This is the Gridded Monthly OCO-2 Carbon Dioxide assimilated dataset. The OCO-2 mission provides the highest quality space-based XCO2 retrievals to date. However, the instrument data are characterized by large gaps in coverage due to OCO-2’s narrow 10-km ground track and an inability to see through clouds and thick aerosols. This global gridded dataset is produced using a data assimilation technique commonly referred to as state estimation within the geophysical literature. Data assimilation synthesizes simulations and observations, adjusting the state of atmospheric constituents like CO2 to reflect observed values, thus gap-filling observations when and where they are unavailable based on previous observations and short transport simulations by GEOS. Compared to other methods, data assimilation has the advantage that it makes estimates based on our collective scientific understanding, notably of the Earth’s carbon cycle and atmospheric transport. OCO-2 GEOS (Goddard Earth Observing System) Level 3 data are produced by ingesting OCO-2 L2 retrievals every 6 hours with GEOS CoDAS, a modeling and data assimilation system maintained by NASA’s Global Modeling and Assimilation Office (GMAO). GEOS CoDAS uses a high-performance computing implementation of the Gridpoint Statistical Interpolation approach for solving the state estimation problem. GSI finds the analyzed state that minimizes the three-dimensional variational (3D-Var) cost function formulation of the state estimation problem.
File name: the file is stored in ". nc4" format, and the file name is "YYYYMM. nc4", where YYYY represents the year and MM represents the month
Reading method: software such as Panoply can directly open to view data information, including longitude and latitude information, carbon dioxide column concentration data, data quality information, etc., and can also use matlab, python and other programming methods to obtain data content.
Nasa, N. (2022). OCO2_ GEOS_ L3CO2_ MONTH 10r dataset in the upper reaches of the Yangtze River (2015-2022). Upper Yangtze River Scientific Data Center, DOI: 10.5067/BGFIODET3HZ8. (Download the reference：
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| East: 112.00 | West: 89.00 |
|---|---|
| South: 24.00 | North: 36.50 |
