MOPITT Retrievals, Forward Model and Cloud Detection

Description of the MOPITT CO Retrieval Algorithm

A paper describing the operational CO retrieval algorithm in detail has recently been accepted by the Journal of Geophysical Research (Deeter et al.). A full mathematical description of the MOPITT retrieval algorithm is contained in Pan et al. (JGR, 1998).

An optimal estimation-based retrieval algorithm and a fast radiative transfer model (see below) are used to invert the measured A- and D-signals to determine the tropospheric CO profile. Retrievals of CO may involve up to twelve measured signals in two distinct bands: a solar reflectance band near 2.3 microns, and a thermal emission band near 4.7 microns. The thermal band signals are sensitive to thermal emission from the earth's surface as well as atmospheric absorption and emission. The solar band signals are sensitive to atmospheric CO through absorption processes only. Currently, only clear-sky radiances (i.e. radiances uncontaminated by clouds) are fed to the retrieval algorithm. A detailed description of the MOPITT cloud-detection algorithm can be found below.

In atmospheric remote sensing, the common problem of inverting a set of measured radiances to determine aspects of the atmospheric state (temperature profile, trace gas mixing ratio profiles, etc.) is often ill-conditioned, meaning that no unique solution exists without added constraints. Thus, additional information of some type is usually required to constrain the retrieval to fall within physically reasonable limits. The CO retrieval algorithm used for MOPITT exploits an optimal estimation technique. More precisely, MOPITT exploits the technique referred to by Rodgers as ``Maximum A Posteriori,'' or ``MAP'' [C. D. Rodgers, Inverse Methods for Atmospheric Sounding: Theory and Practice, World Scientific, 2000]. The general strategy of such techniques is to seek the solution most statistically consistent with both the measured radiances and the typical observed patterns of CO profile variability (as described quantitatively by both the a priori mean profile and the a priori covariance matrix).

The thermal band signals depend not only on the atmospheric CO distribution but also on various other atmospheric quantities (such as the atmospheric temperature and water vapor mixing ratio profiles) and surface parameters (surface temperature and longwave emissivity). Accurate values for all of these geophysical parameters must be obtained to produce accurate retrievals. Atmospheric temperature and water vapor profiles are obtained by spatially and temporally interpolating reanalysis profiles from NCEP to the location and time of each MOPITT pixel. However, sources of geophysical data such as NCEP are unable to provide accurate values of surface temperature and emissivity (both of which are highly variable) at the temporal and spatial resolution demanded by the MOPITT retrievals. Fortunately, information contained in the MOPITT thermal band signals allows retrieval of the surface temperature and emissivity along with the CO profile, and makes external data sources for these quantities necessary only for providing a priori and initial guess values. Thus, rather than assuming fixed values for the surface temerature and emissivity, these two parameters are included in the retrieval state vector (along with the elements of the CO profile). (A closer inspection of the roles of surface temperature and emissivity reveals that their effects on the thermal-band signals are quite similar, although not identical. Thus, it is not entirely unreasonable to think of surface temperature and emissivity as together representing a single degree of freedom with respect to variability in the thermal-band signals.)

The MAP technique combines two independent estimates of the same vector quantity (i.e. the state vector determined solely from the measurement vector and the ``virtual'' measurement represented by the a priori state vector) with generally unequal covariances. Retrievals of the CO profile consist of a ``floating'' surface-level retrieval (tied to the pixel-dependent surface pressure value) and retrievals at up to six fixed pressure levels. In areas of elevated topography where one or more of the fixed pressure level values exceed the actual local surface pressure, that part of the retrieved state vector is filled with the missing-value numerical identifier. The standard seven-level grid employed by MOPITT includes levels at the surface, 850, 700, 500, 350, 250, and 150 mb. The retrieved CO total column value is obtained as a byproduct of the retrieved profile and is obtained simply by integrating the retrieved profile from the surface to the top of the atmosphere. The MOPITT CO "Level 2 Product" therefore consists of retrieved values and estimated uncertainties of the CO profile, CO total column, surface temperature, and surface emissivity. For the CO profile, the retrieved error covariance matrix is also provided. Although this covariance matrix may be useful in and of itself, it is also a necessary element of averaging kernel calculations.

"Phase-I" Operational CO Retrievals (March, 2000 - May, 2001)

Rather than initially attempting to "force" the CO retrieval algorithm to incorporate all available radiances, we have taken a more conservative approach. Specifically, we have chosen to base the retrievals on the minimum number of radiances necessary for a useful CO profile retrieval. The radiances currently used for the Phase I retrievals include the A signal for Channel 7, and the D signals for Channels 1, 3, and 7. The solar CO channels 2 and 6 were excluded from the retrievals because of low observed signal-to-noise ratios. (Promising techniques for reducing the apparent noise in these channels are under development.)

"Phase-II" Operational CO Retrievals (August, 2001 - present)

In May, 2001, a cooler on MOPITT failed and effectively disabled channels 1-4 on MOPITT. After extensive diagnostics and some minor reconfiguration of the Channel 7 PMC, channels 5-8 resumed operations in August, 2001. Retrieval simulations using the 5A, 5D, 7A, and 7D radiances indicate that the retrieval performance (as quantified by the retrieval averaging kernels) using these available radiances should be similar to the performance of Phase-I retrievals based on the 1D, 3D, 7D, and 7A signals. However, these simulations also show that the retrieval results are extremely sensitive to biases in the 5A and 5D radiances. Currently, we are testing a Phase-II product based on the 5A, 5D, and 7D radiances. Early results are promising, however, validation of this product is not yet complete. Therefore, unlike the Phase-I product, the Phase-II product is currently still considered 'beta'.

MOPITT Forward Modeling

Forward modeling of the MOPITT channel radiances must combine accuracy and precision while providing for variations in target and contaminating gases, temperature, viewing geometry and surface properties. To achieve this, a set of radiation models has been developed. The following is a very brief overview of this work. An expandcd, though simplified, discussion can be found in "Channel radiance calculations for MOPITT forward modeling and operational retrievals" (Francis et al., SPIE, 1999). A detailed discussion of the MOPITT forward models is presented in "Radiative transfer modeling for the EOS Terra satellite Measurement of Pollution in the Troposphere (MOPITT) instrument" (Edwards et al., JGR, 1999).

Line-by-line Calculations

Gas correlation spectroscopy introduces a high resolution spectral filter into the measurement process, having line widths of order 0.1 cm^-1. In addition, calculations with spectral resolutions as fine as 0.0025 cm^-1 are required to construct the databases which are key components of the higher-level MOPITT radiation codes MOPABS and MOPFAS discussed below. Line-by-line (LBL) calculations must therefore be performed to provide these filters and databases. These are provided by the general purpose radiance and transmittance model GENLN2. Although too cumbersome for operational use, top-of-atmosphere radiances provided by GENLN2 also give benchmarks against which faster MOPITT codes can be assessed.

MOPABS: An optical-depth lookup table model

An intermediate step in the MOPITT radiation code hierarchy, MOPABS computes channel radiances through a monochromatic absorption coefficient fitting scheme. This technique explicitly mirrors much of the underlying physics of the radiative transfer. It yields transmittance and radiance spectra across each channel passband, which are integrated to give the MOPITT channel signals. The method has essentially LBL accuracy and is considerably faster. Channel radiance calculations for a given test atmosphere can typically be completed in a few minutes. While this is still too slow for operational retrievals, MOPABS is an important tool for the development of a truly fast forward model. In addition, MOPABS has broad applications to other MOPITT work.

MOPFAS: The MOPITT operational fast forward model

MOPFAS achieves faster performance than MOPABS by reformulating the calculation so that time-consuming spectral integrations are avoided. A regression scheme based on the OPTRAN technique is applied to establish a correspondence between channel-integrated transmittances and atmospheric state profiles, such that the former can be inferred accurately and quickly given the latter. The regression maps a set of predictors, obtained from the state profiles, onto corresponding values of channel transmittance obtained from MOPABS. The predictors are functions of absorber amount, pressure, temperature and viewing geometry. The regression coefficients are pre-computed through a least-squares fit over a representative atmospheric ensemble. MOPFAS channel radiances are in good agreement with MOPABS. Typically, MOPFAS and MOPABS have mean differences of 0.05 - 0.1% , with maximum differences of 0.4 - 0.7% depending on channel and band. In addition, a MOPFAS calculation is about 10⁵ times faster than GENLN2 LBL calculations. This is fast enough for MOPFAS to be used in operational retrievals.

Cloud detection

The MOPITT cloud algorithm detects and removes measurements contaminated by clouds before retrieving CO profiles and CO and CH₄ total column amounts. This algorithm combines MOPITT radiances and the MODIS (MODerate-resolution Imaging Spectroradiometer) cloud mask to achieve maximum coverage and accuracy. The cloud detection technique using only MOPITT radiances (MOPCLD) is described by Warner et al. (2001). The collocation method between MOPITT and MODIS field of views, as well as the performance of the hybrid cloud detection, is described by Warner et al. (draft).

The MOPCLD threshold method compares the observed radiances with calculated clear sky radiances, and currently uses only one MOPITT thermal channel at 4.7 µm. The threshold, based on observed channel radiance and forward model calculated clear column radiance, is: R_observed/R_calculated <0.955. Only latitudes within 65° North and South are included in this threshold test to avoid complications due to temperature inversions. MOPITT solar channels are not currently used in the L2 cloud detection processing since a detailed study of the calibration is still underway. This information will be added to MOPITT L2 cloud detection in the ways discussed by Warner et al. (2001).

MOPITT and MODIS instruments are aboard the same satellite platform and their measurements overlap a large geographical area close to nadir and are at the same time. MOPITT sensors scan across orbit to 30° satellite viewing angle on both sides of nadir, pausing for approximately 0.45 seconds to take measurements of an array of four 22km by 22km pixels. MODIS instrument, on the other hand, scans in a continuous circle across orbit and takes measurements of the earth within ±55° satellite angles. The MODIS swaths are more than twice as wide as those of MOPITT and provide complete overlap for MOPITT measurements. The spatial resolution of the MODIS cloud mask is 1x1km, even though some of the MODIS cloud decisions are based on higher resolution measurements (250m x 250m and 500m x 500m). Therefore, each MOPITT pixel is collocated to approximately 484 MODIS 1x1km pixels.

To maximize accuracy and global coverage, MODIS cloud mask and MOPCLD are combined in the MOPITT V3 cloud detection algorithm. A MOPITT pixel is considered clear when both methods agree it is clear and when there is only low cloud in the field of view (FOV). Note that there is a 5% cloud allowance (as determined by MODIS) in each MOPITT pixel for it to be considered as clear. Cloud description flags are included in MOPITT Level 2 files to indicate the cloud decisions made for each pixel (see table below). Additional MODIS flags are used to locate low level clouds when the MODIS cloud mask classifies a pixel as cloudy and MOPCLD classifies it as clear (flag=4). In all other cases, when MODIS cloud mask classifies a pixel as cloudy and MOPCLD classifies it as clear, this pixel is considered cloudy (no retrieval performed). The final decision is clear when MODIS says clear and MOPCLD says cloudy (flag=3). In areas where MODIS cloud mask is not available only MOPCLD is used (flag=1). Only MODIS cloud mask is used in the polar-regions (above 65°N and below 65°S) (flag=5).

Currently, only our best estimates of cloud-free pixels are included in the V3 data files; retrievals are not performed on cloudy pixels. Therefore, users should not need to filter the data according to the cloud flags included in the Level 2 files. For reference, the cloud flags currently used are listed below. See the MOPITT file spec for the complete list of cloud description flags.

Cloud Description Flags used in V3

Flag	Description
1	MOPCLD only clear, thermal only
2	MOPCLD and MODIS cloud mask agree on clear
3	MODIS cloud mask only clear (when MOPCLD cloudy)
4	MOPCLD overriding MODIS cloud mask over low clouds (MODIS test flags used)
5	MODIS cloud mask only, clear over polar regions

References

Deeter, M.N., et al., Operational carbon monoxide retrieval algorithm and selected results for the MOPITT instrument, J. Geophys. Res., 104, in press. (pdf)

Edwards, D.P., C.M. Halvorson, and J.C. Gille, Radiative transfer modeling for the EOS Terra satellite Measurement of Pollution in the Troposphere (MOPITT) instrument, J. Geophys. Res., 104, 16755, 1999. (pdf [file size: 56MB])

Francis, G., et al., Influence of Surface Reflectivity Variability on MOPITT 2.2-2.3µm Channel Radiances and the Retrieval of CO and CH4, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Toronto, June 24-28, 2002. (pdf)

Francis, G., et al., Channel radiance calculations for MOPITT forward modeling and operational retrievals, SPIE 1999, paper and slides.

Pan, Liwen, John C. Gille, David P. Edwards, Paul L. Bailey, and Clive D. Rodgers, Retrieval of tropospheric carbon monoxide for the MOPITT experiment, J. Geophys. Res., 103, 32,277-32,290, 1998. (pdf)

Warner, Juying X., John C. Gille, David P. Edwards, Dan C. Ziskin, Mark W. Smith, Paul L. Bailey, and Laurie Rokke, Cloud detection and clearing for the Earth Observing System Terra satellite Measurements of Pollution in the Troposphere (MOPITT) experiment, Applied Optics, 40, 1269-1284, 2001. (pdf)

Warner, Juying, et al., MOPITT Cloud Detection Algorithm, draft (Word doc).

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Last update: 08 Jan 2003, Louisa Emmons