Advances in Statistical Climatology, Meteorology and Oceanography最新文献

{"title":"Bivariate Gaussian models for wind vectors in a distributional regression framework","authors":"M. Lang, G. Mayr, R. Stauffer, A. Zeileis","doi":"10.5194/ascmo-5-115-2019","DOIUrl":"https://doi.org/10.5194/ascmo-5-115-2019","url":null,"abstract":"Abstract. A new probabilistic post-processing method for wind vectors is presented in a distributional regression framework employing the bivariate Gaussian distribution. In contrast to previous studies, all parameters of the distribution are simultaneously modeled, namely the location and scale parameters for both wind components and also the correlation coefficient between them employing flexible regression splines. To capture a possible mismatch between the predicted and observed wind direction, ensemble forecasts of both wind components are included using flexible two-dimensional smooth functions. This encompasses a smooth rotation of the wind direction conditional on the season and the forecasted ensemble wind direction. The performance of the new method is tested for stations located in plains, in mountain foreland, and within an alpine valley, employing ECMWF ensemble forecasts as explanatory variables for all distribution parameters. The rotation-allowing model shows distinct improvements in terms of predictive skill for all sites compared to a baseline model that post-processes each wind component separately. Moreover, different correlation specifications are tested, and small improvements compared to the model setup with no estimated correlation could be found for stations located in alpine valleys.","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48761250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of initial ocean conditions on temperature and precipitation in a coupled climate model's solution","authors":"R. Tokmakian, P. Challenor","doi":"10.5194/ASCMO-5-17-2019","DOIUrl":"https://doi.org/10.5194/ASCMO-5-17-2019","url":null,"abstract":"Abstract. This paper describes results of an experiment that perturbed the\u0000initial conditions for the ocean's temperature field of the Community Earth\u0000System Model (CESM) with a well defined design. The resulting 30-member\u0000ensemble of CESM simulations, each of 10 years in length, is used to create\u0000an emulator (a nonlinear regression relating the initial conditions to\u0000various outcomes) from the simulators. Through the use of the emulator to\u0000expand the output distribution space, we estimate the spatial uncertainties\u0000at 10 years for surface air temperature, 25 m ocean temperature,\u0000precipitation, and rain. Outside the tropics, basin averages for the\u0000uncertainty in the ocean temperature field range between 0.48 ∘C\u0000(Indian Ocean) and 0.87 ∘C (North Pacific) (2 standard\u0000deviation). The tropical Pacific uncertainty is the largest due to different\u0000phasings of the ENSO signal. Over land areas, the regional temperature\u0000uncertainty varies from 1.03 ∘C (South America) to 10.82 ∘C\u0000(Europe) (2 standard deviation). Similarly, the regional average\u0000uncertainty in precipitation varies from\u00000.001 cm day−1 over Antarctica to\u00000.163 cm day−1 over Australia with a global average of\u00000.075 cm day−1. In general, both temperature and precipitation\u0000uncertainties are larger over land than over the ocean. A maximum covariance\u0000analysis is used to examine how ocean temperatures affect both surface air\u0000temperatures and precipitation over land. The analysis shows that the\u0000tropical Pacific influences the temperature over North America, but the North\u0000America surface temperature is also moderated by the state of the North\u0000Pacific outside the tropics. It also indicates which regions show a high\u0000degree of variance between the simulations in the ensemble and are,\u0000therefore, less predictable. The calculated uncertainties are also compared\u0000to an estimate of internal variability within CESM. Finally, the importance\u0000of feedback processes on the solution of the simulation over the 10 years of\u0000the experiment is quantified. These estimates of uncertainty do not take into\u0000consideration the anthropogenic effect on warming of the atmosphere and ocean.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47423655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NWP-based lightning prediction using flexible count data regression","authors":"T. Simon, G. Mayr, Nikolaus Umlauf, A. Zeileis","doi":"10.5194/ASCMO-5-1-2019","DOIUrl":"https://doi.org/10.5194/ASCMO-5-1-2019","url":null,"abstract":"Abstract. A method to predict lightning by postprocessing numerical weather prediction\u0000(NWP) output is developed for the region of the European Eastern Alps.\u0000Cloud-to-ground (CG) flashes – detected by the ground-based Austrian\u0000Lightning Detection & Information System (ALDIS) network – are counted on\u0000the 18×18 km2 grid of the 51-member NWP ensemble of the European\u0000Centre for Medium-Range Weather Forecasts (ECMWF). These counts serve as the\u0000target quantity in count data regression models for the occurrence of\u0000lightning events and flash counts of CG. The probability of lightning\u0000occurrence is modelled by a Bernoulli distribution. The flash counts are\u0000modelled with a hurdle approach where the Bernoulli distribution is combined\u0000with a zero-truncated negative binomial. In the statistical models the\u0000parameters of the distributions are described by additive predictors, which\u0000are assembled using potentially nonlinear functions of NWP covariates.\u0000Measures of location and spread of 100 direct and derived NWP covariates\u0000provide a pool of candidates for the nonlinear terms. A combination of\u0000stability selection and gradient boosting identifies the nine (three) most\u0000influential terms for the parameters of the Bernoulli (zero-truncated\u0000negative binomial) distribution, most of which turn out to be associated with\u0000either convective available potential energy (CAPE) or convective\u0000precipitation. Markov chain Monte Carlo (MCMC) sampling estimates the final\u0000model to provide credible inference of effects, scores, and\u0000predictions. The selection of terms and MCMC sampling are applied for data of\u0000the year 2016, and out-of-sample performance is evaluated for 2017. The\u0000occurrence model outperforms a reference climatology – based on 7 years of\u0000data – up to a forecast horizon of 5 days. The flash count model is\u0000calibrated and also outperforms climatology for exceedance probabilities,\u0000quantiles, and full predictive distributions.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49657489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial trend analysis of gridded temperature data at varying spatial scales","authors":"O. Haug, T. Thorarinsdottir, S. Sørbye, C. Franzke","doi":"10.5194/ascmo-6-1-2020","DOIUrl":"https://doi.org/10.5194/ascmo-6-1-2020","url":null,"abstract":"Abstract. Classical assessments of trends in gridded temperature data perform\u0000independent evaluations across the grid, thus, ignoring spatial correlations\u0000in the trend estimates. In particular, this affects assessments of trend\u0000significance as evaluation of the collective significance of individual tests\u0000is commonly neglected. In this article we build a space–time hierarchical\u0000Bayesian model for temperature anomalies where the trend coefficient is\u0000modelled by a latent Gaussian random field. This enables us to calculate\u0000simultaneous credible regions for joint significance assessments. In a case\u0000study, we assess summer season trends in 65 years of gridded temperature data\u0000over Europe. We find that while spatial smoothing generally results in larger\u0000regions where the null hypothesis of no trend is rejected, this is not the\u0000case for all subregions.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45903458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hourly probabilistic snow forecasts over complex terrain: a hybrid ensemble postprocessing approach","authors":"R. Stauffer, G. Mayr, Jakob W. Messner, A. Zeileis","doi":"10.5194/ASCMO-4-65-2018","DOIUrl":"https://doi.org/10.5194/ASCMO-4-65-2018","url":null,"abstract":"Abstract. Accurate and high-resolution snowfall and fresh snow forecasts are important for a range of economic sectors as well as for the safety of people and infrastructure, especially in mountainous regions. In this article a new hybrid statistical postprocessing method is proposed, which combines standardized anomaly model output statistics (SAMOS) with ensemble copula coupling (ECC) and a novel re-weighting scheme to produce spatially and temporally high-resolution probabilistic snow forecasts. Ensemble forecasts and hindcasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) serve as input for the statistical postprocessing method, while measurements from two different networks provide the required observations.This new approach is applied to a region with very complex topography in the eastern European Alps. The results demonstrate that the new hybrid method allows one not only to provide reliable high-resolution forecasts, but also to combine different data sources with different temporal resolutions to create hourly probabilistic and physically consistent predictions.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44855294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging","authors":"T. Wong","doi":"10.5194/ASCMO-4-53-2018","DOIUrl":"https://doi.org/10.5194/ASCMO-4-53-2018","url":null,"abstract":"Abstract. Projections of coastal storm surge hazard are a basic requirement for\u0000effective management of coastal risks. A common approach for estimating\u0000hazards posed by extreme sea levels is to use a statistical model, which may\u0000use a time series of a climate variable\u0000as a covariate to modulate the statistical model and account for potentially\u0000nonstationary storm surge behavior (e.g., North Atlantic Oscillation index).\u0000Previous works using nonstationary statistical approaches to assess coastal\u0000flood hazard have demonstrated the importance of accounting for many key\u0000modeling uncertainties. However, many assessments have typically relied on a\u0000single climate covariate, which may leave out important processes and lead to\u0000potential biases in the projected flood hazards. Here, I employ a recently\u0000developed approach to integrate stationary and nonstationary statistical\u0000models, and characterize the effects of choice of covariate time series on\u0000projected flood hazard. Furthermore, I expand upon this approach by\u0000developing a nonstationary storm surge statistical model that makes use of\u0000multiple covariate time series, namely, global mean temperature, sea level,\u0000the North Atlantic Oscillation index and time. Using Norfolk, Virginia, as a\u0000case study, I show that a storm surge model that accounts for additional\u0000processes raises the projected 100-year storm surge return level by up to\u000023 cm relative to a stationary model or one that employs a single covariate\u0000time series. I find that the total model posterior probability associated\u0000with each candidate covariate, as well as a stationary model, is about\u000020 %. These results shed light on how including a wider range of physical\u0000process information and considering nonstationary behavior can better enable\u0000modeling efforts to inform coastal risk management.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46898207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures","authors":"R. Benestad, B. V. van Oort, F. Justino, F. Stordal, Kajsa M. Parding, A. Mezghani, H. Erlandsen, J. Sillmann, Milton E. Pereira-Flores","doi":"10.5194/ASCMO-4-37-2018","DOIUrl":"https://doi.org/10.5194/ASCMO-4-37-2018","url":null,"abstract":"Abstract. A methodology for estimating and downscaling the probability associated with the duration of heatwaves is presented and applied as a case study for Indian wheat crops. These probability estimates make use of empirical-statistical downscaling and statistical modelling of probability of occurrence and streak length statistics, and we present projections based on large multi-model ensembles of global climate models from the Coupled Model Intercomparison Project Phase 5 and three different emissions scenarios: Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5. Our objective was to estimate the probabilities for heatwaves with more than 5 consecutive days with daily maximum temperature above 35 ∘C, which represent a condition that limits wheat yields. Such heatwaves are already quite frequent under current climate conditions, and downscaled estimates of the probability of occurrence in 2010 is in the range of 20 %–84 % depending on the location. For the year 2100, the high-emission scenario RCP8.5 suggests more frequent occurrences, with a probability in the range of 36 %–88 %. Our results also point to increased probabilities for a hot day to turn into a heatwave lasting more than 5 days, from roughly 8 %–20 % at present to 9 %–23 % in 2100 assuming future emissions according to the RCP8.5 scenario; however, these estimates were to a greater extent subject to systematic biases. We also demonstrate a downscaling methodology based on principal component analysis that can produce reasonable results even when the data are sparse with variable quality.","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47292124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimates of climate system properties incorporating recent climate change","authors":"A. Libardoni, C. Forest, A. Sokolov, E. Monier","doi":"10.5194/ASCMO-4-19-2018","DOIUrl":"https://doi.org/10.5194/ASCMO-4-19-2018","url":null,"abstract":"Abstract. Historical time series of surface temperature and ocean heat content changes\u0000are commonly used metrics to diagnose climate change and estimate properties\u0000of the climate system. We show that recent trends, namely the slowing of\u0000surface temperature rise at the beginning of the 21st century and the\u0000acceleration of heat stored in the deep ocean, have a substantial impact on\u0000these estimates. Using the Massachusetts Institute of Technology Earth System\u0000Model (MESM), we vary three model parameters that influence the behavior of\u0000the climate system: effective climate sensitivity (ECS), the effective ocean\u0000diffusivity of heat anomalies by all mixing processes (Kv), and the net\u0000anthropogenic aerosol forcing scaling factor. Each model run is compared to\u0000observed changes in decadal mean surface temperature anomalies and the trend\u0000in global mean ocean heat content change to derive a joint probability\u0000distribution function for the model parameters. Marginal distributions for\u0000individual parameters are found by integrating over the other two parameters.\u0000To investigate how the inclusion of recent temperature changes affects our\u0000estimates, we systematically include additional data by choosing periods that\u0000end in 1990, 2000, and 2010. We find that estimates of ECS increase in\u0000response to rising global surface temperatures when data beyond 1990 are\u0000included, but due to the slowdown of surface temperature rise in the early\u000021st century, estimates when using data up to 2000 are greater than when data\u0000up to 2010 are used. We also show that estimates of Kv increase in\u0000response to the acceleration of heat stored in the ocean as data beyond 1990\u0000are included. Further, we highlight how including spatial patterns of surface\u0000temperature change modifies the estimates. We show that including latitudinal\u0000structure in the climate change signal impacts properties with spatial\u0000dependence, namely the aerosol forcing pattern, more than properties defined\u0000for the global mean, climate sensitivity, and ocean diffusivity.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49665457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecast score distributions with imperfect observations","authors":"J. Bessac, P. Naveau","doi":"10.5194/ascmo-7-53-2021","DOIUrl":"https://doi.org/10.5194/ascmo-7-53-2021","url":null,"abstract":"Abstract. The field of statistics has become one of the mathematical foundations in forecast evaluation studies, especially with regard to computing scoring rules. The classical paradigm of scoring rules is to discriminate between two different forecasts by comparing them with observations.\u0000The probability distribution of the observed record is assumed to be perfect as a verification benchmark.\u0000In practice, however, observations are almost always tainted by errors and uncertainties.\u0000These may be due to homogenization problems, instrumental deficiencies, the need for indirect reconstructions from other sources (e.g., radar data), model errors in gridded products like reanalysis, or any other data-recording issues.\u0000If the yardstick used to compare forecasts is imprecise, one can wonder whether such types of errors may or may not have a strong influence on decisions based on classical scoring rules.\u0000We propose a new scoring rule scheme in the context of models that incorporate errors of the verification data.\u0000We rely on existing scoring rules and incorporate uncertainty and error of the verification data through a hidden variable and the conditional expectation of scores when they are viewed as a random variable.\u0000The proposed scoring framework is applied to standard setups, mainly an additive Gaussian noise model and a multiplicative Gamma noise model.\u0000These classical examples provide known and tractable conditional distributions and, consequently, allow us to interpret explicit expressions of our score.\u0000By considering scores to be random variables, one can access the entire range of their distribution. In particular, we illustrate that the commonly used mean score can be a misleading representative of the distribution when the latter is highly skewed or has heavy tails. In a simulation study, through the power of a statistical test, we demonstrate the ability of the newly proposed score to better discriminate between forecasts when verification data are subject to uncertainty compared with the scores used in practice.\u0000We apply the benefit of accounting for the uncertainty of the verification data in the scoring procedure on a dataset of surface wind speed from measurements and numerical model outputs. Finally, we open some discussions on the use of this proposed scoring framework for non-explicit conditional distributions.\u0000","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47828299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing NARCCAP climate model effects using spatial confidence regions.","authors":"Joshua P French, Seth McGinnis, Armin Schwartzman","doi":"10.5194/ascmo-3-67-2017","DOIUrl":"10.5194/ascmo-3-67-2017","url":null,"abstract":"<p><p>We assess similarities and differences between model effects for the North American Regional Climate Change Assessment Program (NARCCAP) climate models using varying classes of linear regression models. Specifically, we consider how the average temperature effect differs for the various global and regional climate model combinations, including assessment of possible interaction between the effects of global and regional climate models. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We also show conclusively that results from pointwise inference are misleading, and that accounting for multiple comparisons is important for making proper inference.</p>","PeriodicalId":36792,"journal":{"name":"Advances in Statistical Climatology, Meteorology and Oceanography","volume":"3 2","pages":"67-92"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604436/pdf/nihms898559.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35428166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}