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Statistical Downscaling for Rainfall Forecasts Using Modified Constructed Analog Method in Thailand
Patchalai Anuchaivong1, Dusadee Sukawat2, Anirut Luadsong3
1Department of Mathematics, Faculty of Science, King Mongkut's University of Technology Thonburi (KMUTT), 126 Pracha Uthit Road, Bang Mod, Thung Khru, Bangkok 10140, Thailand.
Thescientificworldjournal
|March 21, 2017
Summary
Statistical downscaling using the Modified Constructed Analog Method (MCAM) accurately simulated daily rainfall. This method improved forecast accuracy, reducing errors and closely matching observed precipitation data.
Area of Science:
- Climatology
- Meteorology
- Statistical Modeling
Background:
- General Circulation Models (GCMs) provide large-scale climate data.
- Statistical downscaling bridges GCM outputs with local observations.
- Accurate rainfall simulation is crucial for weather forecasting and climate studies.
Purpose of the Study:
- To evaluate the Modified Constructed Analog Method (MCAM) for simulating daily rainfall.
- To assess the accuracy of MCAM in rainfall prediction using historical data.
- To improve rainfall simulation accuracy for meteorological applications.
Main Methods:
- Employed statistical downscaling with the Modified Constructed Analog Method (MCAM).
- Calculated Euclidean distance to identify analog days for rainfall simulation.
- Utilized a linear combination of 30 analog days and adjusted weights for forecasting.
- Applied the method to daily rainfall data from 1979-2010 across thirty Thai Meteorological Department stations.
Main Results:
- Achieved a high correlation value of 0.8 between simulated and observed rainfall.
- Reduced the percentage error in rainfall simulation by 13.66%.
- MCAM produced a simulated precipitation value of 1094.10 mm, closely approximating the observed 1119.53 mm.
Conclusions:
- The Modified Constructed Analog Method (MCAM) demonstrates significant accuracy in simulating daily rainfall.
- MCAM offers a reliable approach for enhancing rainfall prediction using statistical downscaling.
- This technique provides valuable insights for improving weather forecasting models.
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