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Land Data Assimilation Systems

  • Paul R. Houser
Part of the NATO Science Series book series (NAIV, volume 26)

Abstract

Land surface temperature and wetness conditions affect and are affected by numerous climatological, meteorological, ecological, and geophysical phenomena. Therefore, accurate, high resolution estimates of terrestrial water and energy storages are valuable for predicting climate change, weather, biological and agricultural productivity, and flooding, and for performing a wide array of studies in the broader biogeosciences. In particular, terrestrial stores of energy and water modulate fluxes between the land and atmosphere and exhibit persistence on diurnal, seasonal, and interannual timescales. Furthermore, because soil moisture, temperature, and snow are integrated states, errors in land surface forcing and parametrization accumulate in the representations of these variables in operational numerical weather forecast models, which lead to incorrect surface water and energy partitioning. Therefore, accurate re-initialization of water and energy state variables in these models is crucial.

Keywords

Data Assimilation Land Surface Model Data Assimilation System Moderate Resolution Image Spectroradiometer Global Land Data Assimilation System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Atlas, R. M., and R. Lucchesi, 2000: File Specification for GEOS-DAS Gridded Oulput. Available online: http://dao.gsfc.nasa.gov/DAO_docs/File_Spec_v4.html. Google Scholar
  2. Berg, A. A., J.S. Famiglietti, J. P. Walker, and P.R. Houser, 2003: The Impact of Bias Correction to Reanalysis Products on Simulations of North American Land Surface States and Fluxes. J. Geophys. Res. In Peparation.Google Scholar
  3. Derber, J. C., D.F. Parrish, and S. J. Lord, 1991: The new global operational analysis system at the National Meteorological Center. Wea. and Forecasting, 6, 538–547.CrossRefGoogle Scholar
  4. Famiglietti, J. S., J. A. Devereaux, C. A. Laymon, T. Tsegaye, P. R. Houser, T. J. Jackson, S. T. Graham, M. Rodell, and P. J. van Oevelen, 1999: Ground-based investigation of soil moisture variability within remote sensing footprints during the Southern Great Plains 1997 (SGP97) Hydrology Experiment Wat Resour. Res., 35, 1839–1851.CrossRefGoogle Scholar
  5. Hamill, T.M., R.P. d’Entremont, and J.T. Bunting, 1992: A description of the Air Force real-time nephanalysis model Wea. Forecasting, 7, 288–306.CrossRefGoogle Scholar
  6. Hansen, M.C., R.S. DeFries, J. R. G. Townshend, and R. Sohlberg, 2000: Global land cover classification at 1km spatial resolution using a classification tree approach. International Journal of Remote Sensing, 21, 1331–1364.CrossRefGoogle Scholar
  7. Idso, S.: 1981: A set of equations for the Ml spectrum and 8-and 14-micron and 105-to 125 thermal radiation from cloudless skies. Wat. Resour. Res., 17, 295–304.CrossRefGoogle Scholar
  8. Kalman, R.E., 1960: A new approach to linear filtering and prediction problems. Trans. ASME, Ser. D. J. Basic Eng,. 82, 35–45.CrossRefGoogle Scholar
  9. Kopp, T.J., and R.B. Kiess, 1996: The Air Force Global Weather Central cloud analysis model. AMS 15th Conf. on Weather Analysis and Forecasting, Norfolk, VA, 220–222.Google Scholar
  10. Mitchell, K., P. Houser, E. Wood, J. Schaake, D. Tarpley, D. Lettenmaier, W. Higgins, C. Marshall, D. Lohmann, M. Ek, B. Cosgrove, J. Entin, Q. Duan, R. Pinker, A. Robock, F. Habets, and K. Vinnikov, 1999: GCIP Land Data Assimilation System (IDAS) projectnow underway, GEWEX News 9(4), 3–6.Google Scholar
  11. Myneni, R. B., C. D. Keeling, C. J. Tucker, G. Asar, and R. R. Nemani, 1997: Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386, 698–702.CrossRefGoogle Scholar
  12. Olivera, F., J. S. Famiglietti, and K Asante, 2000: Global-Scale Flow Routing Using a Source-to-Sink Algorithm. Wat Resour. Res., 36, 2197–2207.CrossRefGoogle Scholar
  13. Ottle, C., and D. Vidalmadjar, 1992: Estimation of land surface temperature with NOAA9 data. Rem. Sens. Env., 40, 27–41.CrossRefGoogle Scholar
  14. Owe, M., R. de Jeu, and J.P. Walker, 2001: A Methodology for Surface Soil Moisture and Vegetation Optical Depth Retrieval Using the Microwave Polarization Difference Index. IEEE Transactions on Geoscience and Remote Sensing, 39, 1643–1654.CrossRefGoogle Scholar
  15. Pfaendtner, J., S. Bloom, D. Lamich, M. Seablom, M. Sienkiewicz, J. Stobie, and A. da Silva: 1995: Documentation of the Goddard Earth Observing System (GEOS) Data Assimilation System — Version 1, NASA Technical Memorandum 104606 4, 44 pp.Google Scholar
  16. Radakovich, J. D., P. R. Houser, A. da Silva, and M. G. Bosilovich, 2001: Results from global land-surface data assimilation methods. AMS 5 th Symposium on Integrated Observing Systems, Albuquerque, NM, 14–19 January, 132–134.Google Scholar
  17. Reynolds, C. A., T. J. Jackson, and W. J. Rawls, 1999: Estimating available water content by linking the FAO Soil Map of the World with global soil profile databases and pedo-transfer fonctions. American Geophysical Union, Fall Meeting, Eos Trans. AGU, 80.Google Scholar
  18. Rodell, M. and J. S. Famiglietti, 2001: Terrestrial Water Storage Variations over Illinois: Analysis of Observations and Implications for GRACE. Wat Resour. Res., 37, 1327–1340.CrossRefGoogle Scholar
  19. Rodell, M., and J. S. Famiglietti, 1999: Detectability of variations in continental water storage from satellite observations of the time dependent gravity field Wat Resour. Res., 35, 2705–2723.CrossRefGoogle Scholar
  20. Turk, F. J., G. Rohaly, J. D. Hawkins, E. A Smith, A. Grose, F. S. Marzano, A. Mugnai and V. Levizzani, 2000: Analysis and assimilation of rainfall from blended SSM/I, TRMM and geostationary satellite data. AMS 10th Corf. On Sat. Meteor, and Ocean., Long Beach, CA, 9–14 January, 66–69.Google Scholar
  21. Verdin, K. L., and S. K. Greenlee, 1996: Development of continental scale digital elevation models and extraction of hydrographic features. Proceedings, Third International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Fe, NM, January 21–26, National Center for Geographic Information and Analysis, Santa Barbara, CAGoogle Scholar
  22. Walker, J. P., and P. R. Houser, 2001: A methodology for initializing soil moisture in a global climate model: Assimilation of near-surface soil moisture observations. J. Geophys. Res., 106, 11761–11774.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • Paul R. Houser
    • 1
  1. 1.NASA Goddard Space Flight CenterGreenbeltUSA

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