Merging Multisatellite and Gauge Precipitation Based on Geographically Weighted Regression and Long Short-Term Memory Network

Abstract

To generate high-quality spatial precipitation estimates, merging rain gauges with a single-satellite precipitation product (SPP) is a common approach. However, a single SPP cannot capture the spatial pattern of precipitation well, and its resolution is also too low. This study proposed an integrated framework for merging multisatellite and gauge precipitation. The framework integrates the geographically weighted regression (GWR) for improving the spatial resolution of precipitation estimations and the long short-term memory (LSTM) network for improving the precipitation estimation accuracy by exploiting the spatiotemporal correlation pattern between multisatellite precipitation products and rain gauges. Specifically, the integrated framework was applied to the Han River Basin of China for generating daily precipitation estimates from the data of both rain gauges and four SPPs (TRMM_3B42, CMORPH, PERSIANN-CDR, and GPM-IMAGE) during the period of 2007–2018. The results show that the GWR-LSTM framework significantly improves the spatial resolution and accuracy of precipitation estimates (resolution of correlation coefficient of 0.86, and Kling–Gupta efficiency of 0.6) over original SPPs (resolution of or , correlation coefficient of 0.36–0.54, Kling–Gupta efficiency of 0.30–0.52). Compared with other methods, the correlation coefficient for the whole basin is improved by approximately 4%. Especially in the lower reaches of the Han River, the correlation coefficient is improved by 15%. In addition, this study demonstrates that merging multiple-satellite and gauge precipitation is much better than merging partial products of multiple satellite with gauge observations.

A Method for Merging Multi-Source Daily Satellite Precipitation Datasets and Gauge Observations over Poyang Lake Basin, China

“…However, the coarse resolution and large uncertainties limit its wide application, especially for local studies. A growing number of studies have focused on the acquisition of high-resolution and highaccuracy precipitation fields using fusion approaches, and many studies have concentrated on the fusion of precipitation from two or three sources [25,[28][29][30][31][32]. Few studies have attempted to use more than three precipitation sources in the merging process to fully incorporate valuable precipitation information from different sources.…”

“…By blending station measurements and satellite precipitation products, previous studies have developed many data fusion approaches, including conditional merging [24], weighted fusion [17], geographically weighted regression [25][26][27], optimal interpolation techniques [28], Bayesian estimation [29], and machine learning models [30,31]. Despite the improvements in spatial patterns of precipitation obtained by these approaches, most of them were proposed based on gauge observations and only a single remote sensing precipitation dataset [32,33], ignoring valuable information captured by other satellite datasets.…”

A Downscaling–Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM

“…The Hanjiang River, with a total length of 1532 km, is the largest tributary of the Yangtze River, flowing through five provinces: Shaanxi, Gansu, Sichuan, Henan and Hubei. As shown in Figure 1, the Hanjiang River Basin, with the area of 159,000 km 2 , is located in south of central China, at 30 • 4 ~34 • 11 N and 106 • 5 ~114 • 18 E [36]. The altitude of the river basin is ranging from 9 to 3466 m, lower in the southeastern region.…”

An Integrated Framework for Spatiotemporally Merging Multi-Sources Precipitation Based on F-SVD and ConvLSTM

“…The long short-term memory network (LSTM) is a type of recurrent neural network (RNN) specifically designed to handle long-term dependencies by utilizing a gating mechanism to regulate the flow of information. Shen et al [21] proposed an integrated framework to merge multi-satellite and gauge precipitation data, which integrates the geographically weighted regression to improve the spatial resolution of precipitation estimations and the LSTM to improve the precipitation estimation accuracy by exploiting the temporal correlation pattern between multi-satellite precipitation products and rain gauges. Wu et al [22] proposed a spatiotemporal deep fusion model by combining the convolutional neural networks (CNN) and the LSTM to merge the TRMM 3B42 V7 satellite data, rain gauge data, and thermal infrared images.…”