A hybrid statistical‐dynamical framework for meteorological drought prediction: Application to the southwestern United States

Abstract

AbstractImproving water management in water stressed‐regions requires reliable seasonal precipitation predication, which remains a grand challenge. Numerous statistical and dynamical model simulations have been developed for predicting precipitation. However, both types of models offer limited seasonal predictability. This study outlines a hybrid statistical‐dynamical modeling framework for predicting seasonal precipitation. The dynamical component relies on the physically based North American Multi‐Model Ensemble (NMME) model simulations (99 ensemble members). The statistical component relies on a multivariate Bayesian‐based model that relates precipitation to atmosphere‐ocean teleconnections (also known as an analog‐year statistical model). Here the Pacific Decadal Oscillation (PDO), Multivariate ENSO Index (MEI), and Atlantic Multidecadal Oscillation (AMO) are used in the statistical component. The dynamical and statistical predictions are linked using the so‐called Expert Advice algorithm, which offers an ensemble response (as an alternative to the ensemble mean). The latter part leads to the best precipitation prediction based on contributing statistical and dynamical ensembles. It combines the strength of physically based dynamical simulations and the capability of an analog‐year model. An application of the framework in the southwestern United States, which has suffered from major droughts over the past decade, improves seasonal precipitation predictions (3–5 month lead time) by 5–60% relative to the NMME simulations. Overall, the hybrid framework performs better in predicting negative precipitation anomalies (10–60% improvement over NMME) than positive precipitation anomalies (5–25% improvement over NMME). The results indicate that the framework would likely improve our ability to predict droughts such as the 2012–2014 event in the western United States that resulted in significant socioeconomic impacts.