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Atmosphere
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The Physics, Dynamics, and Prediction of Extreme Weather in a...
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The Physics, Dynamics, and Prediction of Extreme Weather in a Changing Climate

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

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 11360

Special Issue Editors

Dr. Xiaoming Shi

E-Mail Website
Guest Editor
Division of Environment and Sustainability, Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Hong Kong, China
Interests: turbulence; convection; clouds; extreme weather; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dale R. Durran

E-Mail Website
Guest Editor
Department of Atmospheric Sciences, College of the Environment, University of Washington, Seattle, WA 98105, USA
Interests: mesoscale dynamic meteorology; numerical methods; atmospheric predictability; mountain meteorology; atmospheric waves
Prof. Dr. Ji Nie

E-Mail Website
Guest Editor
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Interests: tropical meteorology; convection; monsoon; extreme precipitation; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

As the global temperature keeps rising, the world is facing increasing risks of extreme weather events, such as torrential rainfall, heatwaves, and wildfires, which impact human health and safety and disrupt our societies. Thus, more than ever, we need to improve our understanding of the physics and dynamics of extreme weather and enhance our event prediction skills.

We cordially invite you to contribute to this Special Issue of Atmosphere, which intends to highlight the community's new knowledge of extreme weather processes and advances in our forecasting skills. The topics of interest include, but are not limited to, the following:

  • Tropical cyclones
  • Extreme precipitation and flooding
  • Drought
  • Dust storms
  • Lightning
  • Hailstorms
  • Heatwaves

The investigation approaches can be based on conventional observation, remote sensing, numerical simulations, and/or machine learning.

We believe that process-level understanding is vital for intellectual and practical merits. We also value the development of new methodologies in weather forecasting, data assimilation, climate projection and analysis, and the use of machine learning.

This Special Issue aims to be a collection of contributions that will improve our understanding and prediction of extreme events. Let us make our society more resilient to extreme weather risks through our scientific findings.

Dr. Xiaoming Shi
Prof. Dale R. Durran
Prof. Dr. Ji Nie
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
  • Climate change
  • Climate risks
  • Weather monitoring
  • Weather forecasting
  • Remote sensing
  • Numerical simulation
  • Machine learning

Published Papers (5 papers)

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Research

13 pages, 4164 KiB  
Article
Simulation of an Extreme Precipitation Event Using Ensemble-Based WRF Model in the Southeastern Coastal Region of China
by Lu Gao, Jianhui Wei, Xiangyong Lei, Miaomiao Ma, Lan Wang, Xiaojun Guan and Hui Lin
Atmosphere 2022, 13(2), 194; https://doi.org/10.3390/atmos13020194 - 25 Jan 2022
Cited by 4 | Viewed by 2719
Abstract
Extreme weather events have increased significantly in the past decades due to global warming. As a robust forecast and monitoring tool of extreme weather events, regional climate models have been widely applied on local scales. This study presented a simulation of an extreme [...] Read more.
Extreme weather events have increased significantly in the past decades due to global warming. As a robust forecast and monitoring tool of extreme weather events, regional climate models have been widely applied on local scales. This study presented a simulation of an extreme precipitation event in the Southeastern Coastal Region of China (SEC), where floods, typhoons, and mountain torrents occur frequently using the Weather Research and Forecast model (WRF) driven by GEFS (The Global Ensemble Forecast System) ensemble members (one control run and 20 ensemble members) from 01 UTC 14 June to 18 UTC 16 June 2010. The observations of hourly precipitation records from 68 meteorological stations in the SEC were applied to validate the WRF ensemble simulations with respect to 3-hourly cumulative precipitation (3hP), 6-hourly cumulative precipitation (6hP) and total cumulative precipitation (TCP). The results showed that all WRF 20 ensemble outputs could capture the extreme precipitation events fairly well with the Pearson correlation coefficient ranging from 0.01 to 0.82 and 0.16 to 0.89 for 3 and 6hP, respectively. The normalized root mean square error was comparable between the control run and 20 ensembles for 3hP (0.67 vs. 0.63) and 6hP (0.51 vs. 0.53). In general, WRF underestimated the observations for TCP. The control run (En00) modeled 28.1% less precipitation, while the 20 ensembles modeled 3.9% to 55.5% less precipitation than observations. The ensemble member 12 (En12) showed the best TCP simulation with the smallest bias. The average of 20 ensembles simulated 31.7% less precipitation than observations. The total precipitation was not captured by WRF with a significant bias that ranged from −203.1 to 112.3 mm. The storm centers were generally not captured by WRF in this case study. WRF ensembles underestimated the observation in the central Fujian Province while overestimated in the northern and southern Fujian Province. Although the average of ensembles can reduce the uncertainty to a certain extent, the individual ensemble (e.g., En12) may be more reliable on local scales. Full article
(This article belongs to the Special Issue The Physics, Dynamics, and Prediction of Extreme Weather in a Changing Climate)
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9 pages, 1487 KiB  
Article
Estimating the Impact of Global Warming on Aircraft Takeoff Performance in China
by Wei Yuan, Panxi Dai, Mengxiang Xu, Wei Song and Peng Zhang
Atmosphere 2021, 12(11), 1472; https://doi.org/10.3390/atmos12111472 - 07 Nov 2021
Cited by 4 | Viewed by 1746
Abstract
Aviation operations are significantly affected by weather conditions, such as high-temperature days. Under global warming, rising temperatures decrease the air density and thus, reduce the maximum takeoff weight of an aircraft. In this study, we investigate the impact of global warming on the [...] Read more.
Aviation operations are significantly affected by weather conditions, such as high-temperature days. Under global warming, rising temperatures decrease the air density and thus, reduce the maximum takeoff weight of an aircraft. In this study, we investigate the impact of global warming on the aircraft takeoff performance in 53 airports in China by combining observational data and CMIP6 climate projections. There is a distinct geographic inhomogeneity of critical temperature, above which the takeoff weight decreases significantly with the increasing air temperature, mostly due to differences in airport elevations. By the end of the century, under the SSP5-8.5 scenario (with average warming of 5.2 °C in China), the daily maximum temperature for nearly all summer days in West China and for about half of the summer days in East China exceeds critical temperature, indicating that frequent weight restriction will be necessary. We further examine the reduction in carrying capacity due to climate change. By the end of the century, under the SSP5-8.5 scenario, the summer total carrying capacity will be reduced by about 2.8% averaged over all 53 airports. The impacts on airports in West China are nearly four times greater than those in East China, due to the higher vulnerability and stronger warming in West China. Full article
(This article belongs to the Special Issue The Physics, Dynamics, and Prediction of Extreme Weather in a Changing Climate)
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18 pages, 26325 KiB  
Article
Sensitivity of the Short-Range Precipitation Forecast in South China Region to Sea Surface Temperature Variations
by Diah Valentina Lestari and Xiaoming Shi
Atmosphere 2021, 12(9), 1138; https://doi.org/10.3390/atmos12091138 - 04 Sep 2021
Cited by 1 | Viewed by 1977
Abstract
Ocean variability plays an essential role in the climate system at different time scales through air–sea interactions. Recent studies have addressed the importance of the ocean mixed layer in cooling feedback to tropical cyclones (TCs). However, using constant sea surface temperature (SST) in [...] Read more.
Ocean variability plays an essential role in the climate system at different time scales through air–sea interactions. Recent studies have addressed the importance of the ocean mixed layer in cooling feedback to tropical cyclones (TCs). However, using constant sea surface temperature (SST) in short-range weather forecasts remains common, especially in high-resolution regional models. This study investigates the role of subsurface ocean mixing in the short-range forecast of non-TC extreme rainfall with the Weather Research and Forecast (WRF) model. In the simulations of 26 heavy rainfall cases, we found that using a one-dimensional mixed layer model leads to a 15% enhancement (reduction) of rainfall maximum in six (two) cases compared to using constant SST. When the initial depth of the mixed layer model is perturbed by the amount of daily variability, 13 cases exhibit larger than 15% increases or decreases. A detailed analysis of one case suggests that the radiative process dominates the overall response of SST. The warming and moistening of boundary layer air cause significant strengthening of updrafts in convection. Although the SST change in most cases due to varying mixed layer model setups is less than 0.5 K, convective motions in some cases are surprisingly sensitive to small changes. Full article
(This article belongs to the Special Issue The Physics, Dynamics, and Prediction of Extreme Weather in a Changing Climate)
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13 pages, 33700 KiB  
Article
Abrupt Change Detection Method Based on Features of Lorenz Trajectories
by Chaojiu Da, Binglu Shen, Jian Song, Cairang Xaiwu and Guolin Feng
Atmosphere 2021, 12(6), 781; https://doi.org/10.3390/atmos12060781 - 17 Jun 2021
Cited by 1 | Viewed by 1745
Abstract
This paper presents a definition of bifurcation-type abrupt changes based on the bifurcation features of Lorenz trajectories. These abrupt changes are the result of the transition behavior of dynamical system trajectories among different equilibrium regions. We demonstrate that these bifurcation-type jumps can better [...] Read more.
This paper presents a definition of bifurcation-type abrupt changes based on the bifurcation features of Lorenz trajectories. These abrupt changes are the result of the transition behavior of dynamical system trajectories among different equilibrium regions. We demonstrate that these bifurcation-type jumps can better reflect the nature of abrupt change. In analyzing the features of Lorenz equation trajectories, a dynamical method for detecting bifurcation-type abrupt changes is presented. A numerical solution of the Lorenz equation is adopted, using a curve integral or vector product to construct a time series of positive and negative values. Changes in the sign of this time series accurately determine whether the trajectory is in the right or left equilibrium region, and the points at which the time series is equal to zero are the times at which the trajectory jumps between different equilibrium regions, that is, the occurrence times of bifurcation-type abrupt changes. This method is completely dependent on the dynamical characteristics of the system. A theoretical approach for detecting abrupt climate changes based on the dynamical characteristics of the atmospheric model is described. Compared with the original method of identifying abrupt climate changes, this method has dynamic significance and can detect abrupt changes in multi-dimensional time series. Although this method can be applied theoretically, applications to real atmospheric data first require the data to be smoothed. Full article
(This article belongs to the Special Issue The Physics, Dynamics, and Prediction of Extreme Weather in a Changing Climate)
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13 pages, 5643 KiB  
Article
Non-Stationary Effects of the Arctic Oscillation and El Niño–Southern Oscillation on January Temperatures in Korea
by Jae-Seung Yoon, Il-Ung Chung, Ho-Jeong Shin, Kunmn-Yeong Jang, Maeng-Ki Kim, Jeong-Soo Park, Doo-Sun R. Park, Kyung-On Boo, Young-Hwa Byun and Hyun-Min Sung
Atmosphere 2021, 12(5), 538; https://doi.org/10.3390/atmos12050538 - 22 Apr 2021
Viewed by 2252
Abstract
In recent decades, extremely cold winters have occurred repeatedly throughout the Northern Hemisphere, including the Korean Peninsula (hereafter, Korea). Typically, cold winter temperatures in Korea can be linked to the strengthening of the Siberian High (SH). Although previous studies have investigated the typical [...] Read more.
In recent decades, extremely cold winters have occurred repeatedly throughout the Northern Hemisphere, including the Korean Peninsula (hereafter, Korea). Typically, cold winter temperatures in Korea can be linked to the strengthening of the Siberian High (SH). Although previous studies have investigated the typical relationship between the SH and winter temperatures in Korea, this study uniquely focused on a change in the relationship, which reflects the influence of the Arctic Oscillation (AO) and El Niño–Southern Oscillation (ENSO). A significant change in the 15-year moving correlation between the SH and the surface air temperature average in Korea (K-tas) was observed in January. The correlation changed from −0.80 during 1971–1990 to −0.16 during 1991–2010. The mean sea-level pressure pattern regressed with the temperature, and a singular value decomposition analysis that incorporated the temperature and pressure supports that the negative high correlation during 1971–1990 was largely affected by AO. This connection with AO is substantiated by empirical orthogonal function (EOF) analysis with an upper-level geopotential height at 300 hPa. In the second mode of the EOF, the temperature and pressure patterns were primarily affected by ENSO during 1991–2010. Consequently, the interdecadal change in correlation between K-tas and the SH in January can be attributed to the dominant effect of AO from 1971–1990 and of ENSO from 1991–2010. Our results suggest that the relative importance of these factors in terms of the January climate in Korea has changed on a multidecadal scale. Full article
(This article belongs to the Special Issue The Physics, Dynamics, and Prediction of Extreme Weather in a Changing Climate)
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