Search Results
Combining In Situ and Satellite Observations to Retrieve Salinity and Density at the Ocean Surface(Mercator) tracer power spectral density: (a) SSD, (b) SSS, and (c) SST. 4. Discussion and conclusions The combination of satellite and in situ data represents a powerful approach to extract as much information as possible from available observations and to describe better the environmental parameters characterizing the ocean surface with respect to monoparameter-/monosensor-based approaches. Synergistic approaches are especially needed if aiming to resolve the signals associated with ocean mesoscale
(Mercator) tracer power spectral density: (a) SSD, (b) SSS, and (c) SST. 4. Discussion and conclusions The combination of satellite and in situ data represents a powerful approach to extract as much information as possible from available observations and to describe better the environmental parameters characterizing the ocean surface with respect to monoparameter-/monosensor-based approaches. Synergistic approaches are especially needed if aiming to resolve the signals associated with ocean mesoscale
introduce a test showing how much vertical resolution is needed in the future to maintain an unbiased global subsurface observation system. This study is summarized in section 4 . 2. Data and methods In situ ocean subsurface temperature observations within 1966–2010 from the World Ocean Database 2009 (WOD09; Boyer et al. 2009 ) were used in this study. The data were collected with different instruments, including XBT, Argo (PFL in the WOD09 dataset as in Fig. 1 ), ocean station data (OSD), undulating
introduce a test showing how much vertical resolution is needed in the future to maintain an unbiased global subsurface observation system. This study is summarized in section 4 . 2. Data and methods In situ ocean subsurface temperature observations within 1966–2010 from the World Ocean Database 2009 (WOD09; Boyer et al. 2009 ) were used in this study. The data were collected with different instruments, including XBT, Argo (PFL in the WOD09 dataset as in Fig. 1 ), ocean station data (OSD), undulating
altimetry data have been increasingly applied toward coastal oceans in recent years (e.g., Vignudelli et al. 2011 ; Liu et al. 2012 ). Second, besides the Kuroshio, multiscale eddies play an important role in ocean circulation variability in China’s marginal seas, as seen from both satellite-derived geostrophic currents (e.g., Liu et al. 2008 ) and in situ observations (e.g., Yuan et al. 1998 ; Liu et al. 2000 ). Small errors and uncertainties in geostrophic velocity calculations may affect the
altimetry data have been increasingly applied toward coastal oceans in recent years (e.g., Vignudelli et al. 2011 ; Liu et al. 2012 ). Second, besides the Kuroshio, multiscale eddies play an important role in ocean circulation variability in China’s marginal seas, as seen from both satellite-derived geostrophic currents (e.g., Liu et al. 2008 ) and in situ observations (e.g., Yuan et al. 1998 ; Liu et al. 2000 ). Small errors and uncertainties in geostrophic velocity calculations may affect the
Performance Statistics of a Real-Time Pacific Ocean Weather Sensor Network. Further technical details and validation of wave height measurements can be found in previous work by Raghukumar et al. (2019) . In this analysis, the performance of the Spotter buoy measurement of significant wave height was characterized relative to other methods. In addition to in situ buoys, numerical simulations and satellite observations are used to estimate wave height in the open ocean. However, each method relies upon different assumptions and samples the field over different periods (i
. Further technical details and validation of wave height measurements can be found in previous work by Raghukumar et al. (2019) . In this analysis, the performance of the Spotter buoy measurement of significant wave height was characterized relative to other methods. In addition to in situ buoys, numerical simulations and satellite observations are used to estimate wave height in the open ocean. However, each method relies upon different assumptions and samples the field over different periods (i
-based chemical analysis in samples returned to shipboard or shore-based laboratories. During the past two decades, automated chemical analyzers, based on laboratory procedures, have been developed that allow in situ measurements of nitrate to be made on ocean moorings ( Jannasch et al. 1994 ; Sakamoto et al. 2004 ; Körtzinger et al. 2008 ). These instruments are relatively complex and sustained (multiyear) operations have not been widely reported. More recently, optical nitrate detectors have been
-based chemical analysis in samples returned to shipboard or shore-based laboratories. During the past two decades, automated chemical analyzers, based on laboratory procedures, have been developed that allow in situ measurements of nitrate to be made on ocean moorings ( Jannasch et al. 1994 ; Sakamoto et al. 2004 ; Körtzinger et al. 2008 ). These instruments are relatively complex and sustained (multiyear) operations have not been widely reported. More recently, optical nitrate detectors have been
Optimizing Mooring Placement to Constrain Southern Ocean Air–Sea Fluxespaucity of in situ flux observations, which are challenging to collect because of high winds and high sea state, air–sea fluxes are not well observed in the Southern Ocean, and associated reanalysis data have considerable uncertainties ( Bourassa et al. 2013 ; Gille et al. 2016 ; Potter et al. 2018 ; Swart et al. 2019 ). The Southern Ocean Observing System (SOOS) was established in 2011, with the mission of facilitating the design and implementation of a comprehensive and multidisciplinary
paucity of in situ flux observations, which are challenging to collect because of high winds and high sea state, air–sea fluxes are not well observed in the Southern Ocean, and associated reanalysis data have considerable uncertainties ( Bourassa et al. 2013 ; Gille et al. 2016 ; Potter et al. 2018 ; Swart et al. 2019 ). The Southern Ocean Observing System (SOOS) was established in 2011, with the mission of facilitating the design and implementation of a comprehensive and multidisciplinary
12-month (6 month at starting and ending points) running average has been applied for plotting. The paper is organized as follows: SST datasets from available SST analyses and evaluation datasets from ocean profile measurements and satellite-based observations are described in section 2 . The evaluation datasets are compared with in situ observations from ships, buoys, and Argo floats to ensure their quality in section 3 . The SST analyses are evaluated against those ocean profile measurements
12-month (6 month at starting and ending points) running average has been applied for plotting. The paper is organized as follows: SST datasets from available SST analyses and evaluation datasets from ocean profile measurements and satellite-based observations are described in section 2 . The evaluation datasets are compared with in situ observations from ships, buoys, and Argo floats to ensure their quality in section 3 . The SST analyses are evaluated against those ocean profile measurements
Ocean using 2187 hydrocast stations, far less than were available to Johnson and McPhaden in the Pacific (15 693 stations) or Zhang et al. in the Atlantic (86 131 stations). Qu and Meyers (2005) estimated currents based on in situ data, but they restricted their analysis to the narrow region between Indonesia and Australia, where observations are relatively dense. Also, Qu and Meyers had to calculate density from temperature and a mean T / S relationship because the number of salinity
Ocean using 2187 hydrocast stations, far less than were available to Johnson and McPhaden in the Pacific (15 693 stations) or Zhang et al. in the Atlantic (86 131 stations). Qu and Meyers (2005) estimated currents based on in situ data, but they restricted their analysis to the narrow region between Indonesia and Australia, where observations are relatively dense. Also, Qu and Meyers had to calculate density from temperature and a mean T / S relationship because the number of salinity
application of these fields emphasizes the need to conduct studies to evaluate the accuracies and determine the uncertainty characteristics of the variables in the reanalysis fields. The assimilation of a very wide range of measurements, both in situ and remotely sensed, means the determination of the veracity of the reanalysis fields requires comparisons with independent data that have been withheld from the assimilation procedures. In this study, we use in situ and remotely sensed ocean and atmospheric
application of these fields emphasizes the need to conduct studies to evaluate the accuracies and determine the uncertainty characteristics of the variables in the reanalysis fields. The assimilation of a very wide range of measurements, both in situ and remotely sensed, means the determination of the veracity of the reanalysis fields requires comparisons with independent data that have been withheld from the assimilation procedures. In this study, we use in situ and remotely sensed ocean and atmospheric
. 2011 ; Liang et al. 2019 ; Iizuka and Nakamura 2019 ; Dragaud et al. 2019 ; Aumann et al. 2020 ; Ciani et al. 2020 ). The reliability of SST products strongly depends on the availability of SST observations, among other factors. In situ SST observations are available as early as 1772 in the International Comprehensive Ocean–Atmosphere Dataset (ICOADS; Freeman et al. 2017 ). At that time, SST observations were used by commercial sailing ships to locate the Gulf Stream ( Franklin et al. 1768
. 2011 ; Liang et al. 2019 ; Iizuka and Nakamura 2019 ; Dragaud et al. 2019 ; Aumann et al. 2020 ; Ciani et al. 2020 ). The reliability of SST products strongly depends on the availability of SST observations, among other factors. In situ SST observations are available as early as 1772 in the International Comprehensive Ocean–Atmosphere Dataset (ICOADS; Freeman et al. 2017 ). At that time, SST observations were used by commercial sailing ships to locate the Gulf Stream ( Franklin et al. 1768
