Improving Soil Moisture States in WRF-Hydro Using SWOT Observations

No abstract availabl

Assimilation of GPM-Retrieved Marine Surface Meteorology Variables for Two Winter Storms

No abstract availabl

Demonstration of a Daily High-Resolution (375-m) ALEXI Evapotranspiration Product for the NENA Region

While the current constellation of geostationary sensors provides near-global coverage (60N to 60S) – it requires merging data from 7 satellites [resolving time differences; view angles; atmospheric correction]. Polar orbiting sensors such as MODIS and VIIRS provide daily global coverage of LST at higher resolutions than GEO sensors but at only two times per day

Application of Synthetic TEMPO Products at NASA SPoRT to Accelerate Use in Air Quality and Public Health Decision Support

No abstract availabl

Demonstration of a Daily High-Resolution (375-m) ALEXI Evapotranspiration Product for the NENA Region

No abstract availabl

Development OF A Multi-Scale Framework for Mapping Global Evapotranspiration

As the worlds water resources come under increasing tension due to dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. Remote sensing methods for monitoring consumptive water use (e.g, ET) are becoming increasingly important, especially in areas of significant water and food insecurity. One method to estimate ET from satellite-based methods, the Atmosphere Land Exchange Inverse (ALEXI) model uses the change in mid-morning land surface temperature to estimate the partitioning of sensible and latent heat fluxes which are then used to estimate daily ET. This presentation will outline several recent enhancements to the ALEXI modeling system, with a focus on global ET and drought monitoring

Land Surface Modeling and Evaporative Stress Products for Increased Situational Awareness

No abstract availabl

Using Temporal Changes in Drought Indices to Generate Probabilistic Drought Intensification Forecasts

In this study, the potential utility of using rapid temporal changes in drought indices to provide early warning of an elevated risk for drought development over subseasonal time scales is assessed. Standardized change anomalies were computed each week during the 2000–13 growing seasons for drought indices depicting anomalies in evapotranspiration, precipitation, and soil moisture. A rapid change index (RCI) that encapsulates the accumulated magnitude of rapid changes in the weekly anomalies was computed each week for each drought index, and then a simple statistical method was used to convert the RCI values into drought intensification probabilities depicting the likelihood that drought severity as analyzed by the U.S. Drought Monitor (USDM) would worsen in subsequent weeks. Local and regional case study analyses revealed that elevated drought intensification probabilities often occur several weeks prior to changes in the USDM and in topsoil moisture and crop condition datasets compiled by the National Agricultural Statistics Service. Statistical analyses showed that the RCI-derived probabilities are most reliable and skillful over the central and eastern United States in regions most susceptible to rapid drought development. Taken together, these results suggest that tools used to identify areas experiencing rapid changes in drought indices may be useful components of future drought early warning systems

Exploring the Use of Machine Learning to Develop a Predictive Model for Future Fire Seasons

No abstract availabl