• Akylas, E., Y. Tsakos, M. Tombrou, and D. P. Lalas, 2003: Considerations on minimum friction velocity. Quart. J. Roy. Meteor. Soc., 129, 19291943, https://doi.org/10.1256/qj.01.73.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Andreas, E. L, T. W. Horst, A. A. Grachev, P. O. G. Persson, C. W. Fairall, P. S. Guest, and R. E. Jordan, 2010a: Parametrizing turbulent exchange over summer sea ice and the marginal ice zone. Quart. J. Roy. Meteor. Soc., 136, 927943, https://doi.org/10.1002/qj.618.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Andreas, E. L, P. O. G. Persson, R. E. Jordan, T. W. Horst, P. S. Guest, A. A. Grachev, and C. W. Fairall, 2010b: Parameterizing turbulent exchange over sea ice in winter. J. Hydrometeor., 11, 87104, https://doi.org/10.1175/2009JHM1102.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Assouline, S., D. Li, S. Tyler, J. Tanny, S. Cohen, E. Bou-Zeid, M. Parlange, and G. G. Katul, 2016: On the variability of the Priestley–Taylor coefficient over water bodies. Water Resour. Res., 52, 150163, https://doi.org/10.1002/2015WR017504.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bariteau, L., D. Helmig, C. W. Fairall, J. E. Hare, J. Hueber, and E. K. Lang, 2010: Determination of oceanic ozone deposition by ship-borne eddy covariance flux measurements. Atmos. Meas. Tech., 3, 441455, https://doi.org/10.5194/amt-3-441-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Basu, S., 2019: Hybrid profile-gradient approaches for the estimation of surface fluxes. Bound.-Layer Meteor., 170, 2944, https://doi.org/10.1007/s10546-018-0391-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bianco, L., and Coauthors, 2019: Impact of model improvements on 80-m wind speeds during the second Wind Forecast Improvement Project (WFIP2). Geosci. Model Dev., 12, 48034821, https://doi.org/10.5194/gmd-12-4803-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blomquist, B. W., B. J. Huebert, C. W. Fairall, L. Bariteau, J. B. Edson, J. E. Hare, and W. R. McGillis, 2014: Advances in air–sea CO2 flux measurement by eddy correlation. Bound.-Layer Meteor., 152, 245276, https://doi.org/10.1007/s10546-014-9926-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bou-Zeid, E., W. Anderson, G. G. Katul, and L. Mahrt, 2020: The persistent challenge of surface heterogeneity in boundary-layer meteorology: A review. Bound.-Layer Meteor., 177, 227245, https://doi.org/10.1007/s10546-020-00551-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Businger, J. A., 1973: A note on free convection. Bound.-Layer Meteor., 4, 323326, https://doi.org/10.1007/BF02265241.

  • Cava, D., D. Contini, A. Donateo, and P. Martano, 2008: Analysis of short-term closure of the surface energy balance above short vegetation. Agric. For. Meteor., 148, 8293, https://doi.org/10.1016/j.agrformet.2007.09.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cristea, N. C., S. K. Kampf, and S. J. Burges, 2013: Revised coefficients for Priestley–Taylor and Makkink–Hansen equations for estimating daily reference evapotranspiration. J. Hydrol. Eng., 18, 12891300, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000679.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cuxart, J., and A. A. Boone, 2020: Evapotranspiration over land from a boundary-layer meteorology perspective. Bound.-Layer Meteor., 177, 427459, https://doi.org/10.1007/s10546-020-00550-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cuxart, J., L. Conangla, and M. A. Jiménez, 2015: Evaluation of the surface energy budget equation with experimental data and the ECMWF model in the Ebro Valley. J. Geophys. Res. Atmos., 120, 10081022, https://doi.org/10.1002/2014JD022296.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deardorff, J. W., 1970: Convective velocity and temperature scales for the unstable planetary boundary layer and for Rayleigh convection J. Atmos. Sci., 27, 12111213, https://doi.org/10.1175/1520-0469(1970)027<1211:CVATSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • De Bruin, H. A. R., 1983: A model for the Priestley–Taylor parameter α. J. Climate Appl. Meteor., 22, 572578, https://doi.org/10.1175/1520-0450(1983)022<0572:AMFTPT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • De Bruin, H. A. R., and A. A. M. Holtslag, 1982: A simple parameterization of the surface fluxes of sensible and latent heat during daytime compared with the Penman–Monteith concept. J. Appl. Meteor., 21, 16101621, https://doi.org/10.1175/1520-0450(1982)021<1610:ASPOTS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fairall, C. W., E. F. Bradley, D. P. Rogers, J. B. Edson, and G. S. Young, 1996: Bulk parameterization of air–sea fluxes for Tropical Ocean-Global Atmosphere Coupled-Ocean Atmosphere Response Experiment. J. Geophys. Res., 101, 37473764, https://doi.org/10.1029/95JC03205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fairall, C. W., E. F. Bradley, J. E. Hare, A. A. Grachev, and J. B. Edson, 2003: Bulk parameterization of air–sea fluxes: Updates and verification for the COARE algorithm. J. Climate, 16, 571591, https://doi.org/10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fisher, J. B., K. P. Tu, and D. D. Baldocchi, 2008: Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sens. Environ., 112, 901919, https://doi.org/10.1016/j.rse.2007.06.025.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Foken, T., 2008: The energy balance closure problem: An overview. Ecol. Appl., 18, 13511367, https://doi.org/10.1890/06-0922.1.

  • Foken, T., 2017: Micrometeorology. 2nd ed. Springer, 362 pp., https://doi.org/10.1007/978-3-642-25440-6.

  • Foken, T., F. Wimmer, M. Mauder, C. Thomas, and C. Liebethal, 2006: Some aspects of the energy balance closure problem. Atmos. Chem. Phys., 6, 43954402, https://doi.org/10.5194/acp-6-4395-2006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, Z., H. Liu, G. G. Katul, and T. Foken, 2017a: Non-closure of the surface energy balance explained by phase difference between vertical velocity and scalars of large atmospheric eddies. Environ. Res. Lett., 12, 034025, https://doi.org/10.1088/1748-9326/aa625b.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, Z., and Coauthors, 2017b: A novel approach to evaluate soil heat flux calculation: An analytical review of nine methods. J. Geophys. Res. Atmos., 122, 69346949, https://doi.org/10.1002/2017JD027160.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garratt, J. R., 1992: The Atmospheric Boundary Layer. Cambridge University Press, 316 pp.

  • Grachev, A. A., C. W. Fairall, and S. S. Zilitinkevich, 1997: Surface-layer scaling for the convection-induced stress regime. Bound.-Layer Meteor., 83, 423439, https://doi.org/10.1023/A:1000281625985.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grachev, A. A., C. W. Fairall, and S. E. Larsen, 1998: On the determination of the neutral drag coefficient in the convective boundary layer. Bound.-Layer Meteor., 86, 257278, https://doi.org/10.1023/A:1000617300732.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grachev, A. A., L. Bariteau, C. W. Fairall, J. E. Hare, D. Helmig, J. Hueber, and E. K. Lang, 2011: Turbulent fluxes and transfer of trace gases from ship-based measurements during TexAQS 2006. J. Geophys. Res., 116, D13110, https://doi.org/10.1029/2010JD015502.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grachev, A. A., E. L Andreas, C. W. Fairall, P. S. Guest, and P. O. G. Persson, 2015: Similarity theory based on the Dougherty–Ozmidov length scale. Quart. J. Roy. Meteor. Soc., 141, 18451856, https://doi.org/10.1002/qj.2488.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grachev, A. A., C. W. Fairall, B. W. Blomquist, H. J. S. Fernando, L. S. Leo, S. F. Otárola-Bustos, J. M. Wilczak, and K. L. McCaffrey, 2020: On the surface energy balance closure at different temporal scales. Agric. For. Meteor., 281, 107823, https://doi.org/10.1016/j.agrformet.2019.107823.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hao, Y., J. Baik, and M. Choi, 2019: Developing a soil water index-based Priestley–Taylor algorithm for estimating evapotranspiration over East Asia and Australia. Agric. For. Meteor., 279, 107760, https://doi.org/10.1016/j.agrformet.2019.107760.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Higgins, C. W., 2012: A-posteriori analysis of surface energy budget closure to determine missed energy pathways. Geophys. Res. Lett., 39, L19403, https://doi.org/10.1029/2012GL052918.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jacobs, A. F. G., B. G. Heusinkveld, and A. A. M. Holtslag, 2008: Towards closing the surface energy budget of a mid-latitude grassland. Bound.-Layer Meteor., 126, 125136, https://doi.org/10.1007/s10546-007-9209-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kondo, J., N. Saigusa, and T. Sato, 1990: A parameterization of evaporation from bare soil surfaces. J. Appl. Meteor., 29, 385389, https://doi.org/10.1175/1520-0450(1990)029<0385:APOEFB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kustas, W. P., C. S. T. Daughtry, and P. J. Van Oevelen, 1993: Analytical treatment of the relationship between soil heat flux/net radiation ratio and vegetation indices. Remote Sens. Environ., 46, 319330, https://doi.org/10.1016/0034-4257(93)90052-Y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kustas, W. P., J. M. Norman, T. J. Schmugge, and M. C. Anderson, 2004: Mapping surface energy fluxes with radiometric temperature. Thermal Remote Sensing in Land Surface Processes, D. A. Quattrochi and J. C. Luvall, Eds., CRC Press, 205253.

    • Search Google Scholar
    • Export Citation
  • Leuning, R., E. van Gorsel, W. J. Massman, and P. R. Isaac, 2012: Reflections on the surface energy imbalance problem. Agric. For. Meteor., 156, 6574, https://doi.org/10.1016/j.agrformet.2011.12.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lhomme, J. A., 1997: A theoretical basis for the Priestley–Taylor coefficient. Bound.-Layer Meteor., 82, 179191, https://doi.org/10.1023/A:1000281114105.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Z. L., R. L. Tang, Z. M. Wan, Y. Y. Bi, C. H. Zhou, B. H. Tang, G. J. Yan, and X. Y. Zhang, 2009: A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors, 9, 38013853, https://doi.org/10.3390/s90503801.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liebethal, C., and T. Foken, 2007: Evaluation of six parameterization approaches for the ground heat flux. Theor. Appl. Climatol., 88, 4356, https://doi.org/10.1007/s00704-005-0234-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liebethal, C., B. Huwe, and T. Foken, 2005: Sensitivity analysis for two ground heat flux calculation approaches. Agric. For. Meteor., 132, 253262, https://doi.org/10.1016/j.agrformet.2005.08.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahrt, L., D. Vickers, J. Howell, J. Højstrup, J. M. Wilczak, J. Edson, and J. Hare, 1996: Sea surface drag coefficients in the Risø Air Sea Experiment. J. Geophys. Res., 101, 14 32714 335, https://doi.org/10.1029/96JC00748.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Majozi, N. P., C. M. Mannaerts, A. Ramoelo, R. Mathieu, A. Nickless, and W. Verhoef, 2017: Analysing surface energy balance closure and partitioning over a semi-arid savanna FLUXNET site in Skukuza, Kruger National Park, South Africa. Hydrol. Earth Syst. Sci., 21, 34013415, https://doi.org/10.5194/hess-21-3401-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Masseroni, D., C. Corbari, and M. Mancini, 2014: Limitations and improvements of the energy balance closure with reference to experimental data measured over a maize field. Atmósfera, 27, 335352, https://doi.org/10.1016/S0187-6236(14)70033-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mauder, M., and Coauthors, 2007: The energy balance experiment EBEX-2000. Part II: Intercomparison of eddy-covariance sensors and post-field data processing methods. Bound.-Layer Meteor., 123, 2954, https://doi.org/10.1007/s10546-006-9139-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mauder, M., T. Foken, and J. Cuxart, 2020: Surface-energy-balance closure over land: A review. Bound.-Layer Meteor., 177, 395426, https://doi.org/10.1007/s10546-020-00529-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meyers, T. P., and S. E. Hollinger, 2004: An assessment of storage terms in the surface energy balance of maize and soybean. Agric. For. Meteor., 125, 105115, https://doi.org/10.1016/j.agrformet.2004.03.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monteith, J. L., 1965: Evaporation and environment in the state and movement of water in living organism. Proceedings of the Society for Experimental Biology, Cambridge University Press, 205234.

    • Search Google Scholar
    • Export Citation
  • Olson, J. B., and Coauthors, 2019: Improving wind energy forecasting through numerical weather prediction model development. Bull. Amer. Meteor. Soc., 100, 22012220, https://doi.org/10.1175/BAMS-D-18-0040.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oncley, S. P., and Coauthors, 2007: The energy balance experiment EBEX-2000. Part I: Overview and energy balance. Bound.-Layer Meteor., 123, 128, https://doi.org/10.1007/s10546-007-9161-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Panofsky, H. A., H. Tennekes, D. A. Lenschow, and J. C. Wyngaard, 1977: The characteristics of turbulent velocity components in the surface layer under convective conditions. Bound.-Layer Meteor., 11, 355361, https://doi.org/10.1007/BF02186086.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Penman, H. L., 1948: Natural evaporation from open water, bare soil and grass. Proc. Roy. Soc. London, 193A, 120145, https://doi.org/10.1098/rspa.1948.0037.

    • Search Google Scholar
    • Export Citation
  • Pereira, A. R., 2004: The Priestley–Taylor parameter and the decoupling factor for estimating reference evapotranspiration. Agric. For. Meteor., 125, 305313, https://doi.org/10.1016/j.agrformet.2004.04.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Priestley, C., and R. Taylor, 1972: On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Wea. Rev., 100, 8192, https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schumann, U., 1988: Minimum friction velocity and heat transfer in the rough surface layer of a convective boundary layer. Bound.-Layer Meteor., 44, 311326, https://doi.org/10.1007/BF00123019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shaw, W., and Coauthors, 2019: The second Wind Forecast Improvement Project (WFIP2): General overview. Bull. Amer. Meteor. Soc., 100, 16871699, https://doi.org/10.1175/BAMS-D-18-0036.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stoy, P. C., and Coauthors, 2013: A data-driven analysis of energy balance closure across FLUXNET research sites: The role of landscape scale heterogeneity. Agric. For. Meteor., 171–172, 137152, https://doi.org/10.1016/j.agrformet.2012.11.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Su, Z., 2002: The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrol. Earth Syst. Sci., 6, 85100, https://doi.org/10.5194/hess-6-85-2002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sykes, R. I., D. S. Henn, and W. S. Lewellen, 1993: Surface-layer description under free-convection conditions. Quart. J. Roy. Meteor. Soc., 119, 409421, https://doi.org/10.1002/qj.49711951103.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Venturini, V., L. Rodriguez, and G. Bisht, 2011: Comparison among different modified Priestley and Taylor equations to calculate actual evapotranspiration with MODIS data. Int. J. Remote Sens., 32, 13191338, https://doi.org/10.1080/01431160903547965.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilczak, J. M., S. P. Oncley, and S. A. Stage, 2001: Sonic anemometer tilt correction algorithms. Bound.-Layer Meteor., 99, 127150, https://doi.org/10.1023/A:1018966204465.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilczak, J. M., and Coauthors, 2019: The second Wind Forecast Improvement Project (WFIP2): Observational field campaign. Bull. Amer. Meteor. Soc., 100, 17011723, https://doi.org/10.1175/BAMS-D-18-0035.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, K., and Coauthors, 2002: Energy balance closure at FLUXNET sites. Agric. For. Meteor., 113, 223243, https://doi.org/10.1016/S0168-1923(02)00109-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yaglom, A. M., 1974: Data on turbulence characteristics in the atmospheric surface layer. Izv. Akad. Sci. USSR, 10, 341352.

  • Yao, Y., and Coauthors, 2013: MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestley–Taylor algorithm. Agric. For. Meteor., 171–172, 187202, https://doi.org/10.1016/j.agrformet.2012.11.016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yao, Y., and Coauthors, 2014: Validation and application of the modified satellite-based Priestley–Taylor algorithm for mapping terrestrial evapotranspiration. Remote Sens., 6, 880904, https://doi.org/10.3390/rs6010880.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yao, Y., and Coauthors, 2015: A satellite-based hybrid algorithm to determine the Priestley–Taylor parameter for global terrestrial latent heat flux estimation across multiple biomes. Remote Sens. Environ., 165, 216233, https://doi.org/10.1016/j.rse.2015.05.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zilitinkevich, S. S., A. A. Grachev, and J. C. R. Hunt, 1998: Surface frictional and non-local heat/mass transfer in the shear-free convective boundary layer. Buoyant Convection in Geophysical Flows, E. J. Plate et al., Eds., NATO ASI Series, Vol. 513, Kluwer, 83113, https://doi.org/10.1007/978-94-011-5058-3_4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zilitinkevich, S. S. , and Coauthors, 2005: The effect of large eddies on the convective heat/mass transfer over complex terrain: Advanced theory and its validation against experimental and les data. Croat. Meteor. J., 40, 2026.

    • Search Google Scholar
    • Export Citation
  • Zilitinkevich, S. S., and Coauthors, 2006: The influence of large convective eddies on the surface layer turbulence. Quart. J. Roy. Meteor. Soc., 132, 14231456, https://doi.org/10.1256/qj.05.79.

    • Crossref
    • Search Google Scholar
    • Export Citation
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A Hybrid Bulk Algorithm to Predict Turbulent Fluxes over Dry and Wet Bare Soils

Andrey A. GrachevaNOAA/Physical Sciences Laboratory, Boulder, Colorado
bCooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado

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Christopher W. FairallaNOAA/Physical Sciences Laboratory, Boulder, Colorado

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Byron W. BlomquistaNOAA/Physical Sciences Laboratory, Boulder, Colorado
bCooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado

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Harindra J. S. FernandocDepartment of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana
dDepartment of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, Indiana

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Laura S. LeocDepartment of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana
eDepartment of Physics and Astronomy, Alma Mater Studiorum-University of Bologna, Bologna, Italy

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Sebastián F. Otárola-BustoscDepartment of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana

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James M. WilczakaNOAA/Physical Sciences Laboratory, Boulder, Colorado

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Katherine L. McCaffreyaNOAA/Physical Sciences Laboratory, Boulder, Colorado
bCooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado

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Abstract

Measurements made in the Columbia River basin (Oregon) in an area of irregular terrain during the second Wind Forecast Improvement Project (WFIP2) field campaign are used to develop an optimized hybrid bulk algorithm to predict the surface turbulent fluxes from readily measured or modeled quantities over dry and wet bare or lightly vegetated soil surfaces. The hybrid (synthetic) algorithm combines (i) an aerodynamic method for turbulent flow, which is based on the transfer coefficients (drag coefficient and Stanton number), roughness lengths, and Monin–Obukhov similarity; and (ii) a modified Priestley–Taylor (P-T) algorithm with physically based ecophysiological constraints, which is essentially based on the surface energy budget (SEB) equation. Soil heat flux in the latter case was estimated from measurements of soil temperature and soil moisture. In the framework of the hybrid algorithm, bulk estimates of the momentum flux and the sensible heat flux are derived from a traditional aerodynamic approach, whereas the latent heat flux (or moisture flux) is evaluated from a modified P-T model. Direct measurements of the surface fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made during WFIP2 for different soil conditions (dry and wet) are used to optimize and tune the hybrid bulk algorithm. The bulk flux estimates are validated against the measured eddy-covariance fluxes. We also discuss the SEB closure over dry and wet surfaces at various time scales based on the modeled and measured fluxes. Although this bulk flux algorithm is optimized for the data collected during the WFIP2, a hybrid approach can be used for similar flux-tower sites and field campaigns.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Grachev’s current affiliation: Boundary Layer Research Team/Atmospheric Dynamics and Analytics Branch, DEVCOM Army Research Laboratory, WSMR, New Mexico.

McCaffrey’s current affiliation: S & P Global Commodity Insights, Boulder, Colorado.

Corresponding author: Andrey A. Grachev, andrey.grachev@colorado.edu

Abstract

Measurements made in the Columbia River basin (Oregon) in an area of irregular terrain during the second Wind Forecast Improvement Project (WFIP2) field campaign are used to develop an optimized hybrid bulk algorithm to predict the surface turbulent fluxes from readily measured or modeled quantities over dry and wet bare or lightly vegetated soil surfaces. The hybrid (synthetic) algorithm combines (i) an aerodynamic method for turbulent flow, which is based on the transfer coefficients (drag coefficient and Stanton number), roughness lengths, and Monin–Obukhov similarity; and (ii) a modified Priestley–Taylor (P-T) algorithm with physically based ecophysiological constraints, which is essentially based on the surface energy budget (SEB) equation. Soil heat flux in the latter case was estimated from measurements of soil temperature and soil moisture. In the framework of the hybrid algorithm, bulk estimates of the momentum flux and the sensible heat flux are derived from a traditional aerodynamic approach, whereas the latent heat flux (or moisture flux) is evaluated from a modified P-T model. Direct measurements of the surface fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made during WFIP2 for different soil conditions (dry and wet) are used to optimize and tune the hybrid bulk algorithm. The bulk flux estimates are validated against the measured eddy-covariance fluxes. We also discuss the SEB closure over dry and wet surfaces at various time scales based on the modeled and measured fluxes. Although this bulk flux algorithm is optimized for the data collected during the WFIP2, a hybrid approach can be used for similar flux-tower sites and field campaigns.

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Grachev’s current affiliation: Boundary Layer Research Team/Atmospheric Dynamics and Analytics Branch, DEVCOM Army Research Laboratory, WSMR, New Mexico.

McCaffrey’s current affiliation: S & P Global Commodity Insights, Boulder, Colorado.

Corresponding author: Andrey A. Grachev, andrey.grachev@colorado.edu
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