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Atmosphere
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Non-stationarity (Seasonality and Trends) in Time Series of Meteorological Extreme...
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Non-stationarity (Seasonality and Trends) in Time Series of Meteorological Extreme Events

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 4608

Special Issue Editors

Dr. Kreso Pandzic

E-Mail Website
Guest Editor
Director General Division, Croatian Meteorological andHydrological Service, 10000 Zagreb, Croatia
Interests: climate variations and trends in Croatia and Southeastern Europe; application of complex principal component analysis technique in explaining a connection between local meteorological field patterns; air temperature and precipitation, with patterns of large-scale circulation parameters like sea level air pressure; inhomogeneity of climate and hydrological time series; deterministic chaos; drought/wetness study using the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI); impact of climate variations and trends on water resources and agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Tanja Likso

E-Mail Website
Guest Editor
Head of Data Processing and Management Department, Croatian Meteorological and Hydrological Service, Ravnice 48, 10000 Zagreb, Croatia
Interests: boundary-layer meteorology (application of Monin–Obukhov similarity theory for the wind speed estimation in the lower part of the atmospheric surface layer)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ognjen Bonacci

E-Mail Website
Guest Editor
Faculty of Civil Engineering, Architecture and Geodesy in Split, University of Split, Split, Croatia
Interests: engineering hydrology; karst hydrology; ecohydrology; climate changes; water resources research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Meteorological (i.e., weather and climate) observations indicate that climate warming has grown worse in recent decades. It manifests in the increasingly frequent occurrence of extreme meteorological phenomena, including heat waves, intensive precipitation, and dry spells. Not only their frequency, but also their duration and intensity have increased in recent decades. A similar trend of changes is estimated for the next decades of the 21st century. At the same time, the aforementioned changes are accompanied by a decrease in the frequency of cold waves, including cold spells. As theoretical tools for analysis of meteorological extreme event time series usually require their specific characteristics (such as stationarity), strengthening of non-stationarity because of seasonality and trends becomes a big challenge, including estimation of their outlooks for future.

This issue will give preference to studies of any extreme meteorological phenomena, including air temperature and precipitation time series extremes, preferably using probability distributions adopted for a study of historical time series of extreme meteorological events under conditions of non-stationarity.

As extreme meteorological events produce great economic losses, the articles presented in this issue could be useful for a broad and diverse group of recipients.

Dr. Kreso Pandzic
Dr. Tanja Likso
Prof. Dr. Ognjen Bonacci
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • extreme weather and climate phenomena
  • non-stationarity of time series
  • probability distributions

Published Papers (2 papers)

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Research

27 pages, 22177 KiB  
Article
Seasonal Aspects of Radiative and Advective Air Temperature Populations: A Canadian Perspective
by Ana Žaknić-Ćatović and William A. Gough
Atmosphere 2022, 13(7), 1017; https://doi.org/10.3390/atmos13071017 - 24 Jun 2022
Cited by 2 | Viewed by 1189
Abstract
Canadian high-frequency temperature time series exhibit physical heterogeneity in the coexistence of radiative and advective populations in the total air temperature sample. This work examines forty-five Canadian hourly air temperature records to study seasonal characteristics and variability of radiative and advective population counts [...] Read more.
Canadian high-frequency temperature time series exhibit physical heterogeneity in the coexistence of radiative and advective populations in the total air temperature sample. This work examines forty-five Canadian hourly air temperature records to study seasonal characteristics and variability of radiative and advective population counts and their corresponding temperature biases and trends. The Linear Pattern Discrimination algorithm, conceptualized in a previous study, was adjusted to seasonal analysis on the equinox-to-equinox time scale. Count analysis of radiative and advective days supports the existence of two distinct thermal regimes, Spring–Summer and Fall–Winter. Further, seasonal advective counts for the majority of examined stations typically decrease in numbers. The consistently warmer winter radiative temperature extrema points to the critical role of the advective population in control of the overall temperature magnitude. Canadian northwest warming trends are found to be the highest, indicating the amplifying effect of decreasing advective counts with rapidly increasing temperatures that weaken the advective population’s moderating ability to control the magnitude of the total temperature population. Full article
(This article belongs to the Special Issue Non-stationarity (Seasonality and Trends) in Time Series of Meteorological Extreme Events)
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15 pages, 900 KiB  
Article
A Bayesian Hierarchical Spatial Copula Model: An Application to Extreme Temperatures in Extremadura (Spain)
by J. Agustín García, Mario M. Pizarro, F. Javier Acero and M. Isabel Parra
Atmosphere 2021, 12(7), 897; https://doi.org/10.3390/atmos12070897 - 10 Jul 2021
Cited by 3 | Viewed by 2088
Abstract
A Bayesian hierarchical framework with a Gaussian copula and a generalized extreme value (GEV) marginal distribution is proposed for the description of spatial dependencies in data. This spatial copula model was applied to extreme summer temperatures over the Extremadura Region, in the southwest [...] Read more.
A Bayesian hierarchical framework with a Gaussian copula and a generalized extreme value (GEV) marginal distribution is proposed for the description of spatial dependencies in data. This spatial copula model was applied to extreme summer temperatures over the Extremadura Region, in the southwest of Spain, during the period 1980–2015, and compared with the spatial noncopula model. The Bayesian hierarchical model was implemented with a Monte Carlo Markov Chain (MCMC) method that allows the distribution of the model’s parameters to be estimated. The results show the GEV distribution’s shape parameter to take constant negative values, the location parameter to be altitude dependent, and the scale parameter values to be concentrated around the same value throughout the region. Further, the spatial copula model chosen presents lower deviance information criterion (DIC) values when spatial distributions are assumed for the GEV distribution’s location and scale parameters than when the scale parameter is taken to be constant over the region. Full article
(This article belongs to the Special Issue Non-stationarity (Seasonality and Trends) in Time Series of Meteorological Extreme Events)
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