• Alapaty, K., , Raman S. , , and Niyogi D. S. , 1997: Uncertainty in the specification of surface characteristics: A study of prediction errors in the boundary layer. Bound.-Layer Meteor., 82 , 473500.

    • Search Google Scholar
    • Export Citation
  • Bao, J-W., , and Warner T. T. , 1993: Treatment of on/off switches in the adjoint method: FDDA experiments with a simple model. Tellus, 45A , 525538.

    • Search Google Scholar
    • Export Citation
  • Betts, A. K., , and Ball J. H. , 1998: FIFE surface climate and site-averaged dataset 1987–89. J. Atmos. Sci., 55 , 10911108.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bhumralkar, C. M., 1975: Numerical experiments on the computation of ground surface temperature in an atmospheric general circulation model. J. Appl. Meteor., 14 , 12461258.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonan, G. B., , Pollard D. , , and Thompson S. L. , 1993: Influence of subgrid-scale heterogeneity in leaf area index, stomatal resistance, and soil moisture on grid-scale land–atmosphere interactions. J. Climate, 6 , 18821897.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boni, G., , Castelli F. , , and Entekhabi D. , 2000: Sampling strategies and assimilation of ground temperature for the estimation of surface energy balance components. IEEE Trans. Geosci. Remote Sens.,. 39 , 165172.

    • Search Google Scholar
    • Export Citation
  • Bouyssel, F., , Cassé V. , , and Pailleux J. , 1999: Variational surface analysis from screen level atmospheric parameters. Tellus, 51A , 453468.

    • Search Google Scholar
    • Export Citation
  • Brubaker, K. L., , and Entekhabi D. , 1995: An analytic approach to modeling the land–atmosphere interaction 1. Construct and equilibrium behavior. Water Resour. Res., 31 , 619632.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brutsaert, W., 1975: On a derivable formula for long-wave radiation from clear skies. Water Resour. Res., 11 , 742744.

  • Callies, U., , Rhodin A. , , and Eppel D. P. , 1998: A case study on variational soil moisture analysis from atmospheric observations. J. Hydrol., 212–213 , 95108.

    • Search Google Scholar
    • Export Citation
  • Clapp, R. B., , and Hornberger G. M. , 1978: Empirical equations for some soil hydraulic properties. Water Resour. Res., 14 , 601604.

  • Colello, G. D., , Grivet C. , , Sellers P. J. , , and Berry J. A. , 1998: Modeling of energy, water, and CO2 flux in a temperate grassland ecosystem with SiB2: May–October 1987. J. Atmos. Sci., 55 , 11411169.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cuenca, R. H., , Ek M. , , and Mahrt L. , 1996: Impact of soil water property parameterization on atmospheric boundary layer simulation. J. Geophys. Res., 101 , 72697277.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deardorff, J. W., 1978: Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation. J. Geophys. Res., 83 , 18891903.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Driedonks, A. G. M., 1982: Models and observations of the growth of the atmospheric boundary layer. Bound.-Layer Meteor., 23 , 283306.

  • Ek, M., , and Cuenca R. H. , 1994: Variation in soil parameters: Implications for modeling surface fluxes and atmospheric boundary-layer development. Bound.-Layer Meteor., 70 , 369383.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Entekhabi, D., 1995: Recent advances in land–atmosphere interaction research. Rev. Geophys., 33 , (Suppl.),. 9951003.

  • Errico, R. M., 1997: What is an adjoint model? Bull. Amer. Meteor. Soc., 78 , 25772591.

  • ——, and Vukicevic, T., 1992: Sensitivity analysis using an adjoint of the PSU–NCAR Mesoscale Model. Mon. Wea. Rev., 120 , 16441660.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gill, P. E., , Murray W. , , and Wright M. H. , 1981: Practical Optimization. Academic Press, 401 pp.

  • Hall, M. C. G., 1986: Application of adjoint sensitivity theory to an atmospheric general circulation model. J. Atmos. Sci., 43 , 26442651.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • ——, and Cacuci, D. G., 1983: Physical interpretation of the adjoint functions for sensitivity analysis of atmospheric models. J. Atmos. Sci., 40 , 25372546.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • ——, ——, and Schlesinger, M. E., 1982: Sensitivity analysis of a radiative–convective model by the adjoint method. J. Atmos. Sci., 39 , 20382050.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., 1994: Global Physical Climatology. Academic Press, 411 pp.

  • Hu, Z., , and Islam S. , 1995: Prediction of ground surface temperature and soil moisture content by the force-restore method. Water Resour. Res., 31 , 25312539.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, C. P., , and Entekhabi D. , 1998: Feedbacks in the land-surface and mixed-layer energy budgets. Bound.-Layer Meteor., 88 , 121.

  • Koster, R. D., , and Suarez M. J. , 1996: Energy and water balance calculations in the Mosaic LSM. NASA Tech. Memo. 104606, Vol. 9, 60 pp.

  • LeDimet, F-X., , and Talagrand O. , 1986: Variational algorithms for analysis and assimilation of meteorological observation: Theoretical aspects. Tellus, 38A , 97110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Y., , Navon I. M. , , Courtier P. , , and Gauthier P. , 1993: Variational data assimilation with a semi-implicit global shallow water equation model and its adjoint. Mon. Wea. Rev., 121 , 17591769.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Z., , and Navon I. M. , 1998: Adjoint sensitivity of the earth's radiation budget in the NCEP medium-range forecasting model. J. Geophys. Res., 103 , 38013814.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, J. D., 1980: On the force-restore method for prediction of ground surface temperature. J. Geophys. Res., 85 , 32513254.

  • Louis, J. F., , Tiedtke M. , , and Geleyn J. F. , 1982: A short history of the operational PBL parameterization at ECMWF. Workshop on PBL Parameterization, Reading, United Kingdom, ECMWF, 59–79.

    • Search Google Scholar
    • Export Citation
  • Lu, J., , and Hsieh W. W. , 1997: Adjoint data assimilation in coupled atmosphere–ocean models: Determining model parameters in a simple equatorial model. Quart. J. Roy. Meteor. Soc., 123 , 21152139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McLaughlin, D., 1995: Recent developments in hydrologic data assimilation. Rev. Geophys., 33 , (Suppl.),. 977984.

  • ——, and Townley, L. R., 1996: A reassessment of the groundwater inverse problem. Water Resour. Res., 32 , 11311161.

  • McNaughton, K. G., , and Spriggs T. W. , 1986: A mixed-layer model for regional evaporation. Bound.-Layer Meteor., 34 , 243262.

  • Rabier, F., , Courtier P. , , and Talagrand O. , 1992: An application of adjoint models to sensitivity analysis. Beitr. Phys. Atmos., 65 , 177192.

    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., , Entekhabi D. , , and McLaughlin D. B. , 2000: Downscaling of radiobrightness measurements for soil moisture estimation: A four-dimensional variational data assimilation approach. Water Resour. Res., in press.

    • Search Google Scholar
    • Export Citation
  • Rhodin, A., , Kucharski F. , , Callies U. , , Eppel D. P. , , and Wergen W. , 1999: Variational analysis of effective soil moisture from screen-level atmospheric parameters: Applications to a short-range weather forecast model. Quart. J. Roy. Meteor. Soc., 125 , 24272448.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sellers, P. J., 1985: Canopy reflectance, photosynthesis, and transpiration. Int. J. Remote Sens., 6 , 13351372.

  • ——, Mintz, Y., , Sud Y. C. , , and Dalcher A. , 1986: A simple biosphere model (SiB) for use within general circulation models. J. Atmos. Sci., 43 , 505531.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • ——, Hall, F. G., , Asrar G. , , Strebel D. E. , , and Murphy R. E. , 1992: An overview of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE). J. Geophys. Res., 97 , 18 34518 371.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • ——, and. Coauthors. . 1996: A revised land surface parameterization (SiB2) for atmospheric GCMs. Part I: Model formulation. J. Climate, 9 , 676705.

    • Search Google Scholar
    • Export Citation
  • Smeda, M. S., 1979: A bulk model for the atmospheric planetary boundary layer. Bound.-Layer Meteor., 17 , 411427.

  • Strebel, D. E., , Landis D. R. , , Huemmrich K. F. , , and Meeson B. W. , 1994: Surface Observations and Non-image Data Sets. Vol. 1, NASA, GSFC, Greenbelt, MD, CD-ROM.

    • Search Google Scholar
    • Export Citation
  • Stull, R. B., 1994: An Introduction to Boundary Layer Meteorology. Kluwer Academic, 666 pp.

  • Sun, N-Z., , and Yeh W. W-G. , 1990: Coupled inverse problems in groundwater modeling 1. Sensitivity analysis and parameter identification. Water Resour. Res., 26 , 25072525.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, W-Y., , and Bosilovich M. G. , 1996: Planetary boundary layer and surface layer sensitivity to land surface parameters. Bound.-Layer Meteor., 77 , 353378.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Talagrand, O., , and Courtier P. , 1987: Variational assimilation of meteorological observations with the adjoint vorticity equation. I: Theory. Quart. J. Roy. Meteor. Soc., 113 , 13111328.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tennekes, H., 1973: A model for the dynamics of the inversion above a convective boundary layer. J. Atmos. Sci., 30 , 558567.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thacker, W. C., , and Long R. B. , 1988: Fitting dynamics to data. J. Geophys. Res., 93 , 12271240.

  • Xu, Q., 1996a: Generalized adjoint for physical processes with parameterized discontinuities. Part I: Basic issues and heuristic examples. J. Atmos. Sci., 53 , 11231142.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • ——,. 1996b: Generalized adjoint for physical processes with parameterized discontinuities. Part II: Vector formulations and matching conditions. J. Atmos. Sci., 53 , 11431155.

    • Search Google Scholar
    • Export Citation
  • Yeh, W. W-G., , and Sun N-Z. , 1990: Variational sensitivity analysis, data requirements, and parameter identification in a leaky aquifer system. Water Resour. Res., 26 , 19271938.

    • Search Google Scholar
    • Export Citation
  • Zivković, M., , Louis J-F. , , and Moncet J-L. , 1995: Sensitivity analysis of a radiation parameterization. J. Geophys. Res., 100 , 12 82713 840.

    • Search Google Scholar
    • Export Citation
  • Zou, X., 1997: Tangent linear and adjoint of “on–off” processes and their feasibility for use in 4-dimensional variational data assimilation. Tellus, 49A , 331.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 145 145 12
PDF Downloads 38 38 13

A Coupled Land Surface–Boundary Layer Model and Its Adjoint

View More View Less
  • 1 Ralph M. Parsons Laboratory, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts
© Get Permissions
Restricted access

Abstract

In this paper, a simple coupled land surface–boundary layer model and its adjoint are presented. The primary goal is to demonstrate the capabilities of the adjoint model as a general tool for sensitivity analysis and data assimilation. The adjoint method was chosen primarily for two reasons: 1) the adjoint model can be used not only to obtain parameter sensitivities with greater efficiency but, more important, to provide added insight into the sensitivities as compared with that obtained with traditional simulation techniques (e.g., pathways, time variations in sensitivity) and 2) the adjoint model can be used in a variational data assimilation framework to combine measurements and the model of the physical system optimally in order to estimate state variables and fluxes. Two simple examples are presented to illustrate how the framework can be used for performing both diagnostic sensitivity experiments and hydrologic data assimilation. In the sensitivity experiment, temporal patterns and total influence of model states and parameters on average daily ground temperature are shown. In the synthetic data assimilation example, the adjoint model is used as an estimation tool to initialize the coupled model through assimilation of ground temperature observations. As a result, great improvement was gained in simulation of model states and surface fluxes based only on a minimal set of basic land temperature measurements and the auxiliary parameters: incident solar radiation, large-scale wind speed, and free atmosphere profiles of temperature and humidity. Forthcoming studies will use the framework developed here to examine thoroughly the consequences of using uncoupled versus coupled models of the land and the atmospheric boundary layer (ABL). In assimilation mode, the coupled surface–ABL model and its adjoint will be used to estimate surface fluxes and micrometeorological conditions based on remote sensing measurements of land temperature and minimal auxiliary data.

Corresponding author address: Dara Entekhabi, Ralph M. Parsons Laboratory, Massachusetts Institute of Technology, Room 48-331, Cambridge, MA 02139. Email: darae@mit.edu

Abstract

In this paper, a simple coupled land surface–boundary layer model and its adjoint are presented. The primary goal is to demonstrate the capabilities of the adjoint model as a general tool for sensitivity analysis and data assimilation. The adjoint method was chosen primarily for two reasons: 1) the adjoint model can be used not only to obtain parameter sensitivities with greater efficiency but, more important, to provide added insight into the sensitivities as compared with that obtained with traditional simulation techniques (e.g., pathways, time variations in sensitivity) and 2) the adjoint model can be used in a variational data assimilation framework to combine measurements and the model of the physical system optimally in order to estimate state variables and fluxes. Two simple examples are presented to illustrate how the framework can be used for performing both diagnostic sensitivity experiments and hydrologic data assimilation. In the sensitivity experiment, temporal patterns and total influence of model states and parameters on average daily ground temperature are shown. In the synthetic data assimilation example, the adjoint model is used as an estimation tool to initialize the coupled model through assimilation of ground temperature observations. As a result, great improvement was gained in simulation of model states and surface fluxes based only on a minimal set of basic land temperature measurements and the auxiliary parameters: incident solar radiation, large-scale wind speed, and free atmosphere profiles of temperature and humidity. Forthcoming studies will use the framework developed here to examine thoroughly the consequences of using uncoupled versus coupled models of the land and the atmospheric boundary layer (ABL). In assimilation mode, the coupled surface–ABL model and its adjoint will be used to estimate surface fluxes and micrometeorological conditions based on remote sensing measurements of land temperature and minimal auxiliary data.

Corresponding author address: Dara Entekhabi, Ralph M. Parsons Laboratory, Massachusetts Institute of Technology, Room 48-331, Cambridge, MA 02139. Email: darae@mit.edu

Save