We present further development of the empirical orthogonal functions (EOF)-based retrieval algorithm. The algorithm output is a regression formula that relates principal components of the reflected sunlight spectra with CO2 total column amount. The algorithm was implemented and tested for the observations from the Japanese satellite Greenhouse gases Observing Satellite (GOSAT). Training of the EOF-based algorithm with the collocated ground-based and space-borne data (e.g., Total Carbon Column Observing Network and GOSAT observations, respectively) was shown to impose some errors that were interpreted as a result of implicit averaging over the collocation area. Alternative training with the small subset (∼5 % to 10%) of the full-physics algorithm is free of such errors; however, this option requires additional filtering of the space-borne observations that are strongly affected by atmospheric light scattering. This filtering was implemented by the comparison of the EOF-regression estimates of surface pressure with corresponding meteorological data.
We present satellite-based data of the column-averaged dry air mole fraction of atmospheric carbon dioxide (XCO<sub>2</sub>) and methane (XCH<sub>4</sub>), which were derived from the radiance spectra measured by Greenhouse gases Observing SATellite (GOSAT). We have applied new version of the Photon path-length Probability Density Function (PPDF)-based
algorithm to estimate XCO<sub>2</sub> and PPDF parameters. These parameters serve to allow for optical path modification due to
atmospheric light scattering and they are retrieved simultaneously with CO2 concentration using radiance spectra from
all available GOSAT short wave infrared (SWIR) bands (oxygen A-band, 1.6-μm, and 2.0-μm CO<sub>2</sub> absorption bands). For the methane abundance, retrieved from 1.67-μm absorption band, we applied optical path correction based on PPDF parameters from 1.6-μm CO<sub>2</sub> absorption band. Similarly to widely used CO<sub>2</sub>-proxy technique, this correction assumes identical light path modifications in 1.67-μm and 1.6-μm bands. This approach is believed to offer some advantages over the proxy technique since it does not use any prior assumptions on carbon dioxide concentrations. Both carbon dioxide and methane GOSAT retrievals were validated using ground-based Fourier Transform Spectrometer (FTS)
measurements provided by the Total Carbon Column Observing Network (TCCON). For XCO<sub>2</sub> retrievals we found subppm station-to-station bias (GOSAT versus TCCON); single-scan precision of mostly below 2 ppm (0.5%); and
correlation coefficient for the Northern Hemisphere TCCON stations above 0.8. For XCH4 retrievals over TCCON sites
we found single-scan precision below 1 % and correlation coefficient above 0.8.
Inverse estimation of surface C0<sub>2</sub> fluxes is performed with atmospheric transport model using ground-based and GOSAT observations. The NIES-retrieved C0<sub>2</sub> column mixing (X<sub>c02</sub>) and column averaging kernel are provided by GOSAT Level 2 product v. 2.0 and PPDF-DOAS method. Monthly mean C0<sub>2</sub> fluxes for 64 regions are estimated together with a global mean offset between GOSAT data and ground-based data. We used the fixed-lag Kalman filter to infer monthly fluxes for 42 sub-continental terrestrial regions and 22 oceanic basins. We estimate fluxes and compare results obtained by two inverse modeling approaches. In basic approach adopted in GOSAT Level4 product v. 2.01, we use aggregation of the GOSAT observations into monthly mean over 5x5 degree grids, fluxes are estimated independently for each region, and NIES atmospheric transport model is used for forward simulation. In the alternative method, the model-observation misfit is estimated for each observation separately and fluxes are spatially correlated using EOF analysis of the simulated flux variability similar to geostatistical approach, while transport simulation is enhanced by coupling with a Lagrangian transport model Flexpart. Both methods use using the same set of prior fluxes and region maps. Daily net ecosystem exchange (NEE) is predicted by the Vegetation Integrative Simulator for Trace gases (VISIT) optimized to match seasonal cycle of the atmospheric C0<sub>2</sub> . Monthly ocean-atmosphere C0<sub>2</sub> fluxes are produced with an ocean pC02 data assimilation system. Biomass burning fluxes were provided by the Global Fire Emissions Database (GFED); and monthly fossil fuel C0<sub>2</sub> emissions are estimated with ODIAC inventory. The results of analyzing one year of the GOSAT data suggest that when both GOSAT and ground-based data are used together, fluxes in tropical and other remote regions with lower associated uncertainties are obtained than in the analysis using only ground-based data. With version 2.0 of L2 X<sub>c02</sub> the fluxes appear reasonable for many regions and seasons, however there is a need for improving the L2 bias correction, data filtering and the inverse modeling method to reduce estimated flux anomalies visible in some areas. We also observe that application of spatial flux correlations with EOF based approach reduces flux anomalies.
This paper concerns development of a new retrieval algorithm for the processing of the Greenhouse gases Observing
SATellite (GOSAT) data. GOSAT is scheduled to be launched in 2009 to monitor column amounts of CO<sub>2</sub> and CH<sub>4</sub>. A
nadir-looking Fourier-Transform Spectrometer (FTS) of Short Wavelength Infrared (SWIR, 1.6 microns and 2 microns)
and 0.76 microns oxygen A-band regions are mounted on GOSAT.
We focus on the methane retrievals from 1.67 μm spectral band under conditions of strong optical path modification due
to atmospheric scattering. First, the algorithm of spectral channel selection is proposed to reduce the effects of
uncertainties in water vapor content and solar spectrum. Two techniques for the atmospheric scattering correction are
compared: one uses CO<sub>2</sub> as a proxy gas; the second is based on the simple parameterization of photon path-length
probability density function (PPDF). The latter technique includes the following steps: estimation of PPDF parameters
from radiance spectra in the O<sub>2</sub> A-band and 2.0 -μm CO<sub>2</sub> band, the necessary correction to use these estimated
parameters in the 1.58-μm CO<sub>2</sub> and 1.67-μm CH<sub>4</sub> bands; and, finally, CO<sub>2</sub> and methane retrievals. Both approaches were
verified by numerical simulations using an independent radiative transfer approach to produce radiance spectra expected
for the GOSAT sensor. The accuracy of the retrievals in the presence of aerosols and cirrus cloud is discussed.
The results of model study for CO<sub>2</sub> retrievals from numerically synthesized GOSAT (Greenhouse gases Observing
SATellite) observation data are presented. The GOSAT is scheduled to be launched in 2008 to monitor column amounts
of CO<sub>2</sub> and CH<sub>4</sub>. A nadir-looking Fourier-Transform Spectrometer (FTS) of Short Wavelength Infrared (SWIR, 1.6 μm
and 2 μm) and 0.76 μm oxygen A-band regions will be mounted on GOSAT. To assess CO2 sources and sinks, the
monthly averaged CO2 column amounts estimated by satellite-based measurements should have a precision of within 1%
or better to provide an advantage over existing ground-based measurement networks. This study focuses on CO<sub>2</sub>
retrievals in the presence of cirrus clouds. An important feature of this problem is to apply radiance data measured in
several spectral channels. In particular, 1.58 μm spectral band was utilized for CO<sub>2</sub> total column amount retrievals. The
cloud correction was performed using an original approach that is based on the application of the equivalence theorem
with parameterization of photon path-length probability density function (PPDF). The PPDF parameters were estimated
using nadir radiance in the oxygen A-band and in the H<sub>2</sub>O-saturated area of the 2.0 μm spectral band.
We describe an original methodology to account for aerosol and cirrus cloud contributions to reflected sunlight. This method can be applied to the problem of retrieving greenhouse gases from satellite-observed data and is based on the equivalence theorem with further parameterization of the photon path-length probability density function (PPDF). Monte Carlo simulation was used to validate this parameterization for a vertically non-homogeneous atmosphere including an aerosol layer and cirrus clouds. Initial approximation suggests that the PPDF depends on four parameters that can be interpreted as the effective cloud height, cloud relative reflectance, and two additional factors to account for photon path-length distribution under the cloud. We demonstrate that these parameters can be efficiently retrieved from the nadir radiance measured in the oxygen A-band and from the H<sub>2</sub>O-saturated area of the CO<sub>2</sub> 2.0 ?m spectral band.
A new method is proposed to solve inverse problems with new types of a prior partial information about physical parameters to retrieve. The partial information are relative to integrals of the functions over an arbitrary range of a variable, and/or first and second derivatives of desired functions. They might be provided either form common features of desired solutions or from independent measurements. This inverse problem was solved in the framework of the iterative inversion method under the assumption of log-normal probability density function of measurements. As a typical example of the approach, we examined particle sizing of stratospheric aerosol from multi-wavelength extinction and angular aureole measurements in the visible.
A new method of particle sizing for mixed-phase and ice clouds proposed and tested by numerical simulation in authors' papers is applied here to experimentally measured scattering phase function. The method enables us to identify each component of a bi-component cloud composed of ice crystals and water droplets and to retrieve separately a size distribution for each cloud component. We use mainly available referenced data to test the inversion method with respect to the retrieval of size composition of mixed-phase and ice cloud under both single- and bi-component assumption and try to explain the known fact of the discrepancy between measured scattering phase functions for an ice cloud and those theoretically predicted by the ray tracing treatment, for instance, for convex ice crystals. Applying the inversion method enables us to show that one of effective ways to describe the scattering phase function behavior of mixed-phase and ice clouds is the bi-component assumption. It is rather natural for a mixed-phase cloud because of the existence of water droplets and ice crystals in the cloud simultaneously. On the other hand, one of an important physical reason for the bi-component assumption in an ice cloud lies in the well known fact that, as the cloud is transformed from water phase to ice one a high proportion of the particles can frequently stay as small supercooled water droplets even at very low temperature.
A new method of particle size retrieval is proposed of rice crystal and mixed phase clouds. The method enables us to identify each component of a bi-component cloud composed namely of ice crystals and water droplets and to retrieve separately size distributions of each cloud component. Its capability is explored as usually by using 'synthetic' multi-angular data of scattered light intensity. Various cloud microphysical characteristics are modeled by assuming two non-interacting cloud components such as liquid or supercooled droplets and cubic or hexagonal ice crystals with regular simple geometrical shapes as a first approximation. The sensitivity of the method is tested for different relative concentrations of the cloud components varying over a wide range. Firstly, we investigate the applicability limits of the single-component cloud approximation in retrieving particle size distributions of a bi-component cloud. Secondly, we test the method to retrieve simultaneously the size distributions of both the components in mixed-phase clouds, and discuss the conditions of its applicability.
The problems of optimum inversion in the presence of random noise are analyzed. Two main kinds of noise are considered: the random errors of measurements and random errors of physical model. It is studied the optimization of the numerical inverse problem solution concerning both noises. Using the statistical estimation ideas is discussed for this consideration. Specific features of every noise to influence on the limitation of information content of the optic experiment and on implementation of inversion are distinguished. The quantitative criteria to evaluate information content of input data and procedure of their interpretation are proposed. The latter is aimed to optimize the solution in presence of random errors of the model as well as errors of measurements and, moreover, to correct used model by the measurements being interpreted. An arbitrary accompanied and a priori information can be used. For example, a priori estimations of the sought and model parameters, correlations between them, non-negativity of values etc., can be included. The peculiarity of the inversion method is an essentially large number of variables and increased stability should be provided. The original iterative process of linear inversion characteristic to statistical optimizations are being proposed for this in the algorithm elaborating.
Clouds play an important role in forming the atmospheric radiation budget and are a specific factor for transformation of aerosol components. Cloud composition should be correctly enough taken into account to predict operatively the light propagation trough clouds. This matter is especially of high priority near the sources of industrial pollutions. Cloud microstructure would be the most variable quantity here, because of highly-disperse soot components ingress into clouds. Such components can influence greatly on spectral absorption and reflection characteristics of cloud cover and effect globally on climate. The consistent physical approach to predict the light propagation trough clouds requires the radiative model of clouds should include a complete set of the specific optical characteristics that, according to the radiative transfer equation, are sufficient to simulate the spectral absorption and reflection of cloud cover. Different approaches can be used to determine the above specific characteristics. The most promising one is appeared to be based on the retrieval of these quantities by airborne intercloud measurements of rather a narrow set of cloud light-scattering characteristics. The main object of this report is to investigate the method for solving an inverse problem of light scattering to design a radiative model of liquid-drops clouds comprising soot components. The investigation is carried out within the scope of mathematical simulation.