Verification of Surface Temperature Forecasts from the National Digital Forecast Database over the Western United States

Abstract

Experimental gridded forecasts of surface temperature issued by National Weather Service offices in the western United States during the 2003/04 winter season (18 November 2003–29 February 2004) are evaluated relative to surface observations and gridded analyses. The 5-km horizontal resolution gridded forecasts issued at 0000 UTC for forecast lead times at 12-h intervals from 12 to 168 h were obtained from the National Digital Forecast Database (NDFD). Forecast accuracy and skill are determined relative to observations at over 3000 locations archived by MesoWest. Forecast quality is also determined relative to Rapid Update Cycle (RUC) analyses at 20-km resolution that are interpolated to the 5-km NDFD grid as well as objective analyses obtained from the Advanced Regional Prediction System Data Assimilation System that rely upon the MesoWest observations and RUC analyses. For the West as a whole, the experimental temperature forecasts issued at 0000 UTC during the 2003/04 winter season exhibit skill at lead times of 12, 24, 36, and 48 h on the basis of several verification approaches. Subgrid-scale temperature variations and observational and analysis errors undoubtedly contribute some uncertainty regarding these results. Even though the “true” values appropriate to evaluate the forecast values on the NDFD grid are unknown, it is estimated that the root-mean-square errors of the NDFD temperature forecasts are on the order of 3°C at lead times shorter than 48 h and greater than 4°C at lead times longer than 120 h. However, such estimates are derived from only a small fraction of the NDFD grid boxes. Incremental improvements in forecast accuracy as a result of forecaster adjustments to the 0000 UTC temperature grids from 144- to 24-h lead times are estimated to be on the order of 13%.

Verification of surface minimum, mean, and maximum temperature forecasts in Calabria for summer 2008

“…The R and B matrices depend on the observation (σ 2 o ) and background (σ 2 b ) error covariances, respectively, whose magnitudes are estimated by the Lönnberg and Hollingsworth method (Lönnberg and Hollingsworth, 1986;Xu et al, 2001;Myrick and Horel, 2006). The covariance between observational innovations y o -Hx b is computed as a function of the distance r from all background field-observation pairs:…”

“…Nevertheless, observations can develop systematic errors under particular weather conditions due to position, exposure, and instrumental errors (Myrick and Horel, 2006). Even supposing a perfect instrument, that is, with no observational errors, position is an important issue for regions with complex orography because the model forecast is defined as the average value of the meteorological parameter over the grid box, and is representative of the expected conditions inside the grid box.…”

USA National Phenology Network gridded products documentation

“…Weather prediction has improved dramatically over the past several decades (Slingo and Palmer, 2011) and the NDFD system capitalizes on the expertise of local forecast offices as well as the latest generation of meteorological models at their disposal (Glahn and Ruth, 2003). NDFD forecast performance is regularly evaluated with standard verification statistics (available at www.nws.noaa.gov/ndfd/verification/) and has been formally evaluated in peer-reviewed studies (for example, Myrick and Horel, 2006).…”

Surface high-resolution temperature forecast in southern Italy

“…The R and B matrices depend on the observation (σ 2 o ) and background (σ 2 b ) error covariances, respectively, whose magnitudes are estimated by fitting, as a function of the distance r, the covariance between observational innovations computed from all background field-observation pairs (Lönnberg and Hollingsworth, 1986;Myrick and Horel, 2006). Assuming that: (a) the observational errors are uncorrelated with one another; and (b) the background and the observational errors are uncorrelated, we obtain the following two equations for the innovations' covariance:…”