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Tropical Cyclone Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 8602

Special Issue Editor


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Guest Editor
Earth and Space Sciences (ess) Research Group, Enviromental Sciences and Biochemistry (2012-2021), University of Castilla-La Mancha (UCLM), Avda. Carlos III s/n, E-45071 Toledo, Spain
Interests: precipitation; remote sensing; tropical cyclones; climate change; social sciences; microphysics
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Special Issue Information

Dear Colleagues,

Tropical cyclones cause massive economic losses and sudden mortality over ample coastal areas of the planet. Indeed, hurricanes and typhoons are devastating phenomena that require societal attention. Estimating hurricane intensity, frequency and path in a climate emergency scenario is an active research area, and on those topics remote sensing can play an important role. Optimizing Earth Observation (EO) for emergency management is a timely and relevant research area of societal interest.

In the Physics realm, tropical cyclones are intriguing thermodynamical machines driven by enthalpy exchanges in the sea/atmosphere interface that embeds numerous atmospheric processes. Tropical cyclones form and evolve as the result of complex multi-scale processes and interactions. The role of the thermodynamic and kinematic structure of large-scale environment has long been recognized. Research over the past decade has indicated that the hurricane inner core processes might play a crucial role in determining the storm’s intensity and size. Yet, understanding of these multi-scale processes is still lacking, bringing to the forefront the need to investigate the important role of the convective organization. Also, there are still many unanswered questions about the physical processes that determine tropical cyclone intensity. Remote sensing technology can play an active role to advance such topic.

While tropical cyclones are natural phenomena there is clear evidence that human action is a key ingredient in evaluating their impact. As climate changes due to both anthropogenic and natural influences, it is crucial to improve our understanding and ability to project changes in tropical cyclone activity in all the oceanic basin. Indeed, there is every reason to pool international expertise to increase our knowledge on tropical cyclones.

The Special Issue will be devoted to disseminating research using remote sensing to investigate these interesting meteorological phenomena. Examples include remotely-sensed analyses of tropical cyclones and disturbances using infrared wavelengths and microwave frequencies, rapid intensification methods that use satellite information at some stage, genesis research, and datasets and databases devoted to advance our knowledge on the genesis, intensification and future of tropical cyclones.

The scope is very broad. There are many other such cross-cutting topics that could be contributed to the Special Issue, including insurance, natural hazards assessment and societal/communication topics.

The Special Issue welcomes papers that deal mainly with modeling but use satellite information for illustrative purposes, and case-study contributions making some use of satellite data and flight campaigns. It is also open to radar, dropsonde and general airborne observations of tropical cyclones. Papers on applications of remote sensing to study individual tropical cyclones and numerical case studies on specific hurricanes would be welcome contributions to the Special Issue. Papers on tropical cyclones in the Pacific, Indian and Australian basins will be warmly appreciated to balance the current research bias toward the North Atlantic area.

Prof. Dr. Francisco J. Tapiador
Guest Editor

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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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 Cyclones
  • Hurricanes
  • Typhoons
  • Rapid intensification
  • Tropical depression
  • Fujiwhara effect
  • Severe cyclonic storms
  • Eyewall replacement cycle

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Published Papers (2 papers)

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Research

15 pages, 4456 KiB  
Article
Advanced Machine Learning Methods for Major Hurricane Forecasting
by Javier Martinez-Amaya, Cristina Radin and Veronica Nieves
Remote Sens. 2023, 15(1), 119; https://doi.org/10.3390/rs15010119 - 26 Dec 2022
Cited by 7 | Viewed by 4972
Abstract
Hurricanes, rapidly increasing in complexity and strength in a warmer world, are one of the worst natural disasters in the 21st century. Further studies integrating the changing hurricane features are thus crucial to aid in the prediction of major hurricanes. With this in [...] Read more.
Hurricanes, rapidly increasing in complexity and strength in a warmer world, are one of the worst natural disasters in the 21st century. Further studies integrating the changing hurricane features are thus crucial to aid in the prediction of major hurricanes. With this in mind, we present a new framework based on automated decision tree analysis, which has the capability to identify the most important cloud structural parameters from GOES imagery as predictors for hurricane intensification potential in the Atlantic and Pacific oceans. The proposed framework has been proved effective for predicting major hurricanes with an overall accuracy of 73% from 6 to 54 h in advance (both regions combined). Full article
(This article belongs to the Special Issue Tropical Cyclone Remote Sensing)
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21 pages, 5627 KiB  
Article
A Comparison of Spectral Bin Microphysics versus Bulk Parameterization in Forecasting Typhoon In-Fa (2021) before, during, and after Its Landfall
by Yun Zhang, Zuhang Wu, Lifeng Zhang and Hepeng Zheng
Remote Sens. 2022, 14(9), 2169; https://doi.org/10.3390/rs14092169 - 30 Apr 2022
Cited by 4 | Viewed by 2561
Abstract
Typhoon In-Fa hit continental China in July 2021 and caused an unprecedented rainfall amount, making it a typical case to examine the ability of numerical models in forecasting landfalling typhoons. The record-breaking storm was simulated using a 3-km-resolution weather research and forecast (WRF) [...] Read more.
Typhoon In-Fa hit continental China in July 2021 and caused an unprecedented rainfall amount, making it a typical case to examine the ability of numerical models in forecasting landfalling typhoons. The record-breaking storm was simulated using a 3-km-resolution weather research and forecast (WRF) model with spectral bin microphysics scheme (BIN) and two-moment seven-class bulk parameterization scheme (BULK). The simulations were then separated into three different typhoon landfall periods (i.e., pre-landfall, landfall, and post-landfall). It was found that typhoon intensity prediction is sensitive to microphysical schemes regardless of landfall periods, while typhoon track prediction tends to be more (less) sensitive to microphysical schemes after (before) typhoon landfall. Moreover, significant differences exist between BIN and BULK schemes in simulating the storm intensity, track, and rainfall distribution. BIN scheme simulates stronger (weaker) typhoon intensity than BULK scheme after (before) landfall, while BULK scheme simulates typhoon moving faster (slower) than BIN scheme before (after) landfall. BIN scheme produces much more extensive and homogeneous typhoon rainbands than BULK scheme, whereas BULK scheme produces stronger (weaker) rainfall in the typhoon inner (outer) rainbands. The possible reasons for such differences are discussed. At present, the ability of WRF and other mesoscale models to accurately simulate the typhoon precipitation hydrometeors is still limited. To evaluate the performances of BIN and BULK schemes of WRF model in simulating the condensed water in Typhoon In-Fa, the observed microwave brightness temperature and radar reflectivity from the core observatory of Global Precipitation Mission (GPM) satellite are directly used for validation with the help of a satellite simulator. It is suggested that BIN scheme has better performance in estimating the spatial structure, overall amplitude, and precise location of the condensed water in typhoons before landfall. During typhoon landfall, the performance of BIN scheme in simulating the structure and location of the condensate is close to that of BULK scheme, but the condensate intensity prediction by BIN scheme is still better; BULK scheme performs even better than BIN scheme in the prediction of condensate structure and location after typhoon landfall. Both schemes seem to have poorer performances in simulating the spatial structure of precipitation hydrometeors during typhoon landfall than before/after typhoon landfall. Moreover, BIN scheme simulates more (less) realistic warm (cold) rain processes than BULK scheme, especially after typhoon landfall. BULK scheme simulates more cloud water and larger convective updraft than BIN scheme, and this is also reported in many model studies comparing BIN and BULK schemes. Full article
(This article belongs to the Special Issue Tropical Cyclone Remote Sensing)
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