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Christopher A. Hiemstra, Glen E. Liston, Roger A. Pielke Sr., Daniel L. Birkenheuer, and Steven C. Albers

Abstract

Meteorological forcing data are necessary to drive many of the spatial models used to simulate atmospheric, biological, and hydrological processes. Unfortunately, many domains lack sufficient meteorological data and available point observations are not always suitable or reliable for landscape or regional applications. NOAA’s Local Analysis and Prediction System (LAPS) is a meteorological assimilation tool that employs available observations (meteorological networks, radar, satellite, soundings, and aircraft) to generate a spatially distributed, three-dimensional representation of atmospheric features and processes. As with any diagnostic representation, it is important to ascertain how LAPS outputs deviate from a variety of independent observations. A number of surface observations exist that are not used in the LAPS system, and they were employed to assess LAPS surface state variable and precipitation analysis performance during two consecutive years (1 September 2001–31 August 2003). LAPS assimilations accurately depicted temperature and relative humidity values. The ability of LAPS to represent wind speed was satisfactory overall, but accuracy declined with increasing elevation. Last, precipitation estimates performed by LAPS were irregular and reflected inherent difficulties in measuring and estimating precipitation.

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Steven C. Albers, John A. McGinley, Daniel L. Birkenheuer, and John R. Smart

Abstract

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 am a skin temperature analysis (used to solve for cloud layer heights and coverage) derived from Geostationary Operational Environmental Satellite IR 11.24-µm 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.

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