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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Lindsay M. Sheridan, Jiali Wang, Caroline Draxl, Nicola Bodini, Caleb Phillips, Dmitry Duplyakin, Heidi Tinnesand, Raj K. Rai, Julia E. Flaherty, Larry K. Berg, Chunyong Jung, and Ethan Young
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-115, https://doi.org/10.5194/wes-2024-115, 2024
Revised manuscript under review for WES
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Three recent wind resource datasets are assessed for their skills in representing annual average wind speeds and seasonal, diurnal, and inter-annual trends in the wind resource to support customers interested in small and midsize wind energy.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William Sacks, Ethan Coon, and Robert Hetland
EGUsphere, https://doi.org/10.5194/egusphere-2024-1555, https://doi.org/10.5194/egusphere-2024-1555, 2024
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We integrate E3SM land model (ELM) with the WRF Model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) – Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM, and ESMF caps for ELM initialization, execution, and finalization. The LILAC-ESMF framework maintains the integrity of the ELM’s source code structure and facilitates the transfer of future developments in LSMs to WRF-ELM.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
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This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Chuxuan Li, Alexander L. Handwerger, Jiali Wang, Wei Yu, Xiang Li, Noah J. Finnegan, Yingying Xie, Giuseppe Buscarnera, and Daniel E. Horton
Nat. Hazards Earth Syst. Sci., 22, 2317–2345, https://doi.org/10.5194/nhess-22-2317-2022, https://doi.org/10.5194/nhess-22-2317-2022, 2022
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In January 2021 a storm triggered numerous debris flows in a wildfire burn scar in California. We use a hydrologic model to assess debris flow susceptibility in pre-fire and postfire scenarios. Compared to pre-fire conditions, postfire conditions yield dramatic increases in peak water discharge, substantially increasing debris flow susceptibility. Our work highlights the hydrologic model's utility in investigating and potentially forecasting postfire debris flows at regional scales.
Caleb Phillips, Lindsay M. Sheridan, Patrick Conry, Dimitrios K. Fytanidis, Dmitry Duplyakin, Sagi Zisman, Nicolas Duboc, Matt Nelson, Rao Kotamarthi, Rod Linn, Marc Broersma, Timo Spijkerboer, and Heidi Tinnesand
Wind Energ. Sci., 7, 1153–1169, https://doi.org/10.5194/wes-7-1153-2022, https://doi.org/10.5194/wes-7-1153-2022, 2022
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Adoption of distributed wind turbines for energy generation is hindered by challenges associated with siting and accurate estimation of the wind resource. This study evaluates classic and commonly used methods alongside new state-of-the-art models derived from simulations and machine learning approaches using a large dataset from the Netherlands. We find that data-driven methods are most effective at predicting production at real sites and new models reliably outperform classic methods.
Romit Maulik, Vishwas Rao, Jiali Wang, Gianmarco Mengaldo, Emil Constantinescu, Bethany Lusch, Prasanna Balaprakash, Ian Foster, and Rao Kotamarthi
Geosci. Model Dev., 15, 3433–3445, https://doi.org/10.5194/gmd-15-3433-2022, https://doi.org/10.5194/gmd-15-3433-2022, 2022
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In numerical weather prediction, data assimilation is frequently utilized to enhance the accuracy of forecasts from equation-based models. In this work we use a machine learning framework that approximates a complex dynamical system given by the geopotential height. Instead of using an equation-based model, we utilize this machine-learned alternative to dramatically accelerate both the forecast and the assimilation of data, thereby reducing need for large computational resources.
Jiali Wang, Zhengchun Liu, Ian Foster, Won Chang, Rajkumar Kettimuthu, and V. Rao Kotamarthi
Geosci. Model Dev., 14, 6355–6372, https://doi.org/10.5194/gmd-14-6355-2021, https://doi.org/10.5194/gmd-14-6355-2021, 2021
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Downscaling, the process of generating a higher spatial or time dataset from a coarser observational or model dataset, is a widely used technique. Two common methodologies for performing downscaling are to use either dynamic (physics-based) or statistical (empirical). Here we develop a novel methodology, using a conditional generative adversarial network (CGAN), to perform the downscaling of a model's precipitation forecasts and describe the advantages of this method compared to the others.
Julie Bessac and Philippe Naveau
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 53–71, https://doi.org/10.5194/ascmo-7-53-2021, https://doi.org/10.5194/ascmo-7-53-2021, 2021
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We propose a new forecast evaluation scheme in the context of models that incorporate errors of the verification data. We rely on existing scoring rules and incorporate uncertainty and error of the verification data through a hidden variable and the conditional expectation of scores. By considering scores to be random variables, one can access the entire range of their distribution and illustrate that the commonly used mean score can be a misleading representative of the distribution.
Jaydeep Singh, Narendra Singh, Narendra Ojha, Amit Sharma, Andrea Pozzer, Nadimpally Kiran Kumar, Kunjukrishnapillai Rajeev, Sachin S. Gunthe, and V. Rao Kotamarthi
Geosci. Model Dev., 14, 1427–1443, https://doi.org/10.5194/gmd-14-1427-2021, https://doi.org/10.5194/gmd-14-1427-2021, 2021
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Atmospheric models often have limitations in simulating the geographically complex and climatically important central Himalayan region. In this direction, we have performed regional modeling at high resolutions to improve the simulation of meteorology and dynamics through a better representation of the topography. The study has implications for further model applications to investigate the effects of anthropogenic pressure over the Himalaya.
Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi
Geosci. Model Dev., 12, 4261–4274, https://doi.org/10.5194/gmd-12-4261-2019, https://doi.org/10.5194/gmd-12-4261-2019, 2019
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Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Jiali Wang, Cheng Wang, Vishwas Rao, Andrew Orr, Eugene Yan, and Rao Kotamarthi
Geosci. Model Dev., 12, 3523–3539, https://doi.org/10.5194/gmd-12-3523-2019, https://doi.org/10.5194/gmd-12-3523-2019, 2019
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WRF-Hydro needs to be calibrated to optimize its output with respect to observations. However, when applied to a relatively large domain, both WRF-Hydro simulations and calibrations require intensive computing resources and are best performed in parallel. This study ported an independent calibration tool (parameter estimation tool – PEST) to high-performance computing clusters and adapted it to work with WRF-Hydro. The results show significant speedup for model calibration.
Jeffrey D. Mirocha, Matthew J. Churchfield, Domingo Muñoz-Esparza, Raj K. Rai, Yan Feng, Branko Kosović, Sue Ellen Haupt, Barbara Brown, Brandon L. Ennis, Caroline Draxl, Javier Sanz Rodrigo, William J. Shaw, Larry K. Berg, Patrick J. Moriarty, Rodman R. Linn, Veerabhadra R. Kotamarthi, Ramesh Balakrishnan, Joel W. Cline, Michael C. Robinson, and Shreyas Ananthan
Wind Energ. Sci., 3, 589–613, https://doi.org/10.5194/wes-3-589-2018, https://doi.org/10.5194/wes-3-589-2018, 2018
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This paper validates the use of idealized large-eddy simulations with periodic lateral boundary conditions to provide boundary-layer flow quantities of interest for wind energy applications. Sensitivities to model formulation, forcing parameter values, and grid configurations were also examined, both to ascertain the robustness of the technique and to characterize inherent uncertainties, as required for the evaluation of more general wind plant flow simulation approaches under development.
K. K. Shukla, K. Niranjan Kumar, D. V. Phanikumar, R. K. Newsom, V. R. Kotamarthi, T. B. M. J. Ouarda, and M. V. Ratnam
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-162, https://doi.org/10.5194/amt-2016-162, 2016
Revised manuscript not accepted
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Estimation of Cloud base height was carried out by using various ground based instruments (Doppler Lidar and Ceilometer) and satellite datasets (MODIS) over central Himalayan region for the first time. The present study demonstrates the potential of Doppler Lidar in precise estimation of cloud base height and updraft velocities. More such deployments will be invaluable inputs for regional weather prediction models over complex Himalayan terrains.
Julie Bessac, Pierre Ailliot, Julien Cattiaux, and Valerie Monbet
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 1–16, https://doi.org/10.5194/ascmo-2-1-2016, https://doi.org/10.5194/ascmo-2-1-2016, 2016
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Several multi-site stochastic generators of zonal and meridional components of wind are proposed in this paper. Various questions are explored, such as the modeling of the regime in a multi-site context, the extraction of relevant clusterings from extra variables or from the local wind data, and the link between weather types extracted from wind data and large-scale weather regimes. We also discuss the relative advantages of hidden and observed regime-switching models.
Y. Feng, V. R. Kotamarthi, R. Coulter, C. Zhao, and M. Cadeddu
Atmos. Chem. Phys., 16, 247–264, https://doi.org/10.5194/acp-16-247-2016, https://doi.org/10.5194/acp-16-247-2016, 2016
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Aerosol radiative effects are of great importance for climate studies over South Asia, such as the weakening of the South Asian monsoon in the 20th century. This study reveals the altitude dependence of commonly underestimated aerosol radiative properties over this region. It further demonstrates the importance of constraining aerosol vertical distributions and partitioning of scattering vs absorbing aerosols in simulating the subsequent regional dynamical and hydrological responses to aerosols.
B. A. Drewniak, U. Mishra, J. Song, J. Prell, and V. R. Kotamarthi
Biogeosciences, 12, 2119–2129, https://doi.org/10.5194/bg-12-2119-2015, https://doi.org/10.5194/bg-12-2119-2015, 2015
V. S. Manoharan, R. Kotamarthi, Y. Feng, and M. P. Cadeddu
Atmos. Chem. Phys., 14, 1159–1165, https://doi.org/10.5194/acp-14-1159-2014, https://doi.org/10.5194/acp-14-1159-2014, 2014
Y. Feng, V. Ramanathan, and V. R. Kotamarthi
Atmos. Chem. Phys., 13, 8607–8621, https://doi.org/10.5194/acp-13-8607-2013, https://doi.org/10.5194/acp-13-8607-2013, 2013
B. Drewniak, J. Song, J. Prell, V. R. Kotamarthi, and R. Jacob
Geosci. Model Dev., 6, 495–515, https://doi.org/10.5194/gmd-6-495-2013, https://doi.org/10.5194/gmd-6-495-2013, 2013
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Ola Haug, Thordis L. Thorarinsdottir, Sigrunn H. Sørbye, and Christian L. E. Franzke
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 1–12, https://doi.org/10.5194/ascmo-6-1-2020, https://doi.org/10.5194/ascmo-6-1-2020, 2020
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Trends in gridded temperature data are commonly assessed independently for each grid cell, ignoring spatial coherencies. This may severely affect the interpretation of the results. This article proposes a space–time model for temperatures that allows for joint assessments of the trend across locations. In a case study of summer season trends in Europe, it is found that the region with a significant trend under spatial coherency is vastly different from that under independent assessments.
Alexis Hannart
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 161–171, https://doi.org/10.5194/ascmo-5-161-2019, https://doi.org/10.5194/ascmo-5-161-2019, 2019
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In climate change attribution studies, one often seeks to maximize a signal-to-noise ratio, where the
signalis the anthropogenic response and the
noiseis climate variability. A solution commonly used in D&A studies thus far consists of projecting the signal on the subspace spanned by the leading eigenvectors of climate variability. Here I show that this approach is vastly suboptimal – in fact, it leads instead to maximizing the noise-to-signal ratio. I then describe an improved solution.
Moritz N. Lang, Georg J. Mayr, Reto Stauffer, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 115–132, https://doi.org/10.5194/ascmo-5-115-2019, https://doi.org/10.5194/ascmo-5-115-2019, 2019
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Accurate wind forecasts are of great importance for decision-making processes in today's society. This work presents a novel probabilistic post-processing method for wind vector forecasts employing a bivariate Gaussian response distribution. To capture a possible mismatch between the predicted and observed wind direction caused by location-specific properties, the approach incorporates a smooth rotation of the wind direction conditional on the season and the forecasted ensemble wind direction.
X. Joey Wang, John R. J. Thompson, W. John Braun, and Douglas G. Woolford
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 57–66, https://doi.org/10.5194/ascmo-5-57-2019, https://doi.org/10.5194/ascmo-5-57-2019, 2019
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This paper presents the analysis of data from small-scale laboratory experimental smouldering fires that were digitally video-recorded. The video images of these fires bear a resemblance to remotely sensed images of wildfires and provide an opportunity to fit and assess a spatial model for fire spread that attempts to account for uncertainty in fire growth. We found that the fitting method is feasible, and the spatial model provides a suitable mathematical for the fire spread process.
Robin Tokmakian and Peter Challenor
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 17–35, https://doi.org/10.5194/ascmo-5-17-2019, https://doi.org/10.5194/ascmo-5-17-2019, 2019
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As an example of how to robustly determine climate model uncertainty, the paper describes an experiment that perturbs the initial conditions for the ocean's temperature of a climate model. A total of 30 perturbed simulations are used (via an emulator) to estimate spatial uncertainties for temperature and precipitation fields. We also examined (using maximum covariance analysis) how ocean temperatures affect air temperatures and precipitation over land and the importance of feedback processes.
Thorsten Simon, Georg J. Mayr, Nikolaus Umlauf, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 1–16, https://doi.org/10.5194/ascmo-5-1-2019, https://doi.org/10.5194/ascmo-5-1-2019, 2019
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Lightning in Alpine regions is associated with events such as thunderstorms,
extreme precipitation, high wind gusts, flash floods, and debris flows.
We present a statistical approach to predict lightning counts based on
numerical weather predictions. Lightning counts are considered on a grid
with 18 km mesh size. Skilful prediction is obtained for a forecast horizon
of 5 days over complex terrain.
Tony E. Wong
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 53–63, https://doi.org/10.5194/ascmo-4-53-2018, https://doi.org/10.5194/ascmo-4-53-2018, 2018
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Millions of people worldwide are at a risk of coastal flooding, and this number will increase as the climate continues to change. This study analyzes how climate change affects future flood hazards. A new model that uses multiple climate variables for flood hazard is developed. For the case study of Norfolk, Virginia, the model predicts 23 cm higher flood levels relative to previous work. This work shows the importance of accounting for climate change in effectively managing coastal risks.
Amy Braverman, Snigdhansu Chatterjee, Megan Heyman, and Noel Cressie
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 93–105, https://doi.org/10.5194/ascmo-3-93-2017, https://doi.org/10.5194/ascmo-3-93-2017, 2017
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In this paper, we introduce a method for expressing the agreement between climate model output time series and time series of observational data as a probability value. Our metric is an estimate of the probability that one would obtain two time series as similar as the ones under consideration, if the climate model and the observed series actually shared the same underlying climate signal.
Joshua P. French, Seth McGinnis, and Armin Schwartzman
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 67–92, https://doi.org/10.5194/ascmo-3-67-2017, https://doi.org/10.5194/ascmo-3-67-2017, 2017
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We assess the mean temperature effect of global and regional climate model combinations for the North American Regional Climate Change Assessment Program using varying classes of linear regression models, including possible interaction effects. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We conclusively show that accounting for multiple comparisons is important for making proper inference.
László Varga and András Zempléni
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 55–66, https://doi.org/10.5194/ascmo-3-55-2017, https://doi.org/10.5194/ascmo-3-55-2017, 2017
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This paper proposes a new generalisation of the block bootstrap methodology, which allows for any positive real number as expected block size. We use this bootstrap for determining the p values of a homogeneity test for copulas. The methods are applied to a temperature data set - we have found some significant changes in the dependence structure between the standardised temperature values of pairs of observation points within the Carpathian Basin.
Andrew Poppick, Elisabeth J. Moyer, and Michael L. Stein
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 33–53, https://doi.org/10.5194/ascmo-3-33-2017, https://doi.org/10.5194/ascmo-3-33-2017, 2017
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We show that ostensibly empirical methods of analyzing trends in the global mean temperature record, which appear to de-emphasize assumptions, can nevertheless produce misleading inferences about trends and associated uncertainty. We illustrate how a simple but physically motivated trend model can provide better-fitting and more broadly applicable results, and show the importance of adequately characterizing internal variability for estimating trend uncertainty.
John Tipton, Mevin Hooten, and Simon Goring
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 1–16, https://doi.org/10.5194/ascmo-3-1-2017, https://doi.org/10.5194/ascmo-3-1-2017, 2017
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We present a statistical framework for the reconstruction of historic temperature patterns from sparse, irregular data collected from observer stations. A common statistical technique for climate reconstruction uses modern era data as a set of temperature patterns that can be used to estimate the spatial temperature patterns. We present a framework for exploration of different assumptions about the sets of patterns used in the reconstruction while providing statistically rigorous estimates.
Georgina Davies and Noel Cressie
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 155–169, https://doi.org/10.5194/ascmo-2-155-2016, https://doi.org/10.5194/ascmo-2-155-2016, 2016
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Sea surface temperature (SST) is a key component of global climate models, particularly in the tropical Pacific Ocean where El Niño and La Nina events have worldwide implications. In our paper, we analyse monthly SSTs in the Niño 3.4 region and find a transformation that removes a spatial mean-variance dependence for each month. For 10 out of 12 months in the year, the transformed monthly time series gave more accurate or as accurate forecasts than those from the untransformed time series.
Eric Gilleland, Melissa Bukovsky, Christopher L. Williams, Seth McGinnis, Caspar M. Ammann, Barbara G. Brown, and Linda O. Mearns
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 137–153, https://doi.org/10.5194/ascmo-2-137-2016, https://doi.org/10.5194/ascmo-2-137-2016, 2016
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Several climate models are evaluated under current climate conditions to determine how well they are able to capture frequencies of severe-storm environments (conditions conducive for the formation of hail storms, tornadoes, etc.). They are found to underpredict the spatial extent of high-frequency areas (such as tornado alley), as well as underpredict the frequencies in the areas.
Whitney K. Huang, Michael L. Stein, David J. McInerney, Shanshan Sun, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 79–103, https://doi.org/10.5194/ascmo-2-79-2016, https://doi.org/10.5194/ascmo-2-79-2016, 2016
Sergei N. Rodionov
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 63–78, https://doi.org/10.5194/ascmo-2-63-2016, https://doi.org/10.5194/ascmo-2-63-2016, 2016
David Bolin, Arnoldo Frigessi, Peter Guttorp, Ola Haug, Elisabeth Orskaug, Ida Scheel, and Jonas Wallin
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 39–47, https://doi.org/10.5194/ascmo-2-39-2016, https://doi.org/10.5194/ascmo-2-39-2016, 2016
Julie Bessac, Pierre Ailliot, Julien Cattiaux, and Valerie Monbet
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 1–16, https://doi.org/10.5194/ascmo-2-1-2016, https://doi.org/10.5194/ascmo-2-1-2016, 2016
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Several multi-site stochastic generators of zonal and meridional components of wind are proposed in this paper. Various questions are explored, such as the modeling of the regime in a multi-site context, the extraction of relevant clusterings from extra variables or from the local wind data, and the link between weather types extracted from wind data and large-scale weather regimes. We also discuss the relative advantages of hidden and observed regime-switching models.
E. M. Schliep, A. E. Gelfand, and D. M. Holland
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 59–74, https://doi.org/10.5194/ascmo-1-59-2015, https://doi.org/10.5194/ascmo-1-59-2015, 2015
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There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the US motivates the need for advanced statistical models to predict air quality metrics. We propose a statistical model that jointly models ground-monitoring station data and satellite-obtained data allowing for temporal and spatial misalignment, missingness, and spatially and temporally varying correlation to enhance prediction of particulate matter.
R. Philbin and M. Jun
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 29–44, https://doi.org/10.5194/ascmo-1-29-2015, https://doi.org/10.5194/ascmo-1-29-2015, 2015
T. K. Doan, J. Haslett, and A. C. Parnell
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 15–27, https://doi.org/10.5194/ascmo-1-15-2015, https://doi.org/10.5194/ascmo-1-15-2015, 2015
W. B. Leeds, E. J. Moyer, and M. L. Stein
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 1–14, https://doi.org/10.5194/ascmo-1-1-2015, https://doi.org/10.5194/ascmo-1-1-2015, 2015
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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...
