Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks

Although the resolution of numerical weather prediction models continues to improve, many of the processes that influence precipitation are still not captured adequately by the scales of present operational models, and consequently precipitation forecasts have not yet reached the level of accuracy needed for hydrologic forecasting. Postprocessing of model output to account for local differences can enhance the accuracy and usefulness of these forecasts. Model Output Statistics have performed this important function for a number of years via regression techniques; this paper presents an alternate approach that uses artificial neural networks to produce 6-h precipitation forecasts for specific locations. Tests performed on four locations in the middle Atlantic region of the United States show that the accuracy of the forecasts produced using neural networks compares favorably with those generated using linear regression, especially for heavier precipitation amounts.