Earth Data School
About
Full catalog/MultiInstrumentFusedXCO2
MultiInstrumentFusedXCO2·v3·dataset

Carbon dioxide in the air, daily, many instruments combined

Multi-Instrument Fused bias-corrected XCO2 and other select fields aggregated as Level 4 daily files V3 (MultiInstrumentFusedXCO2)
atmosphere NASA GES_DISC Level 4
In plain English

What it measures. Daily gridded maps of carbon dioxide in the air, blending observations from multiple instruments into a smooth, gap-filled picture.

How it's made. Created by statistically merging daily bias-corrected CO2 data from the OCO-2 and GOSAT satellites and interpolating it onto a grid.

How & where you'd use it. Gives a more complete daily view of atmospheric carbon dioxide than any single instrument, useful for tracking carbon and studying its sources and sinks.

What's measured

ATMOSPHERE › ATMOSPHERIC CHEMISTRY › CARBON AND HYDROCARBON COMPOUNDS › ATMOSPHERIC CARBON DIOXIDE

Coverage & cadence

  • Time span2014-09-06 → 2020-07-31
  • Measured byOCO-2 (OCO SPECTROMETERS) · GOSAT (TANSO-FTS)
  • Processing levelLevel 4
  • Spatial extent-180, -90, 180, 90
  • StatusCOMPLETE

What you can do with it

  • Map air pollutants — NO₂, aerosols, ozone
  • Track greenhouse gases and Earth's energy budget
  • Feed weather and air-quality analysis
Official description

Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Orbiting Carbon Observatory (OCO-2) and Greenhouse Gases Observing Satellite (GOSAT) bias corrected data. This is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page.

Get the data

multiinstrumentfusedxco2_access.py
import earthaccess
earthaccess.login(strategy="netrc")          # free Earthdata Login

results = earthaccess.search_data(
    short_name="MultiInstrumentFusedXCO2",
    version="3",
    bounding_box=(-122.5, 37.2, -121.8, 37.9),  # your area (W,S,E,N)
    temporal=("2024-01-01", "2024-12-31"),       # your dates
)
files = earthaccess.open(results)   # stream straight from GES_DISC
Browsing CMR needs no login. Downloading or streaming bytes needs a free Earthdata Login + the earthaccess package.