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Atmosphere
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Tropical Cyclones: Observation and Prediction
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Tropical Cyclones: Observation and Prediction

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

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 25172

Special Issue Editor

Dr. Evan A. Kalina

E-Mail Website
Guest Editor
1. University of Colorado/Cooperative Institute for Research in Environmental Sciences, Boulder, CO, 80305, United States;
2. NOAA Global Systems Laboratory, Boulder, CO 80305, USA
Interests: tropical cyclones; microphysics; planetary boundary layer; radars; operational modeling; ensembles; model evaluation

Special Issue Information

Dear Colleagues,

From 1990 to 2019, tropical cyclones caused an average of 13,000 fatalities and $35 billion in damages (in 2020 US dollars) on an annual global basis. Because of their substantial impact on life and property, improving tropical cyclone forecasts is a critical priority for many governments and operational weather prediction centers. Observations of tropical cyclones are essential for achieving these improvements. Conventional and satellite observations of tropical cyclones are routinely quality controlled and assimilated into operational numerical weather prediction (NWP) systems, ensuring that an accurate initial condition is supplied to the forecast model. Case studies and statistical analyses that leverage data collected from observing networks and field campaigns have greatly enhanced our scientific understanding of the lifecycle, structure, and governing processes of tropical cyclones. Observations are also frequently used to assess tropical cyclone NWP model performance and to improve their physical parameterization schemes, ultimately leading to more accurate and reliable forecasts.

In this Special Issue, we invite original and review articles that use atmospheric and/or oceanic observations of tropical cyclones from a wide range of in situ and remote sensing platforms, including aircraft, autonomous vehicles, bathythermographs, buoys, disdrometers, dropsondes, lightning detection networks, ocean drifters and floats, radars, satellites, ships, surface weather stations, and underwater gliders. These contributions may describe novel instrument platforms; new or improved techniques for collecting tropical cyclone observations; case studies of tropical cyclones that utilize observations; assimilation of new types of tropical cyclone observations into NWP models; and/or the use of observations to evaluate tropical cyclone NWP models or to develop new physical parameterizations appropriate for tropical cyclone environments. In general, contributions should highlight the unique and essential roles that observations play in furthering our understanding of tropical cyclones and driving improvements in their forecasting.

Dr. Evan A. Kalina
Guest Editor

Manuscript Submission Information

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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 case studies
  • tropical cyclone forecasting
  • data assimilation for tropical cyclones
  • tropical cyclone observations
  • tropical cyclone model evaluation

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Published Papers (8 papers)

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Research

13 pages, 3680 KiB  
Article
The Use of Composite GOES-R Satellite Imagery to Evaluate a TC Intensity and Vortex Structure Forecast by an FV3GFS-Based Hurricane Forecast Model
by Shaowu Bao, Zhan Zhang, Evan Kalina and Bin Liu
Atmosphere 2022, 13(1), 126; https://doi.org/10.3390/atmos13010126 - 13 Jan 2022
Cited by 4 | Viewed by 4445
Abstract
The HAFS model is an effort under the NGGPS and UFS initiatives to create the next generation of hurricane prediction and analysis system based on FV3-GFS. It has been validated extensively using traditional verification indicators such as tracker error and biases, intensity error [...] Read more.
The HAFS model is an effort under the NGGPS and UFS initiatives to create the next generation of hurricane prediction and analysis system based on FV3-GFS. It has been validated extensively using traditional verification indicators such as tracker error and biases, intensity error and biases, and the radii of gale, damaging and hurricane strength winds. While satellite images have been used to verify hurricane model forecasts, they have not been used on HAFS. The community radiative transfer model CRTM is used to generate model synthetic satellite images from HAFS model forecast state variables. The 24 forecast snapshots in the mature stage of hurricane Dorian in 2019 are used to generate a composite model synthetic GOES-R infrared brightness image. The composite synthetic image is compared to the corresponding composite image generated from the observed GOES-R data, to evaluate the model forecast TC vortex intensity, size, and asymmetric structure. Results show that the HAFS forecast TC Dorian agrees reasonably well with the observation, but the forecast intensity is weaker, its overall vortex size smaller, and the radii of its eye and maximum winds larger than the observed. The evaluation results can be used to further improve the model. While these results are consistent with those obtained by traditional verification methods, evaluations based on composite satellite images provide an additional benefit with richer information because they have near-real-times spatially and temporally continuous high-resolution data with global coverage. Composite satellite infrared images could be used routinely to supplement traditional verification methods in the HAFS and other hurricane model evaluations. Note since this study only evaluated one hurricane, the above conclusions are only applicable to the model behavior of the mature stage of hurricane Dorian in 2019, and caution is needed to extend these conclusions to expect model biases in predicting other TCs. Nevertheless, the consistency between the evaluation using composite satellite images and the traditional metrics, of hurricane Dorian, shows that this method has the potential to be applied to other storms in future studies. Full article
(This article belongs to the Special Issue Tropical Cyclones: Observation and Prediction)
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28 pages, 908 KiB  
Article
Forced, Balanced, Axisymmetric Shallow Water Model for Understanding Short-Term Tropical Cyclone Intensity and Wind Structure Changes
by Eric A. Hendricks, Jonathan L. Vigh and Christopher M. Rozoff
Atmosphere 2021, 12(10), 1308; https://doi.org/10.3390/atmos12101308 - 7 Oct 2021
Cited by 2 | Viewed by 2395
Abstract
A minimal modeling system for understanding tropical cyclone intensity and wind structure changes is introduced: Shallow Water Axisymmetric Model for Intensity (SWAMI). The forced, balanced, axisymmetric shallow water equations are reduced to a canonical potential vorticity (PV) production and inversion problem, whereby PV [...] Read more.
A minimal modeling system for understanding tropical cyclone intensity and wind structure changes is introduced: Shallow Water Axisymmetric Model for Intensity (SWAMI). The forced, balanced, axisymmetric shallow water equations are reduced to a canonical potential vorticity (PV) production and inversion problem, whereby PV is produced through a mass sink (related to the diabatic heating) and inverted through a PV/absolute–angular–momentum invertibility principle. Because the invertibility principle is nonlinear, a Newton–Krylov method is used to iteratively obtain a numerical solution to the discrete problem. Two versions of the model are described: a physical radius version which neglects radial PV advection (SWAMI-r) and a potential radius version that naturally includes the advection in the quasi-Lagrangian coordinate (SWAMI-R). In idealized numerical simulations, SWAMI-R produces a thinner and more intense PV ring than SWAMI-r, demonstrating the role of axisymmetric radial PV advection in eyewall evolution. SWAMI-R always has lower intensification rates than SWAMI-r because the reduction in PV footprint effect dominates the peak magnitude increase effect. SWAMI-r is next demonstrated as a potentially useful short-term wind structure forecasting tool using the newly added FLIGHT+ Dataset azimuthal means for initialization and forcing on three example cases: a slowly intensifying event, a rapid intensification event, and a secondary wind maximum formation event. Then, SWAMI-r is evaluated using 63 intensifying cases. Even though the model is minimal, it is shown to have some skill in short-term intensity prediction, highlighting the known critical roles of the relationship between the radial structures of the vortex inertial stability and diabatic heating rate. Because of the simplicity of the models, SWAMI simulations are completed in seconds. Therefore, they may be of some use for hurricane nowcasting to short-term (less than 24 h) intensity and structure forecasting. Due to its favorable assumptions for tropical cyclone intensification, a potential use of SWAMI is a reasonable short-term upper-bound intensity forecast if the storm intensifies. Full article
(This article belongs to the Special Issue Tropical Cyclones: Observation and Prediction)
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27 pages, 13080 KiB  
Article
A Comparison of HWRF Six-Hourly 4DEnVar and Hourly 3DEnVar Assimilation of Inner Core Tail Dopper Radar Observations for the Prediction of Hurricane Edouard (2014)
by Benjamin Davis, Xuguang Wang and Xu Lu
Atmosphere 2021, 12(8), 942; https://doi.org/10.3390/atmos12080942 - 22 Jul 2021
Cited by 9 | Viewed by 2374
Abstract
Six-hourly three-dimensional ensemble variational (3DEnVar) (6H-3DEnVar) data assimilation (DA) assumes constant background error covariance (BEC) during a six-hour DA window and is, therefore, unable to account for temporal evolution of the BEC. This study evaluates the one-hourly 3DEnVar (1H-3DEnVar) and six-hourly 4DEnVar (6H-4DEnVar) [...] Read more.
Six-hourly three-dimensional ensemble variational (3DEnVar) (6H-3DEnVar) data assimilation (DA) assumes constant background error covariance (BEC) during a six-hour DA window and is, therefore, unable to account for temporal evolution of the BEC. This study evaluates the one-hourly 3DEnVar (1H-3DEnVar) and six-hourly 4DEnVar (6H-4DEnVar) DA methods for the analyses and forecasts of hurricanes with rapidly evolving BEC. Both methods account for evolving BEC in a hybrid EnVar DA system. In order to compare these methods, experiments are conducted by assimilating inner core Tail Doppler Radar (TDR) wind for Hurricane Edouard (2014) and by running the Hurricane Weather Research and Forecasting (HWRF) model. In most metrics, 1H-3DEnVar and 6H-4DEnVar analyses and forecasts verify better than 6H-3DEnVar. 6H-4DEnVar produces better thermodynamic analyses than 1H-3DEnVar. Radar reflectivity shows that 1H-3DEnVar produces better structure forecasts. For the first 24–48 h of the intensity forecast, 6H-4DEnVar forecast performs better than 1H-3DEnVar verified against the best track. Degraded 1H-3DEnVar forecasts are found to be associated with background storm center location error as a result of underdispersive ensemble storm center spread. Removing location error in the background improves intensity forecasts of 1H-3DEnVar. Full article
(This article belongs to the Special Issue Tropical Cyclones: Observation and Prediction)
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14 pages, 4915 KiB  
Article
Applications of Radar Data Assimilation with Hydrometeor Control Variables within the WRFDA on the Prediction of Landfalling Hurricane IKE (2008)
by Feifei Shen, Jinzhong Min, Hong Li, Dongmei Xu, Aiqing Shu, Danhua Zhai, Yakai Guo and Lixin Song
Atmosphere 2021, 12(7), 853; https://doi.org/10.3390/atmos12070853 - 30 Jun 2021
Cited by 9 | Viewed by 2483
Abstract
The impact of assimilating radar radial velocity and reflectivity on the analyses and forecast of Hurricane IKE is investigated within the framework of the WRF (Weather Research and Forecasting) model and its three-dimensional variational (3DVar) data assimilation system, including the hydrometeor control variables. [...] Read more.
The impact of assimilating radar radial velocity and reflectivity on the analyses and forecast of Hurricane IKE is investigated within the framework of the WRF (Weather Research and Forecasting) model and its three-dimensional variational (3DVar) data assimilation system, including the hydrometeor control variables. Hurricane IKE in the year 2008 was chosen as the study case. It was found that assimilating radar data is able to effectively improve the small-scale information of the hurricane vortex area in the model background. Radar data assimilation experiments yield significant cyclonic wind increments in the inner-core area of the hurricane, enhancing the intensity of the hurricane in the model background. On the other hand, by extending the traditional control variables to include the hydrometeor control variables, the assimilation of radar reflectivity can effectively adjust the water vapor and hydrometeors of the background, further improving the track and intensity forecast of the hurricane. The precipitation forecast skill is also enhanced to some extent with the radar data assimilation, especially with the extended hydrometeor control variables. Full article
(This article belongs to the Special Issue Tropical Cyclones: Observation and Prediction)
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9 pages, 2187 KiB  
Article
Analog Ensemble Methods for Improving Satellite-Based Intensity Estimates of Tropical Cyclones
by William E. Lewis, Timothy L. Olander, Christopher S. Velden, Christopher Rozoff and Stefano Alessandrini
Atmosphere 2021, 12(7), 830; https://doi.org/10.3390/atmos12070830 - 28 Jun 2021
Cited by 1 | Viewed by 2066
Abstract
Accurate, reliable estimates of tropical cyclone (TC) intensity are a crucial element in the warning and forecast process worldwide, and for the better part of 50 years, estimates made from geostationary satellite observations have been indispensable to forecasters for this purpose. One such [...] Read more.
Accurate, reliable estimates of tropical cyclone (TC) intensity are a crucial element in the warning and forecast process worldwide, and for the better part of 50 years, estimates made from geostationary satellite observations have been indispensable to forecasters for this purpose. One such method, the Advanced Dvorak Technique (ADT), was used to develop analog ensemble (AnEn) techniques that provide more precise estimates of TC intensity with instant access to information on the reliability of the estimate. The resulting methods, ADT-AnEn and ADT-based Error Analog Ensemble (ADTE-AnEn), were trained and tested using seventeen years of historical ADT intensity estimates using k-fold cross-validation with 10 folds. Using only two predictors, ADT-estimated current intensity (maximum wind speed) and TC center latitude, both AnEn techniques produced significant reductions in mean absolute error and bias for all TC intensity classes in the North Atlantic and for most intensity classes in the Eastern Pacific. The ADTE-AnEn performed better for extreme intensities in both basins (significantly so in the Eastern Pacific) and will be incorporated in the University of Wisconsin’s Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) workflow for further testing during operations in 2021. Full article
(This article belongs to the Special Issue Tropical Cyclones: Observation and Prediction)
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11 pages, 1108 KiB  
Article
Predicting Major Storm Surge Levels
by Robert Mendelsohn
Atmosphere 2021, 12(6), 756; https://doi.org/10.3390/atmos12060756 - 10 Jun 2021
Cited by 1 | Viewed by 2848
Abstract
The National Atmospheric and Oceanic Administration (NOAA) calculates the surge probability distribution along the coast from their long-term tidal stations. This process is sufficient for predicting the surge from common storms but tends to underestimate large surges. Across 23 long-term tidal stations along [...] Read more.
The National Atmospheric and Oceanic Administration (NOAA) calculates the surge probability distribution along the coast from their long-term tidal stations. This process is sufficient for predicting the surge from common storms but tends to underestimate large surges. Across 23 long-term tidal stations along the East Coast of the United States, 100-year surges were observed 49 times, although they should have occurred only 23 times. We hypothesize that these 100-year surges are not the tail outcome from common storms but are actually caused by major hurricanes. Matching these 100-year surges with major hurricanes revealed that major hurricanes caused 43 of the 49 surges. We consequently suggest a revised approach to estimating the surge probability distribution. We used tidal data to estimate the probability of common surges but analyzed major hurricane surges separately, using the return rate of major hurricanes and the observed surge from each major hurricane to predict hurricane surges. The revision reveals that expected coastal flooding damage is higher than we thought, especially in the southeast United States. Full article
(This article belongs to the Special Issue Tropical Cyclones: Observation and Prediction)
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24 pages, 1061 KiB  
Article
An Advanced Artificial Intelligence System for Investigating Tropical Cyclone Rapid Intensification with the SHIPS Database
by Yijun Wei and Ruixin Yang
Atmosphere 2021, 12(4), 484; https://doi.org/10.3390/atmos12040484 - 12 Apr 2021
Cited by 19 | Viewed by 3475
Abstract
Currently, most tropical cyclone (TC) rapid intensification (RI) prediction studies are conducted based on a subset of the SHIPS database using a relatively simple model structure. However, variables (features) in the SHIPS database are built upon human expertise in TC intensity studies based [...] Read more.
Currently, most tropical cyclone (TC) rapid intensification (RI) prediction studies are conducted based on a subset of the SHIPS database using a relatively simple model structure. However, variables (features) in the SHIPS database are built upon human expertise in TC intensity studies based on hard and subjective thresholds, and they should be explored thoroughly to make full use of the expertise. Based on the complete SHIPS data, this study constructs a complicated artificial intelligence (AI) system that handles feature engineering and selection, imbalance, prediction, and hyper parameter-tuning, simultaneously. The complicated AI system is used to further improve the performance of the current studies in RI prediction, and to identify other essential SHIPS variables that are ignored by previous studies with variable importance scores. The results outperform most of the earlier studies by approximately 21–50% on POD (Probability Of Detection) with reduced FAR (False Alarm Rate). This study built a baseline for future work on new predictor identification with more complicated AI techniques. Full article
(This article belongs to the Special Issue Tropical Cyclones: Observation and Prediction)
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21 pages, 12594 KiB  
Article
Large Roll Vortices Exhibited by Post-Tropical Cyclone Sandy during Landfall
by James A. Schiavone, Kun Gao, David A. Robinson, Peter J. Johnsen and Mathieu R. Gerbush
Atmosphere 2021, 12(2), 259; https://doi.org/10.3390/atmos12020259 - 16 Feb 2021
Cited by 2 | Viewed by 2448
Abstract
Roll vortices are frequent features of a hurricane’s boundary layer, with kilometer or sub-kilometer horizontal scale. In this study, we found that large roll vortices with O (10 km) horizontal wavelength occurred over land in Post-Tropical Cyclone Sandy (2012) during landfall on New [...] Read more.
Roll vortices are frequent features of a hurricane’s boundary layer, with kilometer or sub-kilometer horizontal scale. In this study, we found that large roll vortices with O (10 km) horizontal wavelength occurred over land in Post-Tropical Cyclone Sandy (2012) during landfall on New Jersey. Various characteristics of roll vortices were corroborated by analyses of Doppler radar observations, a 500 m resolution Weather Research and Forecasting (WRF) simulation, and an idealized roll vortex model. The roll vortices were always linear-shaped, and their wavelengths of 5–14 km were generally larger than any previously published for a tropical cyclone over land. Based on surface wind observations and simulated WRF surface wind fields, we found that roll vortices significantly increased the probability of hazardous winds and likely caused the observed patchiness of treefall during Sandy’s landfall. Full article
(This article belongs to the Special Issue Tropical Cyclones: Observation and Prediction)
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