Browsing by Author "Guo Jin-Yun"

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Oceanic surface geostrophic velocities determined with satellite altimetric crossover method
(2010-11-01)
Guo Jin-Yun
;
Chang Xiao-Tao
;
Hwang Cheinway
;
Sun Jia-Long
;
Han Yan-Ben
If the Coriolis force is balanced by the pressure gradient in the hydrostatic equilibrium. the ocean current is referred to as the geostrophic current Most ocean currents worldwide are approximately geostrophic currents The surface geostrophic velocity is calculated with the satellite altimetric crossover method in this paper The uncertainties of geostrophic velocity determined from satellite altimetric crossover data are analyzed If the accuracy of the sea surface slope along the ascending or descending track of altimetric satellite is up to the 10(-7) level with 50 km of resolution, the geostrophic velocity will have an accuracy of better than 10 cm/s In the areas of low latitude or near the altimetric satellite orbital inclination, the uncertainty of geostrophic velocity determined with the crossover method will be much bigger than that in the low-/mid-latitude areas The Kuroshio area east of Taiwan, China is selected as a testing region A highly accurate and up-to-date geoidal model which is determined using the combined data from the ground-based gravimetry, ship-borne gravimetry, air-borne gravimetry and satellite altimetry, is selected as the reference geoid Sea surface heights from the geophysical data records of TOPEX/Poseidon and Jason-1 from 2002 to 2005 are used to compute highly accurate dynamic heights at three crossover points Geostrophic velocities are then derived at these crossover points and are basically identical to the current velocities provided by the National Center for Ocean Research, Taiwan, China
Oceanic surface geostrophic velocities determined with satellite altimetric crossover method
(2010-11-01)
Guo Jin-Yun
;
Chang Xiao-Tao
;
Hwang Cheinway
;
Sun Jia-Long
;
Han Yan-Ben
If the Coriolis force is balanced by the pressure gradient in the hydrostatic equilibrium. the ocean current is referred to as the geostrophic current Most ocean currents worldwide are approximately geostrophic currents The surface geostrophic velocity is calculated with the satellite altimetric crossover method in this paper The uncertainties of geostrophic velocity determined from satellite altimetric crossover data are analyzed If the accuracy of the sea surface slope along the ascending or descending track of altimetric satellite is up to the 10(-7) level with 50 km of resolution, the geostrophic velocity will have an accuracy of better than 10 cm/s In the areas of low latitude or near the altimetric satellite orbital inclination, the uncertainty of geostrophic velocity determined with the crossover method will be much bigger than that in the low-/mid-latitude areas The Kuroshio area east of Taiwan, China is selected as a testing region A highly accurate and up-to-date geoidal model which is determined using the combined data from the ground-based gravimetry, ship-borne gravimetry, air-borne gravimetry and satellite altimetry, is selected as the reference geoid Sea surface heights from the geophysical data records of TOPEX/Poseidon and Jason-1 from 2002 to 2005 are used to compute highly accurate dynamic heights at three crossover points Geostrophic velocities are then derived at these crossover points and are basically identical to the current velocities provided by the National Center for Ocean Research, Taiwan, China
Optimal threshold algorithm of EnviSat waveform retracking over coastal sea
(2010-04-01)
Guo Jin-Yun
;
Gao Yong-Gang
;
Chang Xiao-Tao
;
Hwang Cheinway
;
土木工程學系
Waveform retracking is an effective method to improve the accuracy of sea surface height measurement with the satellite altimetric technique. The improved threshold retracking algorithm is optimized in the paper. Several waveform retracking algorithms are analyzed. Then a waveform retracking system is programmed. Several retracking methods are used to retrack the EnviSat waveform over the Mediterranean Sea. The improved threshold retracking algorithm is the most robust retracking method and more suitable for the EnviSat waveform retracking over the coastal Mediterranean.
Temporal-spatial variations of sea level over China seas derived from altimeter data of TOPEX/Poseidon, Jason-1 and Jason-2 from 1993 to 2012
(2015-09-01)
Guo Jin-Yun
;
Wang Jian-Bo
;
Hu Zhi-Bo
;
Hwang, Cheinway
;
Chen Chuan-Fa
Variations of sea level are an important issue associated with global climate change, especially global warming. The sea level rising may lead to serious impact on the social-economic development of a country. Global oceans are continuously observed by altimetry satellite missions for more than 20 years. We use altimeter data of TOPEX/Poseidon (T/P), Jason-1 and Jason-2 from 1993 to 2012 to study the sea level change over China seas. China seas located in the western Pacific Ocean are selected as the study area. T/P MGDR data of 11 to 364 cycles span January 1993 to August 2002. Jason-1 GDR data of 1 to 259 cycles span January 2002 to January 2009. Jason-2 GDR data of 1 to 165 cycles span July 2008 to December 2012. Southern oscillation index (SOT) time series span January 1993 to December 2012. Altimetry data in the tandem stages are used to calibrate biases of sea level anomalies (SLAs) from T/P, Jason-1 and Jason-2 point by point. The spatial distribution of China seas\' level change is studied with the continuous tension spline method. Temporal variations of China seas\' level are analyzed with the linear fitting method and the wavelet analysis. Relationships between the El Nino-Southern Oscillation (ENSO) and sea level changes of the South China Sea and the East China Sea are studied with the correlation analysis. By unifying altimetry data point by point in tandem stages, the mean differences of sea level heights for Jason-1 vs T/P and Jason-2 vs Jason-1 are 0. 21 cm and 0. 03 cm, respectively. SLA time series are constructed after corrections of sea level biases from 1993 to 2012. In general, the mean SLA is positive over China seas. SLAs are higher in the south than those in the north, and lower in the west than those in the east. SLAs are negative over the western Bohai Sea, the northern Yellow Sea, the northern Taiwan Strait and the North Bay. SLAs rise with the increasing longitude and decrease with the growing latitude. This spatial distribution of SLAs over China seas is related to the water flux, sea surface wind stress, oceanic dynamics, monsoon, Kuroshio and ENSO. The mean rising rate of sea level for the 20 years is 4. 64 mm . a(-1) over the whole study area. Sea levels are rising in the Bohai Sea, Yellow Sea, East China Sea and South China Sea with rising rates 4. 44 mm . a(-1), 2. 37 mm . a(-1), 3. 02 mm . a(-1) and 4. 25 mm . a(-1), respectively. The annual sea level variation is obvious in the study area. The sea level is higher in summer and autumn than in winter and spring. The main cycles of sea level change include one year and 9 years over the whole seas. There are also minor cycles of 0. 5 years, 1. 5 years, 2 years and 4 years. Cyclical changes of sea level are related to the geographical position, climate, oceanic dynamics and submarine topography. The correlation coefficients between SOIs and SLAs of the South China Sea and the East China Sea are 0.39 and 0.02, respectively. The correlation coefficient is 0. 44 after the two-month delay for the South China Sea. The correlation coefficient is 0. 17 after the four-month delay for the East China Sea. This indicates that the sea level change over the South China Sea may be largely affected by ENSO. Altimetry data of T/P, Jason-1 and Jason-2 are processed to study the sea level change over China seas from 1993 to 2012. We used the altimetry data in tandem stages to calibrate the sea level biases of these three missions to achieve the seamless SLAs point by point. The spatial distribution of sea level change is given with the continuous tension spline method. The sea level is generally rising at the mean rate of 4. 64 mm . a(-1) over China seas in these 20 years. The sea level variations change with different seas and seasons. The mean rising rates are 4. 44 mm . a(-1), 2. 34 mm . a(-1), 3. 02 mm . a(-1) and 4. 25 mm . a(-1) over Bohai Sea, Yellow Sea, East China Sea and South China Sea, respectively. Temporal-spatial change of sea level over the study area is related to oceanic dynamics, submarine topography, monsoon, Kuroshio and ENSO. The main cycles of sea level change are one year and 9 years as derived from the wavelet analysis. El Nino and La Nina events make stronger effects on sea level variations over the South China Sea than the other seas.