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Tropical Cyclones Remote Sensing and Data Assimilation

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: closed (21 March 2020) | Viewed by 75031

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Special Issue Editors

Prof. Dr. Xiaofeng Yang

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Guest Editor

State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, Beijing 100101, China
Interests: satellite oceanography; microwave remote sensing; marine atmospheric boundary layer process studies; marine pollution monitoring; air–sea interactions
Special Issues, Collections and Topics in MDPI journals

Dr. Bryan Stiles

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Guest Editor

Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA
Interests: microwave remote sensing; sea surface winds; sea surface heights; scatterometry; radiometry; synthetic aperture radar; tropical cyclones; planetary science; planetary rotation models; neural networks

Dr. Marcos Portabella

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Guest Editor

Institute of Marine Sciences (ICM-CSIC), Pg. Maritim de la Barceloneta 37-49, 08003 Barcelona, Spain
Interests: satellite remote sensing; scatterometry; synthetic aperture radars; microwave radiometry; sea surface wind, wind stress, and salinity retrievals; calibration; forward modelling; measurement error modelling; quality control; non-linear inversion; data assimilation

Dr. Gang Zheng

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Guest Editor

State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, No.36 Baochubei Road, Hangzhou 310012, China
Interests: satellite oceanography; microwave remote sensing; AI oceanography; tropical cyclone remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Tropical cyclones (TC) are the most severe storm systems in the tropics and among the most destructive natural hazards in the world. Remote sensing systems, such as radiometers and scatterometers, have already proven their worth for observing and forecasting TC. These measurements are critical for short-term forecasting, but also offer the means to better examine the role of extreme conditions for the state of the ocean, at local and global scales. Satellite, airborne, in situ, and numerical weather prediction of high and extreme wind estimates have their own strengths and limitations, as well as different spatial representativeness. A particularly challenging topic is how to define a suitable wind reference under such extreme weather conditions.

In recent years, new EO systems, such as, SMOS, SMAP, Sentinel-1/2, FengYun-3, Suomi-NPP, GOES-R, FengYun-4, etc., have been launched. Frequent observations of surface wind and atmospheric water content near the TC inner core are now possible and are revealing the spatial storm structure with impressive detail. This promotes the improvement of mesoscale and synoptic modeling and data assimilation of the remotely sensed observations in TC forecasting. In addition to new sensors, new techniques for utilizing sensors with long term temporal extent such as, QuikSCAT, ASCAT, WindSAT, SSM/I and GOES, are enabling us to better understand climatological trends in tropical cyclone frequency, intensity, and spatial extent and the impact of these storms on the global air/sea energy balance.

This Special Issue will focus on the newly-developed methods for TC monitoring using state-of-the-art remote sensing techniques. The topics of this Special Issue include, without being limited to, the following subjects:

Remote sensing of wind under TC conditions
Tropical cyclone intensity estimation
Tropical cyclone integrated kinetic energy estimation
Monitoring of cyclone structures
Convection and precipitation observations
Observations of air–sea interaction and intensity changes
Damage assessment using satellite observations
Satellite data assimilation and applications in TC forecasts

Authors are requested to check and follow the specific Instructions to Authors, see:

https://www.mdpi.com/journal/remotesensing/instructions

We look forward to receiving your submissions in this interesting area of specialization.

Dr. Xiaofeng Yang
Dr. Bryan Stiles
Dr. Marcos Portabella
Dr. Gang Zheng
Guest Editors

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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 cyclone
Cyclone intensity
Cyclone track
Cyclone structure
Tropical cyclogenesis
Microwave systems

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Published Papers (14 papers)

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Editorial

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4 pages, 173 KiB

Open AccessEditorial

Editorial for Special Issue “Tropical Cyclones Remote Sensing and Data Assimilation”

by Bryan W. Stiles, Marcos Portabella, Xiaofeng Yang and Gang Zheng

Remote Sens. 2020, 12(18), 3067; https://doi.org/10.3390/rs12183067 - 19 Sep 2020

Viewed by 2409

Abstract

Tropical cyclones (TCs) are essential for many reasons, including their destruction of human lives and property and their effect on heat and nutrient fluxes between the ocean’s surface and its depths. A better understanding of ocean fluxes is needed to predict the impact of global climate change on the oceans and to quantify how ocean heat content modulates the dynamics of global climate change. Similarly, improved modeling of nutrient fluxes is crucial for maintaining fisheries and preserving crucial marine ecosystems to benefit both humanity and marine life. Numerous remote sensors measure crucial geophysical quantities before, during, and after TCs, including sea surface temperature (SST), ocean color, chlorophyll concentration, ocean surface winds, sea surface height, and significant wave height. In this special issue, an international group of researchers have written articles describing (1) novel techniques and remote sensors for measuring the aforementioned quantities in tropical cyclones, (2) methods for validating and improving the accuracy of those measurements and harmonizing them among different sensors, (3) scientific analyses that investigate the relationships between remote-sensed ocean surface measurements and in situ measurements of vertical profiles of ocean temperature, salinity, and current, and (4) strategies for utilizing remote-sensed measurements to improve operational forecasts in order to provide better tropical cyclone warnings to human populations. Full article

(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)

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17 pages, 7066 KiB

Open AccessArticle

On-Board Wind Scatterometry

by Xingou Xu, Xiaolong Dong and Yu Xie

Remote Sens. 2020, 12(7), 1216; https://doi.org/10.3390/rs12071216 - 9 Apr 2020

Cited by 7 | Viewed by 4213

Abstract

Real-time (RT) ocean surface wind can make key improvements to disaster alarmingand safety of maritime navigation to avoid loss in property and human lives. Wind scatterometry is a well-acquainted way of obtaining good quality ocean surface winds, and it has been in application for decades. Existing wind-obtaining chains employ ground stations for receiving observations and can, at best, provide products in around 30 minutes for limited regions. In recent years, a satellite information-obtaining and transmission network is the new trend of Earth observation. In this research, on-board wind retrieval environment and procedures, which are different from traditional wind-obtaining chains, are proposed. First, the establishment of the on-board environment is instructed. Structures of each module are provided. The ground simulation system is been established based on this. After that, existing observing and processing routines of wind scatterometry are described, and then an on-board processing chain proposed and described. Modifications to existing satellite-ground chains are highlighted. The proposed method is validated in Level 0 data from the Chinese–French Oceanic SATellite (CFOSAT). Experiments indicate that the proposed on-board processing procedure can provide comparable results to ground-processed wind products. The root-mean-square error (RMSE) of wind speed for a track of data used in the experiment was about 0.26 m/s, and it was about 0.8° for wind direction. By decreasing wind field result quality, calculation time can be lessened in the on-board environment. However, it is found that in the whole chain of on-board wind generation, the most time-consuming procedure is observation-obtaining. The proposed on-board processing method can achieve good wind accuracy while meeting RT applications with good processing time. This provides a good complement to existing on-board-observing-ground-processing chains for RT applications. Full article

(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)

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24 pages, 6394 KiB

Open AccessArticle

Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data

by Juhyun Lee, Jungho Im, Dong-Hyun Cha, Haemi Park and Seongmun Sim

Remote Sens. 2020, 12(1), 108; https://doi.org/10.3390/rs12010108 - 28 Dec 2019

Cited by 90 | Viewed by 9996

Abstract

For a long time, researchers have tried to find a way to analyze tropical cyclone (TC) intensity in real-time. Since there is no standardized method for estimating TC intensity and the most widely used method is a manual algorithm using satellite-based cloud images, there is a bias that varies depending on the TC center and shape. In this study, we adopted convolutional neural networks (CNNs) which are part of a state-of-art approach that analyzes image patterns to estimate TC intensity by mimicking human cloud pattern recognition. Both two dimensional-CNN (2D-CNN) and three-dimensional-CNN (3D-CNN) were used to analyze the relationship between multi-spectral geostationary satellite images and TC intensity. Our best-optimized model produced a root mean squared error (RMSE) of 8.32 kts, resulting in better performance (~35%) than the existing model using the CNN-based approach with a single channel image. Moreover, we analyzed the characteristics of multi-spectral satellite-based TC images according to intensity using a heat map, which is one of the visualization means of CNNs. It shows that the stronger the intensity of the TC, the greater the influence of the TC center in the lower atmosphere. This is consistent with the results from the existing TC initialization method with numerical simulations based on dynamical TC models. Our study suggests the possibility that a deep learning approach can be used to interpret the behavior characteristics of TCs. Full article

(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)

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18 pages, 5673 KiB

Open AccessArticle

Examination of Surface Wind Asymmetry in Tropical Cyclones over the Northwest Pacific Ocean Using SMAP Observations

by Ziyao Sun, Biao Zhang, Jun A. Zhang and William Perrie

Remote Sens. 2019, 11(22), 2604; https://doi.org/10.3390/rs11222604 - 6 Nov 2019

Cited by 22 | Viewed by 4438

Abstract

Tropical cyclone (TC) surface wind asymmetry is investigated by using wind data acquired from an L-band passive microwave radiometer onboard the NASA Soil Moisture Active Passive (SMAP) satellite between 2015 and 2017 over the Northwest Pacific (NWP) Ocean. The azimuthal asymmetry degree is defined as the factor by which the maximum surface wind speed is greater than the mean wind speed at the radius of the maximum wind (RMW). We examined storm motion and environmental wind shear effects on the degree of TC surface wind asymmetry under different intensity conditions. Results show that the surface wind asymmetry degree significantly decreases with increasing TC intensity, but increases with increasing TC translation speed, for tropical storm and super typhoon strength TCs; whereas no such relationship is found for typhoon and severe typhoon strength TCs. However, the degree of surface wind asymmetry increases with increasing wind shear magnitude for all TC intensity categories. The relative strength between the storm translation speed and the wind shear magnitude has the potential to affect the location of the maximum wind speed. Moreover, the maximum degree of wind asymmetry is found when the direction of the TC motion is nearly equal to the direction of the wind shear. Full article

(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)

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16 pages, 3749 KiB

Open AccessArticle

A Radar Radial Velocity Dealiasing Algorithm for Radar Data Assimilation and its Evaluation with Observations from Multiple Radar Networks

by Guangxin He, Juanzhen Sun, Zhuming Ying and Lejian Zhang

Remote Sens. 2019, 11(20), 2457; https://doi.org/10.3390/rs11202457 - 22 Oct 2019

Cited by 5 | Viewed by 3984

Abstract

Automated and accurate radar dealiasing algorithms are very important for their assimilation into operational numerical weather forecasting models. A radar radial velocity dealiasing algorithm aimed at radar data assimilation is introduced and assessed using from several S-band and C-band radar observations under the severe weather conditions of hurricanes, typhoons, and deep continental convection in this paper. This dealiasing algorithm, named automated dealiasing for data assimilation (ADDA), is a further development of the dealiasing algorithm named the China radar network (CINRAD) improved dealiasing algorithm (CIDA), originally developed for China’s CINRAD (China Next Generation Weather Radar) radar network. The improved scheme contains five modules employed to remove noisy data, select the suitable first radial, preserve the convective regions, execute multipass dealiasing in both azimuthal and radial directions and conduct the final local dealiasing with an error check. This new dealiasing algorithm was applied to two hurricane cases, two typhoon cases, and three intense-convection cases that were observed from the CINRAD of China, Taiwan‘s radar network, and NEXRAD (Next Generation Weather Radar) of the U.S. with a continuous period of more than 12 h for each case. The dealiasing results demonstrated that ADDA performed better than CIDA for all selected cases. This algorithm not only produced a high success rate for the S-band radar, but also a reasonable performance for the C-band radar. Full article

(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)

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19 pages, 9615 KiB

Open AccessEditor’s ChoiceArticle

Upper Ocean Response to Two Sequential Tropical Cyclones over the Northwestern Pacific Ocean

by Jue Ning, Qing Xu, Tao Feng, Han Zhang and Tao Wang

Remote Sens. 2019, 11(20), 2431; https://doi.org/10.3390/rs11202431 - 19 Oct 2019

Cited by 18 | Viewed by 4388

Abstract

The upper ocean thermodynamic and biological responses to two sequential tropical cyclones (TCs) over the Northwestern Pacific Ocean were investigated using multi-satellite datasets, in situ observations and numerical model outputs. During Kalmaegi and Fung-Wong, three distinct cold patches were observed at sea surface. The locations of these cold patches are highly correlated with relatively shallower depth of the 26 °C isotherm and mixed layer depth (MLD) and lower upper ocean heat content. The enhancement of surface chlorophyll a (chl-a) concentration was detected in these three regions as well, mainly due to the TC-induced mixing and upwelling as well as the terrestrial runoff. Moreover, the pre-existing ocean cyclonic eddy (CE) has been found to significantly modulate the magnitude of surface cooling and chl-a increase. With the deepening of the MLD on the right side of TCs, the temperature of the mixed layer decreased and the salinity increased. The sequential TCs had superimposed effects on the upper ocean response. The possible causes of sudden track change in sequential TCs scenario were also explored. Both atmospheric and oceanic conditions play noticeable roles in abrupt northward turning of the subsequent TC Fung-Wong. Full article

(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)

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25 pages, 32341 KiB

Open AccessArticle

Statistical Characterization of the Observed Cold Wake Induced by North Atlantic Hurricanes

by Koen Haakman, Juan-Manuel Sayol, Carine G. van der Boog and Caroline A. Katsman

Remote Sens. 2019, 11(20), 2368; https://doi.org/10.3390/rs11202368 - 12 Oct 2019

Cited by 11 | Viewed by 4147

Abstract

This work quantifies the magnitude, spatial structure, and temporal evolution of the cold wake left by North Atlantic hurricanes. To this end we composited the sea surface temperature anomalies (SSTA) induced by hurricane observations from 2002 to 2018 derived from the international best track archive for climate stewardship (IBTrACS). Cold wake characteristics were distinguished by a set of hurricane and oceanic properties: Hurricane translation speed and intensity, and the characteristics of the upper ocean stratification represented by two barrier layer metrics: Barrier layer thickness (BLT) and barrier layer potential energy (BLPE). The contribution of the above properties to the amplitude of the cold wake was analyzed individually and in combination. The mean magnitude of the hurricane-induced cooling was of 1.7 °C when all hurricanes without any distinction were considered, and the largest cooling was found for slow-moving, strong hurricanes passing over thinner barrier layers, with a cooling above 3.5 °C with respect to pre-storm sea surface temperature (SST) conditions. On average the cold wake needed about 60 days to disappear and experienced a strong decay in the first 20 days, when the magnitude of the cold wake had decreased by 80%. Differences between the cold wakes yielded by mostly infrared and merged infrared and microwave remote sensed SST data were also evaluated, with an overall relative underestimation of the hurricane-induced cooling of about 0.4 °C for infrared-mostly data. Full article

(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)

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25 pages, 22001 KiB

Open AccessArticle

Ocean Response to Successive Typhoons Sarika and Haima (2016) Based on Data Acquired via Multiple Satellites and Moored Array

by Han Zhang, Xiaohui Liu, Renhao Wu, Fu Liu, Linghui Yu, Xiaodong Shang, Yongfeng Qi, Yuan Wang, Xunshu Song, Xiaohui Xie, Chenghao Yang, Di Tian and Wenyan Zhang

Remote Sens. 2019, 11(20), 2360; https://doi.org/10.3390/rs11202360 - 11 Oct 2019

Cited by 29 | Viewed by 6260

Abstract

Tropical cyclones (TCs) are natural disasters for coastal regions. TCs with maximum wind speeds higher than 32.7 m/s in the north-western Pacific are referred to as typhoons. Typhoons Sarika and Haima successively passed our moored observation array in the northern South China Sea in 2016. Based on the satellite data, the winds (clouds and rainfall) biased to the right (left) sides of the typhoon tracks. Sarika and Haima cooled the sea surface ~4 and ~2 °C and increased the salinity ~1.2 and ~0.6 psu, respectively. The maximum sea surface cooling occurred nearly one day after the two typhoons. Station 2 (S2) was on left side of Sarika’s track and right side of Haima’s track, which is studied because its data was complete. Strong near-inertial currents from the ocean surface toward the bottom were generated at S2, with a maximum mixed-layer speed of ~80 cm/s. The current spectrum also shows weak signal at twice the inertial frequency (2f). Sarika deepened the mixed layer, cooled the sea surface, but warmed the subsurface by ~1 °C. Haima subsequently pushed the subsurface warming anomaly into deeper ocean, causing a temperature increase of ~1.8 °C therein. Sarika and Haima successively increased the heat content anomaly upper than 160 m at S2 to ~50 and ~100 m°C, respectively. Model simulation of the two typhoons shows that mixing and horizontal advection caused surface ocean cooling, mixing and downwelling caused subsurface warming, while downwelling warmed the deeper ocean. It indicates that Sarika and Haima sequentially modulated warm water into deeper ocean and influenced internal ocean heat budget. Upper ocean salinity response was similar to temperature, except that rainfall refreshed sea surface and caused a successive salinity decrease of ~0.03 and ~0.1 psu during the two typhoons, changing the positive subsurface salinity anomaly to negative Full article

(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)

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22 pages, 7446 KiB

Open AccessArticle

Chlorophyll Concentration Response to the Typhoon Wind-Pump Induced Upper Ocean Processes Considering Air–Sea Heat Exchange

by Yupeng Liu, Danling Tang and Morozov Evgeny

Remote Sens. 2019, 11(15), 1825; https://doi.org/10.3390/rs11151825 - 4 Aug 2019

Cited by 45 | Viewed by 6666

Abstract

The typhoon Wind-Pump induced upwelling and cold eddy often promote the significant growth of phytoplankton after the typhoon. However, the importance of eddy-pumping and wind-driven upwelling on the sea surface chlorophyll a concentration (Chl-a) during the typhoon are still not clearly distinguished. In addition, the air–sea heat flux exchange is closely related to the upper ocean processes, but few studies have discussed its role in the sea surface Chl-a variations under typhoon conditions. Based on the cruise data, remote sensing data, and model data, this paper analyzes the contribution of the vertical motion caused by the eddy-pumping upwelling and Ekman pumping upwelling on the surface Chl-a, and quantitatively analyzes the influence of air–sea heat exchange on the surface Chl-a after the typhoon Linfa over the northeastern South China Sea (NSCS) in 2009. The results reveal the Wind Pump impacts on upper ocean processes: (1) The euphotic layer-integrated Chl-a increased after the typhoon, and the increasing of the surface Chl-a was not only the uplift of the deeper waters with high Chl-a but also the growth of the phytoplankton; (2) The Net Heat Flux (air–sea heat exchange) played a major role in controlling the upper ocean physical processes through cooling the SST and indirectly increased the surface Chl-a until two weeks after the typhoon; (3) the typhoon-induced cyclonic eddy was the most important physical process in increasing the surface Chl-a rather than the Ekman pumping and wind-stirring mixing after typhoon; (4) the spatial shift between the surface Chl-a blooms and the typhoon-induced cyclonic eddy could be due to the Ekman transport; (5) nutrients uplifting and adequate light were two major biochemical elements supplying for the growth of surface phytoplankton. Full article

(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)

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18 pages, 5180 KiB

Open AccessArticle

Objective Estimation of Tropical Cyclone Intensity from Active and Passive Microwave Remote Sensing Observations in the Northwestern Pacific Ocean

by Kunsheng Xiang, Xiaofeng Yang, Miao Zhang, Ziwei Li and Fanping Kong

Remote Sens. 2019, 11(6), 627; https://doi.org/10.3390/rs11060627 - 14 Mar 2019

Cited by 16 | Viewed by 5039

Abstract

A method of estimating tropical cyclone (TC) intensity based on Haiyang-2A (HY-2A) scatterometer, and Special Sensor Microwave Imager and Sounder (SSMIS) observations over the northwestern Pacific Ocean is presented in this paper. Totally, 119 TCs from the 2012 to 2017 typhoon seasons were selected, based on satellite-observed data and China Meteorological Administration (CMA) TC best track data. We investigated the relationship among the TC maximum-sustained wind (MSW), the microwave brightness temperature (TB), and the sea surface wind speed (SSW). Then, a TC intensity estimation model was developed, based on a multivariate linear regression using the training data of 96 TCs. Finally, the proposed method was validated using testing data from 23 other TCs, and its root mean square error (RMSE), mean absolute error (MAE), and bias were 5.94 m/s, 4.62 m/s, and −0.43 m/s, respectively. Full article

(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)

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25 pages, 16602 KiB

Open AccessArticle

Stepped Frequency Microwave Radiometer Wind-Speed Retrieval Improvements

by Joseph W. Sapp, Suleiman O. Alsweiss, Zorana Jelenak, Paul S. Chang and James Carswell

Remote Sens. 2019, 11(3), 214; https://doi.org/10.3390/rs11030214 - 22 Jan 2019

Cited by 48 | Viewed by 7872

Abstract

With the operational deployment of the Stepped Frequency Microwave Radiometer (SFMR), hurricane reconnaissance and research aircraft provide near real-time observations of the 10

m

ocean-surface wind-speed both within and around tropical cyclones. Hurricane specialists use these data to assist in determining wind radii [...] Read more.

With the operational deployment of the Stepped Frequency Microwave Radiometer (SFMR), hurricane reconnaissance and research aircraft provide near real-time observations of the 10

m

ocean-surface wind-speed both within and around tropical cyclones. Hurricane specialists use these data to assist in determining wind radii and maximum sustained winds—critical parameters for determining and issuing watches and warnings. These observations are also used for post-storm analysis, model validation, and ground truth for aircraft- and satellite-based wind sensors. We present observations on the current operational wind-speed and rain-rate SFMR retrieval procedures in the tropical cyclone environment and propose suggestions to improve them based on observed wind-speed biases. Using these new models in the SFMR retrieval process, we correct an approximate 10% low bias in the wind-speed retrievals from 15 to 45

m

s

⁻¹ with respect to GPS dropwindsondes. In doing so, we eliminate the rain-contaminated wind-speed retrievals below 45 mm

h

⁻¹ at tropical storm- and hurricane-force speeds present in the current operational model. We also update the SFMR radiative transfer model to include recent updates to smooth-ocean emissivity and atmospheric opacity models. All corrections were designed such that no changes to the current SFMR calibration procedures are required. Full article

(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)

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15 pages, 8157 KiB

Open AccessArticle

Seismological Observations of Ocean Swells Induced by Typhoon Megi Using Dispersive Microseisms Recorded in Coastal Areas

by Jianmin Lin, Sunke Fang, Xiaofeng Li, Renhao Wu and Hong Zheng

Remote Sens. 2018, 10(9), 1437; https://doi.org/10.3390/rs10091437 - 8 Sep 2018

Cited by 8 | Viewed by 4923

Abstract

Typhoons in the western Pacific Ocean can generate extensive ocean swells, some of which propagate toward Taiwan, Luzon, and the Ryukyu Islands, impacting the coasts and generating double-frequency (DF) microseisms. The dispersion characteristics of DF microseisms relevant to the propagation of ocean swells were analyzed using the fractional Fourier transform (FrFT) to obtain the propagation distance and track the origins of typhoon-induced swells through seismic observations. For the super typhoon Megi in 2010, the origin of the induced ocean swells was tracked and localized accurately using seismic records from stations in eastern Taiwan. The localized source regions and calculated wave periods of the ocean swells are in good agreement with values predicted by ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF). However, localized deviations may depend on the effective detection of dispersive DF microseisms, which is tied to both coastline geometry and the geographic locations of seismic stations. This work demonstrates the effectiveness of seismological methods in observing typhoon-induced swells. The dispersion characteristics of DF microseisms recorded by coastal stations could be used as a proxy measure to track and monitor typhoon-induced swells across oceans. Full article

(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)

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17 pages, 12832 KiB

Open AccessArticle

An Empirical Algorithm to Retrieve Significant Wave Height from Sentinel-1 Synthetic Aperture Radar Imagery Collected under Cyclonic Conditions

by Weizeng Shao, Yuyi Hu, Jingsong Yang, Ferdinando Nunziata, Jian Sun, Huan Li and Juncheng Zuo

Remote Sens. 2018, 10(9), 1367; https://doi.org/10.3390/rs10091367 - 28 Aug 2018

Cited by 23 | Viewed by 4462

Abstract

In this study, an empirical algorithm is proposed to retrieve significant wave height (SWH) from dual-polarization Sentinel-1 (S-1) synthetic aperture radar (SAR) imagery collected under cyclonic conditions. The retrieval scheme is based on the well-known CWAVE empirical function that is here updated to deal with multi-polarization S-1 SAR measurements collected using the interferometric wide (IW) and the Extra Wide-Swath (EW) imaging modes, under cyclonic conditions. First, a training dataset that consists of six S-1 SAR images collected under cyclonic conditions is exploited to both tune the retrieval function and to check the soundness of the retrievals against the co-located WAVEWATCH-III (WW3) numerical simulations. The comparison of simulation from the WW3 model and measurements from altimeter Jason-2 shows a 0.29m root mean square error (RMSE) of significant wave height (SWH). Then, a testing data-set that consists of two S-1 SAR images is exploited to provide a preliminary validation. The results, verified against both WW3 and European Centre for Medium-Range Weather Forecasts (ECMWF) data, show the soundness of the herein approach. Full article

(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)

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7 pages, 1545 KiB

Open AccessLetter

On Extreme Winds at L-Band with the SMAP Synthetic Aperture Radar

by Alexander G. Fore, Simon H. Yueh, Bryan W. Stiles, Wenqing Tang and Akiko K. Hayashi

Remote Sens. 2019, 11(9), 1093; https://doi.org/10.3390/rs11091093 - 8 May 2019

Cited by 7 | Viewed by 3495

Abstract

In this letter, we discuss some observations of the Soil Moisture Active Passive (SMAP) mission’s high-resolution synthetic aperture radar (SAR) for extreme winds and tropical cyclones. We find that the L-band cross-polarized backscatter is far more sensitive to wind speed at extreme winds than the co-polarized backscatter and it is essential to observations of extreme winds with L-band SAR. We introduce a cyclone wind speed retrieval algorithm and apply it to the limited SMAP SAR dataset of cyclones. We show that the SMAP SAR instrument is capable of measuring extreme winds up to the category 5 (70 m/s) wind speed regime providing unique capabilities as compared to traditional scatterometers with C and Ku-band radars. Full article

(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)

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