Evaluations of WRF Sensitivities in Surface Simulations with an Ensemble Prediction System

• Published in: Atmosphere 2018-03, Vol.9 (3), p.106
• Main Authors: Pan, Linlin, Liu, Yubao, Knievel, Jason, Delle Monache, Luca, Roux, Gregory
• Format: Article
• Language: English
• Subjects: Atmospheric boundary layer; Atmospheric models; Atmospheric research; Boundary layers; Clouds; Computer simulation; Data assimilation; Data collection; Humidity; Mathematical models; Microphysics; Modelling; operational ensemble system; Parameterization; Performance measurement; Perturbation methods; Physics; Root-mean-square errors; Sensitivity analysis; Simulation; Specific humidity; surface simulation; Surface temperature; Surface wind; Weather forecasting; Wind direction; Wind speed; WRF sensitivities
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos9030106

This paper investigates the sensitivities of the Weather Research and Forecasting (WRF) model simulations to different parameterization schemes (atmospheric boundary layer, microphysics, cumulus, longwave and shortwave radiations and other model configuration parameters) on a domain centered over the inter-mountain western United States (U.S.). Sensitivities are evaluated through a multi-model, multi-physics and multi-perturbation operational ensemble system based on the real-time four-dimensional data assimilation (RTFDDA) forecasting scheme, which was developed at the National Center for Atmospheric Research (NCAR) in the United States. The modeling system has three nested domains with horizontal grid intervals of 30 km, 10 km and 3.3 km. Each member of the ensemble system is treated as one of 48 sensitivity experiments. Validation with station observations is done with simulations on a 3.3-km domain from a cold period (January) and a warm period (July). Analyses and forecasts were run every 6 h during one week in each period. Performance metrics, calculated station-by-station and as a grid-wide average, are the bias, root mean square error (RMSE), mean absolute error (MAE), normalized standard deviation and the correlation between the observation and model. Across all members, the 2-m temperature has domain-average biases of −1.5–0.8 K; the 2-m specific humidity has biases from −0.5–−0.05 g/kg; and the 10-m wind speed and wind direction have biases from 0.2–1.18 m/s and −0.5–4 degrees, respectively. Surface temperature is most sensitive to the microphysics and atmospheric boundary layer schemes, which can also produce significant differences in surface wind speed and direction. All examined variables are sensitive to data assimilation.