scholarly journals Optimal Configuration Method of Sampling Points Based on Variability of Sea Surface Temperature

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Chang Liu ◽  
Yuning Lei ◽  
Feng Gao ◽  
Meizhen Zhao
Keyword(s):  
Spatial Variability ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Optimal Configuration ◽  
In Situ Observation ◽  
Distribution Method ◽  
Sampling Points ◽  
Compression Sensing

In situ observation is one of the most direct and efficient ways to understand the ocean, but it is usually limited in terms of spatial and temporal coverage. The determination of optimal sampling strategies that effectively utilize available resources to maximize the information content of the collected ocean data is becoming an open problem. The historical sea surface temperature (SST) dataset contains the spatial variability information of SST, and this prior knowledge can be used to optimize the configuration of sampling points. Here, a configuration method of sampling points based on the variability of SST is studied. Firstly, in order to get the spatial variability of SST in the ocean field to be sampled, the historical SST data of the field is analyzed. Then, K-means algorithm is used to cluster the subsampled fields to make the configuration of sampling points more suitable. Finally, to evaluate the sampling performance of the new configuration method of sampling points, the SST field is reconstructed by the method based on compression sensing algorithm. Results show that the proposed optimal configuration method of sampling points significantly outperforms the traditional random sampling points distribution method in terms of reconstruction accuracy. These results provide a new method for configuring sampling points of ocean in situ observation with limited resources.

scholarly journals Assessment and Improvement of Global Gridded Sea Surface Temperature Datasets in the Yellow Sea Using In Situ Ocean Buoy and Research Vessel Observations

2020 ◽  
Vol 12 (5) ◽  
pp. 759
Author(s):  
Kyungman Kwon ◽  
Byoung-Ju Choi ◽  
Sung-Dae Kim ◽  
Sang-Ho Lee ◽  
Kyung-Ae Park
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Yellow Sea ◽  
Bias Correction ◽  
Sea Surface ◽  
The Yellow Sea ◽  
In Situ Observation ◽  
Observation Data ◽  
Warm Bias

The sea surface temperature (SST) is essential data for the ocean and atmospheric prediction systems and climate change studies. Five global gridded sea surface temperature products were evaluated with independent in situ SST data of the Yellow Sea (YS) from 2010 to 2013 and the sources of SST error were identified. On average, SST from the gridded optimally interpolated level 4 (L4) datasets had a root mean square difference (RMSD) of less than 1 °C compared to the in situ observation data of the YS. However, the RMSD was relatively high (2.3 °C) in the shallow coastal region in June and July and this RMSD was mostly attributed to the large warm bias (>2 °C). The level 3 (L3) SST data were frequently missing in early summer because of frequent sea fog formation and a strong (>1.2 °C/12 km) spatial temperature gradient across the tidal mixing front in the eastern YS. The missing data were optimally interpolated from the SST observation in offshore warm water and warm biased SST climatology in the region. To fundamentally improve the accuracy of the L4 gridded SST data, it is necessary to increase the number of SST observation data in the tidally well mixed region. As an interim solution to the warm bias in the gridded SST datasets in the eastern YS, the SST climatology for the optimal interpolation can be improved based on long-term in situ observation data. To reduce the warm bias in the gridded SST products, two bias correction methods were suggested and compared. Bias correction methods using a simple analytical function and using climatological observation data reduced the RMSD by 19–29% and 37–49%, respectively, in June.

scholarly journals Thermocline Model for Estimating Argo Sea Surface Temperature

2022 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Zhang ChunLing ◽  
Zhang Meng-Li ◽  
Wang Zhen-Feng ◽  
Hu Song ◽  
Wang Dan-Yang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Global Ocean ◽  
Observation System ◽  
In Situ Observation ◽  
Argo Data ◽  
Argo Profiles ◽  
Mean Square Errors

Argo has become an important constituent of the global ocean observation system. However, due to the lack of sea surface measurements from most Argo profiles, the application of Argo data is still limited. In this study, a thermocline model was constructed based on three key thermocline parameters, i.e, thermocline upper depth, the thermocline bottom depth, and thermocline temperature gradient. Following the model, we estimated the sea surface temperature of Argo profiles by providing the relationship between sea surface and subsurface temperature. We tested the effectiveness of our proposed model using statistical analysis and by comparing the sea surface temperature with the results obtained from traditional methods and in situ observations in the Pacific Ocean. The root mean square errors of results obtained from thermocline model were found to be significantly reduced compared to the extrapolation results and satellite retrieved temperature results. The correlation coefficient between the estimation result and in situ observation was 0.967. Argo surface temperature, estimated by the thermocline model, has been theoretically proved to be reliable. Thus, our model generates theoretically feasible data present the mesoscale phenomenon in more detail. Overall, this study compensates for the lack surface observation of Argo, and provides a new tool to establish complete Argo data sets.

scholarly journals Validation of Sea Surface Temperature from GCOM-C Satellite Using iQuam Datasets and MUR-SST in Indonesian Waters

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Bambang Sukresno ◽  
Dinarika Jatisworo ◽  
Rizki Hanintyo
Keyword(s):  
High Resolution ◽  
Error Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Important Variable ◽  
Sea Surface ◽  
Analysis Method ◽  
Validation Process ◽  
Indonesian Seas

Sea surface temperature (SST) is an important variable in oceanography. One of the SST data can be obtained from the Global Observation Mission-Climate (GCOM-C) satellite. Therefore, this data needs to be validated before being applied in various fields. This study aimed to validate SST data from the GCOM-C satellite in the Indonesian Seas. Validation was performed using the data of Multi-sensor Ultra-high Resolution sea surface temperature (MUR-SST) and in situ sea surface temperature Quality Monitor (iQuam). The data used are the daily GCOM-C SST dataset from January to December 2018, as well as the daily dataset from MUR-SST and iQuam in the same period. The validation process was carried out using the three-way error analysis method. The results showed that the accuracy of the GCOM-C SST was 0.37oC.

scholarly journals RETRIEVAL OF SEA SURFACE TEMPERATURE OVER POTERAN ISLAND WATER OF INDONESIA WITH LANDSAT 8 TIRS IMAGE: A PRELIMINARY ALGORITHM

2015 ◽  
Vol XL-2/W4 ◽  
pp. 87-90 ◽  
Author(s):  
M. A. Syariz ◽  
L. M. Jaelani ◽  
L. Subehi ◽  
A. Pamungkas ◽  
E. S. Koenhardono ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Brightness Temperature ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
In Situ Data ◽  
The North ◽  
Thermal Band

The Sea Surface Temperature (SST) retrieval from satellites data Thus, it could provide SST data for a long time. Since, the algorithms of SST estimation by using Landsat 8 Thermal Band are sitedependence, we need to develop an applicable algorithm in Indonesian water. The aim of this research was to develop SST algorithms in the North Java Island Water. The data used are in-situ data measured on April 22, 2015 and also estimated brightness temperature data from Landsat 8 Thermal Band Image (band 10 and band 11). The algorithm was established using 45 data by assessing the relation of measured in-situ data and estimated brightness temperature. Then, the algorithm was validated by using another 40 points. The results showed that the good performance of the sea surface temperature algorithm with coefficient of determination (<i>R</i><sup>2</sup>) and Root Mean Square Error (<i>RMSE</i>) of 0.912 and 0.028, respectively.

scholarly journals Adjusting for Desert-Dust-Related Biases in a Climate Data Record of Sea Surface Temperature

2020 ◽  
Vol 12 (16) ◽  
pp. 2554
Author(s):  
Christopher J. Merchant ◽  
Owen Embury
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Dust Aerosol ◽  
Sea Surface ◽  
Climate Data ◽  
Desert Dust ◽  
Data Record ◽  
Climate Data Record

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol.

scholarly journals Satellite-based time-series of sea-surface temperature since 1981 for climate applications

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Christopher J. Merchant ◽  
Owen Embury ◽  
Claire E. Bulgin ◽  
Thomas Block ◽  
Gary K. Corlett ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ocean Model ◽  
Sea Surface ◽  
Climate Data ◽  
Satellite Measurements ◽  
Data Record ◽  
The Mean ◽  
Climate Data Record

Abstract A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 1012 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km2 and 45 km2. The mean density of good-quality observations is 13 km−2 yr−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

scholarly journals Evaluation of global monitoring and forecasting systems at Mercator Océan

Ocean Science ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 57-81 ◽  
Author(s):  
J.-M. Lellouche ◽  
O. Le Galloudec ◽  
M. Drévillon ◽  
C. Régnier ◽  
E. Greiner ◽  
...  
Keyword(s):  
Quality Control ◽  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Altimeter Data ◽  
Open Boundary ◽  
Vertical Profiles ◽  
Validation Procedure

Abstract. Since December 2010, the MyOcean global analysis and forecasting system has consisted of the Mercator Océan NEMO global 1/4° configuration with a 1/12° nested model over the Atlantic and the Mediterranean. The open boundary data for the nested configuration come from the global 1/4° configuration at 20° S and 80° N. The data are assimilated by means of a reduced-order Kalman filter with a 3-D multivariate modal decomposition of the forecast error. It includes an adaptive-error estimate and a localization algorithm. A 3-D-Var scheme provides a correction for the slowly evolving large-scale biases in temperature and salinity. Altimeter data, satellite sea surface temperature and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. In addition to the quality control performed by data producers, the system carries out a proper quality control on temperature and salinity vertical profiles in order to minimise the risk of erroneous observed profiles being assimilated in the model. This paper describes the recent systems used by Mercator Océan and the validation procedure applied to current MyOcean systems as well as systems under development. The paper shows how refinements or adjustments to the system during the validation procedure affect its quality. Additionally, we show that quality checks (in situ, drifters) and data sources (satellite sea surface temperature) have as great an impact as the system design (model physics and assimilation parameters). The results of the scientific assessment are illustrated with diagnostics over the year 2010 mainly, assorted with time series over the 2007–2011 period. The validation procedure demonstrates the accuracy of MyOcean global products, whose quality is stable over time. All monitoring systems are close to altimetric observations with a forecast RMS difference of 7 cm. The update of the mean dynamic topography corrects local biases in the Indonesian Throughflow and in the western tropical Pacific. This improves also the subsurface currents at the Equator. The global systems give an accurate description of water masses almost everywhere. Between 0 and 500 m, departures from in situ observations rarely exceed 1 °C and 0.2 psu. The assimilation of an improved sea surface temperature product aims to better represent the sea ice concentration and the sea ice edge. The systems under development are still suffering from a drift which can only be detected by means of a 5-yr hindcast, preventing us from upgrading them in real time. This emphasizes the need to pursue research while building future systems for MyOcean2 forecasting.

Sign in / Sign up

Export Citation Format

Share Document