Correcting temperature and humidity forecasts using Kalman filtering: potential for agricultural protection in Northern Greece

A correction method for the provision of accurate near-surface temperature and humidity forecasts is developed, based on the combination of a Kalman theory filtering technique and an empirical method with exponential smoothing. The combined method is applied on high-resolution weather forecasts provided by an operational model in Greece, over a basin in the northern part of the country, where agricultural protection is of great importance, especially due to mildew in potatoes, which represents a constant threat for farmers. The application of the method has shown that it can substantially reduce errors of the near-surface temperature and humidity forecasts provided for 2–3 days ahead in time. Based on these corrected forecasts, farmers can then schedule their fungicide spraying programs according to the expected weather, thus reducing the cost and the ecological impact of frequent preventive spraying interventions.