Presenting a Long-Term, Reprocessed Dataset of Global Sea Surface Temperature Produced Using the OSTIA System
Abstract
:1. Introduction
1.1. The Purpose of the Reprocessed Dataset
1.2. Overview of Currently Available Long-Term Daily SST Products
2. Description of the OSTIA Climate Configuration
2.1. Assimilation of Input Datasets
2.2. Satellite Dataset Quality Control (QC)
2.3. In Situ Dataset QC
- A platform-dependent uncertainty.
- Reject points with a QC value greater than one (one is a pass and four is a fail).
- Add HadIOD bias corrections to the in situ SST values (in HadIOD.1.2.0.0, the correction values for buoys are zero, but this step is good practice).
- For each observation, an error standard deviation value is assigned by combining, in quadrature, a platform-dependent error variance (0.12 K) with a geographic uncertainty value extracted from the matching location in the spatial error variance data file.
2.4. Selection of Reference Satellite Sensor
3. Input Datasets
3.1. Satellite Datasets
3.1.1. Sensors Used and Their Timeline
3.1.2. Sources of Satellite Datasets
3.1.3. Satellite Instrument Issues
3.1.4. Comparison of Infra-Red (IR) and Passive Microwave (PMW) Datasets
3.2. Sea Ice Datasets
3.3. In Situ Datasets
4. Methodology Used to Analyze the Accuracy of CMEMS v2.0
4.1. Source of Data
4.2. Methodology
- Each daily analysis data file contains data from midnight to midnight for that day.
- The SIRDS dataset is a monthly dataset containing observation data for the entire month as well as their latitude/longitude locations.
- Any SIRDS observation where its timestamp is outside the analysis dataset’s time window is removed.
- Any SIRDS observation with a quality flag is removed.
- For each analysis grid cell, the SIRDS observations within that cell are extracted. Both SIRDS and analysis values are then saved as a matchup dataset.
- The matchup dataset is then used for calculating the mean and standard deviation of the analysis minus in situ differences, which can then be used for validation of the analysis.
4.3. Use of Bootstrapping to Calculate Confidence Intervals for Bias and Standard Deviation Statistics
- The L4 analyses were calculated using a variety of inputs, including in situ buoy data (see Section 2). This effect was minimized by using in situ data from outside the data assimilation window. Nevertheless, errors in the analysis were not truly independent of the in situ buoy data.
- The uncertainty calculated for each in situ measurement did not consider the error caused by the offset of the analysis day (since this is very difficult to quantify).
- Representativity error: the difference that occurred due to the mismatch between the analysis cell size and the location of the in situ data point within this pixel.
- Uncertainty arising from the Argo and drifting buoys not measuring the foundation SST directly.
- Take an original dataset A containing η number of data points.
- Randomly draw η number of data points from A to create a resampled dataset B. Due to the random nature of the selection, B will contain some data points more than once.
- Save the resampled dataset B.
- Repeat the resampling process for the number of chosen iterations (i = 10,000 in this case).
- For each of the iterations, the statistic x can be calculated (mean and std dev in this case).
- The result is a bootstrap distribution indicating the variability in x.
- The confidence intervals are then calculated from this bootstrap distribution (5th and 95th percentile in this case).
5. Assessment of the Accuracy of CMEMS v2.0
5.1. Results
- From 2003 onwards, the buoy and Argo validation statistics are similar. This implies that, even though the buoy dataset is offset by two days, it still remains a representative validation dataset that can be used for assessing the quality of the analyses prior to the Argo era.
- The Argo dataset is initially very noisy from 2000 to 2003 due to the sparse network. The noise reduces as the network matures.
- The analysis minus in situ standard deviation values for CMEMS v2.0 are slightly lower than those of v1.2.
- The analysis minus in situ mean difference values show that the SSTs produced by CMEMS v2.0 are closer to zero (less biased) relative to the in situ temperatures than CMEMS v1.2.
- CMEMS v2.0 shows a cool bias between 2012 and 2020; this is investigated further in Section 5.2.
- The validation statistics display a clear seasonal variability.
- The shaded areas that represent the 5th and 95th percentiles of the dataset are extremely small, indicating the high precision of the datasets. Consequently, they are difficult to observe in the plots.
5.2. In Situ vs. Analysis—Investigation into 2012–2020 Bias Increase
6. Evaluation of Analysis Uncertainties
6.1. Mathematical Background
6.2. Analysis Uncertainty Evaluation Plots—Methodology
- The in situ observations were matched up with the corresponding SST analysis grid cell.
- The differences between the matched-up in situ observations and the SST analysis values were binned according to the associated analysis uncertainty values. Twenty bins of 0.05 K (ranging from zero to one) were used.
- If there were more than 100 matchups in a bin, the following statistics were calculated and plotted (steps 4–7).
- The RSD, plotted on the y-axis with cyan vertical lines (Equation (2)).
- The median values, plotted with red crosses.
- The robust standard error (Equation (3)) was calculated for the data points in each bin and plotted with red vertical bars.
- The green dashed line signifies the expected RSD envelope, which was calculated using Equation (4) using the expected in situ uncertainty.
6.3. Results
7. Investigation of Feature Resolution Using Power Spectra and Horizontal Gradients
7.1. Introduction to Concept
- The Gulf Stream.
- Agulhas current retroreflection.
- Kuroshio current.
7.2. Feature Resolution—Methodology
- Loop through each day in the L4 dataset and extract a region of interest (ROI).
- For each daily ROI dataset, loop though the latitude coordinates.
- For each latitude, calculate a cross section using the procedure below:
- Extract all points on the longitude.
- Detrend the dataset (by taking the result of a linear least-squares fit to the dataset and then subtracting this from the dataset).
- Calculate a spectral analysis.
- Store this value.
- At the end of the latitude loop, the mean is taken of all the values from step 3 to give the spectrum of that entire ROI for that day, which is then saved into an array.
- The spectrum values for each day are then averaged and plotted.
7.3. Feature Resolution—Results and Implications
7.4. Horizontal Gradients—Methodology
- For each grid cell, check if any of the surrounding pixels are land or ice, and if so, ignore it.
- Find the X gradient (Xgrad). This is the SST difference between the pixels on either side of the grid cell (step 1) divided by the distance between those points.
- Find the Y gradient (Ygrad). This is the SST difference between the pixels above and below the grid cell (step 1) divided by the distance between those points.
- The horizontal gradient for grid cell (step 1) is then calculated using Equation (5):
7.5. Horizontal Gradients—Results
8. Volcanic Eruption Case Studies
8.1. Introduction
8.2. Mt. Pinatubo
8.2.1. Introduction and Limitations of Validation Datasets
8.2.2. Validation Dataset and Methodology
- Take the L4 analysis dataset for the region and time period (in the case of Mt. Pinatubo, this is latitude −20°S to 30°N and the time period is January 1990 to December 1992).
- Calculate the monthly average of the L4 data (step 1).
- Take the HadSST4 median actual SST for the region and time period (HadSST4 is already a monthly dataset).
- The HadSST4 dataset can have gaps in its coverage. Mask out the L4 analysis data from step 2 wherever there are gaps in the HadSST4 dataset.
- Take the spatial average of the L4 analysis (Step 4) and subtract by the spatial average of the HadSST4 dataset (Step 3) and plot.
- Loop through each of the HadSST4 ensemble members.
- For each of the 200 ensemble members, extract the region and time period.
- Take the monthly spatial average of these data and save.
- Take the 200 ensemble members from step 3 and calculate the standard deviation for each month.
- Calculate the monthly HadSST4 measurement and sampling error uncertainty standard deviations.
- Combine the monthly ensemble standard deviations (step 4) in quadrature with the monthly measurement/sampling uncertainties (step 5).
- Multiply step 6 by two and use this to plot the ±2-sigma uncertainty range.
8.2.3. Results
- A new version of Radiative Transfer (RTTOV) software (v12.3) used for cloud detection and SST retrieval.
- Use of bias-aware Optimal Estimation (OE) retrieval for AVHRR. In v2.1, the bias-corrections were a post hoc adjustment of the SSTs.
- Improved processing to reduce AVHRR data gaps.
- Inclusion of tropospheric dust aerosol in the model, which should fix the large cold dust biases seen in v2 SSTs.
- More sophisticated quality filtering allows more usable data to be obtained from the AVHRR-12 dataset.
8.3. El Chichón
8.3.1. Introduction
8.3.2. Results
9. Conclusions
10. List of Acronyms
AMSR | Advanced Microwave Scanning Radiometer |
ATSR | Along-Track Scanning Radiometer |
AVHRR | Advanced Very-High-Resolution Radiometer |
C3S | Copernicus Climate Change Services |
CDR | Climate Data Record |
CMEMS | Copernicus Marine Environment Monitoring Service |
DMSP | Defence Meteorological Satellite Program |
DOISST | Daily Optimum Interpolation Sea Surface Temperature |
ECMWF | European Centre for Medium-Range Weather Forecasting |
ERS | Earth Remote Sensing Satellite |
ESA SST CCI | European Space Agency SST Copernicus Climate Initiative |
EUMETSAT | European Organisation for the Exploitation of Meteorological Satellites |
GAC | Global Area Coverage |
GCOM-W | Global Change Observation Mission—Water |
GCOS | Global Climate Observing System |
GHRSST | Group for High-Resolution SST |
GMI | GPM Microwave Imager |
GOES | Geostationary Operational Environmental Satellite |
GPM | Global Precipitation Measurement |
GTMBA | Global Tropical Moored Buoy Array |
GTS | Global Telecommunication System |
HadIOD | Met Office Hadley Centre Integrated Ocean Database |
HadSST4 | Hadley Centre’s Sea Surface Temperature Dataset |
HIRS | High-Resolution Infrared Radiation Sounder |
ICOADS | International Comprehensive Ocean–Atmosphere Dataset |
IR | Infra-Red |
JAXA | Japan Aerospace Exploration Agency |
L2P | Level 2 Preprocessed |
L3C | Level-3 Collated |
L3U | Level-3 Uncollated |
L4 | Level 4 |
MAD | Median Absolute Deviation |
MCS | Marine Cold Spell |
MetDB | Met Office Observations Database |
MGDSST | The Merged Satellite and In Situ Data Global Daily Sea Surface Temperature |
MHW | Marine Heatwave |
NEMO | Nucleus for European Modeling of the Ocean |
NOAA | National Oceanic and Atmospheric Administration |
NRT | Near Real Time |
NWP | Numerical Weather Prediction |
OE | Optimal Estimation |
OI | Optimal Interpolation |
OSI-SAF | Ocean and Sea Ice Satellite Application Facility hosted by EUMETSAT |
OSTIA | Ocean Sea Temperature and Ice Analysis |
PMW | Passive MicroWave |
PODAAC | Physical Oceanography Distributed Active Archive Centre |
QC | Quality Control |
REMSS | Remote Sensing Systems |
RFI | Radio Frequency Interference |
ROI | Region of Interests |
RSD | Robust Standard Deviation |
RSE | Robust Standard Error |
RTTOV | Full expansion: RTTOV = Radiative Transfer for TOVS. TOVS = TIROS Operational Vertical Sounder. TIROS = Television Infrared Observation Satellite. |
SEVIRI | Spinning Enhanced Visible and InfraRed Imager |
SIRDS | SST CCI Independent Reference Dataset |
SLSTR | Sea and Land Surface Temperature Radiometer |
SMMR | Scanning Multichannel Microwave Radiometer |
SNPP | Suomi National Polar-Orbiting Partnership |
SO2 | Sulfur Dioxide |
SQG | Surface Quasi-Geostrophic |
SSH | Sea Surface Height |
SSM/I | Special Sensor Microwave/Imager |
SSMIS | Special Sensor Microwave Imager/Sounder |
SST | Sea Surface Temperature |
UKEOCIS | UK Earth Observation Climate Information Service |
UKMCAS | UK Marine and Climate Advisory Service |
VIIRS | Visible Infrared Imaging Radiometer Suite |
ZGM | Zero Gyro Mode |
11. Dataset Locations
- PODAAC product name: OSTIA-UKMO-L4-GLOB-REP-v2.0
- Data source: NEAR-GOOS RRTDB
- Webpage URL: https://www.data.jma.go.jp/gmd/goos/data/database.html (accessed on 28 August 2024).
- Download location: https://www.data.jma.go.jp/gmd/goos/data/pub/JMA-product/mgd_sst_glb_D/Readme_mgd_sst_glb_D (accessed on 28 August 2024).
- PODAAC product name: AVHRR_OI-NCEI-L4-GLOB-v2.1
- Citation: [78]
- Webpage URL: https://data.marine.copernicus.eu/product/SST_GLO_SST_L4_REP_OBSERVATIONS_010_024/description (accessed on 28 August 2024).
- Dataset DOI: https://doi.org/10.48670/moi-00169
- Webpage URL: https://catalogue.ceda.ac.uk/uuid/4a9654136a7148e39b7feb56f8bb02d2 (accessed on 28 August 2024).
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Analysis Name | SST Type | Time Span | Grid Res (Degrees) | Satellite Datasets Used | In situ Dataset |
---|---|---|---|---|---|
MDGSST | SST depth (1 m) | 1 January 1982–Present | 0.25° (approx. 27.78 km at the equator) | AVHRR and MetOp-A, WINDSAT, AMSR2/E | Ships and buoys |
NOAA DOISST v2.1 | 0.2 m nominal | 1 September 1981–Present | AVHRR 7-19 and MetOp-A/B AVHRR | Ships, moored and drifter buoys, and Argo | |
ESA SST CCI v2.1 | Daily average SST depth (0.2 m) | 1 September 1981 to 31 December 2016 2017–2022 from C3S | 0.05° (approx. 5.55 km at the equator) | AVHRR, AATSR and ATSR1/2 SLSTR in C3S extension | Does not use in situ |
CMEMS analysis v2.0 | Foundation SST | 1 October 1981-Present (with a 6-month delay) | AVHRR 7-19, MetOp-A, ATSR1/2, AATSR, GOES-13/16, SEVIRI, SLSTR-A/B, AMSR2/E, and GMI | Moored and drifter buoys | |
Previous version: CMEMS analysis v1.2 | 1 January 1985–31 December 2007 | AVHRR, AATSR and ATSR1/2 | Ships, moored and drifter buoys |
Instrument | Start/End Date | Platform | Dataset Provider | Spectrum and (Orbit) | Resolution of Input Dataset | File Format |
---|---|---|---|---|---|---|
AVHRR 7 | 24 August 1981–18 February 1985 | NOAA-7 | ESA SST CCI-CDR version 2.0. (Not to be confused with the L4 CDR dataset). | IR (LEO) | 0.05 deg | L3U |
AVHRR 9 | 4 January 1985–28 October 1988 | NOAA-9 | ||||
AVHRR 11 | 12 October 1988–1 September 1994 | NOAA-11 | ||||
AVHRR 12 | 16 September 1991–14 December 1998 | NOAA-12 | ||||
AVHRR 14 | 19 January 1995–31 December 1999 | NOAA-14 | ||||
AVHRR 15 | 1 January 1999–31 December 2001 | NOAA-15 | ||||
AVHRR 16 | 1 June 2003–31 December 2006 | NOAA-16 | ||||
AVHRR 17 | 1 December 2002–31 December 2009 | NOAA-17 | ||||
AVHRR 18 | 5 June 2005–31 December 2009 | NOAA-18 | ||||
AVHRR 19 | 22 February 2009–31 December 2016 | NOAA-19 | ||||
AVHRR MTA | 21 November 2006–31 December 2016 | MetOp-A | ||||
AVHRR 19 | 1 January 2017–31 December 2018 | NOAA-19 | C3S | L3C | ||
AVHRR MTA | 1 January 2017–30 September 2021 | MetOp-A | ||||
AVHRR MTB | 1 October 2021–Present | MetOp-B | ||||
ATSR1 | 1 November 1991–1 January 1996 | ERS-1 | ESA SST CCI | L3U | ||
ATSR2 | 1 August 1995–22 June 2003 | ERS-2 | ||||
AATSR | 1 December 2002–31 December 2012 | EnviSat | ||||
ABI-13 | 1 April 2010–14 December 2017 | GOES-13 | EUMETSAT OSI SAF | IR (GEO) | L3C | |
ABI-16 | 17 December 2017–Present | GOES-16 | ||||
SEVIRI | 1 June 2004–Present | MetoSat 8-11 | ||||
SLSTR A | 1 January 2017–Present | Sentinel-3a | C3S | IR (LEO) | ||
SLSTR B | 15 December 2019–Present | Sentinel-3b | ||||
AMSR 2 | 1 December 2013–Present | GCOM-W | REMSS | PMW (LEO) | 25 km | L2P |
GMI | 4 March 2014–Present | GPM_Core | L3U |
Sensor | Start Date | End Date |
---|---|---|
AVHRR 7 | 24 August 1981 | 31 January 1985 |
AVHRR 9 | 1 February 1985 | 31 August 1988 |
No Instrument | 1 September 1988 | 12 October 1988 |
AVHRR 11 | 13 October 1988 | 31 December 1991 |
ATSR1 | 1 January 1992 | 31 July 1995 |
ATSR2 | 01 August 1995 | 30 November 1995 |
AVHRR 14 | 1 December 1995 | 30 June 1996 |
ATSR2 | 1 July 1996 | 31 July 2002 |
AATSR | 1 August 2002 | 31 March 2012 |
AVHRR MTA | 1 April 2012 | 31 December 2016 |
SLSTR A | 1 January 2017 | 31 December 2021 |
OSI-450 | OSI-430 | OSI-430-b | ||||||
---|---|---|---|---|---|---|---|---|
Satellite | Start | End | Satellite | Start | End | Satellite | Start | End |
Nimbus 7 SMMR | 1 January 1979 | 1 August 1987 | DMSP F17 SSMIS | 1 January 2016 | 1 December 2018 | DMSP F16 SSMIS | 1 January 2016 | Present |
DMSP F8 SSM/I | 1 July 1987 | 1 December 1991 | DMSP F18 SSMIS | DMSP F17 SSMIS | ||||
DMSP F10 SSM/I | 1 January 1991 | 1 November 1997 | DMSP F18 SSMIS | |||||
DMSP F11 SSM/I | 1 January 1992 | 1 December 1999 | ||||||
DMSP F13 SSM/I | 1 March 1995 | 1 December 2008 | ||||||
DMSP F14 SSM/I | 1 May 1997 | 1 August 2008 | ||||||
DMSP F15 SSM/I | 1 February 2000 | 1 July 2006 | ||||||
DMSP F16 SSMIS | 1 November 2005 | 1 December 2013 | ||||||
DMSP F17 SSMIS | 1 December 2006 | 1 December 2015 | ||||||
DMSP F18 SSMIS | 1 March 2010 | 1 December 2015 |
Buoy Time Period 1985–2007 | Argo Time Period 2000–2007 | Buoy Time Period 1982–2020 | Argo Time Period 2000–2020 | |||||
---|---|---|---|---|---|---|---|---|
Analysis − Buoy Bias | Analysis − Buoy Std Dev | Analysis − Argo Bias | Analysis − Argo Std Dev | Analysis − Buoy Bias | Analysis − Buoy Std Dev | Analysis − Argo Bias | Analysis − Argo Std Dev | |
CMEMS v1.2 | −0.09018 | 0.63064 | −0.09884 | 0.56086 | ||||
CMEMS v2.0 | 0.00334 | 0.56782 | −0.00668 | 0.46151 | −0.01039 | 0.51653 | −0.02664 | 0.41992 |
Analysis − Obs Mean Percentile | Analysis − Obs Std Dev Percentile | |||||
---|---|---|---|---|---|---|
Analysis − Obs Mean | 5th | 95th | Analysis − Obs Std Dev | 5th | 95th | |
CMEMS v1.2 | −0.136164 | −0.141289 | −0.130924 | 0.630929 | 0.612625 | 0.650864 |
CMEMS v2.0 | −0.100210 | −0.104891 | −0.095304 | 0.580533 | 0.559674 | 0.602480 |
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Worsfold, M.; Good, S.; Atkinson, C.; Embury, O. Presenting a Long-Term, Reprocessed Dataset of Global Sea Surface Temperature Produced Using the OSTIA System. Remote Sens. 2024, 16, 3358. https://doi.org/10.3390/rs16183358
Worsfold M, Good S, Atkinson C, Embury O. Presenting a Long-Term, Reprocessed Dataset of Global Sea Surface Temperature Produced Using the OSTIA System. Remote Sensing. 2024; 16(18):3358. https://doi.org/10.3390/rs16183358
Chicago/Turabian StyleWorsfold, Mark, Simon Good, Chris Atkinson, and Owen Embury. 2024. "Presenting a Long-Term, Reprocessed Dataset of Global Sea Surface Temperature Produced Using the OSTIA System" Remote Sensing 16, no. 18: 3358. https://doi.org/10.3390/rs16183358