巴格达站降雨事件 WRF 模型中不同物理方案的敏感性

Raghad H. Ahmed, Thaer O. Roomi, Hazim H. Hussain, Zeinab Salah
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摘要

天气研究和预报模型(WRF)提供了许多物理选项,用户可以根据不同的尺度、地区和应用对其进行修改。本研究的目的是测试 WRF 模式中不同物理方案对伊拉克降雨事件的敏感性。在这项研究中,对 WRF 气候版本的六种不同物理配置进行了评估,以模拟伊拉克的降雨事件。测试了两个行星边界层(PBL)、三个积云(CUM)和两个微物理(MIC)方案之间的可能组合。研究区域为东经 35 度-55 度、北纬 290 度-38 度所包围的地区,通常包括伊拉克地区。WRF 模型安装在 Linux 平台上,纵向和经向网格大小为 10 公里。为了进行六种不同的模拟,并为伊拉克地区选择性能最佳的配置,对单个网格点 (巴格达站)的大气参数(温度、压力和总降水量)进行了模型数据和 ECMWF 模型输出测 试。使用统计方法对模式输出进行了测试:偏差(BE)、平均绝对误差(MAE)和均方根误差(RMSE)。结果显示,所有模拟预测的降雨量值都接近实际值,但发现云微物理设置对温度偏差的影响最大,而积云参数化设置对降雨量的影响最大。
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Sensitivity of Different Physical Schemes in WRF Model of a Rainfall Event in Baghdad Station
The Weather Research and Forecasting model (WRF) offers a number of physical options that let users modify it to different scales, regions, and applications. The aim of this study is to test the sensitivity of different physics schemes in the WRF model for rainfall events over Iraq. In this study, six different physics configurations of the climate version of WRF were evaluated for simulation of a rainfall event in Iraq. Possible combinations among two Planetary Boundary Layers (PBL), three Cumulus (CUM) and two Microphysics (MIC) schemes were tested. The study area is the region surrounded by the longitudes 35o E-55o E and latitudes 290o N–38o N, which typically includes the Iraq region. The WRF model is installed on a Linux platform with a 10 km grid size in the zonal and meridional directions. For the six different simulations and the process of choosing the best performing configuration for the Iraq region, the model outputs tested for a single grid point (Baghdad station) of the atmospheric parameters (temperature, pressure and total precipitation) with modeled data and ECMWF. Model outputs using statistical methods: Bias Error (BE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results show All the simulations predict rainfall with values close to the actual but it was discovered that the cloud microphysics setup had the greatest impact on temperature biases, whereas the cumulus parameterization setup has the greatest impact on precipitation.
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