Advances in Statistical Climatology, Meteorology and Oceanography

Abstract. This paper presents a statistical analysis of air temperature data from 32 stations in Italy and the UK up to 2000 m above sea level from 2002 to 2021. The data came from both highland and lowland areas in order to evaluate the differences due to both location and elevation. The analysis focused on detecting trends at annual and monthly timescales, employing ordinary least-squares (OLS), robust S-estimator regression, and Mann–Kendall (MK) and Sen's slope methods. Hierarchical clustering (HCA) using dynamic time warping (DTW) was then applied to the monthly data to analyze the intra-annual pattern similarity of trends within and across the groups. Two different regions of Europe were chosen because of the different climate and temperature trends – namely, the northern UK (smaller trends) and the northwest Italian Alps (larger trends). The main novelty of the work is to show that stations with similar locations and altitudes have similar monthly slopes by quantifying them using DTW and clustering. These results reveal the nonrandomness of different trends throughout the year and between different parts of Europe, with a modest influence of altitude in wintertime. The findings revealed that group average trends were close to the National Oceanic and Atmospheric Administration (NOAA) values for the areas in Italy and the UK, confirming the validity of analyzing a small number of stations. More interestingly, intra-annual patterns were detected commonly at the stations of each of the groups and are clearly different between them. Confirming the different climates, most highland and lowland stations in Italy exhibit statistically significant positive trends, while in the UK, both highland and lowland stations show statistically nonsignificant negative trends. Hierarchical clustering in combination with DTW showed consistent similarity between monthly patterns of means and trends within the group of stations and inconsistent similarity between patterns across groups. The use of the 12 distance correlation matrices (dcor) (one for each month) also contributes to what is the main result of the paper, which is to clearly show the different temporal patterns in relation to location and (in some months) altitude. The anomalous behaviors detected at 3 of the 32 stations, namely Valpelline, Fossano, and Aonoch Mòr, can be attributed, respectively, to the facts that Valpelline is the lowest-elevation station in its group; Fossano is the southernmost of the Italian stations, with some sublittoral influence; and Aonoch Mòr has a large number of missing values. In conclusion, these results improve our understanding of temperature spatio-temporal dynamics in two very different regions of Europe and emphasize the importance of consistent analysis of data to assess the ongoing effects of climate change. The intra-annual time patterns of temperature trends could also be compared with climate model results.

Chalachew Muluken Liyew

Elvira Di Nardo

Rosa Meo

Stefano Ferraris

Advances in statistical climatology, meteorology and oceanography

Identifying time patterns of highland and lowland air temperature trends in Italy and the UK across monthly and annual scales

识别意大利和英国高地与低地气温趋势的时间模式：基于月度和年度尺度

Abstract. Gaussian process (GP) regression is a flexible modeling technique used to predict outputs and to capture uncertainty in the predictions. However, the GP regression process becomes computationally intensive when the training spatial dataset has a large number of observations. To address this challenge, we introduce a scalable GP algorithm, termed MuyGPs, which incorporates nearest-neighbor and leave-one-out cross-validation during training. This approach enables the evaluation of large spatial datasets with state-of-the-art accuracy and speed in certain spatial problems. Despite these advantages, conventional quadratic loss functions used in the MuyGPs optimization, such as root mean squared error (RMSE), are highly influenced by outliers. We explore the behavior of MuyGPs in cases involving outlying observations and, subsequently, develop a robust approach to handle and mitigate their impact. Specifically, we introduce a novel leave-one-out loss function based on the pseudo-Huber function (LOOPH) that effectively accounts for outliers in large spatial datasets within the MuyGPs framework. Our simulation study shows that the LOOPH loss method maintains accuracy despite outlying observations, establishing MuyGPs as a powerful tool for mitigating unusual observation impacts in the large data regime. In the analysis of US ozone data, MuyGPs provides accurate predictions and uncertainty quantification, demonstrating its utility in managing data anomalies. Through these efforts, we advance the understanding of GP regression in spatial contexts.

Juliette Mukangango

Amanda Muyskens

Benjamin W. Priest

A robust approach to Gaussian process implementation

高斯过程实现的鲁棒方法

Abstract. Event attribution methods are increasingly routinely used to assess the role of climate change in individual weather events. In order to draw robust conclusions about whether changes observed in the real world can be attributed to anthropogenic climate change, it is necessary to analyse trends in observations alongside those in climate models, where the factors driving changes in weather patterns are known. Here we present a quantitative statistical synthesis method, developed over 8 years of conducting rapid probabilistic event attribution studies, to combine quantitative attribution results from multi-model ensembles and other, qualitative, lines of evidence in a single framework to draw quantitative conclusions about the overarching role of human-induced climate change in individual weather events.

Friederike E. L. Otto

Clair Barnes

Sjoukje Philip

Sarah Kew

Geert Jan van Oldenborgh

Robert Vautard

Formally combining different lines of evidence in extreme-event attribution

极端事件归因中不同证据线的形式化结合

Abstract. Low-visibility conditions enforce special procedures that reduce the operational flight capacity at airports. Accurate and probabilistic forecasts of these capacity-reducing low-visibility procedure (lvp) states help the air traffic management in optimizing flight planning and regulation. In this paper, we investigate nowcasts, medium-range forecasts, and the predictability limit of the lvp states at Vienna International Airport. The forecasts are generated with boosting trees, which outperform persistence, climatology, direct output of numerical weather prediction (NWP) models, and ordered logistic regression. The boosting trees consist of an ensemble of decision trees grown iteratively on information from previous trees. Their input is observations at Vienna International Airport as well as output of a high resolution and an ensemble NWP model. Observations have the highest impact for nowcasts up to a lead time of +2 h. Afterwards, a mix of observations and NWP forecast variables generates the most accurate predictions. With lead times longer than +7 h, NWP output dominates until the predictability limit is reached at +12 d. For lead times longer than +2 d, output from an ensemble of NWP models improves the forecast more than using a deterministic but finer resolved NWP model. The most important predictors for lead times up to +18 h are observations of lvp and dew point depression as well as NWP dew point depression. At longer lead times, dew point depression and evaporation from the NWP models are most important.

Sebastian J. Dietz

Philipp Kneringer

Georg J. Mayr

Achim Zeileis

Low-visibility forecasts for different flight planning horizons using tree-based boosting models

基于树增强模型的不同飞行规划视野低能见度预报

Abstract. Extreme temperature and precipitation events occurring in Australia in recent decades have caused significant socio-economic and environmental impacts, and thus determining the factors contributing to these extremes is an active area of research. Many recently occurring record-breaking temperature and rainfall events have now been examined from an extreme event attribution (EEA) perspective. This paper describes a set of studies that have examined the causes of extreme climate events using various general circulation models (GCMs), presenting a comprehensive methodology for GCM-based attribution of extremes of temperature and precipitation observed on large spatial and temporal scales in Australia. First, we review how Coupled Model Intercomparison Project Phase 5 (CMIP5) models have been used to examine the changing odds of observed extremes. Second, we review how a large perturbed initial condition ensemble of a single climate model (CESM) has been used to quantitatively examine the changing characteristics of Australian heat extremes. For each approach, methodological details and applications are provided and limitations highlighted. The conclusions of this methodological review discuss the limitations and uncertainties associated with this approach and identify key unexplored applications of GCM-based attribution of extremes. Ideally, this information will be useful for the application of the described extreme event attribution approaches elsewhere.

Sophie C. Lewis

Sarah E. Perkins-Kirkpatrick

Andrew D. King

Approaches to attribution of extreme temperature and precipitation events using multi-model and single-member ensembles of general circulation models

利用多模式和单成员集合的通用环流模型进行极端温度和降水事件归因的方法

Abstract. Comparisons of observed and modeled climate behavior often focus on central tendencies, which overlook other important distributional characteristics related to quantiles and variability. We propose two permutation procedures, standard and stratified, for assessing the accuracy of climate models. Both procedures eliminate the need to model cross-correlations in the data, encouraging their application in a variety of contexts. By making only slightly stronger assumptions, the stratified procedure dramatically strengthens the ability to detect a difference in the distribution of observed and climate model data. The proposed procedures allow researchers to identify potential model deficiencies over space and time for a variety of distributional characteristics, providing a more comprehensive assessment of climate model accuracy, which will hopefully lead to further model refinements. The proposed statistical methodology is applied to temperature data generated by the state-of-the-art North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX).

Joshua P. French

Piotr S. Kokoszka

Seth McGinnis

Spatiotemporal functional permutation tests for comparing observed climate behavior to climate model projections

时空功能置换检验用于比较观测气候行为与气候模型预测

Abstract. Although, by now, ensemble-based probabilistic forecasting is the most advanced approach to weather prediction, ensemble forecasts still suffer from a lack of calibration and/or display systematic bias, thus requiring some post-processing to improve their forecast skill. Here, we focus on visibility, a weather quantity that plays a crucial role in, for example, aviation and road safety or ship navigation, and we propose a parametric model where the predictive distribution is a mixture of a gamma and a truncated normal distribution, both right censored at the maximal reported visibility value. The new model is evaluated in two case studies based on visibility ensemble forecasts of the European Centre for Medium-Range Weather Forecasts covering two distinct domains in central and western Europe and two different time periods. The results of the case studies indicate that post-processed forecasts are substantially superior to raw ensembles; moreover, the proposed mixture model consistently outperforms the Bayesian model averaging approach used as a reference post-processing technique.

Ágnes Baran

Sándor Baran

Parametric model for post-processing visibility ensemble forecasts

后处理能见度集合预报的参数模型

涉及主题	数学物理地质学计算机科学统计生物地理海洋学气候学气象学生态学环境科学经济人工智能机器学习量子力学计量经济学气候变化气候模式工程类大气科学降水哲学古生物学
出版信息	出版商: Copernicus Publications，出版周期: ，期刊类型: journal，开源期刊: 是
基本数据	创刊年份: 2019，原创研究文献占比: 0.00%，自引率:N.A.， Gold OA占比: 0.00%
平均审稿周期	网友分享经验：26 Weeks

Advances in Statistical Climatology, Meteorology and Oceanography

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Identifying time patterns of highland and lowland air temperature trends in Italy and the UK across monthly and annual scales

A robust approach to Gaussian process implementation

Formally combining different lines of evidence in extreme-event attribution

Low-visibility forecasts for different flight planning horizons using tree-based boosting models

Approaches to attribution of extreme temperature and precipitation events using multi-model and single-member ensembles of general circulation models

Identifying time patterns of highland and lowland air temperature trends in Italy and the UK across monthly and annual scales

Formally combining different lines of evidence in extreme-event attribution

A robust approach to Gaussian process implementation

Spatiotemporal functional permutation tests for comparing observed climate behavior to climate model projections

Parametric model for post-processing visibility ensemble forecasts

ISSN	2364-3579, 2364-3587
h-index	7