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
Related authors
No articles found.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Related subject area
Statistics
Applying different methods to model dry and wet spells at daily scale in a large range of rainfall regimes across Europe
Comparison of climate time series – Part 5: Multivariate annual cycles
Regridding uncertainty for statistical downscaling of solar radiation
Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate change
Statistical modeling of the space–time relation between wind and significant wave height
Modeling general circulation model bias via a combination of localized regression and quantile mapping methods
Evaluation of simulated responses to climate forcings: a flexible statistical framework using confirmatory factor analysis and structural equation modelling – Part 1: Theory
Evaluation of simulated responses to climate forcings: a flexible statistical framework using confirmatory factor analysis and structural equation modelling – Part 2: Numerical experiment
Comparing climate time series – Part 2: A multivariate test
Forecast score distributions with imperfect observations
Novel measures for summarizing high-resolution forecast performance
Copula approach for simulated damages caused by landfalling US hurricanes
Nonstationary extreme value analysis for event attribution combining climate models and observations
Comparing climate time series – Part 1: Univariate test
A statistical approach to fast nowcasting of lightning potential fields
Spatial trend analysis of gridded temperature data at varying spatial scales
An improved projection of climate observations for detection and attribution
Bivariate Gaussian models for wind vectors in a distributional regression framework
Fitting a stochastic fire spread model to data
Influence of initial ocean conditions on temperature and precipitation in a coupled climate model's solution
NWP-based lightning prediction using flexible count data regression
An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging
Probabilistic evaluation of competing climate models
Assessing NARCCAP climate model effects using spatial confidence regions
Generalised block bootstrap and its use in meteorology
Estimating trends in the global mean temperature record
Reconstruction of spatio-temporal temperature from sparse historical records using robust probabilistic principal component regression
Analysis of variability of tropical Pacific sea surface temperatures
Evaluating NARCCAP model performance for frequencies of severe-storm environments
Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions
A comparison of two methods for detecting abrupt changes in the variance of climatic time series
Calibrating regionally downscaled precipitation over Norway through quantile-based approaches
Comparison of hidden and observed regime-switching autoregressive models for (u, v)-components of wind fields in the northeastern Atlantic
Autoregressive spatially varying coefficients model for predicting daily PM2.5 using VIIRS satellite AOT
Bivariate spatial analysis of temperature and precipitation from general circulation models and observation proxies
Joint inference of misaligned irregular time series with application to Greenland ice core data
Simulation of future climate under changing temporal covariance structures
Giorgio Baiamonte, Carmelo Agnese, Carmelo Cammalleri, Elvira Di Nardo, Stefano Ferraris, and Tommaso Martini
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 51–67, https://doi.org/10.5194/ascmo-10-51-2024, https://doi.org/10.5194/ascmo-10-51-2024, 2024
Short summary
Short summary
In hydrology, the probability distributions are used to determine the probability of occurrence of rainfall events. In this study, two different methods for modeling rainfall time characteristics have been applied: a direct method and an indirect method that make it possible to relax the assumptions of the renewal process. The analysis was extended to two additional time variables that may be of great interest for practical hydrological applications: wet chains and dry chains.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 1–27, https://doi.org/10.5194/ascmo-10-1-2024, https://doi.org/10.5194/ascmo-10-1-2024, 2024
Short summary
Short summary
This paper introduces a method to assess whether two data sets come from the same source. Current methods do not adequately consider spatial and temporal correlations and their annual cycles in a comprehensive test. This method addresses that gap, thereby providing a new and rigorous tool for evaluating the realism of climate simulations and measuring changes in variability over time.
Maggie D. Bailey, Douglas Nychka, Manajit Sengupta, Aron Habte, Yu Xie, and Soutir Bandyopadhyay
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 103–120, https://doi.org/10.5194/ascmo-9-103-2023, https://doi.org/10.5194/ascmo-9-103-2023, 2023
Short summary
Short summary
To ensure photovoltaic (PV) plants last, we need to understand how climate change affects solar radiation. Climate models help predict future radiation for PV plants, but there is often uncertainty. We explore this uncertainty and its impact on building PV plants. We highlight the importance of considering uncertainties for accurate planning and management. A California case study shows a practical application for the solar industry.
Joel Zeder and Erich M. Fischer
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 83–102, https://doi.org/10.5194/ascmo-9-83-2023, https://doi.org/10.5194/ascmo-9-83-2023, 2023
Short summary
Short summary
The intensities of recent heatwave events, such as the record-breaking heatwave in early June 2021 in the Pacific Northwest area, are substantially altered by climate change. We further quantify the contribution of the local weather situation and the land surface conditions with a statistical model suited for extreme data. Based on this method, we can answer
what ifquestions, such as estimating the change in the 2021 heatwave temperature if it happened in a world without climate change.
Said Obakrim, Pierre Ailliot, Valérie Monbet, and Nicolas Raillard
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 67–81, https://doi.org/10.5194/ascmo-9-67-2023, https://doi.org/10.5194/ascmo-9-67-2023, 2023
Short summary
Short summary
Ocean wave climate has a significant impact on human activities, and its understanding is of socioeconomic and environmental importance. In this study, we propose a statistical model that predicts wave heights in a location in the Bay of Biscay. The proposed method allows us to understand the spatiotemporal relationship between wind and waves and predicts well both wind seas and swells.
Benjamin James Washington, Lynne Seymour, and Thomas L. Mote
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 1–28, https://doi.org/10.5194/ascmo-9-1-2023, https://doi.org/10.5194/ascmo-9-1-2023, 2023
Short summary
Short summary
We develop new methodology to statistically model known bias in general atmospheric circulation models. We focus on Puerto Rico specifically because of other important ongoing and long-term ecological and environmental research taking place there. Our methods work even in the presence of Puerto Rico's broken climate record. With our methods, we find that climate change will not only favor a warmer and wetter climate in Puerto Rico, but also increase the frequency of extreme rainfall events.
Katarina Lashgari, Gudrun Brattström, Anders Moberg, and Rolf Sundberg
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 225–248, https://doi.org/10.5194/ascmo-8-225-2022, https://doi.org/10.5194/ascmo-8-225-2022, 2022
Short summary
Short summary
This work theoretically motivates an extension of the statistical model used in so-called detection and attribution studies to structural equation modelling. The application of one of the models suggested is exemplified in a small numerical study, whose aim was to check the assumptions typically placed on ensembles of climate model simulations when constructing mean sequences. he result of this study indicated that some ensembles for some regions may not satisfy the assumptions in question.
Katarina Lashgari, Anders Moberg, and Gudrun Brattström
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 249–271, https://doi.org/10.5194/ascmo-8-249-2022, https://doi.org/10.5194/ascmo-8-249-2022, 2022
Short summary
Short summary
The performance of a new statistical framework containing various structural equation modelling (SEM) models is evaluated in a pseudo-proxy experiment in comparison with the performance of statistical models used in many detection and attribution studies. Each statistical model was fitted to seven continental-scale regional temperature data sets. The results indicated the SEM specification is the most appropriate for describing the underlying latent structure of the simulated data analysed.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 73–85, https://doi.org/10.5194/ascmo-7-73-2021, https://doi.org/10.5194/ascmo-7-73-2021, 2021
Short summary
Short summary
After a new climate model is constructed, a natural question is whether it generates realistic simulations. Here,
realisticdoes not mean that the detailed patterns on a particular day are correct, but rather that the statistics over many years are realistic. Past approaches to answering this question often neglect correlations in space and time. This paper proposes a method for answering this question that accounts for correlations in space and time.
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
Short summary
Short summary
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.
Eric Gilleland
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 13–34, https://doi.org/10.5194/ascmo-7-13-2021, https://doi.org/10.5194/ascmo-7-13-2021, 2021
Short summary
Short summary
Verifying high-resolution weather forecasts has become increasingly complicated,
and simple, easy-to-understand summary measures are a good alternative. Recent work has demonstrated some common pitfalls with many such summaries. Here, new summary measures are introduced that do not suffer from these drawbacks, while still providing meaningful information.
Thomas Patrick Leahy
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 1–11, https://doi.org/10.5194/ascmo-7-1-2021, https://doi.org/10.5194/ascmo-7-1-2021, 2021
Short summary
Short summary
This study looked at estimating damages caused by hurricanes in the United States. It assessed the relationship between the maximum wind speed at landfall and the resulting damage caused. The study found that the complex processes that determine the size of the damages inflicted could be estimated using this simple relationship. This work could be used to examine how often extreme damage events are likely to occur and the impact of stronger hurricane winds on the US Atlantic and Gulf coasts.
Yoann Robin and Aurélien Ribes
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 205–221, https://doi.org/10.5194/ascmo-6-205-2020, https://doi.org/10.5194/ascmo-6-205-2020, 2020
Short summary
Short summary
We have developed a new statistical method to describe how a severe weather event, such as a heat wave, may have been influenced by climate change. Our method incorporates both observations and data from various climate models to reflect climate model uncertainty. Our results show that both the probability and the intensity of the French July 2019 heatwave have increased significantly in response to human influence. We find that this heat wave might not have been possible without climate change.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 159–175, https://doi.org/10.5194/ascmo-6-159-2020, https://doi.org/10.5194/ascmo-6-159-2020, 2020
Short summary
Short summary
Scientists often are confronted with the question of whether two time series are statistically distinguishable. This paper proposes a test for answering this question. The basic idea is to fit each time series to a time series model and then test whether the parameters in that model are equal. If a difference is detected, then new ways of visualizing those differences are proposed, including a clustering technique and a method based on optimal initial conditions.
Joshua North, Zofia Stanley, William Kleiber, Wiebke Deierling, Eric Gilleland, and Matthias Steiner
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 79–90, https://doi.org/10.5194/ascmo-6-79-2020, https://doi.org/10.5194/ascmo-6-79-2020, 2020
Short summary
Short summary
Very short-term forecasting, called nowcasting, is used to monitor storms that pose a significant threat to people and infrastructure. These threats could include lightning strikes, hail, heavy precipitation, strong winds, and possible tornados. This paper proposes a fast approach to nowcasting lightning threats using simple statistical methods. The proposed model results in fast nowcasts that are more accurate than a competitive, computationally expensive, approach.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
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...
