The Development of a Two-Step Merging and Downscaling Method for Satellite Precipitation Products&#x0D;

Lu Lu,Tang Tang,Liu Liu,Wang Wang,Wei Wei,Zhang Zhang

doi:10.3390/rs12030398

Abstract

Low accuracy and coarse spatial resolution are the two main drawbacks of satellite precipitation products. Therefore, calibration and downscaling are necessary before these products are applied. This study proposes a two-step framework to improve the accuracy of satellite precipitation estimates. The first step is data merging based on optimum interpolation (OI), and the second step is downscaling based on geographically weighted regression (GWR); therefore, the framework is called OI-GWR. An Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) product is used to demonstrate the effectiveness of OI-GWR in the Tianshan Mountains, China. First, the original IMERG precipitation data (OIMERG) are merged with rain gauge data using the OI method to produce corrected IMERG precipitation data (CIMERG). Then, using CIMERG as the first guess and the normalized difference vegetation index (NDVI) as the auxiliary variable, GWR is utilized for spatial downscaling. The two-step OI-GWR method is compared with several traditional methods, including GWR downscaling (Ori_GWR) and spline interpolation. The cross-validation results show that (1) the OI method noticeably improves the accuracy of OIMERG, and (2) the 1-km downscaled data obtained using OI-GWR are much better than those obtained from Ori_GWR, spline interpolation, and OIMERG. The proposed OI-GWR method can contribute to the development of high-resolution and high-accuracy regional precipitation datasets. However, it should be noted that the method proposed in this study cannot be applied in regions without any meteorological stations. In addition, further efforts will be needed to achieve daily- or hourly-scale downscaling of precipitation.

Highlights

Precipitation is the main component of the global water cycle and plays a critical role in Earth’s energy balance [1,2]
Sharifi et al [21] obtained 1-km daily precipitation data in northeast Austria using multiple linear regression (MLR), artificial neural networks (ANNs) and spline interpolation based on 1-km cloud optical thickness (COT), cloud effective radius (CER), and cloud water path (CWP) data
During the process of variable selection for geographically weighted regression (GWR), when the variables with the best global correlation with precipitation were applied for GWR downscaling, the obtained results were not optimal

Summary

Introduction

Precipitation is the main component of the global water cycle and plays a critical role in Earth’s energy balance [1,2]. Because of the coarse spatial resolution of satellite precipitation products, their application in hydrological and climatic models at the watershed scale is restricted. For this reason, many researchers have focused on developing statistical downscaling methods for satellite or reanalysis precipitation products [10,11,12]. Based on the method of Immerzeel, Jia et al [14] established a functional relationship between 3B43 precipitation data and other variables (i.e., altitude and NDVI) using multiple linear regression (MLR) and obtained downscaled annual data at a 1 km resolution for the Qaidam Basin in China. Ma et al [22] obtained 1-km hourly precipitation data in the southeast coast region of China based on COT, CER, and cloud top height (CTH) data from Himawari 8

Methods

Results

Conclusion