Colorado State University

Refereed Publications

Zhang, Y., X.-C. Chen, M. M. Bell, : Improving Monsoonal Precipitation Nowcast using Satellite All-Sky Infrared Radiances: Real-Time Ensemble Data Assimilation and Forecast during PRECIP. Wea. Forecasting, in press ,

Key Points

  • Plain Language Summary

    During the summers of 2020–2021, the PSU WRF-EnKF data assimilation and forecast system was run in real-time in advance of the 2022 Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP), assimilating all-sky (clear-sky and cloudy) infrared radiances from the geostationary satellites into a numerical weather prediction model and providing ensemble forecasts. This study presents the first-of-its-kind systematic evaluation of the impacts of assimilating all-sky infrared radiances on short-term qualitative precipitation forecasts using multi- year, multi-region, real-time ensemble forecasts. Results suggest that rainfall forecasts are improved out to at least 4–6 hours with the assimilation of all-sky infrared radiances, comparable to the influence of assimilating radar observations, with benefits in forecasting large-scale environments and representing atmospheric uncertainties as well.


    The Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) aims to improve our understanding of extreme rainfall processes in the East Asian summer monsoon. A convection- permitting ensemble-based data assimilation and forecast system (the PSU WRF-EnKF system) was run in real-time in the summers of 2020 to 2021 in advance of the 2022 field campaign, assimilating all-sky infrared (IR) radiances from the geostationary Himawari-8 and GOES-16 satellites, and providing 48-hour ensemble forecasts every day for weather briefings and discussions. This is the first time that all-sky IR data assimilation has been performed in a real- time forecast system at a convection-permitting resolution for several seasons. Compared with retrospective forecasts that exclude all-sky IR radiances, rainfall predictions are statistically significantly improved out to at least 4–6 hours for the real-time forecasts, which is comparable to the timescale of improvements gained from assimilating observations from the dense ground-based Doppler weather radar network. The assimilation of all-sky IR radiances also reduced the forecast errors of large-scale environments and helped to maintain a more reasonable ensemble spread compared with the counterpart experiments that didn’t assimilate all-sky IR radiances. The results indicate strong potential for improving routine short-term quantitative precipitation forecasts using these high-spatiotemporal-resolution satellite observations in the future.

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    We would like to thank Drs. Rosimar Rios-Berrios (NCAR), James H. Ruppert, Jr. (University of Oklahoma), and many PRECIP participants for their helpful discussions and feedback on the configuration of the PSU WRF-EnKF system and the planning of the real-time modeling for the field campaign, and Dr. Kristen Rasmussen (Colorado State University) for securing the computational resources for the 2022 real-time forecasts. We would also like to thank the three anonymous reviewers, whose comments prominently improved this manuscript. This work is supported by NSF grants AGS-1712290, AGS-1854607, and AGS-1854559, ONR grant N000141812517, NOAA NGGPS grant through University of Michigan Subcontract 3004628721, NOAA grant NA18OAR4590369, and NASA grant 80NSSC19K0728. The real-time and retrospective data assimilations and forecasts are performed on the Cheyenne supercomputer (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory (CISL) sponsored by the NSF, and the Stampede 2 supercomputer of the Texas Advanced Computing Center (TACC) through the Extreme Science and Engineering Discovery Environment (XSEDE) program (now the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support program, or ACCESS) supported by the National Science Foundation (NSF).