A real-time system to estimate weather conditions at high-resolution
A system known as High-Resolution Aggregation of Data (HiRAD) has been built and deployed for deriving present weather and other weather-related variables at arbitrary points within the Continental United States (CONUS). The technique adapts a layered approach in which a sequence of refinements to a first guess field is made based on a variety of observational data sources. The initial guess is derived from hourly updated RUC analyses and short-range forecast grids down-scaled using high resolution, terrain-based climatological gradients. These data are then corrected based on interpolating errors from the most recent surface and satellite observations. Radar data is then incorporated using rapid-update, human-edited radar mosaics. The radar data is calibrated in real-time at known observation points and then blended into the previous estimates based on confidence of the radar observations. Using the continuous field of radar calibration statistics, initial estimates of ground-based precipitation can be iteratively corrected and tuned, resulting in a final improved estimate of the present weather and associated meteorological fields. Lightning strike data is then incorporated to derive thunder estimates. Finally, various quality control and “aberrant” weather checks are applied.
The paper will describe in detail the various methodologies used in HiRAD's derivation of current weather conditions. Examples of HiRAD output will be presented, as well as results of statistical experiments performed to test the quality and validity of the meteorological results. More detailed reports on the validation of HiRAD (Koval et al.) as well as methods and results of quality control of the input data (Rose et al.) also will be presented in companion papers at this conference.