Authors: Qian Liu*, George Mason University
Topics: Applied Geography
Keywords: TRMM Data, Rainfall Amount, Conditional Rainfall Rate, Temporal Resolution, Spatial Resolution
Session Type: Paper
Start / End Time: 2:40 PM / 4:20 PM
Room: Jackson, Marriott, 5th Floor
Presentation File: No File Uploaded
Precipitation data simulated by global or regional climate models are often too coarse in spatial and temporal resolutions to be directly usable to applications at local scales. To overcome the poor spatiotemporal representation of precipitation data from climate models, downscaling techniques have been used to improve spatial and temporal resolutions. It is necessary to investigate the scale issue of precipitation data to evaluate and improve downscaling methods. The objective of this paper is to investigate the statistical characteristics of satellite remote sensing precipitation data products (TRMM 3B42V7) such as precipitation amount, frequency conditional rainfall rate and coefficient of variance (CV), at different spatial and temporal resolutions (0.25, 0.5,1.0, 2.0 and 2.5 degree at 3, 6, 12, 24 hourly, pentad and monthly) of the tropical area for all the four seasons over a long-term period from year 1998 to 2017. The probability distributions (PDF) and cumulative distribution functions(CDF) of rainfall amount at all the resolutions are computed and modelled as a mixed distribution, and analysed with the Kolmogrov-Smironov (KS) test. The distribution patterns are also fitted with both Gamma and Lognormal distributions. The results of this study provide insights on the scale characteristics of precipitation data which are helpful to improve the precipitation downscaling methods.