Abstract
Investigating the characteristics of model-forecast errors using various statistical and object-oriented methods is necessary for providing useful guidance to end-users 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.
Highlights
The random and systematic errors 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
Investigating the characteristics of model-forecast errors using various statistical and object-oriented methods is necessary for providing useful guidance to end-users and model developers as well
The NCAR-AirDat weather research and forecasting (WRF)-based real-time fourdimensional data assimilation (RTFDDA) and forecasting system, 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, Fig. 14 Same as Fig. 12 except for the wind-speed biases provides an exceptionally useful forecast dataset for studying the error properties of the WRF model forecasts, in terms of a longer time period and a broader coverage of geographic regions than previously studied
Summary
Jimenez and Dudhia (2012) added a new surface sink term in the WRF momentum equation to take into account the effects of the unresolved terrain, which improved climatological and intra-diurnal wind speed variability Data assimilation presents another means to reduce model biases by improving the quality of initial conditions. One significant component of the WRF-RTFDDA system is a data assimilation component that continuously assimilates meteorological observations as they become available, enabling the system to generate model-assimilated and model-adjusted datasets that both define the current atmospheric conditions and serve as the initial conditions for subsequent model forecasts (Liu et al 2008a) This high-resolution CONUS-scale system provides an exceptionally useful forecast dataset for studying the error properties of the WRF model forecasts, in terms of a longer time period and a broader coverage of geographic regions than previously studied. The system cold starts once a week on Saturdays at 18Z
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