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Genetic Ensemble (G-Ensemble) for Meteorological Prediction Enhancement

机译:增强气象预报的遗传集合体(G-集合体)

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摘要

The need for reliable predictions in environmental modelling is long known. Particularly, the predicted weather and meteorological information about the future atmospheric state is crucial and necessary for almost all other areas of environmental modelling. Additionally, right decisions to prevent damages and save lives could be taken depending on a reliable meteorological prediction process. Lack and uncertainty of input data and parameters constitute the main source of errors for most of these models. In recent years, evolutionary optimisation methods have become popular to solve the input parameter problem of environmental models. We propose a new prediction scheme that uses a Genetic Algorithm for parameter estimation in Numerical Weather Prediction Models (NWP) to enhance prediction results. The new approach is called Genetic Ensemble (G-Ensemble) and it has been tested using historical data of a well known weather catastrophe: Hurricane Katrina that occurred in 2005 in the Gulf of Mexico. Obtained results provide significant improvements in weather prediction.
机译:人们早就知道在环境建模中需要可靠的预测。特别是,对于几乎所有其他环境建模领域,有关未来大气状态的预测天气和气象信息都是至关重要的,也是必不可少的。另外,可以根据可靠的气象预测过程来做出防止损坏和挽救生命的正确决定。输入数据和参数的缺乏和不确定性是大多数此类模型的主要误差来源。近年来,进化优化方法已成为解决环境模型输入参数问题的一种流行方法。我们提出了一种新的预测方案,该方案使用遗传算法对数值天气预报模型(NWP)中的参数进行估计,以增强预测结果。这种新方法称为遗传合奏(G-Ensemble),并已使用已知的气象灾难的历史数据进行了测试:2005年发生在墨西哥湾的卡特里娜飓风。获得的结果大大改善了天气预报。

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