Joint Poster Session JP1.6 Building a Gridded Climatological Dataset for Use in the Statistical Interpretation of Numerical Weather Prediction Models

Monday, 20 June 2005
Rachel A. Trimarco, NOAA/NWS, Silver Spring, MD; and K. L. Sheets and K. K. Hughes

Handout (622.4 kB)

The Meteorological Development Laboratory (MDL) of NOAA's National Weather Service (NWS) currently produces forecast guidance using the Model Output Statistics (MOS) technique for around 1700 sites across the United States. Recently, a new generation of statistical guidance has been developed for grids with the resolution of the National Digital Forecast Database (NDFD). These grids will become part of the National Digital Guidance Database (NDGD), which is to be utilized in the Weather Forecast Office (WFO) forecast process. The gridded guidance is produced by performing a sophisticated objective analysis of the traditional MOS station forecasts. The first guess forecast for this analysis is a MOS forecast valid at 5-km gridpoints and created by a generalized operator regression equation. Since observations are not always available every 5 km to facilitate station forecasts, nor are they uniformly distributed, a gridded climatological dataset was needed to supplement the available coarser resolution data and to provide greater detail.

Grids containing monthly normal maximum and minimum temperatures were created by using Geographic Information Systems (GIS) techniques which aided in combining data from multiple sources and formatting the data to match the appropriate grid characteristics. The grids are 30-year climatologies obtained from merging Oregon State University's Parameter-elevation Regressions on Independent Slopes Model (PRISM) data spanning the years 1971 – 2000 over the CONUS and the University Corporation for Atmospheric Research's (UCAR) International Comprehensive Ocean – Atmosphere Data Set (ICOADS), which provided data spanning the same time frame over the oceans and the Great Lakes. The output GIS data were then used to create normal temperatures every fifth day by applying a cubic spline interpolation algorithm to fit the monthly normals and then interpolate to the appropriate day. These gridded datasets will be used as predictors in the gridded MOS forecast guidance for temperatures, which is derived from the National Centers for Environmental Prediction's (NCEP) Global Forecast System (GFS) numerical weather prediction model. This presentation focuses on the techniques used to generate the climatological datasets, as well as their importance to the gridded MOS system.

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