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- Author or Editor: P. Bonelli x
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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.
Abstract
The analysis of several precipitation events occurring during June 1990 in the Alpine region is performed using the Pennsylvania State University–National Center for Atmospheric Research Fifth-Generation Mesoscale Model, version 1. A high-resolution dataset provided by Monitoring Precipitation Activity in the Padana Region observational campaign (June 1990) is used to verify the model forecast.
Comparisons between model simulations, using different cumulus convective schemes associated with either an explicit computation of cloud water and rain (EXP) or a nonconvective scheme (NEXP), have been performed. The comparisons of EXP versus NEXP give indications of the ability of a cumulus scheme to handle nonconvective precipitation. On the other hand, comparing the schemes allows for evaluation of the ability to reproduce total and convective precipitation. The results show that the amount and the areal extent of the total precipitation are well reproduced if a cumulus scheme is associated with EXP; the differences between the simulations performed using EXP and NEXP are reduced if the precipitation is driven by a strong large-scale forcing such as a front. The comparison of the cumulus convection parameterizations highlights the different responses of the schemes to the meteorological situation. When the explicit computation of cloud water and rain is used, a good localization of the rain cells and a fair estimation of the amount of precipitation are obtained using either the Kain–Fritsch or the Grell cumulus convection parameterizations. On the other hand, the Anthes–Kuo scheme produces a strong overestimation of the precipitation regardless of the meteorological forcing and with both EXP and NEXP. The bias and the threat score for the cases analyzed confirm this finding.
Sensitivities to different initial conditions for the same case show that the precipitation forecast depends on the strength of the signal contained in the initial conditions.
Abstract
The analysis of several precipitation events occurring during June 1990 in the Alpine region is performed using the Pennsylvania State University–National Center for Atmospheric Research Fifth-Generation Mesoscale Model, version 1. A high-resolution dataset provided by Monitoring Precipitation Activity in the Padana Region observational campaign (June 1990) is used to verify the model forecast.
Comparisons between model simulations, using different cumulus convective schemes associated with either an explicit computation of cloud water and rain (EXP) or a nonconvective scheme (NEXP), have been performed. The comparisons of EXP versus NEXP give indications of the ability of a cumulus scheme to handle nonconvective precipitation. On the other hand, comparing the schemes allows for evaluation of the ability to reproduce total and convective precipitation. The results show that the amount and the areal extent of the total precipitation are well reproduced if a cumulus scheme is associated with EXP; the differences between the simulations performed using EXP and NEXP are reduced if the precipitation is driven by a strong large-scale forcing such as a front. The comparison of the cumulus convection parameterizations highlights the different responses of the schemes to the meteorological situation. When the explicit computation of cloud water and rain is used, a good localization of the rain cells and a fair estimation of the amount of precipitation are obtained using either the Kain–Fritsch or the Grell cumulus convection parameterizations. On the other hand, the Anthes–Kuo scheme produces a strong overestimation of the precipitation regardless of the meteorological forcing and with both EXP and NEXP. The bias and the threat score for the cases analyzed confirm this finding.
Sensitivities to different initial conditions for the same case show that the precipitation forecast depends on the strength of the signal contained in the initial conditions.
