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Atmospheric Composition

Observations from the APS instrument aboard the Glory satellite present new opportunities and challenges for utilizing aerosol remote sensing data. The measurements themselves are unprecedented in terms of the accuracy and the multitude of information provided. The overall goal of this proposal is to maximize the value of these observations for improving our understanding of the human impact on both climate and air quality. We propose to initiate work towards assimilation of the data within a chemical transport model (GEOS-Chem) using the four dimensional variational technique (4D-Var). With the long-term goal being eventual assimilation of validated APS products as they become available, this proposal will address three specific modeling objectives that are necessary preliminary steps. (1) First, sensitivity analysis will be performed with the GEOS-Chem adjoint to map the information content of APS observations in terms of the extent to which they constrain aerosol distributions and composition, and hence radiative forcing, at spatial and temporal scales beyond those of the measurement window. This will help overcome a shortcoming of the APS instrument, which is the narrow satellite track associated with making such accurate measurements. While previous assimilation studies have focused almost entirely on aerosol optical depth alone, simultaneous assimilation of additional aerosol optical properties (single scattering albedo, refractive index) and size information (efective mode radius and width) will pose challenges in maintaining a consistent, physical representation of the aerosol distribution in models. Through detailed adjoint sensitivity analysis, we will explore the model relationships and propagation of uncertainty between the APS observed quantities and other aspects of the model, such as emissions and assumed dry size. This is a necessary for utilizing the observations to inform a process based model in a meaningful manner. (2) In the second step of this proposal, this sensitivity analysis will be used to construct inverse modeling tests using simulated APS observations to determine the extent to which APS data can resolve errors in different sets of model parameters. The results will indicate the value of the data for constraining microphysical properties vs aerosol source strengths. Such exploratory calculations are a requisite component of tackling new inverse modeling problems which are likely to be ill-posed. (3) In parallel, we will also investigate the potential for joint assimilation of APS data along with measurements of gaseous aerosol precursors from other A-Train satellite instruments. Limitations of previous aerosol remote sensing assimilation studies are the assumptions made concerning the relationship between aerosol optical depth and particle composition. This ultimately limits the utility of such efforts for air quality studies, which need constraints on species-specific aerosol sources for design of control strategies. Focusing on the inorganic fraction of the aerosol fine particulate matter, we will consider simultaneous assimilation of measurements such as SO2 and NOx from OMI and NH3 from TES. The degree to which joint assimilation of each with the APS data will help constrain emissions of specific inorganic aerosol species will be quantified by measuring the combined sensitivity of the corresponding model estimates with respect to emissions. Overall, accomplishing these three research objectives will provide a powerful basis for long-term utilization of APS observations for improving our understanding of aerosol processes, consistent with NASA's strategic goal to study planet Earth from space.

Project PI: Daven Henze/University of Colorado at Boulder

CU Mechanical Engineering Dept 1111 Engineering Drive ECME 114 Boulder, CO 80309

Phone: (303)492-8716

Fax: (303)492-3498

Email: daven.henze@colorado.edu

http://spot.colorado.edu/~henzed/

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Started: Sep 27, 2010

Last Activity: Jan 04, 2011

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