Location-Specific Prediction of the Probability of Occurrence and Quantity of Precipitation over the Western Himalayas

Abstract

Northwest India is composed, in part, of complex Himalayan mountain ranges having different altitudes and orientations, causing the prevailing weather conditions to be complex. During winter, a large amount of precipitation is received in this region due to eastward-moving low pressure synoptic weather systems called western disturbances (WDs). The objective of the present study is to use the perfect prognostic method (PPM) for probability of precipitation (PoP) forecasting and quantitative precipitation forecasting (QPF). Three observatories in the western Himalayan region, namely, Sonamarg, Haddan Taj, and Manali, are selected for development of statistical dynamical models for location-specific prediction of the occurrence and quantity of precipitation. Reanalysis data from the National Centers for Environmental Prediction (NCEP), and upper-air and surface observations from the India Meteorological Department (IMD), are used to develop statistical dynamical models for PoP and QPF for winter, that is, December, January, February, and March (DJFM). Models are developed with data from DJFM 1984?96 and tested with data from DJFM 1996?97. Four experiments are carried out with four different sets of predictors to evaluate the performance of the models with independent datasets. They are 1) NCEP?NCAR reanalysis data, 2) operational analyses from the National Centre for Medium Range Weather Forecasting (NCMRWF) in India, 3) day 1 forecasts with a T80 global spectral model at NCMRWF, and 4) forecasts from the regional fifth-generation Pennsylvania State University?NCAR Mesoscale Model (MM5) day 1 forecast. Forecast skills are examined for these four experiments and for direct numerical model outputs of T80 day 1 and MM5 day 1 forecasts at these three stations. It is found that a best prediction is made with an accuracy of 89% at Haddan Taj using the MM5 day 1 forecast as predictors in the PoP model. In the case of the QPF model, a maximum 85% accuracy is achieved using the MM5 day 1 forecast variables as predictors. Thus, use of numerical model output from MM5 as predictors in statistical dynamical models based on the PPM concept provides definite improvements in the prediction of occurrence and quantity of precipitation as compared to the direct numerical model output.