The Local Analysis and Prediction System combines numerous data sources into a set of analyses and forecasts on a 10-km grid with high temporal resolution. To arrive at an analysis of cloud cover, several input analyses are combined with surface aviation observations and pilot reports of cloud layers. These input analyses are a skin temperature analysis (used to solve for cloud layer heights and coverage) derived from Geostationary Operational Environmental Satellite IR 11.24-mm data, other visible and multispectral imagery, a three-dimensional temperature analysis, and a three-dimensional radar reflectivity analysis derived from full volumetric radar data. Use of a model first guess for clouds is currently being phased in. The goal is to combine the data sources to take advantage of their strengths, thereby automating the synthesis similar to that of a human forecaster.The design of the analysis procedures and output displays focuses on forecaster utility. A number of derived fields are calculated including cloud type, liquid water content, ice content, and icing severity, as well as precipitation type, concentration, and accumulation. Results from validating the cloud fields against independent data obtained during the Winter Icing and Storms Project are presented.Forecasters can now make use of these analyses in a variety of situations, such as depicting sky cover and radiation characteristics over a region, three-dimensionally delineating visibility and icing conditions for aviation, depicting precipitation type, rain and snow accumulation, etc.

As new observation systems are developed and deployed, new and presumably more precise information is becoming available for weather forecasting and climate monitoring. To take advantage of these new observations, it is desirable to have schemes to accurately retrieve the information before statistical analyses are performed so that statistical computation can be more effectively used where it is needed most. The authors propose a sequential variational approach that possesses advantages of both a standard statistical analysis [such as with a three-dimensional variational data assimilation (3DVAR) or Kalman filter] and a traditional objective analysis (such as the Barnes analysis). The sequential variational analysis is multiscale, inhomogeneous, anisotropic, and temporally consistent, as shown by an idealized test case and observational datasets in this study. The real data cases include applications in two-dimensional and three-dimensional space and time for storm outflow boundary detection (surface application) and hurricane data assimilation (three-dimensional space application). Implemented using a multigrid technique, this sequential variational approach is a very efficient data assimilation method.

A multisensor applications development and evaluation system at the National Severe Storms Laboratory addresses significant gaps in both our knowledge and capabilities for accurate high-resolution precipitation estimates at the national scale. W ater is a precious resource and, when excessive or in short supply, a source of many hazards. It is essential to monitor and predict water-related hazards, such as floods, droughts, debris flows, and water quality, and to determine current and future availability of water resources. Accurate quantitative precipitation estimates (QPE) and very short term quantitative precipitation forecasts (VSTQPF) provide key input to these assessments. [QPE and VSTQPF are hereafter referred to collectively as quantitative precipitation information (QPl}.] To meet these needs at the national scale, accurate QPI is needed at various temporal and spatial scales for the entire United States, its territories, and immediate surrounding areas. Temporal scales range from minutes to several hours for tiash flood prediction. QPI products can then be aggregated to support longer-term applications for water supply prediction. Spatial scales range from a few square kilometers or less for urban flash flood predictit)n.

Field studies in support of the Winter Icing and Storms Project (WISP) were conducted in the Colorado Front Range area from 1 February to 31 March 1990 (WISP90) and from 15 January to 5 April 1991 (WISP91). The main goals of the project are to study the processes leading to the formation and depletion of supercooled liquid water in winter storms and to improve forecasts of aircraft icing. During the two field seasons, 2 research aircraft, 4 Doppler radars, 49 Mesonet stations, 7 CLASS sounding systems, 3 microwave radiometers, and a number of other facilities were deployed in the Front Range area. A comprehensive dataset was obtained on 8 anticyclonic storms, 16 cyclonic storms, and 9 frontal passages. This paper describes the objectives of the experiment, the facilities employed, the goals and results of a forecasting exercise, and applied research aspects of WISP. Research highlights are presented for several studies under way to illustrate the types of analysis being pursued. The examples chosen include topics on anticyclonic upslope storms, heavy snowfall, large droplets, shallow cold fronts, ice crystal formation and evolution, and numerical model performance.

No abstract