Improving Ensemble Weather Prediction System Initialization: Disentangling the Contributions from Model Systematic Errors and Initial Perturbation Size

Abstract

AbstractCharacteristics of the European Centre for Medium-Range Weather Forecast’s (ECMWF’s) 0000 UTC diagnosed 2-m temperatures (T2m) from 4D-Var and global ensemble forecasts initial conditions were examined in 2018 over the contiguous United States at 1/2° grid spacing. These were compared against independently generated, upscaled high-resolution T2m analyses that were created with a somewhat novel data assimilation methodology, an extension of classical optimal interpolation (OI) to surface data analysis. The analysis used a high-resolution, spatially detailed climatological background and was statistically unbiased. Differences of the ECMWF 4D-Var T2m initial states from the upscaled OI reference were decomposed into a systematic component and a residual component. The systematic component was determined by applying a temporal smoothing to the time series of differences between the ECMWF T2m analyses and the OI analyses. Systematic errors at 0000 UTC were commonly 1 K or more and larger in the mountainous western United States, with the ECMWF analyses cooler than the reference. The residual error is regarded as random in character and should be statistically consistent with the spread of the ensemble of initial conditions after inclusion of OI analysis uncertainty. This analysis uncertainty was large in the western United States, complicating interpretation. There were some areas suggestive of an overspread initial ensemble, with others underspread. Assimilation of more observations in the reference OI analysis would reduce analysis uncertainty, facilitating more conclusive determination of initial-condition ensemble spread characteristics.