PANDA-C: Prediction AND Data Assimilation for Cloud

This past year MMM scientists continued to work on developing a new data assimilation (DA) system as part of the Prediction AND Data Assimilation for Cloud (PANDA-C) project sponsored by the U.S. Air Force. This project has strong overlap in goals with the United Kingdom Met Office (UKMO) NextGen DA effort and the Joint Effort for Data assimilation Integration (JEDI) within the Joint Center for Satellite Data Assimilation (JCSDA).

The goal is to develop a prototype next-generation data assimilation system that has sufficient modularity and flexibility to allow its use with different forecast models, including NCAR's Model for Prediction Across Scales (MPAS) and LFRic, the new generation model under development at the UKMO. The project also includes subtasks to advance verification of U.S. Air Force cloud analyses and forecasts, and for research toward rapidly updating, all-sky radiance data assimilation, especially using observations from geostationary platforms.

MMM research in FY2019 enabled scientists to run one-month MPAS-DA cycling at 120-km mesh with the assimilation of radiosonde, aircraft, satellite track winds, and GNSSRO data using different DA methods (3DVar, 3DEnVar, and hybrid-3DEnVar).

20-h accumulated rainfall (>500 mm) for an extreme event in Guangzhou, China from (a) observation, and WRF 3km forecasts initialized from the analysis using (b) 3DVar without AHI radiances, (c) 3DVar with AHI radiances, and (d) 4DVar with every 10-min AHI radiances
Figure: 20-h accumulated rainfall (>500 mm) for an extreme event in Guangzhou, China from (a) observation, and WRF 3km forecasts initialized from the analysis using (b) 3DVar without AHI radiances, (c) 3DVar with AHI radiances, and (d) 4DVar with every 10-min AHI radiances.

Additional work included experiments with clear-sky and all-sky GOES-ABI and Himawari-AHI radiance DA, and cloud verification work.