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Data assimilation of radar reflectivity volumes in a LETKF scheme

机译：Letkf计划中雷达反射率卷的数据同化

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摘要

Quantitative precipitation forecast (QPF) is still a challenge for numerical weather prediction (NWP), despite the continuous improvement of models and data assimilation systems. In this regard, the assimilation of radar reflectivity volumes should be beneficial, since the accuracy of analysis is the element that most affects short-term QPFs. Up to now, few attempts have been made to assimilate these observations in an operational set-up, due to the large amount of computational resources needed and due to several open issues, like the rise of imbalances in the analyses and the estimation of the observational error. In this work, we evaluate the impact of the assimilation of radar reflectivity volumes employing a local ensemble transform Kalman filter (LETKF), implemented for the convection-permitting model of the COnsortium for Small-scale MOdelling (COSMO). A 4-day test case on February 2017 is considered and the verification of QPFs is performed using the fractions skill score (FSS) and the SAL technique, an object-based method which allows one to decompose the error in precipitation fields in terms of structure (S), amplitude (A) and location (L). Results obtained assimilating both conventional data and radar reflectivity volumes are compared to those of the operational system of the Hydro-Meteo-Climate Service of the Emilia-Romagna Region (Arpae-SIMC), in which only conventional observations are employed and latent heat nudging (LHN) is applied using surface rainfall intensity (SRI) estimated from the Italian radar network data. The impact of assimilating reflectivity volumes using LETKF in combination or not with LHN is assessed. Furthermore, some sensitivity tests are performed to evaluate the effects of the length of the assimilation window and of the reflectivity observational error (roe). Moreover, balance issues are assessed in terms of kinetic energy spectra and providing some examples of how these af- fect prognostic fields. Results show that the assimi
机译：尽管模型和数据同化系统的持续改进，定量降水预测（QPF）仍为数值天气预报（NWP）仍为挑战。在这方面，雷达反射率体积的同化应该是有益的，因为分析的准确性是大多数影响短期QPF的元素。到目前为止，由于需要的几种开放问题，很少有几次尝试在运营建设中同化这些观察，并且由于几个公开问题，如分析中的不平衡的增加和观察到的估计错误。在这项工作中，我们评估了采用本地集合变换卡尔曼滤波器（Letkf）的雷达反射率卷的同化的影响，为小规模建模的联盟的对流允许模型实现了。考虑了4天的测试用案，并考虑了使用馏分技能评分（FSS）和SAL技术进行QPFS的验证，这是一种基于对象的方法，该方法允许一个人在结构方面分解降水场中的误差（s），幅度（a）和位置（l）。与Emilia-Romagna地区（ARPAE-SIMC）的水力 - 地区气候服务的操作系统相比，获得的结果与emilia-romagna区域（ARPAE-SIMC）的操作系统相比，其中仅采用常规观察和潜热亮种（使用来自意大利雷达网络数据估计的表面降雨强度（SRI）施用LHN。评估使用LETKF与LHN组合使用Letkf的反射率体积的影响。此外，进行一些敏感性测试以评估同化窗口和反射率观测误差（ROE）的长度的效果。此外，在动力学能谱方面评估了平衡问题，并提供了一些预后领域的一些例子。结果表明Assimi

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