| Abstract | Landslide, flash floods etc. are inevitable problems in Bhutan, especially during the rainy season. Even though the rainfall is useful for providing water, it creates havoc when it exceeds certain amount. Hence, the prediction of rainfall is very necessary to avoid natural disasters. The aim of this study is to forecast rainfall for Bhutan using a limited area model. The study results will be useful in providing warning to avoid adverse consequences of floods and landslides. The Weather Research and Forecasting (WRF) model is used for forecasting the rainfall. The intial and boundary conditions for the model are provided by the gridded meteorological output from the Global Forecast System (GFS) model of National Centers for Environmental Prediction (NCEP). Considering the restrictions and requirements imposed by computing power and the simulation time, a three level nested grid structure of 75, 15 and 5km grid sizes are used. The vertical space is divided into 31 unequally spaced layers. The model results are compared with the observed rainfall data using the Thiessen Polygon method. The field data is collected from meteorological stations in Bhutan and basic analyses are carried out to understand the rainfall patterns. Even though Bhutan is a very small country, the variations in rainfall are extremes. The southern part of Bhutan receives more rainfall than the other parts due to the monsoon wind from the Bay of Bengal which is laden with moisture. The regional and local differences in rainfall are due to the altitude, the incoming wind directions, aspects, rain shadow effects and the elevation. The visual comparison of the observed and the computed rainfall distribution shows that the model underestimates the rainfall in the wet periods in most of the stations. It, however, provides good results when the rainfall is very less or when there are no rains. During the dry periods, the model predicts the rainfall well except for a small area within Bhutan. There is a potential for improvement in the forecast results if the hourly instead of daily data is used and also if the local terrestrial data is used instead of the global dataLandslide, flash floods etc. are inevitable problems in Bhutan, especially during the rainy season. Even though the rainfall is useful for providing water, it creates havoc when it exceeds certain amount. Hence, the prediction of rainfall is very necessary to avoid natural disasters. The aim of this study is to forecast rainfall for Bhutan using a limited area model. The study results will be useful in providing warning to avoid adverse consequences of floods and landslides. The Weather Research and Forecasting (WRF) model is used for forecasting the rainfall. The intial and boundary conditions for the model are provided by the gridded meteorological output from the Global Forecast System (GFS) model of National Centers for Environmental Prediction (NCEP). Considering the restrictions and requirements imposed by computing power and the simulation time, a three level nested grid structure of 75, 15 and 5km grid sizes are used. The vertical space is divided into 31 unequally spaced layers. The model results are compared with the observed rainfall data using the Thiessen Polygon method. The field data is collected from meteorological stations in Bhutan and basic analyses are carried out to understand the rainfall patterns. Even though Bhutan is a very small country, the variations in rainfall are extremes. The southern part of Bhutan receives more rainfall than the other parts due to the monsoon wind from the Bay of Bengal which is laden with moisture. The regional and local differences in rainfall are due to the altitude, the incoming wind directions, aspects, rain shadow effects and the elevation. The visual comparison of the observed and the computed rainfall distribution shows that the model underestimates the rainfall in the wet periods in most of the stations. It, however, provides good results when the rainfall is very less or when there are no rains. During the dry periods, the model predicts the rainfall well except for a small area within Bhutan. There is a potential for improvement in the forecast results if the hourly instead of daily data is used and also if the local terrestrial data is used instead of the global data |