Taiyuan Ligong Daxue xuebao (Jul 2024)
A Terrain-Based Bias-Correction Method for Precipitation Data Based on Deep Learning and Spatiotemporal Correlation Modeling
Abstract
Purposes How to perform bias-correction for precipitation data based on terrain information has been an important problem in meteorological big data research. However, the existing precipitation terrain-based bias-correction methods mostly suffer from two limitations. First, most of them are designed based on statistical models or conventional machine learning models, thus fails to go beyond to the deep learning models with more powerful feature learning ability. Second, they also lack the ability to incorporate the spatiotemporal correlation information in meteorological data for enhancing the bias-correction quality. Methods To address these limitations, in this paper, a terrain-based bias-correction method for precipitation data based on deep learning and spatiotemporal correlation modeling is presented. First, the precipitation data and multi-source terrain information with alignment and encoding based on the longitude and latitude are preprocessed, and thus the corresponding data matrices are constructed. Then the precipitation and terrain data in the spatial and temporal neighborhood of each grid are adopted to achieve the multi-source spatiotemporal information modeling, and a down-sampling strategy is used to alleviate the imbalance between the precipitation grids and the non-precipitation grids. Finally, the deep neural network is constructed for regression and bias-correction. The experiments are conducted on the real meteorological data from over 4 000 meteorological observation stations and over 150 thousand meteorological grids in Guangdong Province. Findings Experimental results have verified the significance influence of the spatiotemporal modeling strategy over precipitation bias correction quality and performance advantage of the proposed method over the baseline methods.
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