We present a detailed description of the TES methanol (CH3 OH) retrieval algorithm, along with initial global results showing the seasonal and spatial distribution of methanol in the lower troposphere. The full development of the TES methanol retrieval is described, including microwindow selection, error analysis, and the utilization of a priori and initial guess information provided by the GEOS-Chem chemical transport model. Retrieval simulations and a sensitivity analysis using the developed retrieval strategy show that TES: (i) generally provides less than 1.0 piece of information, (ii) is sensitive in the lower troposphere with peak sensitivity typically occurring between ∼900–700 hPa (∼1– 3 km) at a vertical resolution of ∼5 km, (iii) has a limit of detectability between 0.5 and 1.0 ppbv Representative Volume Mixing Ratio (RVMR) depending on the atmospheric conditions, corresponding roughly to a profile with a maximum concentration of at least 1 to 2 ppbv, and (iv) in a simulation environment has a mean bias of 0.16 ppbv with a standard deviation of 0.34 ppbv. Applying the newly derived TES retrieval globally and comparing the results with corresponding GEOS-Chem output, we find generally consistent large-scale patterns between the two. However, TES often reveals higher methanol concentrations than simulated in the Northern Hemisphere spring, summer and fall. In the Southern Hemisphere, the TES methanol observations indicate a model overestimate over the bulk of South America from December through July, and a model underestimate during the biomass burning season.
Methanol from TES global observations: retrieval algorithm and seasonal and spatial variability
Cady-Pereira, K.E., M.W. Shephard, D. Millet, M. Luo, K.C. Wells, Y. Xiao, V.H. Payne, and J. Worden (2012), Methanol from TES global observations: retrieval algorithm and seasonal and spatial variability, Atmos. Chem. Phys., 12, 8189-8203, doi:10.5194/acp-12-8189-2012.
Abstract
PDF of Publication
Download from publisher's website
Research Program
Atmospheric Composition Modeling and Analysis Program (ACMAP)