Comparing Area Probability Forecasts of (Extreme) Local Precipitation Using Parametric and Machine Learning Statistical Postprocessing Methods
by K.R.P. Whan (KNMI), M.J. Schmeits (KNMI),
Probabilistic forecasts, which communicate forecast uncertainties, enable users to make better weather-based decisions. Using precipitation and numerous instability indices from the deterministic model HARMONIE–AROME (HA; a nonhydrostatic numerical weather prediction model) as potential predictors, we generate summer areal probabilistic maximum hourly precipitation forecasts across 11 regions of the Netherlands. We compare the skill of three statistical postprocessing methods: an extended logistic regression (ELR), a zero-adjusted gamma distribution (ZAGA), and a machine learning-based method, quantile regression forests (QRF). Forecast skill for low and moderate precipitation thresholds increases with the inclusion of extra predictors, in addition to HA precipitation. HA precipitation is the most important predictor at all lead times in ELR and QRF, while in ZAGA, the most important predictor for the location parameter shifts over lead times from HA precipitation to indices of atmospheric instability. All three methods improve upon a climatological forecast for low and moderate precipitation thresholds. ZAGA and QRF are generally the most skillful methods at moderate thresholds. QRF tends to be the most skillful method at higher thresholds, particularly during the afternoon period. Forecasts are reliable at low and moderate thresholds but tend to be overconfident at higher thresholds. QRF and ZAGA have more potential economic value than the deterministic forecast, with value remaining at high thresholds. A maximum local hourly precipitation threshold of 30 mm h^−1 (a criterion in the Royal Netherlands Meteorological Institute’s code yellow warning for severe thunderstorms) is skillfully forecast by QRF in the afternoon period at short lead times.
Whan, K.R.P. and M.J. Schmeits, Comparing Area Probability Forecasts of (Extreme) Local Precipitation Using Parametric and Machine Learning Statistical Postprocessing Methods