ANNOUNCEMENTS
Modeling extreme precipitation event is a continuing challenge in many domains, as these events rely on multi-scale interactions as well as on configuration of model like initialization schemes, spacing of grid and physical parameterizations. Heavy precipitation events in every part of the world are responsible for massive floods. In recent years, Numerical Weather Prediction models including Weather Research and Forecasting model (WRF), forecasts and simulations of weather conditions for future have become an essential part of the research, in the domain of both atmospheric sciences and hydrology. In case of forecasting, the performance in land use land cover (LULC) have been shown to have significant effects on climate across various pathways that modulate rainfall and land surface temperature. With this context, the WRF model is being incorporated in the present study to evaluate the impact of land use land cover changes on rainfall forecast. The model was implemented with 3 types of different land use data set viz. MODIS data, USGS and AWiFS (ISRO) land use data. The research was carried out for India’s North Eastern Region (NER). In the present study, the efficiency of WRF model is assessed by comparing the model simulated rainfall with the observational gridded rainfall data (0.25â° × 0.25â°) from IMD. In addition, BIAS and RMSE are determined for each experiment. Results reveals that the impact of land use land cover changes on rainfall forecast and other meteorological parameters such as surface wind, humidity, surface temperature and dewpoint. It is found that the high resolution and updated AWiFS (ISRO) land use data improves the model forecast to a significant extent in comparison to USGS and MODIS land use data which highlights the importance of land use land cover effect in atmospheric process with the need for an updated LULC for meteorological modeling.
Keywords: Numerical Weather Prediction (NWP), Weather Research & Forecast (WRF), LULC, USGS, MODIS, Bias, RMSE.
