• Atlas, R., 1997: Atmospheric observations and experiments to assess their usefulness in data assimilation. J. Meteor. Soc. Japan, 75, 111130.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bocquet, M., and et al. , 2015: Data assimilation in atmospheric chemistry models: Current status and future prospects for coupled chemistry meteorology models. Atmos. Chem. Phys., 15, 53255358, doi:10.5194/acp-15-5325-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chai, T., G. R. Carmichael, Y. Tang, A. Sandu, A. Heckel, A. Richter, and J. P. Burrows, 2009: Regional NOx emission inversion through a four-dimensional variational approach using SCIAMACHY tropospheric NO2 column observations. Atmos. Environ., 43, 50465055, doi:10.1016/j.atmosenv.2009.06.052.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clemitshaw, K., 2004: A review of instrumentation and measurement techniques for ground-based and airborne field studies of gas-phase tropospheric chemistry. Crit. Rev. Environ. Sci. Technol., 34, 1108, doi:10.1080/10643380490265117.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., 2008: Fuzzy verification of high-resolution gridded forecasts: A review and proposed framework. Meteor. Appl., 15, 5164, doi:10.1002/met.25.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elbern, H., and H. Schmidt, 2001: Ozone episode analysis by four-dimensional variational chemistry data assimilation. J. Geophys. Res., 106, 35693590, doi:10.1029/2000JD900448.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elbern, H., A. Strunk, H. Schmidt, and O. Talagrand, 2007: Emission rate and chemical state estimation by 4-dimensional variational inversion. Atmos. Chem. Phys., 7, 37493769, doi:10.5194/acp-7-3749-2007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, G., H. X. Lin, A. W. Heemink, A. J. Segers, S. Lu, and T. Palsson, 2015: Assimilating aircraft-based measurements to improve forecast accuracy of volcanic ash transport. Atmos. Environ., 115, 170184, doi:10.1016/j.atmosenv.2015.05.061.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, G., A. Heemink, S. Lu, A. Segers, K. Weber, and H.-X. Lin, 2016: Model-based aviation advice on distal volcanic ash clouds by assimilating aircraft in situ measurements. Atmos. Chem. Phys., 16, 91899200, doi:10.5194/acp-16-9189-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, G., F. Prata, H. X. Lin, A. Heemink, A. Segers, and S. Lu, 2017: Data assimilation for volcanic ash plumes using a satellite observational operator: A case study on the 2010 Eyjafjallajökull volcanic eruption. Atmos. Chem. Phys., 17, 11871205, doi:10.5194/acp-17-1187-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gilleland, E., D. Ahijevych, B. G. Brown, B. Casati, and E. E. Ebert, 2009: Intercomparison of spatial forecast verification methods. Wea. Forecasting, 24, 14161430, doi:10.1175/2009WAF2222269.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gilleland, E., D. A. Ahijevych, B. G. Brown, and E. E. Ebert, 2010: Verifying forecasts spatially. Bull. Amer. Meteor. Soc., 91, 13651373, doi:10.1175/2010BAMS2819.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haben, S. A., A. S. Lawless, and N. K. Nichols, 2011a: Conditioning and preconditioning of the variational data assimilation problem. Comput. Fluids, 46, 252256, doi:10.1016/j.compfluid.2010.11.025.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haben, S. A., A. S. Lawless, and N. K. Nichols, 2011b: Conditioning of incremental variational data assimilation, with application to the Met Office system. Tellus, 63A, 782792, doi:10.1111/j.1600-0870.2011.00527.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huneeus, N., F. Chevallier, and O. Boucher, 2012: Estimating aerosol emissions by assimilating observed aerosol optical depth in a global aerosol model. Atmos. Chem. Phys., 12, 45854606, doi:10.5194/acp-12-4585-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jacquez, J. A., and P. Greif, 1985: Numerical parameter identifiability and estimability: Integrating identifiability, estimability, and optimal sampling design. Math. Biosci., 77, 201227, doi:10.1016/0025-5564(85)90098-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kawabata, T., H. Iwai, H. Seko, Y. Shoji, K. Saito, S. Ishii, and K. Mizutani, 2014: Cloud-resolving 4D-Var assimilation of Doppler wind lidar data on a meso-gamma-scale convective system. Mon. Wea. Rev., 142, 44844498, doi:10.1175/MWR-D-13-00362.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lahoz, W., B. Khattatov, and R. Menard, 2010: Data Assimilation: Making Sense of Observations. 1st ed. Springer-Verlag, 718 pp., doi:10.1007/978-3-540-74703-1.

    • Crossref
    • Export Citation
  • Lamsal, L. N., and et al. , 2011: Application of satellite observations for timely updates to global anthropogenic NOx emission inventories. Geophys. Res. Lett., 38, L05810, doi:10.1029/2010GL046476.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenc, A. C., N. E. Bowler, A. M. Clayton, S. R. Pring, and D. Fairbairn, 2015: Comparison of hybrid-4DEnVar and hybrid-4DVar data assimilation methods for global NWP. Mon. Wea. Rev., 143, 212229, doi:10.1175/MWR-D-14-00195.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, S., H. X. Lin, A. W. Heemink, G. Fu, and A. J. Segers, 2016a: Estimation of volcanic ash emissions using trajectory-based 4D-Var data assimilation. Mon. Wea. Rev., 144, 575589, doi:10.1175/MWR-D-15-0194.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, S., H. X. Lin, A. W. Heemink, A. J. Segers, and G. Fu, 2016b: Estimation of volcanic ash emissions through assimilating satellite data and ground-based observations. J. Geophys. Res. Atmos., 121, 10 97110 994, doi:10.1002/2016JD025131.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McMurry, P., 2000: A review of atmospheric aerosol measurements. Atmos. Environ., 34, 19591999, doi:10.1016/S1352-2310(99)00455-0.

  • Meirink, J. F., P. Bergamaschi, and M. C. Krol, 2008: Four-dimensional variational data assimilation for inverse modelling of atmospheric methane emissions: Method and comparison with synthesis inversion. Atmos. Chem. Phys., 8, 63416353, doi:10.5194/acp-8-6341-2008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mittermaier, M., and N. Roberts, 2010: Intercomparison of spatial forecast verification methods: Identifying skillful spatial scales using the fractions skill score. Wea. Forecasting, 25, 343354, doi:10.1175/2009WAF2222260.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Paulino, C., and C. de Bragança Pereira, 1994: On identifiability of parametric statistical models. J. Ital. Stat. Soc., 3, 125151, doi:10.1007/BF02589044.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prata, A. J., and A. T. Prata, 2012: Eyjafjallajökull volcanic ash concentrations determined using Spin Enhanced Visible and Infrared Imager measurements. J. Geophys. Res., 117, D00U23, doi:10.1029/2011jd016800.

    • Search Google Scholar
    • Export Citation
  • Robert, C., S. Durbiano, E. Blayo, J. Verron, J. Blum, and F. X. Le Dimet, 2005: A reduced-order strategy for 4D-Var data assimilation. J. Mar. Syst., 57, 7082, doi:10.1016/j.jmarsys.2005.04.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rothenberg, T. J., 1971: Identification in parametric models. Econometrica, 39, 577591, doi:10.2307/1913267.

  • Talagrand, O., and P. Courtier, 1987: Variational assimilation of meteorological observations with the adjoint vorticity equation. I: Theory. Quart. J. Roy. Meteor. Soc., 113, 13111328, doi:10.1002/qj.49711347812.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., and et al. , 2014: Assimilation of lidar signals: Application to aerosol forecasting in the western Mediterranean basin. Atmos. Chem. Phys., 14, 12 03112 053, doi:10.5194/acp-14-12031-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Webley, P. W., T. Steensen, M. Stuefer, G. Grell, S. Freitas, and M. Pavolonis, 2012: Analyzing the Eyjafjallajökull 2010 eruption using satellite remote sensing, lidar and WRF-Chem dispersion and tracking model. J. Geophys. Res., 117, D00U26, doi:10.1029/2011JD016817.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 17 17 4
PDF Downloads 13 13 1

Evaluation Criteria on the Design for Assimilating Remote Sensing Data Using Variational Approaches

View More View Less
  • 1 Delft Institute of Applied Mathematics, Delft University of Technology, Delft, Netherlands
  • | 2 Department of Climate, Air and Sustainability, TNO, Utrecht, Netherlands
  • | 3 Delft Institute of Applied Mathematics, Delft University of Technology, Delft, Netherlands
© Get Permissions
Restricted access

Abstract

Remote sensing, as a powerful tool for monitoring atmospheric phenomena, has been playing an increasingly important role in inverse modeling. Remote sensing instruments measure quantities that often combine several state variables as one. This creates very strong correlations between the state variables that share the same observation variable. This may cause numerical problems resulting in a low convergence rate or inaccurate estimates in gradient-based variational assimilation if improper error statistics are used. In this paper, two criteria or scoring rules are proposed to quantify the numerical robustness of assimilating a specific set of remote sensing observations and to quantify the reliability of the estimates of the parameters. The criteria are derived by analyzing how the correlations are created via shared observation data and how they may influence the process of variational data assimilation. Experimental tests are conducted and show a good level of agreement with theory. The results illustrate the capability of the criteria to indicate the reliability of the assimilation process. Both criteria can be used with observing system simulation experiments (OSSEs) and in combination with other verification scores.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Sha Lu, s.lu-1@tudelft.nl

Abstract

Remote sensing, as a powerful tool for monitoring atmospheric phenomena, has been playing an increasingly important role in inverse modeling. Remote sensing instruments measure quantities that often combine several state variables as one. This creates very strong correlations between the state variables that share the same observation variable. This may cause numerical problems resulting in a low convergence rate or inaccurate estimates in gradient-based variational assimilation if improper error statistics are used. In this paper, two criteria or scoring rules are proposed to quantify the numerical robustness of assimilating a specific set of remote sensing observations and to quantify the reliability of the estimates of the parameters. The criteria are derived by analyzing how the correlations are created via shared observation data and how they may influence the process of variational data assimilation. Experimental tests are conducted and show a good level of agreement with theory. The results illustrate the capability of the criteria to indicate the reliability of the assimilation process. Both criteria can be used with observing system simulation experiments (OSSEs) and in combination with other verification scores.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Sha Lu, s.lu-1@tudelft.nl
Save