five

OCO-2 Level 1B calibrated, geolocated calibration spectra, Retrospective Processing V10r (OCO2_L1B_Calibration) at GES DISC

收藏
Global Change Master Directory (GCMD)2017-06-17 更新2026-04-25 收录
下载链接:
https://cmr.earthdata.nasa.gov/search/concepts/C1685783883-GES_DISC.html
下载链接
链接失效反馈
官方服务:
资源简介:
Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r. Version 8r is the current version of the data set. Version 7r has been superseded by Version 8r. The Orbiting Carbon Observatory is the first NASA missiondesigned to collect space-based measurements of atmospheric carbon dioxidewith the precision, resolution, and coverage needed to characterize theprocesses controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements ofreflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and inmolecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Their raw data numbers (DN) are delivered correlated in time tothe Level 1B process as Level 1A products. Each band has 1016 spectralelements, although some are masked out in the L2 retrieval.This L1B product results from calibration mode measurements (e.g., Lunar,Solar, Dark observations), and thus it differs from the OCO2_L1B_Science(L1bSc) product. The differences in the product formats are only in the geolocation information provided. Whereas the L1bSc products report geolocation data for each sounding, calibration products report the directionof the boresight vector.This is the retrospective processing where the calibration data is estimated from the full timeseries of data (before, during, and after the measurements), and is expected to be of slightly higher quality.
提供机构:
GES_DISC
创建时间:
2017-06-17
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