1.3
Machine Learning Enhancement of Storm Scale Ensemble Probabilistic Precipitation Forecasts

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Monday, 7 January 2013: 2:00 PM
Machine Learning Enhancement of Storm Scale Ensemble Probabilistic Precipitation Forecasts
Room 18A (Austin Convention Center)
David John Gagne II, Univ. of Oklahoma, Norman, OK; and A. McGovern and M. Xue

Precipitation forecasts provide both a crucial service for the general populace and a challenging forecasting problem due to the complex, multi-scale interactions required for precipitation formation. The Center for the Analysis and Prediction of Storms (CAPS) Storm Scale Ensemble Forecast (SSEF) system is a promising method of providing high resolution forecasts of the intensity and uncertainty in precipitation forecasts. The SSEF incorporates multiple models with multiple parameterization scheme combinations and produces forecasts every 4 km over the continental US. The SSEF precipitation forecasts exhibit significant negative biases and placement errors. In order to correct these issues, multiple machine learning algorithms have been applied to the SSEF precipitation forecasts to correct the forecasts using the NSSL National Mosaic and Multisensor QPE (NMQ) grid as verification. The 2010 runs of the SSEF were used for training and verification. Two levels of post-processing are performed. In the first, probabilities of any precipitation are determined and used to find optimal thresholds for the precipitation areas. Then, three types of forecasts are produced in those areas. First, the probability of the 1-hour accumulated precipitation exceeding a threshold is predicted with random forests, logistic regression, and multivariate adaptive regression splines (MARS). Second, deterministic forecasts based on a correction from the ensemble mean are made with linear regression, random forests, and MARS. Third, fixed probability interval forecasts are made with quantile regressions and quantile regression forests. Models are generated from points sampled from the western, central, and eastern sections of the domain. Verification statistics and case study results show improvements in the reliability and skill of the forecasts compared to the original ensemble while controlling for the over-prediction of the precipitation areas and without sacrificing smaller scale details from the model runs.