Expanding The Variational Methods In The LAPS Moisture Analysis

Abstract

The Local Analysis and Prediction System (LAPS) analyzes three-dimensional moisture and other state variables each hour (or less) over a high resolution relocatable domain. LAPS analyses have been used to initialize local-scale, high-resolution models such as the Colorado State University's Regional Atmospheric Modeling System (RAMS) model and NCAR's MM5 (mesoscale model, version 5) on a routine basis as a means to utilize local data in the forecast model. LAPS has been integrated into the Advanced Weather Information Processing System (AWIPS) as part of the National Weather Service (NWS) modernization. Research to expand LAPS capabilities is one avenue toward providing advanced technologies and new innovations to the operational forecaster. This paper describes work in progress and the next step toward advancing the variational technique in the LAPS moisture analysis. To date, the variational step has been used only with GOES sounder radiances. Other moisture variables were analyzed separately and either merged with that variational result or with the background field prior to the variational step (Birkenheuer 2000, 1999). This change will enable the use of more data in the variational framework. The solution strategy allows different data sources to be represented by different terms in the minimized functional. The functional can be automatically adjusted to match the datasets present. More important, this approach accommodates nonlinear functionals. 1.1 Brief History of LAPS Under development since 1990, LAPS combines nationally disseminated data with local data for realtime objective analyses of all data available to the local weather forecast office. LAPS analyses are of suitable quality to initialize local-scale forecast models. Such models can address specific problems of a small forecast domain with greater detail than can be achieved with nationally disseminated model guidance (Snook et al. 1998). The LAPS system is routinely tested with new data sources and innovative improvements, using more conventional data, which have potential for national dissemination. During the 1980s FSL conducted forecast exercises to test its workstation prototypes. Forecasters were burdened with the impossible task of reviewing all the incoming data made possible through new technologies, while producing timely forecasts. It became obvious that local data needed to be objectively analyzed in conjunction with nationally disseminated data. Conceived as a resolution to this challenge, LAPS was designed to analyze all local data in real time on an affordable computer workstation and use its own output fields to initialize local-scale forecast models. So far LAPS has been interfaced with RAMS and MM5, but it can function with any weather prediction model. A more detailed review of LAPS is available in McGinley et al. (1991). LAPS integrates all state-of-the-art data as they become routinely available to a field forecast office. Advanced data include Doppler reflectivity and velocity fields, satellite observations including GOES infrared (IR) image data in AWIPS format, wind profiler data, automated aircraft reports, and dual-channel groundbased radiometer data. New data sources included here are GOES-derived layer precipitable water data (GVAP), and Global Positioning System (GPS) data.