Synoptic fingerprints of Zonda wind from a statistical prediction model

Abstract

AbstractZonda wind is a typical downslope windstorm over the eastern slopes of the Central Andes in Argentina, which produces extremely warm and dry conditions and has substantial socioeconomic impacts. In this study, we propose a new statistical model for Zonda prediction based on the “synoptic fingerprints” of atmospheric diagnostic variables from ERA5. The model combines principal component analysis (PCA) and logistic regression to establish a relationship between the observed occurrence and the PCA loading component of a predictor variable. This approach enables us to determine the probability of Zonda occurrence at selected stations and identify the synoptic structure features (fingerprints) associated with Zonda events. The obtained fields successfully discriminate between Zonda and non‐Zonda events, suggesting that the available information in the reanalysis data is sufficient for predicting the presence of Zonda. The synoptic fingerprints generated by the model reveal a cross‐barrier pressure gradient resulting from a negative geopotential height anomaly at low levels. The cross‐barrier flow remains unimpeded by the Andes, leading to forced vertical motions on the windward side, accompanied by cooling and precipitation. On the lee side, sinking motions, warming and drying are observed, further facilitated by favourable mid‐ and upper‐level conditions that establish the Zonda wind. The model performs comparably to previous research, with the best results achieved using low‐level variables as predictors. Key performance measures, including the area under the receiver operating curve (ROC) (AUC) of 0.9468 and a Brier score lower than 0.1, demonstrate the model's effectiveness. Using a 0.5 threshold, the accuracy, F1 score and correct alarm ratio (CAR) all exceed 88%, with a probability of detection (POD) higher than 90%. Studies on this type of downslope windstorm are scarce in South America, making this work a significant contribution to understanding synoptic‐scale atmospheric structures associated with Zonda occurrences.