Abstract
The simulations of rainfall from historical data were created in this study by using statistical downscaling. Statistical downscaling techniques are based on a relationship between the variables that are solved by the General Circulation Models (GCMs) and the observed predictions. The Modified Constructed Analog Method (MCAM) is a technique in downscaling estimation, suitable for rainfall simulation accuracy using weather forecasting. In this research, the MCAM was used to calculate the Euclidean distance to obtain the number of analog days. Afterwards, a linear combination of 30 analog days is created with simulated rainfall data which are determined by the corresponding 5 days from the adjusted weights of the appropriate forecast day. This method is used to forecast the daily rainfall and was received from the Thai Meteorological Department (TMD) from the period during 1979 to 2010 at thirty stations. The experiment involved the use of rainfall forecast data that was combined with the historical data during the rainy season in 2010. The result showed that the MCAM gave the correlation value of 0.8 resulting in a reduced percentage error of 13.66%. The MCAM gave the value of 1094.10 mm which was the closest value to the observed precipitation of 1119.53 mm.
Highlights
It is difficult to predict the exact amount of precipitation in future events and prevent the likelihood of natural disasters
This is another way that the application of statistical downscaling can be used for rainfall forecasting by using the Modified Constructed Analog Method (MCAM) in Thailand. This paper introduces another method for the development of rainfall forecasting in Thailand
The MCAM is used for statistical downscaling with the four predictors (T850, G850, Q850, and mean sea level pressure (MSLP)) when the amount of precipitation is being compared at the stations
Summary
It is difficult to predict the exact amount of precipitation in future events and prevent the likelihood of natural disasters. The first method is statistical forecasting, based on finding the relationship between climatology data from past forecasts and future forecasts. This method is relatively simple but the relationship may suddenly change and it makes the forecasts less accurate. The second method is dynamical forecasting based on a climate model This method requires a high-performance computer to generate sophisticated models and may require large amounts of input data. The third method is hybrid forecasting which is based on the combination between statistical forecasting and dynamical forecasting which are applied together [1]. In a more recent variety of articles, downscaling is widely used and applied in climatology for situations such as the construction, simulation, and prediction of the mean, minimum, and maximum air temperature and rainfall for
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