Improvement of LAPS Wind Analysis By Including Background Error Statistics

Abstract

The NOAA Earth System Research Laboratory’s Local Analysis and Prediction System (LAPS) has been ported and used in many numerical weather forecast centers around the world. LAPS is a mesoscale meteorological data assimilation tool that employs a suite of observations to generate a realistic, spatially distributed, time-evolving, threedimensional representation of atmospheric features and processes (McGinley et al. 1991; Albers 1995; Birkenheuer 1999). The system has been consistently producing favorable and realistic meteorological analyses (with initial diabatic and cloudy atmospheric conditions) and ensuing forecasts. Over the years, LAPS has continued to evolve and upgrade to include more and better schemes and capabilities. Currently, the background at each grid point is given an “observation” increment of zero with an appropriate constant weight corresponding to the background error (Albers 1995). This study describes new work that has been done to include an ensemble-based estimate of background error statistics and to improve the weighting of the model background information in the LAPS data assimilation system. With appropriate background covariances retrieved from the ensemble method, the wind analysis is verified by comparing the current LAPS analysis that has constant weighting of background errors (CON) with this upgraded LAPS analysis designated VAR. Both the CON and VAR analyses are then compared with the withheld Radiosonde Observations (RAOBs).