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Este estudio presenta un análisis de los sistemas convectivos de mesoescala (SCM) de verano que se desarrollaron en el noroeste de México durante los intensos episodios de El Niño Oscilación del Sur ocurridos de 1997 a 1999. Los resultados del análisis de datos geoestacionarios indican que el mayor número de SCM se asociaron con el episodio de El Niño de 1997, y tuvieron un periodo activo más prolongado. Durante el episodio de La Niña de 1999 se observó un menor número de SCM, los cuales se desarrollaron durante un periodo activo menor. La ocurrencia de SCM se vincula con la localización de la cresta y las anomalías anticiclónicas a alturas geopotenciales de 500 hPa y 200 hPa, respectivamente. ABSTRACTThis study presents an analysis of the summertime mesoscale convective systems (MCSs) that developed in northwestern Mexico during the strong ENSO events of [1997][1998][1999]. From the analysis of geostationary data, results indicate that the largest number of MCSs was associated with the 1997 El Niño event throughout a longer active period. During the La Niña event of 1999 fewer MCSs were observed, which had developed over a shorter active period. The occurrence of MCSs is linked to the location of the ridge and the anticyclonic anomalies at 500 hPa and 200 hPa, respectively.

A cluster-based artificial neural network model called CLASO (Classification-Assemblage-Association) has been proposed to predict the maximum of the 24-h moving average of PM 10 concentration on the next day in the three largest metropolitan areas of Mexico. The model is a self-organised, real-time learning neural network, which builds its topology via a process of pattern classification by using an historical database. This process is based on a supervised clustering technique, assigning a class to each centroid of the hidden layer, employing the Euclidean distance as a hierarchical criterion. A set of ARIMA models was compared with CLASO model in the forecast performance of the 24-h average PM 10 concentration on the next day. In general, CLASO model produced more accurate predictions of the maximum of the 24-h moving average of PM 10 concentration than the ARIMA models, although the latter showed a minor tendency to underpredict the results. The CLASO model solely requires to be built a historical database of the air quality parameter, an initial radius of classification and the learning factor. CLASO has demonstrated acceptable predictions of 24-h average PM 10 concentration by using exclusively regressive PM 10 concentrations. The forecasting capabilities of the model were found to be satisfactory compared to the classical models, demonstrating its potential application to the other major pollutants used in the Mexican air quality index.

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