Spatially Downscaling IMERG at Daily Scale Using Machine Learning Approaches Over Zhejiang, Southeastern China

Abstract

Precipitation estimates with high accuracy and fine spatial resolution play an important role in the field of meteorology, hydrology, and ecology. In this study, support vector machine (SVM) and back-propagation neural network (BPNN) machine learning algorithms were used to downscale the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) data at daily scale through four events selected from 2017 and 2018 by establishing the relationships between precipitation and six environmental variables over Zhejiang, Southeastern China. The downscaled results were validated by ground observations, and we found that (1) generally, the SVMbased products had better performance and finer spatial textures than the BPNN-based products, the multiple linear regression (MLR)-based products, and the original IMERG;(2) all downscaled products decreased the degree of overestimation of the original IMERG at heavy-precipitation regions to a certain extent; (3) for heavy-precipitation events in the plum rain season, the downscaled products based on SVM and BPNN both improved prediction accuracy compared to the MLR-based products and the original IMERG considering the validations against ground observations. R 2 maximally increased from 0.344 to 0.615 for the SVM-based products and from 0.344 to 0.435 for the BPNN-based products compared to the original IMERG; and (4) for typhoon precipitation events, the SVM-based products still showed better accuracy with R 2 maximally increased from 0.492 to 0.615 compared to the original IMERG. In contrast, the performance of BPNN-based products was not satisfying and showed no significant differences with the performance of MLR-based products. This study provided a potential solution for generating downscaled satellite-based precipitation products at meteorological scales with finer accuracy and spatial resolutions.

Quantitative Precipitation Estimation Model Integrating Meteorological and Geographical Factors at Multiple Spatial Scales

“…The rapid development of machine learning, especially deep learning in recent years has advanced the research of QPE in the meteorological community (Teschl et al, 2006;Gagne et al, 2014;Kühnlein et al, 2014;Sorooshian et al, 2016;Beusch et al, 2018;Chen et al, 2020;Min et al, 2020;Zhang et al, 2020;Wu et al, 2021). In the era of big data, machine learning has great potential for parsing the underlying patterns of huge data without assuming any physical relationships.…”

MSG-GAN-SD: A Multi-Scale Gradients GAN for Statistical Downscaling of 2-Meter Temperature over the EURO-CORDEX Domain

“…Artificial Neural Networks (ANNs) have been used in [23] to perform precipitation downscaling, applying a further residual correction method based on Particle Swarm Optimization (PSO), an Imperialist Competitive Algorithm (ICA), and a Genetic Algorithm (GA). Furthermore, a Back-Propagation Neural Network (BPNN) and Support Vector Machine (SVM) fusion approach was adopted in [24] to downscale precipitation. Several works moved towards deep architectures in the context of Deep Learning (DL), especially concerning Long-Short Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs).…”

Fusion of Surface Soil Moisture Data for Spatial Downscaling of Daily Satellite Precipitation Data

“…In addition, machine learning-based models have also been developed for precipitation downscaling, due to their ability to characterize spatial features from training data [28], [29]. Common examples include support vector machine [30], random forest [31], XGBoost [32], artificial neural network [33], and convolutional neural network [34], etc. These methods always require a large number of training data as input [29].…”