Satellite images in the visible spectral range contain high‐resolution cloud information, but have not been assimilated directly before. This paper presents a case‐study on the assimilation of visible Meteosat SEVIRI images in a convective‐scale data assimilation system based on a local ensemble transform Kalman filter (LETKF) in a near‐operational set‐up. For this purpose, a fast look‐up table‐based forward operator is used to generated synthetic satellite images from the model state. Single‐observation experiments show that the assimilation of visible reflectances improves cloud cover under most conditions and often reduces temperature and humidity errors. In cycled experiments for two summer days with convective precipitation, the assimilation strongly reduces the errors of cloud cover and improves the precipitation forecast. While these results are promising, several issues are identified that limit the efficacy of the assimilation process. First, the linearity assumption of the LETKF can lead to errors as reflectance is a nonlinear function of the model state. Second, errors can arise from the fact that visible reflectances alone are ambiguous and only weakly sensitive to the water phase and cloud‐top height. And lastly, it is not obvious how to localise vertical covariances as visible reflectances are sensitive to clouds at all heights. For the latter reason, no vertical localisation was used in this study. To investigate the robustness of the results, the horizontal localisation scale, the assigned observation error and the spatial density of observations were varied in sensitivity experiments. The best results were obtained for an observation error close to the Desroziers estimate. High observation density combined with small localisation radii resulted in the smallest 1 hr forecast error. These settings were also beneficial for 3 hr forecasts, but forecasts at that lead time were less sensitive to the observation density and the localisation scale.

中文翻译：

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吸收可见卫星图像进行对流尺度数值天气预报：一个案例研究

可见光谱范围内的卫星图像包含高分辨率的云信息，但之前并未被直接吸收。本文介绍了一个对流尺度数据同化系统中的可见Meteosat SEVIRI图像同化的案例研究，该系统基于在近似操作环境中的局部集成变换卡尔曼滤波器（LETKF）。为此，使用基于快速查找表的前向运算符从模型状态生成合成卫星图像。单观测实验表明，在大多数情况下，可见反射的同化可以改善云量，并且通常可以减少温度和湿度误差。在两个夏季的对流降水的循环实验中，同化极大地减少了云层覆盖的误差并改善了降水预报。尽管这些结果令人鼓舞，但发现了一些限制同化过程功效的问题。首先，由于反射率是模型状态的非线性函数，因此LETKF的线性假设可能会导致误差。其次，可能会因以下事实而产生错误：仅可见反射率是模棱两可的，并且仅对水相和云顶高度敏感。最后，由于垂直反射对所有高度的云都敏感，因此如何定位垂直协方差并不明显。由于后者的原因，本研究中未使用垂直定位。为了研究结果的鲁棒性，在敏感性实验中改变了水平定位范围，分配的观测误差和观测的空间密度。对于接近Desroziers估计的观察误差，可获得最佳结果。高观测密度和较小的定位半径导致最小的1小时预测误差。这些设置对于3个小时的预报也很有用，但在提前期进行的预报对观测密度和本地化规模的敏感度较低。