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Calibration of WRF model parameters using multiobjective adaptive surrogate model-based optimization to improve the prediction of the Indian summer monsoon
Climate Dynamics ( IF 4.6 ) Pub Date : 2020-05-31 , DOI: 10.1007/s00382-020-05288-1
Sandeep Chinta , C. Balaji

Sensitive parameters of a numerical weather prediction model substantially influence the model prediction. Weather research and forecasting (WRF) model parameters are assigned default values based on theoretical and experimental analysis by the scheme developers. Calibrating the sensitive parameters of the model has the potential to improve model prediction. The objective of this study is to improve the prediction of the Indian summer monsoon by calibrating the WRF model parameters. A multiobjective adaptive surrogate model-based optimization (MO-ASMO) method is used to calibrate nine sensitive parameters from five physics parameterization schemes. Normalized root-mean-square error values corresponding to four meteorological variables precipitation, surface air temperature, surface air pressure, and wind speed are minimized by calibrating the WRF model sensitive parameters for high-intensity precipitation events of the Indian summer monsoon (ISM). Twelve high-intensity four-day precipitation events of ISM during the years 2015–2017 over the monsoon core region in India are considered to calibrate the model parameters. MO-ASMO method outputs a set of nondominated solutions for the model parameters that reduce the model prediction error. A decision analysis method is used to identify the best solution among the nondominated solutions, which contains the calibrated values of the parameters. A comparison of the default and calibrated parameter values across various precipitation events, driving data, and physical processes in the monsoon core region are carried out. Eighteen high-intensity four-day precipitation events of ISM during the years 2014–2018 are chosen to validate the robustness of the calibrated parameters. The WRF model is run with two different boundary data to verify the effectiveness of the calibrated parameters against the default parameters. The model calibrated parameters obtained using the MO-ASMO method are superior to the default parameters across various precipitation events and boundary data over the monsoon core region during the Indian summer monsoon.



中文翻译:

使用基于多目标自适应替代模型的优化对WRF模型参数进行标定,以改善印度夏季风的预测

数值天气预报模型的敏感参数会极大地影响模型预测。方案开发人员根据理论和实验分析为天气研究和预报(WRF)模型参数分配默认值。校准模型的敏感参数具有改善模型预测的潜力。这项研究的目的是通过校准WRF模型参数来改善印度夏季风的预测。一种基于多目标自适应替代模型的优化方法(MO-ASMO)用于从五个物理参数化方案中校准九个敏感参数。对应于四个气象变量降水,地表气温,地表气压,通过为印度夏季风(ISM)的高强度降水事件校准WRF模型敏感参数,可以将风速和风速降至最低。考虑到印度季风核心地区2015-2017年间的十二次ISM高强度四天降水事件,以校准模型参数。MO-ASMO方法为模型参数输出一组非支配解,从而减少了模型预测误差。决策分析方法用于在非支配解中确定最佳解,其中包含参数的校准值。进行了季风核心区各种降水事件,驱动数据和物理过程的默认参数值和校准参数值的比较。选择2014-2018年间发生的18次ISM高强度四天降水事件,以验证校准参数的稳健性。WRF模型使用两个不同的边界数据运行,以对照默认参数验证校准参数的有效性。在印度夏季风期间,使用MO-ASMO方法获得的模型校准参数优于各种降水事件和季风核心区域边界数据的默认参数。

更新日期:2020-07-16
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