4:15 PM - 4:30 PM
[ACG41-28] Development of GSMaP precipitation estimation algorithm considering regional characteristics
Keywords:preciapitation, passive microwave radiometer, infrared radiometer
The Global Satellite Mapping of Precipitation (GSMaP) is one of the global distributions of precipitation products using multiple microwave radiometers (PMWs) on board low Earth orbit and infrared (IR) imagers from geostationary meteorological satellites with 1-hour and 0.1 degrees resolution. Since PMWs observation cannot be observed globally for less than 3 hours, precipitation estimates from IR observations fill the gaps using the relationship between PMWs estimated rain rate and IR brightness temperature, called the PMW-IR combined algorithm.
Completed the 25-year record of previous data for the latest version, evaluation studies are conducted such as the differences between PMWs only and PMW-IR, comparing other satellite precipitation products, and so on. Their results showed that PMW-IR estimates are relatively degraded, for example, severe underestimation in the western coast of the Indian subcontinent and overestimation in the southern Thailand. This study reports on possible improvement methods that are currently being implemented in preparation for the next major algorithm upgrade scheduled for April 2026.
The PMW-IR combined algorithm is implemented using the Kalman filter approach and noise tables are generated for each 10 degrees of latitude over land and ocean. This band is common to Africa, Asia, and the Amazon region, where the different precipitation types of precipitation dominate. Therefore, we reconsidered the noise tables to precipitation regimes which is an a priori classification from 3-dimensional rainfall precipitation profiles and reanalysis data. In this presentation, we will show the relationship between precipitation and infrared brightness temperatures and the differences in precipitation distributions.
Completed the 25-year record of previous data for the latest version, evaluation studies are conducted such as the differences between PMWs only and PMW-IR, comparing other satellite precipitation products, and so on. Their results showed that PMW-IR estimates are relatively degraded, for example, severe underestimation in the western coast of the Indian subcontinent and overestimation in the southern Thailand. This study reports on possible improvement methods that are currently being implemented in preparation for the next major algorithm upgrade scheduled for April 2026.
The PMW-IR combined algorithm is implemented using the Kalman filter approach and noise tables are generated for each 10 degrees of latitude over land and ocean. This band is common to Africa, Asia, and the Amazon region, where the different precipitation types of precipitation dominate. Therefore, we reconsidered the noise tables to precipitation regimes which is an a priori classification from 3-dimensional rainfall precipitation profiles and reanalysis data. In this presentation, we will show the relationship between precipitation and infrared brightness temperatures and the differences in precipitation distributions.