Articles | Volume 2, issue 1
https://doi.org/10.5194/ascmo-2-39-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/ascmo-2-39-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
09 Jun 2016
Calibrating regionally downscaled precipitation over Norway through quantile-based approaches
David Bolin
Dept. of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
Arnoldo Frigessi
Dept. of Biostatistics, University of Oslo, Oslo, Norway
Norwegian Computing Center, Oslo, Norway
Peter Guttorp
Dept. of Statistics, University of Washington, Seattle, WA, USA
Norwegian Computing Center, Oslo, Norway
Norwegian Computing Center, Oslo, Norway
Elisabeth Orskaug
Norwegian Computing Center, Oslo, Norway
Ida Scheel
Dept. of Mathematics, University of Oslo, Oslo, Norway
Jonas Wallin
Dept. of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
Related authors
No articles found.
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.
Related subject area
Statistics
Spatiotemporal methods for estimating subsurface ocean thermal response to tropical cyclones
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
A conditional approach for joint estimation of wind speed and direction under future climates
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
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
Addison J. Hu, Mikael Kuusela, Ann B. Lee, Donata Giglio, and Kimberly M. Wood
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 69–93, https://doi.org/10.5194/ascmo-10-69-2024, https://doi.org/10.5194/ascmo-10-69-2024, 2024
Short summary
Short summary
We introduce a new statistical framework to estimate the change in subsurface ocean temperature following the passage of a tropical cyclone (TC). Our approach combines tools handling seasonal variations and spatial dependence in the data, culminating in a three-dimensional characterization of the interaction between TCs and the ocean. Our work allows us to obtain new scientific insights, and we expect it to be generally applicable to studying the impact of TCs on other ocean phenomena.
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.
Qiuyi Wu, Julie Bessac, Whitney Huang, Jiali Wang, and Rao Kotamarthi
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 205–224, https://doi.org/10.5194/ascmo-8-205-2022, https://doi.org/10.5194/ascmo-8-205-2022, 2022
Short summary
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.
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
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
Bjørge, D. and Haugen, J. E.: Simulation of present-day climate in HIRHAM using 'perfect' boundaries, RegClim Techn. Report 1, NILU, 1998.
Bolin, D., Lindström, J., Eklundh, L., and Lindgren, F.: Fast estimation of spatially dependent temporal vegetation trends using Gaussian Markov random fields, Comput. Statist. and Data Anal., 53, 2885–2896, 2009.
Bolin, D., Frigessi, A., Guttorp, P., Haug, O., Orskaug, E., Scheel, I., and Wallin, J.: BiasCorrection code, available at: https://github.com/JonasWallin/BiasCorrection, last access: 16 March 2016..
Christensen, J. H., Boberg, F., Christensen, O. B., and Lucas-Picher, P.: On the need for bias correction of regional climate change projections of temperature and precipitation, Geophys. Res. Lett., 35, L20709, https://doi.org/10.1029/2008GL035694, 2008.
Christensen, J. H., Kjellstrom, E., Giorgi, F., Lenderink, G., and Rummukainen, M.: Assigning relative weights to regional climate models: Exploring the concept, Clim. Res., 44, 179–194, 2010.
Doksum, K.: Empirical probability plots and statistical inference for nonlinear models in the two-sample case, Ann. Statist., 2, 267–277, 1974.
ENSEMBLES project: RT3/RT2B data portal, available at: http://ensemblesrt3.dmi.dk, last access: 1 November 2009.
Fang, G. H., Yang, J., Chen, Y. N., and Zammit, C.: Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China, Hydrol. Earth Syst. Sci., 19, 2547–2559, https://doi.org/10.5194/hess-19-2547-2015, 2015.
Førland, E., Allerup, P., Dahlström, B., Elomaa, E., Jónsson, T., Madsen, H., Perälä, J., Rissanen, P., Vedin, H., and Vejen, F.: Manual for operational correction of Nordic precipitation data, Tech. Rep. Report 24/96 KLIMA, Norwegian Metorological Institute, 1996.
Furevik, T., Bentsen, M., Drange, H., Kindem, I., Kvamstø, N., and Sorteberg, A.: Description and evaluation of the Bergen climate model: ARPEGE coupled with MICOM, Clim. Dynam., 21, 27–51, 2003.
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012.
Guttorp, P.: Statistics and climate, Ann. Rev. Statist. Applic., 1, 87–101, 2014.
Jansson, A., Tveito, O. E., Pirinen, P., and Scharling, M.: NORDGRID – a preliminary investigation on the potential for creation of a joint Nordic gridded climate dataset, Tech. Rep. 03/2007, Norwegian Meteorological Institute, 2007.
Kerkhoff, C., Künsch, H. R., and Schär, C.: Assessment of bias assumptions for climate models, J. Climate, 27, 6799–6818, 2014.
Lenderink, G., Buishand, A., and van Deursen, W.: Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach, Hydrol. Earth Syst. Sci., 11, 1145–1159, https://doi.org/10.5194/hess-11-1145-2007, 2007.
Maraun, D., Wetterhall, F., Ireson, A., Chandler, R., Kendon, E., Widmann, M., Brienen, S., Rust, H., Sauter, T., Themeßl, M., Venema, V., Chun, K., Goodess, C., Jones, R., Onof, C., Vrac, M., and Thiele-Eich, I.: Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user, Rev. Geophys., 48, rG3003, https://doi.org/10.1029/2009RG000314, 2010.
Matérn, B.: Spatial variation, Meddelanden från statens skogsforskningsinstitut, 49, 1960.
Maule, C., Thejll, P., Christensen, J., Svendsen, S., and Hannaford, J.: Improved confidence in regional climate model simulations of precipitation evaluated using drought statistics from the ENSEMBLES models, Clim. Dynam., 40, 155–173, 2013.
METOBS: Norwegian Meteorological Institute, Climatology Division, met.no gridded climate data 1 × 1 km2, version 1.1, available at: ftp://ftp.met.no/projects/klimagrid, last access: 28 June 2010.
Mishra, V., Kumar, D., Ganguly, A. R., Sanjay, J., Mujundar, M., Krishnan, R., and Shah, R. D.: Reliability of regional and global climate models to simulate precipitation extremes over India, J. Geophys. Res.-Atmos., 119, 9301–9323, 2014.
Monjo, R., Chust, G., and Caselles, V.: Probabilistic correction of RCM precipitation in the Basque Country (Northern Spain), Theor. Appl. Climatol., 117, 217–219, 2014.
Orskaug, E., Scheel, I., Frigessi, A., Guttorp, P., Haugen, J. E., Tveito, O. E., and Haug, O.: Evaluation of a dynamic downscaling of precipitation over the Norwegian mainland, Tellus A, 63, 746–756, 2011.
Piani, C., Haerter, J. O., and Coppola, E.: Statistical bias correction for daily precipitation in regional climate models over Europe, Theor. Appl. Climatol., 99, 87–192, 2010.
Räty, O., Räisänen, J., and Ylhäisi, J.: Evaluation of delta change and bias correction methods for future daily precipitation: intermodel cross-validation using ENSEMBLES simulations, Clim. Dynam., 42, 2287–2303, 2014.
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, available at: https://www.R-project.org/, last access: 20 June 2015.
Rummukainen, M.: State-of-the-art with regional climate models, Wiley Interdisciplinary Reviews: Climate Change, 1, 82–96, 2010.
Samuelsson, P., Jones, C. G., Willén, U., Ullerstig, A., Gollvik, S., Hansson, U., Jansson, C., Kjellström, E., Nikulin, G., and Wyser, K.: The Rossby Centre Regional Climate model RCA3: model description and performance, Tellus A, 63, 4–23, 2011.
Scheel, I. and Hinnerichsen, M.: The Impact of Climate Change on Precipitation-related Insurance Risk: A Study of the Effect of Future Scenarios on Residential Buildings in Norway., The Geneva Papers, 37, 365–376, 2012.
Schmidli, J., Goodess, C. M., Frei, C., Haylock, M. R., Hundecha, Y., Ribalaygua, J., and Schmith, T.: Statistical and dynamical downscaling of precipitation: An evaluation and comparison of scenarios for the European Alps, J. Geophys. Res., 112, D04105, https://doi.org/10.1029/2005JD007026, 2007.
Smith, R. L., Berliner, L. M., and Guttorp, P.: Statisticians comment on status of climate change science, AmStat News, 2010.
Teutschben, C. and Seibert, J.: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods, J. Hydrol., 457, 12–29, 2012.
Themeßl, M., Gobiet, A., and Leuprecht, A.: Empirical-statistical downscaling and error correction of daily precipitation from regional climate models, Int. J. Climatol., 31, 1530–1544, 2011.
Uppala, S. M., Kållberg, P. W., and Simmons, A. J.: The ERA-40 reanalysis, Q. J. Roy. Meteor. Soc., 131, 2961–3012, 2005.
