Enhancing data assimilation of GPM observations

This chapter describes the authors’ recent achievements on enhancing data assimilation of satellite precipitation observations using a global ensemble data assimilation system known as the Nonhydrostatic ICosahedral Atmospheric Model (NICAM)-Local Ensemble Transform Kalman Filter (LETKF). In precipitation science, satellite data have been providing precious, fundamental information, while numerical models have been playing an equally important role. Data assimilation integrates the numerical models and real-world data and brings synergy. We have been working on assimilating the Global Precipitation Measurement (GPM) data into the NICAM using the LETKF. We continue our effort on “Enhancing Precipitation Prediction Algorithm by Data Assimilation of GPM Observations” funded by JAXA, following successful completion of the 3-year project titled “Enhancing Data Assimilation of GPM Observations” from April 2016 to March 2019. The project first started in April 2013 on “Ensemble-based Data Assimilation of Tropical Rainfall Measuring Mission/GPM Precipitation Measurements,” where we developed a global data assimilation system NICAM-LETKF from scratch. This chapter highlights the recent achievements.