# Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
## 1 Introduction  
Precipitation plays a significant role in global water cycles and energy exchanges [1]. Accurate precipitation information is highly desirable in various scientific and application fields such as water resource management, weather prediction, as well as disaster monitoring and control [2]. Currently, there are three kinds of independent instruments to obtaining precipitation data, including gauges, weather radar and satellite-based sensors [3]. Though conventional point-based measurements from rain gauges could provide relatively accurate rainfall values at the point scale, they are not suitable for providing continuous spatial precipitation distributions [4]. Weather radar can obtain rainfall data with finer spatiotemporal resolutions. However, weather radar has disadvantages due to errors from various sources such as beam blockage in complex terrain and range degradation issues [5]. While satellite-based remote sensing has great potentials to provide comprehensive estimates of precipitation globally with reasonable spatiotemporal resolutions and accuracy. Several projects, including the Global Precipitation Climatology Project (GPCP) [6; 7; 8], the Global Satellite Mapping of Precipitation (GsMaP) project [9], and the Tropical Rainfall Measuring Mission(TRMM) [10; 11; 12], had calculated satellite-based precipitation estimates and published various precipitation products with different temporal and spatial resolutions for free, such as the TRMM Multisatellite Precipitation Analysis (TMPA) data set [12]. The TRMM, carrying a signal-frequency precipitation radar (PR; the Ku-band at 13.8 GHz) and a multichannel TRMM Microwave Imager (TMI; frequencies range between 10 and 85.5 GHz), had provided a range of precipitation products since 1997 [10] and intended to provide the 'best' satellite precipitation estimates [12]. TMPA data had been widely applied in various fields, such as hydrological modeling [13], flood prediction [14] and climatology [15], over the past two decades. With the success of TRMM, the Global Precipitation Measurement (GPM) Core Observatory was launched in February 2014 by a joint effort of the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA). The GPM Core Observatory carried a dual-frequency precipitation radar (DPR; the Ku-band at 13.6 GHz and Ka-band at 35.5 GHz) and a conical-scanning multichannel GPM Microwave Imager (GMI; frequencies range between 10 and 183 GHz). Compared with TRMM's instruments, GPM extended its sensor packages and could detect light and solid precipitation more accurately than TRMM sensors [2; 16]. Numerous studies had examined the accuracy of GPM rainfall products. The performance of the Integrated Multi-satellite Retrievals for GPM (IMERG) and TMPA products were evaluated over mainland China at various spatiotemporal resolutions [17]. In southeast India, the accuracy of IMERG data were validated and compared with those of TMPA and GsMaP data [18]. While in Far East Asia, the IMERG and TMAP data were employed to evaluate the topographical and seasonal precipitation features applied [19]. These studies mainly demonstrated that IMERG had a better ability to provide more accurate rainfall estimate data than TMPA data. However, the spatial resolution of TMPA data was too coarse (0.25\({}^{\circ}\)) and could not meet the demands of some researches, especially at the regional and basin scales. As a result, numerous downscaling models combining different environmental variables as auxiliary data had been explored to derive precipitation estimates at 1 km scale. To downscale TMPA data, the exponential regression model between TMPA and the normalized difference vegetation index (NDVI) was firstly applied in [20]. Based on this downscaling method, a statistical regression downscaling algorithm, introducing the NDVI and the digital elevation model (DEM), was developed [21]. The regression kriging method was also found to be feasible to downscale TMPA data [22].