Sparse gauge network and heterogeneous terrains pose great challenges for conducting accurate short-range quantitative precipitation estimates (QPE). This research combines the quality-controlled gauge network with the short-range quantitative precipitation forecast (QPF) to provide the QPE in the mountainous Southwest. A Space and Time Mesoscale Analysis System (STMAS), which is a multigrid variational data assimilation system, was used to produce 6-h QPE analysis by assimilating the quality-controlled gauge data in conjunction with 0-6-h QPFs. The 6-h gauge data was based on the hourly gauge data from the Hydrometeorological Automated Data System (HADS) and went through a quality control procedure. During the 2005/06, 2006/07, and 2007/08 winters of the Hydrometeorological Testbed (HMT)-West campaign, high-resolution (3 km) ensemble forecasts were implemented in the Northern California region. The ensemble forecast system used four model configurations based on the Weather Research and Forecasting (WRF) mesoscale model with different microphysical schemes and dynamic cores. All models were diabatically initialized by the local analysis and prediction system (LAPS), which greatly reduces the “spin-up” problem for the 0-6-h QPF. The STMAS processed the 6-h gauge data as the precipitation observations and the 0-6-h ensemble-mean QPF as the background information. The 3-km QPE analysis was produced by the STMAS for 11 archived intensive operation periods (IOPs) during the two winters. Compared with the 4-km NCEP Stage IV precipitation analyses, the STMAS-generated QPE shows more details in complex terrains. The STMAS-generated QPE also shows consistent results compared with independent gauge and radar data and reasonable skill for verifying the 6-h QPF from other model forecasts. The impacts of selecting the gauge radius on the STMAS-generated QPE were also examined. This study indicates that the STMAS is able to provide effective QPE. More studies on the STMAS-generated QPE and other analysis fields are desirable.
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