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首页> 外文期刊>International Journal of Applied Mathematics & Statistics >Calibrating the Rainfall Forecast of the HyBMG Outputs Using Bayesian Model Averaging: A Case Study
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Calibrating the Rainfall Forecast of the HyBMG Outputs Using Bayesian Model Averaging: A Case Study

机译：使用贝叶斯模型平均校正HyBMG输出的降雨预测：一个案例研究

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摘要

Indonesia which is located in tropical region has a unique weather characteristic with rainfall happening seasonally. However, the climate change happened during the last decade has introduced a difficulty in predicting the rainfall events in Indonesia, which then influences the agricultural sector in Indonesia. Jember is one of the districts in Indonesia which has a relatively big contribution to the rice production in Indonesia especially East Java. Therefore, forecasting rainfall in Jember is a crucial work. Several ways have been carried out by BMKG (Agency for Meteorology, Cimatology and Geophysics) Indonesia in order to have a reliable rainfall forecast in Indonesia. One of the approaches applied by BMKG is by using the outputs of HyBMG consisting of four statistical models, where each output is treated as a single forecast. This paper discusses the result of forecasting rainfall in the area of study using the combination of the HyBMG outputs. The combined forecast is also calibrated using two different estimation procedures of Bayesian Model Averaging (BMA) i.e. Expectation Maximization (EM) and Markov Chain Monte Carlo (MCMC). The results show that both approaches have their own strength. BMA-EM is quiet simple to be applied but the forecast performance really depends on the choice of the prior distribution of the rainfall event while the MCMC is quiet flexible but it needs a longer computation time. This paper compares the result of both approaches and the performance of is very competitive. However, both calibration procedures outperform the original forecast generated by HyBMG.
机译：位于热带地区的印度尼西亚具有独特的天气特征，季节性降雨是季节性的。但是，过去十年中发生的气候变化给预测印度尼西亚的降雨事件带来了困难，从而影响了印度尼西亚的农业部门。珍贝（Jember）是印度尼西亚的地区之一，对印度尼西亚特别是东爪哇的稻米生产贡献较大。因此，预测Jember的降雨量是一项至关重要的工作。印度尼西亚BMKG（气象，气候和地球物理机构）已经采取了几种方法，以便对印度尼西亚进行可靠的降雨预报。 BMKG应用的方法之一是使用HyBMG的输出，该输出由四个统计模型组成，其中每个输出都被视为一个单独的预测。本文讨论了使用HyBMG输出的组合预测研究区域降雨量的结果。还使用贝叶斯模型平均（BMA）的两种不同估计程序（即期望最大化（EM）和马尔可夫链蒙特卡洛（MCMC））来校准组合的预测。结果表明，两种方法都有自己的优势。 BMA-EM非常安静，易于应用，但预报性能确实取决于降雨事件的先验分布选择，而MCMC非常灵活，但需要更长的计算时间。本文比较了两种方法的结果，并且其性能非常有竞争力。但是，两种校准程序都优于HyBMG生成的原始预测。

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