Improving PERSIANN-CCS Rainfall Estimation using Passive Microwave Rainfall Estimation

Abstract:

This presentation discusses the recent improvements to the PERSIANN-CCS (Precipitation Estimation from remotely Sensed Information using Artificial Neural Networks-Cloud Classification System). The PERSIANN-CCS is one of the algorithms being integrated in the IMERG (Integrated Multi-Satellite Retrievals for the Global Precipitation Mission GPM) to estimate precipitation at 0.04^o lat-long scale at every 30-minute interval. While PERSIANN-CCS has a relatively fine temporal and spatial resolution for generating rainfall estimation over the globe, it sometimes underestimates or overestimates over some regions, depending on certain conditions. In this study, improving the PERSIANN-CCS precipitation estimation using long-term passive microwave (PMW) rainfall estimation is explored. The adjustment is proceeded by matching the probability distribution of PERSIANN-CCS estimates to the PMW rainfall estimation. Four years of concurrent samples from 2008 to 2011 were used in the calibration while one year (2012) of the data was used for the validation of the PMW-adjusted PERSIANN-CCS estimates. Samples over a 5 ^o x5 ^o lat-long coverage were collected and an adjustment look up table for each month covering 60^oS-60^oN was generated. The validation of PERSIANN-CCS estimation before and after PMW adjustment over CONUS using radar data was investigated. The results show that the adjustment has different impact on the PERSIANN-CCS rain estimates depending on the location and time of the year. PERSIANN-CCS adjustments were found to be more significant over high latitude and winter time periods and less significant over the low latitude and summer time period.