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Atmosphere
Special Issues
Investigating Tropical Cyclone Intensity Changes with Advanced Data Analysis Techniques
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Investigating Tropical Cyclone Intensity Changes with Advanced Data Analysis Techniques

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

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 8067

Special Issue Editors

Dr. Ruixin Yang

E-Mail Website
Guest Editor
Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA
Interests: tropical cyclones; data mining; advanced data analysis techniques; climate changes
Dr. Yun Li

E-Mail Website
Guest Editor
Department of Geography and GeoInformation Science, George Mason University, Fairfax, VA 22030, USA
Interests: data discovery; spatiotemporal analysis; GeoAI; natural disaster
Dr. Yijun Wei

E-Mail Website
Guest Editor
Optum, Eden Prairie, MN, USA
Interests: machine learning; data mining; artificial intelligence; tropical cyclone; climate; agriculture; healthcare

Special Issue Information

Dear Colleagues,

Tropical cyclones (TCs) is the most damaged natural hazards on continuous basis as residents in vulnerable areas may need to be alert to them almost daily during local TC seasons. Predicting TC behaviors and being prepared for TC attacks have been the endless efforts by human beings, and the forecasting records can be traced back to well more than one century ago. At present, with the TC track forecasting is with relatively accurate results, the TC intensity forecasting is one of the most important challenges for the weather prediction. The techniques for TC intensity changes include numerical simulations, case studies, composite analysis for identifying important features, climatic and persistence models, as well as for developing statistical models. As we are in the “big data” era, many researchers have been trying to leverage modern data analysis techniques for improving the TC intensity forecasting.

In this special issue, we invite original and review articles that use advanced data analysis techniques for the investigation of TCs, mainly on intensity changes such as TC genesis, intensification, and rapid intensity changes. Those contribution may leverage the diverse data sources, in situ observations, airborne measurements, satellite remote sensing data, and reanalysis gridded data arrays, and cloud-based platforms. The advanced techniques include but are not limited to

  • Artificial intelligence (AI) for enhancing TC intensity change prediction capabilities
  • Explainable statistical/machine learning methods of discovering precursors to TC intensity changes
  • Automated machine learning frameworks for improving TC intensity change rapid intensification

In summary, contributing papers should highlight the roles of advanced data analysis techniques in the investigation of TC intensity changes.

Dr. Ruixin Yang
Dr. Yun Li
Dr. Yijun Wei
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

  • tropical cyclone intensity prediction
  • rapid intensity changes
  • composite analysis
  • data mining for tropical cyclones
  • deep learning implementation and result interpretation

Published Papers (4 papers)

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Research

26 pages, 4045 KiB  
Article
Investigating Tropical Cyclone Rapid Intensification with an Advanced Artificial Intelligence System and Gridded Reanalysis Data
by Yijun Wei, Ruixin Yang and Donglian Sun
Atmosphere 2023, 14(2), 195; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos14020195 - 17 Jan 2023
Cited by 2 | Viewed by 1315
Abstract
Rapid Intensification (RI) in Tropical Cyclone (TC) development is one of the most difficult and still challenging tasks in weather forecasting. In addition to the dynamical numerical simulations, commonly used techniques for RI (as well as TC intensity changes) analysis and prediction are [...] Read more.
Rapid Intensification (RI) in Tropical Cyclone (TC) development is one of the most difficult and still challenging tasks in weather forecasting. In addition to the dynamical numerical simulations, commonly used techniques for RI (as well as TC intensity changes) analysis and prediction are the composite analysis and statistical models based on features derived from the composite analysis. Quite a large number of such selected and pre-determined features related to TC intensity change and RI have been accumulated by the domain scientists, such as those in the widely used SHIPS (Statistical Hurricane Intensity Prediction Scheme) database. Moreover, new features are still being added with new algorithms and/or newly available datasets. However, there are very few unified frameworks for systematically distilling features from a comprehensive data source. One such unified Artificial Intelligence (AI) system was developed for deriving features from TC centers, and here, we expand that system to large-scale environmental condition. In this study, we implemented a deep learning algorithm, the Convolutional Neural Network (CNN), to the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis data and identified and refined potentially new features relevant to RI such as specific humidity in east or northeast, vorticity and horizontal wind in north and south relative to the TC centers, as well as ozone at high altitudes that could help the prediction and understanding of the occurrence of RI based on the deep learning network (named TCNET in this study). By combining the newly derived features and the features from the SHIPS database, the RI prediction performance can be improved by 43%, 23%, and 30% in terms of Kappa, probability of detection (POD), and false alarm rate (FAR) against the same modern classification model but with the SHIPS inputs only. Full article
(This article belongs to the Special Issue Investigating Tropical Cyclone Intensity Changes with Advanced Data Analysis Techniques)
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17 pages, 4650 KiB  
Article
Discovering Precursors to Tropical Cyclone Rapid Intensification in the Atlantic Basin Using Spatiotemporal Data Mining
by Yun Li, Ruixin Yang, Hui Su and Chaowei Yang
Atmosphere 2022, 13(6), 882; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13060882 - 28 May 2022
Viewed by 1695
Abstract
Regarded as one of the most dangerous types of natural disaster, tropical cyclones threaten the life and health of human beings and often cause enormous economic loss. However, intensity forecasting of tropical cyclones, especially rapid intensification forecasting, remains a scientific challenge due to [...] Read more.
Regarded as one of the most dangerous types of natural disaster, tropical cyclones threaten the life and health of human beings and often cause enormous economic loss. However, intensity forecasting of tropical cyclones, especially rapid intensification forecasting, remains a scientific challenge due to limited understanding regarding the intensity change process. We propose an automatic knowledge discovery framework to identify potential spatiotemporal precursors to tropical cyclone rapid intensification from a set of tropical cyclone environmental fields. Specifically, this framework includes (1) formulating RI and non-RI composite environmental fields from historical tropical cyclones using NASA MERRA2 data; (2) utilizing the shared nearest neighbor-based clustering algorithm to detect regions representing relatively homogeneous behavior around tropical cyclone centers; (3) determining candidate precursors from significantly different regions in RI and non-RI groups using a spatiotemporal statistical method; and (4) comparing candidates to existing predictors to select potential precursors. The proposed knowledge discovery framework is applied separately to different factors, including 200 hPa zonal wind, 850–700 hPa relative humidity, and 850–200 hPa vertical shear, to detect potential precursors. Compared to the existing predictors manually labeled, i.e., U200 and U20C, RHLO, and SHRD in the Statistical Hurricane Intensity Prediction Scheme, our automatically discovered precursors have a comparable or better capability for estimating the probability of rapid intensification. Full article
(This article belongs to the Special Issue Investigating Tropical Cyclone Intensity Changes with Advanced Data Analysis Techniques)
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11 pages, 3120 KiB  
Article
Tropical Cyclone Intensity Prediction Using Deep Convolutional Neural Network
by Xiao-Yan Xu, Min Shao, Pu-Long Chen and Qin-Geng Wang
Atmosphere 2022, 13(5), 783; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13050783 - 12 May 2022
Cited by 8 | Viewed by 2715
Abstract
In this study, deep convolutional neural network (CNN) models of stimulated tropical cyclone intensity (TCI), minimum central pressure (MCP), and maximum 2 min mean wind speed at near center (MWS) were constructed based on ocean and atmospheric reanalysis, as well Best Track of [...] Read more.
In this study, deep convolutional neural network (CNN) models of stimulated tropical cyclone intensity (TCI), minimum central pressure (MCP), and maximum 2 min mean wind speed at near center (MWS) were constructed based on ocean and atmospheric reanalysis, as well Best Track of tropical hurricane data over 2014–2018. In order to explore the interpretability of the model structure, sensitivity experiments were designed with various combinations of predictors. The model test results show that simplified VGG-16 (VGG-16 s) outperforms the other two general models (LeNet-5 and AlexNet). The results of the sensitivity experiments display good consistency with the hypothesis and perceptions, which verifies the validity and reliability of the model. Furthermore, the results also suggest that the importance of predictors varies in different targets. The top three factors that are highly related to TCI are sea surface temperature (SST), temperature at 500 hPa (TEM_500), and the differences in wind speed between 850 hPa and 500 hPa (vertical wind shear speed, VWSS). VWSS, relative humidity (RH), and SST are more significant than MCP. For MWS and SST, TEM_500, and temperature at 850 hPa (TEM_850) outweigh the other variables. This conclusion also implies that deep learning could be an alternative way to conduct intensive and quantitative research. Full article
(This article belongs to the Special Issue Investigating Tropical Cyclone Intensity Changes with Advanced Data Analysis Techniques)
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20 pages, 1690 KiB  
Article
An Advanced Artificial Intelligence System for Identifying the Near-Core Impact Features to Tropical Cyclone Rapid Intensification from the ERA-Interim Data
by Yijun Wei, Ruixin Yang, Jason Kinser, Igor Griva and Olga Gkountouna
Atmosphere 2022, 13(5), 643; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13050643 - 19 Apr 2022
Cited by 1 | Viewed by 1750
Abstract
Prediction of tropical cyclone (TC) intensity is one of the ground challenges in weather forecasting, and rapid intensification (RI) is a key part of that prediction. Most of the current RI studies are based on a selected variable (feature) set, which is accumulated [...] Read more.
Prediction of tropical cyclone (TC) intensity is one of the ground challenges in weather forecasting, and rapid intensification (RI) is a key part of that prediction. Most of the current RI studies are based on a selected variable (feature) set, which is accumulated based on expert expertise in past studies of TC intensity changes and RI. Are there any more important variables in TC intensity predictions that were not identified in past studies? A systematic and comprehensive search for those variables from vast amounts of gridded data, satellite images, and other historically collected data could be helpful for answering the above question. Artificial intelligence (AI) has the capabilities to distill features in large array data, and it is helpful in identifying new features related to TC intensity changes in general and RI in particular. Here, we leverage the local linear embedding (LLE) dimension reduction techniques to the European Centre for Medium-Range Weather Forecasts ERA-Interim reanalysis data for identifying new variables related to RI. In addition to the well-known features in the SHIPS (statistical hurricane intensity prediction scheme) database, we identified other significant features, such as 400 and 450 hPa meridional wind, 1000 hPa potential vorticity, and vertical pressure speed, that could help the understanding and prediction of RI occurrences. Furthermore, our AI system outperforms our baseline model with SHIPS data only by 26.6% and 8.4% in kappa and PSS (Peirce’s skill score), respectively. Full article
(This article belongs to the Special Issue Investigating Tropical Cyclone Intensity Changes with Advanced Data Analysis Techniques)
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