2016 STAR Seminars

The NOAA Satellite and Information Service has a variety of satellite products for estimating snow cover and snow properties from both geostationary and polar-orbiting satellites. Visible imagers provide the extent of snow cover as binary maps, and also the fraction of snow cover within each image element (pixel). Passive microwave instruments provide snow cover, snow depth, and snow water equivalent. There are also snow products that use multiple instruments, and others that have an interactive element. This presentation will provide an overview of NOAA's snow products, briefly describing each and providing a high-level assessment of their strengths and weaknesses. International snow product intercomparison projects will also be discussed.

Remote attendance is via GotoWebinar. Registration link is below. Dial-in information is provided after registration.

Jeff Key bio

The National Weather Service (NWS) Office of Water Prediction (OWP), in conjunction with the National Center for Atmospheric Research (NCAR) and the NWS National Centers for Environmental Prediction (NCEP) recently implemented version 1.0 of the National Water Model (NWM) into operations. This model is an hourly cycling uncoupled analysis and forecast system that provides streamflow for 2.7 million river reaches and other hydrologic information on 1km and 250m grids. It provides complementary hydrologic guidance at current NWS river forecast locations and significantly expands guidance coverage and type in underserved locations.

The core of this system is the NCAR-supported community Weather Research and Forecasting (WRF)-Hydro hydrologic model. It ingests forcing from a variety of sources including Multi-Sensor Multi-Radar (MRMS) radar-gauge observed precipitation data and High Resolution Rapid Refresh (HRRR), Rapid Refresh (RAP), Global Forecast System (GFS) and Climate Forecast System (CFS) forecast data. WRF-Hydro is configured to use the Noah-Multi Parameterization (Noah-MP) Land Surface Model (LSM) to simulate land surface processes. Separate water routing modules perform diffusive wave surface routing and saturated subsurface flow routing on a 250m grid, and Muskingum- Cunge channel routing down National Hydrogaphy Dataset Plus V2 (NHDPlusV2) stream reaches. River analyses and forecasts are provided across a domain encompassing the Continental United States (CONUS) and hydrologically contributing areas, while land surface output is available on a larger domain that extends beyond the CONUS into Canada and Mexico (roughly from latitude 19N to 58N). The system includes an analysis and assimilation configuration along with three forecast configurations. These include a short-range 15 hour deterministic forecast, a medium-Range 10 day deterministic forecast and a long-range 30 day 16-member ensemble forecast. United Sates Geologic Survey (USGS) streamflow observations are assimilated into the analysis and assimilation configuration, and all four configurations benefit from the inclusion of 1,260 reservoirs.

About Brian Cosgrove

Brian is the Project Leader for the National Water Model (NWM) at the National Weather Service Office of Water Prediction (NWS/OWP), where he plans and executes implementations of the model with NCAR and NCEP partners, and serves as the OWP-NCEP liaison, coordinating hydrologic activities between OWP and NCEP Centers such as the Environmental Modeling Center (EMC) and the Weather Prediction Center (WPC).

The Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) is a reference observing network designed to provide long-term, climate quality atmospheric data from the troposphere and stratosphere (and surface). GRUAN reference (mainly radiosonde at this time) observations are calibrated through an unbroken traceability chain to SI or community standards with the uncertainty interval in each step in the chain fully characterized, meaning the resulting estimates can be used with high confidence that the true measurement is within the interval. Among the primary objectives of GRUAN is the constraining and inter-calibration of data from other more spatially extensive observing systems such as satellites and the current radiosonde (RAOB) network.

In this context, GRUAN and NOAA have coordinated to create a baseline dataset of collocated GRUAN radiosonde and global satellite observations. This is embodied in the NOAA Products Validation System + (NPROVS+) which has routinely compiled collocations of GRUAN RAOB and satellite sounding observations (from multiple satellites) since 2013, roughly the time that sounding products from hyper-spectral infer-red and microwave sensor suite onboard the S- NPP satellite began being produced. NPROVS+ also integrates JPSS funded radiosondes synchronized to S-NPP overpass at DOE Atmospheric Radiation Measurement (ARM) sites and also for selected data intensive experiments typically targeting the Tropics.

These radiosondes are subsequently processed into reference observations courtesy of GRUAN providing the "sweetest of fruit" not only for sounding Cal/Val but also satellite sensor and associated atmospheric Radiative Transfer (RT) model assessments. The following seminar presents current status of GRUAN and NPROVS+ and highlights their impact on the JPSS Cal/Val program for operational atmospheric sounding products. Topics include the integration of GRUAN uncertainty estimates for more robust Cal/Val, special value of the synchronized "reference" observations and examples of feedback to GRUAN.

Tony Reale bio

Part 1 Abstract:
Coastal zones around the world have come under intensive pressure due to ever-increasing human activity and the frequent occurrence of natural hazards such as earthquake or hurricane. Therefore, the interpretation of coastal land-use and the corresponding coastline changes are important for urban planning, coastal erosion monitoring, and infrastructure construction. Traditionally, coastal zone mapping is done using field survey, which is usually expensive and time consuming, or by using aerial photographs but these are affected by cloud cover or solar illumination, and can be limited in coverage. In recent years, Synthetic Aperture Radar (SAR) satellite remote sensing has proven to be a valuable tool for mapping land cover and coastline changes. In this presentation, using both single- and fully- polarimetric polarization SAR data from Radarsat-2, ALOS-1/2 and Cosmo-Skymed, we explore the image intensity and extra phase information within the SAR data and develop algorithms for coastal zone classification and coastline detections. The algorithms have been validated with survey maps.

About Xiaofeng Li

Xiaofeng Li is with GST at the National Environmental Satellite, Data, and Information Service (NESDIS), National Oceanic Atmospheric Administration (NOAA), College Park, MD, USA. He has 19 years of experience in developing many operational satellite ocean remote sensing products at NESDIS. He is the author of more than 100 peer-reviewed publications, mostly in SAR applications in ocean and atmospheric sciences. Dr. Li currently serves as the Associate Editor for the International Journal of Remote Sensing and Remote Sensing. He is also an Editorial Board Member of the International Journal of Digital Earth. He was the Guest Editor of the International Journal of Remote Sensing special issue on "Remote Sensing of the China Seas (2014)" and the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing special issue on "Remote Sensing of the World Oceans (2016)."

Part 2 Abstract:
Near real-time satellite-derived flood maps are invaluable to river forecasters and decision-makers for disaster monitoring and relief efforts. With the support from the JPSS (Joint-Polar Satellite System) Proving Ground and Risk Reduction Program (JPSS/PGRR), a flood detection package has been developed using SNPP/VIIRS (Suomi National Polar-orbiting Partnership/ Visible Infrared Imaging Radiometer Suite) imagery to generate daily near real-time flood maps automatically for National Weather Service (NWS)-River Forecast Centers (RFC) in the USA. In this package, a series of algorithms have been developed including water detection, cloud shadow removal, terrain shadow removal, minor flood detection, water fraction retrieval and flooding water determination. The package has been running routinely with the direct broadcast SNPP/VIIRS data since 2014. Flood maps were carefully evaluated by river forecasters using airborne imagery and hydraulic observations. Offline validation was also made via visual inspection with VIIRS false-color composite images on more than 10,000 granules across a variety of scenes and comparison with river gauge observations year-round and NOAA flood outlook and warning products. Evaluation of the product has shown high accuracy, and the promising performance of the package has won positive feedback and recognition from end-users.

The National Weather Service's National Water Center (NWC) is collaborating with NCEP, NCAR, and NWS field offices to implement a new nationwide, high resolution, operational hydrologic and streamflow model-WRF-Hydro. Running on the NOAA WCOSS supercomputer system, and scheduled for implementation in late FY16, this model will provide revolutionary water resource prediction capabilities on a 1km/250m grid and across 2.6 million river basins. WRF-Hydro will form the core of the cross-agency NWC Centralized Water Forecasting Project. It will further the NWS' mission to protect lives and property by providing flood, drought and water resource forecast guidance and situational awareness to NWS field offices and Centers, and partner agencies like the Federal Emergency Management Agency (FEMA). This presentation features an overview of this research-to-operations effort, and the preparation of real-time meteorological inputs to the modeling processes.

About David Kitzmiller

David Kitzmiller is a meteorologist in the Interdisciplinary Science and Engineering Division of the National Water Center, National Weather Service. He served as group leader for hydrometeorology in the Hydrologic Science and Modeling Branch of the former Office of Hydrologic Development from 2002 to 2015. His career in the National Weather Service began in the Techniques Development Laboratory, now the Meteorological Development Laboratory, in 1984. He has worked in the development and implementation of applications of digital radar, satellite, and wind profiler observations, as applied to severe storm detection and precipitation estimation and prediction.

The attached PDF contains single page summaries of all the talks and posters planned to be presented by NOAA/NESDIS/STAR researchers at the 2016 AMS Meetings.