Assessment of predictability in Downscaling GEFS Precipitation Forecasts
Abstract
The NOAA Physical Sciences Laboratory produces the Global Ensemble Forecasting System (GEFS) which comprises 11 ensemble members (1 control and 10 perturbation runs) for over a 36-year period (December 1984 to present), with forecasts initialized every day for the next 16 days (first 8-day forecasts obtained from a high-resolution grid and the next 8-day forecasts from a low-resolution grid). The system provides 36 variables related to a wide range of hydrometeorological processes. In this study, we assess the predictability of precipitation within the context of statistical downscaling using a wide range of predictor variables. We use feedforward backpropagation neural networks with a suite of training algorithms to determine which variables (features) are of most relevance at different forecast lead times. The outcome of this study will significantly benefit short-term flood forecasting using GEFS data.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMH165.0006D
- Keywords:
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- 1816 Estimation and forecasting;
- HYDROLOGY;
- 1817 Extreme events;
- HYDROLOGY;
- 1942 Machine learning;
- INFORMATICS;
- 4337 Remote sensing and disasters;
- NATURAL HAZARDS