Development of an index for frost prediction: Technique and validation

Abstract

AbstractAn index for frost prediction is proposed and calibrated against observations. It takes into account: (1) the main meteorological variables that favour or oppose to frost; (2) weights attributed to these variables; and (3) means and standard deviations of these variables, only for cases in which frost occurs, as defined by observation of temperatures that are ≤ 6°C. The meteorological variables used for the frost index IG (from the Portuguese, Índice de Geada) are numerically predicted by a regional weather forecast model. An outcome of the calibration processes results is that temperature has the largest contribution, followed by pressure and winds, while the other variables were adjusted to obey the restriction that the sum of weights are equal to 1. After index calibration and threshold determination, the method was applied for the 2017 winter season, and a case study for May 2018 was also considered. In order to verify whether the new index can satisfactorily contribute to the weather forecasting, the results using the IG were compared with the temperature outputs of the numerical regional model. It was found that for three selected areas, and for all the forecasted hours, the IG produces better results than the model's direct temperature forecasts. Thus, it was concluded that the use of the IG in an operational environment potentially provides considerable improvement in the predictive skill of frost events.