Evaluating Forecast Skills of Moisture from Convective-Permitting WRF-ARW Model during 2017 North American Monsoon Season

Abstract

This paper examines the ability of the Weather Research and Forecasting model forecast to simulate moisture and precipitation during the North American Monsoon GPS Hydrometeorological Network field campaign that took place in 2017. A convective-permitting model configuration performs daily weather forecast simulations for northwestern Mexico and southwestern United States. Model precipitable water vapor (PWV) exhibits wet biases greater than 0.5 mm at the initial forecast hour, and its diurnal cycle is out of phase with time, compared to observations. As a result, the model initiates and terminates precipitation earlier than the satellite and rain gauge measurements, underestimates the westward propagation of the convective systems, and exhibits relatively low forecast skills on the days where strong synoptic-scale forcing features are absent. Sensitivity analysis shows that model PWV in the domain is sensitive to changes in initial PWV at coastal sites, whereas the model precipitation and moisture flux convergence (QCONV) are sensitive to changes in initial PWV at the mountainous sites. Improving the initial physical states, such as PWV, potentially increases the forecast skills.

Keywords

• weather research and forecasting model
• convective-permitting parameterizations
• global forecast system model
• north american mesoscale model
• north american monsoon precipitation
• precipitable water vapor
• moisture flux convergence
• global positioning system
• forecast skills of moisture
• sensitivity analysis