INTERCOMPARISON OF DIFFERENT RAINFALL PRODUCTS AND VALIDATION OF WRF MODELLED RAINFALL ESTIMATION IN N-W HIMALAYA DURING MONSOON PERIOD
- IIRS, Indian Institute of Remote Sensing, 4 Kalidas road, Dehradun, India
Keywords: WRF, TRMM, GPM, IMD, ERA-Interim, FAR, POD, POFD, Bias, Error
Abstract. Extreme precipitation events are responsible for major floods in any part of the world. In recent years, simulations and projection of weather conditions to future, with Numerical Weather Prediction (NWP) models like Weather Research and Forecast (WRF), has become an imperative component of research in the field of atmospheric science and hydrology. The validation of modelled forecast is thus have become matter of paramount importance in case of forecasting. This study delivers an all-inclusive assessment of 5 high spatial resolution gridded precipitation products including satellite data products and also climate reanalysis product as compared to WRF precipitation product. The study was performed in river basins of North Western Himalaya (NWH) in India. Performance of WRF model is evaluated by comparing with observational gridded (0.25° × 0.25°) precipitation data from Indian Meteorological Department (IMD). Other products include TRMM Multi Satellite Precipitation Analysis (TMPA) 3B42-v7 product (0.25° × 0.25°) and Global Precipitation Measurement (GPM) product (0.1° × 0.1°). Moreover, climate reanalysis rainfall product from ERA Interim is also used. Bias, Mean Absolute Error, Root Mean Square Error, False Alarm Ratio (FAR), Probability of False Detection (POFD), and Probability of Detection (POD) were calculated with particular rainfall thresholds. TRMM and GPM products were found to be sufficiently close to the observations. All products showed better performance in the low altitude areas i.e. in planes of Upper Ganga and Yamuna basin and Indus basin, and increase in error as topographical variation increases. This study can be used for identifying suitability of WRF forecast data and assessing performance of other rainfall datasets as well.