• Alavi, N., Bélair S. , Fortin V. , Zhang S. , Husain S. Z. , Carrera M. , and Abrahamowicz M. , 2016: Warm season evaluation of soil moisture prediction in the Soil, Vegetation, and Snow (SVS) scheme. J. Hydrometeor., doi:10.1175/JHM-D-15-0189.1, in press.

    • Search Google Scholar
    • Export Citation
  • Alfieri, J. G., Niyogi D. , Blanken P. D. , Chen F. , LeMone M. A. , Mitchell K. E. , Ek M. B. , and Kumar A. , 2008: Estimation of the minimum canopy resistance for croplands and grasslands using data from the 2002 International H2O project. Mon. Wea. Rev., 136, 44524469, doi:10.1175/2008MWR2524.1.

    • Search Google Scholar
    • Export Citation
  • Arora, V. K., 2003: Simulating energy and carbon fluxes over winter wheat using coupled land surface and terrestrial ecosystem models. Agric. For. Meteor., 118, 2147, doi:10.1016/S0168-1923(03)00073-X.

    • Search Google Scholar
    • Export Citation
  • Arora, V. K., and Boer G. J. , 2005: A parameterization of leaf phenology for the terrestrial ecosystem component of climate models. Global Change Biol., 11, 3959, doi:10.1111/j.1365-2486.2004.00890.x.

    • Search Google Scholar
    • Export Citation
  • Ball, J. T., Woodrow I. E. , and Berry J. A. , 1987: A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. Progress in Photosynthesis Research, J. Biggens, Ed., Springer, 221–224, doi:10.1007/978-94-017-0519-6_48.

  • Bélair, S., Crevier L.-P. , Mailhot J. , Bilodeau B. , and Delage Y. , 2003a: Operational implementation of the ISBA land surface scheme in the Canadian regional weather forecast model. Part I: Warm season results. J. Hydrometeor., 4, 352370, doi:10.1175/1525-7541(2003)4<352:OIOTIL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bélair, S., Brown R. , Mailhot J. , Bilodeau B. , and Crevier L.-P. , 2003b: Operational implementation of the ISBA land surface scheme in the Canadian regional weather forecast model. Part II: Cold season results. J. Hydrometeor., 4, 371386, doi:10.1175/1525-7541(2003)4<371:OIOTIL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bernier, N. B., Bélair S. , Bilodeau B. , and Tong L. , 2011: Near-surface and land surface forecast system of the Vancouver 2010 Winter Olympic and Paralympic Games. J. Hydrometeor., 12, 508530, doi:10.1175/2011JHM1250.1.

    • 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, doi:10.1175/1520-0450(1975)014<1246:NEOTCO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Blackadar, A.-K., 1976: Modeling the nocturnal boundary layer. Preprints, Third Symp. on Atmospheric Turbulence, Diffusion and Air Quality, Raleigh, NC, Amer. Meteor. Soc., 4649.

  • Candille, G., Cote C. , Houtekamer P. L. , and Pellerin G. , 2007: Verification of an ensemble prediction system against observations. Mon. Wea. Rev., 135, 26882699, doi:10.1175/MWR3414.1.

    • Search Google Scholar
    • Export Citation
  • Carrera, M. L., Bélair S. , Fortin V. , Bilodeau B. , Charpentier D. , and Doré L. , 2010: Evaluation of snowpack simulations over the Canadian Rockies with an experimental hydrometeorological modeling system. J. Hydrometeor., 11, 11231140, doi:10.1175/2010JHM1274.1.

    • Search Google Scholar
    • Export Citation
  • Carrera, M. L., Bélair S. , and Bilodeau B. , 2015: The Canadian Land Data Assimilation System (CaLDAS): Description and synthetic evaluation study. J. Hydrometeor., 16, 12931314, doi:10.1175/JHM-D-14-0089.1.

    • Search Google Scholar
    • Export Citation
  • Collatz, G. J., Ribas-Carbo M. , and Berry J. A. , 1992: Coupled photosynthesis–stomatal conductance model for leaves of plants. Aust. J. Plant Physiol., 19, 519538, doi:10.1071/PP9920519.

    • 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, doi:10.1029/JC083iC04p01889.

    • Search Google Scholar
    • Export Citation
  • de Rosnay, P., and Coauthors, 2009: AMMA Land Surface Model Intercomparison Experiment coupled to the Community Microwave Emission Model: ALMIP–MEM. J. Geophys. Res., 114, D05108, doi:10.1029/2008JD010724.

    • Search Google Scholar
    • Export Citation
  • Drusch, M., and Viterbo P. , 2007: Assimilation of screen-level variables in ECMWF’s Integrated Forecast System: A study on the impact on the forecast quality and analyzed soil moisture. Mon. Wea. Rev., 135, 300314, doi:10.1175/MWR3309.1.

    • Search Google Scholar
    • Export Citation
  • Drusch, M., Holmes T. , de Rosnay P. , and Balsamo G. , 2009: Comparing ERA-40-based L-band brightness temperatures with Skylab observations: A calibration/validation study using the Community Microwave Emission Model. J. Hydrometeor., 10, 213226, doi:10.1175/2008JHM964.1.

    • Search Google Scholar
    • Export Citation
  • Dupont, S., Otte T. L. , and Ching J. K. S. , 2004: Simulation of meteorological fields within and above urban and rural canopies with a mesoscale model (MM5). Bound.-Layer Meteor., 113, 111158, doi:10.1023/B:BOUN.0000037327.19159.ac.

    • Search Google Scholar
    • Export Citation
  • Entekhabi, D., and Coauthors, 2010: The Soil Moisture Active Passive (SMAP) mission. Proc. IEEE, 98, 704716, doi:10.1109/JPROC.2010.2043918.

    • Search Google Scholar
    • Export Citation
  • Farquhar, G. D., von Caemmere S. , and Berry J. A. , 1980: A biochemical model of photosynthetic assimilation in leaves of species. Planta, 149, 7890, doi:10.1007/BF00386231.

    • Search Google Scholar
    • Export Citation
  • Grell, G. A., Dudhia J. , and Stauffer D. R. , 1994: A description of the Fifth generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-398+STR, 121 pp., doi:10.5065/D60Z716B.

  • Husain, S. Z., Bélair S. , Mailhot J. , and Leroyer S. , 2013: Improving the representation of the nocturnal near-neutral surface layer in the urban environment with a mesoscale atmospheric model. Bound.-Layer Meteor., 147, 525551, doi:10.1007/s10546-013-9798-x.

    • Search Google Scholar
    • Export Citation
  • Husain, S. Z., Bélair S. , and Leroyer S. , 2014a: Influence of soil moisture on urban microclimate and surface-layer meteorology in Oklahoma City. J. Appl. Meteor. Climatol., 53, 8398, doi:10.1175/JAMC-D-13-0156.1.

    • Search Google Scholar
    • Export Citation
  • Husain, S. Z., Separovic L. , Yu W. , and Fernig D. , 2014b: Extended-range high-resolution dynamical downscaling over a continental-scale spatial domain with atmospheric and surface nudging. J. Geophys. Res. Atmos., 119, 13 72013 750, doi:10.1002/2014JD022195.

    • Search Google Scholar
    • Export Citation
  • Jacobson, M. Z., 1999: Effects of soil moisture on temperatures, winds, and pollutant concentrations in Los Angeles. J. Appl. Meteor., 38, 607616, doi:10.1175/1520-0450(1999)038<0607:EOSMOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Jarvis, P. G., 1976: The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philos. Trans. Roy. Soc. London, B273, 593610, doi:10.1098/rstb.1976.0035.

    • Search Google Scholar
    • Export Citation
  • Kerr, Y. H., and Coauthors, 2010: The SMOS mission: New tool for monitoring key elements of the global water cycle. Proc. IEEE, 98, 666687, doi:10.1109/JPROC.2010.2043032.

    • Search Google Scholar
    • Export Citation
  • Kirdyashev, K. P., Chukhlantsev A. A. , and Shutko A. M. , 1979: Microwave radiation of the earth’s surface in the presence of vegetation cover. Radiotekh. Elektron, 24, 256264.

    • Search Google Scholar
    • Export Citation
  • Kumar, A., Chen F. , Barlage M. , Ek M. B. , and Niyogi D. , 2014: Assessing impacts of integrating MODIS vegetation data in the Weather Research and Forecasting (WRF) Model coupled to two different canopy-resistance approaches. J. Appl. Meteor. Climatol., 53, 13621380, doi:10.1175/JAMC-D-13-0247.1.

    • Search Google Scholar
    • Export Citation
  • Lespinas, F., Fortin V. , Roy G. , Rasmussen P. , and Stadnyk T. , 2015: Performance evaluation of the Canadian Precipitation Analysis (CaPA). J. Hydrometeor., 16, 20452064, doi:10.1175/JHM-D-14-0191.1.

    • Search Google Scholar
    • Export Citation
  • Leuning, R., 1995: A critical appraisal of a combined stomatal–photosynthesis model for C3 plants. Plant Cell Environ., 18, 339355, doi:10.1111/j.1365-3040.1995.tb00370.x.

    • Search Google Scholar
    • Export Citation
  • Mahfouf, J.-F., 1991: Analysis of soil moisture from near-surface parameters: A feasibility study. J. Appl. Meteor., 30, 15341547, doi:10.1175/1520-0450(1991)030<1534:AOSMFN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mahfouf, J.-F., Brasnett B. , and Gagnon S. , 2007: A Canadian Precipitation Analysis (CaPA) project: Description and preliminary results. Atmos.–Ocean, 45, 117, doi:10.3137/ao.v450101.

    • Search Google Scholar
    • Export Citation
  • Martilli, A., Clappier A. , and Rotach M. W. , 2002: An urban surface exchange parameterisation for mesoscale models. Bound.-Layer Meteor., 104, 261304, doi:10.1023/A:1016099921195.

    • Search Google Scholar
    • Export Citation
  • Masson, V., 2000: A physically-based scheme for the urban energy budget in atmospheric models. Bound.-Layer Meteor., 94, 357397, doi:10.1023/A:1002463829265.

    • Search Google Scholar
    • Export Citation
  • Niyogi, D., Alapaty K. , Raman S. , and Chen F. , 2009: Development and evaluation of a coupled photosynthesis-based gas exchange evapotranspiration model (GEM) for mesoscale weather forecasting applications. J. Appl. Meteor. Climatol., 48, 349368, doi:10.1175/2008JAMC1662.1.

    • Search Google Scholar
    • Export Citation
  • Noilhan, J., and Planton S. , 1989: A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev., 117, 536549, doi:10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Schenk, H. J., and Jackson R. B. , 2003: Global distribution of root profiles in terrestrial ecosystems data set reference document. Oak Ridge National Laboratory Distributed Active Archive Center, doi:10.3334/ORNLDAAC/660.

  • Schmugge, T. J., and Choudhury B. J. , 1981: A comparison of radiative transfer models for predicting the microwave emission from soil. Radio Sci., 16, 927938, doi:10.1029/RS016i005p00927.

    • Search Google Scholar
    • Export Citation
  • Separovic, L., Husain S. Z. , Yu W. , and Fernig D. , 2014: High-resolution surface analysis for extended-range downscaling with limited-area atmospheric models. J. Geophys. Res. Atmos., 119, 13 65113 682, doi:10.1002/2014JD022387.

    • Search Google Scholar
    • Export Citation
  • Summer, M. E., 2000: Handbook of Soil Science. CRC Press, 2313 pp.

  • Wang, J. R., and Schmugge T. , 1980: An empirical model for the complex dielectric permittivity of soils as a function of water content. IEEE Trans. Geosci. Remote Sens., 18, 288295, doi:10.1109/TGRS.1980.350304.

    • Search Google Scholar
    • Export Citation
  • Wigneron, J. P., Laguerre L. , and Kerr Y. H. , 2001: A simple parameterization of the L-band microwave emission from rough agricultural soils. IEEE Trans. Geosci. Remote Sens., 39, 16971707, doi:10.1109/36.942548.

    • Search Google Scholar
    • Export Citation
  • Ye, N., Walker J. P. , Guerschman J. , Ryu D. , and Gurney R. J. , 2015: Standing water effect on soil moisture retrieval from L-band passive microwave observations. Remote Sens. Environ., 169, 232242, doi:10.1016/j.rse.2015.08.013.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 5 5 5
PDF Downloads 2 2 2

The Multibudget Soil, Vegetation, and Snow (SVS) Scheme for Land Surface Parameterization: Offline Warm Season Evaluation

View More View Less
  • 1 Meteorological Research Division, Environment and Climate Change Canada, Dorval, Québec, Canada
  • | 2 Meteorological Service of Canada, Environment and Climate Change Canada, Dorval, Québec, Canada
  • | 3 Meteorological Research Division, Environment and Climate Change Canada, Dorval, Québec, Canada
  • | 4 Meteorological Service of Canada, Environment and Climate Change Canada, Dorval, Québec, Canada
Restricted access

Abstract

A new land surface parameterization scheme, named the Soil, Vegetation, and Snow (SVS) scheme, was recently developed at Environment and Climate Change Canada to replace the operationally used Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme. The new scheme is designed to address a number of weaknesses and limitations of ISBA that have been identified over the last decade. Unlike ISBA, which calculates a single energy budget for the different land surface components, SVS introduces a new tiling approach that includes separate energy budgets for bare ground, vegetation, and two different snowpacks (over bare ground and low vegetation and under high vegetation). The inclusion of a photosynthesis module as an option to determine the surface stomatal resistance is another significant addition in SVS. The representation of vertical water transport through soil has also been substantially improved in SVS with the introduction of multiple soil layers. Overall, offline simulations conducted in the present study demonstrated clear improvements in warm season meteorological predictions with SVS compared to the ISBA scheme. The results also revealed considerable reduction of standard error in the SVS-predicted L-band brightness temperature. This demonstrates the scheme’s ability for better hydrological prediction and its potential for providing more accurate soil moisture analysis. The impact of the photosynthesis module within the current implementation of SVS is, however, found to be negligible on near-surface meteorological prediction and slightly negative for brightness temperature.

Denotes Open Access content.

Corresponding author address: Syed Zahid Husain, Meteorological Research Division, Environment and Climate Change Canada, 2121 Route Transcanadienne, Dorval QC H9P 1J3, Canada. E-mail: syed.husain@canada.ca

Abstract

A new land surface parameterization scheme, named the Soil, Vegetation, and Snow (SVS) scheme, was recently developed at Environment and Climate Change Canada to replace the operationally used Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme. The new scheme is designed to address a number of weaknesses and limitations of ISBA that have been identified over the last decade. Unlike ISBA, which calculates a single energy budget for the different land surface components, SVS introduces a new tiling approach that includes separate energy budgets for bare ground, vegetation, and two different snowpacks (over bare ground and low vegetation and under high vegetation). The inclusion of a photosynthesis module as an option to determine the surface stomatal resistance is another significant addition in SVS. The representation of vertical water transport through soil has also been substantially improved in SVS with the introduction of multiple soil layers. Overall, offline simulations conducted in the present study demonstrated clear improvements in warm season meteorological predictions with SVS compared to the ISBA scheme. The results also revealed considerable reduction of standard error in the SVS-predicted L-band brightness temperature. This demonstrates the scheme’s ability for better hydrological prediction and its potential for providing more accurate soil moisture analysis. The impact of the photosynthesis module within the current implementation of SVS is, however, found to be negligible on near-surface meteorological prediction and slightly negative for brightness temperature.

Denotes Open Access content.

Corresponding author address: Syed Zahid Husain, Meteorological Research Division, Environment and Climate Change Canada, 2121 Route Transcanadienne, Dorval QC H9P 1J3, Canada. E-mail: syed.husain@canada.ca
Save