A Gridded Monthly Precipitation Merged Rain Gauge and Satellite Analysis Dataset for the Tian Shan Range between 1981 and 2019

Abstract

Abstract An accurate and reliable precipitation product on regular grids is essential for understanding trends and variability within climate studies, for weather forecasting, and in hydrology and agrometeorology applications. However, the construction of high-resolution spatiotemporal precipitation grid products is challenging for complex terrain with sparse rain gauge networks and when only coarse spatial resolutions of satellite data are available. The objective of this study was to consequently provide a practical method to create a grid precipitation product by merging accurate quantitative observations from weather stations with continuous spatial information and from a satellite-based estimate product. The new gridded precipitation product exhibits a monthly temporal resolution and a spatial resolution of 0.01° for the Tian Shan range, extending back to 1981. To overcome the limitation of low densities and sparse distributions of meteorological stations in the complex terrain of the Tian Shan, a suitable interpolation of Australian National University Spline (ANUSPLIN) was used to interpolate grid precipitation based on in situ data. The interpolation grid precipitation was then merged with the satellite precipitation product developed by the U.S. Geological Survey and the Climate Hazards Group. After evaluation and validation using withheld stations and comparison to reference datasets, the result indicated that the merged product exhibits considerable promise for application in complex terrain. The method can be widely applied and is expected to construct precipitation products with high spatial and temporal resolution by merging multiple precipitation data sources. Significance Statement The purpose of this study is to construct a gridded precipitation dataset by merging the interpolation precipitation based on in situ observation and a satellite precipitation product in arid mountain regions. This is important because gridded precipitations are essential to the evaluation of climate model output and detection of trends in mean climate and climate extremes. Our results present a guide on merging frameworks to construct precipitation datasets used multisource data sources for regions with complex topography, precipitation scarcity, and ungauged and sparse collection.