scholarly journals Blending Sea Surface Temperatures from Multiple Satellites and In Situ Observations for Coastal Oceans

2009 ◽  
Vol 26 (7) ◽  
pp. 1415-1426 ◽  
Author(s):  
Yi Chao ◽  
Zhijin Li ◽  
John D. Farrara ◽  
Peter Hung
Keyword(s):  
Coastal Upwelling ◽  
Sea Surface ◽  
High Temporal Resolution ◽  
Potential Applications ◽  
Independent Observations ◽  
Ocean Flows ◽  
In Situ Observations ◽  
Continuous Fields ◽  
Mean Square Errors

Abstract A two-dimensional variational data assimilation (2DVAR) method for blending sea surface temperature (SST) data from multiple observing platforms is presented. This method produces continuous fields and has the capability of blending multiple satellite and in situ observations. In addition, it allows specification of inhomogeneous and anisotropic background correlations, which are common features of coastal ocean flows. High-resolution (6 km in space and 6 h in time) blended SST fields for August 2003 are produced for a region off the California coast to demonstrate and evaluate the methodology. A comparison of these fields with independent observations showed root-mean-square errors of less than 1°C, comparable to the errors in conventional SST observations. The blended SST fields also clearly reveal the finescale spatial and temporal structures associated with coastal upwelling, demonstrating their utility in the analysis of finescale flows. With the high temporal resolution, the blended SST fields are also used to describe the diurnal cycle. Potential applications of this SST blending methodology in other coastal regions are discussed.

scholarly journals Seawater Salinity Estimating Module Based on the Sound Velocity Measurements

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
А. P. Tolstosheev ◽  
E. G. Lunev ◽  
S. V. Motyzhev ◽  
V. Z. Dykman ◽  
◽  
...  
Keyword(s):  
Sound Velocity ◽  
Ocean Dynamics ◽  
Sea Surface ◽  
Temperature Measurements ◽  
Velocity Measurements ◽  
In Situ Tests ◽  
Salinity Field ◽  
In Situ Observations

Purpose. Reliability of knowledge about the ocean dynamics and climate variability is largely limited for lack of systematic in situ observations of the sea surface layer salinity, which is one of the basic hydrological parameters determining circulation and stratification of the water masses. The study is aimed at developing an autonomous device for long-term monitoring of salinity in the seawater upper layer. Methods and Results. One of the most effective tools for in situ observations of the ocean upper layer is the global network of surface drifting buoys – drifters. At present, the network consists of more than 1500 buoys, but only a few of them provide sea surface salinity observations within the framework of a limited number of pilot experiments. In the drifters, salinity is calculated by the traditional method using the results of the electrical conductivity and temperature measurements. There are a few problems related both to the principle of determining salinity by this method and to providing long-term stable running of conductivity sensors under the conditions of pollution and biological fouling. A drifter equipped with the module for the sound velocity and temperature measurements used for calculating salinity by an alternative method just aboard the drifter, was developed in Marine Hydrophysical Institute, Russian Academy of Sciences. The sound velocity and temperature module includes a specially designed time-of-flight sound velocity sensor with the fixed base and a quartz temperature sensor. In course of two years, numerous laboratory and in situ tests of several prototypes of the sound velocity and temperature module were performed. The laboratory tests showed that the repeatability limits for the results of the sound velocity measurements in the distilled water were 0.02 m/s. According to the data of the long-term in situ tests performed at intensive biological fouling, the error of salinity estimation resulted from of the sound velocity and temperature measurements were within 0.05 ‰. This result permits to expect that the sound velocity and temperature module parameters will remain stable in real conditions of long-term autonomous operation. Conclusions. The obtained results make it possible to recommend application of the drifters equipped with the modules for the sound velocity and temperature measurements as an effective tool for regular operational monitoring of the salinity field of the upper sea layer.

scholarly journals Validation of satellite-based sea surface temperature products against in situ observations off the western coast of Sumatra

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Qoosaku Moteki
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Optimal Interpolation ◽  
Western Coast ◽  
Mean Square ◽  
Ensemble Spread ◽  
In Situ Observations ◽  
High Clouds

AbstractThis study validated the sea surface temperature (SST) datasets from the Group for High-Resolution SST Multi Product Ensemble (GMPE), National Oceanic and Atmospheric Administration (NOAA) Optimal Interpolation (OI) SST version 2 and 2.1 (OIv2 and OIv2.1), and Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) in the area off the western coast of Sumatra against in situ observations. Furthermore, the root mean square differences (RMSDs) of OIv2, OIv2.1, and ECCO2 were investigated with respect to GMPE, whose small RMSD < 0.2 K against in situ observations confirmed its suitability as a reference. Although OIv2 showed a large RMSD (1–1.5 K) with a significant negative bias, OIv2.1 (RMSD < 0.4 K) improved remarkably. In the average SST distributions for December 2017, the differences among the 4 datasets were significant in the areas off the western coast of Sumatra, along the southern coast of Java, and in the Indonesian inland sea. These results were consistent with the ensemble spread distribution obtained with GMPE. The large RMSDs of OIv2 corresponded to high clouds, and it was suggested that the change in the satellites used for SST estimation contributed to the improvement in OIv2.1.

scholarly journals A comparison of atmospheric CO<sub>2</sub> flux signals obtained from GEOS-Chem flux inversions constrained by in situ or GOSAT observations

2018 ◽  
Author(s):  
Saroja M. Polavarapu ◽  
Feng Deng ◽  
Brendan Byrne ◽  
Dylan B. A. Jones ◽  
Micheal Neish
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Co2 Flux ◽  
Boreal Summer ◽  
Spatial Structures ◽  
Independent Observations ◽  
In Situ Observations ◽  
The Difference ◽  
Observing Systems

Abstract. The CO2 flux signal is defined as the difference of the four-dimensional CO2 field obtained by integrating an atmospheric transport model with posterior fluxes and that obtained with prior fluxes. It is a function of both the model and the prior fluxes and it can provide insight into how posterior fluxes inform CO2 distributions. Here, we use the GEOS-Chem transport model constrained by either GOSAT or in situ observations to obtain two sets of posterior flux estimates in order to compare the flux signals obtained from the two different observing systems. Flux signals are also computed using two different models. The global flux signal in the troposphere primarily reflects the northern extratropics whereas the global flux signal in the stratosphere mainly reflects tropical contributions. While both observing systems constrain the global budget for 2010 equally well, stronger seasonal variations of the flux signal are obtained with GOSAT. Posterior CO2 distributions obtained with in situ observations better agree with TCCON measurements over an 18-month time period, but GOSAT-informed posterior fluxes better constrain the seasonal cycle at northern extratropical sites. Zonal standard deviations of the flux signal exceed the minimal value (defined by uncertainty in meteorological analyses) through most of the year when GOSAT observations are used, but when in situ observations are used, the minimum value is exceeded only in boreal summer. This indicates a potential for flux estimates constrained by GOSAT data to retrieve spatial structures within a zonal band throughout the year in the tropics and through most of the year in the northern extratropics. Verification of such spatial structures will require a dense network of independent observations.

Time-series within automatically generated ROIs from wildlife cameras are well able to explain variability in forest phenology on a temperature gradient

2020 ◽  
Author(s):  
Lars Uphus ◽  
Annette Menzel
Keyword(s):  
Time Series ◽  
Temperature Gradient ◽  
Time Lapse ◽  
Relevant Information ◽  
High Temporal Resolution ◽  
Deciduous Species ◽  
Multiple Thresholds ◽  
In Situ Observations ◽  
Wildlife Cameras

&lt;p&gt;Using RGB camera data (e.g. webcams, wildlife cameras) has great potential to measure forest phenology over climate gradients, because of its very high temporal resolution, while at the same time being more objective and less time consuming than in situ observations. To make images useful for the purpose of measuring phenological events, such as Start of Season (SOS) and End of Season (EOS), there is need to derive Regions of Interest (ROI) objectively and (semi-)automatically. In order to answer this need, Bothmann et al. (2017) proposed a method which randomly sets a number of pinpricks in the image and calculates how greenness over time from all other pixels correlates to these different pinpricks. Subsequently, ROIs are created by discarding the pixels with low correlation, using multiple thresholds.&amp;#160;Despite its advantage of being automated and more objective compared to prevailing expert-based ROIs, and therefore its potential applicability for phenological research using a large amount of cameras, the method has not been reproduced for this purpose so far. Therefore, we assess here how well this method is able to separate foliage of different deciduous species from evergreens and phenologically irrelevant components in time-lapse wildlife camera data and in that way how suitable it is in explaining variation in phenology over a temperature gradient. We used 73 Cuddleback wildlife cameras troughout Bavaria which were installed within nine quadrants of 6*6 kilometers spanning a temperature gradient of 2.5&amp;#176;C. Hourly taken images of deciduous forests in spring, summer and autumn 2019 were analysed. Half of them were facing canopy, and half of them were facing understory. We applied the principles of the method from Bothmann et al. (2017) and assigned the best matching ROI to foliage of &lt;em&gt;Fagus sylvatica&lt;/em&gt; or other deciduous species. Within this ROI, mean Green Chromatic Coordinate (GCC), a greenness index, over all pixels within the ROI, was derived per time-stamp. Afterwards, a time-series was calculated on these GCC values and with a suitable combination of curve-fitting techniques, SOS and EOS were derived, expressed in Day of Year (DOY). We compared these SOS and EOS dates with weekly in situ observations of spring and autumn phenology, which were taken in the same quadrants. Despite that Bothmann's method was developed on a single tower-mounted scientific webcam which viewed on canopy from above, while we made use of wildlife cameras at 73 different locations facing either understory perpendicular or canopy from below, it was able to distinguish &lt;em&gt;F. sylvatica&lt;/em&gt; and other deciduous foliage from phenologically less relevant information. Time-series derived from these ROIs were able to explain variability in phenology between understory and canopy and over the temperature gradient similarly and supplementary to in situ observations.&amp;#160;&lt;/p&gt;

Could an ocean climate atlas generated by a model compete with an observational one?

2020 ◽  
Author(s):  
Georgy I. Shapiro ◽  
Jose M. Gonzalez-Ondina ◽  
Xavier Francis ◽  
Hyee S. Lim ◽  
Ali Almehrezi
Keyword(s):  
Standard Error ◽  
Sea Surface ◽  
Vertical Profiles ◽  
Individual Data ◽  
Model Simulations ◽  
Ocean Climate ◽  
Data Points ◽  
In Situ Observations ◽  
The Mean

&lt;p&gt;Modern numerical ocean models have matured over the last decades and are able to provide accurate fore- and hind-cast of the ocean state. The most accurate data could be obtained from the reanalysis where the model run in a hindcast mode with assimilation of available observational data. An obvious benefit of model simulation is that it provides the spatial density and temporal resolution which cannot be achieved by in-situ observations or satellite derived measurements. It is not unusual that even a relatively small area of the ocean model can have in access of 100,000 nodes in the horizontal, each containing vertical profiles of temperature, salinity, velocity and other ocean parameters with a temporal resolution theoretically as high as a few minutes. Remotely sensed (satellite) observations of sea surface temperature can compete with the models in terms of spatial resolution, however they only produce data at the sea surface not the vertical profiles. On the other hand, in-situ observations have a benefit of being much more precise than model simulations. For instance a widely used CTD profiler SBE 911plus has accuracy of about 0.001 &amp;#176;C, which is not achievable by models.&lt;/p&gt;&lt;p&gt;In the creation of a climatic atlas the higher accuracy of individual profiles provided by in-situ measurements may become less beneficial. Assuming the normal distribution of data at each location, the standard error of the mean (SEM) is calculated as SE=S/SQRT(N), where S is the standard deviation of individual data points around the mean, and N is the number of data points. The climatic data are obtained by averaging a large number of individual data points, and here the benefit of having more data points may become a greater advantage than the accuracy of a single observation. &amp;#160;&lt;/p&gt;&lt;p&gt;In this study we have created an ocean climate atlas for the northern part of the Indian Ocean including the Red Sea and the Arabian Gulf using model generated data. The data were taken from Copernicus Marine Environment Monitoring Service (CMEMS) reanalysis product GLOBAL_REANALYSIS_PHY_001_030 with 1/12&amp;#176; horizontal resolution and 50 vertical levels for the period 1998 to 2017. The model component is the NEMO platform driven at the surface by ECMWF ERA-Interim reanalysis. The model assimilates along track altimeter data, satellite Sea Surface Temperature, as well as in-situ temperature and salinity vertical profiles where available.&amp;#160;The monthly data from CMEMS were then averaged over 20 years to produce an atlas at the surface, 10, 20, 30, 75, 100, 125, 150, 200, 250, 300, 400, and 500 m depths. &amp;#160;The standard error of the mean has been calculated for each point and each depth level on the native grid (1/12 degree).&lt;/p&gt;&lt;p&gt;The atlas based on model simulations was compared with the latest version of the World Ocean Atlas (WOA)&amp;#160; 2018 published by the NCEI.&amp;#160; WOA has objectively analysed climatological mean fields on a &amp;#188; &amp;#160;degree grid. The differences between the mean values and SEMs from observational and simulated atlases are analysed, and the potential causes of mismatch are discussed.&lt;/p&gt;

scholarly journals Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

2019 ◽  
Vol 11 (7) ◽  
pp. 750 ◽  
Author(s):  
Emmanuel Dinnat ◽  
David Le Vine ◽  
Jacqueline Boutin ◽  
Thomas Meissner ◽  
Gary Lagerloef
Keyword(s):  
Dielectric Constant ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
In Situ Observations ◽  
The Impact

Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency’s (ESA’s) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration’s (NASA’s) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from the Argo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS. We also identify the impact of the corrections for atmospheric effects recently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.

scholarly journals Validation of Satellite Sea Surface Temperatures and Long-Term Trends in Korean Coastal Regions over Past Decades (1982–2018)

2020 ◽  
Vol 12 (22) ◽  
pp. 3742
Author(s):  
Eun-Young Lee ◽  
Kyung-Ae Park
Keyword(s):  
Cold Water ◽  
Sea Surface ◽  
Tidal Mixing ◽  
The Yellow Sea ◽  
Coastal Regions ◽  
Water Temperature Data ◽  
Long Term Trends ◽  
Mean Square Errors

Validation of daily Optimum Interpolation Sea Surface Temperature (OISST) data from 1982 to 2018 was performed by comparison with quality-controlled in situ water temperature data from Korea Meteorological Administration moored buoys and Korea Oceanographic Data Center observations in the coastal regions around the Korean Peninsula. In contrast to the relatively high accuracy of the SSTs in the open ocean, the SSTs of the coastal regions exhibited large root-mean-square errors (RMSE) ranging from 0.75 K to 1.99 K and a bias ranging from −0.51 K to 1.27 K, which tended to be amplified towards the coastal lines. The coastal SSTs in the Yellow Sea presented much higher RMSE and bias due to the appearance of cold water on the surface induced by vigorous tidal mixing over shallow bathymetry. The long-term trends of OISSTs were also compared with those of in situ water temperatures over decades. Although the trends of OISSTs deviated from those of in situ temperatures in coastal regions, the spatial patterns of the OISST trends revealed a similar structure to those of in situ temperature trends. The trends of SSTs using satellite data explained about 99% of the trends in in situ temperatures in offshore regions (>25 km from the shoreline). This study discusses the limitations and potential of global SSTs as well as long-term SST trends, especially in Korean coastal regions, considering diverse applications of satellite SSTs and increasing vulnerability to climate change.

scholarly journals The 25-Day-Period Large-Scale Oscillations in the Argentine Basin Revisited

2005 ◽  
Vol 35 (8) ◽  
pp. 1473-1479 ◽  
Author(s):  
Chang-Kou Tai ◽  
Lee-Lueng Fu
Keyword(s):  
Simulation Study ◽  
Large Scale ◽  
Space Time ◽  
Sea Surface ◽  
Time Sampling ◽  
Numerical Studies ◽  
In Situ Observations ◽  
Ocean Topography ◽  
Argentine Basin

Abstract From sea surface height measurements made by the Ocean Topography Experiment (TOPEX)/Poseidon satellite, Fu et al. found and described large-scale oscillations at the period of 25 days in the Argentine Basin of the South Atlantic Ocean. These oscillations were previously hinted at by in situ observations. Only the extensive space–time sampling capability of TOPEX/Poseidon, however, was able to give a complete description of the phenomenon as a counterclockwise-rotating dipole centered at 45°S, 317°E over the Zapiola Rise. Fu et al. also undertook theoretical and numerical studies to suggest that the phenomenon is a resonantly excited barotropic normal mode of the locally closed f/H contour. In a simulation study, however, they also found that the space–time smoothing scheme employed would probably lower the amplitude of the estimated phenomenon by 30%–40%. By reprocessing the data using a different method and showing the amplitude to be almost 2 times as large, in this note it is confirmed that this is indeed the case. The original 5-yr study has also been extended to nearly 10 yr, demonstrating that the same phenomenon has persisted for almost 10 yr.

Sign in / Sign up

Export Citation Format

Share Document