Planetary boundary layer parametrization (PBL) is a key factor influencing weather forecast skills. This article provides a quantitative illustration of the segregation of forecast errors and their growth arising from different PBLs of the Weather Research and Forecasting (WRF) model. The systematic (root mean square) components of forecast errors arising from four different PBLs of the WRF over the state of Odisha (India) and its surrounding regions are elucidated. Error characterizations are carried out for the forecast lead time up to 96 h (day-4) for two contrasting monsoon seasons, i.e., 2013 (normal) and 2014 (deficit). A total of 1112 simulations are carried out for each initial condition, i.e., May 15 to September 29 for both monsoon seasons using four PBL schemes, i.e., Yonsei University (YSU), Mellor–Yamada–Nakanishi-Niino (MYNN), Asymmetric Convective Model version 2 (ACM2), and medium-range forecast (MRF). The overall results suggest the errors in thermodynamical variables (i.e., temperature and relative humidity) are large compared to the dynamical variable (i.e., wind). For the normal monsoon year (i.e., 2013), the MRF and MYNN exhibit the lowest root mean square error (RMSE) of temperature compared to ACM2 and YSU, whereas MRF (MYNN) shows the lowest (highest) error growth at 24–48 h (72–96 h). The deficit year (2014) has a higher temperature error compared to that of the normal monsoon year (2013), which might be due to frequent monsoon break periods. The spatial distribution of wind exhibits the lowest systematic error for MRF with lead time up to 48 h. The subsequent decrease (increase) of convergence (divergence) of error flux over northern Odisha and increase (decrease) of the same over southern Odisha suggests that the error propagation occurs from north to south. In general, both the convergence and divergence of error energy are found to be weak in MRF and MYNN, attributable to the lower error growth rate and hence the smaller systematic errors compared to ACM2 and YSU for both these monsoon seasons. It is also found that the systematic component of the linear (nonlinear) error growth rate is contributed by the physics (dynamics) components of the model. These findings will provide guidance for the model community and operational agencies to make an optimal choice of PBL parameterizations, particularly for the monsoon forecast.

中文翻译：

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夏季季风季节印度奥里萨邦和邻近地区行星边界层参数化预报误差的分离

行星边界层参数化 (PBL) 是影响天气预报技能的关键因素。本文对天气研究和预报 (WRF) 模型的不同 PBL 产生的预报误差的分离及其增长进行了定量说明。阐明了由 WRF 的四个不同 PBL 在奥里萨邦（印度）及其周边地区引起的预测误差的系统（均方根）分量。对于两个对比鲜明的季风季节，即 2013 年（正常）和 2014 年（赤字），对长达 96 小时（第 4 天）的预测提前期进行了误差表征。对于每个初始条件，即 5 月 15 日至 9 月 29 日，使用四种 PBL 方案，即延世大学 (YSU)、Mellor-Yamada-Nakanishi-Niino (MYNN)、非对称对流模型第 2 版 (ACM2) 和中期预报 (MRF)。总体结果表明，与动态变量（即风）相比，热力学变量（即温度和相对湿度）的误差较大。对于正常季风年（即 2013 年），与 ACM2 和 YSU 相比，MRF 和 MYNN 表现出最低的温度均方根误差 (RMSE)，而 MRF (MYNN) 在 24-48 之间表现出最低（最高）误差增长小时（72-96 小时）。与正常季风年（2013年）相比，赤字年（2014年）的温度误差更大，这可能是由于频繁的季风中断期。风的空间分布表现出最低的 MRF 系统误差，前置时间长达 48 小时。随后奥里萨邦北部误差通量收敛（发散）的减少（增加）和奥里萨邦南部的收敛（发散）增加（减少）表明误差传播是从北向南发生的。一般而言，在 MRF 和 MYNN 中，误差能量的收敛和发散均较弱，这归因于误差增长率较低，因此与这两个季风季节的 ACM2 和 YSU 相比，系统误差较小。还发现线性（非线性）误差增长率的系统分量是由模型的物理（动力学）分量贡献的。这些发现将为模型社区和运营机构提供指导，以做出 PBL 参数化的最佳选择，尤其是季风预测。