Remotely sensed observations of atmospheric composition require an estimate
of surface pressure. This estimate can either come from an instrument with
sensitivity in an O

The present surface-based network of observing systems has been
shown to be inadequate for reducing uncertainty in surface flux estimates of
CO

Satellites measure the top-of-atmosphere radiance, which is sensitive to the
integrated amount of absorbing tracer along the photon path. This amount may
change with either changes in mixing ratio or of the mass of air through
which photons pass. Source–sink processes only change mixing ratio. Thus, for
best use in flux inversions, retrievals of X

In

In this paper, we present a methodology to partially answer the question of
cost versus benefit for an active O

The paper is laid out as follows. Section

LAS instruments measure the difference in
transmitted/received energies at two or more wavelengths (which for the
two-line case we call “

The observation operator maps the model state (

For ASCENDS, three candidate spectral lines have been identified as having
the most potential for estimating absorption due to CO

The online and offline wavelengths are chosen with consideration of the sensitivity of transmission to
atmospheric temperature, water vapor and pressure, since variability in these quantities will contribute to the
overall uncertainty in the retrieved gas number densities. For example,

From

Figure

From

For the observation operators in Eqs. (

For a linear retrieval, the matrix

Assuming a single sounding in the retrieval (or spatially uncorrelated
errors),

In Sects.

The observed differential absorptions

In order to compute the information in each observation we summarize the preceding subsections in terms of expressions for the total uncertainty in each observation, which is just the sum of the three components described at length above.

For

Similarly, for

In Sect.

A minimum requirement for the O

For a given choice of CO

In the following subsections, we outline the procedure for computing the
uncertainties in the observations

Atmospheric state uncertainties were computed from an extensive set of observation and model prediction
pairs derived from surface weather observation station reports (METAR/SYNOP)

The differential absorption cross section

Sample sets of simulated absorption cross section for representative CO

In order to compute

Identically to the method described in

Ensemble mean weighting functions derived from NWP global vertical temperature and moisture
profiles. The upper panel shows the average weighting function for the online wavelength only, and the lower plot
shows the average weighting function for the differential optical depth calculation that uses an additional offline
wavelength to subtract the contribution of aerosols and other scatterers. These plots show the impact of the
online wavelength: the instruments sampling the 1.571 and 1.572

The standard deviation of the differences between the model-predicted

Layer optical depths for the desired wavenumbers, at standard layer heights between the surface and
100 km above the surface, were computed by combining the atmospheric state vectors with a nominal CO

The variability in the differential optical depth weighting functions due to differences in NWP temperature and moisture as a function of vertical height. The values are the standard deviations of the difference between the average weighting function values and the ensemble members as a percentage of the mean weighting function value.

Each of the 2500+ pairs of observation- and model-based weighting
functions were constructed by dividing the
discrete optical depth for each layer by the layer mixing ratio (i.e., 385 ppm for CO

The variance of the differences between differential optical depth WFs derived from observed and
modeled atmospheric soundings as a function of pressure (in 25 mb layers). The enhanced variability in the
1.571

Assuming that the differences in optical depths derived from NWP and observed environments have the same
distribution as the true errors, the sample error covariance

The in-layer covariance of the differences between differential optical depths derived from observed and
modeled atmospheric soundings, as a function of pressure (in 25 mb layers). The covariances between the CO

Applying Eq. (

Temperature- and humidity-induced weighting function relative
uncertainty for

It is important to note that these computations assume a perfect radiative transfer model and as such do not contain systematic errors in the spectroscopic characterizations themselves. Quantifying the impact of such errors is beyond the scope of this paper, and we assume that the state of the art radiative transfer models will be used in any operational retrievals.

Surface pressure relative uncertainty for

In the context of surface pressure errors, with scalars

The observed surface pressure values were extracted from 107
airport and/or permanent surface weather observation station reports for the same contiguous United States
(CONUS) and global regions described above along with their corresponding NWP model values. The NWP
model values were corrected to the observed station height using a standard lapse rate relationship. The
resulting 1

The values of

The upper bound given by Eq. (

Upper bounds on the O

The 0.76

Examining the column for the 1.26

Perhaps most surprising is that our analysis concludes that neither O

The preceding work defines an information-based measurement precision requirement for an O

The authors realize that we have only explored a small set of candidate wavelengths in the spectral bands of interest.
We stress, however, that we are exploring exactly those lines being targeted by current ASCENDS instrument design teams.
It is beyond the scope of this paper to provide a complete characterization of all CO

In the context of ever improving global NWP models, it is important to note that we expect global NWP surface pressure
errors to trend toward the lower end of our

Crowell would like to acknowledge grant support from NASA Headquarters and the Langley Research Center (NASA). Rayner is in receipt of an Australian Professorial Fellowship (DP1096309). Zaccheo is funded in part by a grant from NASA Headquarters. Edited by: G. Ehret

Equations Eqs. (