[preprint] MAR: Monthly AIRS Radiances (2002-09 through 2024-08)

This dataset contains monthly-mean radiance observations from the NASA AIRS instrument (Chahine, 2006) on a regular, lat-lon grid. Data are available from 22 years of observations from September 2002 through August 2024. Profiles with non-nominal quality flags are excluded. Data are separated by zenith viewing angle, and orbit type (ascending vs. descending). The number of aggregated observations is included to allow for tests of statistical significance. To distinguish between clear and cloudy scenes, we identify clear-sky observations following the methods of DeSouza-Machado et al., 2025. Namely, profiles with the top 10% of radiances in the 1231 cm-1 window channel are labeled as clear-sky. DeSouza-Machado et al., 2025 show that while this methodology is sensitive to cloud contamination in regions with high cloud cover and frequent temperature inversions, it provides a good estimate of clear-sky conditions with global coverage. Clear-sky profiles are identified separately for each 16-day AIRS orbit cycle and are then aggregated to monthly mean fields. Spatial grid spacing is 5 degrees in longitude, with variable latitude spacing to give a roughly equal number of observations in each gridcell. 120 spectral channels on the AIRS L1C spectral grid are included here. These channels represent a subset of the 470 channels identified as the most suitable for climate analysis studies in Strow et al., 2020 (see Section 4.2). A list of channels can be found at https://doi.org/10.5281/zenodo.3878740. Data are available in the Zarr data format to be efficiently accessed and reconfigured for different user purposes. Each spectral channel is ~370mb and the entire 470 channel dataset is ~175GB (this preliminary dataset includes every fourth channel to meet the Zenodo memory limit, but following peer review the full dataset will be made available). The included python file (MARexample_AIRSobs_averaging.py) gives an example of reading these data directly from Zenodo. Zarr format 2 is used to remain compatible with both Zarr 2 and 3 versions. These data were produced for Shaw et al. (in prep.), which serves to document them. Please acknowledge and cite Shaw et al. if you use these data per the license. References: Chahine, M. T., and Coauthors (2006). AIRS: Improving Weather Forecasting and Providing New Data on Greenhouse Gases. Bull. Amer. Meteor. Soc., 87, 911–926, https://doi.org/10.1175/BAMS-87-7-911 DeSouza-Machado, S., Strow, L. L., & Kramer, R. J. (2025). Geophysical trends inferred from 20 years of AIRS infrared global observations. Journal of Geophysical Research: Atmospheres, 130, e2025JD043501. https://doi.org/10.1029/2025JD043501 Strow, L. L. and DeSouza-Machado, S. (2020). Establishment of AIRS climate-level radiometric stability using radiance anomaly retrievals of minor gases and sea surface temperature. Atmos. Meas. Tech., 13, 4619–4644, https://doi.org/10.5194/amt-13-4619-2020本数据集包含美国国家航空航天局（NASA）大气红外探测仪（AIRS）的月平均辐射观测值，采用规则经纬网格（lat-lon grid）布设，数据覆盖2002年9月至2024年8月共计22年的观测序列。已剔除质量标记不符合标称标准的探测廓线，数据按天顶观测角与轨道类型（升轨与降轨）进行分类。数据集附带聚合观测次数字段，可用于开展统计显著性检验。为区分晴空与云天场景，本数据集采用DeSouza-Machado等人（2025）提出的方法识别晴空观测：将1231 cm⁻¹窗口通道内辐射亮温排名前10%的探测廓线标记为晴空廓线。DeSouza-Machado等人（2025）的研究表明，尽管该方法在高云量与频繁逆温区域易受云污染影响，但仍可在全球范围内较好地估算晴空条件。晴空廓线基于每16天的AIRS轨道周期单独识别，随后聚合为月平均场。 空间网格的经度间隔为5°，纬度间隔可变，以确保每个网格单元内的观测数量大致相等。本数据集包含AIRS L1C光谱网格上的120个光谱通道，这些通道是Strow等人（2020）筛选出的最适用于气候分析研究的470个通道的子集（详见第4.2节）。通道清单可访问：https://doi.org/10.5281/zenodo.3878740。 数据采用Zarr数据格式（Zarr）存储，可高效访问并根据不同用户需求进行重构。单个光谱通道的数据量约为370 MB，完整的470通道数据集总容量约为175 GB。本预发布数据集仅包含每4个通道中的1个以适配Zenodo的存储限制，经同行评议后将发布完整数据集。附带的Python脚本MARexample_AIRSobs_averaging.py提供了直接从Zenodo读取该数据集的示例。本数据集采用Zarr格式第2版，以兼容Zarr 2与Zarr 3版本。 本数据集由Shaw等人（待发表）研制并完成文档化，若您依据授权使用该数据集，请注明并引用Shaw等人的相关成果。 参考文献： Chahine, M. T. 等（2006）. AIRS：改进天气预报并提供温室气体新观测数据. 美国气象学会公报（Bull. Amer. Meteor. Soc.）, 87, 911–926, https://doi.org/10.1175/BAMS-87-7-911 DeSouza-Machado, S., Strow, L. L. 与Kramer, R. J.（2025）. 基于20年AIRS红外全球观测数据反演的地球物理趋势. 地球物理研究杂志：大气圈（Journal of Geophysical Research: Atmospheres）, 130, e2025JD043501, https://doi.org/10.1029/2025JD043501 Strow, L. L. 与DeSouza-Machado, S.（2020）. 利用微量气体与海表温度的辐射异常反演确定AIRS气候级辐射稳定性. 大气测量技术（Atmos. Meas. Tech.）, 13, 4619–4644, https://doi.org/10.5194/amt-13-4619-2020