摘要
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 </span><span style="font-size:12px;font-family:Verdana;">studies, and for the evaluation of regional model simulations. In this paper, four satellite derived precipitation datasets: </span></span><span style="font-family:Verdana;font-size:12px;">Climate Prediction Center</span><span style="font-family:""><span style="font-size:12px;font-family:Verdana;"> MORPHING (CMORPH), Tropical Rainfall Measuring Mission (TRMM), the Precipitation Estimation Algorithm from Remotely-Sensed Information using an Artificial </span><span style="font-size:12px;font-family:Verdana;">Neural Network (PERSIANN), and the global Satellite Mapping of</span><span style="font-size:12px;font-family:Verdana;"> Precipitation (GSMaP) </span></span><span style="font-family:""><span style="font-size:12px;font-family:Verdana;">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</span><sup><span style="font-size:12px;font-family:Verdana;">nd</span></sup><span style="font-size:12px;font-family:Verdana;"> May 2019 to 4</span><sup><span style="font-size:12px;font-family:Verdana;">th</span></sup><span style="font-size:12px;font-family:Verdana;"> May 2019 at the time of Cyclone </span></span><span style="font-family:Verdana;font-size:12px;">“</span><span style="font-family:Verdana;font-size:12px;">FANI</span><span style="font-family:Verdana;font-size:12px;">”</span><span style="font-family:Verdana;font-size:12px;">. It is found that the satellite derive</span><span style="font-family:Verdana;font-size:12px;">d</span><span style="font-family:Verdana;font-size:12px;"> precipitation datasets </span><span style="font-family:Verdana;font-size:12px;">are </span><span style="font-family:Verdana;font-size:12px;">reasonably matched with the observed but slightly different.
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 </span><span style="font-size:12px;font-family:Verdana;">studies, and for the evaluation of regional model simulations. In this paper, four satellite derived precipitation datasets: </span></span><span style="font-family:Verdana;font-size:12px;">Climate Prediction Center</span><span style="font-family:""><span style="font-size:12px;font-family:Verdana;"> MORPHING (CMORPH), Tropical Rainfall Measuring Mission (TRMM), the Precipitation Estimation Algorithm from Remotely-Sensed Information using an Artificial </span><span style="font-size:12px;font-family:Verdana;">Neural Network (PERSIANN), and the global Satellite Mapping of</span><span style="font-size:12px;font-family:Verdana;"> Precipitation (GSMaP) </span></span><span style="font-family:""><span style="font-size:12px;font-family:Verdana;">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</span><sup><span style="font-size:12px;font-family:Verdana;">nd</span></sup><span style="font-size:12px;font-family:Verdana;"> May 2019 to 4</span><sup><span style="font-size:12px;font-family:Verdana;">th</span></sup><span style="font-size:12px;font-family:Verdana;"> May 2019 at the time of Cyclone </span></span><span style="font-family:Verdana;font-size:12px;">“</span><span style="font-family:Verdana;font-size:12px;">FANI</span><span style="font-family:Verdana;font-size:12px;">”</span><span style="font-family:Verdana;font-size:12px;">. It is found that the satellite derive</span><span style="font-family:Verdana;font-size:12px;">d</span><span style="font-family:Verdana;font-size:12px;"> precipitation datasets </span><span style="font-family:Verdana;font-size:12px;">are </span><span style="font-family:Verdana;font-size:12px;">reasonably matched with the observed but slightly different.
作者
Deepa Roy
S. M. Quamrul Hassan
Syeda Sabrina Sultana
Deepa Roy;S. M. Quamrul Hassan;Syeda Sabrina Sultana(Department of Mathematics, Jashore University of Science and Technology, Jashore, Bangladesh;Storm Warning Center, Bangladesh Meteorological Department, Dhaka, Bangladesh;Regional Integrated Multi-Hazard Early Warning System, Dhaka, Bangladesh)
