Analysis of the surface temperature and wind forecast errors of the NCAR-AirDat operational CONUS 4-km WRF forecasting system

Abstract

Investigating the characteristics of modelforecast errors using various statistical and object-oriented methods is necessary for providing useful guidance to endusers and model developers as well. To this end, the random and systematic errors (i.e., biases) of the 2-m temperature and 10-m wind predictions of the NCAR-AirDat weather research and forecasting (WRF)-based real-time four-dimensional data assimilation (RTFDDA) and forecasting system are analyzed. This system has been running operationally over a contiguous United States (CONUS) domain at a 4-km grid spacing with four forecast cycles daily from June 2009 to September 2010. In the result an exceptionally useful forecast dataset was generated and used for studying the error properties of the model forecasts, in terms of both a longer time period and a broader coverage of geographic regions than previously studied. Spatiotemporal characteristics of the errors are investigated based on the 24-h forecasts between June 2009 and April 2010, and the 72-h forecasts between May and September 2010. It was found that the biases of both wind and temperature forecasts vary greatly seasonally and diurnally, with dependency on the forecast length, station elevation, geographical location, and meteorological conditions. The temperature showed systematic cold biases during the daytime at all station elevations and warm biases during the nighttime above 1,000 m above sea level (ASL), while below 600 m ASL cold biases occurred during the nighttime. The forecasts of surface wind speed exhibited strong positive biases during the nighttime, while the negative biases were observed in the spring and summer afternoons. The surface wind speed was mostly over-predicted except for the stations located between 1,000 and 2,100 m ASL, for which negative biases were identified for most forecast cycles. The highest wind-speed errors were found over the high terrain and near sea-level stations. The wind-direction errors were relatively large at the high-terrain elevation in the Rocky and Appalachian mountain ranges and the western coastal areas and the error structure exhibited notable diurnal variability.

Multiyear applications of WRF/Chem over continental U.S.: Model evaluation, variation trend, and impacts of boundary conditions

“…The MB of −0.9°C for 2010, in this study, is comparable to the surface temperature biases from the models in Brunner et al []. Wyszogrodzki et al [] also reported that a 4 km WRF forecasting simulation over contiguous U.S. showed systematic cold biases at all stations during the daytime and at stations below 600 m above sea level. Wyszogrodzki et al [] and Brunner et al [] attributed the cold surface temperature biases to deficiencies in the radiation schemes; however, this study shows that the radiation variables are overpredicted for all years (Table ).…”

Impact of four‐dimensional data assimilation (FDDA) on urban climate analysis

“…The STD is increased when observations are assimilated; that is, the climate spread of the system increases with the data assimilation. Since the mesoscale model often underestimates the spread when comparing with observations [e.g., Houtekamer et al ., ], for example, the surface temperature of WRF tends to have cold bias during daytime and warm bias during the night time [e.g., Pan et al ., ; Wyszogrodzki et al ., ], the increase of the spread indicates an improvement of the dynamical downscaling capability using FDDA.…”

Evaluation of WRF Model Forecasts and Their Use for Hydroclimate Monitoring over Southern South America

“…Larger values are found over arid and mountainous regions, both near the Andes and toward the northeast corner of the domain, likely related to the topographical features of the Brazilian Highlands. As for precipitation, these values are not different from those found over the United States (e.g., Cheng and Steenburgh 2005;Wyszogrodzki et al 2013).…”