caa a22 4500 605474125 CHVBK 20210128100346.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00382-014-2137-1 doi (NATIONALLICENCE)springer-10.1007/s00382-014-2137-1 Downscaling near-surface wind over complex terrain using a physically-based statistical modeling approach [Elektronische Daten] [Hsin-Yuan Huang, Scott Capps, Shao-Ching Huang, Alex Hall] 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 <1.5ms−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. Springer-Verlag Berlin Heidelberg, 2014 Near-surface wind nationallicence Dynamical downscaling nationallicence Statistical downscaling nationallicence Complex terrain nationallicence Huang Hsin-Yuan Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, 7343 Math Science Building, Los Angeles, CA, USA aut Capps Scott Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, USA aut Huang Shao-Ching Institute for Digital Research and Education, University of California, Los Angeles, Los Angeles, CA, USA aut Hall Alex Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, USA aut Climate Dynamics Springer Berlin Heidelberg 44/1-2(2015-01-01), 529-542 0930-7575 44:1-2<529 2015 44 382 https://doi.org/10.1007/s00382-014-2137-1 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00382-014-2137-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Huang Hsin-Yuan Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, 7343 Math Science Building, Los Angeles, CA, USA aut NATIONALLICENCE 700 1- Capps Scott Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, USA aut NATIONALLICENCE 700 1- Huang Shao-Ching Institute for Digital Research and Education, University of California, Los Angeles, Los Angeles, CA, USA aut NATIONALLICENCE 700 1- Hall Alex Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, USA aut NATIONALLICENCE 773 0- Climate Dynamics Springer Berlin Heidelberg 44/1-2(2015-01-01), 529-542 0930-7575 44:1-2<529 2015 44 382