The Sensitivity of The Measurement Of Pollution In The Troposphere (MOPITT) Retrievals of Carbon Monoxide to the Lowermost Troposphere

In this thesis, the measurements of carbon monoxide (CO) obtained from an nadir sounding Measurement Of Pollution In The troposphere (MOPITT) instrument are used. Atmospheric CO is one of the most abundant and widely distributed air pollutant and is a very important indirect greenhouse gas via its reaction with the OH radical which in turn controls the oxidising capacity of the troposphere. In this thesis, the MOPITT Level 2 Version 3 (L2V3) retrieved CO data is primarily used and compared with recently released (April 2009) Level 2 Version 4 (L2V4) retrieved CO data to examine the potential of the MOPITT instrument to differentiate emission features in the lowermost troposphere including mega-cities. This study develops a novel robust methodology using day-night difference profile simulations to examine the ability of the instrument to identify CO enhancements in the lowermost layer of the atmosphere using ‘typical’ averaging kernels. More realistic CO profiles from the TOMCAT model are then used to validate this methodology. The day-night difference simulations are performed for the Indian subcontinent. It is shown that for L2V3, the daytime and nighttime degrees of freedom for a signal (DOFS) exhibit a bi-modal distribution for all selected Indian regions. The L2V3 simulation study clearly demonstrates, for higher DOFS, that day700-night700 differences give a closer differentiation of lowermost CO than other measures for MOPITT data, the first time that this has been processed. For L2V4, similar DOFS distributions are observed for the Indian subcontinent. The L2V4 simulation study also demonstrates for the first time that day850-night700 CO differences give a closer differentiation of lowermost CO by taking account of L2V4 day and night a priori mixing ratios. Finally, the methodologies developed in chapter 3 and 4 are applied to identify spatially isolated signals of lowermost CO for one year of data i.e. 2007. Features associated with nearly 100 cities are identified, the use of thresholds for higher DOFS retrievals and the use of non-surface retrieval levels with less tie to a priori. The significant step forward being consistent day-night differences for two different analyses (L2V3, L2V4).