# Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
## 4 Discussion  
### Anomalies in the Inland Water Bodies in TP  
Systematic anomalies were observed in the western and northern part of the TP in the original TMPA 3B43 data. The phenomena were also found in [25; 48; 49]. They concluded that the systematic anomalies on the TP interior in satellite-based precipitation estimates were mainly affected by some water bodies. The distribution of inland water bodies was shown in Figure 11c. It was obtained from the Global Lakes and Wetlands Database (GLWD) (downloaded from: [[https://www.worldwildlife.org/publications/global-lakes-and-wetlands-database-large-lake-polygons-level-1](https://www.worldwildlife.org/publications/global-lakes-and-wetlands-database-large-lake-polygons-level-1)]([https://www.worldwildlife.org/publications/global-lakes-and-wetlands-database-large-lake-polygons-level-1](https://www.worldwildlife.org/publications/global-lakes-and-wetlands-database-large-lake-polygons-level-1))). The spatial distributions of systematic anomalies were very close to those of inland water bodies. We further investigated the precipitation estimates covered by anomalies compared with rain gauges or its neighbors. Sites for seven anomalies which were located near water bodies were analyzed, as shown in Figure 12. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & & **Jan** & **Feb** & **Mar** & **Apr** & **May** & **Jun** & **Jul** & **Aug** & **Sep** & **Oct** & **Nov** & **Dec** \\ \hline Downscaled results & R\({}^{2}\) & 0.57 & 0.60 & 0.57 & 0.59 & 0.68 & 0.80 & 0.75 & 0.70 & 0.76 & 0.52 & 0.58 & 0.42 \\ based on TMPA & Bias (\%) & 12.23 & 20.54 & 25.67 & 14.30 & 10.40 & 16.86 & 19.59 & 11.35 & 11.25 & 7.02 & 21.23 & 13.79 \\ data at 10 km & MAE (mm) & 3.79 & 3.77 & 7.20 & 13.31 & 18.22 & 24.77 & 22.33 & 23.38 & 21.64 & 9.67 & 3.38 & 1.99 \\  & RMSE (mm) & 9.70 & 6.22 & 13.76 & 21.30 & 26.60 & 36.00 & 30.41 & 37.90 & 29.51 & 14.64 & 5.36 & 3.35 \\ Downscaled results & R\({}^{2}\) & 0.58 & 0.68 & 0.63 & 0.63 & 0.79 & 0.82 & 0.81 & 0.77 & 0.79 & 0.68 & 0.60 & 0.46 \\ based on TMPA & Bias (\%) & 10.76 & 14.93 & 22.75 & 13.23 & 7.45 & 11.27 & 13.44 & 7.26 & 11.96 & 6.48 & 20.23 & 8.47 \\ data at 1 km & MAE (mm) & 3.09 & 2.72 & 4.95 & 10.89 & 13.82 & 20.88 & 19.47 & 18.17 & 18.90 & 7.49 & 2.39 & 1.68 \\  & RMSE (mm) & 8.96 & 5.50 & 8.86 & 17.64 & 17.81 & 27.60 & 24.57 & 28.21 & 25.20 & 10.14 & 3.15 & 2.88 \\ Downscaled results & R\({}^{2}\) & 0.60 & 0.74 & 0.64 & 0.61 & 0.80 & 0.85 & 0.81 & 0.79 & 0.82 & 0.68 & 0.63 & 0.51 \\ based on IMERG & Bias (\%) & 8.12 & 6.47 & 14.76 & 3.38 & 2.56 & 7.29 & 8.63 & 4.11 & 8.42 & 4.48 & 14.87 & 6.72 \\ data at 10 km & MAE (mm) & 2.01 & 3.00 & 4.66 & 8.80 & 10.11 & 15.12 & 15.33 & 13.00 & 12.00 & 5.00 & 2.10 & 1.00 \\  & RMSE (mm) & 7.50 & 4.78 & 8.32 & 14.32 & 16.77 & 20.21 & 19.56 & 22.00 & 18.32 & 9.09 & 3.06 & 2.71 \\ \hline \end{tabular}
\end{table} 
Table 2: Coefficients of determination (R\({}^{2}\)), bias, mean absolute error (MAE) and root mean square error (RMSE) for the downscaled results based on TMPA data at 10 km, the downscaled results based on TMPA data at 1 km and downscaled results based on IMERG data at 1 km, against ground observations at each month in 2015. It was obvious that TMPA data tended to overestimate precipitation over inland water bodies, with mean bias \(\sim\) 78%. In contrast, IMERG data agreed quite well with ground observations (bias \(\sim\) 10%). The PMW retrievals might be the main source of the anomalies, and the deficiencies of PMW retrievals in the TMPA products (GPROF2010) led to the systematic overestimation. While IMERG employed the latest version of Goddard Profiling Algorithm (GPROF2014) to compute precipitation estimates from all PMW sensors onboard GPM satellites, it provided more consistent precipitation estimates over water bodies.