HR: 15:10h
AN: A13G-06    [Abstracts]
TI: On the role of data assimilation in the remote sensing and modeling of tropospheric gases and their sources
AU: * Yudin, V A
EM: vyudin@ucar.edu
AF: National Center for Atmospheric Research, NCAR/ACD, Boulder, CO 80305 United States
AU: Petron, G
EM: gap@ucar.edu
AF: National Center for Atmospheric Research, NCAR/ACD, Boulder, CO 80305 United States
AU: Lamarque, J
EM: lamar@ucar.edu
AF: National Center for Atmospheric Research, NCAR/ACD, Boulder, CO 80305 United States
AU: Gille, J C
EM: gille@ucar.edu
AF: National Center for Atmospheric Research, NCAR/ACD, Boulder, CO 80305 United States
AU: Edwards, D P
EM: edwards@ucar.edu
AF: National Center for Atmospheric Research, NCAR/ACD, Boulder, CO 80305 United States
AU: Emmons, L K
EM: emmons@ucar.edu
AF: National Center for Atmospheric Research, NCAR/ACD, Boulder, CO 80305 United States
AU: Deeter, M N
EM: mnd@ucar.edu
AF: National Center for Atmospheric Research, NCAR/ACD, Boulder, CO 80305 United States
AB: The paper highlights the role of data assimilation schemes in the global monitoring of tropospheric gases and combined model-data analysis of the ground-based and multi-platform satellite constituent observations. The key novel assignments of these data fusion tools (along with the traditional function of the global mapping) will be (a) identification of the systematic data-model discrepancies (biases); (b) evaluation of the consistency between the multi-instrument space and in situ data; and (c) implication for constraining the input model sources with appropriate error analysis. Correction of the data biases and optimization of the strength of sources over "observable" regions can help produce the "bias-free" assimilative constituent maps and short-term forecasts of targeted species in the data constrained chemistry transport models (CTM). These assimilative fields and forecasts can provide in turn time-evolving a priori information in the constituent retrievals from the satellite radiance data. Several illustrations of the timeliness of this strategy application to the evaluation of carbon monoxide concentrations and budget with the multi-year MOPITT data and MOZART CTM will be discussed. The perspectives of the combined state (concentration)-parameter (emission) estimation will be highlighted with the toy model scenarios and synthetic data that mimic the satellite constituent retrievals (profiles and errors) including their seasonal and year-to-year variations induced by the surface emissions.
DE: 0300 ATMOSPHERIC COMPOSITION AND STRUCTURE
DE: 0322 Constituent sources and sinks
DE: 0365 Troposphere: composition and chemistry
DE: 0368 Troposphere: constituent transport and chemistry
DE: 0520 Data analysis: algorithms and implementation
SC: Atmospheric Sciences [A]
MN: Fall Meeting 2005