Observing storm surges in China's coastal areas by integrating multi-source satellite altimeters

Ji, Tao; Li, Guosheng; Zhang, Yue

doi:10.1016/j.ecss.2019.05.006

Cited by 14 publications

(17 citation statements)

References 36 publications

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“…SLA data require various geophysical and environmental corrections, including dynamic atmospheric correction (DAC), tidal correction (including solid earth tides, pole tides, and loading tides), sea state bias (SSB), tropospheric correction (dry troposphere and wet tropospheric correction), ionospheric correction (iono), and instrument calibration (inst). The calculation formula of SLA is as follows:

SLA = Orbit - Range - DAC - solid earth tide - pole tide - loading tide - tide - SSB - dry tropo - wet tropo - iono - MSS

where Orbit is the distance between the satellite's center of mass and the reference surface (ellipsoid WGS84), Range is the retracked distance between the instrument and the mean reflected sea level, and MSS is the mean sea surface height (SSH), observed by the satellite altimeter for 20 years (Ji et al, 2019).…”

Section: Datamentioning

confidence: 99%

“…In order to ensure the consistency of the two data sets, it is necessary to remove the atmospherically induced sea level caused by wind and atmospheric pressure from the tide gauge records (Marcos et al, 2015). Hence, we retained DAC corrections in SLA to be consistent with tide gauge observations (Antony et al, 2014; Ji et al, 2019). Furthermore, we defined SSH as the result of the sum of SLA and DAC.…”

Section: Datamentioning

confidence: 99%

“…Storm surge events were identified from residuals using 3 standard deviations and occurred must be met for more than 18 hr of continuous occurrence as a criterion. Then we identified the storm surge events, the storm surge heights, and the event duration in the history of each station (Ji et al, 2019; K. Zhang et al, 2000).…”

Section: Datamentioning

confidence: 99%

“…In addition, in recent years, the damage caused by storm surge disasters has been the highest among various marine disasters in China, with the overall increasing trend. Storm surges have become one of the natural disasters that seriously threaten the socioeconomic development of the southeastern coastal areas of China (Ji et al, 2019). Typhoon Rammasun, for example, was the strongest typhoon that landed in China since 1949.…”

Section: Introductionmentioning

confidence: 99%

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Historical Reconstruction of Storm Surge Activity in the Southeastern Coastal Area of China for the Past 60 Years

Liu

2020

Earth and Space Science

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It is a very difficult task to analyze the spatial and temporal variability of storm surge activity with current monitoring methods, because tide gauge observation, satellite altimeter, and numerical simulation do not ensure sufficient spatial and temporal resolution and temporal coverage at the same time. We propose to use a reanalysis data set, which covers the period from 1958 to 2016, to statistically reconstruct the daily maximum storm surge levels in the southeastern coastal area of China. Then, we used the Geographical Differential Analysis (GDA) calibration to correct the reconstruction results. The verification of the statistical model and the calibration results show that both the statistical model and the GDA calibration have a good accuracy in terms of high correlations and small errors. However, they also reveal a variable spatial quality of the multivariate statistical model in the reconstructed maximum storm surge levels, especially in the extreme events of the storm surge activity, the daily maximum surge is often underestimated. The GDA calibration result readily solves this problem, as it can accurately reflect not only the daily maximum storm surge levels but also the whole spatial and temporal dynamic storm surge evolution process. Therefore, this work provides an effective method to establish long‐term sequence and high‐precision daily maximum storm surge levels. This method can reveal the spatial and temporal evolution features of storm surge intensity at a climatic scale and main variability features. Moreover, it can predict possible trends in the geospatial distribution pattern of storm surge intensity in the context of future climate change.

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SLA = Orbit - Range - DAC - solid earth tide - pole tide - loading tide - tide - SSB - dry tropo - wet tropo - iono - MSS

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Historical Reconstruction of Storm Surge Activity in the Southeastern Coastal Area of China for the Past 60 Years

Liu

2020

Earth and Space Science

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“…Although these areas are heavily influenced by extreme sea level events, few studies have analyzed storm surge activity under the influence of climate change in this specific area (Cid et al., 2018). Globally, these extreme events are hotspots of high risk threatening the survival and development of human beings in coastal areas, and are becoming a major challenge facing the international community (Ji et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Features of Storm Surge Activity and Its Response to Climate Change in the Southeastern Coastal Area of China in the Past 60 years

Liu

et al. 2021

JGR Atmospheres

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The southeastern coastal areas of China have been hit hardest by storm surges in terms of frequency, direct economic losses, and casualties. This is particularly true for the provinces of Zhejiang, Fujian, and Guangdong, where both the risk and the extent of disasters are much higher than in the coastal areas of northern China. In addition, in recent years, the damage caused by storm surge disasters has been the highest among various marine disasters in China, with steady increases on a yearly basis. Storm surges have become one of the natural disasters that seriously threaten the socioeconomic development of the southeastern coastal areas of China (Ji et al., 2019). Typhoon Rammasun (2014), for example, was the strongest typhoon that landed in China since 1949. It generated storm surges of more than 2 m at several tide gauges in the provinces of Guangdong, Guangxi, and Hainan, resulting in the death of 88 people and in losses of $7.2 billion (US) (Wankang et al., 2019). In 2013, Typhoon Tiantu struck Guangdong, Fujian and nearby areas, generating a maximum storm surge of more than 2 m along the Guangdong coast, and causing an economic loss of the US $1.82 billion (H. Zhang & Sheng, 2015). As another example, typhoon Dan landed in the province of Fujian on October 9, 1999, causing 34 deaths and more than the US $2.4 billion in losses, as well as massive destruction of coastal facilities (Wankang et al., 2019).

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Mapping the spatiotemporal variability in global storm surge water levels using satellite radar altimetry

Bij de Vaate,

Slobbe,

Verlaan

2024

Ocean Dynamics

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Multi-mission satellite altimetry data have been used to study the spatial and temporal variability in global storm surge water levels. This was done by means of a time-dependent extreme value analysis applied to the monthly maximum detided water levels. To account for the limited temporal resolution of the satellite data, the data were first stacked on a $$\varvec{5}^{\varvec{\circ }} \times \varvec{5}^{\varvec{\circ }}$$ 5 ∘ × 5 ∘ grid. Moreover, additional scaling was applied to the extreme value analysis for which the scaling factors were determined by means of a resampling method using reanalysis data. In addition to the conventional analysis using data from tide gauges, this study provides an insight in the ocean-wide storm surge properties. Nonetheless, where possible, results were compared to similar information derived from tide gauge data. Except for secular changes, the satellite-derived results are comparable to the information derived from tide gauges (correlation $$> \varvec{0.5}$$ > 0.5 ), although the tide gauges show more local variability. Where limited correlation was observed for the secular change, it was suggested that the satellites may not be able to fully capture the temporal variability in the short-lived, tropical storms, as opposed to extra-tropical storms.

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Observing storm surges in China's coastal areas by integrating multi-source satellite altimeters

Cited by 14 publications

References 36 publications

Historical Reconstruction of Storm Surge Activity in the Southeastern Coastal Area of China for the Past 60 Years

Historical Reconstruction of Storm Surge Activity in the Southeastern Coastal Area of China for the Past 60 Years

Spatiotemporal Features of Storm Surge Activity and Its Response to Climate Change in the Southeastern Coastal Area of China in the Past 60 years

Mapping the spatiotemporal variability in global storm surge water levels using satellite radar altimetry

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