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http://dx.doi.org/10.12989/was.2019.28.2.129

Spatial correlation-based WRF observation-nudging approach in simulating regional wind field  

Ren, Hehe (Key Lab of Smart Prevention and Mitigation for Civil Engineering Disasters of the Ministry of Industry and Information, Harbin Institute of Technology)
Laima, Shujin (Key Lab of Smart Prevention and Mitigation for Civil Engineering Disasters of the Ministry of Industry and Information, Harbin Institute of Technology)
Chen, Wen-Li (Key Lab of Smart Prevention and Mitigation for Civil Engineering Disasters of the Ministry of Industry and Information, Harbin Institute of Technology)
Guo, Anxin (Key Lab of Smart Prevention and Mitigation for Civil Engineering Disasters of the Ministry of Industry and Information, Harbin Institute of Technology)
Li, Hui (Key Lab of Smart Prevention and Mitigation for Civil Engineering Disasters of the Ministry of Industry and Information, Harbin Institute of Technology)
Publication Information
Wind and Structures / v.28, no.2, 2019 , pp. 129-140 More about this Journal
Abstract
Accurately simulating the wind field of large-scale region, for instant urban areas, the locations of large span bridges, wind farms and so on, is very difficult, due to the complicated terrains or land surfaces. Currently, the regional wind field can be simulated through the combination of observation data and numerical model using observation-nudging in the Weather Research and Forecasting model (WRF). However, the main drawback of original observation-nudging method in WRF is the effects of observation on the surrounding field is fully mathematical express in terms of temporal and spatial, and it ignores the effects of terrain, wind direction and atmospheric circulation, while these are physically unreasonable for the turbulence. For these reasons, a spatial correlation-based observation-nudging method, which can take account the influence of complicated terrain, is proposed in the paper. The validation and comparation results show that proposed method can obtain more reasonable and accurate result than original observation-nudging method. Finally, the discussion of wind field along bridge span obtained from the simulation with spatial correlation-based observation-nudging method was carried out.
Keywords
wind field; complex terrain; spatial correlation-based WRF observation-nudging method; long-span bridges; wind heterogeneous distribution character; local wind environment;
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Times Cited By KSCI : 7  (Citation Analysis)
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1 Davis, C., Warner, T., Astling, E. and Bowers, J. (1999), "Development and application of an operational, relocatable, mesogamma scale weather analysis and forecasting system", Tellus Series A-dynamic Meteorology & Oceanography, 51(5), 710-727.   DOI
2 Dhunny, A.Z., Lollchund, M.R. and Rughooputh, S.D. (2015), "A high-resolution mapping of wind energy potentials for Mauritius using Computational Fluid Dynamics (CFD)", Wind Struct., 20(4), 565-578.   DOI
3 Fast, J.D. (1995), "Mesoscale modeling and four-dimensional data assimilation in areas of highly complex terrain", J. Appl. Meteor., 34, 2762-2782.   DOI
4 Grell, G.A. and Freitas, S.R. (2014), "A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling", Atmosp. Chem. Phys., 14(10), 5233-5250.   DOI
5 Hong, S., Dudhia, J. and Chen, S. (2004), "A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation", Mon. Weather Rev., 132, 103-120.   DOI
6 Hong, S., Noh, Y. and Dudhia, J. (2006), "A new vertical diffusion package with an explicit treatment of entrainment processes", Mon. Weather Rev., 134, 2318-2341.   DOI
7 Iacono, M.J., Delamere, J.S., Mlawer, E.J., Shephard, M.W., Clough, S.A. and Collins, W.D. (2008), "Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models", J. Geophys. Res. Atmosp., 113, D13103.   DOI
8 Leslie, L.M., LeMarshall, J.F., Morrison, R.P., Spinoso, C., Purser, R.J. and Pescod, N. (1998), "Improved hurricane track forecasting from the continuous assimilation of high-quality satellite wind data", Mon. Weather Rev., 126, 1248-1258.   DOI
9 Bitsuamlak, G.T. and Abdi, D. (2016), "Wind flow simulations in idealized and real built environments with models of various level of complexity", Wind Struct., 22(4), 503-524.   DOI
10 Chen, F. and Dudhia, J. (2001), "Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. part I: model implementation and sensitivity", Mon. Weather Rev., 129, 569-585.   DOI
11 Cheng, X.L., Li, J., Hu, F., Xu, J. and Zhu, R. (2015), "Refined numerical simulation in wind resource assessment", Wind Struct., 20(1), 59-74.   DOI
12 Li, S.W., Hu, Z.Z., Tse, K.T. and Weerasuriya, A.U. (2016), "Wind direction field under the influence of topography: part II: CFD investigations", Wind Struct., 22(4), 477-501.   DOI
13 Liu, S., Pan, W., Zhang, H., Cheng, X., Long, Z. and Chen, Q. (2017), "CFD simulations of wind distribution in an urban community with a full-scale geometrical model", Build. Environ., 117, 11-23.   DOI
14 Liu, Y., Bourgeois, A., Warner, T., Swerdlin, S. and Hacker, J. (2005), "An implementation of obs-nudging-based FDDA into WRF for supporting ATEC test operations", WRF/MM5 user's workshop, Colorado, USA, June.
15 Liu, Y., Warner, T., Vincent, C., Wu, W. and Mahoney, B. (2011), "Simultaneous nested modeling from the synoptic scale to the LES scale for wind energy applications", J. Wind Eng. Ind. Aerod., 99(4), 308-319.   DOI
16 Liu, Y., Chen, F., Warner, T. and Basara, J. (2006), "Verification of a mesoscale data-assimilation and forecasting system for the Oklahoma City area during the joint urban 2003 field project", J. Appl. Meteor., 45(7), 912-929.   DOI
17 Liu, Y., Warner, T.T., Bowers, J.F., Carson, L.P., Chen, F. and Clough, C.A. (2008), "The operational mesogamma-scale analysis and forecast system of the U.S. Army test and evaluation command. part I: overview of the modeling system, the forecast products, and how the products are used", J. Appl. Meteor., 47(4), 1077-1092.   DOI
18 Liu, Y., Yu, W., Vandenberghe, F., Hahmann, F., Warner, A. and Swerdlin, S. (2006), "Assimilation of diverse meteorological datasets with a four-dimensional mesoscale analysis and forecast system", Proceedings of the 10th Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans and Land Surface, Atlanta, USA, January.
19 Mlawer, E., Taubman, S., Brown, P., Iacono, M. and Clough, S. (1997), "Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave", J. Geophys. Res., 102(D14), 16 663-16 682.   DOI
20 Monti, P., Cantelli, A., Leuzzi, G., Valerio, G. and Pilotti, M. (2017), "Numerical simulations of mountain winds in an alpine valley", Wind Struct., 24(6), 565-578.   DOI
21 Pan, L.L., Chen, S.H., Dan, C., Lin, M.Y., Hart, Q. and Zhang, M.H. (2011), "Influences of climate change on California and Nevada regions revealed by a high-resolution dynamical downscaling study", Clim. Dynam., 37, 2005-2020.   DOI
22 Shen, L., Han, Y., Cai, C.S., Dong, G., Zhang, J. and Hu, P. (2017), "LES of wind environments in urban residential areas based on an inflow turbulence generating approach", Wind Struct., 24(1), 1-24.   DOI
23 Rife, D.L., Davis, C.A., Liu, Y. and Warner, T.T. (2004), "Predictability of low-level winds by mesoscale meteorological models", Mon. Weather Rev., 132(11), 2553-2569.   DOI
24 Rife, D.L., Warner, T.T., Chen, F. and Astling, E.G. (2002), "Mechanisms for diurnal boundary layer circulations in the Great Basin Desert", Mon. Weather Rev., 130(4), 921-938.   DOI
25 Seaman, N.L., Stauffer, D.R. and Lario-Gibbs, A.M. (1995), "A multiscale four-dimensional data assimilation system applied in the San Joaquin Valley during SARMAP. part I: modeling design and basic performance characteristics", J. Appl. Meteor., 34, 1739-1761.   DOI
26 Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M. and Duda, M. (2008), "A description of the advanced research WRF version 3", NCAR Technical, 113, 7-25.
27 Stauffer, D.R. and Seaman, N.L. (1994), "Multiscale fourdimensional data assimilation", J. Appl. Meteor., 33, 416-434.   DOI
28 Warner, T.T., Bowers, J.F., Swerdlin, S.P. and Beitler, B.A. (2004), "A rapidly deployable operational mesoscale modeling system for emergency-response applications", Bull. Am. Meteorol. Soc., 85(5), 709-716.   DOI
29 Weerasuriya, A.U., Hu, Z.Z., Li, S.W. and Tse, K.T. (2016), "Wind direction field under the influence of topography, part I: a descriptive model", Wind Struct., 22(4), 455-476.   DOI
30 Xu, M., Liu, Y., Davis, C.A. and Warner, T.T. (2002), "Sensitivity study on nudging parameters for a mesoscale FDDA system". Proceedings of the 19th Conference on Weather Analysis and Forecasting/15th Conference on Numerical Weather Prediction, San Antonio, USA, July.
31 Zajaczkowski, F.J., Haupt, S.E. and Schmehl, K.J. (2011), "A preliminary study of assimilating numerical weather prediction data into computational fluid dynamics models for wind prediction", J. Wind Eng. Ind. Aerod., 99(4), 320-329.   DOI
32 Zhang, Y., Liu, Y. and Nipen, T. (2016), "Evaluation of the impacts of assimilating the TAMDAR data on 12/4 km grid WRF-based RTFDDA simulations over the CONUS", 2016, 1-13.