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A statistical method for forecasting extreme daily temperatures using ECMWF 2-m temperatures and ground station measurements

  • P. Boi (a1)

Abstract

A recursive statistical method is given to post-process the 2-metre temperature ECMWF General Circulation Model by using the SAR network of surface stations in Sardinia. The method calculates daily maximum and minimum temperature forecasts up to 5 days ahead for 55 stations. Forecasts over one year for 55 ground stations have been analysed. A comparison with Direct Model Output (DMO) and persistence is then made, giving special attention to the winter season and to freezing conditions. The error analysis shows a BIAS very close to zero (between 0°C and 0.5°C) on all the stations and a marked reduction of mean absolute error compared to the DMO forecast. The error reduction is more marked in the case of freezing conditions than with the DMO forecast. Such situations are difficult to forecast and the very high BIAS of DMO has not been eliminated completely. The advantages of the procedure are: (a) there is no need to store large numbers of measurements and large amounts of model forecast data; (b) the estimated correction is easily adapted to new meteorological conditions; (c) the procedure is transparent to the updating of the model; and (d) the procedure is very easy to implement operationally.

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A statistical method for forecasting extreme daily temperatures using ECMWF 2-m temperatures and ground station measurements

  • P. Boi (a1)

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