Abstract. We evaluate the uncertainties of methane optimal estimation retrievals from single-footprint thermal infrared observations from the Atmospheric Infrared Sounder (AIRS). These retrievals are primarily sensitive to atmospheric methane in the mid-troposphere through the lower stratosphere
(∼2 to ∼17 km). We compare them to in situ observations made from aircraft during the
HIAPER Pole to Pole Observations (HIPPO) and Atmospheric Tomography Mission (ATom) campaigns, and
from the NOAA GML aircraft network, between the surface and 5–13 km, across a range of
years, latitudes between 60∘ S to 80∘ N, and over land and ocean. After a
global, pressure-dependent bias correction, we find that the land and ocean have similar biases
and that the reported observation error (combined measurement and interference errors) of
∼27 ppb is consistent with the SD between aircraft and individual AIRS
observations. A single observation has measurement (noise related) uncertainty of
∼17 ppb, a ∼20 ppb uncertainty from radiative interferences (e.g., from
water or temperature), and ∼30 ppb due to “smoothing error”, which is partially
removed when making comparisons to in situ measurements or models in a way that accounts for this
regularization. We estimate a 10 ppb validation uncertainty because the aircraft typically
did not measure methane at altitudes where the AIRS measurements have some sensitivity, e.g., the
stratosphere, and there is uncertainty in the truth that we validate against. Daily averaging only
partly reduces the difference between aircraft and satellite observation, likely because of
correlated errors introduced into the retrieval from temperature and water vapor. For example,
averaging nine observations only reduces the aircraft–model difference to ∼17 ppb
vs. the expected ∼10 ppb. Seasonal averages can reduce this ∼17 ppb
uncertainty further to ∼10 ppb, as determined through comparison with NOAA aircraft,
likely because uncertainties related to radiative effects of temperature and water vapor are
reduced when averaged over a season.
Ground-based temperature and humidity profiling: combining active and passive remote sensors