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Supermodding – A special footprint operator for mesoscale data assimilation using scatterometer winds
Quarterly Journal of the Royal Meteorological Society ( IF 3.0 ) Pub Date : 2021-01-20 , DOI: 10.1002/qj.3979 Máté Mile 1 , Roger Randriamampianina 1 , Gert‐Jan Marseille 2 , Ad Stoffelen 2
Quarterly Journal of the Royal Meteorological Society ( IF 3.0 ) Pub Date : 2021-01-20 , DOI: 10.1002/qj.3979 Máté Mile 1 , Roger Randriamampianina 1 , Gert‐Jan Marseille 2 , Ad Stoffelen 2
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Satellite observation footprints may extend over many grid points of a high‐resolution limited‐area model, while small and fast model scales may not be traceable by the observing system. We discuss the spatial representation of scatterometer ocean surface winds for the representation of high‐resolution model state variables. A prototype observation operator called supermodding is studied in the variational assimilation framework in order to avoid correcting unconstrained small scales during the assimilation procedure. The challenges connected to small scales that are represented by the mesoscale model with respect to the application of the supermodding operator are discussed through idealised experiments. These results show that the application of the supermodding operator is able to avoid correcting unconstrained small scales, putting focus on the large scales only during data assimilation. Departure‐based diagnostics show that the footprint representation helps to reduce the standard deviation of observation minus background departures (4–5% reduction) while the statistics for the supermodding method show a further reduction (8–11%). The impact of the supermodding approach is discussed through a forecast sensitivity study using a moist total energy norm (MTEN)‐based technique and verification of the forecasts against observations. It is shown that the impact of the supermodding method to ASCAT data assimilation on the upper‐air AROME‐Arctic forecasts is observed over sea and near the surface, and that it is progressively shifted towards 700–800 hPa levels. Both supermodding at 30 km and at 60 km (i.e., twice the effective resolution of the applied scatterometer observations) show significant and consistent improvement on forecasts of lower tropospheric wind and temperature compared to the operational assimilation technique, as such demonstrating the robustness of the supermodding technique.
中文翻译：
Supermodding –特殊的足迹操作器，用于使用散射仪风进行中尺度数据同化
卫星观测足迹可能会扩展到高分辨率有限区域模型的许多网格点，而小型和快速的模型比例可能无法由观测系统追踪。我们讨论了散射计海洋表面风的空间表示，以表示高分辨率的模型状态变量。为了避免在同化过程中校正无约束的小尺度，在变分同化框架中研究了一种称为“ supermodding”的原型观测算子。通过理想化的实验，讨论了由中尺度模型代表的与小尺度有关的挑战，这些挑战涉及超级调节算子的应用。这些结果表明，超级修饰算子的应用能够避免校正无约束的小尺度，仅在数据同化时才将重点放在大规模上。基于出发点的诊断表明，足迹表示法有助于减少观测值的标准偏差减去背景偏差（减少4–5％），而采用超级改装方法的统计数据则进一步减少（8–11％）。通过使用基于湿总能量范数（MTEN）的技术进行的预测敏感性研究以及对观测值与观测值的验证，讨论了超级替代方法的影响。结果表明，在海上和近地表观察到了叠加方法对ASCAT数据同化对高空AROME-Arctic预报的影响，并且逐渐向700-800 hPa的水平移动。两者都在30 km和60 km处超调（即，
更新日期：2021-03-07
中文翻译：
Supermodding –特殊的足迹操作器，用于使用散射仪风进行中尺度数据同化
卫星观测足迹可能会扩展到高分辨率有限区域模型的许多网格点，而小型和快速的模型比例可能无法由观测系统追踪。我们讨论了散射计海洋表面风的空间表示，以表示高分辨率的模型状态变量。为了避免在同化过程中校正无约束的小尺度，在变分同化框架中研究了一种称为“ supermodding”的原型观测算子。通过理想化的实验，讨论了由中尺度模型代表的与小尺度有关的挑战，这些挑战涉及超级调节算子的应用。这些结果表明，超级修饰算子的应用能够避免校正无约束的小尺度，仅在数据同化时才将重点放在大规模上。基于出发点的诊断表明，足迹表示法有助于减少观测值的标准偏差减去背景偏差（减少4–5％），而采用超级改装方法的统计数据则进一步减少（8–11％）。通过使用基于湿总能量范数（MTEN）的技术进行的预测敏感性研究以及对观测值与观测值的验证，讨论了超级替代方法的影响。结果表明，在海上和近地表观察到了叠加方法对ASCAT数据同化对高空AROME-Arctic预报的影响，并且逐渐向700-800 hPa的水平移动。两者都在30 km和60 km处超调（即，
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