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A Stochastic Model of Surface Wind Speed for Air Quality Control Purposes

G. FinziDipartimento di Elettronica, Centro Teoria dei Sistemi CNR, Politecnico, Milano, Italy

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P. BonelliENEL, Centro Ricerche Termiche e Nucleari, Milano, Italy

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G. BacciCentro Meteorologico Regionale, Linate, Italy

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Abstract

Wind speed in the lower layers of the atmosphere is a relevant factor in pollutant dispersion and therefore its forecast is an essential datum for any reliable real-time predictor of ambient concentrations. Here a stochastic model for one-day forecasts of the surface daily wind speed at a site is described. According to the model, this speed is a linear combination of the surface wind speed of the previous day, the daily variation of the 500 mb wind speed over the site, the daily variation of the 500 mb geopotential over the site, and noise. Moreover, the coefficients of the linear combination are assumed to depend both on a “synoptic category” (defined in light of the prevailing synoptic circulation) and on the 500 mb wind direction over the site. The forecast performance is satisfactory for the two sites in the Po Valley (northern Italy) where the model has been tested. Furthermore, it is much better than the performance of a simple autoregressive model. Since such a model uses recent values of the forecast variable (surface daily wind speed) as its only information, the difference in performance shows the importance of using additional meteorological information (500 mb wind and geopotential, and “synoptic category”) for obtaining a reliable forecast.

Abstract

Wind speed in the lower layers of the atmosphere is a relevant factor in pollutant dispersion and therefore its forecast is an essential datum for any reliable real-time predictor of ambient concentrations. Here a stochastic model for one-day forecasts of the surface daily wind speed at a site is described. According to the model, this speed is a linear combination of the surface wind speed of the previous day, the daily variation of the 500 mb wind speed over the site, the daily variation of the 500 mb geopotential over the site, and noise. Moreover, the coefficients of the linear combination are assumed to depend both on a “synoptic category” (defined in light of the prevailing synoptic circulation) and on the 500 mb wind direction over the site. The forecast performance is satisfactory for the two sites in the Po Valley (northern Italy) where the model has been tested. Furthermore, it is much better than the performance of a simple autoregressive model. Since such a model uses recent values of the forecast variable (surface daily wind speed) as its only information, the difference in performance shows the importance of using additional meteorological information (500 mb wind and geopotential, and “synoptic category”) for obtaining a reliable forecast.

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