In a study published in the Journal of Hydrometeorology, the scientists compared 4-km CPM results with 28-km DDM results for one snow season (October 1, 2013–May 31, 2014) on the Third Pole. The CPM- and DDM-simulated precipitation, as well as three merged gridded precipitation datasets, were evaluated against in situ observations below 4800 m. The five precipitation datasets (CPM, DDM, CMFD, COPRPH, and TRMM) showed large differences across the Third Pole, with underestimation by TRMM and overestimation by CPM and DDM compared to actual observations. The most significant difference occurred in the Brahmaputra Grand Canyon. Given the substantial uncertainty in observed precipitation in the high mountains, snow cover simulated by a high-resolution land data assimilation system was used to indirectly evaluate the above precipitation data using MODIS observations. The simulated snow cover fraction was greatly underestimated using all the merged precipitation datasets. However, simulations using DDM- and CPM-generated precipitation as input outperformed those using other gridded precipitation data, showing lower biases, higher pattern correlations, and closer probability distribution functions than runs driven by the merged precipitation datasets. 

 

The findings of this study generally support the assumption that high-resolution CPM-produced precipitation data is useful in land surface and hydrology simulations in high mountain regions without reliable in situ precipitation observations.