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- Title
Towards an Accurate and Reliable Downscaling Scheme for High-Spatial-Resolution Precipitation Data.
- Authors
Zhu, Honglin; Liu, Huizeng; Zhou, Qiming; Cui, Aihong
- Abstract
Accurate high-spatial-resolution precipitation is significantly important in hydrological and meteorological modelling, especially in rain-gauge-sparse areas. Some methods and strategies have been applied for satellite-based precipitation downscaling, residual correction and precipitation calibration. However, which downscaling scheme can provide reliable high-resolution precipitation efficiently remains unanswered. To address this issue, this study aimed to present a framework combining the machine learning downscaling algorithm and post-process procedures. Firstly, four ML-based models, namely support vector regression, random forest, spatial random forest (SRF) and eXtreme gradient boosting (XGBoost), were tested for downscaling and compared with conventional downscaling methods. Then, the effectiveness of the residual correction process using ordinary Kriging and the calibration process using the geographical difference analysis (GDA) method was investigated. The results showed that the ML-based methods had better performance than the conventional regression and interpolation approaches. The SRF and XGBoost outperformed others in generating accurate precipitation estimation with a high resolution. The GDA calibration process significantly improved the downscaled results. However, the residual correction process decreased the downscaling performance of the ML-based models. Combining the SRF or XGBoost downscaling algorithm with the GDA calibration method could be a promising downscaling scheme for precipitation data. The scheme could be used to generate high-resolution precipitation, especially in areas urgently requiring data, which would benefit regional water resource management and hydrological disaster prevention.
- Subjects
DOWNSCALING (Climatology); WATER management; BOOSTING algorithms; MACHINE learning; ATMOSPHERIC models; RAIN gauges; RANDOM forest algorithms; PRECIPITATION gauges
- Publication
Remote Sensing, 2023, Vol 15, Issue 10, p2640
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs15102640