Three-way error analysis between AATSR, AMSR-E and in situ sea surface temperature observations

Using co-locations 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 (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 also that the covariances between the observations due to representativity error are negligible. Overall, the AATSR observations are shown to have a very small standard deviation of error of 0.16K, whilst the buoy SSTs have an error of 0.23K and the AMSR-E SST observations have an error of 0.42K. 1

Independent uncertainty estimates for coefficient based sea surface temperature retrieval from the Along-Track Scanning Radiometer instruments

We establish a methodology for calculating uncertainties in sea surface temperature estimates from coefficient based satellite retrievals. The uncertainty estimates are derived independently of in-situ data. This enables validation of both the retrieved SSTs and their uncertainty estimate using in-situ data records. The total uncertainty budget is comprised of a number of components, arising from uncorrelated (eg. noise), locally systematic (eg. atmospheric), large scale systematic and sampling effects (for gridded products). The importance of distinguishing these components arises in propagating uncertainty across spatio-temporal scales. We apply the method to SST data retrieved from the Advanced Along Track Scanning Radiometer (AATSR) and validate the results for two different SST retrieval algorithms, both at a per pixel level and for gridded data. We find good agreement between our estimated uncertainties and validation data. This approach to calculating uncertainties in SST retrievals has a wider application to data from other instruments and retrieval of other geophysical variables

Observational needs of sea surface temperature

Sea surface temperature (SST) is a fundamental physical variable for understanding, quantifying and predicting complex interactions between the ocean and the atmosphere. Such processes determine how heat from the sun is redistributed across the global oceans, directly impacting large- and small-scale weather and climate patterns. The provision of daily maps of global SST for operational systems, climate modeling and the broader scientific community is now a mature and sustained service coordinated by the Group for High Resolution Sea Surface Temperature (GHRSST) and the CEOS SST Virtual Constellation (CEOS SST-VC). Data streams are shared, indexed, processed, quality controlled, analyzed, and documented within a Regional/Global Task Sharing (R/GTS) framework, which is implemented internationally in a distributed manner. Products rely on a combination of low-Earth orbit infrared and microwave satellite imagery, geostationary orbit infrared satellite imagery, and in situ data from moored and drifting buoys, Argo floats, and a suite of independent, fully characterized and traceable in situ measurements for product validation (Fiducial Reference Measurements, FRM). Research and development continues to tackle problems such as instrument calibration, algorithm development, diurnal variability, derivation of high-quality skin and depth temperatures, and areas of specific interest such as the high latitudes and coastal areas. In this white paper, we review progress versus the challenges we set out 10 years ago in a previous paper, highlight remaining and new research and development challenges for the next 10 years (such as the need for sustained continuity of passive microwave SST using a 6.9 GHz channel), and conclude with needs to achieve an integrated global high-resolution SST observing system, with focus on satellite observations exploited in conjunction with in situ SSTs. The paper directly relates to the theme of Data Information Systems and also contributes to Ocean Observing Governance and Ocean Technology and Networks within the OceanObs2019 objectives. Applications of SST contribute to all the seven societal benefits, covering Discovery; Ecosystem Health & Biodiversity; Climate Variability & Change; Water, Food, & Energy Security; Pollution & Human Health; Hazards and Maritime Safety; and the Blue Economy

Sea surface temperature for climate from the along-track scanning radiometers

This thesis describes the construction of a sea surface temperature (SST) dataset from Along-Track Scanning Radiometer (ATSR) observations suitable for climate applications. The algorithms presented here are now used at ESA for reprocessing of historical ATSR data and will be the basis of the retrieval used on the forthcoming SLSTR instrument on ESA’s Sentinel-3 satellite. In order to ensure independence of ATSR SSTs from in situ measurements, the retrieval uses physics-based methods through the use of radiative transfer (RT) simulations. The RT simulations are based on the Reference ForwardModel line-by-line model linked to a new sea surface emissivity model which accounts for surface temperature, wind speed, viewing angle and salinity, and to a discrete ordinates scattering (DISORT) model to account for aerosol. An atmospheric profile dataset, based on full resolution ERA-40 numerical weather prediction (NWP) data, is defined and used as input to the RTmodel. Five atmospheric trace gases (N2O, CH4, HNO3, and CFC-11 and CFC-12) are identified as having temporal and geographical variability which have a significant (∼0.1K) impact on RT simulations. Several additional trace gases neglected in previous studies are included using fixed profiles contributing ∼0.04K to RT simulations. Comparison against ATSR-2 and AATSR observations indicates that RT model biases are reduced from 0.2–0.5K for previous studies to ∼0.1K. A new coefficient-based SST retrieval scheme is developed from the RT simulations. Coefficients are banded by total column water vapour (TCWV) from NWP analyses reducing simulated regional biases to <0.1K compared to ∼0.2K for global coefficients. An improved treatment of the instrument viewing geometry decreases simulated view-angle related biases from ∼0.1K to <0.005K for the day-time dual-view retrieval. To eliminate inter-algorithmbiases due to remaining RT model biases and uncertainty in the characterisation of the ATSR instruments the offset coefficient for each TCWV band is adjusted to match the results from a reference channel combination. As infrared radiometers are sensitive to the skin SST while in situ buoys measure SST at some depth below the surface an adjustment for the skin effect and diurnal stratification is included. The samemodel allows adjustment for the differing time of observation between ATSR-2 and AATSR to prevent the diurnal cycle being aliased into the final record. The RT simulations are harmonised between sensors using a double-difference technique eliminating discontinuities in the final SST record. Comparison against in situ drifting and tropical moored buoys shows the new SST dataset is of high quality. Systematic differences between ATSR retrieved SST and in situ drifters show zonal, regional, TCWV, and wind speed biases are less than 0.1K except for themost extreme cases (TCWV <5 kgm−2). The precision of ATSR retrieved SSTs is ∼0.15 K, lower than the precision ofmeasurement of the global ensemble of in situ drifting buoys. From 1995 onwards the ARC SSTs are stable with instability of less than 5mK year−1 to 95% confidence (demonstrated for tropical regions)

The impact of satellite derived land surface temperatures on numerical weather predication analyses and forecasts

Land surface temperature (LST) observations from a variety of satellite instruments operating in the infrared have been compared to estimates of surface temperature from the Met Office operational numerical weather prediction (NWP) model. The comparisons show that during the day the NWP model can under predict the surface temperature by up to 10 K in certain regions such as the Sahel and Southern Africa. By contrast at night the differences are generally smaller. Matchups have also been performed between satellite LSTs and observations from an in situ radiometer located in Southern England within a region of mixed land use. These matchups demonstrate good agreement at night and suggest that the satellite uncertainties in LST are less than 2 K. The Met Office surface analysis scheme has been adapted to utilize nighttime LST observations. Experiments using these analyses in an NWP model have shown a benefit to the resulting forecasts of near surface air temperature, particularly over Africa

Inter-calibration of HY-1B/COCTS thermal infrared channels with MetOp-A/IASI

The Chinese Ocean Color and Temperature Scanner (COCTS) on board the Haiyang-1B (HY-1B) satellite has two thermal infrared channels (9 and 10) centred near 11 μm and 12 μm respectively which are intended for sea surface temperature (SST) observations. In order to improve the accuracy of COCTS SSTs, the inter-calibration of COCTS thermal infrared radiance is carried out. The Infrared Atmospheric Sounding Interferometer (IASI) on board MetOp-A satellite is used as inter-calibration reference owing to its hyperspectral nature and high-quality measurements. The inter-calibration of HY-1B COCTS thermal infrared radiances with IASI is undertaken for data from the period 2009 to 2011 located in the northwest Pacific. Collocations of COCTS radiance with IASI are identified within a temporal window of 30 minutes, a spatial window of 0.12° and an atmospheric path tolerance of 3%. Matched IASI spectra are convolved with the COCTS spectral response functions, while COCTS pixels within the footprint of each IASI pixel are spatially averaged, thus creating matched IASI-COCTS radiance pairs that should agree well in the absence of satellite biases. The radiances of COCTS 11 and 12 μm channel are lower than IASI with relatively large biases, and a strong dependence of difference on radiance in the case of 11 μm channel. We use linear robust regression for different four detectors of COCTS separately to obtain the inter-calibration coefficients to correct the COCTS radiance. After correction, the mean values of COCTS 11 and 12 μm channel minus IASI radiance are -0.02 mW m-2 cm sr-1 and -0.01 mW m-2 cm sr-1 respectively, with corresponding standard deviations of 0.51 mW m-2 cm sr-1 and 0.57 mW m-2 cm sr-1. Striped noise is present in COCTS original radiance imagery associated with inconsistency between four detectors, and inter-calibration is shown to reduce, although not eliminate, the striping. The calibration accuracy of COCTS is improved after inter-calibration, that is potentially useful for improving COCTS SST accuracy in the future

Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data

Land surface temperature (LST) is an important indicator of global ecological environment and climate change. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the recently launched Sentinel-3 satellites provides high-quality observations for estimating global LST. The algorithm of the official SLSTR LST product is a split-window algorithm (SWA) that implicitly assumes and utilizes knowledge of land surface emissivity (LSE). The main objective of this study is to investigate alternative SLSTR LST retrieval algorithms with an explicit use of LSE. Seventeen widely accepted SWAs, which explicitly utilize LSE, were selected as candidate algorithms. First, the SWAs were trained using a comprehensive global simulation dataset. Then, using simulation data as well as in-situ LST, the SWAs were evaluated according to their sensitivity and accuracy: eleven algorithms showed good training accuracy and nine of them exhibited low sensitivity to uncertainties in LSE and column water vapor content. Evaluation based on two global simulation datasets and a regional simulation dataset showed that these nine SWAs had similar accuracy with negligible systematic errors and RMSEs lower than 1.0 K. Validation based on in-situ LST obtained for six sites further confirmed the similar accuracies of the SWAs, with the lowest RMSE ranges of 1.57–1.62 K and 0.49−0.61 K for Gobabeb and Lake Constance, respectively. While the best two SWAs usually yielded good accuracy, the official SLSTR LST generally had lower accuracy. The SWAs identified and described in this study may serve as alternative algorithms for retrieving LST products from SLSTR data

Use of satellite observations for operational oceanography: recent achievements and future prospects

The paper gives an overview of the development of satellite oceanography over the past five years focusing on the most relevant issues for operational oceanography. Satellites provide key essential variables to constrain ocean models and/or serve downstream applications. New and improved satellite data sets have been developed and have directly improved the quality of operational products. The status of the satellite constellation for the last five years was, however, not optimal. Review of future missions shows clear progress and new research and development missions with a potentially large impact for operational oceanography should be demonstrated. Improvement of data assimilation techniques and developing synergetic use of high resolution satellite observations are important future priorities

Half a century of satellite remote sensing of sea-surface temperature

Sea-surface temperature (SST) was one of the first ocean variables to be studied from earth observation satellites. Pioneering images from infrared scanning radiometers revealed the complexity of the surface temperature fields, but these were derived from radiance measurements at orbital heights and included the effects of the intervening atmosphere. Corrections for the effects of the atmosphere to make quantitative estimates of the SST became possible when radiometers with multiple infrared channels were deployed in 1979. At the same time, imaging microwave radiometers with SST capabilities were also flown. Since then, SST has been derived from infrared and microwave radiometers on polar orbiting satellites and from infrared radiometers on geostationary spacecraft. As the performances of satellite radiometers and SST retrieval algorithms improved, accurate, global, high resolution, frequently sampled SST fields became fundamental to many research and operational activities. Here we provide an overview of the physics of the derivation of SST and the history of the development of satellite instruments over half a century. As demonstrated accuracies increased, they stimulated scientific research into the oceans, the coupled ocean-atmosphere system and the climate. We provide brief overviews of the development of some applications, including the feasibility of generating Climate Data Records. We summarize the important role of the Group for High Resolution SST (GHRSST) in providing a forum for scientists and operational practitioners to discuss problems and results, and to help coordinate activities world-wide, including alignment of data formatting and protocols and research. The challenges of burgeoning data volumes, data distribution and analysis have benefited from simultaneous progress in computing power, high capacity storage, and communications over the Internet, so we summarize the development and current capabilities of data archives. We conclude with an outlook of developments anticipated in the next decade or so