The Advanced Geostationary Radiation Imager (AGRI) carried by the FengYun-4A (FY-4A) satellite enables the continuous observation of local weather. However, FY-4A/AGRI infrared satellite observations are strongly influenced by clouds, which complicates their use in all-sky data assimilation. The presence of clouds leads to increased uncertainty, and the observation-minus-background (O−B) differences can significantly deviate from the Gaussian distribution assumed in the variational data assimilation theory. In this study, we introduce two cloud-affected (Ca) indices to quantify the impact of cloud amount and establish dynamic observation error models to address biases between O−B and Gaussian distributions when assimilating all-sky data from FY-4A/AGRI observations. For each Ca index, we evaluate two dynamic observation error models: a two-segment and a three-segment linear model. Our findings indicate that the three-segment linear model we propose better conforms to the statistical characteristics of FY-4A/AGRI observations and improves the Gaussianity of the O−B probability density function. Dynamic observation error models developed in this study are capable of handling cloud-free or cloud-affected FY-4A/AGRI observations in a uniform manner without cloud detection.

中文翻译：

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FY-4A/AGRI全天红外辐射观测同化误差模型

风云四号A（FY-4A）卫星搭载的先进对地静止辐射成像仪（AGRI）能够对当地天气进行连续观测。然而，FY-4A/AGRI红外卫星观测受到云层的强烈影响，这使得它们在全天数据同化中的使用变得复杂。云的存在会导致不确定性增加，并且观测减去背景（O−B）差异可能会显着偏离变分数据同化理论中假设的高斯分布。在本研究中，我们引入了两个云影响（Ca）指数来量化云量的影响，并建立动态观测误差模型，以解决同化 FY-4A/AGRI 观测中的全天空数据时 O−B 分布和高斯分布之间的偏差。对于每个 Ca 指数​​，我们评估两个动态观测误差模型：两段线性模型和三段线性模型。我们的研究结果表明，我们提出的三段线性模型更符合FY-4A/AGRI观测的统计特征，并提高了OB概率密度函数的高斯性。本研究开发的动态观测误差模型能够以统一的方式处理无云或受云影响的 FY-4A/AGRI 观测，而无需云检测。