[1] Measurements of Pollution in the Troposphere (MOPITT) is a new remote sensing instrument aboard the Earth Observing System (EOS) ''Terra'' satellite which exploits gas correlation radiometry principles to quantify tropospheric concentrations of carbon monoxide (CO) and methane (CH 4 ). The MOPITT CO retrieval algorithm employs a nonlinear optimal estimation method to iteratively solve for the CO profile which is statistically most consistent with both the satellite-measured radiances and a priori information. The algorithm's theoretical basis is described in terms of the observed radiances and their weighting functions, the a priori information, and the retrieval averaging kernels. Examples of actual CO retrievals over scenes with contrasting pollution conditions are demonstrated, and interpreted in the context of the retrieval averaging kernels and a priori.

Evaluation of the fidelity of these simulations has been hampered by sparse surface networks and the limitation of current satellite retrievals to oceanic regions. Large differences between various models and between models and observations suggest that an optimal aerosol model has not been developed to date. Global annual mean burdens of sulfate simulated by a 7313

.[1] Validation of the Measurements of Pollution in the Troposphere (MOPITT) retrievals of carbon monoxide (CO) has been performed with a varied set of correlative data. These include in situ observations from a regular program of aircraft observations at five sites ranging from the Arctic to the tropical South Pacific Ocean. Additional in situ profiles are available from several short-term research campaigns situated over North and South America, Africa, and the North and South Pacific Oceans. These correlative measurements are a crucial component of the validation of the retrieved CO profiles and columns from MOPITT. The current validation results indicate good quantitative agreement between MOPITT and in situ profiles, with an average bias less than 20 ppbv at all levels. Comparisons with measurements that were timed to sample profiles coincident with MOPITT overpasses show much less variability in the biases than those made by various groups as part of research field experiments. The validation results vary somewhat with location, as well as a change in the bias between the Phase 1 and Phase 2 retrievals (before and after a change in the instrument configuration due to a cooler failure). During Phase 1, a positive bias is found in the lower troposphere at cleaner locations, such as over the Pacific Ocean, with smaller biases at continental sites. However, the Phase 2 CO retrievals show a negative bias at the Pacific Ocean sites. These validation comparisons provide critical assessments of the retrievals and will be used, in conjunction with ongoing improvements to the retrieval algorithms, to further reduce the retrieval biases in future data versions.

This paper presents results of the inverse modeling of carbon monoxide surface sources on a monthly and regional basis using the MOPITT (Measurement Of the Pollution In The Troposphere) CO retrievals. The targeted time period is from April 2000 to March 2001. A sequential and time-dependent inversion scheme is implemented to correct an a priori set of monthly mean CO sources. The a posteriori estimates for the total anthropogenic (fossil fuel + biofuel + biomass burning) surface sources of CO in TgCO/yr are 509 in Asia, 267 in Africa, 140 in North America, 90 in Europe and 84 in Central and South America. Inverting on a monthly scale allows one to assess a corrected seasonality specific to each source type and each region. Forward CTM simulations with the a posteriori emissions show a substantial improvement of the agreement between modeled CO and independent in situ observation

[1] A three-dimensional (3-D) inverse modeling scheme is used to constrain the direct surface emissions of carbon monoxide CO. A priori estimates of CO emissions are taken from various inventories and are included in the IMAGES model to compute the distribution of CO. The modeled CO mixing ratios are compared with observations at 39 CMDL stations, averaged over the years [1990][1991][1992][1993][1994][1995][1996]. The interannual variability of CO sources is therefore ignored. We show that the method used (time-dependent synthesis inversion) is able to adjust the surface fluxes on a monthly basis in order to improve the agreement between the observed and the modeled CO mixing ratios at the stations. The Earth surface is divided into regions. The spatial distribution of CO sources is fixed inside each of these regions. The inversion scheme optimizes the intensities of the emissions fluxes for the following processes: technological activities, forest and savanna burning, agricultural waste burning and fuelwood use, soil/vegetation emissions, and oceanic emissions. The inversion significantly reduces the uncertainties on the surface sources over Europe, North America and Asia. The most striking result is the increase (almost by a factor of 2) of CO flux from Asia in all a posteriori scenarios. The uncertainties on the Southern Hemisphere emissions remain large after the inversion, because the current observational surface network is too sparse at these latitudes. The inversion, moreover, shifts the peak in biomass burning emissions in the Southern Hemisphere by one month. This temporal shift ensures a better match of the observed and modeled CO seasonal cycle at the Ascension Island station. We also attempted to optimize the annual and global productions of CO due to methane and NMHC. With the current set of data, the scheme was not able to differentiate between these two sources, and hence only the total chemical production of CO can be optimized.