NSSL scientists Jidong Gao, David Stensrud and the University of Oklahoma School of Meteorology professor Xuguang Wang have received a significant research grant from the National Science Foundation to develop new techniques that will help improve convective-scale (1km) weather prediction.
Currently, most convective-scale data assimilation rely on techniques that were developed for larger-scale weather, where the rules of the atmospheric dynamics are usually different from those of thunderstorm events. To make convective-scale data assimilation more realistic and able to predict individual storms, they must effectively use Doppler radar data as a jumping off point.
The scientists propose to explore new techniques to feed (assimilate) operational WSR-88D radar data into convective scale models, and evaluate the results. This research will help improve our understanding of storm-scale data assimilation and dynamics, and lead to better detection and prediction of thunderstorm hazards. The award continues to draw upon NOAA’s critical investment in the WSR-88D network, and will provide synergistic support to NOAA’s Warn-on-Forecast project.
NSSL scientists Jidong Gao, David Stensrud, and Louis Wicker were among five invited guest editors for a special issue of Advances in Meteorology, an open access international journal. This special issue focuses on high-resolution storm-scale computer models that ingest or assimilate radar data.
With the steady increase in computing power, operational centers throughout the world are preparing to run their weather computer models at resolutions high enough to predict individual thunderstorms. To do this, the models will be required to ingest observations.
This opportunity increases the demand for using radar data in storm-scale data assimilation in order to insert storm structures into model initial conditions.
The potential for successfully assimilating radar data into storm-scale numerical weather prediction (NWP) models is challenged by data quality control, proper estimation of the background error statistics, and the estimation of atmospheric state variables that are not directly observed by radar.
This special issue focuses on progress in some of these important areas. There are 12 papers published in this special issue, including seven papers from NSSL and five papers from other institutions. This special issue can be found at: http://www.hindawi.com/journals/amete/si/567170/