Large-scale circulation anomalies conducive to extreme precipitation events and derivation of daily rainfall in northeastern Mexico and southeastern Texas

The severe impacts of climate variability and climate hazards on society re veal the increasing need for improving regional and local-scale climate dia gnosis. A new downscaling approach for climate diagnosis is presented here. It is based on artificial neural network (ANN) techniques that derive rela tionships from the large-and local-scale atmospheric controls to the local winter climate. This study documents the large-scale conditions associated with extreme precipitation events in northeastern Mexico and southeastern T exas during the 1985-93 period, and demonstrates the ability of ANN to simu late realistic relationships between circularion-humidity fields and daily precipitation at local scale. The diagnostic model employs a neural network that preclassifies the winter circulation and humidity fields into different patterns. The results from this neural network classification approach, known as a self-organizing map (SOM), indicate that negative (positive) anomalies of winter precipitation over the study area are associated with 1) a weaker (stronger) Aleutian lo w, 2) a stronger (weaker) North Pacific high, 3) a negative (positive) phas e of the Pacific-North American pattern, and 4) cold (warm) ENSO events. Th e atmospheric patterns classified with the SOM technique are then used as i nput to another neural network (feed-forward ANN) that captures over 60% of the daily rainfall variance over the region. This further reveals that the SOM preclassification of days with similar atmospheric conditions succeede d in emphasizing the differences of the atmospheric variance that are condu cive to extreme precipitation. This resulted in a downscaling model that is highly sensitive to local- and large-scale weather anomalies associated wi th ENSO warm events and cold air outbreaks.