The impact of spatial resolution on resolving spatial precipitation patterns in the Himalayas

Abstract

Frequently used gridded meteorological datasets poorly represent precipitation in the Himalaya due to their relatively low spatial resolution and the associated coarse representation of the complex topography. Dynamical downscaling using high-resolution atmospheric models may improve the accuracy and quality of the precipitation fields, as simulations at higher spatial resolution are more capable of resolving the interaction between the topography and the atmosphere. However, most physics parameterizations (e.g. microphysics, cumulus, planetary boundary layer and land surface) are designed for grid spacings of 1 km or more, what complicates high-resolution modeling around this critical resolution. In this study, the WRF (Weather Research and Forecasting) model is used to determine which resolution is required to most accurately simulate monsoon and winter precipitation in the Nepalese Himalayas. Four model nests are set up with grid spacings of 25, 5, 1 and 0.5 km, respectively, and a typical summer (18th to 28th of July 2014) and winter (10th to 20th of February 2014) period are simulated. Simulated precipitation fields are compared with observational data obtained from automatic weather stations, pluviometers and tipping buckets in the Langtang catchment. Results show that, despite issues with the quality of the observational data due to under catch of snowfall, the highest resolution of 500 meter provides the best match with the observations and gives the most plausible spatial distribution of precipitation. The quality of the wind and temperature fields is also improved, whereby the cold temperature bias caused by the ERA-Interim data is decreased.