E-mail Jason Otkin
Drought Monitoring and Prediction
Flash drought development across the contiguous U.S. has been examined using the Evaporative Stress Index (ESI), which identifies anomalies in evapotranspiration (ET) using thermal infrared (TIR) imagery from geostationary satellites and a two-source energy balance model that considers vegetated and bare soil surface components. Land surface temperatures obtained from TIR imagery respond quickly to changing soil moisture and vegetation conditions. Recent work has shown that the ESI is capable of providing early warning of an increased likellihood for "flash drought" development characterized by rapid deterioration in vegetation health and rapid soil moisture depletion occurring over sub-seasonal time scales. The ESI is computed each week across the entire U.S., with archived and current images displayed at http://hrsl.ba.ars.usda.gov/drought/.
For additional information on the ESI and associated drought monitoring products, please watch this webinar. If you are interested in providing your input concerning ways you could use drought early warning information to better prepare for drought development, describing the characteristics of rapid onset, flash drought events, and proposing ways to improving the usefulness of the ESI as a drought early warning tool, please complete this online questionnaire.
Funding Sources: NOAA Climate Program Office Sectoral Applications Research Program (SARP); NOAA Climate Office Modeling, Analysis, Predictions, and Projections (MAPP); NASA Applied Sciences Program; NASA Soil Moisture Active Passive (SMAP)
Ensemble and Variational Data Assimilation
Infrared observations from geosynchronous and polar orbiting satellite sensors provide valuable information about the horizontal and vertical distribution of clouds and atmospheric water vapor. By using advanced data assimilation techniques, such as the Ensemble Kalman filter (EnKF), these observations can improve the accuracy of atmospheric analyses used to initialize numerical weather prediction models. The assimilation of cloudy observations is very challenging due to a variety of reasons, but has gained increased attention in recent years as operational forecast centers move towards all-sky data assimilation. Several regional-scale Observing System Simulation Experiments (OSSEs) have been performed to examine the potential ability of clear and cloudy-sky infrared observations from the GOES-R Advanced Baseline Imager (ABI) to improve forecasts of high impact weather events. Recent work has also explored the impact of simultaneously assimilating ABI brightness temperatures and WSR-88D radar reflectivity and radial velocity observations at convection-resolving resolutions. The impact of assimilating real cloudy and water vapor sensitive infrared brightness temperatures from the MSG SEVIRI sensor is also being assessed using the COSMO/KENDA EnKF data assimilation system being developed by the German Deutscher Wetterdienst (DWD). Last, efforts are also underway to assess the error characteristics of cloudy infrared and precipitation-affected microwave brightness temperatures in the GFS model when using the Community Radiative Transfer Model (CRTM).
Funding Sources: NOAA GOES-R Risk Reduction Program; NOAA GOES-R Algorithm Working Group; NOAA OSSE Testbed; COSMO Model Consortium; Joint Center for Satellite Data Assimilation; NOAA National Environmental Satellite Data and Information Service (NESDIS); NASA Data for Operation and Assessment Program; NOAA Joint Technology Transfer Initiative (JTTI)
Model Validation Using Satellite Observations
Cloud and moisture fields from high-resolution numerical model simulations are difficult to validate due to a scarcity of conventional observations with high temporal and spatial resolution capable of capturing their potentially rapid evolution. Satellite observations from geostationary and polar-orbiting platforms, however, provide valuable information about the cloud and moisture fields through raw brightness temperatures and retrieved cloud products, such as cloud top pressure and cloud water path. Over the past few years, we have performed several studies that examined the accuracy of various planetary boundary layer and cloud microphysical parameterization schemes in the WRF model using brightness temperatures and cloud data from GOES and MODIS. Other studies have used data from SEVIRI and CloudSat to examine the accuracy of the cloud fields in the large-scale, high-resolution model simulations used to generate synthetic satellite datasets. These studies have shown that the combined numerical weather prediction and forward radiative transfer modeling framework is capable of producing accurate infrared brightness temperatures that can be used to validate high-resolution numerical models. Two new model validation projects are also underway. The first project is being used to develop a near realtime GOES-based satellite verification system for the HRRR model that will provide forecasters objective measures to quickly determine the accuracy of the many overlapping forecast cycles at the current time. Simulated and observed satellite imagery for each forecast cycle, along with the verification measures, are displayed each hour on our project webpage (http://cimss.ssec.wisc.edu/hrrrval/). The second project is using simulated infrared and microwave brightness temperatures to assess the accuracy of the HWRF model.
Funding Sources: NOAA GOES-R Risk Reduction Program; NOAA GOES-R Algorithm Working Group; NOAA Hurricane Forecast Improvement Project (HFIP); NOAA Next Generation Global Prediction System (NGGPS)
Synthetic Satellite Dataset Generation
Output from high-resolution Weather Research and Forecasting (WRF) model simulations has been used in recent years to generate synthetic brightness temperatures for future satellite sensors, such as the GOES-R ABI. A synthetic brightness temperature dataset is most useful for satellite retrieval algorithm development and measurement demonstration activities if it realistically depicts the vertical and horizontal structure of the cloud field and accurately represents the spatial distribution of clear and cloudy areas. High-resolution model simulations are computationally very expensive to perform, thus, synthetic satellite datasets are typically generated for individual case studies depicting the evolution of the atmospheric state over 1-3 day periods. The CIMSS GOES-R proxy data team has used supercomputing resources within the NSF-sponsored TeraGrid and XSEDE supercomputing networks to perform several WRF model simulations covering extensive geographic areas with high spatial resolution. The high fidelity of these datasets as demonstrated by validation studies supports a realistic demonstration of GOES-R measurement capabilities and retrieval algorithms.
Funding Source: NOAA GOES-R Algorithm Working Group
Synthetic Satellite Imagery for Realtime Model Forecasts
As part of the CIMSS GOES-R Proving Ground activities, synthetic ABI infrared brightness temperatures are generated each day using model output from 4-km resolution WRF model forecasts produced at the National Severe Storms Laboratory. The synthetic satellite observations are converted into various formats for use by forecasters and researchers, with animations available here. Participants at the NOAA Hazardous Weather Testbed (HWT) Spring Experiment use the forecast imagery to prepare and modify forecasts of thunderstorm activity. Simulated observations for all 16 ABI bands are also being produced each day using the WRF-Chem model run on the NOAA/NESDIS S4 Supercomputer at the University of Wisconsin-Madison. The simulated satellite datasets are being used to exercise the GOES-R ABI processing system prior to the launch of the GOES-R satellite in 2016. Simulated satellite imagery has proven useful for forecasters because it provides an integrated view of the atmosphere that promotes an efficient interpretation of model features important to thunderstorm development without having to view multiple individual fields.
Funding Sources: NOAA GOES-R Algorithm Working Group; NOAA GOES-R Proving Ground; NOAA GOES-R Risk Reduction