OU/NSSL CLAMPS AERIoe Temperature and Water Vapor Profile Data from LAPSE-RATE

The AERIoe algorithm (Turner and Loehnert 2014, Turner and Blumberg 2018) retrieves profiles of temperature and water vapor mixing ratio, together with cloud properties for a single-layer cloud (i.e., LWP, effective radius), from AERI-observed infrared radiance spectrum. The data can be used to characterize the evolution of the planetary boundary layer and boundary layer clouds. This dataset was collected at the Moffat Consolidated School in Moffat, CO during the LAPSE-RATE field campaign. The AERIoe retrieval was run at 15-minute resolution to match the cadence of the UAS that was colocated with the CLAMPS facility. This is a physical-iterative retrieval method. The retrieval of thermodynamic profiles from spectral infrared radiance observations is an ill-posed problem, and thus constraints need to be included in the retrieval algorithm to provide physically plausible results. Here, we use a climatology of 2022 radiosonde profiles collected from Denver during July as our prior information in an optimal estimation framework. As the method uses an optimal estimation framework, a full error covariance matrix of each solution is included in the output file. The 1-sigma uncertainty of each retrieved variable, which is derived from the error covariance matrix, is included for each scientific field and is named "sigma_X", where "X" is the name of the scientific field (e.g., 'temperature'). The information content in the AERI observations, which is in the "dfs" field, on the thermodynamic profiles is primarily concentrated in the lowest 3 km or up to cloud base; the retrieved data should not be used above that level (or used with caution). References: Turner, D. D., and U. Löhnert, 2014: Information Content and Uncertainties in Thermodynamic Profiles and Liquid Cloud Properties Retrieved from the Ground-Based Atmospheric Emitted Radiance Interferometer (AERI). <em>J. Appl. Meteor. Climatol.</em>, <strong>53</strong>, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1. Turner, D. D., and W. G. Blumberg, 2019: Improvements to the AERIoe Thermodynamic Profile Retrieval Algorithm. <em>IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing</em>, <strong>12</strong>, 1339–1354, https://doi.org/10.1109/JSTARS.2018.2874968.AERIoe算法（AERIoe，Turner与Loehnert 2014，Turner与Blumberg 2018）可基于地基大气发射辐射干涉仪（Atmospheric Emitted Radiance Interferometer，AERI）观测得到的红外辐射光谱，反演温度与水汽混合比廓线，以及单层云的云属性（即液态水路径（Liquid Water Path，LWP）、有效半径）。该数据集可用于表征行星边界层与边界层云的演变过程。本数据集采集于美国科罗拉多州莫法特市的莫法特联合学校，采集时段为LAPSE-RATE野外试验期间。AERIoe反演以15分钟分辨率运行，以匹配与CLAMPS设施共址的无人机系统（Unmanned Aerial System，UAS）的观测频次。该方法属于物理迭代反演方法。通过红外光谱辐射观测反演热力学廓线属于不适定问题，因此需在反演算法中加入约束条件，以获得物理上合理的反演结果。本研究采用2022年7月在丹佛市采集的无线电探空廓线气候态作为最优估计框架下的先验信息。由于该方法采用最优估计框架，输出文件中包含每个反演解的完整误差协方差矩阵。每个反演变量的1σ不确定性（由误差协方差矩阵推导得到）将随每个科学变量一并输出，命名格式为"sigma_X"，其中"X"为科学变量的名称（例如"temperature"）。AERI观测中包含于"dfs"字段内的、针对热力学廓线的信息含量主要集中在最低3 km范围内，或直至云底高度；反演数据不应在该高度以上使用（或需谨慎使用）。参考文献：Turner, D. D., 与U. Löhnert, 2014: Information Content and Uncertainties in Thermodynamic Profiles and Liquid Cloud Properties Retrieved from the Ground-Based Atmospheric Emitted Radiance Interferometer (AERI). *J. Appl. Meteor. Climatol.*, **53**, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1. Turner, D. D., 与W. G. Blumberg, 2019: Improvements to the AERIoe Thermodynamic Profile Retrieval Algorithm. *IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing*, **12**, 1339–1354, https://doi.org/10.1109/JSTARS.2018.2874968.