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