scholarly journals Use of Uncertainty Inflation in OSTIA to Account for Correlated Errors in Satellite-Retrieved Sea Surface Temperature Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1083
Author(s):  
Rebecca Reid ◽  
Simon Good ◽  
Matthew J. Martin
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
European Space Agency ◽  
Sea Surface ◽  
Observation Error ◽  
Correlated Errors ◽  
Negative Results ◽  
Error Correlation ◽  
Correlation Information ◽  
Analysis System

Sea surface temperature (SST) analysis systems such as the Operational Sea Surface Temperature and Ice Analysis (OSTIA) use statistical methods to combine observations together with a first guess field to create spatially complete maps of SST. These commonly assume that observation errors are uncorrelated, yet some errors (such as due to retrieval issues) can be correlated. Information about errors is used by the analysis system to determine the weighting to apply to the observations, hence this incorrect assumption could degrade the analysis. A common technique to mitigate for this is to inflate the observation uncertainties. Using information on observation error correlations provided with data produced by the European Space Agency (ESA) SST Climate Change Initiative (CCI) project, idealised tests were carried out to determine how this inflation technique can best be applied. These showed that applying inflation in situations where the observation errors are correlated over similar or larger distances to the errors in the background can cause unpredictable and sometimes negative results. However, in situations where the observation error correlation length scale is relatively small, inflation should improve the analysis. These findings were adapted to the OSTIA system and various configurations were tested. It was found that the inflation methods did not affect statistics of differences between the analyses and independent Argo reference data. However, the SST gradients were affected, particularly if some observation uncertainties were inflated but others were not. The results from both the idealised tests and the application to the real system therefore highlight that it is challenging to implement the inflation method in the case of an SST analysis system and show the need for assimilation schemes that can make full use of observation error correlation information.

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 Sea Surface Temperature Analysis for Fengyun-3C Data Using Oriented Elliptic Correlation Scales

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8067
Author(s):  
Zhihong Liao ◽  
Bin Xu ◽  
Junxia Gu ◽  
Chunxiang Shi
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Kalman Filtering ◽  
Sea Surface ◽  
Quality Analysis ◽  
Observation Error ◽  
Change Analysis ◽  
Background Error ◽  
Filtering Method ◽  
Correlation Scale

Sea surface temperature (SST) is critical for global climate change analysis and research. In this study, we used visible and infrared scanning radiometer (VIRR) sea surface temperature (SST) data from the Fengyun-3C (FY-3C) satellite for SST analysis, and applied the Kalman filtering methods with oriented elliptic correlation scales to construct SST fields. Firstly, the model for the oriented elliptic correlation scale was established for SST analysis. Secondly, observation errors from each type of SST data source were estimated using the optimal matched datasets, and background field errors were calculated using the model of oriented elliptic correlation scale. Finally, the blended SST analysis product was obtained using the Kalman filtering method, then the SST fields using the optimum interpolation (OI) method were chosen for comparison to validate results. The quality analysis for 2016 revealed that the Kalman analysis with a root-mean-square error (RMSE) of 0.3243 °C had better performance than did the OI analysis with a RMSE of 0.3911 °C, which was closer to the OISST product RMSE of 0.2897 °C. The results demonstrated that the Kalman filtering method with dynamic observation error and background error estimation was significantly superior to the OI method in SST analysis for FY-3C SST data.

scholarly journals Harmonization of Space-Borne Infra-Red Sensors Measuring Sea Surface Temperature

2020 ◽  
Vol 12 (6) ◽  
pp. 1048 ◽  
Author(s):  
Christopher J. Merchant ◽  
Thomas Block ◽  
Gary K. Corlett ◽  
Owen Embury ◽  
Jonathan P. D. Mittaz ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Land Surface ◽  
Optimal Estimation ◽  
Sea Surface ◽  
Observation Error ◽  
Climate Data ◽  
Water Vapor Path ◽  
Along Track ◽  
Reference Sensor

Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing.

An operational analysis system for the global diurnal cycle of sea surface temperature: implementation and validation

2017 ◽  
Vol 143 (705) ◽  
pp. 1787-1803 ◽  
Author(s):  
J. While ◽  
C. Mao ◽  
M. J. Martin ◽  
J. Roberts-Jones ◽  
P. A. Sykes ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Diurnal Cycle ◽  
Sea Surface ◽  
Operational Analysis ◽  
Analysis System

scholarly journals Sea surface temperature datasets for climate applications from Phase 1 of the European Space Agency Climate Change Initiative ( SST CCI )

2014 ◽  
Vol 1 (2) ◽  
pp. 179-191 ◽  
Author(s):  
Christopher J. Merchant ◽  
Owen Embury ◽  
Jonah Roberts‐Jones ◽  
Emma Fiedler ◽  
Claire E. Bulgin ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Phase 1 ◽  
European Space Agency ◽  
Sea Surface ◽  
Change Initiative ◽  
Space Agency

Holocene sea surface temperature variations recorded in corals from Kikai Island, Japan

2017 ◽  
Vol 51 (4) ◽  
pp. e9-e14 ◽  
Author(s):  
Hiroto Kajita ◽  
Atsuko Yamazaki ◽  
Takaaki Watanabe ◽  
Chung-Che Wu ◽  
Chuan-Chou Shen ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Temperature Variations
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