Climate Dynamics

, Volume 44, Issue 1, pp 529-542

Downscaling near-surface wind over complex terrain using a physically-based statistical modeling approach

  • Hsin-Yuan HuangAffiliated withJoint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles Email author 
  • , Scott B. CappsAffiliated withDepartment of Atmospheric and Oceanic Sciences, University of California, Los Angeles
  • , Shao-Ching HuangAffiliated withInstitute for Digital Research and Education, University of California, Los Angeles
  • , Alex HallAffiliated withDepartment of Atmospheric and Oceanic Sciences, University of California, Los Angeles

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A physically-based statistical modeling approach to downscale coarse resolution reanalysis near-surface winds over a region of complex terrain is developed and tested in this study. Our approach is guided by physical variables and meteorological relationships that are important for determining near-surface wind flow. Preliminary fine scale winds are estimated by correcting the course-to-fine grid resolution mismatch in roughness length. Guided by the physics shaping near-surface winds, we then formulate a multivariable linear regression model which uses near-surface micrometeorological variables and the preliminary estimates as predictors to calculate the final wind products. The coarse-to-fine grid resolution ratio is approximately 10–1 for our study region of southern California. A validated 3-km resolution dynamically-downscaled wind dataset is used to train and validate our method. Winds from our statistical modeling approach accurately reproduce the dynamically-downscaled near-surface wind field with wind speed magnitude and wind direction errors of <1.5 ms−1 and 30°, respectively. This approach can greatly accelerate the production of near-surface wind fields that are much more accurate than reanalysis data, while limiting the amount of computational and time intensive dynamical downscaling. Future studies will evaluate the ability of this approach to downscale other reanalysis data and climate model outputs with varying coarse-to-fine grid resolutions and domains of interest.


Near-surface wind Dynamical downscaling Statistical downscaling Complex terrain