A Novel Reference-Based and Gradient-Guided Deep Learning Model for Daily Precipitation Downscaling

Abstract

The spatial resolution of precipitation predicted by general circulation models is too coarse to meet current research and operational needs. Downscaling is one way to provide finer resolution data at local scales. The single-image super-resolution method in the computer vision field has made great strides lately and has been applied in various fields. In this article, we propose a novel reference-based and gradient-guided deep learning model (RBGGM) to downscale daily precipitation considering the discontinuity of precipitation and ill-posed nature of downscaling. Global Precipitation Measurement Mission (GPM) precipitation data, variables in ERA5 re-analysis data, and topographic data are selected to perform the downscaling, and a residual dense attention block is constructed to extract features of them. By exploring the discontinuous feature of precipitation, we introduce gradient feature to reconstruct precipitation distribution. We also extract the feature of high-resolution monthly precipitation as a reference feature to resolve the ill-posed nature of downscaling. Extensive experimental results on benchmark data sets demonstrate that our proposed model performs better than other baseline methods. Furthermore, we construct a daily precipitation downscaling data set based on GPM precipitation data, ERA5 re-analysis data and topographic data.

A Multiscale Attention Network for the Classification of Lightning Safety Risk Warnings

“…Machine learning has achieved notable results in security risk assessment in multiple elds and has strong data processing and realtime computing capabilities, which can make up for the shortcomings of traditional methods [10][11]. In recent years, many scholars have attempted to apply machine learning methods to atmospheric-related elds [6, [12][13][14][15][16] and have achieved good prediction results. The results have indicated that the use of arti cial intelligence algorithms in lightning warning research can improve prediction accuracy, effectiveness, and superiority [17][18].…”

Joint Generalized Neural Models and Censored Spatial Copulas for Probabilistic Rainfall Forecasting

“…To provide local-scale precipitation forecasting, it is common practice to downscale coarse CM forecasts to a finer grid with the use of post-processing methods. Similar to the works by Woo and Wong [2017], Vandal et al [2018], Adewoyin et al [2021], Xiang et al [2022] and Wang et al [2023], one of our main aims is to perform statistical downscaling of rainfall, that is predicting high-resolution precipitation from low-resolution weather variables. By conditioning on forecasts of future weather variables, our model can gain in reach due to the accuracy of CM weather forecasts on longer-time frames compared to statistical models directly predicting the weather.…”

Downscaling Daily Satellite-Based Precipitation Estimates Using MODIS Cloud Optical and Microphysical Properties in Machine-Learning Models

“…The resolution of the precipitation dataset is downscaled to a finer resolution using the auxiliary data products through the regression procedure. This data-driven downscaling method requires a large amount of data in order to establish statistical relationships and, unlike the dynamical methods, has lower computational demands [12,[26][27][28].…”