Abstract
Precipitation, a basic component of the water cycle, is significantly important for meteorological, climatological and hydrological research. However, accurate estimation on the precipitation remains considerably challenging because of the sparsity of gauge networks and the large spatial variability of precipitation over mountainous regions. Moreover, meteorological stations in mountainous areas are often dispersed and have difficulty in accurately reflecting the intensity and evolution of precipitation events. In this study, we proposed a novel method to produce high-quality, high-resolution precipitation estimates in the Tianshan Mountains, China, based on area-to-point kriging (ATPK) downscaling and a two-step correction, i.e., probability density function matching-optimum interpolation (PDF-OI). We obtained 1-km hourly precipitation data in the Tianshan Mountains by merging estimates from the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) product with observations from 1065 meteorological stations in the warm season (May to September) during 2016–2018. The spatial resolution and accuracy of the merged precipitation data greatly increased compared to IMERG. According to a cross-validation with gauged observations, the correlation coefficient (CC), probability of detection (POD) and critical success index (CSI) increased from 0.30, 0.50 and 0.24 for IMERG to 0.63, 0.65 and 0.38, respectively, for the merged estimates, and the root mean squared error (RMSE), mean error (ME) and false alarm ratio (FAR) decreased from 0.46 to 0.38 mm/h, 0.06 to 0.05 mm/h and 0.69 to 0.52, respectively. The proposed method will be useful for developing high-resolution precipitation estimates in mountainous areas such as central Asia and the Belt and Road Initiative regions.
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Acknowledgments
This work was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (2020D01A137), the National Natural Science Foundation of China (42071075, 41901363), Tianshan Youth Project of Xinjiang Uigur Autonomous Region — Outstanding young talents (2019Q039), National Key R&D Program of China (2019YFC1510503), and the Basic Research Operating Expenses of the Central Level Non-profit Research Institutes (IDM2020006).
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Lu, Xy., Chen, Yy., Tang, Gq. et al. Quantitative estimation of hourly precipitation in the Tianshan Mountains based on area-to-point kriging downscaling and satellite-gauge data merging. J. Mt. Sci. 19, 58–72 (2022). https://doi.org/10.1007/s11629-021-6901-5
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DOI: https://doi.org/10.1007/s11629-021-6901-5