scholarly journals Three-Way Error Analysis between AATSR, AMSR-E, and In Situ Sea Surface Temperature Observations

2008 ◽  
Vol 25 (7) ◽  
pp. 1197-1207 ◽  
Author(s):  
Anne G. O’Carroll ◽  
John R. Eyre ◽  
Roger W. Saunders
Keyword(s):  
Standard Deviation ◽  
Error Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Data Sources ◽  
Independent Data ◽  
Earth Observing System ◽  
Along Track

Abstract Using collocations of three different observation types of sea surface temperatures (SSTs) gives enough information to enable the standard deviation of error on each observation type to be derived. SSTs derived from the Advanced Along-Track Scanning Radiometer (AATSR) and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) instruments are used, along with SST observations from buoys. Various assumptions are made within the error theory, including that the errors are not correlated, which should be the case for three independent data sources. An attempt is made to show that this assumption is valid and that the covariances between the different observations because of representativity error are negligible. Overall, the spatially averaged nighttime AATSR dual-view three-channel bulk SST observations for 2003 are shown to have a very small standard deviation of error of 0.16 K, whereas the buoy SSTs have an error of 0.23 K and the AMSR-E SST observations have an error of 0.42 K.

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.

Validation of the AATSR Meteo Product Sea Surface Temperature

2006 ◽  
Vol 23 (5) ◽  
pp. 711-726 ◽  
Author(s):  
A. G. O'Carroll ◽  
J. G. Watts ◽  
L. A. Horrocks ◽  
R. W. Saunders ◽  
N. A. Rayner
Keyword(s):  
Standard Deviation ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
European Space Agency ◽  
Tropical Rainfall Measuring Mission ◽  
Daily Basis ◽  
Mean Difference ◽  
Sea Surface ◽  
Lower Latitude

Abstract The Advanced Along Track Scanning Radiometer (AATSR) Sea Surface Temperature (SST) Meteo product, a fast-delivery level-2 product at 10 arc min spatial resolution, has been available from the European Space Agency (ESA) since 19 August 2002. Validation has been performed on these data at the Met Office on a daily basis, with a 2-day lag from data receipt. Meteo product skin SSTs have been compared with point measurements of buoy SST, a 1° climate SST analysis field compiled from in situ measurements and Advanced Very High Resolution Radiometer (AVHRR) SSTs, and a 5° latitude–longitude 5-day averaged in situ dataset. Comparisons of the AATSR Meteo product against Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) SSTs are also presented. These validation results have confirmed the AATSR Meteo product skin SST to be within ±0.3 K of in situ data. Comparisons of the AATSR skin SSTs against buoy SSTs, from 19 August 2002 to 20 August 2003, give a mean difference (AATSR – buoy) of 0.04 K (standard deviation = 0.28 K) during nighttime, and a mean difference of 0.02 K (standard deviation = 0.39 K) during the day. Analyses of the buoy matchups have shown that there is no cool skin effect observed in the nighttime observations, implying that the three-channel AATSR product skin SST may be 0.1–0.2 K too warm. Comparisons with TMI SSTs confirm that the lower-latitude SSTs are not significantly affected by residual cloud contamination.

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 Optimal error analysis of MJO prediction associated with uncertainties in sea surface temperature over Indian Ocean

2020 ◽  
Vol 54 (9-10) ◽  
pp. 4331-4350
Author(s):  
Xiaojing Li ◽  
Youmin Tang ◽  
Lei Zhou ◽  
Zhixiong Yao ◽  
Zheqi Shen ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Error Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Optimal Error

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.

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