An Assessment of Spatial Distribution of Four Different Satellite-Derived Rainfall Estimations and Observed Precipitation over Bangladesh

Abstract

Given that precipitation is a major component of the earth's water and energy cycles, reliable information on the monthly spatial distribution of precipitation is also crucial for climate science, climatological water-resource research studies, and for the evaluation of regional model simulations. In this paper, four satellite derived precipitation datasets: Climate Prediction Center MORPHING (CMORPH), Tropical Rainfall Measuring Mission (TRMM), the Precipitation Estimation Algorithm from Remotely-Sensed Information using an Artificial Neural Network (PERSIANN), and the global Satellite Mapping of Precipitation (GSMaP) are spatially analyzed and compared with the observed precipitation data provided by Bangladesh Meteorological Department (BMD). For this study, the different precipitations data sets are spatially analyzed from 2 nd May 2019 to 4 th May 2019 at the time of Cyclone "FANI". It is found that the satellite derived precipitation datasets are reasonably matched with the observed but slightly different.

Evaluation of Daily and Hourly Performance of Multi-Source Satellite Precipitation Products in China’s Nine Water Resource Regions

“…PERSIANN, developed by the Hydrometeorology and Remote Sensing Research Center at California State University, Central Coast [17], is a comprehensive dataset of multi-satellite rainfall estimates obtained using an artificial neural network algorithm. It estimates precipitation by employing the PERSIANN algorithm on GridSat-B1 infrared satellite data and trains the artificial neural network using hourly precipitation data from the National Centers for Environmental Prediction (NCEP) Phase IV.…”