A 3D gamma-index provides an acceptance criterion for forecasts by checking agreement within explicit spatial, temporal, and intensity tolerances instead of exact matches.
Brown, Barbara Casati, and Elizabeth E
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LSTM networks predict HRRR forecast errors with average improvements of 48% for precipitation, 25% for temperature, and 15% for wind using mesonet ground truth.
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A Tolerance-Based Framework for Spatio-Temporal Forecast Validation Using the gamma-Index
A 3D gamma-index provides an acceptance criterion for forecasts by checking agreement within explicit spatial, temporal, and intensity tolerances instead of exact matches.
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Predicting Forecast Error for the HRRR Using LSTM Neural Networks: A Comparative Study Using New York and Oklahoma State Mesonets
LSTM networks predict HRRR forecast errors with average improvements of 48% for precipitation, 25% for temperature, and 15% for wind using mesonet ground truth.