# Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
## 3 Results  
### Original Results and Validations Using Ground Observations at Annual and Monthly Scales  
Figure 3a-c represented the annual precipitation maps derived from original TMPA, resampled IMERG and original IMERG in the year 2015 over the TP, respectively. Over the region, both TMPA and IMERG data, as well as the resampled IMERG, captured the typical southeast-northwest decreasing precipitation patterns, which were consistent with the patterns measured by rain gauges displayed in Figure 1. TMPA and IMERG patterns were close as they utilized similar sensors including IR and PMW sensors, to retrieve precipitation. However, TMPA presented some exceptional values in the western and northern parts of the TP relative to the neighboring pixels. These anomalies did not appear in IMERG estimates resulting in more continuous precipitation trends. Figure 3: _Cont._  
To compare the performance of the original TMPA data with resampled IMERG, as well as the original IMERG data, we validated these products against ground observations from 113 rain gauge stations on the TP in 2015. Figure 4a shown the scatterplot of the validation between ground observations and the original TMPA data at 0.25\({}^{\circ}\) resolution. Figure 4b,c demonstrate the validations of resampled IMERG data at 0.25\({}^{\circ}\) resolution and original IMERG data at 0.1\({}^{\circ}\) resolution, against ground observations, respectively. Both satellite precipitation products generally overestimated precipitation over the TP compared to ground observations. The original TMPA was the mostly overestimated product compared to ground observations (R\({}^{2}\)\(\sim\) 0.67, bias \(\sim\) 22.40%). IMERG shown improved accuracy (R\({}^{2}\)\(\sim\) 0.74, bias \(\sim\) 12.23%) at 0.1\({}^{\circ}\) resolution. The resampled IMERG data also outperformed the original TMPA data, with R\({}^{2}\)\(\sim\) 0.73 and bias \(\sim\) 12.40%, at the same spatial resolution of 0.25\({}^{\circ}\). Figure 4: _Cont._  
Figure 3: Spatial distribution of precipitation maps generated from (**a**) original TRMM Multisatellite Precipitation Analysis (TMPA) data (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)), (**b**) resampled IMERG data (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)) and (**c**) original Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) data (0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\)), over the TP in 2015. Figure 5 displayed the monthly mean precipitation from 113 rain gauges, the original TMPA, resampled IMERG and the original IMERG, respectively. Both TMPA and IMERG successfully captured the precipitation dynamics intra-annually, with heavy precipitation occurred at June, July, August and September (referred as wet seasons), while little precipitation happened in November, December, January and February (referred as dry seasons). IMERG estimates were better than TMPA estimates, due to its values were closer to the ground measured precipitation amounts. Table 1 presented the statistical results at monthly scale. Both TMPA and IMERG products performed better during the wet seasons and performed worse during the dry seasons. During dry seasons, the volume of precipitation was lower, and the durations of the precipitation was shorter in time which made the detection more complicated by PWM sensor. The performance of IMERG was slightly better than TMPA during the dry seasons with relatively higher values of R\({}^{2}\) and lower values of bias, MAE and RMSE, respectively. One of the possible reasons for the improvements may attribute to the extended sensors that equipped on IMERG (DPR and GMI) than sensors equipped on TMPA (PR and TMI), leading to a more accurate detection of light and solid precipitation. Another likely reason to explain why IMERG performed better than TMPA during the dry season was that the different PWM retrievals. IMERG employed the GPROF2014 while TMPA employed GPROF2010.