用全局敏感性分析和机器学习确定孟加拉湾热带气旋预报的WRF模式敏感参数
Determining the sensitive parameters of WRF model for the prediction of Tropical cyclones in Bay of Bengal using Global sensitivity analysis and Machine learning
摘要：
本研究的重点是确定参数的天气研究和预报（WRF）模式，强烈影响孟加拉湾（BoB）地区的热带气旋的预测。三个全局敏感性分析（SA）的方法，即莫里斯一次（MOAT），多元自适应回归样条（MARS），和代理的Sobol'，以确定最敏感的参数24个可调参数对应的7个参数化方案的WRF模式。本研究选择了2011年至2018年间鲍勃上空不同类别的10个热带气旋，例如气旋风暴、严重气旋风暴和非常严重的气旋风暴。24个参数的敏感性得分进行了评估，为8个气象变量。所有变量的参数敏感性结果在三种SA方法中是一致的，24个参数中的8个对总体敏感性评分的贡献为80%-90%。结果表明，当可用样本数超过200时，以高斯过程回归为代理模型的Sobol方法可以得到可靠的灵敏度结果。当与观测值相比时，模型模拟具有最小RMSE值的参数被认为是最佳参数。比较观测和模式模拟与默认和最佳参数显示，预测与最佳的一组参数产生了19.65%的改进，在地面风，6.5%的地面温度，和13.2%的降水预测，相比，默认的一组参数。
英文摘要：
The present study focuses on identifying the parameters from the Weather Research and Forecasting (WRF) model that strongly influence the prediction of tropical cyclones over the Bay of Bengal (BoB) region. Three global sensitivity analysis (SA) methods namely the Morris One-at-A-Time (MOAT), Multivariate Adaptive Regression Splines (MARS), and surrogate-based Sobol' are employed to identify the most sensitive parameters out of 24 tunable parameters corresponding to seven parameterization schemes of the WRF model. Ten tropical cyclones across different categories, such as cyclonic storms, severe cyclonic storms, and very severe cyclonic storms over BoB between 2011 and 2018, are selected in this study. The sensitivity scores of 24 parameters are evaluated for eight meteorological variables. The parameter sensitivity results are consistent across three SA methods for all the variables, and 8 out of the 24 parameters contribute 80%-90% to the overall sensitivity scores. It is found that the Sobol' method with Gaussian progress regression as a surrogate model can produce reliable sensitivity results when the available samples exceed 200. The parameters with which the model simulations have the least RMSE values when compared with the observations are considered as the optimal parameters. Comparing observations and model simulations with the default and optimal parameters shows that predictions with the optimal set of parameters yield a 19.65% improvement in surface wind, 6.5% in surface temperature, and 13.2% in precipitation predictions, compared to the default set of parameters.
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