The provision of high resolution near real-time rainfall data has made satellite rainfall products very potential for monitoring hydrological hazards. However, a major challenge in their direct-use can be problematic due to measurement error. In this study, an attempt was made to correct the bias of Global Satellite Mapping of Precipitation near-real-time (GSMaP_NRT) product. Physical factors, including topography, season, windspeed and cloud types were accounted for correcting bias. Peninsular Malaysia was used as the case study area. Gridded rainfall, developed from 80 gauges for the period 2000–2018, was used along with physical factors in a two-stage procedure. The model consisted of a classifier to categorise rainfall of different intensity and regression models to predict rainfall amount of different intensity class. An ensemble tree-based learning algorithm, called random forest, was used for classification and regression. The results revealed a big improvement of near-real-time GSMaP_NRT product after bias correction (GSMaP_BC) compared to the gauge corrected version (GSMaP_GC). Accuracy evaluation for complete timeseries indicated about 110% reduction of normalized root-mean-square error (NRMSE) in GSMaP_BC (0.8) compared to GSMaP_NRT (1.7) and GSMaP_GC (1.75). On the other hand, the bias of GSMaP_BC became nearly zero (0.3) compared to 2.1 and − 3.1 for GSMaP_NRT and GSMaP_GC products. The spatial correlation of GSMaP_BC was >0.7 with observed rainfall data for all months compared to 0.2–0.78 for GSMaP_NRT and GSMaP_GC, indicating capability of GSMaP_BC to replicate spatial pattern of rainfall. The bias-corrected near-real-time GSMaP data can be used for monitoring and forecasting floods and hydrological phenomena in the absence of dense rain-gauge network in areas, frequently experience hydro-meteorological hazards.

高分辨率近实时降雨数据的提供使卫星降雨产品非常有可能用于监测水文灾害。然而，由于测量误差，直接使用它们的主要挑战可能会成问题。在这项研究中，试图纠正全球卫星降水近实时（GSMaP_NRT）产品的偏差。纠正地形偏差的因素包括地形，季节，风速和云类型。马来西亚半岛被用作案例研究区域。在两个阶段的过程中，结合了80种测量仪的网格雨量和2000年至2018年期间的物理因子，将其与物理因子一起使用。该模型由对不同强度的降雨进行分类的分类器和对不同强度的降雨量进行预测的回归模型组成。基于集合树的学习算法，称为随机森林，用于分类和回归。结果表明，与量表校正版本（GSMaP_GC）相比，偏差校正（GSMaP_BC）后的近实时GSMaP_NRT产品有了很大的改进。完整时间序列的准确性评估表明，与GSMaP_NRT（1.7）和GSMaP_GC（1.75）相比，GSMaP_BC（0.8）中的标准化均方根误差（NRMSE）降低了约110％。另一方面，与GSMaP_NRT和GSMaP_GC产品的2.1和− 3.1相比，GSMaP_BC的偏差几乎变为零（0.3）。GSMaP_BC的空间相关性在所有月份的观测降雨数据中均大于0.7，而GSMaP_NRT和GSMaP_GC的空间相关性则为0.2–0.78，表明GSMaP_BC具有复制降雨空间模式的能力。