Ensemble‐Based Data Assimilation of GPM DPR Reflectivity: Cloud Microphysics Parameter Estimation With the Nonhydrostatic Icosahedral Atmospheric Model (NICAM). Issue 5 (2nd March 2023)

• Record Type: Journal Article
• Authors: Kotsuki, Shunji; Terasaki, Koji; Satoh, Masaki; Miyoshi, Takemasa
• Is Part Of: Journal of geophysical research. Volume 128:Issue 5(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-03-02
• Subjects: data assimilation -- GPM DPR -- parameter estimation -- cloud microphysics -- radar reflectivity; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2022JD037447
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Direct assimilation of Dual‐frequency Precipitation Radar (DPR) data of the Global Precipitation Measurement (GPM) core satellite is challenging mainly due to its long revisiting intervals relative to the time scale of precipitation, and precipitation location errors. This study explores a method for improving precipitation forecasts using GPM DPR through model parameter estimation. We developed a 28 km mesh global atmospheric data assimilation system that integrates the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) and Local Ensemble Transform Kalman Filter (LETKF) coupled with a satellite radar simulator. Using the NICAM‐LETKF and GPM DPR observations, this study estimates a model cloud physics parameter corresponding to snowfall terminal velocity. To overcome the difficulties of long revisiting intervals and precipitation location errors, we propose a parameter estimation method based on a two‐dimensional histogram known as the contoured frequency by temperature diagram (CFTD). Parameter estimation effectively mitigated the gap between simulated and observed CFTD, resulting in improved 6 hr precipitation forecasts. Plain Language Summary: Direct assimilation of satellite‐borne radar data into weather forecasting models is challenging mainly due to its long revisiting intervals and precipitation location errors. This study explores a method for improving precipitation forecasts using the satellite radar data for optimizing an uncertain parameter in a weather forecasting model. We estimated a model cloud physics parameter corresponding to snowfall terminal velocity. Parameter estimation effectively mitigated the gap between simulated and observed radar reflectivity, resulting in improved 6 hr precipitation forecasts. Key Points: Direct assimilation of GPM DPR reflectivity is challenging due to its long revisiting intervals relative to the time scale of precipitation A new model parameter estimation approach based on a reflectivity‐temperature histogram is used to improve global precipitation forecasts Parameter estimation of snow terminal velocity mitigated the gap between simulated and observed reflectivity, resulting in improved forecasts