Abstract
Environmental monitoring of Earth from space has provided invaluable information for understanding land–atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications is often limited by their coarse spatial resolutions. In this study, we propose a downscaling approach based on precipitation–land surface characteristics. Daytime land surface temperature, nighttime land surface temperature, and day–night land surface temperature differences were introduced as variables in addition to the Normalized Difference Vegetation Index (NDVI), the Digital Elevation Model (DEM), and geolocation (longitude, latitude). Four machine learning regression algorithms, the classification and regression tree (CART), the k-nearest neighbors (k-NN), the support vector machine (SVM), and random forests (RF), were implemented to downscale monthly TRMM 3B43 V7 precipitation data from 25 km to 1 km over North China for the purpose of comparison of algorithm performance. The downscaled results were validated based on observations from meteorological stations and were also compared to a previous downscaling algorithm. According to the validation results, the RF-based model produced the results with the highest accuracy. It was followed by SVM, CART, and k-NN, but the accuracy of the downscaled results using SVM relied greatly on residual correction. The downscaled results were well correlated with the observations during the year, but the accuracies were relatively lower in July to September. Downscaling errors increase as monthly total precipitation increases, but the RF model was less affected by this proportional effect between errors and observation compared with the other algorithms. The variable importances of the land surface temperature (LST) feature variables were higher than those of NDVI, which indicates the significance of considering the precipitation–land surface temperature relationship when downscaling TRMM 3B43 V7 precipitation data.
Highlights
Attaining accurate and fine spatial resolution precipitation data is very important for understanding land surface processes and global climate change
The downscaling method is based on two assumptions: (1) the precipitation has a spatial learning regression models; and (2) the models established at low spatial resolution can be used relationship the land surface characteristics, and relationship besurface addressed by machine to predict with the precipitation at fine resolution with thethis higher resolutioncan land characteristics learning regression models; theland models established at low and spatial resolutionas can be used dataset
We introduced land surface temperature features in addition to Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), and geolocations for downscaling of monthly Tropical Rainfall Measuring Mission (TRMM) 3B43 V7 precipitation data from a spatial resolution of 0.25◦ to one of 1 km over North China
Summary
Attaining accurate and fine spatial resolution precipitation data is very important for understanding land surface processes and global climate change. 2016, 8, 835 weather radar systems can provide spatial precipitation information but validation of ground radar rainfall products and the high uncertainties are major challenges for broad utilization in hydrologic application [4,5]. The development of satellite sensors and remote sensing technology has resulted in multiple sources of precipitation datasets [7,8,9,10,11,12,13,14,15,16,17,18] that provide more reliable estimations of precipitation over un-gauged areas compared with various interpolation methods.
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