All measurements of

Atmospheric carbon dioxide (

OCO-2 and GOSAT share a common observational approach: solar reflectance
spectra centered around 1.6 and 2.0

Early analysis of

Several retrieval codes that have been used to analyze GOSAT and OCO-2
spectra treat this problem differently. For example, the RemoTeC algorithm
does not retrieve the surface pressure from the spectra. It uses the surface
pressure from the meteorological reanalysis

In this analysis, we address two issues with the OCO-2 v8 estimate of surface
pressure: erroneous surface pressure values from the meteorological
reanalysis due to small miss-specifications of the geolocations of OCO-2's
eight footprints in the instrument-to-spacecraft pointing offsets and
erroneous surface pressure estimates due to sampling the meteorological
reanalysis at incorrect times. We illustrate how, using improved knowledge of
the surface pressure, we can improve the bias correction and reduce errors in

OCO-2 v8

OCO-2 target mode observation over Lauder, New Zealand, on
17 February 2015. Panel

Illustration of the OCO-2 spacecraft body axes.

Biases in OCO-2

Another source for erroneous surface pressure estimates in v8 is caused by a
temporal sampling error of the surface pressure estimate from the
meteorological reanalysis. The prior surface pressure is taken from the
GEOS5-FP-IT 3-hourly output. A coding error in the meteorological sampling
algorithm caused for some soundings the surface pressure estimate to be
sampled as much as 3 h after the overpass time. This mostly affected
soundings of orbits whose first and last soundings fully lie between synoptic
GEOS5-FP-IT's 3-hourly outputs (00:00, 03:00, etc.); the soundings in such an
orbit would be erroneously sampled at the upper bounding synoptic time for
that orbit. For example, for an orbit whose soundings lie fully between 06:00
and 09:00 UTC, the OCO-2 meteorological sampling algorithm erroneously
samples the GEOS5-FP-IT surface pressure field at 09:00 UTC for each
sounding in that orbit. On average, this introduced a mean prior surface
pressure error of about

The core of the OCO-2 instrument is a three-channel grating spectrometer that
records spectra of reflected sunlight in the

OCO-2 pointing offsets for each footprint and spectral band for the

To obtain the best estimate for the geolocation of the eight footprints, the
following must be known: (1) the location of the spacecraft along the orbit
track, (2) the pointing of the instrument boresight relative to a local
coordinate system, and (3) the relative pointing of the fields of view (FOV)
of the eight footprints in the three spectrometers. A Global Positioning
System (GPS) sensor provides the location of the observatory along its orbit
track. The on-board star tracker determines the orientation of the
observatory relative to fixed stars. The relative alignment of the eight
footprints is characterized with respect to the spacecraft body axes. The
spatial FOV, defined along the long axis of the slit by the eight footprints,
is aligned parallel with the spacecraft

Change in altitude

The analysis of the IOC lunar data exposed some deficiencies of its usage in
elaborating footprint geolocations. Lunar data are typically taken in
so-called single pixel mode when each pixel of the array is read out
individually. This is in contrast to normal operations where 20 spatial pixel
samples are co-added to form each footprint. In addition, the moon only
illuminates a fraction of the FPA. Furthermore, defocus compromises the
analysis of the strong

To overcome the aforementioned limitations for the v0006 configuration, the
IOC lunar data results were used to constrain the pointing vector for FP 6
and 7, whereas for the other FPs the ground test results were used. Here, we
follow a different approach to derive new pointing offsets. We shift from
estimating geolocations with lunar images, which are strictly geometric
measurements, to optimizing footprint geolocations with retrieval variables.
We utilize the ACOS Level 2 Full Physics (L2FP) algorithm and its associated
prescreeners, the A-band Preprocessor (ABP) and the IMAP-DOAS Preprocessor
(IDP) to estimate footprint geolocations. The ABP performs a fast retrieval
of surface pressure using the

We identify two desert areas in the Northern and Southern Hemisphere with
topographic relief and frequent clear-sky conditions during nadir and glint
observations to derive new footprint geolocations: a remote area in the Death
Valley National Park, CA, USA, and an area in the Atacama Desert, Chile. The
Death Valley National Park area ranges from 35 to 37

OCO-2 v9 instrument-to-spacecraft pointing offsets for each spectral
band along the

We run the ABP and IDP for a set of different pointing offsets for which the
relative footprint positions of the v0006 configuration are preserved. If not
otherwise stated, in the following we refer to the pointing offset of FP 4 of
the

Figure

Standard deviation of

Standard deviation of

Figure

Mean difference between v9 and v8 (v9–v8) surface pressure prior
for April 2016. Data are aggregated into 2

Difference between v9 and v8 (v9–v8) of the surface pressure prior
standard deviation in each grid cell for April 2016. Data are aggregated into
2

To evaluate the impact of the updated footprint geolocations we sample the
surface pressure from GEOS5-FP-IT with the updated meteorological sampling
algorithm (that was corrected for the time sampling error) at the footprint
geolocations of the

Overview of the truth proxy training data sets for v9.

Models that contribute to the multi-model median truth proxy data set.

Parametric bias correction coefficients and reference values for v9 over land and ocean.

Our improved knowledge of OCO-2's footprint geolocations and the update of
the meteorological sampling algorithm reduce errors in bias-corrected

The parametric bias correction accounts for spurious variability in

For v9, we define two different

Contributions of the parametric bias correction terms to raw

Difference between v9

Relative increase of soundings that pass the v9 filtration scheme
compared to v8 for the entire year 2016. Data are aggregated into
2

Similar to v8, we use three truth proxies to derive the parametric bias
correction coefficients for co_grad_del, DWS and the revised

Filter variables and limits for the

Stations used in the TCCON truth proxy data set.

Bad soundings (e.g., those affected by clouds and low continuum level
signal-to-noise ratio) are mostly screened out by the ABP and IDP
before the ACOS L2FP algorithm performs retrievals. Some soundings that pass
the prescreening criteria, however, show errors in raw

We introduce the new filter variables

The global scaling factor corrects for an overall bias in

We use the same geographic and temporal co-location criteria for OCO-2 data
from direct overpasses of TCCON stations as in

Here, we evaluate the impact of the changes made in v9 on bias-corrected

v8

Global difference between v8 and v9 (v9–v8) bias-corrected

Figure

The update of the pointing vector that is used to derive the geolocation for
OCO-2's eight footprints, together with an update of the meteorological
sampling algorithm that corrects for a temporal sampling coding error,
provides a better estimate for the surface pressure in OCO-2's v9 data
product. Biases in

Accurate knowledge of the surface pressure and its estimate is crucial to
retrieve

All of the OCO-2 data products are publicly
available through the NASA Goddard Earth Science Data and Information
Services Center (GES DISC) for distribution and archiving
(

Column-averaged dry air mole fractions of

MK performed substantial data analysis regarding the derivation of new pointing offsets, the revised bias correction, and the global scaling factor for v9. CO was involved in nearly all aspects of this work, in particular the revised bias correction, quality filtering, and the global scaling factor for v9. BF implemented many tests and performed data analysis. AE provided project leadership and algorithm guidance. CM and RN helped to understand the origin of the topography-related bias and contributed to the selection of the training data sets. PW provided critical guidance on nearly all aspects of the work, throughout all stages.

The authors declare that they have no conflict of interest.

We thank David Crisp for helpful discussions on the viewing geometry of the
observatory. We thank Callum McCracken for contributing to
Fig.