The efficacy of the standard performance metrics [mean bias (Bias), root mean square deviation (RMSD), and correlation coefficient (CC)] compared to a new symmetric index of agreement (λ) for the evaluation of numerical weather prediction models is investigated in this study. It evaluates the weather research and forecasting (WRF) model with the global precipitation measurement’s (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) and station rainfall, as the reference datasets. This study uses three IMERG products, namely: GPM infrared-microwave merged gauge corrected (GMS), GPM microwave-calibrated infrared (GIR), and GPM inter-calibrated microwave (GMW) rainfall. The analysis showed that WRF rainfall when compared to different reference datasets is producing similar RMSD values but significantly different Bias values. This behavior is because of the inverse relationship between Bias and standard deviation of residual (σR\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma_{{\text{R}}}$$\end{document}). It is so because RMSD is a function of both. However, λ is able to appropriately represent the distinct performances of WRF. The regions with contradictory behavior of RMSD and CC are also appropriately represented in λ. The evaluation using λ showed that WRF is comparable to GMS and GIR, except for GMW. The performance of WRF was not found to be very promising when compared to station rainfall, which is attributed to WRFs representation efficiency and the effect of topography. However, a comparison of IMERG products with station rainfall showed that GMS was the most agreeable followed by GIR and GMW. The study also showed that the efficacy of λ is related to its non-linear relationship with Bias and CC.

中文翻译：

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研究一种新的对称一致性指数在评估印度东北部 WRF 模拟夏季风降雨量时的效果

研究了标准性能指标 [平均偏差 (Bias)、均方根偏差 (RMSD) 和相关系数 (CC)] 与用于评估数值天气预报模型的新对称一致性指数 (λ) 的功效在这个研究中。它使用全球降水测量 (GPM) 综合多卫星检索 GPM (IMERG) 和台站降雨作为参考数据集来评估天气研究和预报 (WRF) 模型。本研究使用三种 IMERG 产品，即：GPM 红外-微波合并规范校正 (GMS)、GPM 微波校准红外 (GIR) 和 GPM 间校准微波 (GMW) 降雨。分析表明，与不同的参考数据集相比，WRF 降雨产生了相似的 RMSD 值，但有显着不同的偏差值。这种行为是因为偏差和残差标准偏差之间的反比关系 (σR\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma_{{\text{R}}}$$\end{document}）。之所以如此，是因为 RMSD 是两者的函数。然而，λ 能够恰当地代表 WRF 的不同性能。RMSD 和 CC 具有矛盾行为的区域也适当地用 λ 表示。使用 λ 的评估表明，除了 GMW，WRF 与 GMS 和 GIR 相当。与站点降雨量相比，WRF 的性能并不是很有希望，这归因于 WRF 的表示效率和地形的影响。然而，IMERG 产品与站点降雨量的比较表明，GMS 最令人满意，其次是 GIR 和 GMW。该研究还表明，λ 的功效与其与 Bias 和 CC 的非线性关系有关。