2018 Vol. 37, No. 1
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2018, 37(1): .
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2018, 37(1): .
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2018, 37(1): 1-10.
doi: 10.1007/s13131-018-1153-0
Abstract:
Combining a linear regression and a temperature budget formula, a multivariate regression model is proposed to parameterize and estimate sea surface temperature (SST) cooling induced by tropical cyclones (TCs). Three major dynamic and thermodynamic processes governing the TC-induced SST cooling (SSTC), vertical mixing, upwelling and heat flux, are parameterized empirically using a combination of multiple atmospheric and oceanic variables: sea surface height (SSH), wind speed, wind curl, TC translation speed and surface net heat flux. The regression model fits reasonably well with 10-year statistical observations/reanalysis data obtained from 100 selected TCs in the northwestern Pacific during 2001-2010, with an averaged fitting error of 0.07 and a mean absolute error of 0.72℃ between diagnostic and observed SST cooling. The results reveal that the vertical mixing is overall the pre dominant process producing ocean SST cooling, accounting for 55% of the total cooling. The upwelling accounts for 18% of the total cooling and its maximum occurs near the TC center, associated with TC-induced Ekman pumping. The surface heat flux accounts for 26% of the total cooling, and its contribution increases towards the tropics and the continental shelf. The ocean thermal structures, represented by the SSH in the regression model, plays an important role in modulating the SST cooling pattern. The concept of the regression model can be applicable in TC weather prediction models to improve SST parameterization schemes.
Combining a linear regression and a temperature budget formula, a multivariate regression model is proposed to parameterize and estimate sea surface temperature (SST) cooling induced by tropical cyclones (TCs). Three major dynamic and thermodynamic processes governing the TC-induced SST cooling (SSTC), vertical mixing, upwelling and heat flux, are parameterized empirically using a combination of multiple atmospheric and oceanic variables: sea surface height (SSH), wind speed, wind curl, TC translation speed and surface net heat flux. The regression model fits reasonably well with 10-year statistical observations/reanalysis data obtained from 100 selected TCs in the northwestern Pacific during 2001-2010, with an averaged fitting error of 0.07 and a mean absolute error of 0.72℃ between diagnostic and observed SST cooling. The results reveal that the vertical mixing is overall the pre dominant process producing ocean SST cooling, accounting for 55% of the total cooling. The upwelling accounts for 18% of the total cooling and its maximum occurs near the TC center, associated with TC-induced Ekman pumping. The surface heat flux accounts for 26% of the total cooling, and its contribution increases towards the tropics and the continental shelf. The ocean thermal structures, represented by the SSH in the regression model, plays an important role in modulating the SST cooling pattern. The concept of the regression model can be applicable in TC weather prediction models to improve SST parameterization schemes.
2018, 37(1): 11-19.
doi: 10.1007/s13131-018-1154-z
Abstract:
A new method to determine wave directions from nautical X-band images is proposed. The signatures of ocean waves show obvious scale and directional characteristics in nautical X-band radar images. Curvelet transform (CT) possesses very high scale and directional sensitivities. Therefore, it has good capability to analyze ocean wave fields. The radar images are decomposed at different scales, in different directions, and at different positions by CT, and curvelet coefficients are obtained. Given to the scale and directional characteristics of surface waves, the information of ocean waves is centralized in the curvelet coefficients of certain directions and at certain scales. Therefore, the wave orientations can be determined. The 180 ambiguity is removed by calculating cross-correlation coefficients (CCCs) between continuous collected images. The proposed method is verified by the dataset collected on the Northwest coast of the Zhangzi Island in the Yellow Sea of China from March to April 2009.
A new method to determine wave directions from nautical X-band images is proposed. The signatures of ocean waves show obvious scale and directional characteristics in nautical X-band radar images. Curvelet transform (CT) possesses very high scale and directional sensitivities. Therefore, it has good capability to analyze ocean wave fields. The radar images are decomposed at different scales, in different directions, and at different positions by CT, and curvelet coefficients are obtained. Given to the scale and directional characteristics of surface waves, the information of ocean waves is centralized in the curvelet coefficients of certain directions and at certain scales. Therefore, the wave orientations can be determined. The 180 ambiguity is removed by calculating cross-correlation coefficients (CCCs) between continuous collected images. The proposed method is verified by the dataset collected on the Northwest coast of the Zhangzi Island in the Yellow Sea of China from March to April 2009.
2018, 37(1): 20-29.
doi: 10.1007/s13131-018-1155-y
Abstract:
The fog occurs frequently over the Yellow Sea in spring (April-May), a climatical period of Asian monsoon transition. A comprehensive survey of the characteristic weather pattern and the air-sea condition is provided associated with the fog for the period of 1960-2006. The sea fog is categorized by airflow pathways of backward trajectory cluster analysis with the surface observations derived from international comprehensive ocean-atmosphere dataset (I_COADS) I_COADS datasets and contemporaneous wind fields from the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis. On the basis of the airflow paths, the large-scale lower-tropospheric circulation patterns and the associated surface divergence, the distribution of a vertical humidity, the horizontal water vapor transportation and the air-sea temperature difference are investigated and the major findings are summarized as follows. (1) Four primary clusters of the airflow paths that lead to spring sea fog formation are identified. They are originated from the northwest, east, southeast and southwest of the Yellow Sea, respectively. (2) Springtime Yellow Sea fog occurs under two typical weather patterns: the Yellow Sea high (YSH) and cyclone and anticyclone couplet (CAC). Each pattern appears by about equal chance in April but the YSH occurrence drops to around one third and the CAC rises to around two third of chance in May. (3) The common feature in the two types of synoptic conditions is that surface divergence center is located over the Yellow Sea. (4) For the YSH type of fog, water vapor comes mainly from local evaporation with a well-defined dry layer present in the lower atmosphere; for the CAC type of fog, however, water vapor comes mainly from areas outside the Yellow Sea with a thick surface layer of high humidity. (5) With the differences in weather patterns and its associated vertical distribution of the humidity and the transportation of water vapor, there are two types of sea fogs. Most fogs of the CAC types are “warm” fog, while fogs of YSH type have nearly equal chance to be “warm” and “cold” fog.
The fog occurs frequently over the Yellow Sea in spring (April-May), a climatical period of Asian monsoon transition. A comprehensive survey of the characteristic weather pattern and the air-sea condition is provided associated with the fog for the period of 1960-2006. The sea fog is categorized by airflow pathways of backward trajectory cluster analysis with the surface observations derived from international comprehensive ocean-atmosphere dataset (I_COADS) I_COADS datasets and contemporaneous wind fields from the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis. On the basis of the airflow paths, the large-scale lower-tropospheric circulation patterns and the associated surface divergence, the distribution of a vertical humidity, the horizontal water vapor transportation and the air-sea temperature difference are investigated and the major findings are summarized as follows. (1) Four primary clusters of the airflow paths that lead to spring sea fog formation are identified. They are originated from the northwest, east, southeast and southwest of the Yellow Sea, respectively. (2) Springtime Yellow Sea fog occurs under two typical weather patterns: the Yellow Sea high (YSH) and cyclone and anticyclone couplet (CAC). Each pattern appears by about equal chance in April but the YSH occurrence drops to around one third and the CAC rises to around two third of chance in May. (3) The common feature in the two types of synoptic conditions is that surface divergence center is located over the Yellow Sea. (4) For the YSH type of fog, water vapor comes mainly from local evaporation with a well-defined dry layer present in the lower atmosphere; for the CAC type of fog, however, water vapor comes mainly from areas outside the Yellow Sea with a thick surface layer of high humidity. (5) With the differences in weather patterns and its associated vertical distribution of the humidity and the transportation of water vapor, there are two types of sea fogs. Most fogs of the CAC types are “warm” fog, while fogs of YSH type have nearly equal chance to be “warm” and “cold” fog.
2018, 37(1): 30-39.
doi: 10.1007/s13131-018-1156-x
Abstract:
The air temperature is one of the most important parameters used for monitoring the Arctic climate change. The constellation observing system for meteorology, ionosphere, and climate and Formosa Satellite Mission 3 (COSMIC/FORMOSAT-3) radio occultation (RO) “wet” temperature product (i.e., “wetPrf”) is used to analyze the Arctic air temperature profiles at 925-200 hPa in 2007-2012. The “wet” temperatures are further compared with radiosonde (RS) observations. The results from the spatially and temporally synchronized RS and COSMIC observations show that their temperatures agree well with each other, especially at 400 hPa. Comparisons of seasonal temperatures and anomalies from the COSMIC and homogenized RS observations suggest that the limited number of COSMIC observations during the spatial matchup may be insufficient to describe the small-scale spatial structure of temperature variations. Furthermore, comparisons of the seasonal temperature anomalies from the RS and 5°×5° gridded COSMIC observations at 400 hPa during the sea ice minimum (SIM) of 2007 and 2012 are also made. The results reveal that similar Arctic temperature variation patterns can be obtained from both RS and COSMIC observations over the land area, while extra information can be further provided from the densely distributed COSMIC observations. Therefore, despite COSMIC observations being unsuitable to describe the Arctic temperatures in the lowest level, they provide a complementary data source to study the Arctic upper-air temperature variations and related climate change.
The air temperature is one of the most important parameters used for monitoring the Arctic climate change. The constellation observing system for meteorology, ionosphere, and climate and Formosa Satellite Mission 3 (COSMIC/FORMOSAT-3) radio occultation (RO) “wet” temperature product (i.e., “wetPrf”) is used to analyze the Arctic air temperature profiles at 925-200 hPa in 2007-2012. The “wet” temperatures are further compared with radiosonde (RS) observations. The results from the spatially and temporally synchronized RS and COSMIC observations show that their temperatures agree well with each other, especially at 400 hPa. Comparisons of seasonal temperatures and anomalies from the COSMIC and homogenized RS observations suggest that the limited number of COSMIC observations during the spatial matchup may be insufficient to describe the small-scale spatial structure of temperature variations. Furthermore, comparisons of the seasonal temperature anomalies from the RS and 5°×5° gridded COSMIC observations at 400 hPa during the sea ice minimum (SIM) of 2007 and 2012 are also made. The results reveal that similar Arctic temperature variation patterns can be obtained from both RS and COSMIC observations over the land area, while extra information can be further provided from the densely distributed COSMIC observations. Therefore, despite COSMIC observations being unsuitable to describe the Arctic temperatures in the lowest level, they provide a complementary data source to study the Arctic upper-air temperature variations and related climate change.
2018, 37(1): 40-49.
doi: 10.1007/s13131-018-1157-9
Abstract:
In this study, numerical prediction of surges associated with a storm was made through the method of lines (MOL) in coordination with the newly proposed RKARMS (4, 4) method for the meghna estuarine region, along the coast of Bangladesh. For this purpose, the vertically integrated shallow water equations (SWEs) in Cartesian coordinates were firstly transformed into ordinary differential equations (ODEs) of initial valued, which were then soloved using the new RKARMS (4, 4) method. Nested grid technique was employed for resolving the complexities of the region of interest with minimum cost. Fresh water discharge through the lower Meghna River was taken into account along the north east corner of the innermost child scheme. Numerical experiments were performed with the severe cyclone on April 1991 that crossed the coast over the study area. Simulated results by the study were found to be in good agreement with some reported data and were found to compare well with the results obtained by the MOL in addition with the classical 4th order Runge-Kutta (RK (4, 4)) method and the standard finite difference method (FDM).
In this study, numerical prediction of surges associated with a storm was made through the method of lines (MOL) in coordination with the newly proposed RKARMS (4, 4) method for the meghna estuarine region, along the coast of Bangladesh. For this purpose, the vertically integrated shallow water equations (SWEs) in Cartesian coordinates were firstly transformed into ordinary differential equations (ODEs) of initial valued, which were then soloved using the new RKARMS (4, 4) method. Nested grid technique was employed for resolving the complexities of the region of interest with minimum cost. Fresh water discharge through the lower Meghna River was taken into account along the north east corner of the innermost child scheme. Numerical experiments were performed with the severe cyclone on April 1991 that crossed the coast over the study area. Simulated results by the study were found to be in good agreement with some reported data and were found to compare well with the results obtained by the MOL in addition with the classical 4th order Runge-Kutta (RK (4, 4)) method and the standard finite difference method (FDM).
2018, 37(1): 50-59.
doi: 10.1007/s13131-018-1158-8
Abstract:
The natural gas generation process is simulated by heating source rocks of the Yacheng Formation, including the onshore-offshore mudstone and coal with kerogens of Type II2-III in the Qiongdongnan Basin. The aim is to quantify the natural gas generation from the Yacheng Formation and to evaluate the geological prediction and kinetic parameters using an optimization procedure based on the basin modeling of the shallow-water area. For this, the hydrocarbons produced have been grouped into four classes (C1, C2, C3 and C4-6). The results show that the onset temperature of methane generation is predicted to occur at 110℃ during the thermal history of sediments since 5.3 Ma by using data extrapolation. The hydrocarbon potential for ethane, propane and heavy gaseous hydrocarbons (C4-6) is found to be almost exhausted at geological temperature of 200℃ when the transformation ratio (TR) is over 0.8, but for which methane is determined to be about 0.5 in the shallow-water area. In contrast, the end temperature of the methane generation in the deep-water area was over 300℃ with a TR over 0.8. It plays an important role in the natural gas exploration of the deep-water basin and other basins in the broad ocean areas of China. Therefore, the natural gas exploration for the deep-water area in the Qiongdongnan Basin shall first aim at the structural traps in the Ledong, Lingshui and Beijiao sags, and in the forward direction of the structure around the sags, and then gradually develop toward the non-structural trap in the deep-water area basin of the broad ocean areas of China.
The natural gas generation process is simulated by heating source rocks of the Yacheng Formation, including the onshore-offshore mudstone and coal with kerogens of Type II2-III in the Qiongdongnan Basin. The aim is to quantify the natural gas generation from the Yacheng Formation and to evaluate the geological prediction and kinetic parameters using an optimization procedure based on the basin modeling of the shallow-water area. For this, the hydrocarbons produced have been grouped into four classes (C1, C2, C3 and C4-6). The results show that the onset temperature of methane generation is predicted to occur at 110℃ during the thermal history of sediments since 5.3 Ma by using data extrapolation. The hydrocarbon potential for ethane, propane and heavy gaseous hydrocarbons (C4-6) is found to be almost exhausted at geological temperature of 200℃ when the transformation ratio (TR) is over 0.8, but for which methane is determined to be about 0.5 in the shallow-water area. In contrast, the end temperature of the methane generation in the deep-water area was over 300℃ with a TR over 0.8. It plays an important role in the natural gas exploration of the deep-water basin and other basins in the broad ocean areas of China. Therefore, the natural gas exploration for the deep-water area in the Qiongdongnan Basin shall first aim at the structural traps in the Ledong, Lingshui and Beijiao sags, and in the forward direction of the structure around the sags, and then gradually develop toward the non-structural trap in the deep-water area basin of the broad ocean areas of China.
2018, 37(1): 60-72.
doi: 10.1007/s13131-018-1159-7
Abstract:
Three comprehensive surveys were performed in the Changjiang (Yangtze River) Estuary (CJE) to understand the biogenic silica (BSi) composition, behavior and budget. It is indicated that the BSi is composed of phytoliths, phytoplankton and sponges; phytolith BSi has 16 forms and account for 23% to 83% of the bulk BSi in the maximum turbidity zone. The budget shows that the major exogenous BSi source in the water column of the CJE is the riverine input, accounting for 95% of the total BSi input. Dominant processes that maintain BSi levels in the water column are the primary production (55 Gmol/a) and the subsequent BSi sedimentation (46 Gmol/a); and the BSi pool produced by the primary production represents two point three times the BSi loading of the Changjiang River and 63% of the BSi output, respectively. The net export (26 Gmol/a) of BSi from the CJE to the East China Sea and Yellow Sea roughly equals the riverine BSi loading. The observed total accumulation of BSi is one point seven times larger than the loading of total BSi output, with 53% to 88% of phytolith BSi and their assemblage, indicating that there has already been a “filter” of terrestrial BSi. The reverse weathering in sediments is an important process for the reactive silica removal in the CJE due to authigenic alterations. It is indicated that the phytolith fluxes in the suspended load represent a significant BSi source in the estuary, and the CJE would act as a net BSi sink.
Three comprehensive surveys were performed in the Changjiang (Yangtze River) Estuary (CJE) to understand the biogenic silica (BSi) composition, behavior and budget. It is indicated that the BSi is composed of phytoliths, phytoplankton and sponges; phytolith BSi has 16 forms and account for 23% to 83% of the bulk BSi in the maximum turbidity zone. The budget shows that the major exogenous BSi source in the water column of the CJE is the riverine input, accounting for 95% of the total BSi input. Dominant processes that maintain BSi levels in the water column are the primary production (55 Gmol/a) and the subsequent BSi sedimentation (46 Gmol/a); and the BSi pool produced by the primary production represents two point three times the BSi loading of the Changjiang River and 63% of the BSi output, respectively. The net export (26 Gmol/a) of BSi from the CJE to the East China Sea and Yellow Sea roughly equals the riverine BSi loading. The observed total accumulation of BSi is one point seven times larger than the loading of total BSi output, with 53% to 88% of phytolith BSi and their assemblage, indicating that there has already been a “filter” of terrestrial BSi. The reverse weathering in sediments is an important process for the reactive silica removal in the CJE due to authigenic alterations. It is indicated that the phytolith fluxes in the suspended load represent a significant BSi source in the estuary, and the CJE would act as a net BSi sink.
2018, 37(1): 73-88.
doi: 10.1007/s13131-017-1118-8
Abstract:
As an active back-arc basin, the Okinawa Trough is located in the southeastern region of the East China Sea shelf and is strongly influenced by the subduction of the Philippine Sea Plate. Major element, trace element and Sr-Nd-Pb isotopic composition data are presented for volcanic rocks from the Iheya Ridge (IR), the middle Okinawa Trough. The IR rocks record large variations in major elements and range from basalts to rhyolites. Similar trace element distribution characteristics together with small variations in 87Sr/86Sr (0.703 862-0.704 884), 144Nd/143Nd (0.512 763-0.512 880) and Pb isotopic ratios, demonstrate that the IR rocks are derived from a similar magma source. The fractional crystallization of olivine, clinopyroxene, plagioclase, and amphibole, as well as accessory minerals, can reasonably explain the compositional variations of these IR rocks. The simulations suggest that approximately 60% and 75% fractionation of an evolved basaltic magma can produce trace element compositions similar to those of the intermediate rocks and acid rocks, respectively. The analysis of their Sr-Nd-Pb isotopic content ratios suggest that the source of the rocks from the IR is close to the depleted mantle (DM) but extends to the enriched mantle (EMII), indicating that the mantle source of these rocks is a mixture between the DM and EMII end members. The simulations show that the source of the IR volcanic rocks can be best interpreted as the result of the mixing of approximately 0.8%-2.0% subduction sediment components and 98.0%-99.2% mantle-derived melts.
As an active back-arc basin, the Okinawa Trough is located in the southeastern region of the East China Sea shelf and is strongly influenced by the subduction of the Philippine Sea Plate. Major element, trace element and Sr-Nd-Pb isotopic composition data are presented for volcanic rocks from the Iheya Ridge (IR), the middle Okinawa Trough. The IR rocks record large variations in major elements and range from basalts to rhyolites. Similar trace element distribution characteristics together with small variations in 87Sr/86Sr (0.703 862-0.704 884), 144Nd/143Nd (0.512 763-0.512 880) and Pb isotopic ratios, demonstrate that the IR rocks are derived from a similar magma source. The fractional crystallization of olivine, clinopyroxene, plagioclase, and amphibole, as well as accessory minerals, can reasonably explain the compositional variations of these IR rocks. The simulations suggest that approximately 60% and 75% fractionation of an evolved basaltic magma can produce trace element compositions similar to those of the intermediate rocks and acid rocks, respectively. The analysis of their Sr-Nd-Pb isotopic content ratios suggest that the source of the rocks from the IR is close to the depleted mantle (DM) but extends to the enriched mantle (EMII), indicating that the mantle source of these rocks is a mixture between the DM and EMII end members. The simulations show that the source of the IR volcanic rocks can be best interpreted as the result of the mixing of approximately 0.8%-2.0% subduction sediment components and 98.0%-99.2% mantle-derived melts.
2018, 37(1): 89-96.
doi: 10.1007/s13131-018-1160-1
Abstract:
Sediment samples obtained from the South Mid-Atlantic Ridge are analyzed by a gas chromatography-mass spectrometer (GC-MS) for the abundances and distributions of hydrocarbons. The hydrocarbons in the samples exhibit a bimodal distribution of n-alkanes and are rich in 3-methylalkanes, 8-methylalkanes and 2, 4, (n-1)-trimethylalkanes, which may be the result of metabolic activity of benthic microorganism. Terpanes, hopanes and steranes are all enriched in the samples, which also support the microbial origin of hydrocarbons in samples. Bitumen and hydrocarbons in the samples show a trend that the contents are the highest in the Samples 22V-TVG10 and 26V-TVG05 collected near hydrothermal areas, and the lowest in samples 22IV-TVG01, 22V-TVG11, and 22V-TVG14 collected far from the hydrothermal areas, which suggest the possible influence on the samples by hydrothermal activity.
Sediment samples obtained from the South Mid-Atlantic Ridge are analyzed by a gas chromatography-mass spectrometer (GC-MS) for the abundances and distributions of hydrocarbons. The hydrocarbons in the samples exhibit a bimodal distribution of n-alkanes and are rich in 3-methylalkanes, 8-methylalkanes and 2, 4, (n-1)-trimethylalkanes, which may be the result of metabolic activity of benthic microorganism. Terpanes, hopanes and steranes are all enriched in the samples, which also support the microbial origin of hydrocarbons in samples. Bitumen and hydrocarbons in the samples show a trend that the contents are the highest in the Samples 22V-TVG10 and 26V-TVG05 collected near hydrothermal areas, and the lowest in samples 22IV-TVG01, 22V-TVG11, and 22V-TVG14 collected far from the hydrothermal areas, which suggest the possible influence on the samples by hydrothermal activity.
2018, 37(1): 97-107.
doi: 10.1007/s13131-017-1123-y
Abstract:
The size-fractionated biomass and primary production of phytoplankton, and the influence of environmental factors on it were studied in the Dongsha natural gas hydrate zone of the northern South China Sea in May 2013. Low nutrient, low chlorophyll a (Chl a) and primary productivity characteristics were found in these waters. The phenomena of subsurface Chl a maximum layers (SCMLs) and primary production maximum layers (SPMLs) were observed in the Dongsha waters. There were significant differences in the size-fractionated biomass and primary production that showed picophytoplankton > nanophytoplankton > microphytoplankton in terms of biomass and degree of contribution to production. Vertical biomass distribution indicated there were considerable differences among different phytoplankton within the euphotic zone (Zeu) in spring. For example, microphytoplankton was distributed evenly in the euphotic layer and nanophytoplankton was mainly distributed in the subsurface or in the middle of the euphotic layer, while picophytoplankton was mainly distributed in the middle or bottom of the euphotic layer. Smaller cell size and larger relative surface area allow picophytoplankton to benefit from nutrient competition and to hold a dominant position in the tropical oligotrophic waters of low latitudes. There was a positive correlation between size-fractionated biomass and temperature with pH and a negative correlation between size-fractionated biomass and silicate with phosphate. There was a positive correlation between size-fractionated primary production and temperature and a negative correlation between size-fractionated biomass and salinity with phosphate. Phosphate was an important factor influencing the size structure of phytoplankton. Meanwhile, irradiation and the euphotic layer were more important in regulating the vertical distribution of size-fractionated phytoplankton in the Dongsha natural gas hydrate zone.
The size-fractionated biomass and primary production of phytoplankton, and the influence of environmental factors on it were studied in the Dongsha natural gas hydrate zone of the northern South China Sea in May 2013. Low nutrient, low chlorophyll a (Chl a) and primary productivity characteristics were found in these waters. The phenomena of subsurface Chl a maximum layers (SCMLs) and primary production maximum layers (SPMLs) were observed in the Dongsha waters. There were significant differences in the size-fractionated biomass and primary production that showed picophytoplankton > nanophytoplankton > microphytoplankton in terms of biomass and degree of contribution to production. Vertical biomass distribution indicated there were considerable differences among different phytoplankton within the euphotic zone (Zeu) in spring. For example, microphytoplankton was distributed evenly in the euphotic layer and nanophytoplankton was mainly distributed in the subsurface or in the middle of the euphotic layer, while picophytoplankton was mainly distributed in the middle or bottom of the euphotic layer. Smaller cell size and larger relative surface area allow picophytoplankton to benefit from nutrient competition and to hold a dominant position in the tropical oligotrophic waters of low latitudes. There was a positive correlation between size-fractionated biomass and temperature with pH and a negative correlation between size-fractionated biomass and silicate with phosphate. There was a positive correlation between size-fractionated primary production and temperature and a negative correlation between size-fractionated biomass and salinity with phosphate. Phosphate was an important factor influencing the size structure of phytoplankton. Meanwhile, irradiation and the euphotic layer were more important in regulating the vertical distribution of size-fractionated phytoplankton in the Dongsha natural gas hydrate zone.
2018, 37(1): 108-114.
doi: 10.1007/s13131-018-1161-0
Abstract:
The popular methods to estimate wave height with high-frequency (HF) radar depend on the integration over the second-order spectral region and thus may come under from even not strong external interference. To improve the accuracy and increase the valid detection range of the wave height measurement, particularly by the small-aperture radar, it is turned to singular peaks which often exceed the power of other frequency components. The power of three kinds of singular peaks, i.e., those around ±1, ±√2 and ±1/√2 times the Bragg frequency, are retrieved from a one-month-long radar data set collected by an ocean state monitoring and analyzing radar, model S (OSMAR-S), and in situ buoy records are used to make some comparisons. The power response to a wave height is found to be described with a new model quite well, by which obvious improvement on the wave height estimation is achieved. With the buoy measurements as reference, a correlation coefficient is increased to 0.90 and a root mean square error (RMSE) is decreased to 0.35 m at the range of 7.5 km compared with the results by the second-order method. The further analysis of the fitting performance across range suggests that the √2 peak has the best fit and maintains a good performance as far as 40 km. The correlation coefficient is 0.78 and the RMSE is 0.62 m at 40 km. These results show the effectiveness of the new empirical method, which opens a new way for the wave height estimation with the HF radar.
The popular methods to estimate wave height with high-frequency (HF) radar depend on the integration over the second-order spectral region and thus may come under from even not strong external interference. To improve the accuracy and increase the valid detection range of the wave height measurement, particularly by the small-aperture radar, it is turned to singular peaks which often exceed the power of other frequency components. The power of three kinds of singular peaks, i.e., those around ±1, ±√2 and ±1/√2 times the Bragg frequency, are retrieved from a one-month-long radar data set collected by an ocean state monitoring and analyzing radar, model S (OSMAR-S), and in situ buoy records are used to make some comparisons. The power response to a wave height is found to be described with a new model quite well, by which obvious improvement on the wave height estimation is achieved. With the buoy measurements as reference, a correlation coefficient is increased to 0.90 and a root mean square error (RMSE) is decreased to 0.35 m at the range of 7.5 km compared with the results by the second-order method. The further analysis of the fitting performance across range suggests that the √2 peak has the best fit and maintains a good performance as far as 40 km. The correlation coefficient is 0.78 and the RMSE is 0.62 m at 40 km. These results show the effectiveness of the new empirical method, which opens a new way for the wave height estimation with the HF radar.
2018, 37(1): 115-129.
doi: 10.1007/s13131-018-1162-z
Abstract:
Haiyang-2A (HY-2A) is China’s first ocean dynamic environment satellite and the radar altimeter is one of its main payloads. One of the main purposes of the radar altimeter is to measure the sea surface height (SSH). The SSH determined from the altimeter range measurements includes some range and geophysical corrections. These corrections largely affect the accuracy of the SSH measurements. The range and the geophysical corrections are reprocessed and the altimeter waveforms in HY-2A sensor interim geophysical data set records (S-IGDR) are retracked from June 1, 2014 to June 14, 2014, and the accuracy of the reprocessed SSH measurements is evaluated. The methods of the range and geophysical corrections used to reprocess HY-2A altimeter data are validated by using these methods to reprocess the Jason-2 range and geophysical corrections and comparing the results with the range and geophysical corrections in Jason-2 geophysical dataset records (GDR) product. A crossover analysis is used to evaluate the accuracy of the reprocessed HY-2A SSH measurements. The standard deviation (STD) of the crossover SSH differences for HY-2A is around 4.53 cm while the STD of the SSH differences between HY-2A and Jason-2 is around 5.22 cm. The performance of the reprocessed HY-2A SSH measurements is significantly improved with respect to the SSH measurements derived from HY-2A interim geophysical dataset records (IGDR) product. The 2015-2016 El Niño has been the strongest El Niño event since 1997-1998. The range and the geophysical corrections in HY-2A IGDR are reprocessed and sea level anomalies are used to monitor the 2015-2016 El Niño. The results show that the HY-2A altimeter can well observe the 2015-2016 El Niño.
Haiyang-2A (HY-2A) is China’s first ocean dynamic environment satellite and the radar altimeter is one of its main payloads. One of the main purposes of the radar altimeter is to measure the sea surface height (SSH). The SSH determined from the altimeter range measurements includes some range and geophysical corrections. These corrections largely affect the accuracy of the SSH measurements. The range and the geophysical corrections are reprocessed and the altimeter waveforms in HY-2A sensor interim geophysical data set records (S-IGDR) are retracked from June 1, 2014 to June 14, 2014, and the accuracy of the reprocessed SSH measurements is evaluated. The methods of the range and geophysical corrections used to reprocess HY-2A altimeter data are validated by using these methods to reprocess the Jason-2 range and geophysical corrections and comparing the results with the range and geophysical corrections in Jason-2 geophysical dataset records (GDR) product. A crossover analysis is used to evaluate the accuracy of the reprocessed HY-2A SSH measurements. The standard deviation (STD) of the crossover SSH differences for HY-2A is around 4.53 cm while the STD of the SSH differences between HY-2A and Jason-2 is around 5.22 cm. The performance of the reprocessed HY-2A SSH measurements is significantly improved with respect to the SSH measurements derived from HY-2A interim geophysical dataset records (IGDR) product. The 2015-2016 El Niño has been the strongest El Niño event since 1997-1998. The range and the geophysical corrections in HY-2A IGDR are reprocessed and sea level anomalies are used to monitor the 2015-2016 El Niño. The results show that the HY-2A altimeter can well observe the 2015-2016 El Niño.
