A Novel Real-Time Error Adjustment Method With Considering Four Factors for Correcting Hourly Multi-Satellite Precipitation Estimates

Abstract

High-accuracy near-real-time satellite precipitation estimates (SPEs) provide an opportunity for hydrometeorologists to improve the forecasting of extreme events, such as flood, landslide, tropical cyclone, and other extreme events, at the large scale. However, the currently operational near-real-time SPEs still have larger errors and uncertainties. In this study, we found that there exists a clear relationship of spatial plane function (SPF) between retrieval errors of SPEs and four crucial factors including topography, seasonality, climate type, and rain rate. Based on this finding, we proposed a novel error adjustment method to correct the near-real-time hourly global satellite mapping of precipitation (GSMaP-NRT) estimates in real-time. The new satellite precipitation dataset, namely, ILSF-RT, was then inter-compared with the latest near-real-time GSMaP product suite (i.e., GSMaP-NRT and GSMaP-Gauge-NRT). Verification results show that the proposed method can effectively reduce the retrieval errors of GSMaP-NRT for various terrains and rain rates over different seasons and climate-type areas. The new ILSF-RT even exhibits a general improvement over the GSMaP-Gauge-NRT estimates. Furthermore, one important merit of the new method is that it can perform rather well in validation even when not much historical data were applied as training samples in calibration, for example, during the generation of ILSF-RT, only 45 data pairs of satellite retrievals and ground observations were used for winter season over Chinese arid areas. However, the results of bias score show that the current method seems unsuitable to adjust the rainfall events with higher rain rates (>=1 mm hr^−1), which needs to be further improved.