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14 pages, 5326 KiB  
Article
A Circulation Study Based on the 2022 Sino–Vietnamese Joint Survey Data from the Beibu Gulf
by Zhi Zeng, Jinwen Liu, Xin Zhao, Zhijie Chen, Yanyu Chen, Bo Chen, Maochong Shi and Wei He
Water 2024, 16(20), 2943; https://doi.org/10.3390/w16202943 - 16 Oct 2024
Viewed by 200
Abstract
This study analyzed the horizontal and vertical distribution characteristics of temperature and salinity in the central and eastern regions of the Beibu Gulf, based on conductivity measurements in summer 2022, temperature, and depth (CTD) measurement data from the Sino–Vietnamese cooperative project “Demonstration Study [...] Read more.
This study analyzed the horizontal and vertical distribution characteristics of temperature and salinity in the central and eastern regions of the Beibu Gulf, based on conductivity measurements in summer 2022, temperature, and depth (CTD) measurement data from the Sino–Vietnamese cooperative project “Demonstration Study on Ecological Protection and Management in Typical Bays: Seasonal Survey of the Beibu Gulf”. Furthermore, the study utilized the computational results from the numerical Finite-Volume Coastal Ocean Model (FVCOM) to elucidate the intrinsic patterns that formed the temperature and salinity distribution characteristics in August 2022 from both thermodynamic and dynamic perspectives. The circulation in the Beibu Gulf drives external seawater to move northward from the bay mouth. During this movement, numerous upwelling areas are created by lateral Ekman transport. The formation of different scales of cyclonic and anticyclonic vortices and current convergence zones is influenced by topography, runoff, and the water flux from the Qiongzhou Strait, which are key factors in the formation of upwelling and downwelling. The surface circulation in August 2022 significantly differed from the 20-year average surface circulation, with an influx of 1.15 × 104 m3/s more water entering the Beibu Gulf compared to the multi-year average. The water flux from the Qiongzhou Strait is a critical factor affecting the circulation patterns in the Beibu Gulf. The northeastern waters of the Beibu Gulf are characterized by current convergence zones, where extensive upwelling occurs. The rich nutrient salts in these areas promote the reproduction and growth of phytoplankton and zooplankton, making this the most favorable ecological environment in the Beibu Gulf and serving as a natural reserve for fisheries, coral reefs, dugongs, and Bryde’s whales. Full article
(This article belongs to the Section Oceans and Coastal Zones)
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Figure 1

Figure 1
<p>Locations of the Sino–Vietnam joint temperature and salinity survey stations in 2022 and bathymetry map.</p>
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<p>Measured spatial distribution of temperature ((<b>top</b>), °C) and salinity (<b>bottom</b>) in the surface and bottom layers of Beibu Gulf. The dashed lines denote the data boundaries.</p>
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<p>The temperature and salinity distribution across sections.</p>
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<p>The temperature and salinity distribution across sections.</p>
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<p>(<b>a</b>) Unstructured mesh of the nested model in the Beibu Gulf and SCS and (<b>b</b>) observation stations for runoff (yellow dots), tides (blue dots), and currents (red dot, A). RR denotes Red River. PR denotes Pearl River.</p>
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<p>Simulated sea surface temperature (SST) distribution in the Beibu Gulf for August 2022.</p>
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<p>Average circulation in the Beibu Gulf in the (<b>a</b>) surface, (<b>b</b>) middle, (<b>c</b>) bottom, and (<b>d</b>) full layers in August 2022.</p>
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<p>Multi-year average (1993–2012) circulation in the Beibu Gulf in August at the (<b>a</b>) surface, (<b>b</b>) middle, and (<b>c</b>) bottom, with (<b>d</b>) vertical averaging.</p>
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<p>Multi-year average (1993–2012) of the (<b>a</b>) middle- and (<b>b</b>) bottom-layer circulation in the Beibu Gulf in February.</p>
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<p>Distribution of coral reefs in the northeastern Beibu Gulf [<a href="#B30-water-16-02943" class="html-bibr">30</a>].</p>
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20 pages, 5623 KiB  
Article
Tropical Cyclone Wind Direction Retrieval Based on Wind Streaks and Rain Bands in SAR Images
by Zhancai Liu, Hongwei Yang, Weihua Ai, Kaijun Ren, Shensen Hu and Li Wang
Remote Sens. 2024, 16(20), 3837; https://doi.org/10.3390/rs16203837 (registering DOI) - 15 Oct 2024
Viewed by 304
Abstract
Tropical cyclones (TCs) are associated with severe weather phenomena, making accurate wind field retrieval crucial for TC monitoring. SAR’s high-resolution imaging capability provides detailed information for TC observation, and wind speed calculations require wind direction as prior information. Therefore, utilizing SAR images to [...] Read more.
Tropical cyclones (TCs) are associated with severe weather phenomena, making accurate wind field retrieval crucial for TC monitoring. SAR’s high-resolution imaging capability provides detailed information for TC observation, and wind speed calculations require wind direction as prior information. Therefore, utilizing SAR images to retrieve TC wind fields is of significant importance. This study introduces a novel approach for retrieving wind direction from SAR images of TCs through the classification of TC sub-images. The method utilizes a transfer learning-based Inception V3 model to identify wind streaks (WSs) and rain bands in SAR images under TC conditions. For sub-images containing WSs, the Mexican-hat wavelet transform is applied, while for sub-images containing rain bands, an edge detection technique is used to locate the center of the TC eye and subsequently the tangent to the spiral rain bands is employed to determine the wind direction associated with the rain bands. Wind direction retrieval from 10 SAR TC images showed an RMSD of 19.52° and a correlation coefficient of 0.96 when compared with ECMWF and HRD observation wind directions, demonstrating satisfactory consistency and providing highly accurate TC wind directions. These results confirm the method’s potential applications in TC wind direction retrieval. Full article
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Figure 1

Figure 1
<p>Example of geophysical phenomena in SAR images. The first row represents wind streaks (G), the second row depicts rain bands (I), and the third row illustrates other geophysical phenomena (A).</p>
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<p>Flowchart of retrain recognition model based on transfer learning and wind direction retrieval from TCs SAR images.</p>
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<p>The architecture of transfer learning.</p>
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<p>The wind direction of rain band locations existing in Northern Hemisphere TCs.</p>
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<p>Accuracy and loss of training set (blue lines) and validation set (orange lines).</p>
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<p>Sub-image recognition results of SAR TC images. “G” represents WSs, “I” represents rain bands and “A” denotes other geophysical phenomena.</p>
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<p>Wind direction retrieval from SAR TC sub-images using 2-D Mexican-hat wavelet transform. (<b>a</b>) SAR sub-image; (<b>b</b>) The result of FFT; (<b>c</b>) The result of Mexico-hat wavelet transformation; (<b>d</b>) The wind direction of the sub-image.</p>
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<p>The Canny edge detection results for TC Douglas. The NRCS for VV and VH polarizations are presented in (<b>a</b>,<b>d</b>), respectively; the rain band distributions for VV and VH polarizations are shown in (<b>b</b>,<b>e</b>), respectively; the TC eye positions for VV and VH polarizations are depicted in (<b>c</b>,<b>f</b>), respectively.</p>
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<p>The Canny edge detection results for TC Larry. The NRCS for VV and VH polarizations are presented in (<b>a</b>,<b>d</b>), respectively; the rain band distributions for VV and VH polarizations are shown in (<b>b</b>,<b>e</b>), respectively; the TC eye positions for VV and VH polarizations are depicted in (<b>c</b>,<b>f</b>), respectively.</p>
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<p>Schematic diagram of wind directions with 180° ambiguity and reference wind direction. For the two predicted wind directions <math display="inline"><semantics> <msub> <mi>θ</mi> <msub> <mi>p</mi> <mn>1</mn> </msub> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>θ</mi> <msub> <mi>p</mi> <mn>2</mn> </msub> </msub> </semantics></math> that are aligned but point in opposite directions, the smaller the <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>θ</mi> </mrow> </semantics></math> calculated relative to the reference wind direction <math display="inline"><semantics> <msub> <mi>θ</mi> <mi>t</mi> </msub> </semantics></math>, the closer it is to the true wind direction.</p>
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<p>The wind field rotational pattern of TCs. (<b>a</b>) TCs in the Northern Hemisphere. (<b>b</b>) TCs in the Southern Hemisphere.</p>
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<p>The wind direction retrieval results for TC Douglas, acquired on 25 July 2020. (<b>a</b>) Quick-look from the VV polarized SAR image over TC Douglas; (<b>b</b>) The wind direction retrieval results; (<b>c</b>) The ECMWF wind direction; (<b>d</b>) Comparison of the retrieved wind direction with ECMWF and HRD observation wind direction.</p>
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<p>The wind direction retrieval results for TC Larry, acquired on 7 September 2021. (<b>a</b>) Quick-look from the VV polarized SAR image over TC Larry; (<b>b</b>) The wind direction retrieval results; (<b>c</b>) The ECMWF wind direction; (<b>d</b>) Comparison of the retrieved wind direction with ECMWF and HRD observation wind direction.</p>
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<p>Comparison of wind directions retrieved from 10 SAR TCs images with ECMWF reanalysis and HRD observation wind directions.</p>
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27 pages, 7003 KiB  
Article
Resonant Forcing by Solar Declination of Rossby Waves at the Tropopause and Implications in Extreme Precipitation Events and Heat Waves—Part 2: Case Studies, Projections in the Context of Climate Change
by Jean-Louis Pinault
Atmosphere 2024, 15(10), 1226; https://doi.org/10.3390/atmos15101226 - 14 Oct 2024
Viewed by 269
Abstract
Based on the properties of Rossby waves at the tropopause resonantly forced by solar declination in harmonic modes, which was the subject of a first article, case studies of heatwaves and extreme precipitation events are presented. They clearly demonstrate that extreme events only [...] Read more.
Based on the properties of Rossby waves at the tropopause resonantly forced by solar declination in harmonic modes, which was the subject of a first article, case studies of heatwaves and extreme precipitation events are presented. They clearly demonstrate that extreme events only form under specific patterns of the amplitude of the speed of modulated airflows of Rossby waves at the tropopause, in particular period ranges. This remains true even if extreme events appear as compound events where chaos and timing are crucial. Extreme events are favored when modulated cold and warm airflows result in a dual cyclone-anticyclone system, i.e., the association of two joint vortices of opposite signs. They reverse over a period of the dominant harmonic mode in spatial and temporal coherence with the modulated airflow speed pattern. This key role could result from a transfer of humid/dry air between the two vortices during the inversion of the dual system. Finally, focusing on the two period ranges 17.1–34.2 and 8.56–17.1 days corresponding to 1/16- and 1/32-year period harmonic modes, projections of the amplitude of wind speed at 250 mb, geopotential height at 500 mb, ground air temperature, and precipitation rate are performed by extrapolating their amplitude observed from January 1979 to March 2024. Projected amplitudes are regionalized on a global scale for warmest and coldest half-years, referring to extratropical latitudes. Causal relationships are established between the projected amplitudes of modulated airflow speed and those of ground air temperature and precipitation rate, whether they increase or decrease. The increase in the amplitude of modulated airflow speed of polar vortices induces their latitudinal extension. This produces a tightening of Rossby waves embedded in the polar and subtropical jet streams. In the context of climate change, this has the effect of increasing the efficiency of the resonant forcing of Rossby waves from the solar declination, the optimum of which is located at mid-latitudes. Hence the increased or decreased vulnerability to heatwaves or extreme precipitation events of some regions. Europe and western Asia are particularly affected, which is due to increased activity of the Arctic polar vortex between longitudes 20° W and 40° E. This is likely a consequence of melting ice and changing albedo, which appears to amplify the amplitude of variation in the period range 17.1–34.2 days of poleward circulation at the tropopause of the Arctic polar cell. Full article
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Figure 1
<p>Wind velocity at 250 mb (<b>a</b>,<b>b</b>), geopotential height at 500 mb (<b>c</b>,<b>d</b>), and precipitation rate (<b>e</b>,<b>f</b>), on 13 January 2011: (<b>a</b>,<b>c</b>,<b>e</b>) amplitude and (<b>b</b>,<b>d</b>,<b>f</b>) phase. The wind speed phase (<b>b</b>) indicates when the modulated airflows are coldest. The scale of amplitudes of geopotential height variation is referring to negative anomalies.</p>
Full article ">Figure 2
<p>Amplitude of variation of wind speed at 250 mb (<b>a</b>,<b>b</b>), geopotential height at 500 mb (<b>c</b>,<b>d</b>), and ground air temperature (<b>e</b>,<b>f</b>) in the period range 17.1–34.2 days (harmonic 1/16 year). The coordinates of each mesh considered are indicated in each of the figures. Values are averaged from October to March, that is over the coldest/warmest half-year in the northern/southern hemisphere (<b>a</b>,<b>c</b>,<b>e</b>) and from April to September, that is over the warmest/coldest half-year in the northern/southern hemisphere (<b>b</b>,<b>d</b>,<b>f</b>). The second-degree polynomial and the projected trend are represented.</p>
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<p>Wind velocity at 250 mb (<b>a</b>,<b>b</b>), geopotential height at 500 mb, and air temperature 2 m above the ground on 25 August 2022 in the period range 17.1 to 34.2 days: (<b>a</b>,<b>c</b>,<b>e</b>) amplitude and (<b>b</b>,<b>d</b>,<b>f</b>) phase. The wind speed phase (<b>b</b>) indicates when the modulated airflows are warmest. The scale of amplitudes of geopotential height variation is referring to positive anomalies.</p>
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<p>Wind velocity at 250 mb (<b>a</b>,<b>b</b>), geopotential height at 500 mb (<b>c</b>,<b>d</b>), and precipitation rate (<b>e</b>,<b>f</b>), on 17 July 2021: (<b>a</b>,<b>c</b>,<b>e</b>) amplitude and (<b>b</b>,<b>d</b>,<b>f</b>) phase. The wind speed phase (<b>b</b>) indicates when the modulated airflows are coldest. The scale of amplitudes of geopotential height is referring to negative anomalies.</p>
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<p>Wind velocity at 250 mb (<b>a</b>,<b>b</b>), geopotential height at 500 mb (<b>c</b>,<b>d</b>), and precipitation rate (<b>e</b>,<b>f</b>), on 29 August 2005: (<b>a</b>,<b>c</b>,<b>e</b>) amplitude and (<b>b</b>,<b>d</b>,<b>f</b>) phase. The wind speed phase (<b>b</b>) indicates when the modulated airflows are coldest.</p>
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<p>Wind velocity at 250 mb in the Northern Hemisphere on 29 August 2005 in the period range 8.56 to 17.1 days. Amplitude (m/s) in (<b>a</b>), and phase in (<b>b</b>). The wind speed phase (<b>b</b>) indicates when the modulated airflows are coldest.</p>
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<p>Projected amplitude in the period range 17.1 to 34.2 days of the variations in wind speed at 250 mb averaged over the coldest half-year (referring to extratropical latitudes).</p>
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<p>Projected amplitude in the period range 17.1 to 34.2 days of the variations in wind speed at 250 mb averaged over the warmest half-year (referring to extratropical latitudes).</p>
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<p>Projected amplitude in the period range 17.1 to 34.2 days of the variations in geopotential height at 500 mb averaged over the coldest half-year (referring to extratropical latitudes).</p>
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<p>Projected amplitude in the period range 17.1 to 34.2 days of the variations in geopotential height at 500 mb averaged over the warmest half-year (referring to extratropical latitudes).</p>
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<p>Projected amplitude in the period range 17.1 to 34.2 days of the variations in ground air temperature averaged over the coldest half-year (referring to extratropical latitudes).</p>
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<p>Projected amplitude in the period range 17.1 to 34.2 days of the variations in ground air temperature averaged over the warmest half-year (referring to extratropical latitudes).</p>
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<p>Projected amplitude in the period range 17.1 to 34.2 days of the variations in extratropical precipitation rate averaged over the coldest half-year.</p>
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<p>Projected amplitude in the period range 17.1 to 34.2 days of the variations in extratropical precipitation rate averaged over the warmest half-year.</p>
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22 pages, 7116 KiB  
Article
Regional Mean Sea Level Variability Due to Tropical Cyclones: Insights from August Typhoons
by MyeongHee Han, SungHyun Nam and Hak-Soo Lim
J. Mar. Sci. Eng. 2024, 12(10), 1830; https://doi.org/10.3390/jmse12101830 - 14 Oct 2024
Viewed by 430
Abstract
This study investigates the interannual variations in regional mean sea levels (MSLs) of the northeast Asian marginal seas (NEAMS) during August, focusing on the role of typhoon activity from 1993 to 2019. The NEAMS are connected to the Pacific through the East China [...] Read more.
This study investigates the interannual variations in regional mean sea levels (MSLs) of the northeast Asian marginal seas (NEAMS) during August, focusing on the role of typhoon activity from 1993 to 2019. The NEAMS are connected to the Pacific through the East China Sea (ECS) and narrow, shallow straits in the east, where inflow from the southern boundary (ECS), unless balanced by eastern outflow, leads to significant convergence or divergence, as well as subsequent changes in regional MSLs. Satellite altimetry and tide-gauge data reveal that typhoon-induced Ekman transport plays a key role in MSL variability, with increased inflow raising MSLs during active typhoon seasons. In contrast, weak typhoon activity reduces inflow, resulting in lower MSLs. This study’s findings have significant implications for coastal management, as the projected changes in tropical cyclone frequency and intensity due to climate change could exacerbate sea level rise and flooding risks. Coastal communities in the NEAMS region will need to prioritize enhanced flood defenses, early warning systems, and adaptive land use strategies to mitigate these risks. This is the first study to link typhoon frequency directly to NEAMS MSL variability, highlighting the critical role of wind-driven processes in regional sea level changes. Full article
(This article belongs to the Special Issue Air-Sea Interaction and Marine Dynamics)
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Figure 1
<p>Domains of the NEAMS (gray shading), including Area A, where typhoon activity was assessed; Areas B and C, where inflow and outflow occur, respectively (black arrows indicate basic flow transport); and Areas D and E, which are related to time series of zonal and meridional winds. Typhoon tracks are superimposed with symbol sizes and color scales according to maximum sustained wind speeds of 17, 33, 43, 49, 58, and 70 m s<sup>−1</sup> for the months of August from 1993 to 2019. ES, YS, BS, ECS, SCS, SO, and PO denote the East Sea (Sea of Japan), Yellow Sea, Bohai Sea, East China Sea, South China Sea, Sea of Okhotsk, and Pacific Ocean, respectively. KS, TAS, TSS, and SS represent the Korea/Tsushima Strait, Taiwan Strait, Tsugaru Strait, and Soya Straits, respectively.</p>
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<p>Time series of the summer (August) MSLs in the NEAMS region from 1993 to 2019, based on satellite altimetry (red open circles) and tide-gauge observations (black open squares), both adjusted to exclude the inverted barometer effect. The tide-gauge data have been referenced to a common vertical datum to match the MSL with satellite altimetry over the 27-year period. Dashed red and black dotted lines indicate the respective trends for satellite and tide-gauge measurements.</p>
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<p>Time series of the detrended summer NEAMS MSL anomalies (black filled diamond) derived from satellite altimetry data, SLC by Ekman transport anomaly in Area B (red filled square), and SLC by Ekman transport anomaly differences between Areas B and C (red open triangle), derived from ERA5 data from 1993 to 2019. The correlation coefficients between the NEAMS MSL and SLC by Ekman transport anomaly in Area B and between the NEAMS MSL and SLC by Ekman transport anomaly differences between Areas B and C are both 0.65 (<span class="html-italic">p</span>-value &lt; 0.01).</p>
Full article ">Figure 4
<p>(<b>a</b>) Time series of detrended summer NEAMS MSL anomalies (black solid line) derived from satellite altimetry data and typhoon occurrence in the ECS (red dashed line) from 1993 to 2019. Summers with relatively high MSL changes (<span class="html-italic">Period H</span>; &gt;2 cm, black solid diamonds) are denoted by orange and green shading, while summers with relatively low MSL changes (<span class="html-italic">Period L</span>; &lt;−2 cm) are denoted by black-filled diamonds. During each summer throughout <span class="html-italic">Period H</span>, three or more typhoons passed through Area A (red solid circles), with the exception of the summers of 2001 and 2002 (green shading,) while two or fewer typhoons occurred during each summer of <span class="html-italic">Period L</span> (red open circles) (correlation coefficient = 0.54; <span class="html-italic">p</span>-value &lt; 0.01). Error bar indicates the positive and negative standard deviations of daily detrended summer NEAMS MSL. Time series of detrended summer (<b>b</b>) ES (green triangle, correlation coefficient = 0.49; <span class="html-italic">p</span> = 0.01) and (<b>c</b>) YS (blue square, correlation coefficient = 0.55; <span class="html-italic">p</span>-value &lt; 0.01) MSL anomalies derived from satellite altimetry data from 1993 to 2019. Error bar indicates the positive and negative standard deviations of daily detrended summer ES and YS MSLs.</p>
Full article ">Figure 5
<p>Surface wind stress vectors (black arrows) and typhoon tracks (dotted lines) for (<b>a</b>) composite <span class="html-italic">Period H</span> and (<b>b</b>) composite <span class="html-italic">Period L</span>. The legend for dotted lines can be found in the upper-left corner with criteria for maximum wind speed in m s<sup>−1</sup> similar to <a href="#jmse-12-01830-f001" class="html-fig">Figure 1</a>. Area A and coastlines are denoted by red dashed box and gray lines, respectively.</p>
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<p>Surface wind stress vectors (black arrows) and typhoon tracks (dotted lines) in (<b>a</b>) 2001 and (<b>b</b>) 2002. Area A and coastlines are denoted by a red dashed box and gray lines, respectively.</p>
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<p>Time series of hourly zonal and meridional winds averaged over (<b>a</b>) Area D, summer 2001, and (<b>b</b>) Area E, summer 2002, are plotted using blue squares and red circles, respectively. Monthly mean zonal and meridional winds and zero wind speed lines are represented by the dotted blue and red horizontal lines and solid black lines, respectively. Gray shading represents typhoons Pabuk and Rusa during 19–20 August 2001 and 30–31 August 2002, respectively.</p>
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<p>(<b>a</b>) Correlation map of MSL atmospheric pressure changes related to NEAMS MSL changes during August 1993–2019. In (<b>a</b>), contour intervals are 0.05, and correlations with confidence levels &lt; 90% are discarded. Schematics demonstrate the ocean inflow and outflow (black filled arrows) of the NEAMS and NEAMS MSL anomalies (red and blue) in August driven by (<b>b</b>,<b>c</b>) convergence and (<b>d</b>) divergence during <span class="html-italic">Periods H</span> and <span class="html-italic">L</span>, respectively, related to inflow Ekman transport (black open arrows) induced by wind (blue shaded arrows). In (<b>b</b>), the large L represents the typhoon center. Composite sea level (ADT) anomalies for (<b>b</b>) <span class="html-italic">Period H</span>, (<b>c</b>) summers of 2001 and 2002, and (<b>d</b>) <span class="html-italic">Period L</span> in August obtained from satellite altimeters are indicated using colors.</p>
Full article ">Figure 9
<p>Time series of detrended summer NEAMS MSL anomalies derived from satellite altimetry data (black open diamonds, NEAMS) by heat transport (red open circles, HT) and salt transport (blue open squares, ST) differences in anomalies over the ESC (Area B) and the Tsugaru and Soya Straits, by net surface heat flux (magenta asterisk, HF) derived from the Ocean Reanalysis System 5 (ORAS5), and by subtracting MSL by HT, ST, and HF from altimetry MSL (green open triangle, mass transport (MT)) from 1993 to 2019.</p>
Full article ">
30 pages, 566 KiB  
Article
Area–Time-Efficient High-Radix Modular Inversion Algorithm and Hardware Implementation for ECC over Prime Fields
by Yamin Li
Computers 2024, 13(10), 265; https://doi.org/10.3390/computers13100265 - 12 Oct 2024
Viewed by 286
Abstract
Elliptic curve cryptography (ECC) is widely used for secure communications, because it can provide the same level of security as RSA with a much smaller key size. In constrained environments, it is important to consider efficiency, in terms of execution time and hardware [...] Read more.
Elliptic curve cryptography (ECC) is widely used for secure communications, because it can provide the same level of security as RSA with a much smaller key size. In constrained environments, it is important to consider efficiency, in terms of execution time and hardware costs. Modular inversion is a key time-consuming calculation used in ECC. Its hardware implementation requires extensive hardware resources, such as lookup tables and registers. We investigate the state-of-the-art modular inversion algorithms, and evaluate the performance and cost of the algorithms and their hardware implementations. We then propose a high-radix modular inversion algorithm aimed at reducing the execution time and hardware costs. We present a detailed radix-8 hardware implementation based on 256-bit primes in Verilog HDL and compare its cost performance to other implementations. Our implementation on the Altera Cyclone V FPGA chip used 1227 ALMs (adaptive logic modules) and 1037 registers. The modular inversion calculation took 3.67 ms. The AT (area–time) factor was 8.30, outperforming the other implementations. We also present an implementation of ECC using the proposed radix-8 modular inversion algorithm. The implementation results also showed that our modular inversion algorithm was more efficient in area–time than the other algorithms. Full article
(This article belongs to the Special Issue Using New Technologies in Cyber Security Solutions (2nd Edition))
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Figure 1

Figure 1
<p>Block diagram of the proposed mixed radix-8 modular inversion circuit.</p>
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<p>Waveform of the proposed radix-8 modular inversion algorithm.</p>
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<p>Latency and AT comparison of modular inversion algorithms. Details (year and first author’s name) of the numbers [<span class="html-italic">n</span>] (algorithm) on the horizontal axis are in <a href="#computers-13-00265-t007" class="html-table">Table 7</a>.</p>
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<p>The ECDH key exchange algorithm uses scalar point multiplication, which uses two operations, point addition and point doubling, which use the four basic modular operations (addition, subtraction, multiplication, and inversion).</p>
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<p>Waveform of the scalar point multiplication <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mi>d</mi> <mi>P</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>=</mo> <mo>[</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>]</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mo>[</mo> <mi>q</mi> <mi>x</mi> <mo>,</mo> <mi>q</mi> <mi>y</mi> <mo>]</mo> </mrow> </semantics></math> using proposed the radix-8 modular inversion algorithm.</p>
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<p>ECC latency and AT comparison of modular inversion algorithms. Details (year and first author’s name) of the numbers [<span class="html-italic">n</span>] (algorithm) on the horizontal axis are in <a href="#computers-13-00265-t010" class="html-table">Table 10</a>.</p>
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<p>Block diagrams of scalar point multiplication circuits. (<b>a</b>) Traditional scalar point multiplication circuit; (<b>b</b>) Montgomery ladder scalar point multiplication circuit.</p>
Full article ">
20 pages, 13269 KiB  
Article
Relationship between Tibetan Plateau Surface Heat Fluxes and Daily Heavy Precipitation in the Middle and Lower Yangtze River Basins (1980–2022)
by Lu Li, Xiaohua Dong, Yaoming Ma, Hanyu Jin, Chong Wei and Bob Su
Remote Sens. 2024, 16(20), 3779; https://doi.org/10.3390/rs16203779 - 11 Oct 2024
Viewed by 315
Abstract
Variable heat fluxes over the Tibetan Plateau (TP) interact thermally with the atmosphere, affecting weather in surrounding areas, particularly in the Middle and Lower Yangtze River (MLYR). However, the circulation patterns and time-lag effects between TP heat fluxes and MLYR precipitation remain unclear. [...] Read more.
Variable heat fluxes over the Tibetan Plateau (TP) interact thermally with the atmosphere, affecting weather in surrounding areas, particularly in the Middle and Lower Yangtze River (MLYR). However, the circulation patterns and time-lag effects between TP heat fluxes and MLYR precipitation remain unclear. This study identified 577 large-scale daily heavy precipitation events (LSDHPEs) in the MLYR from 1980 to 2022. We analyzed the weather causation and spatiotemporal correlations between the TP surface heat fluxes and MLYR LSDHPEs using self-organizing map clustering, singular value decomposition, and harmonic analysis of time series. The results found two dominant synoptic patterns of LSDHPEs at 500 hPa: one, driven by anticyclonic and cyclonic circulations coinciding with shifts in the West Pacific subtropical high and South Asian high, increased from 2000 to 2022; the other, influenced by MLYR cyclonic circulation, showed a significant decrease. For the first time, we revealed lagged effects of the latent heat anomalies (with a lag time of 1–10 d and 130–200 d) and sensible heat anomalies (with a lag time of 2–4 months) over the TP during LSDHPEs in the MLYR. The results may enhance our understanding of TP heat flux anomalies as precursor signals for early warning of heavy rainfall and flooding in the MLYR. Full article
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<p>Location of the study area.</p>
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<p>Research framework of this study.</p>
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<p>(<b>a</b>,<b>b</b>) Temporal distribution of LSDHPEs selected from MSWEP, CPC, and CN05.1 in MLYR. (<b>c</b>) The average annual precipitation of MLYR from CN05.1 during 1980–2022. (<b>d</b>) Spatial distribution of heavy precipitation events selected from CN05.1 in MLYR.</p>
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<p>Synoptic patterns of SOM correspond to the May to August period during 1980–2022 at 500 hPa. (Contour represents mean geopotential height anomalies. Arrows represent the wind field anomalies. The yellow line represents the WNPSH outlined by 5880 gpm contour at 500 hPa. The green line represents the SAH outlined by a 12500 gpm contour at 200 hPa. The green dashed line indicates the SAH at 200 hPa relative to the climatic mean from May to August 1980–2022.)</p>
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<p>Composite mean synoptic fields of LSDHPEs in SOM patterns at 500 hPa. (The contours represent geopotential height anomalies. The left color bar represents the water vapor flux at 500 hPa. The yellow line represents the WNPSH outlined by 5880 gpm contour at 500 hPa. The green line represents the SAH outlined by the 12500 gpm contour at 200 hPa. The green dashed line indicates the SAH at 200 hPa relative to the climatic mean from May to August during 1980–2022.)</p>
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<p>Mean air temperature at 500hPa and the total CAPE field of the LSC patterns. (The contours represent the CAPE. The left color bar represents the temperature at 500 hPa).</p>
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<p>The annual occurrence of SOM and LSC patterns. (The blue dots and black dots indicate the occurrence of the SOM and LSC patterns, respectively. Solid lines represent the trend line from 1980 to 2022. Dashed lines represent the trend line of the corresponding dots from 2000 to 2022. The slope and P values (parentheses) are displayed in the top left corner.)</p>
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<p>Time-lagged correlation coefficients between the surface heat flux anomalies over the TP and the precipitation anomalies in LSDHPEs. ((<b>a</b>), the correlation between the anomalous LH on the TP and the anomalous precipitation over the MLYR. (<b>b</b>), the correlation between the anomalous SH on the TP and the anomalous precipitation over the MLYR. All values (bar) passed the 95% significance level test; the baseline indicates the mean value of the correlation coefficient. Red line represents the fitted curve obtained by using the HANTS algorithm.).</p>
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<p>The spatial distribution of heterogeneous CCs between the anomalous LH and anomalous precipitation with a time lag of 7 days (<b>a</b>,<b>b</b>). The distribution of heterogeneous CCs between the anomalous SH and anomalous precipitation with a time lag of 90 days (<b>c</b>,<b>d</b>). (The slash lines are statistically significant at 5% level. The statistical histograms in each subfigure are the distribution of the CCs that passed the significance test.)</p>
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<p>Box plots of RMSE, CC, and RB for three rainfall products at rainfall observation sites in the MLYR.</p>
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<p>Evaluating the accuracy of SOM for different numbers of nodes.</p>
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<p>Water vapor flux anomalies of the LSDHPEs in SOM patterns at 850 hPa.</p>
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<p>Temperature and water vapor flux of the LSC patterns at 850 hPa.</p>
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<p>The synoptic patterns of the LSC at 200 hPa.</p>
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<p>(<b>a</b>) Lagged correlation coefficient curves of the mean TP heat flux and MLYR mean precipitation. (<b>b</b>) Lagged correlation coefficient curves of the mean heat flux in the critical area A and the MLYR mean precipitation. Dots represent significance at the 95% confidence level.</p>
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17 pages, 7040 KiB  
Article
Observation of Statistical Characteristics and Vertical Structures of Surface Warm Cyclonic Eddies and Cold Anticyclonic Eddies in the North Pacific Subtropical Countercurrent Region
by Yaowei Ma, Qinghong Li, Xiangjun Yu, Song Li and Xingyu Zhou
J. Mar. Sci. Eng. 2024, 12(10), 1783; https://doi.org/10.3390/jmse12101783 - 8 Oct 2024
Viewed by 422
Abstract
Conventional wisdom about mesoscale eddies is that cyclonic (anticyclonic) eddies are commonly associated with cold(warm) surface cores. Nevertheless, plenties of surface warm cyclonic eddies (WCEs) and cold anticyclonic eddies (CAEs) in the North Pacific Subtropical Countercurrent (STCC) region are observed by a synergistic [...] Read more.
Conventional wisdom about mesoscale eddies is that cyclonic (anticyclonic) eddies are commonly associated with cold(warm) surface cores. Nevertheless, plenties of surface warm cyclonic eddies (WCEs) and cold anticyclonic eddies (CAEs) in the North Pacific Subtropical Countercurrent (STCC) region are observed by a synergistic investigation based on data from satellite altimetry, microwave radiometer, and Argo float profiles in this study. The results indicate that these two types of abnormal eddies (WCEs and CAEs) are prevalent in the STCC region, comprising approximately 30% of all eddies detected via satellite observations. We then analyze their spatial-temporal distribution characteristics and composite vertical structures. A statistical comparison with surface cold cyclonic eddies (CCEs) and warm anticyclonic eddies (WAEs) reveals notable differences between the anomalous and typical eddies. Additionally, we present the composite vertical structures of temperature and salinity anomalies for the anomalous eddies across five delineated subregions within an eddy-coordinate system. Furthermore, the close relationship between these abnormal eddies and subsurface-intensified mesoscale eddies are discussed. Full article
(This article belongs to the Section Physical Oceanography)
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<p>(<b>a</b>) Topographic maps of the North Pacific Subtropical Countercurrent region, based on the ETOPO1 dataset (doi:10.7289/V5C8276M), where black lines give the dividing lines between five areas (Areas A to E). (<b>b</b>) Spatial distribution of the base-10 logarithm of eddy kinetic energy (EKE, cm<sup>2</sup>/s<sup>2</sup>) in the STCC region (20°–28° N, 120°–160° E), based on daily sea level anomaly data provided by the Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO) from January 2007 to December 2014.</p>
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<p>Example cases of (<b>a</b>) cold anticyclonic eddy and (<b>b</b>) warm cyclonic eddy detected from the combination of SSHA and SSTA maps provided by satellite observation. The red (blue) contour line depicts the boundary of the CAE (WCE) case. Arrows and shading represent the surface geostrophic currents and temperature anomalies after band-pass filtering, respectively.</p>
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<p>Spatial distributions of the occurrence frequency of the (<b>a</b>) WCEs, (<b>b</b>) CCEs, (<b>c</b>) CAEs, and (<b>d</b>) WAEs in 0.5° × 0.5° bins. Vertical dash lines are the boundaries between different Areas A and E.</p>
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<p>Monthly distributions of four types of eddies. The yellow, blue, purple, and red bars represent warm cyclonic eddies, cold cyclonic eddies, cold anticyclonic eddies, and warm anticyclonic eddies, respectively.</p>
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<p>Yearly distributions of four types of eddies. The yellow, blue, purple, and red bars represent warm cyclonic eddies, cold cyclonic eddies, cold anticyclonic eddies, and warm anticyclonic eddies, respectively.</p>
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<p>UF and UB line in the Mann–Kendall test result for a monthly number of four types of eddies (<b>a</b>) wce, (<b>b</b>) cce, (<b>c</b>) cae, and (<b>d</b>) wae.</p>
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<p>UF and UB line in Mann–Kendall test result for an annual number of four types of eddies (<b>a</b>) wce, (<b>b</b>) cce, (<b>c</b>) cae, and (<b>d</b>) wae.</p>
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<p>Statistical histogram of the eddy amplitude (units: cm) and eddy radius (units: km) of the four types of eddies in the STCC region. Comparisons between (<b>a</b>) the eddy amplitude of CCEs and WCEs, (<b>b</b>) the eddy amplitude of WAEs and CAEs, (<b>c</b>) the eddy radius of CCEs and WCEs, and (<b>d</b>) the eddy radius of WAEs and CAEs are shown. The yellow, blue, purple, and red bars represent warm cyclonic eddies, cold cyclonic eddies, cold anticyclonic eddies, and warm anticyclonic eddies, respectively.</p>
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<p>Spatial distributions of the Argo profiles captured by (<b>a</b>) CCEs, (<b>b</b>) WAEs, (<b>c</b>) WCEs, and (<b>d</b>) CAEs. The profile numbers are shown at the northeastern corner of each subfigure.</p>
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<p>Vertical zonal sections of the temperature anomalies <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>′</mo> </msup> </mrow> </semantics></math> (°C) of composite CAE (cold anticyclonic eddy) in Areas A–E (<b>a</b>–<b>e</b>).</p>
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<p>Same as <a href="#jmse-12-01783-f010" class="html-fig">Figure 10</a>, but for temperature anomalies <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>′</mo> </msup> </mrow> </semantics></math> (°C) of composite WCE.</p>
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<p>Same as <a href="#jmse-12-01783-f010" class="html-fig">Figure 10</a>, but for the salinity anomalies <math display="inline"><semantics> <msup> <mi>S</mi> <mo>′</mo> </msup> </semantics></math> (PSU) of composite CAE.</p>
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<p>Same as <a href="#jmse-12-01783-f010" class="html-fig">Figure 10</a>, but for the salinity anomalies <math display="inline"><semantics> <msup> <mi>S</mi> <mo>′</mo> </msup> </semantics></math> (PSU) of composite WCE.</p>
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19 pages, 12548 KiB  
Article
Comparison of Different Numerical Methods in Modeling of Debris Flows—Case Study in Selanac (Serbia)
by Jelka Krušić, Manuel Pastor, Saeid M. Tayyebi, Dragana Đurić, Tina Đurić, Mileva Samardžić-Petrović, Miloš Marjanović and Biljana Abolmasov
Appl. Sci. 2024, 14(19), 9059; https://doi.org/10.3390/app14199059 - 8 Oct 2024
Viewed by 702
Abstract
Flow-type landslides are not typical in this region of the Balkans. However, after the Tamara cyclone event in 2014, numerous such occurrences have been observed in Serbia. This paper presents the initial results of a detailed investigation into debris flows in Serbia, comparing [...] Read more.
Flow-type landslides are not typical in this region of the Balkans. However, after the Tamara cyclone event in 2014, numerous such occurrences have been observed in Serbia. This paper presents the initial results of a detailed investigation into debris flows in Serbia, comparing findings from two programs: RAMMS DBF and Geoflow SPH. Located in Western Serbia, the Selanac debris flow is a complex event characterized by significant depths in the initial block and entrainment zone. Previous field investigations utilized ERT surveys, supplemented by laboratory tests, to characterize material behavior. Approximately 450,000 m3 of material began to flow following an extreme precipitation period, ultimately traveling 1.2 km to the deposition zone. For validation purposes, ERT profiles from both the deposition zone and the source area were utilized, with particular attention given to areas where entrainment was substantial, as this had a significant impact on the final models. The first objective of this research is to conduct a detailed investigation of debris flow using field investigations: geophysical (ERT) and aerial photogrammetry. The second objective is to evaluate the capacity of two debris flow propagation models to simulate the reality of these phenomena. The GeoFlow-SPH code overestimated the maximum propagation thickness in comparison to the RAMMS model. The numerical results regarding final depths closely align, especially when considering the estimated average depth in the deposition zone. The results confirm the necessity of using multiple simulation codes to more accurately predict specific events. Full article
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<p>Location of the study area with measured precipitation amounts at the nearest weather stations.</p>
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<p>Geological map of the terrain, detailed at 1:10,000 of the general geological map (OGK) at 1:100,000, Ljubovija.</p>
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<p>Orthophoto of the Selanac debris flow obtained by UAV photogrammetric survey with characterization of basic geometric elements.</p>
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<p>Map obtained by overlapping two epochs of the Digital Terrain Model (DTM).</p>
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<p>Position of the ERT profiles on the Digital Terrain Model.</p>
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<p>Results of ERT profile 5.</p>
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<p>USCS diagram based on soil consistency parameters.</p>
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<p>SPH interactions for two-phase model.</p>
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<p>(<b>a</b>) Area of entrainment in the transportation zone; (<b>b</b>) entrainment model of material in the transportation zone; (<b>c</b>) final deposition model; (<b>d</b>) maximum flow velocity model of the Selanac debris flow.</p>
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<p>(<b>a</b>,<b>b</b>) Model for calculating achieved maximum flow velocities; (<b>c</b>) model of the material entrainment rate (er); (<b>d</b>) model for simulating material movement and final deposition depths.</p>
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<p>Comparative view of final material depths at position of ERT 5 profile.</p>
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27 pages, 7826 KiB  
Article
Comprehensive Comparison of Seven Widely-Used Planetary Boundary Layer Parameterizations in Typhoon Mangkhut Intensification Simulation
by Lei Ye, Yubin Li, Ping Zhu, Zhiqiu Gao and Zhihua Zeng
Atmosphere 2024, 15(10), 1182; https://doi.org/10.3390/atmos15101182 - 30 Sep 2024
Viewed by 458
Abstract
Numerical experiments using the WRF model were conducted to analyze the sensitivity of Typhoon Mangkhut intensification simulations to seven widely used planetary boundary layer (PBL) parameterization schemes, including YSU, MYJ, QNSE, MYNN2, MYNN3, ACM2, and BouLac. The results showed that all simulations generally [...] Read more.
Numerical experiments using the WRF model were conducted to analyze the sensitivity of Typhoon Mangkhut intensification simulations to seven widely used planetary boundary layer (PBL) parameterization schemes, including YSU, MYJ, QNSE, MYNN2, MYNN3, ACM2, and BouLac. The results showed that all simulations generally reproduced the tropical cyclone (TC) track and intensity, with YSU, QNSE, and BouLac schemes better capturing intensification processes and closely matching observed TC intensity. In terms of surface layer parameterization, the QNSE scheme produced the highest Ck/Cd ratio, resulting in stronger TC intensity based on Emanuel’s potential intensity theory. In terms of PBL parameterization, the YSU and BouLac schemes, with the same revised MM5 surface layer scheme, simulated weaker turbulent diffusivity Km and shallower mixing height, leading to stronger TC intensity. During the intensification period, the BouLac, YSU, and QNSE PBL schemes exhibited stronger tangential wind, radial inflow within the boundary layer, and updraft around the eye wall, consistent with TC intensity results. Both PBL and surface layer parameterization significantly influenced simulated TC intensity. The QNSE scheme, with the largest Ck/Cd ratio, and the YSU and BouLac schemes, with weaker turbulent diffusivity, generated stronger radial inflow, updraft, and warm core structures, contributing to higher storm intensity. Full article
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<p>Model domain configuration and the best track of Mangkhut from JTWC during 0000 UTC 7th to 0000 UTC 13 September 2018. Colored dots indicate the 6 hourly TC track, and colors indicate the categories of TC intensity (DB: disturbance; TD: tropical depression; TS: tropical storm; TY: typhoon; ST: super typhoon). And d01, d02, and d03 represent the domain 01, 02, and 03, respectively.</p>
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<p>(<b>a</b>) TC track, (<b>b</b>) time series of track error (unit: km), (<b>c</b>) minimum central sea level pressure (MSLP) (unit: hPa), and (<b>d</b>) maximum sustained wind speed (VMAX) (unit: m s<sup>−1</sup>) from the JTWC best track data, ERA5 reanalysis data, and the numerical simulations from 0600 UTC 7th to 0000 UTC 13 September 2018.</p>
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<p>Time series of area-averaged (<b>a</b>) surface enthalpy flux (unit: W m<sup>−2</sup>) and (<b>b</b>) surface momentum flux (unit: kg m<sup>−1</sup> s<sup>−2</sup>) from TC center to 200 km radius from 0600 UTC 7th to 0000 UTC 13 September 2018.</p>
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<p>The variation of surface exchange coefficients for (<b>a1</b>–<b>g1</b>) momentum <span class="html-italic">C<sub>d</sub></span> and (<b>a2</b>–<b>g2</b>) heat <span class="html-italic">C<sub>k</sub></span> with 10 m wind speed at 1200 UTC 12 September 2018; numerical simulations (thick gray dots) compared with data from Large and Pond [<a href="#B69-atmosphere-15-01182" class="html-bibr">69</a>], Powell et al. [<a href="#B70-atmosphere-15-01182" class="html-bibr">70</a>], Donelan et al. [<a href="#B71-atmosphere-15-01182" class="html-bibr">71</a>], Black et al. [<a href="#B72-atmosphere-15-01182" class="html-bibr">72</a>], Zhang et al. [<a href="#B73-atmosphere-15-01182" class="html-bibr">73</a>], Haus et al. [<a href="#B74-atmosphere-15-01182" class="html-bibr">74</a>], Bell et al. [<a href="#B75-atmosphere-15-01182" class="html-bibr">75</a>], and Richter et al. [<a href="#B76-atmosphere-15-01182" class="html-bibr">76</a>].</p>
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<p>The variation of surface exchange coefficient ratio (ratio of <span class="html-italic">C<sub>k</sub></span>/<span class="html-italic">C<sub>d</sub></span>) with 10 m wind speed at 1200 UTC 12 September 2018 from different numerical simulations.</p>
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<p>Height–radius distribution of azimuthally averaged turbulent diffusivity for momentum (unit: m<sup>2</sup> s<sup>−1</sup>) from (<b>a1</b>–<b>a4</b>) YSU, (<b>b1</b>–<b>b4</b>) MYJ, (<b>c1</b>–<b>c4</b>) QNSE, (<b>d1</b>–<b>d4</b>) MYNN2, (<b>e1</b>–<b>e4</b>) MYNN3, (<b>f1</b>–<b>f4</b>) ACM2, and (<b>g1</b>–<b>g4</b>) BouLac, at (<b>a1</b>–<b>g1</b>) 0600 UTC 7th, (<b>a2</b>–<b>g2</b>) 0000 UTC 9th, (<b>a3</b>–<b>g3</b>) 0000 UTC 11th, and (<b>a4</b>–<b>g4</b>) 1200 UTC 12 September 2018.</p>
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<p>Height–radius distribution of azimuthally averaged turbulent diffusivity for momentum (unit: m<sup>2</sup> s<sup>−1</sup>) from (<b>a1</b>–<b>a4</b>) YSU, (<b>b1</b>–<b>b4</b>) MYJ, (<b>c1</b>–<b>c4</b>) QNSE, (<b>d1</b>–<b>d4</b>) MYNN2, (<b>e1</b>–<b>e4</b>) MYNN3, (<b>f1</b>–<b>f4</b>) ACM2, and (<b>g1</b>–<b>g4</b>) BouLac, at (<b>a1</b>–<b>g1</b>) 0300 UTC 7th, (<b>a2</b>–<b>g2</b>) 0400 UTC 7th, (<b>a3</b>–<b>g3</b>) 0500 UTC 7th, and (<b>a4</b>–<b>g4</b>) 0600 UTC 7 September 2018.</p>
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<p>Comparison of turbulent diffusivity for momentum (<span class="html-italic">K<sub>m</sub></span>) between numerical simulations and observations at 0600 UTC 7th (in red), 0000 UTC 9th (in green), 0000 UTC 11th (in yellow), and 1200 UTC 12th (in blue) September 2018. In (<b>a</b>–<b>g</b>), OBS (in black) represents observations from Zhang et al. [<a href="#B80-atmosphere-15-01182" class="html-bibr">80</a>], which are based on in situ data at 450 m altitude.</p>
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<p>Height–radius distribution of azimuthally averaged tangential wind (unit: m s<sup>−1</sup>) from (<b>a1</b>–<b>a4</b>) ERA5, (<b>b1</b>–<b>b4</b>) YSU, (<b>c1</b>–<b>c4</b>) MYJ, (<b>d1</b>–<b>d4</b>) QNSE, (<b>e1</b>–<b>e4</b>) MYNN2, (<b>f1</b>–<b>f4</b>) MYNN3, (<b>g1</b>–<b>g4</b>) ACM2, and (<b>h1</b>–<b>h4</b>) BouLac at (<b>a1</b>–<b>h1</b>) 0600 UTC 7th, (<b>a2</b>–<b>h2</b>) 0000 UTC 9th, (<b>a3</b>–<b>h3</b>) 0000 UTC 11th, and (<b>a4</b>–<b>h4</b>) 1200 UTC 12 September 2018.</p>
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<p>As in <a href="#atmosphere-15-01182-f009" class="html-fig">Figure 9</a> but for radial wind (unit: m s<sup>−1</sup>), with (<b>a1</b>–<b>a4</b>) ERA5, (<b>b1</b>–<b>b4</b>) YSU, (<b>c1</b>–<b>c4</b>) MYJ, (<b>d1</b>–<b>d4</b>) QNSE, (<b>e1</b>–<b>e4</b>) MYNN2, (<b>f1</b>–<b>f4</b>) MYNN3, (<b>g1</b>–<b>g4</b>) ACM2, and (<b>h1</b>–<b>h4</b>) BouLac at (<b>a1</b>–<b>h1</b>) 0600 UTC 7th, (<b>a2</b>–<b>h2</b>) 0000 UTC 9th, (<b>a3</b>–<b>h3</b>) 0000 UTC 11th, and (<b>a4</b>–<b>h4</b>) 1200 UTC 12 September 2018.</p>
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<p>As in <a href="#atmosphere-15-01182-f009" class="html-fig">Figure 9</a> but for upward vertical wind (unit: m s<sup>−1</sup>), with (<b>a1</b>–<b>a4</b>) ERA5, (<b>b1</b>–<b>b4</b>) YSU, (<b>c1</b>–<b>c4</b>) MYJ, (<b>d1</b>–<b>d4</b>) QNSE, (<b>e1</b>–<b>e4</b>) MYNN2, (<b>f1</b>–<b>f4</b>) MYNN3, (<b>g1</b>–<b>g4</b>) ACM2, and (<b>h1</b>–<b>h4</b>) BouLac at (<b>a1</b>–<b>h1</b>) 0600 UTC 7th, (<b>a2</b>–<b>h2</b>) 0000 UTC 9th, (<b>a3</b>–<b>h3</b>) 0000 UTC 11th, and (<b>a4</b>–<b>h4</b>) 1200 UTC 12 September 2018.</p>
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<p>Height–radius distribution of azimuthally averaged equivalent potential temperature (unit: K) from (<b>a1</b>–<b>a4</b>) YSU, (<b>b1</b>–<b>b4</b>) MYJ, (<b>c1</b>–<b>c4</b>) QNSE, (<b>d1</b>–<b>d4</b>) MYNN2, (<b>e1</b>–<b>e4</b>) MYNN3, (<b>f1</b>–<b>f4</b>) ACM2, and (<b>g1</b>–<b>g4</b>) BouLac, at (<b>a1</b>–<b>g1</b>) 0000 UTC 11th, (<b>a2</b>–<b>g2</b>) 1200 UTC 11th, (<b>a3</b>–<b>g3</b>) 0000 UTC 12th, and (<b>a4</b>–<b>g4</b>) 1200 UTC 12 September 2018.</p>
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<p>Height–radius distribution of azimuthally averaged specific humidity difference (unit: g kg<sup>−1</sup>) between YSU and (<b>a</b>) MYJ, (<b>b</b>) QNSE, (<b>c</b>) MYNN2.5, (<b>d</b>) MYNN3, (<b>e</b>) ACM2, and (<b>f</b>) BouLac at (<b>1</b>) 0600 UTC 7, (<b>2</b>) 0000 UTC 9, (<b>3</b>) 0000 UTC 11, and (<b>4</b>) 1200 UTC 12, September 2018.</p>
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16 pages, 9032 KiB  
Article
Assessing Vulnerability to Cyclone Hazards in the World’s Largest Mangrove Forest, The Sundarbans: A Geospatial Analysis
by Mohammed, Fahmida Sultana, Ariful Khan, Sohag Ahammed, Md. Shamim Reza Saimun, Md Saifuzzaman Bhuiyan, Sanjeev K. Srivastava, Sharif A. Mukul and Mohammed A. S. Arfin-Khan
Forests 2024, 15(10), 1722; https://doi.org/10.3390/f15101722 - 29 Sep 2024
Viewed by 835
Abstract
The Sundarbans is the world’s largest contiguous mangrove forest with an area of about 10,000 square kilometers and shared between Bangladesh and India. This world-renowned mangrove forest, located on the lower Ganges floodplain and facing the Bay of Bengal, has long served as [...] Read more.
The Sundarbans is the world’s largest contiguous mangrove forest with an area of about 10,000 square kilometers and shared between Bangladesh and India. This world-renowned mangrove forest, located on the lower Ganges floodplain and facing the Bay of Bengal, has long served as a crucial barrier, shielding southern coastal Bangladesh from cyclone hazards. However, the Sundarbans mangrove ecosystem is now increasingly threatened by climate-induced hazards, particularly tropical cyclones originating from the Indian Ocean. To assess the cyclone vulnerability of this unique ecosystem, using geospatial techniques, we analyzed the damage caused by past cyclones and the subsequent recovery across three salinity zones, i.e., Oligohaline, Mesohaline, and Polyhaline. Our study also examined the relationship between cyclone intensity with the extent of damage and forest recovery. The findings of our study indicate that the Polyhaline zone, the largest in terms of area and with the lowest elevation, suffered the most significant damage from cyclones in the Sundarbans region, likely due to its proximity to the most cyclone paths. A correlation analysis revealed that cyclone damage positively correlated with wind speed and negatively correlated with the distance of landfall from the center of the Sundarbans. With the expectation of more extreme weather events in the near future, the Sundarbans mangrove forest faces a potentially devastating outlook unless both natural protection processes and human interventions are undertaken to safeguard this critical ecosystem. Full article
(This article belongs to the Special Issue Biodiversity, Health, and Ecosystem Services of Mangroves)
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<p>Path of the studied cyclones in the Bangladesh Sundarbans and the coverage of three saline zones (i.e., Polyhaline, Mesohaline, and Oligohaline) in the area. The red dot mark indicates the center point of the Sundarbans mangrove forest.</p>
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<p>Flowchart of the working procedures of cyclone damage and recovery analysis.</p>
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<p>Transition of the four NDVI classes after the occurrence of cyclones and their transitioned amount in square kilometers (km<sup>2</sup>). C1, C2, C3, and C4 represent Class 1, Class 2, Class 3, and Class 4 of pre-cyclone and post-cyclone NDVI classes, respectively. The total amount of area (in km<sup>2</sup>) of each class of pre-cyclone and post-cyclone NDVI is given under the class identifier (C1, C2, C3, and C4). Gray-colored inset boxes represent the unchanged area of each NDVI class after cyclone occurrence. A distinct colored number at the end of each line illustrates the amount of area (in km<sup>2</sup>) shifted to other classes.</p>
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<p>Transitioned area under different NDVI classes according to salinity zones in Bangladesh Sundarbans after the occurrence of 12 studied cyclones, where (<b>a</b>) represents the extent of the three saline zones.</p>
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<p>Four NDVI classes of both, pre-cyclone NDVI and post-cyclone NDVI, for the three saline zones of Bangladesh Sundarbans, where (<b>a</b>) represents the extent of the three saline zones.</p>
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<p>Cyclone-damaged areas in different saline zones are sorted by the year the cyclone occurred.</p>
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<p>Cyclone-recovered areas in different saline zones sorted by the year of cyclone occurred.</p>
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<p>Mean saline zone-wise values of (<b>a</b>) the damaged area and (<b>b</b>) the recovered area. Here, similar letters were used to represent no significant difference among saline zone-wise damaged and recovered areas, respectively.</p>
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<p>Results of correlation analysis of damage percentage (%) with cyclone variables, distance, and wind speed shown by (<b>a</b>) correlation matrix showing correlation coefficient values among different values; (<b>b</b>) results of Pearson’s correlation among damage percentage and wind speed (<b>c</b>); results of Pearson’s correlation among damage percentage and distance.</p>
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18 pages, 5148 KiB  
Article
Trends and Periodicities of Tropical Cyclone Frequencies and the Correlations with Ocean Drivers
by Guoyou Li, Huabin Shi and Zhiguo He
J. Mar. Sci. Eng. 2024, 12(10), 1707; https://doi.org/10.3390/jmse12101707 - 26 Sep 2024
Viewed by 505
Abstract
This study presents a comprehensive analysis on the variations in the tropical cyclone (TC) frequencies during 1980–2021, including the linear trends, periodicities, and their variabilities on both global and basin-wise scales. An increasing trend in the annual number of global TCs is identified, [...] Read more.
This study presents a comprehensive analysis on the variations in the tropical cyclone (TC) frequencies during 1980–2021, including the linear trends, periodicities, and their variabilities on both global and basin-wise scales. An increasing trend in the annual number of global TCs is identified, with a significant rising trend in the numbers of tropical storms (maximum sustained wind 35 ktsUmax<64 kts) and intense typhoons (Umax96 kts) and a deceasing trend for weak typhoons (64 ktsUmax<96 kts). There is no statistically significant trend shown in the global Accumulated Cyclone Energy (ACE). On a regional scale, the Western North Pacific (WNP) and Eastern North Pacific (ENP) are the regions of the first- and second-largest numbers of TCs, respectively, while the increased TC activity in the North Atlantic (NA) contributes the most to the global increase in TCs. It is revealed in the wavelet transformation for periodicity analysis that the variations in the annual number of TCs with different intensities mostly show an inter-annual period of 3–7 years and an inter-decadal one of 10–13 years. The inter-annual and inter-decadal periods are consistent with those in the ENSO-related ocean drivers (via the Niño 3.4 index), Southern Oscillation Index (SOI), and Inter-decadal Pacific Oscillation (IPO) index. The inter-decadal variation in 10–13 years is also observed in the North Atlantic Oscillation (NAO) index. The Tropical North Atlantic (TNA) index and Atlantic Multi-decadal Oscillation (AMO) index, on the other hand, present the same inter-annual period of 7–10 years as that in the frequencies of all the named TCs in the NA. Further, the correlations between TC frequencies and ocean drivers are also quantified using the Pearson correlation coefficient. These findings contribute to an enhanced understanding of TC activity, thereby facilitating efforts to predict particular TC activity and mitigate the inflicted damage. Full article
(This article belongs to the Section Physical Oceanography)
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<p>Locations of defined ocean basins. NIO: North Indian Ocean; WNP: Western North Pacific Ocean; ENP: Eastern North Pacific Ocean; NA: North Atlantic Ocean; SIO: South Indian Ocean; SP: South Pacific Ocean.</p>
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<p>Time series (solid lines) and its linear trend (dashed lines) of (<b>a</b>) annual numbers of all named TCs; the annual numbers as well as proportions of (<b>b</b>) TSs, (<b>c</b>) WTYs, and (<b>d</b>) ITYs on the globe scale. In subfigures (<b>b</b>–<b>d</b>), the blue lines represent the series of annual numbers, while the red ones are for the proportions. The shaded areas represent the 95% confidence interval in the linear regression, and the <math display="inline"><semantics> <mrow> <mi>p</mi> </mrow> </semantics></math>-value for the statistical significance of the linear trend is included.</p>
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<p>Time series (solid line) and the linear trend (dashed line) of annual values of global ACE. The shaded area represents the 95% confidence interval of the linear regression, and the <math display="inline"><semantics> <mrow> <mi>p</mi> </mrow> </semantics></math>-value for the statistical significance of the linear trend is included.</p>
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<p>(<b>a</b>) Linear trend in the annual numbers of all named TCs in hemispheres and six ocean basins. Red columns represent increasing trends while blue ones decreasing. One and two asterisks above the bars indicate statistical significance levels of the linear trend of 10% and 5%, respectively; (<b>b</b>) the 42-year-averaged proportions (columns) of all named TC numbers generated in hemispheres and six ocean basins in global total TCs and their linear trends (numbers above the columns, in the unit of %/year). Two asterisks above the numbers indicate statistical significance levels of the linear trend of 5%.</p>
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<p>(<b>a</b>) Linear trend in the annual numbers of TSs in hemispheres and six ocean basins. Red columns represent increasing trends while blue ones decreasing. Two asterisks above the bars indicate statistical significance levels of the linear trend of 5%; (<b>b</b>) the 42-year-averaged proportions (columns) of TS numbers generated in hemispheres and six ocean basins in global total TSs and their linear trends (numbers above the columns, in the unit of %/year). Two asterisks above the numbers indicate statistical significance levels of the linear trend of 5%.</p>
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<p>(<b>a</b>) Linear trend in the annual numbers of WTYs in hemispheres and six ocean basins. Red columns represent increasing trends while blue ones decreasing. One and two asterisks above the bars indicate statistical significance levels of the linear trend of 10% and 5%, respectively; (<b>b</b>) the 42-year-averaged proportions (columns) of WTY numbers generated in hemispheres and six ocean basins in global total WTYs and their linear trends (numbers above the columns, in the unit of %/year). One and two asterisks above the numbers indicate statistical significance levels of the linear trend of 10% and 5%, respectively.</p>
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<p>(<b>a</b>) Linear trend in the annual numbers of ITYs in hemispheres and six ocean basins. Red columns represent increasing trends while blue ones decreasing. One and two asterisks above the bars indicate statistical significance levels of the linear trend of 10% and 5%, respectively; (<b>b</b>) the 42-year-averaged proportions (columns) of ITY numbers generated in hemispheres and six ocean basins in global total ITYs and their linear trends (numbers above the columns, in the unit of %/year). Two asterisks above the numbers indicate statistical significance levels of the linear trend of 5%.</p>
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<p>(<b>a</b>) Linear trend in the annual values of ACE in hemispheres and six ocean basins. Red columns represent increasing trends while blue ones decreasing. Two asterisks above the bars indicate statistical significance levels of the linear trend of 5%; (<b>b</b>) the 42-year-averaged proportions (columns) of ACE in hemispheres and six ocean basins in global ACE and their linear trends (numbers above the columns, in the unit of %/year). Two asterisks above the numbers indicate statistical significance levels of the linear trend of 5%.</p>
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<p>Wavelet coefficient magnitude scalograms and power spectrums of annual numbers of all named TCs across the entire globe, the two hemispheres, and six ocean basins. The white dashed lines in magnitude scalograms indicate the cone of influence.</p>
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<p>Time series (solid lines) and its linear trend (dashed lines) of (<b>a</b>) Niño 3.4 index, (<b>b</b>) SOI, (<b>c</b>) IPO index, (<b>d</b>) IOD index, (<b>e</b>) TNA index, (<b>f</b>) NAO index, and (<b>g</b>) AMO index. The shaded areas represent the 95% confidence interval in the linear regression, and the <math display="inline"><semantics> <mrow> <mi>p</mi> </mrow> </semantics></math>-values for the statistical significance of the linear trend are included.</p>
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<p>Wavelet coefficient magnitude scalograms and power spectrums of annual values of (<b>a</b>) Niño 3.4 index, (<b>b</b>) SOI, (<b>c</b>) IPO index, (<b>d</b>) IOD index, (<b>e</b>) TNA index, (<b>f</b>) NAO index, and (<b>g</b>) AMO index. The white dashed lines in magnitude scalograms indicate the cone of influence.</p>
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24 pages, 3135 KiB  
Review
Current Status of Remote Sensing for Studying the Impacts of Hurricanes on Mangrove Forests in the Coastal United States
by Abhilash Dutta Roy, Daria Agnieszka Karpowicz, Ian Hendy, Stefanie M. Rog, Michael S. Watt, Ruth Reef, Eben North Broadbent, Emma F. Asbridge, Amare Gebrie, Tarig Ali and Midhun Mohan
Remote Sens. 2024, 16(19), 3596; https://doi.org/10.3390/rs16193596 - 26 Sep 2024
Viewed by 951
Abstract
Hurricane incidents have become increasingly frequent along the coastal United States and have had a negative impact on the mangrove forests and their ecosystem services across the southeastern region. Mangroves play a key role in providing coastal protection during hurricanes by attenuating storm [...] Read more.
Hurricane incidents have become increasingly frequent along the coastal United States and have had a negative impact on the mangrove forests and their ecosystem services across the southeastern region. Mangroves play a key role in providing coastal protection during hurricanes by attenuating storm surges and reducing erosion. However, their resilience is being increasingly compromised due to climate change through sea level rises and the greater intensity of storms. This article examines the role of remote sensing tools in studying the impacts of hurricanes on mangrove forests in the coastal United States. Our results show that various remote sensing tools including satellite imagery, Light detection and ranging (LiDAR) and unmanned aerial vehicles (UAVs) have been used to detect mangrove damage, monitor their recovery and analyze their 3D structural changes. Landsat 8 OLI (14%) has been particularly useful in long-term assessments, followed by Landsat 5 TM (9%) and NASA G-LiHT LiDAR (8%). Random forest (24%) and linear regression (24%) models were the most common modeling techniques, with the former being the most frequently used method for classifying satellite images. Some studies have shown significant mangrove canopy loss after major hurricanes, and damage was seen to vary spatially based on factors such as proximity to oceans, elevation and canopy structure, with taller mangroves typically experiencing greater damage. Recovery rates after hurricane-induced damage also vary, as some areas were seen to show rapid regrowth within months while others remained impacted after many years. The current challenges include capturing fine-scale changes owing to the dearth of remote sensing data with high temporal and spatial resolution. This review provides insights into the current remote sensing applications used in hurricane-prone mangrove habitats and is intended to guide future research directions, inform coastal management strategies and support conservation efforts. Full article
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<p>PRISMA workflow representing the systematic literature review process.</p>
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<p>Applications of remote sensing for studying impacts of hurricanes on mangroves.</p>
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<p>Coastal southeastern United States showing some locations where studies on hurricane impact on mangroves were carried out, that included (<b>A</b>) Everglades National Park, Florida, (<b>B</b>) Florida Keys, (<b>C</b>) Port Fourchon, Louisiana, (<b>D</b>) (Inset): Puerto Rico.</p>
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<p>The regional frequency of remote sensing based peer-reviewed articles published on studying impacts of hurricanes on mangroves in the United States from January 2010 to September 2024.</p>
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<p>Percentage breakdown of sensors used for studying impacts of hurricanes on mangroves in the United States from January 2010 to September 2024.</p>
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<p>Data analysis methods used to study the impacts of hurricanes on mangroves in the United States from January 2010 to September 2024.</p>
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19 pages, 7507 KiB  
Article
Spatiotemporal Climatology of Georgia Tropical Cyclones and Associated Rainfall
by Reilly Corkran, Jill Trepanier and Vincent Brown
J. Mar. Sci. Eng. 2024, 12(10), 1693; https://doi.org/10.3390/jmse12101693 - 24 Sep 2024
Viewed by 858
Abstract
Tropical cyclones (TCs), often characterized by high wind speeds and heavy rainfall, cause widespread devastation, affecting millions of people and leading to economic losses worldwide. TC-specific research in Georgia is scarce, likely due to the minimal geographical extent of its coast and the [...] Read more.
Tropical cyclones (TCs), often characterized by high wind speeds and heavy rainfall, cause widespread devastation, affecting millions of people and leading to economic losses worldwide. TC-specific research in Georgia is scarce, likely due to the minimal geographical extent of its coast and the infrequency of direct landfalls. Research on Georgia TCs does not account for storms that make landfall in other southeastern states (e.g., Florida) and continue north, northeast, or northwest into Georgia. This study used the North Atlantic Basin hurricane database (HURDAT2) to quantify the spatiotemporal patterns of direct and indirect landfalling of Georgia tropical cyclones (>16 ms−1) from 1851 to 2021. TC-induced rainfall was also quantified using rainfall data (nClimGrid-Daily and nClimGrid) from 1951 to 2021 to estimate the proportion of Georgia’s total annual and monthly rainfall attributed to TCs. A multi-methodological approach, incorporating statistics and mapping, is employed to assess the trends of Georgia’s tropical cyclones and the associated rainfall. The study analyzed 113 TCs and found that, on average, less than one TC annually (x¯ = 0.66) traverses the state. September averaged the highest percentage (25%) of TC-induced rainfall, followed by October (14%), and August (13%). This pattern aligns with the TC season, with the highest frequency of TCs occurring in September (n = 35), followed by August (n = 25), and October (n = 18). We found that 10% of tropical storms make landfall on the coastline, while the remaining 91% enter Georgia by making landfall in Florida (92%), Louisiana (7%), or South Carolina (1%) first. A threat of TCs during the peak of the season emphasizes the importance of heightened awareness, increased planning practices, and resource allocation during these periods to protect Georgia’s history and natural beauty, and its residents. Full article
(This article belongs to the Special Issue Coastal Disaster Assessment and Response)
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<p>(<b>a</b>) Track map of direct landfalling tropical cyclones on the coastline of Georgia from 1851 to 2021; (<b>b</b>) track map of indirect landfalling tropical cyclones in the state of Georgia from 1851 to 2021.</p>
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<p>Heat map of first tropical cyclone entry points into Georgia, 1851–2021.</p>
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<p>Heat map of tropical cyclone exit points along the Georgia border, 1851–2021.</p>
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<p>Georgia’s annual tropical cyclone occurrence (1851–2021) categorized by intensity.</p>
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<p>The seasonality of tropical cyclones within the Georgia state boundary separated by intensity.</p>
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<p>Distribution of tropical cyclone maximum intensity within the state of Georgia from 1851 to 2021.</p>
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<p>Tropical cyclone tracks used to find the annual TC precipitation in Georgia from 1951 to 2021.</p>
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<p>The five geographic regions of Georgia with county delineation and the Coastal Plain divided into the upper and lower regions.</p>
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<p>Annual TC precipitation percentage in Georgia from 1951 to 2021.</p>
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<p>TC Tracks that occurred from May to November in Georgia from 1951 to 2021; monthly average rainfall data attributed to tropical cyclones from May to November.</p>
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<p>Average TC precipitation percentage for May–November in Georgia from 1951 to 2021.</p>
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13 pages, 601 KiB  
Article
Exploring Experimental Isotope Scaling and Density Limit in Tokamak Transport
by Jan Weiland, Tariq Rafiq and Eugenio Schuster
Plasma 2024, 7(3), 780-792; https://doi.org/10.3390/plasma7030041 - 23 Sep 2024
Viewed by 361
Abstract
As it turns out, both isotope scaling and density limits are phenomena closely linked to fluid closure. The necessity to include ion viscosity arises for both phenomena. Thus, we have added ion viscosity to our model. The experimental isotope scaling has been successfully [...] Read more.
As it turns out, both isotope scaling and density limits are phenomena closely linked to fluid closure. The necessity to include ion viscosity arises for both phenomena. Thus, we have added ion viscosity to our model. The experimental isotope scaling has been successfully recovered in our fluid model through parameter scans. Although ion viscosity typically exerts a small effect, the density limit is manifested by increasing the density by approximately tenfold from the typical experimental density. In our case, this increase originates from the density in the Cyclone base case. Notably, these phenomena would not manifest with a gyro-Landau fluid closure. The isotope scaling is nullified by the addition of a gyro-Landau term, while the density limit results from permitting ion viscosity to become comparable to the gyro-Landau term. The mechanism of zonal flows, demonstrated analytically for the Dimits upshift, yields insights into the isotope scaling observed in experiments. In our approach, ion viscosity is introduced in place of the Landau fluid resonances found in some fluid models. This implies that the mechanism of isotope scaling operates at the level of fluid closure in connection with the generation of zonal flows. The strength of zonal flows in our model has been verified, particularly in connection with the successful simulation of the nonlinear Dimits shift. Consequently, a role is played by our approach in the temperature perturbation part of the Reynolds stress. Full article
(This article belongs to the Special Issue New Insights into Plasma Theory, Modeling and Predictive Simulations)
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<p>This figure exhibits similarity to the suppression of explosive instability by a nonlinear frequency shift [<a href="#B27-plasma-07-00041" class="html-bibr">27</a>]. Reproduced from [I. Holod, J. Weiland, and A. Zagorodny Physics of Plasmas 9, 1217 (2002)], with the permission of AIP Publishing.</p>
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<p>(<b>a</b>) Normalized growth rate and normalized flow shear (<b>b</b>) transport (ion thermal diffusivity) are derived for Cyclone parameters (<math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mi>e</mi> </msub> <mo>=</mo> <mn>4.5</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>19</mn> </msup> <mo> </mo> <msup> <mi mathvariant="normal">m</mi> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>, R = 2.5 m, T = 2.0 keV, a = 0.63 m, q = 1.4, s = 0.78, B = 2.0 T) based on the findings in Ref. [<a href="#B7-plasma-07-00041" class="html-bibr">7</a>], incorporating ion viscosity corresponding to hydrogen in Equation (<a href="#FD6-plasma-07-00041" class="html-disp-formula">6</a>). The data is considered at half the radius. The strong flow shear at marginal stability arises from the fluid resonance described in Equation (<a href="#FD6-plasma-07-00041" class="html-disp-formula">6</a>). Waves with wavelengths approaching the system size will inevitably reach marginal stability at some point, leading to strong damping. Consequently, transport is heavily influenced by the fluid closure. The reactive closure results from the detuning of wave–particle resonances due to resonance broadening or nonlinear frequency shifts.</p>
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<p>Transport as a function of normalized ion temperature gradients for both hydrogen and tritium shows a decrease in transport with a higher isotope mass. These results are obtained by adding ion viscosity to the calculations conducted in Ref. [<a href="#B7-plasma-07-00041" class="html-bibr">7</a>]. The data here is considered at the edge, where sharper gradients are present.</p>
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<p>The ion temperature is depicted by the full line before and after the L–H transition in a simulation of ITER, utilizing the Weiland fluid model along with the neoclassical module. This is a global simulation showing the central ion temperature: <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi mathvariant="normal">e</mi> </msub> <mo>∼</mo> <mn>20.0</mn> </mrow> </semantics></math> keV and the H-mode barrier.</p>
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<p>(<b>a</b>) Normalized growth rates and normalized flow shear (<b>b</b>) transport (ion thermal diffusivity) illustrate similar variations as seen in <a href="#plasma-07-00041-f002" class="html-fig">Figure 2</a>. Both the rotation and Dimits shift exhibit a decreasing trend, consistent with the observations in Ref. [<a href="#B7-plasma-07-00041" class="html-bibr">7</a>] for the gyro-Landau fluid model. This particular case corresponds to a density of <math display="inline"><semantics> <mrow> <mn>4.5</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>20</mn> </msup> <mo> </mo> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>. It illustrates how thermal diffusivity increases with higher density. The exact density limit depends on when zonal flows become too weak to absorb the inward inverse turbulent cascade.</p>
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22 pages, 1283 KiB  
Article
Dynamic Approach to Update Utility and Choice by Emerging Technologies to Reduce Risk in Urban Road Transportation Systems
by Francesco Russo, Antonio Comi and Giovanna Chilà
Future Transp. 2024, 4(3), 1078-1099; https://doi.org/10.3390/futuretransp4030052 - 20 Sep 2024
Viewed by 489
Abstract
International research attention on evacuation issues has increased significantly following the human and natural disasters at the turn of the century, such as 9/11, Hurricane Katrina, Cyclones Idai and Kenneth, the Black Saturday forest fires and tsunamis in Japan. The main problem concerning [...] Read more.
International research attention on evacuation issues has increased significantly following the human and natural disasters at the turn of the century, such as 9/11, Hurricane Katrina, Cyclones Idai and Kenneth, the Black Saturday forest fires and tsunamis in Japan. The main problem concerning when a disaster can occur involves studying the risk reduction. Risk, following all the theoretical and experimental studies, is determined by the product of three components: occurrence, vulnerability and exposure. Vulnerability can be improved over time through major infrastructure actions, but absolute security cannot be achieved. When the event will occur with certainty, only exposure remains to reduce the risk to people before the effect hits them. Exposure can be improved, under fixed conditions of occurrence and vulnerability, by improving evacuation. The main problem in terms of evacuating the population from an area is the available transport system, which must be used to its fullest. So, if the system is well managed, the evacuation improves (shorter times), meaning the exposure is reduced, and therefore, the risk is reduced. A key factor in the analysis of transport systems under emergency conditions is the behavior of the user, and therefore, the study of demand. This work identifies the main research lines that are useful for studying demand under exposure-related risk conditions. The classification of demand models that simulate evacuation conditions in relation to the effect on the transportation system is summarized. The contribution proposes a model for updating choice in relation to emergency conditions and utility. The contribution of emerging ICTs to actualization is formally introduced into the models. Intelligent technologies make it possible to improve user decisions, reducing exposure and therefore risk. The proposed model moves within the two approaches of the literature: it is an inter-period dynamic model with the probability expressed within the discrete choice theory; furthermore, it is a sequential dynamic model with the probability dependent on the previous choices. The contribution presents an example of application of the model, developing a transition matrix considering the case of choice updating under two extreme conditions. Full article
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<p>The literature and proposed dynamic models.</p>
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<p>Main milestones: decision maker; <span class="html-italic">risk process</span>; and user behavior.</p>
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<p>Comparison between the classical and proposed learning processes updating utility.</p>
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<p>Network test.</p>
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<p>Network test case: transition matrices in the equiprobable case and disruption of one link.</p>
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