Measuring atmospheric conditions above convective storms using spaceborne instruments is challenging. The operational retrieval framework of current hyperspectral infrared sounders adopts a cloud-clearing scheme that is unreliable in overcast conditions. To overcome this issue, previous studies have developed an optimal estimation method that retrieves the temperature and humidity above high thick clouds by assuming a slab of cloud. In this study, we find that variations in the effective radius and density of cloud ice near the tops of convective clouds lead to non-negligible spectral uncertainties in simulated infrared radiance spectra. These uncertainties cannot be fully eliminated by the slab-cloud assumption. To address this problem, a synergistic retrieval method is developed here. This method retrieves temperature, water vapor, and cloud properties simultaneously by incorporating observations from active sensors in synergy with infrared radiance spectra. A simulation experiment is conducted to evaluate the performance of different retrieval strategies using synthetic radiance data from the Atmospheric Infrared Sounder (AIRS) and cloud data from CloudSat/CALIPSO. In this experiment, we simulate infrared radiance spectra from convective storms through a combination of a numerical weather prediction model and a radiative transfer model. The simulation experiment shows that the synergistic method is advantageous, as it shows high retrieval sensitivity to the temperature and ice water content near the cloud top. The synergistic method more than halves the root-mean-square errors in temperature and column integrated water vapor compared to prior knowledge based on the climatology. It can also improve the quantification of the ice water content and effective radius compared to prior knowledge based on retrievals from active sensors. Our results suggest that existing infrared hyperspectral sounders can detect the spatial distributions of temperature and humidity anomalies above convective storms.

中文翻译：

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使用高光谱红外测深仪对云上温度和水汽反演进行基于模拟实验的评估

使用星载仪器测量对流风暴上方的大气条件具有挑战性。当前高光谱红外探测器的操作检索框架采用在阴天条件下不可靠的清云方案。为了克服这个问题，之前的研究已经开发了一种最佳估计方法，通过假设一片云来检索高厚云上方的温度和湿度。在这项研究中，我们发现对流云顶部附近云冰的有效半径和密度的变化导致模拟红外辐射光谱中不可忽略的光谱不确定性。这些不确定性不能通过板云假设完全消除。为了解决这个问题，这里开发了一种协同检索方法。该方法检索温度、水蒸气、通过将有源传感器的观测与红外辐射光谱协同作用，同时结合云特性。使用来自大气红外测深仪 (AIRS) 的合成辐射数据和来自 CloudSat/CALIPSO 的云数据进行模拟实验，以评估不同检索策略的性能。在这个实验中，我们通过数值天气预报模型和辐射传输模型的组合来模拟来自对流风暴的红外辐射光谱。仿真实验表明，协同方法对云顶附近的温度和冰水含量具有较高的反演灵敏度，具有优越性。与基于气候学的先验知识相比，协同方法将温度和柱子积分水汽的均方根误差减少了一半以上。与基于主动传感器检索的先验知识相比，它还可以改进冰水含量和有效半径的量化。我们的结果表明，现有的红外高光谱探测器可以检测对流风暴上方温度和湿度异常的空间分布。