Comparing Local Analysis Prediction System (LAPS) Diagnoses with Observations

Meteorological forcing data are necessary to drive many of the spatial models used to simulate atmospheric, biological, and hydrological processes. Unfortunately, many areas (e.g., high elevation mountains, deserts) lack meteorological data and available point observations are not always suitable for landscape or regional applications, especially in mountainous regions. The local analysis prediction system (LAPS, http://laps.fsl.noaa.gov/) is a meteorological diagnostic tool that employs observations (meteorological networks, radar, satellite, soundings, and aircraft) to generate a spatially distributed, three-dimensional, representation of atmospheric features and processes. Data produced by LAPS include wind speed, wind direction, surface temperature, relative humidity, surface pressure, precipitation, and cloud cover, among others. Because LAPS is a spatially distributed representation of meteorological observations, it provides valuable opportunities for users that require local (10 km horizontal grid increment) meteorological data to drive distributed land surface and ecosystem models over large regions (1.29 x 10⁶ km²). As with any diagnostic representation, it is important to ascertain how LAPS outputs deviate from a variety of observations at different spatial and temporal scales. Since many observations are integrated into LAPS, they can not be used to assess diagnostic performance. Fortunately, a number of observations exist that are not used in the LAPS system. These observations include GLOBE (http://www.globe.gov), the cold lands processes experiment, (CLPX, http://www.nohrsc.nws.gov/~cline/clp.html), and other observation networks that generate comparable data to those employed in LAPS. Independent data sets were collected and compared with LAPS outputs from 1 September 2001 to 1 September 2003. The best model and observed correlations were tied to relative humidity and air temperature. Poorer relationships were identified among observed wind speed and direction data. The relationships among LAPS diagnoses and observed data with respect to site locations, local land cover, temporal resolution of observations, and season of the comparison are described with respect to LAPS diagnoses and observed data, and example applications of the LAPS data are provided.