Articles | Volume 8, issue 2
https://doi.org/10.5194/ascmo-8-205-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-8-205-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
02 Dec 2022
| 02 Dec 2022
A conditional approach for joint estimation of wind speed and direction under future climates
Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA
Julie Bessac
Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
Whitney Huang
School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, USA
Jiali Wang
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
Rao Kotamarthi
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
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Cited articles
Abatzoglou, J. T., Hatchett, B. J., Fox-Hughes, P., Gershunov, A., and Nauslar,
N. J.: Global climatology of synoptically-forced downslope winds, 41, 31–50,
https://doi.org/10.1002/joc.6607, 2021. a
Ailliot, P., Bessac, J., Monbet, V., and Pene, F.: Non-homogeneous hidden
Markov-switching models for wind time series, J. Statist.
Plan. Inf., 160, 75–88, 2015. a
Akinsanola, A. A., Ogunjobi, K. O., Abolude, A. T., and Salack, S.: Projected
changes in wind speed and wind energy potential over West Africa in CMIP6
models, Environ. Res. Lett., 16, 044033, https://doi.org/10.1088/1748-9326/abed7a, 2021. a
Bessac, J., Ailliot, P., Cattiaux, J., and Monbet, V.: Comparison of hidden and
observed regime-switching autoregressive models for (u,v)-components of wind
fields in the Northeast Atlantic, Adv. Statist. Climatol.,
Meteorology and Oceanography, 2, 1–16, 2016. a
Bessac, J., Monahan, A. H., Christensen, H. M., and Weitzel, N.: Stochastic
Parameterization of Subgrid-Scale Velocity Enhancement of Sea Surface Fluxes,
Mon. Weather Rev., 147, 1447–1469, https://doi.org/10.1175/MWR-D-18-0384.1, 2019. a
Bessac, J., Christensen, H. M., Endo, K., Monahan, A. H., and Weitzel, N.:
Scale-aware space-time stochastic parameterization of subgrid-scale velocity
enhancement of sea surface fluxes, J. Adv. Model. Earth
Sy., 13, e2020MS002367, https://doi.org/10.1029/2020MS002367,
2021. a, b
Bogardi, I. and Matyasovzky, I.: Estimating daily wind speed under climate
change, Solar Energ., 57, 239–248, 1996. a
Breslow, P. B. and Sailor, D. J.: Vulnerability of wind power resources to
climate change in the continental United States, Renew. Energ., 27,
585–598, 2002. a
Brown, B. G., Katz, R. W., and Murphy, A. H.: Time series models to simulate
and forecast wind speed and wind power, J. Clim. Appl.
Meteorol., 23, 1184–1195, 1984. a
Bukovsky, M. S. and Karoly, D. J.: A regional modeling study of climate change
impacts on warm-season precipitation in the central United States, J.
Climate, 24, 1985–2002, 2011. a
Cheng, C. S., Lopes, E., Fu, C., and Huang, Z.: Possible impacts of climate
change on wind gusts under downscaled future climate conditions: Updated
for Canada, J. Climate, 27, 1255–1270, 2014. a
Coles, S. G. and Walshaw, D.: Directional modelling of extreme wind speeds,
J. Roy. Statist. Soc. C, 43,
139–157, 1994. a
Constantinescu, E., Zavala, V., Rocklin, M., Lee, S., and Anitescu, M.: A
computational framework for uncertainty quantification and stochastic
optimization in unit commitment with wind power generation, IEEE T. Power Syst., 26, 431–441, https://doi.org/10.1109/TPWRS.2010.2048133, 2011. a
Cooley, D., Thibaud, E., Castillo, F., and Wehner, M. F.: A nonparametric
method for producing isolines of bivariate exceedance probabilities,
Extremes, 22, 1–18, 2019. a
Dempster, A. P., Laird, N. M., and Rubin, D. B.: Maximum likelihood from
incomplete data via the EM algorithm, J. Roy. Statist.
Soc. B, 39, 1–22, 1977. a
Deser, C., Knutti, R., Solomon, S., and Phillips, A. S.: Communication of the
role of natural variability in future North American climate, Nat. Clim.
Change, 2, 775–779, 2012. a
Di Luca, A., de Elía, R., and Laprise, R.: Potential for added value in
precipitation simulated by high-resolution nested regional climate models and
observations, Clim. Dynam., 38, 1229–1247, 2012. a
Donner, L.J., Wyman, B. L., Hemler, R. S., Horowitz, L. W., Ming, Y., Zhao, M., Golaz, J. C., Ginoux, P., Lin, S. J., Schwarzkopf, M. D., and Austin, J.: The
dynamical core, physical parameterizations, and basic simulation
characteristics of the atmospheric component AM3 of the GFDL global coupled
model CM3, J. Climate, 24, 3484–3519, 2011. a
Efron, B. and Tibshirani, R. J.: An introduction to the bootstrap, CRC press, https://doi.org/10.1201/9780429246593,
1994. a
Fayle, C. E.: A short history of the world's shipping industry, Taylor &
Francis, https://doi.org/10.4324/9781315020006, 2006. a
Fisher, N. I.: Statistical analysis of circular data, cambridge university
press, https://doi.org/10.1002/bimj.4710380307, 1995. a
Gao, M., Ding, Y., Song, S., Lu, X., Chen, X., and McElroy, M. B.: Secular
decrease of wind power potential in India associated with warming in the
Indian Ocean, Sci. Adv., 4, eaat5256, https://doi.org/10.1126/sciadv.aat5256, 2018. a
Gao, Y., Fu, J. S., Drake, J., Liu, Y., and Lamarque, J.-F.: Projected changes
of extreme weather events in the eastern United States based on a high
resolution climate modeling system, Environ. Res. Lett., 7,
044025, https://doi.org/10.1088/1748-9326/7/4/044025, 2012. a
Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. C.,
Jayne, S. R., Lawrence, D. M., Neale, R. B., Rasch, P. J., Vertenstein, M., and Worley, P. H.: The community climate system model version 4, J. Climate, 24,
4973–4991, 2011. a
Giorgi, F. and Mearns, L. O.: Introduction to Special Section: Regional Climate Modeling Revisited, J. Geophys. Res., 104, 6335–6352,
https://doi.org/10.1029/98JD02072, 1999. a
Hawkins, E. and Sutton, R.: The potential to narrow uncertainty in regional
climate predictions, B. Am. Meteorol. Soc., 90,
1095–1108, 2009. a
He, Y., Monahan, A. H., Jones, C. G., Dai, A., Biner, S., Caya, D., and Winger,
K.: Probability distributions of land surface wind speeds over North America,
J. Geophys. Res.-Atmos., 115, 1–19, 2010. a
Hill, G. W.: Algorithm 518: Incomplete Bessel Function I 0. The Von
Mises Distribution [S14], ACM T. Math. Softw., 3, 279–284, 1977. a
Holmes, J. D.: Wind loading of structures, CRC press, https://doi.org/10.1201/b18029, 2018. a
Hornik, K. and Grün, B.: movMF: an R package for fitting mixtures of von
Mises-Fisher distributions, J. Stat. Softw., 58, 1–31,
2014. a
Hsu, S. A., Meindl, E. A., and Gilhousen, D. B.: Determining the power-law
wind-profile exponent under near-neutral stability conditions at sea, J. Appl. Meteorol., 33, 757–765, 1994. a
Irish, J. L., Resio, D. T., and Ratcliff, J. J.: The influence of storm size on
hurricane surge, J. Phys. Oceanogr., 38, 2003–2013, 2008. a
Jones, C. D., Hughes, J. K., Bellouin, N., Hardiman, S. C., Jones, G. S., Knight, J., Liddicoat, S., O'Connor, F. M., Andres, R. J., Bell, C., Boo, K.-O., Bozzo, A., Butchart, N., Cadule, P., Corbin, K. D., Doutriaux-Boucher, M., Friedlingstein, P., Gornall, J., Gray, L., Halloran, P. R., Hurtt, G., Ingram, W. J., Lamarque, J.-F., Law, R. M., Meinshausen, M., Osprey, S., Palin, E. J., Parsons Chini, L., Raddatz, T., Sanderson, M. G., Sellar, A. A., Schurer, A., Valdes, P., Wood, N., Woodward, S., Yoshioka, M., and Zerroukat, M.: The HadGEM2-ES implementation of CMIP5 centennial simulations, Geosci. Model Dev., 4, 543–570, https://doi.org/10.5194/gmd-4-543-2011, 2011. a
Kneib, T., Silbersdorff, A., and Säfken, B.: Rage against the mean – a
review of distributional regression approaches, Econom. Statist., https://doi.org/10.1016/j.ecosta.2021.07.006, online first,
2021. a, b
Li, X., Zhong, S., Bian, X., and Heilman, W. E.: Climate and climate
variability of the wind power resources in the Great Lakes region of the
United States, J. Geophys. Res.-Atmos., 115, 1–15, 2010. a
Liang, X.-Z., Kunkel, K. E., Meehl, G. A., Jones, R. G., and Wang, J. X.:
Regional climate models downscaling analysis of general circulation models
present climate biases propagation into future change projections,
Geophys. Res. Lett., 35, 1–5, 2008. a
Lu, R., Turan, O., and Boulougouris, E.: Voyage optimization, prediction of
ship specific fuel consumption for energy efficient shipping, 3rd International Conference onTechnologies, Operations, Logistics and Modelling for Low Carbon Shipping, London, United Kingdom, 1–11, 2013. a
Lucas-Picher, P., Caya, D., de Elía, R., and Laprise, R.: Investigation of
regional climate models' internal variability with a ten-member ensemble of
10-year simulations over a large domain, Clim. Dynam., 31, 927–940,
2008. a
Mardia, K. and Sutton, T.: On the modes of a mixture of two von Mises
distributions, Biometrika, 62, 699–701, 1975. a
Mardia, K. V.: Statistics of directional data, J. Roy. Stat.
Soc. B, 37, 349–371, 1975. a
Mardia, K. V. and Jupp, P. E.: Directional statistics, vol. 494, John Wiley &
Sons, ISBN 978-0-471-95333-3, 2009. a
Mendis, P., Ngo, T., Haritos, N., Hira, A., Samali, B., and Cheung, J.: Wind
loading on tall buildings, Electronic Journal of Structural Engineering, 41–54,
2007. a
Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P. C., Ebisuzaki,
W., Jović, D., Woollen, J., Rogers, E., Berbery, E. H., and Ek, M. B.: North
American regional reanalysis, B. Am. Meteorol.
Soc., 87, 343–360, 2006. a
Mosteller, F. and Tukey, J. W.: Data analysis and regression: a second course
in statistics, ISBN 9780201048544, 1977. a
Musial, W., Spitsen, P., Duffy, P., Beiter, P., Marquis, M., Hammond, R., and Shields, M.: Offshore Wind Market Report: 2022 Edition (No. NREL/TP-5000-83544), National Renewable Energy Lab. (NREL), Golden, CO (United States), 2022. a
Pinson, P.: Wind energy: Forecasting challenges for its operational
management, Stat. Sci., 28, 564–585, 2013. a
Pinson, P., Madsen, H., Nielsen, H., Papaefthymiou, G., and Klöckl, B.:
From probabilistic forecasts to statistical scenarios of short-term wind
power production, Wind Energy, 12, 51–62, 2009. a
Pryor, S. C. and Barthelmie, R. J.: Climate change impacts on wind energy: A
review, Renewable and sustainable energy reviews, 14, 430–437, 2010. a
Pryor, S. C., Barthelmie, R. J., Clausen, N.-E., Drews, M., MacKellar, N., and
Kjellström, E.: Analyses of possible changes in intense and extreme wind
speeds over northern Europe under climate change scenarios, Clim. Dynam.,
38, 189–208, 2012. a
Reyers, M., Moemken, J., and Pinto, J. G.: Future changes of wind energy
potentials over Europe in a large CMIP5 multi-model ensemble,
Int. J. Climatol., 36, 783–796, 2016. a
Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann,
G., Nakicenovic, N., and Rafaj, P.: RCP 8.5 – A scenario of comparatively
high greenhouse gas emissions, Clim. Change, 109, 33–57, 2011. a
Rusu, L., Raileanu, A. B., and Onea, F.: A comparative analysis of the wind and
wave climate in the Black Sea along the shipping routes, Water, 10, 924, https://doi.org/10.3390/w10070924,
2018. a
Sailor, D. J., Smith, M., and Hart, M.: Climate change implications for wind
power resources in the Northwest United States, Renewable Energy, 33,
2393–2406, 2008. a
Sherwood, S. C., Bony, S., and Dufresne, J.-L.: Spread in model climate
sensitivity traced to atmospheric convective mixing, Nature, 505, 37–42,
2014. a
Solari, S. and Losada, M. Á.: Simulation of non-stationary wind speed and
direction time series, J. Wind Eng. Ind.
Aerod., 149, 48–58, 2016. a
Toro, G. R., Resio, D. T., Divoky, D., Niedoroda, A. W., and Reed, C.:
Efficient joint-probability methods for hurricane surge frequency analysis,
Ocean Eng., 37, 125–134, 2010. a
Wang, J. and Kotamarthi, V. R.: High-resolution dynamically downscaled
projections of precipitation in the mid and late 21st century over North
America, Earth's Future, 3, 268–288, 2015. a
Wang, J., Swati, F., Stein, M. L., and Kotamarthi, V. R.: Model performance in
spatiotemporal patterns of precipitation: New methods for identifying value
added by a regional climate model, J. Geophys. Res.-Atmos., 120, 1239–1259, 2015. a
Westerling, A. L., Cayan, D. R., Brown, T. J., Hall, B. L., and Riddle, L. G.:
Climate, Santa Ana winds and autumn wildfires in southern California,
Eos, 85, 289–296, 2004. a
Wilby, R. L., Wigley, T., Conway, D., Jones, P., Hewitson, B., Main, J., and
Wilks, D.: Statistical downscaling of general circulation model output: A
comparison of methods, Water Resour. Res., 34, 2995–3008, 1998. a
Woodruff, J. D., Irish, J. L., and Camargo, S. J.: Coastal flooding by tropical
cyclones and sea-level rise, Nature, 504, 44–52, 2013. a
Wu, Q.: QiuyiWu/Wind-Project: ANLWindProject (ANLWindProject), Zenodo [code], https://doi.org/10.5281/zenodo.7358862, 2022. a
Wu, Q., Bessac, J., Huang, W., and Wang, J.: Wind Data for Station-wise assessment of wind speed and direction under future climates across the United States, Zenodo [data set], https://doi.org/10.5281/zenodo.6425797, 2022. a
Xia, Y., Mitchell, K., Ek, M., Cosgrove, B., Sheffield, J., Luo, L., Alonge,
C., Wei, H., Meng, J., and Livneh, B.: Continental-scale water and energy
flux analysis and validation for North American Land Data Assimilation System
project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow,
J. Geophys. Res.-Atmos., 117, 1–27, 2012a. a
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., and Livneh, B.: Continental-scale water and energy
flux analysis and validation for the North American Land Data Assimilation
System project phase 2 (NLDAS-2): 1. Intercomparison and application of model
products, J. Geophys. Res.-Atmos., 117, 1–27,
2012b. a
Zannetti, P.: Air pollution modeling: theories, computational methods and
available software, Springer Science & Business Media, ISBN 1853121002, 2013. a
Zeng, X., Zhang, Q., Johnson, D., and Tao, W.-K.: Parameterization of wind
gustiness for the computation of ocean surface fluxes at different spatial
scales, Mon. Weather Rev., 130, 2125–2133, 2002. a
Zhang, K., Zhao, C., Wan, H., Qian, Y., Easter, R. C., Ghan, S. J., Sakaguchi, K., and Liu, X.: Quantifying the impact of sub-grid surface wind variability on sea salt and dust emissions in CAM5, Geosci. Model Dev., 9, 607–632, https://doi.org/10.5194/gmd-9-607-2016, 2016. a
Zobel, Z., Wang, J., Wuebbles, D. J., and Kotamarthi, V. R.: Analyses for
high-resolution projections through the end of the 21st century for
precipitation extremes over the United States, Earth's Future, 6, 1471–1490,
2018a. a
Zobel, Z., Wang, J., Wuebbles, D. J., and Kotamarthi, V. R.: Evaluations of
high-resolution dynamically downscaled ensembles over the contiguous United
States, Clim. Dynam., 50, 863–884, 2018b. a
Short summary
We study wind conditions and their potential future changes across the U.S. via a statistical conditional framework. We conclude that changes between historical and future wind directions are small, but wind speeds are generally weakened in the projected period, with some locations being intensified. Moreover, winter wind speeds are projected to decrease in the northwest, Colorado, and the northern Great Plains (GP), while summer wind speeds over the southern GP slightly increase in the future.
We study wind conditions and their potential future changes across the U.S. via a statistical...
