Test of Power Transformation Function to Hydrometeor and Water Vapor Mixing Ratios for Direct Variational Assimilation of Radar Reflectivity Data
Test of Power Transformation Function to Hydrometeor and Water Vapor Mixing Ratios for Direct Variational Assimilation of Radar Reflectivity Data
复制标题
雷达反射率数据直接变分同化中水凝物和水汽混合比的功率变换函数测试
DOI：
10.1175/waf-d-22-0158.1
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发表时间：
2023
影响因子：
2.9
通讯作者：
Carlin, Jacob T.
中科院分区：
文献类型：
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作者：
Hu, Jiafen;Gao, Jidong;Liu, Chengsi;Zhang, Guifu;Heinselman, Pamela;Carlin, Jacob T.
将雷达反射率同化到对流尺度NWP模型中一直是雷达数据同化中的一个具有挑战性的课题。一个主要原因是反射率正演观测算子是高度非线性的。为了解决这一挑战，本研究将一个幂变换函数应用于WRF模型的水成物和水蒸气混合比变量。对2019年发生的5次高影响天气事件进行了3次三维变分数据同化实验并进行了比较：(i)使用原始水流星混合比作为控制变量同化反射率的控制实验，（ii）使用功率转换水流星混合比作为控制变量同化反射率的实验，以及（iii）使用功率转换水流星和水蒸气混合比（qυ）作为控制变量同化反射率和检索伪水蒸气观测的实验。从5个案例中对0 - 3小时的预测进行了定性和定量评价。两种功率转换混合比实验的分析和预测效果均优于控制实验。值得注意的是，在所有情况下，用功率转换的流体作为附加控制变量同化伪水汽可以提高分析和短期预报的性能。此外，使用功率变换的两个实验的代价函数最小化的收敛速度比控制实验快。在任何资料同化方案中有效利用雷达反射率观测仍然是一个重要的研究课题，因为反射率观测明确地包括有关水成物的信息，也隐含地包括有关风暴内湿度分布的信息。然而，由于反射率正演观测算子是高度非线性的，反射率很难同化。本研究旨在确定一种更有效的方法，将反射率吸收到对流尺度NWP模式中，以提高高影响天气事件预测的准确性。
Assimilating radar reflectivity into convective-scale NWP models remains a challenging topic in radar data assimilation. A primary reason is that the reflectivity forward observation operator is highly nonlinear. To address this challenge, a power transformation function is applied to the WRF Model’s hydrometeor and water vapor mixing ratio variables in this study. Three 3D variational data assimilation experiments are performed and compared for five high-impact weather events that occurred in 2019: (i) a control experiment that assimilates reflectivity using the original hydrometeor mixing ratios as control variables, (ii) an experiment that assimilates reflectivity using power-transformed hydrometeor mixing ratios as control variables, and (iii) an experiment that assimilates reflectivity and retrieved pseudo–water vapor observations using power-transformed hydrometeor and water vapor mixing ratios (qυ) as control variables. Both qualitative and quantitative evaluations are performed for 0–3-h forecasts from the five cases. The analysis and forecast performance in the two experiments with power-transformed mixing ratios is better than the control experiment. Notably, the assimilation of pseudo–water vapor with power-transformedqυas an additional control variable is found to improve the performance of the analysis and short-term forecasts for all cases. In addition, the convergence rate of the cost function minimization for the two experiments that use the power transformation is faster than that of the control experiments.Significance StatementThe effective use of radar reflectivity observations in any data assimilation scheme remains an important research topic because reflectivity observations explicitly include information about hydrometeors and also implicitly include information about the distribution of moisture within storms. However, it is difficult to assimilate reflectivity because the reflectivity forward observation operator is highly nonlinear. This study seeks to identify a more effective way to assimilate reflectivity into a convective-scale NWP model to improve the accuracy of predictions of high-impact weather events.