Objective and Forecaster Evaluation of Current NWS Mesoscale Surface Analysis Techniques with the Ensemble Kalman Filter

Real-time mesoscale surface analyses are very important to National Weather Service (NWS) forecast operations. Routine, gridded analyses provide forecasters with a spatially and temporally consistent characterization of the current weather situation at high resolution. These analyses are also used to verify a suite of operational guidance models and the NWS National Digital Forecast Database (NDFD). A leading NWS method to produce mesoscale (5 km and 2.5 km) surface analyses is the real time mesoscale analysis system (RTMA), which is a two-dimensional variational data assimilation system. A secondary NWS surface analysis technique is Match-Observations-All (MOA), a method that fits to observations exactly while spreading information spatially through weighting functions. The purpose of this study is to compare surface analyses produced by an ensemble Kalman filter (EnKF), a data assimilation system that uses flow-dependent covariances, to those of the RTMA and MOA.

Results from an objective evaluation of the RTMA and EnKF systems against independent wind and temperature observations show improved wind analyses with the EnKF, but improved temperature analyses from the RTMA. The potential role of surface biases and lack of inflation in the EnKF will be discussed. The sensitivity of these results to different terrain features in the Pacific Northwest, such as coastlines and complex topography, will also be shown. Furthermore, results from a subjective forecaster evaluation involving four NWS forecast offices (Seattle, Portland, Spokane, and Pendleton) will be presented. This subjective evaluation compared RTMA, EnKF, and MOA wind and temperature analyses, and assessed the usefulness of EnKF uncertainty in the NWS analysis and short-term forecasting process.