltech and a Senior Research Scientist at NASA's Jet Propulsion Laboratory. His research has elucidated how rainfall extremes change with climate, how changes in cloud cover can destabilize the climate system, and how winds and weather on planetary bodies such as Jupiter and Titan come about. He is currently leading the Climate Modeling Alliance (clima.caltech.edu), which is building a new Earth system model that automatically learns from diverse data sources.
Abstract: NOAA CoastWatch/OceanWatch provides easy access for everyone to global and regional satellite data products for use in understanding, managing and protecting ocean and coastal resources and for assessing impacts of environmental change in ecosystems, weather, and climate. A CoastWatch objective is to educate and train users about using satellite data and CoastWatch products. The CoastWatch Data Portal is a collection of services that facilitate the discovery and utilization of satellite ocean remote sensing products. In this seminar, Michael Soracco will provide an overview of these services and demonstrate the map viewer for the CoastWatch Data Portal. You will learn how to display, explore, and access sea surface temperature, salinity, color, wind, synthetic aperture radar and sea level anomaly data products available through NOAA CoastWatch. https://coastwatch.noaa.gov/cw/index.html
Bio(s): Michael Soracco is the HelpDesk Coordinator for NOAA CoastWatch/OceanWatch/PolarWatch. He specializes in user access by developing and maintaining data products, the data portal (and website) and runs the helpdesk. Michael studied aerospace engineering for his B.AE at Georgia Institute of Technology and earned his Master's degree in Business at Central Michigan University. He served as a commissioned officer in the NOAA Corps servicing equatorial moored buoys, conducting coastal hydrographic surveys, and as an ocean remote sensing operations officer. He has developed and taught the NOAA GIS course and is a fundamental source of the CoastWatch program's corporate memory.
Abstract: I describe the application of convolutional neural networks (CNN), a type of deep-learning method, to high-impact weather. CNNs are specially designed to learn directly from spatial grids, which improves both skill and interpretability. Specifically, I develop and test CNNs for two tasks. The first is tornado prediction, where two CNNs predict next-hour tornado occurrence for a given storm, using datasets similar to those used by forecasters in real-time operations. The tornado models achieve an area under the receiver-operating-characteristic curve (AUC) of 0.94 and critical success index (CSI) of ~0.3. This is competitive with a machine-learning model currently used in operations, which suggests that the CNNs would also be suitable for operations. Specialized machine-learning-interpretation methods highlight the importance of a deep reflectivity core and strong mesocyclone, as well as low-level instability and wind shear in the surrounding environment. Also, interpretation methods suggest that a rear-flank downdraft with too much precipitation and negative buoyancy can lead to tornadogenesis failure, which corroborates some previous literature. The second task is front detection, where a CNN draws warm and cold fronts in reanalysis data. I use the CNN-detected fronts to create a 40-year climatology over North America. On a large scale, fronts are most common in the mid-latitude cyclone track, which migrates poleward from winter to summer, equatorward during El Nio, and poleward during La Nia. Also, the cyclone track appears to be migrating poleward as a consequence of global warming. These results are broadly consistent with the few pre-existing climatologies, although there are some discrepancies that should be investigated in the future. Overall, I demonstrate that deep learning can be used to advance both the prediction and understanding of high-impact weather.
Bio(s): Dr. Ryan Lagerquist recently graduated with a Ph.D. in Meteorology from the University of Oklahoma. He has been researching machine-learning applications in atmospheric science for 8 years with organizations including Environment Canada, the University of Alberta, Google, NCAR, and CIMMS. Ryan begins a postdoc with the Cooperative Institute for Research in the Atmosphere (CIRA) in June. Ryan is also program co-chair of the 2021 Artificial Intelligence conference at the AMS annual meeting.
Abstract: Data-driven algorithms, in particular neural networks, can emulate the effect of sub-grid scale processes in coarse-resolution climate models if trained on high-resolution climate simulations. However, they may violate key physical constraints, lack interpretability, and make large errors when evaluated outside of their training set. First, we show that nonlinear physical constraints can be enforced in neural networks, either approximately by adapting the loss function or to within machine precision by adapting the architecture. As these physical constraints are insufficient to guarantee generalizability, we additionally propose a framework to incorporate physical rescalings within the neural network: By aligning the distributions of both input and output variables across climates, we transform extrapolation into interpolation and significantly improve the ability of neural networks to generalize to unseen climates. Third, we present recent tools designed to interpret machine-learning parametrizations of convection, which we leverage to show that two sets of neural networks trained on different datasets behave consistently with observations. Our interpretability tools can further diagnose the stability of machine-learning parametrizations when coupled to atmospheric fluid dynamics, which helps the ultimate goal of improving the performance and stability of coupled online climate simulations.
Bio(s): Tom Beucler is a project scientist affiliated with UC Irvine and Columbia University. He is interested in atmospheric physics, machine learning and climate risk analysis.
Abstract: The success of a machine learning system depends on not only the choice of machine learning algorithm but also on how the the whole machine learning pipeline is constructed. In this presentation, the key components of the machine learning pipeline, including problem definition, preprocessing, choosing appropriate algorithms, training, evaluation, and interpretation will be described. Common approaches in the atmospheric sciences for each component will be explained and linked with examples from machine learning applications in the atmospheric sciences. Finally, challenges of transitioning machine learning systems to operational use will be discussed.
Bio(s): David John Gagne is a Machine Learning Scientist in the Computational Information Systems Laboratory (CISL) and the Research Applications Laboratory (RAL) at the National Center for Atmospheric Research (NCAR). His research focuses on developing machine learning systems to improve the prediction and understanding of high impact weather, and to enhance weather and climate models. During his time at NCAR, he has collaborated with interdisciplinary teams to produce machine learning systems to study hail, tornadoes, hurricanes, and renewable energy. He has also developed short courses and hackathons to provide atmospheric scientists hands-on experience with machine learning. Gagne received his Ph.D. in meteorology from the University of Oklahoma in 2016 and completed an Advanced Study Program postdoctoral fellowship at NCAR in 2018. In addition to his duties at NCAR, he also serves as chair of the American Meteorological Society Artificial Intelligence Committee.
Abstract: This introductory talk provides basic information about mostly used machine learning (ML) techniques and some ML applications developed to enhance different components of Numerical Weather Prediction (NWP) systems. Basic groups of ML applications that have been already developed for NWP systems are overviewed.Major challenges that NWP currently faces are discussed. It is shown that many of these problems can be resolved or alleviated using ML techniques. ML applications developed for NWP model initialization/data assimilation, model improvements, and model output post processing are discussed. Several examples of such application (ML satellite retrieval algorithm, ML fast parameterizations of subgrid processes, and ML nonlinear ensembles) are introduced to illustrate the capabilities of ML techniques. Advantages and limitations of ML techniques are discussed.
Bio(s): Dr. Vladimir M. Krasnopolsky got his M.S. in Theoretical and Computational Physics and Ph. D. in Theoretical Nuclear Physics from the Moscow State University (Russia). He worked in the field of theoretical nuclear physics at the Institute of Nuclear Physics (Moscow State University) before coming to the US in 1989. Since 1990 he has been working in the field of numerical weather and climate prediction and AI applications. Vladimir works on applications of remote sensing and satellite data in meteorology, oceanography, and numerical weather and climate prediction. Dr. Krasnopolsky also works with various machine learning techniques. He developed multiple neural network applications for numerical weather and climate prediction. Dr. Krasnopolsky published two books, two book chapters, over 70 papers in refereed scientific journals. He is a member (formerly Chair) of the Committee on Computational and Artificial Intelligence Applications in Environmental Science of American Meteorological Society, a member of the IEEE/Computational Intelligence Society Task Force Computational intelligence in earth and environmental sciences, and a member of the International Neural Network Society Working Group Computational intelligence in earth and environmental sciences. In 2018 Vladimir was awarded AMS Distinguished Scientific Committee award for Contributions to advancing the application of artificial neural networks to earth science problems and in particular emulations of complex multidimensional mappings.
Abstract: The field of Artificial Intelligence (AI), including applications in the environmental sciences, is evolving at an accelerating pace. Its progress has been made possible by developments in the computer sciences, the availability of larger and more comprehensive environmental data sets, and the ever-increasing availability of affordable computing power. The presentation will start with the early days of the field, including how the term was coined by John McCarthy. We will then cover the progression of the field, including its ups and downs, through a series of examples.
The American Meteorological Society AI workshops and conferences allow to track this progression. Expert systems were the method of choice in the eighties while Neural Networks took over in the nineties followed by a broadening of the methods including fuzzy logic, tree-based methods, genetic algorithms, support vector machines... At the 2019 and 2020 AMS AI conferences deep learning became by far the method of choice with 36% and over 50% of the presentations based on this new method. We will trace back this explosive growth to its roots including Imagenet, AlexNet and the importance of the datasets in a sense driving the development of these methods.
While the AI methods have changed considerably over the years, the topics not so much. The first AMS AI conference in 1998 included talks on precipitation predictions, satellite retrieval and pattern recognition, climate classification and prediction, image processing, decision aids and natural language systems. We will introduce selected environmental applications and methods developed at the Conrad Blucher Institute (CBI) to provide local operational predictions including for water levels, coastal flooding, coastal fog, and a model designed and implemented to predict the cold stunning of sea turtles. These methods take advantage of the flexibility of AI to combine real-time environmental measurements and numerical weather predictions, typically from NOAA, as the predictors to different types of AI models.
We are expecting the fast growth of AI/ML to continue and as the method is becoming one of the main approaches to better predict and gather a deeper understanding of a wide variety of complex and nonlinear processes in the earth sciences. The presentation will conclude with the introduction of some of the present AI related research questions such as the quantification of uncertainties, interpretability, incorporating domain-knowledge in model design and the further potential for AI applications in the environmental sciences.
Bio(s): Philippe Tissot is the Interim Director of the Conrad Blucher Institute and an Associate Research Professor at Texas A&M University-Corpus Christi. For the past 20 years, his research has focused on the development of artificial intelligence methods and other models for the analysis and predictions of environmental systems and coastal physical processes. Projects have included the development and implementation of predictive models supporting navigation and coastal management. Other studies have included the modeling and impact of relative sea level rise and storm surge, the spatial variability of subsidence at the regional scale, tidal studies and local hydrodynamic models. His team's models have been used for over a decade for the prediction of cold stunning of sea turtles allowing to interrupt navigation ahead of these events and other preparation by local stakeholders. Other work has included ML predictions of thunderstorms and the development of ML algorithms to take advantage of 3D point clouds of marsh environments and urban runoff water quality modeling. Dr. Tissot has authored or co-authored over 40 peer reviewed articles, 200 proceedings, abstracts and technical presentations, a Physical Science textbook for future K-12 teachers, and 2 US Patents. Professor Tissot is a member and former chair of the American Meteorological Society Committee on Artificial Intelligence Applications to Environmental Science.
Abstract: CubeSats are revolutionizing the way we make Earth Observations. These low-cost mini satellites are described as cube-shaped spacecrafts with units of 1U =10X10x10cm. SeaHawk, with a total weight of 5kg, is the first 3U CubeSat specifically designed to hold an ocean color instrument payload (HawkEye). SeaHawk was built as part of SOCON (Sustained Ocean Color Observations Using Nanosatellites, http://www.uncw.edu/socon), an ongoing proof of concept project at the University of North Carolina at Wilmington (UNCW). This seminar will provide an overview of SeaHawk's characteristics and mission status. HawkEye's specifications are similar to SeaWiFS (one of the most successful ocean color mission to date) in that it is an 8-band multi-spectral ocean color sensor,except band 7 was modified to improve atmospheric correction and the SNR is>50% that of SeaWiFS. However, HawkEye was designed to fit a 1U cube, it has ~130m spatial resolution, it does not saturate over land, and was built with low-cost, off-the-shelf materials. SeaHawk follows a sun-synchronous Low Earth Orbit at a nominal height of 575km, orbiting 15 times a day, with a swath of 216 x 480km and a repeat time of about 18 days. SeaHawk-1 was launched in December 2018 as part of SpaceX first ride-share mission. Once fully commissioned, data will be available at no cost through NASA's OBPG (https://oceancolor.gsfc.nasa.gov). The ocean color community will also be able to submit requests for image acquisition (e.g. for field support) through UNCW. This project was possible thanks to the Gordon and Betty Moore Foundation and a Space Act Agreement between NASA and UNCW.
Bio(s): Dr. Sara Rivero-Calle is a Post-Doctoral researcher at the University of North Carolina Wilmington. Her interest in bio-optics and satellite remote sensing started at the University of Puerto Rico, where she earned her M.Sc. degree in Biological Oceanography working on the ecology of sponges in mesophotic coral reefs using Autonomous Underwater Vehicles. She then earned a PhD degree in Oceanography from Johns Hopkins University, where she studied long term changes in the North Atlantic phytoplankton communities using the Continuous Plankton Recorder survey, the largest and longest ongoing phytoplankton sampling effort. Sara is interested in projects that involve large datasets, combining remote sensing, modeling and in situ data to answer large-scale ecological questions. She is currently working on the topic of finescale variability and subpixel variability, combining satellite products, ARGOfloats, HPLC pigments and numerical models. At UNCW, Sara is Science Lead and an active member of the Management team of SOCON: Sustained Ocean Color Observations Using Nanosatellites. SOCON recently built and launched SeaHawk-1,the first ocean color CubeSat mission.
Abstract: The NOAA global observing system (GOS) contains a large variety of observing platforms, including geostationary and polar-orbiting satellites, radiosonde,aircraft, surface stations, ships, buoys, etc. Despite the comprehensiveness of the observing system, many critical gaps exist in spatial/temporal coverage,spectral coverage, and resolution. To address these gaps, the NOAA/NESDIS Technology Maturation Program funded one of projects to explore use of emerging internet platforms (such as Loon high altitude balloons and SpaceX StarLink Satellites) for hosting remote sensing instruments. This talk summarizes feasibility assessment on potentials payload hosting opportunities that can benefit NOAA GOS system, which mainly focuses on Loon platforms and also extends to recent SpaceX StarLink constellations. First, the Loon platform characteristics and flight dynamics are comprehensively surveyed to explore the capability and limitation for Loon as a hosting platform. Second, by comparing GOES-16 Advanced Baseline Imager (ABI) with collocated Loon infrared thermometer measurements,we demonstrate that the Loon platform can served as a validation platform for future NOAA satellite sensors. Third, through simulation studies, observational geometry (e.g., footprint size, swath width, pointing accuracy) and weighting functions are studied for the scenarios that the Loon platform can host passive microwave instruments. More importantly, we demonstrate that balloon-based GPS radio occultation (RO) measurements can be complementary to current satellite based GPSRO systems. Efforts have been devoted to develop the capability of simulating the GPSRO slant path and bending angle from the balloon platform at~20 km, utilizing current constellation of Global Navigation Satellite Systems.Based on the calculations, the sampling characteristics and spatial and temporal coverage as well as the advantages and disadvantages are discussed.Based on this, the Observing System Simulation Experiment (OSSE) is designed to assess possible impacts on Global Forecasting System (GFS) forecasting capabilities by adding balloon-based GPSRO observations. The impacts are demonstrated and compared to those from space-based GPSRO observations. Finally,SpaceX StarLink constellation are simulated and potential hosting opportunities are discussed.
Dr. Likun Wang is now working in NOAA/NESDIS/STAR as a contract scientist employed by Riverside Technology, inc for Research Technology Maturation for the Exploitation of Emerging Technologies (RTMEE) Contract, including near space payload hosting platform assessment, Artificial Intelligence (AI) technology demonstration, and geostationary sounder proxy data simulations. With more than 15 years of progressive working experiences of NOAA's satellite sensors, Dr. Likun Wang has been responsible for the pre- and post-launch calibration testing data analysis, inter-calibration for post-launch instrument monitoring and assessment, ground processing software development, configuration and calibration parameter refining, and new algorithm design and integration. He currently serves the chair of World Meteorological Organization (WMO) sponsored Global Space-based Inter-Calibration System (GSICS) infrared sensor working group. Likun Wang received his B.S. degree in atmospheric sciences and the M.S. degree in meteorology from Peking University, Beijing, China, in 1996 and 1999, respectively, and the Ph.D. degree in atmospheric sciences from University of Alaska Fairbanks, in 2004.
NOCCG Seminar crosslisted with OneNOAA and STAR Seminars
Presenter(s): Dr. Emily Smail, CISESS-University of Maryland and SOCD
Sponsor(s): NOAA Ocean Color Coordinating Group (NOCCG). This seminar will not be recorded. Slides may be shared upon request (send email to the POC listed below).
Abstract: The Group on Earth Observations(GEO) is an international partnership working to improve the availability, access and use of Earth observations (EO) for the benefit of society. GEO's 109 Member Countries and 132 Participating Organizations work to actively improve and coordinate global Earth Observation systems and promote broad, open data sharing. GEO's priority engagement areas include the United Nations 2030 Agenda for Sustainable Development. GEO's initiative for oceans and coasts, GEO Blue Planet, is working to support Sustainable Development Goal (SDG) 14 "Conserve and sustainably use the oceans, seas and marine resources for sustainable development". This presentation will provide an overview of GEO Blue Planet's work in support of SDG 14 with a focus on activities to support the United Nations Environment Programme to develop the methodology for reporting on an SDG indicator for coastal eutrophication and applications for visualizing this data that are being developed in collaboration with Esri.
Bio(s): Emily Smail is the Executive Director of the GEO BluePlanet Initiative and a Senior Faculty Specialist at the NOAA-University of Maryland Cooperative Institute for Climate and Satellites.She also serves as the co-chair of the GEO AquaWatch Initiative's outreach and user Engagement working group and supports outreach and education efforts for the NOAA CoastWatch/OceanWatch program. Previously, Dr. Smail worked in informal science Education at the Waikiki Aquarium and policy in the United States Senate through the Knauss Marine Policy Fellowship Program. She received a B.S. in Biology from the Pennsylvania State University and a Ph.D. in Biology from the University of Southern California where her research focused on water quality and marine biogeochemistry.