Sea Surface Temperature Retrieval Research Articles

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

Abstract 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 (e.g. noise), locally systematic (e.g. 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.

Preliminary Inter-Comparison between AHI, VIIRS and MODIS Clear-Sky Ocean Radiances for Accurate SST Retrievals

Clear-sky brightness temperatures (BT) in five bands of the Advanced Himawari Imager (AHI; flown onboard Himawari-8 satellite) centered at 3.9, 8.6, 10.4, 11.2, and 12.3 µm (denoted by IR37, IR86, IR10, IR11, and IR12, respectively) are used in the NOAA Advanced Clear-Sky Processor for Oceans (ACSPO) sea surface temperature (SST) retrieval system. Here, AHI BTs are preliminarily evaluated for stability and consistency with the corresponding VIIRS and MODIS BTs, using the sensor observation minus model simulation (O-M) biases and corresponding double differences. The objective is to ensure accurate and consistent SST products from the polar and geo sensors, and to prepare for the launch of the GOES-R satellite in 2016. All five AHI SST bands are found to be largely in-family with their polar counterparts, but biased low relative to the VIIRS and MODIS (which, in turn, were found to be stable and consistent, except for Terra IR86, which is biased high by 1.5 K). The negative biases are larger in IR37 and IR12 (up to ~−0.5 K), followed by the three remaining longwave IR bands IR86, IR10, and IR11 (from −0.3 to −0.4 K). These negative biases may be in part due to the uncertainties in AHI calibration and characterization, although uncertainties in the coefficients of the Community Radiative Transfer Model (CRTM, used to generate the “M” term) may also contribute. Work is underway to add AHI analyses in the NOAA Monitoring of IR Clear-Sky Radiances over Oceans for SST (MICROS) system and improve AHI BTs by collaborating with the sensor calibration and CRTM teams. The Advanced Baseline Imager (ABI) analyses will be also added in MICROS when GOES-R is launched in late 2016 and the ABI IR data become available.

Assimilating Retrievals of Sea Surface Temperature from VIIRS and AMSR2

AbstractExperiments are carried out to assess the potential contributions of two new satellite datasets, derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi–National Polar-Orbiting Partnership satellite and the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission–Water (GCOM-W) satellite, to the quality of global sea surface temperature (SST) analyses at the Canadian Meteorological Centre (CMC). The new datasets are assimilated both separately and together. Verification of the analyses against independent data shows that the VIIRS and AMSR2 datasets yield analyses with similar global average errors, with the VIIRS analysis performing better during some seasons and the AMSR2 analysis performing better in others. Seasonal cloudiness in some regions diminishes the availability of VIIRS retrievals, resulting in better performance by the AMSR2 analysis. Both datasets were assimilated together along with data from the Advanced Very High Resolution Radiometer (AVHRR), ice data, and in situ data in an updated version of the CMC analysis produced on a 0.1° grid. Verification against independent data shows that the new analysis performed very well, with global average standard deviation consistently better than that of the international Group for High Resolution SST (GHRSST) Multiproduct Ensemble (GMPE) real-time system. This analysis is shown to outperform the currently operational CMC SST analysis, with most of the improvement being due to its assimilation of the VIIRS and AMSR2 retrievals and a further small gain being due to changes to the analysis methodology (including higher resolution).

Hybrid cloud and error masking to improve the quality of deterministic satellite sea surface temperature retrieval and data coverage

Abstract In the infrared region, the quality of sea surface temperature (SST) retrievals critically depends on the cloud detection scheme. More than 5 million matchups, where the surface and top of atmosphere measurements are available, have been carefully analyzed to understand clouds related errors and to develop the advanced cloud detection scheme for improvement of satellite SST quality. The effectiveness of a Bayesian cloud detection (BCD) scheme, operationally implemented at the NOAA Office of Satellite Product Operations (OSPO) for the GOES-Imager, has been examined using an experimental filter and it is found that this scheme is not optimal. Thus, a new algorithm for cloud and error masking (CEM) scheme is proposed for physical SST retrievals. This is based on a quasi-deterministic approach combined with an approximated radiative transfer model and the functional spectral differences at pixel level. Although, traditionally the validation of cloud detection algorithms have often been reported qualitatively using visual inspection of imagery, we have made a quantitative validation of the cloud algorithm for its intended purpose by determining the quality of satellite SST retrievals against in situ data. Results show that CEM can reduce the root mean square error in SST by an average of 22% while increasing the data coverage by an average of 38% compared to the operationally implemented BCD at OSPO, as assessed over a period of fifty months.

Validation of S-NPP VIIRS Sea Surface Temperature Retrieved from NAVO

The validation of sea surface temperature (SST) retrieved from the new sensor Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) Satellite is essential for the interpretation, use, and improvement of the new generation SST product. In this study, the magnitude and characteristics of uncertainties in S-NPP VIIRS SST produced by the Naval Oceanographic Office (NAVO) are investigated. The NAVO S-NPP VIIRS SST and eight types of quality-controlled in situ SST from the National Oceanic and Atmospheric Administration in situ Quality Monitor (iQuam) are condensed into a Taylor diagram. Considering these comparisons and their spatial coverage, the NAVO S-NPP VIIRS SST is then validated using collocated drifters measured SST via a three-way error analysis which also includes SST derived from Moderate Resolution Imaging Spectro-radiometer (MODIS) onboard AQUA. The analysis shows that the NAVO S-NPP VIIRS SST is of high accuracy, which lies between the drifters measured SST and AQUA MODIS SST. The histogram of NAVO S-NPP VIIRS SST root-mean-square error (RMSE) shows normality in the range of 0–0.6 °C with a median of ~0.31 °C. Global distribution of NAVO VIIRS SST shows pronounced warm biases up to 0.5 °C in the Southern Hemisphere at high latitudes with respect to the drifters measured SST, while near-zero biases are observed in AQUA MODIS. It means that these biases may be caused by the NAVO S-NPP VIIRS SST retrieval algorithm rather than the nature of the SST. The reasons and correction for this bias need to be further studied.

A Physical Deterministic Inverse Method for Operational Satellite Remote Sensing: An Application for Sea SurfaceTemperature Retrievals

We propose a new deterministic approach for remote sensing retrieval, called modified total least squares (MTLS), built upon the total least squares (TLS) technique. MTLS implicitly determines the optimal regularization strength to be applied to the normal equation first-order Newtonian retrieval using all of the noise terms embedded in the residual vector. The TLS technique does not include any constraint to prevent noise enhancement in the state space parameters from the existing noise in measurement space for an inversion with an ill-conditioned Jacobian. To stabilize the noise propagation into parameter space, we introduce an additional empirically derived regularization proportional to the logarithm of the condition number of the Jacobian and inversely proportional to the L2-norm of the residual vector. The derivation, operational advantages and use of the MTLS method are demonstrated by retrieving sea surface temperature from GOES-13 satellite measurements. An analytic equation is derived for the total retrieval error, and is shown to agree well with the observed error. This can also serve as a quality indicator for pixel-level retrievals. We also introduce additional tests from the MTLS solutions to identify contaminated pixels due to residual clouds, error in the water vapor profile and aerosols. Comparison of the performances of our new and other methods, namely, optimal estimation and regression-based retrieval, is performed to understand the relative prospects and problems associated with these methods. This was done using operational match-ups for 42 months of data, and demonstrates a relatively superior temporally consistent performance of the MTLS technique.

RETRIEVAL OF SEA SURFACE TEMPERATURE OVER POTERAN ISLAND WATER OF INDONESIA WITH LANDSAT 8 TIRS IMAGE: A PRELIMINARY ALGORITHM

Abstract. 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 (R2) and Root Mean Square Error (RMSE) of 0.912 and 0.028, respectively.

What is the Role of the Sea Surface Temperature Uncertainty in the Prediction of Tropical Convection Associated with the MJO?

Abstract This study investigated the influence of the uncertainty in the sea surface temperature (SST) on the representation of the intraseasonal rainfall variability associated with the Madden–Julian oscillation (MJO) and how this influence varies with convection parameterization. The study was motivated by the fact that there exist substantial differences in observational SST analyses, and by the possibility that lacking sufficient accuracy for SSTs in dynamical models may degrade the MJO simulation and prediction. Experiments for the DYNAMO intensive observing period were carried out using the NCEP atmospheric Global Forecast System (GFS) with three convection schemes forced by three SST specifications. The SST specifications included the widely used National Climatic Data Center (NCDC) daily SST analysis, the TRMM Microwave Imager (TMI) SST retrieval, and an SST climatology that only contains climatological seasonal cycle. The experiments show that for all convection schemes, the advantage of using observed (TMI and NCDC) SSTs over the climatology SSTs can be seen as early as 5 days to 1 week after the start of the forecast. Further, the prediction with TMI SSTs was more skillful than that with the NCDC SSTs, indicating that the current level of SST uncertainties in the observational analyses can lead to large differences when they are used as the lower boundary conditions. The results suggest that the simulation and prediction can be improved with an atmosphere-only model forced by more accurate SSTs, or with a coupled atmosphere–ocean model that has a more realistic representation of the SST variability. Differences in the prediction among the convection schemes are also presented and discussed.

Detector-level spectral characterization of the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite long-wave infrared bands M15 and M16.

The Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) sensor data record (SDR) product achieved validated maturity status in March 2014 after roughly two years of on-orbit characterization (S-NPP spacecraft launched on 28 October 2011). During post-launch analysis the VIIRS Sea Surface Temperature (SST) Environmental Data Record (EDR) team observed an anomalous striping pattern in the daytime SST data. Daytime SST retrievals use the two VIIRS long-wave infrared bands: M15 (10.7μm) and M16 (11.8μm). To assess possible root causes due to detector-level spectral response function (SRF) effects, a study was conducted to compare the radiometric response of the detector-level and operational-band averaged SRFs of VIIRS bands M15 and M16. The study used simulated hyperspectral blackbody radiance data and clear-sky ocean hyperspectral radiances under different atmospheric conditions. It was concluded that the SST product is likely impacted by small differences in detector-level SRFs and that if users require optimal radiometric performance, detector-level processing is recommended for both SDR and EDR products. Future work should investigate potential SDR product improvements through detector-level processing in support of the generation of Suomi NPP VIIRS climate quality SDRs.

Brightness temperature model of sea foam layer at L-band

Permittivity of a sea foam layer is very important in investigating ocean brightness temperature model. At microwave frequency, the Rayleigh method is developed to estimate the effective permittivity of the sea foam layer. To simplify the tedious calculation of sea foam effective permittivity at L band (1.4 GHz), Pade’ approximation is adopted to fit the sea foam effective permittivity computed by the Rayleigh method. With this fitting formula, a new brightness temperature model of sea foam layer defined by certain geophysical parameters, such as air volume fraction (AVF), sea surface temperature (SST), sea surface salinity (SSS) and thickness of foam layer d, is given. Furthermore, the sensitivities of the brightness temperature model to SST, SSS, d and AVF of a sea foam layer at L band are discussed. The sensitivities are ranked from most to least in the order: (1) d; (2) AVF; (3) SSS; (4) SST. This result indicates that the measurement errors of d and AVF have significant impacts on the retrievals of SSS and SST. With the experimental brightness temperature data, the SSS and AFV are retrieved by cost function.

Sensitivity of infrared sea surface temperature retrievals to the vertical distribution of airborne dust aerosol

Sea surface temperature retrievals using the Advanced Very High Resolution Radiometer are highly sensitive to cloud cover and coarse mode aerosol particles such as dust. Operationally, techniques are used to flag contaminated retrievals; however, these techniques are less precise in removing dust-contaminated values. A commonly stated metric of quality for SST daytime retrievals is 0.5°C; thus dust contents that produce errors greater than this value should be of concern. Here we report on significant correlation between potential SST error and observed aerosol optical depths (AOD) that was found in the tropical region dominated by Saharan dust. Utilizing a radiative transfer model with variable dust contents and typical vertical distributions, errors greater than the desired 0.5°C accuracy are observed for dust AODs as low as 0.05. Errors of over 1°C occur with 0.25 AOD. Analysis of the AERONET data from Cape Verde, which includes the Saharan Air Layer off the west coast of Africa, reveals that 90% of the days during the boreal summer are found to have AOD amounts that correspond to error greater than 0.5°C. We found that a correction accurate within 0.25°C requires a mean accuracy of 0.1 AOD and proper vertical placement of the dust layer within 250m. While empirical SST retrievals often have some measure of climatological dust contamination built into them, this work shows that typical variability in dust loadings is a non-trivial error source against SST retrieval goals.

Removing Solar Radiative Effect from the VIIRS M12 Band at 3.7 μm for Daytime Sea Surface Temperature Retrievals

AbstractOperational sea surface temperature (SST) retrieval algorithms are stratified into nighttime and daytime. The nighttime algorithm uses two split-window Visible Infrared Imaging Radiometer Suite (VIIRS) bands—M15 and M16, centered at ~11 and ~12 m, respectively—and a shortwave infrared band—M12, centered at ~3.7 m. The M12 is most transparent and critical for accurate SST retrievals. However, it is not used during the daytime because of contamination by solar radiation, which is reflected by the ocean surface and scattered by atmospheric aerosols. As a result, daytime VIIRS SST and cloud mask products and applications are degraded and inconsistent with their nighttime counterparts. This study proposes a method to remove the solar contamination from the VIIRS M12 based on theoretical radiative transfer model analyses. The method uses either of the two VIIRS shortwave bands, centered at 1.6 m (M10) or 2.25 m (M11), to correct for the effect of solar reflectance in M12. Subsequently, the corrected daytime brightness temperature in M12 can be used as input into nighttime cloud mask and SST algorithms. Preliminary comparisons with the European Centre for Medium-Range Weather Forecasts (ECMWF) SST analysis suggest that the daytime SST products can be improved and potentially reconciled with the nighttime SST product. However, more substantial case studies and assessments using different SST products are required before the transition of this research work into operational products.

The Impact of Radio-Frequency Interference on WindSat Ocean Surface Observations

To study the effects of radio-frequency interference (RFI) on remote sensing data, we examined five years of WindSat ocean observations from regions affected by reflected X-band emissions from geostationary communication satellites. We compared measured brightness temperatures to modeled brightness temperatures obtained using mitigated retrievals as input to the WindSat parameterized radiative transfer model, demonstrating a considerable contribution to the radiometric measurements from interference. Comparisons of potentially contaminated retrievals of sea surface temperature (SST), wind speed, and wind direction with surface reanalyses confirmed that the presence of RFI can bias retrievals while also increasing retrieval uncertainty. Mitigation removed most of the biases. The quality of mitigated SST and wind speed retrievals was comparable to uncontaminated retrievals; however, the performance of first-rank wind directions was noticeably degraded. Recommendations are given on the use of contaminated and mitigated data, from radiance assimilation to climate studies.

Retrieval of Sea and Land Surface Temperature From SVISSR/FY-2C/D/E Measurements

This paper addresses the retrieval of sea surface temperature (SST) and land surface temperature (LST) from the measurements acquired by the Stretched Visible and Infrared Spin Scan Radiometer (SVISSR) on FengYun (FY) 2C/D/E satellites. First, the generalized split-window algorithms for SST and LST retrieval were developed using the moderate spectral resolution atmospheric transmittance algorithm and computer model (MODTRAN) fed with the parameter-adjusted standard model atmospheres. Then, the developed algorithms were applied to SST and LST retrieval from the SVISSR/FY-2C/D measurements in September 2007 and the SVISSR/FY-2E measurements in May 2010 over a large study area (latitude: 10° S-50° N; longitude: 60° E-130° E). Finally, the derived SVISSR/FY-2 SST and LST were, respectively, cross-validated with the MODIS/Terra SST and LST products. The results show the following: 1) the generalized split-window algorithms developed in this work are valid for both SST and LST retrieval from SVISSR/FY-2C/D/E measurements; 2) in contrast to the MODIS/Terra SST and LST products, the errors in nighttime retrieval are usually less than that in daytime retrieval, and the errors in SVISSR/FY-2 SST are generally less than the errors in SVISSR/FY-2 LST; and 3) for both daytime and nighttime, the total errors in SVISSR/FY-2C/D/E LST are, respectively, 0.3 ± 1.6 K, 0.3 ± 1.9 K, and 1.0 ± 1.8 K, while the total errors in SVISSR/FY-2C/D/E SST are, respectively, -0.6 ± 1.3 K, -0.2 ± 1.5 K, and 0.4 ± 1.8 K.

Evaluation and selection of SST regression algorithms for JPSS VIIRS

AbstractTwo global level 2 sea surface temperature (SST) products are generated at NOAA from the Suomi National Polar‐Orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) sensor data records (L1) with two independent processing systems, the Joint Polar Satellite System (JPSS) Interface Data Processing Segment (IDPS) and the NOAA heritage Advanced Clear‐Sky Processor for Oceans (ACSPO). The two systems use different SST retrieval and cloud masking algorithms. Validation against in situ and L4 analyses has shown suboptimal performance of the IDPS product. In this context, existing operational and proposed SST algorithms have been evaluated for their potential implementation in IDPS. This paper documents the evaluation methodology and results. The performance of SST retrievals is characterized with bias and standard deviation with respect to in situ SSTs and sensitivity to true SST. Given three retrieval metrics, all being variable in space and with observational conditions, an additional integral metric is needed to evaluate the overall performance of SST algorithms. Therefore, we introduce the Quality Retrieval Domain (QRD) as a part of the global ocean, where the retrieval characteristics meet predefined specifications. Based on the QRDs analyses for all tested algorithms over a representative range of specifications for accuracy, precision, and sensitivity, we have selected the algorithms developed at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI‐SAF) for implementation in IDPS and ACSPO. Testing the OSI‐SAF algorithms with ACSPO and IDPS products shows the improved consistency between VIIRS SST and Reynolds L4 daily analysis. Further improvement of the IDPS SST product requires adjustment of the VIIRS cloud and ice masks.

The effects of anomalous atmospheres on the accuracy of infrared sea-surface temperature retrievals: Dry air layer intrusions over the tropical ocean

The effects of layers in the atmosphere with anomalously low moisture content on the accuracy of the sea-surface temperature (SST) derived from measurements of infrared radiometers on earth observation satellites are quantified using measurements taken from research cruises and numerical simulations. Radiosonde data from areas of the oceans that are seasonally affected by intrusions of dry air masses of continental origin were used with the Line-By-Line Radiative Transfer Model (LBLRTM) to simulate brightness temperatures measured in MODIS bands 31 and 32 (at wavelengths of ~11μm and ~12μm). The radiosonde datasets contained profiles with and without a dry layer, representing the baseline (no dry layer aloft) and the anomalous conditions (dry layer present). MODIS SST retrieval algorithm versions 5 and 6 were applied to the simulated brightness temperatures to obtain the SST and the retrieval errors were examined. Whereas the average errors for the baseline ‘no dry layer’ conditions range between 0.29 and 0.72K, in the case of very deep dry layers the errors can be >1K. Simulations were also performed for atmospheric profiles that were created from the measured profiles by ‘drying’ layers at various altitudes. It was found that the retrieval errors 1) depend on the amount of water vapor in the atmosphere, 2) change in a systematic way dependent on the presence and characteristics of a dry layer, 3) dry layers in the lower troposphere make the SST retrieval errors more positive, and dry layers in the upper troposphere tend to introduce SST retrieval errors that are more negative.

Analysis of the potential and limitations of microwave radiometry for the retrieval of sea surface temperature: Definition of MICROWAT, a new mission concept

The sensitivity of passive microwave observations to the sea surface temperature (SST) is carefully analyzed, with the objective of designing an optimized satellite instrument, MICROwave Wind And Temperature (MICROWAT), dedicated to an “all‐weather” estimation of the SST at high spatial resolution (15 km). Our study stresses the importance of low‐frequency observations around 6 GHz for accurate SST retrieval. Compared to the 11 GHz channel, the 6 GHz channel provides more sensitivity to the low SSTs and offers lower instrument noise, thanks to possibly broader channel bandwidths. However, it requires much larger antenna size for a given spatial resolution. Two instrument concepts have been suggested, one using a classic real aperture antenna and the other using synthetic interferometric antennas. This first analysis shows that 2‐D interferometric systems would be very complex and would not satisfy the user requirements in terms of SST accuracy. A 1‐D interferometric system could be proposed, but its development requires additional investigation. A dedicated conical scanner onboard a microsatellite with a 6 m antenna and channels at 6.9 and 18.7 GHz (both with V and H polarizations) can provide an SST accuracy of 0.3 K with a 15 km spatial resolution, with today's technology.

A Hybrid Cloud Detection Algorithm to Improve MODIS Sea Surface Temperature Data Quality and Coverage Over the Eastern Gulf of Mexico

Cloud contamination can lead to significant biases in sea surface temperature (SST) as estimated from satellite measurements. The effectiveness of four cloud detection algorithms for the Moderate Resolution Imaging Spectroradiometer (MODIS) in retaining valid SST data and masking cloud-contaminated data was assessed for all 2125 daytime and nighttime images during 2010 over the eastern Gulf of Mexico and including the east coast of Florida. None of the cloud detection algorithms was found to be sufficient to reliably differentiate clouds from valid SST, particularly during anomalously cold events. The strengths and weaknesses of each algorithm were identified, and a new hybrid cloud detection algorithm was developed to maximize valid data retention while excluding cloud-contaminated pixels. The hybrid algorithm was based on a decision tree, which includes a set of rules to use existing algorithms in different ways according to time and location. Comparing with >10000 concurrent in situ SST measurements from buoys, images processed with the hybrid algorithm showed increases in data capture and improved accuracy statistics over most existing algorithms. In particular, while keeping the same accuracy, the hybrid algorithm resulted in nearly 20% more SST retrievals than the most accurate algorithm (Quality SST) currently being used for operational processing. The increases in both data coverage and SST range should improve MODIS data products for more reliable SST retrievals in near real time, thus enhancing the ocean observing capacity to detect anomaly events and study short- and long-term SST changes in coastal environments.

Extended optimal estimation techniques for sea surface temperature from the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI)

Sea surface temperature (SST) can be estimated from day and night observations of the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) by optimal estimation (OE). We show that exploiting the 8.7μm channel, in addition to the “traditional” wavelengths of 10.8 and 12.0μm, improves OE SST retrieval statistics in validation. However, the main benefit is an improvement in the sensitivity of the SST estimate to variability in true SST.In a fair, single-pixel comparison, the 3-channel OE gives better results than the SST estimation technique presently operational within the Ocean and Sea Ice Satellite Application Facility. This operational technique is to use SST retrieval coefficients, followed by a bias-correction step informed by radiative transfer simulation. However, the operational technique has an additional “atmospheric correction smoothing”, which improves its noise performance, and hitherto had no analogue within the OE framework. Here, we propose an analogue to atmospheric correction smoothing, based on the expectation that atmospheric total column water vapour has a longer spatial correlation length scale than SST features. The approach extends the observations input to the OE to include the averaged brightness temperatures (BTs) of nearby clear-sky pixels, in addition to the BTs of the pixel for which SST is being retrieved. The retrieved quantities are then the single-pixel SST and the clear-sky total column water vapour averaged over the vicinity of the pixel. This reduces the noise in the retrieved SST significantly. The robust standard deviation of the new OE SST compared to matched drifting buoys becomes 0.39K for all data. The smoothed OE gives SST sensitivity of 98% on average. This means that diurnal temperature variability and ocean frontal gradients are more faithfully estimated, and that the influence of the prior SST used is minimal (2%). This benefit is not available using traditional atmospheric correction smoothing.

Data Fusion Between Microwave and Thermal Infrared Radiometer Data and Its Application to Skin Sea Surface Temperature, Wind Speed and Salinity Retrievals

Method for data fusion between Microwave Scanning Radiometer: MSR and Thermal Infrared Radiometer: TIR derived skin sea surface temperature: SSST, wind speed: WS and salinity is proposed. SSST can be estimated with MSR and TIR radiometer data. Although the contribution ocean depth to MSR and TIR radiometer data are different each other, SSST estimation can be refined through comparisons between MSR and TIR derived SSST. Also WS and salinity can be estimated with MSR data under the condition of the refined SSST. Simulation study results support the idea of the proposed data fusion method.