five

Establishment of AIRS Climate-Level Radiometric Stability using Radiance Anomaly Retrievals of Minor Gases and SST

收藏
NIAID Data Ecosystem2026-03-11 收录
下载链接:
https://zenodo.org/record/3878740
下载链接
链接失效反馈
官方服务:
资源简介:
Temperature, H2O, and O3 profiles, as well as CO2, N2O, CH4, CFC12, and SST scalar anomalies are computed using a clear subset of AIRS observations over ocean for the first 16-years of NASA's EOS-AQUA AIRS operation. The AIRS Level 1c radiances are averaged over 16 days and 40 equal-area zonal bins and then converted to brightness temperature anomalies. Geophysical anomalies are retrieved from the brightness temperature anomalies using a relatively standard optimal estimation approach. The CO2, N2O, CH4, and CFC12 anomalies are derived by applying a vertically uniform multiplicative shift to each gas in order to obtain an estimate for the ngas mixing ratio. The minor gas anomalies are compared to the NOAA ESRL in-situ values and used to estimate the radiometric stability of the AIRS radiances. Similarly the retrieved SST anomalies are compared to the SST values used in the ERA-Interim reanalysis and to NOAA's OISST SST product. These inter-comparisons strongly suggest that many AIRS channels are stable to better than 0.02 to 0.03 K/Decade, well below climate trend levels, indicating that the AIRS blackbody is not drifting. However, detailed examination of the anomaly retrieval residuals (observed minus computed) show various small unphysical shifts that correspond to AIRS hardware events (shutdowns, etc.). Some examples are given highlighting how the AIRS radiances stability could be improved, especially for channels sensitive to N2O and CH4. The AIRS short wave channels exhibit larger drifts that make them unsuitable for climate trending, and they are avoided in this work. The AIRS Level 2 surface temperature retrievals only use short wave channels. We summarize how these short wave drifts impacts recently published comparisons of AIRS surface temperature trends to other surface climatologies.
创建时间:
2020-06-05
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作