Nowcasting Applications of the Space—Time Mesoscale Analysis System

In recent years the number of surface observations over the U.S. with spatially dense time and space coverage has grown rapidly, resulting in an essentially national mesonetwork offering the possibility of performing frequent monitoring of mesoscale features that may generate significant local weather, as well as hazards to aviation. Despite these advances, the density of this data remains highly variable across the country, literally being composed of “oases and deserts” of data. Traditional approaches to objective analysis (successive correction methods, optimal interpolation, etc.) have great difficulty properly representing details in the “oasis” regions without introducing undesirable noise in the analyzed fields in the “desert” regions. Model-based surface analysis approaches are limited by the resolution of the model, the accuracy of the model background forecast fields, and the assumptions underlying the model and data assimilation system. At the same time, the National Digital Forecast Database (NDFD) has a nominal grid spacing of 5 km across the United States, necessitating the development of a high-quality, very detailed, and robust surface mesoanalysis system.

Given these needs, NOAA/FSL has been developing a 5-km resolution surface mesoanalysis scheme offering real-time analyses at 15-min intervals where the data support such detail, while avoiding noise in other parts of the analysis domain where only coarser-scale features can be resolved. The Space-Time Mesoscale Analysis System (STMAS) is designed to analyze small-scale features such as thunderstorm gust fronts and lake breezes with excellent spatial-temporal coherence, and to be compatible with current AWIPS workstation display capabilities. STMAS includes a robust data quality control method, which is being modified to work as a Kalman filter operating in observation space. The STMAS analysis incorporates an iterative space-time recursive filter within a variational framework, using a previous analysis as the background. Local wavelet techniques are also being tested to further promote the resolving capabilities of the analysis system without introducing undesirable noise in the data-sparse regions.

This paper will discuss applications of STMAS for such purposes as nowcasting, verification of high-resolution numerical model forecasts, and aviation forecasting. We will demonstrate the capability of STMAS to reveal important mesoscale features that lead to hazardous local weather. We will emphasize how the huge number of surface observations now available can be exploited for mesoscale diagnosis and nowcasting. Finally, plans for incorporating information from STMAS into the Real-Time Mesoscale Analysis (RTMA), a 5-km resolution hourly product under development by NCEP and FSL, will be discussed.