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<p>Accurate prediction of hourly precipitation is particularly important for the early warning systems of floods and storms. However, the intermittent nature of precipitation which is characterized by long dry periods with sudden bursts of heavy rainfall events, makes forecasting a challenging task for usual forecasting methods. This study proposes a zero-adjusted gamma generalized additive model for location, scale, and shape (ZAGA-GAMLSS) estimated using <Emphasis FontCategory="NonProportional">gamboostLSS</Emphasis> for probabilistic hourly precipitation forecasting. Three models incorporating lagged precipitation, meteorological variables, and neighboring station information are evaluated using a rolling-origin validation strategy with a four-year training window and a 120-h forecast horizon. The proposed models are compared with persistence, ARIMA, Random Forest, and LSTM benchmarks using point and interval forecast measures. The results demonstrate improved overall forecasting performance while providing calibrated prediction intervals and notably improved prediction of heavy rainfall events. The proposed framework offers a flexible approach for probabilistic precipitation forecasting and uncertainty quantification in flood forecasting applications.</p>

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Evaluating statistical methods for modeling and forecasting hourly precipitations

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • Rohan Hemant Chhatre,
  • James O’Donnell,
  • Nalini Ravishanker

摘要

Accurate prediction of hourly precipitation is particularly important for the early warning systems of floods and storms. However, the intermittent nature of precipitation which is characterized by long dry periods with sudden bursts of heavy rainfall events, makes forecasting a challenging task for usual forecasting methods. This study proposes a zero-adjusted gamma generalized additive model for location, scale, and shape (ZAGA-GAMLSS) estimated using gamboostLSS for probabilistic hourly precipitation forecasting. Three models incorporating lagged precipitation, meteorological variables, and neighboring station information are evaluated using a rolling-origin validation strategy with a four-year training window and a 120-h forecast horizon. The proposed models are compared with persistence, ARIMA, Random Forest, and LSTM benchmarks using point and interval forecast measures. The results demonstrate improved overall forecasting performance while providing calibrated prediction intervals and notably improved prediction of heavy rainfall events. The proposed framework offers a flexible approach for probabilistic precipitation forecasting and uncertainty quantification in flood forecasting applications.