Abstract
In this study we developed and examined a hybrid modeling approach integrating physically-based equations and statistical downscaling to estimate fine-scale daily-mean surface turbulent fluxes (i.e., sensible and latent heat fluxes) for a region of southern California that is extensively covered by varied vegetation types over a complex terrain. The selection of model predictors is guided by physical parameterizations of surface flux used in land surface models and analysis showing net shortwave radiation that is a major source of variability in the surface energy budget. Through a structure of multivariable regression processes with an application of near-surface wind estimates from a previous study, we successfully reproduce dynamically-downscaled 3 km resolution surface flux data. The overall error in our estimates is less than 20 % for both sensible and latent heat fluxes, while slightly larger errors are seen in high-altitude regions. The major sources of error in estimates include the limited information provided in coarse reanalysis data, the accuracy of near-surface wind estimates, and an ignorance of the nonlinear diurnal cycle of surface fluxes when using daily-mean data. However, with reasonable and acceptable errors, this hybrid modeling approach provides promising, fine-scale products of surface fluxes that are much more accurate than reanalysis data, without performing intensive dynamical simulations.
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Acknowledgments
This work was supported by the California Energy Commission and the California Institute of Energy and the Environment under agreement #500-11-033. Additional funding was provided by the National Science Foundation Grant EF-1065863, as well as the City of Los Angeles and the U.S. Department of Energy as part of the American Recovery and Reinvestment Act of 2009. The authors would also like to thank the reviewers for their valuable comments.
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Huang, HY., Hall, A. A physically-based hybrid framework to estimate daily-mean surface fluxes over complex terrain. Clim Dyn 46, 3883–3897 (2016). https://doi.org/10.1007/s00382-015-2810-z
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DOI: https://doi.org/10.1007/s00382-015-2810-z