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Atmosphere
Special Issues
Prediction, Observation, and Monitoring of Weather and Climate Extremes
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Prediction, Observation, and Monitoring of Weather and Climate Extremes

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

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 9573

Special Issue Editors

Dr. Brian Odhiambo Ayugi

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Guest Editor
Department of Civil Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Interests: climatology; climate change; extreme events; model evaluation
Special Issues, Collections and Topics in MDPI journals
Dr. Victor Ongoma

E-Mail Website
Guest Editor
International Water Research Institute, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco
Interests: meteorology; climate change; climate services; sustainable development
Dr. Kenny T.C. Lim Kam Sian

E-Mail Website
Guest Editor
School of Atmospheric Science and Remote Sensing, Wuxi University, Wuxi 214105, China
Interests: climate change; precipitation variability; extreme events; weather prediction

Special Issue Information

Dear Colleagues,

The increase in the intensity and frequency of weather and climate extremes is a growing concern. The resulting disasters greatly impact the natural environment and human society. Projections show a high likelihood of an increase in extreme events as a result of global warming. Therefore, the accurate prediction and timely monitoring of extreme events are of great significance in saving lives and minimizing the destruction of property.

Although extreme events have been studied extensively from univariate perspectives with varying indices developed for monitoring individual and regional events, their variation and formation mechanism on a local to regional scale is unclear, especially those that manifest concurrent occurrences. For example, the variability of Eurasian temperatures and the number of extreme-cold events are increasing under the current climate situation. In addition, the capability of monitoring and forecasting extreme events is also very limited, and the forecast time for different phenomena may differ. Although it is challenging to predict such events, efforts and progress have been made and are worth acknowledging and sharing.

To better understand regional weather and climate characteristics under global warming, we expect to know how extreme-event monitoring techniques and indices perform in different areas. For the simulation and prediction of extreme events, we expect to obtain more insights into the ability of numerical models to adequately simulate extreme events in different areas and at different time scales. To improve the skill of model simulation and prediction, we expect to understand the predictability of extreme events based on dynamic models, physical–empirical models, statistical events, dynamic–statistical models, and deep-learning approaches.

In this context, for this Special Issue of Atmosphere, we are calling for submissions related but not limited to the above questions. Articles that may contribute to a better understanding of extreme-weather/climate-event monitoring and predictions are invited.

Dr. Brian Odhiambo Ayugi
Dr. Victor Ongoma
Dr. Kenny T.C. Lim Kam Sian
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

  • weather and climate extremes
  • model evaluation
  • climate variability
  • severe weather forecasting
  • climate change projection
  • extreme indices
  • compound events

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Published Papers (4 papers)

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25 pages, 7234 KiB  
Article
Meteorological Drought Variability over Africa from Multisource Datasets
by Kenny T. C. Lim Kam Sian, Xiefei Zhi, Brian O. Ayugi, Charles Onyutha, Zablon W. Shilenje and Victor Ongoma
Atmosphere 2023, 14(6), 1052; https://doi.org/10.3390/atmos14061052 - 19 Jun 2023
Cited by 3 | Viewed by 1981
Abstract
This study analyses the spatiotemporal variability of meteorological drought over Africa and its nine climate subregions from an ensemble of 19 multisource datasets (gauge-based, satellite-based and reanalysis) over the period 1983–2014. The standardized precipitation index (SPI) is used to represent drought on a [...] Read more.
This study analyses the spatiotemporal variability of meteorological drought over Africa and its nine climate subregions from an ensemble of 19 multisource datasets (gauge-based, satellite-based and reanalysis) over the period 1983–2014. The standardized precipitation index (SPI) is used to represent drought on a 3-month scale. We analyse various drought characteristics (duration, events, frequency, intensity, and severity) for all drought months, and moderate, severe, and extreme drought conditions. The results show that drought occurs across the continent, with the equatorial regions displaying more negative SPI values, especially for moderate and severe droughts. On the other hand, Eastern Sahara and Western Southern Africa portray less negative SPI values. The study also reveals that extreme drought months have the largest interannual variability, followed by all drought months and severe drought months. The trend analysis of SPI shows a significantly increasing trend in drought episodes over most regions of Africa, especially tropical areas. Drought characteristics vary greatly across different regions of Africa, with some areas experiencing longer and more severe droughts than others. The equatorial region has the highest number of drought events, with longer durations for severe and extreme drought months. The Eastern Sahara region has a low number of drought events but with longer durations for moderate, severe, and extreme drought months, leading to an overall higher drought severity over the area. In contrast, Western Southern Africa and Madagascar display a consistently low drought severity for all categories. The study demonstrates the importance of conducting drought analysis for different drought levels instead of using all drought months. Drought management and adaptation strategies need to enhance community resilience to changing drought situations and consider drought variability in order to mitigate different impacts of drought across the continent. Full article
(This article belongs to the Special Issue Prediction, Observation, and Monitoring of Weather and Climate Extremes)
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14 pages, 2794 KiB  
Article
Multi-Level Circulation Pattern Classification Based on the Transfer Learning CNN Network
by Yanzhang Liu, Jinqi Cai and Guirong Tan
Atmosphere 2022, 13(11), 1861; https://doi.org/10.3390/atmos13111861 - 9 Nov 2022
Cited by 2 | Viewed by 1934
Abstract
Deep learning artificial intelligence technology, which has the advantages of nonlinear mapping ability, massive information extraction ability, spatial-temporal modeling ability, and so on, provides new ideas and methods for further improving the accuracy of weather and climate extreme event prediction. A transfer learning [...] Read more.
Deep learning artificial intelligence technology, which has the advantages of nonlinear mapping ability, massive information extraction ability, spatial-temporal modeling ability, and so on, provides new ideas and methods for further improving the accuracy of weather and climate extreme event prediction. A transfer learning CNN (Convolutional Neural Networks) classification model is established to classify the circulation patterns, along with the newly reconstructed dataset of regional persistent historical heavy rain events, daily rainfall data of 2474 observational stations, and the NCEP/NCAR global reanalysis data of daily geopotential height field in 1981–2018. Different from previous classifications, usually with one level variable, here, in addition to 500 hPa heights, 200 hPa zonal winds and 850 hPa meridional winds over the key areas are also considered in the model. The results show that the multi-level circulation pattern classification based on the transfer learning CNN network has a higher accuracy in the independent test than the single-level model, with the accuracy reaching 92.5% (while only 85% for the single-level model). The spatial correlation coefficient of precipitation between each typical mode and related patterns obtained by the multi-level transfer learning CNN classification is greater than that obtained by the single-level transfer learning CNN, and the variance of 500 hPa heights between each typical mode and the associated patterns is also greater than that obtained by the single-level transfer learning CNN. These results show that the performance of the classification by the multi-level transfer learning CNN model is better than that by the single-level transfer learning CNN. The study is helpful to develop circulation classifications related to large-scale weather or climate disaster events and then to provide a physical basis for further improving the forecast effect and extending the valid time of the forecast through combining the numerical model products. Full article
(This article belongs to the Special Issue Prediction, Observation, and Monitoring of Weather and Climate Extremes)
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25 pages, 8545 KiB  
Article
Features and Evolution of Autumn Weather Regimes in the Southeast China
by Yongdi Wang and Xinyu Sun
Atmosphere 2022, 13(10), 1734; https://doi.org/10.3390/atmos13101734 - 21 Oct 2022
Cited by 1 | Viewed by 1550
Abstract
Autumn is the transitional season when the atmospheric circulation pattern changes from summer to winter. The temperature and precipitation in Southeastern China in autumn are significantly influenced by the change in circulation patterns, and both show significant uniqueness. The clustering method can be [...] Read more.
Autumn is the transitional season when the atmospheric circulation pattern changes from summer to winter. The temperature and precipitation in Southeastern China in autumn are significantly influenced by the change in circulation patterns, and both show significant uniqueness. The clustering method can be used to observe the changes of circulation patterns in detail and to observe and analyze the transition from warm to cold seasons from a detailed view of the daily circulation pattern perspective. This method may have important research implications on how to study the generation and dissipation of extreme weather events. The Self-Organizing Maps (SOM) method is used to a 500 hPa geopotential height and 850 hPa wind and sea level pressure for 1981–2020 to identify the characteristic weather patterns (WTs) in autumn (September–November) over Southeastern China. Characteristics of the captured WTs are also analyzed in terms of the distribution characteristics of weather patterns, occurrence frequency, typical progression, precipitation and extreme precipitation (EP), temperature and extreme high temperature (EHT), and the relationship with atmospheric teleconnection. Nine WTs were identified in autumn, which represents a series of weather situations consisting of troughs and ridges. On this basis, these WTs were used to carry out the differentiation of seasonal differences between early and late autumn. The maximum mean and extreme precipitation occur in several early season patterns (WT1, WT2, WT4, and WT7). It is highly likely that extremely high temperatures occur in the WT1 and WT2 patterns. The most common progression between WTs is WT7−WT1−WT2−WT4 in the early season. This seasonality allows us to distinguish between early and late seasons based on daily weather types. A preliminary trend analysis suggests that patterns in the early season occur more frequently and last longer in the early season, and patterns in the late season occur less frequently and later. That is, the longer cool season pattern is shifting to the shorter warm season pattern. In addition, the persistence of both cool and warm patterns increased during 2001–2020 relative to 1981–2000, and the risk of both flooding and drought occurrence is on the rise. Full article
(This article belongs to the Special Issue Prediction, Observation, and Monitoring of Weather and Climate Extremes)
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10 pages, 2430 KiB  
Commentary
Arctic Climate Extremes
by James E. Overland
Atmosphere 2022, 13(10), 1670; https://doi.org/10.3390/atmos13101670 - 13 Oct 2022
Cited by 5 | Viewed by 2445
Abstract
There are multiple extreme events underway in the Arctic that are beyond previous records: rain in Greenland, Alaska weather variability, and ecosystem reorganizations in the Barents and the northern Bering Sea associated with climate change and sea-ice loss. Such unique extreme events represent [...] Read more.
There are multiple extreme events underway in the Arctic that are beyond previous records: rain in Greenland, Alaska weather variability, and ecosystem reorganizations in the Barents and the northern Bering Sea associated with climate change and sea-ice loss. Such unique extreme events represent a philosophical challenge for interpretation, i.e., a lack of statistical basis, as well as important information for regional adaptation to climate change. These changes are affecting regional food security, human/wildlife health, cultural activities, and marine wildlife conservation. Twenty years ago, the Arctic was more resilient to climate change than now, as sea ice had a broader extent and was three times thicker than today. These new states cannot be assigned probabilities because one cannot a priori conceive of these states. They often have no historical analogues. A way forward for adaptation to future extremes is through scenario/narrative approaches; a recent development in climate change policy is through decision making under deep uncertainty (DMDU). Full article
(This article belongs to the Special Issue Prediction, Observation, and Monitoring of Weather and Climate Extremes)
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