Models to improve winter minimum surface temperature forecasts, Delhi, India

Abstract

Accurate forecasts of minimum surface temperature during winter help in the prediction of cold-wave conditions over northwest India. Statistical models for forecasting the minimum surface temperature at Delhi during winter (December, January and February) are developed by using the classical method and the perfect prognostic method (PPM), and the results are compared. Surface and upper air data are used for the classical method, whereas for PPM additional reanalysis data from the National Center of Environmental Prediction (NCEP) US are incorporated in the model development. Minimum surface temperature forecast models are developed by using data for the winter period 1985-89. The models are validated using an independent dataset (winter 1994-96). It is seen that by applying PPM, rather than the classical method, the model's forecast accuracy is improved by about 10% (correct to within ± 2 °C).