Group For High Resolution Sea Surface Temperature Research Articles

Validation and Application of Satellite-Derived Sea Surface Temperature Gradients in the Bering Strait and Bering Sea

The Arctic is one of the most important regions in the world’s oceans for understanding the impacts of a changing climate. Yet, it is also difficult to measure because of extreme weather and ice conditions. In this work, we directly compare four datasets from the Group for High-Resolution Sea Surface Temperature (GHRSST) with a NASA Saildrone deployment along the Alaskan Coast and the Bering Sea and Bering Strait. The four datasets used are the Remote Sensing Systems Microwave Infrared Optimally Interpolated (MWIR) product, the Canadian Meteorological Center (CMC) product, the Daily Optimally Interpolated Product (DOISST), and the Operational Sea Surface Temperature and Ice Analysis (OSTIA) product. Spatial sea surface temperature (SST) gradients were derived for both the Saildrone deployment and GHRSST products, with the GHRSST products collocated with the Saildrone deployment. Overall, statistics indicate that the OSTIA product had a correlation of 0.79 and a root mean square difference of 0.11 °C/km when compared with Saildrone. CMC had the highest correlation of 0.81. Scatter plots indicate that OSTIA had the slope closest to one, thus best reproducing the magnitudes of the Saildrone gradients. Differences increased at latitudes > 65°N where sea ice would have a greater impact. A trend analysis was then performed on the gradient fields. Overall, positive trends in gradients occurred in areas along the coastal regions. A negative trend occurred at approximately 60°N. A major finding of this study is that future work needs to revolve around the impact of changing ice conditions on SST gradients. Another major finding is that a northward shift in the southern ice edge occurred after 2010 with a maxima at approximately 2019. This indicates that the shift of the southern ice edge is not gradual but has dramatically increased over the last decade. Future work needs to revolve around examining the possible causes for this northward shift.

Comparing eight remotely sensed sea surface temperature products and Bayesian maximum entropy-based data fusion products

Sea surface temperature is an important oceanographic physical attribute. In this work, 5 kinds of infrared SST data sets (including FY3C-VIRR, MetopB-AVHRR3, MODIS-Aqua, MODIS-Terra, SNPP-VIIRS) and 3 kinds of microwave SST data sets (including AMSR-2, GMI, WindSat) were considered for fusion purposes. The area with geographical coordinates ranging from 100° W to 180° W and from 10° N to 10° S was chosen as the study area. Using the Bayesian maximum entropy (BME) method, four SST fusion products were generated from January 1 to January 31, 2020, with temporal resolution of 1 day, spatial resolution of 0.25 decimal degrees, and coverage rate of 100%. The Holdout cross-validation method was employed to evaluate the performance of BME for SST interpolation purposes by using two data sets, i.e., WinSat SST and MODIS-Terra SST data; and the validation results showed that the mean absolute error (MAE) values of the WindSat SST data and the MODIS-Terra SST data were 0.117 mg/m3 and 0.191 mg/m3, respectively, and the root mean square error (RMSE) values were 0.158 mg/m3 and 0.321 mg/m3, respectively. In addition, four SST interpolation products were compared with the Optimum Interpolation Sea Surface Temperature (OISST) and the Group for High-Resolution Sea Surface Temperature (GHRSST) products by combining Argo data with Buoy data treated as in-situ measured data sets. Three (MW-WindSat SST product, ALL-Terra SST product and ALL-WindSat SST product) of the four interpolation products performed better than the OISST and GHRSST data sets. Among them, the ALL-WindSat SST product considering WindSat SST data and other 7 SST data as hard and soft data exhibited the best performance in terms of all three indicators: the coefficient of determination value (0.744), the MAE value (0.82 mg/m3) and the RMSE value (1.082 mg/m3). In sum, the present work made full use of the inclusiveness and integration abilities of the BME method for various types of data, and demonstrated its high accuracy in a number of real world data, thus demonstrating its considerable potential in the context of marine data interpolation and fusion.

Seasonal variability in spatial patterns of sea surface cold- and warm fronts over the continental shelf of the northern South China Sea

Ubiquitous fronts are a key part of energy transfer from large scales to small scales and exert a great impact on material exchange and biogeochemical processes. The spatial pattern and seasonal variability of cold- and warm fronts over the wide shelf of the northern South China Sea (SCS) are investigated using a 20-year time series (2002−2021) of 1-km spatial resolution Group for High Resolution Sea Surface Temperature (GHRSST) images. Our analysis shows distinct spatial and temporal variability in the occurrence of the cold- and warm fronts. Over the inner shelf (depth <50 m), the band-shaped cold fronts are predominately observed during spring through autumn from east of Hainan Island to Taiwan Shoal, with the presence of the maximum intensity and probability in winter. The frontal formations are possibly associated with the joint effect of the Guangdong Coastal Current (GCC) and the South China Sea Warm Current. During summer, the inshore fronts have relatively low probability and gradient magnitude. The warm fronts mainly occur off the western Guangdong coast possibly due to the southwestward-flowing GCC, whereas the cold fronts dominate off the eastern Guangdong coast and the eastern Hainan Island largely because of the coastal upwelling. Over the outer shelf (depth >50 m), the finer-scale cold- and warm fronts are discretely observed, with relatively weaker intensity and lower probability. The frontal activities are very vigorous in winter but slightly quiescent in summer, apparently resulting from the influence of the rich submesoscale processes in the SCS. This study could help improve our understanding of the SCS oceanic multiscale dynamics.

A zonal and segmentation analysis of the relationships between variations in sea-surface temperatures of the western Pacific Ocean and Southeast Asian rainfall

Abstract With the objective of understanding how terrestrial precipitation in Southeast Asia (SEA) responds to both temporal and spatial variations in sea-surface temperature (SST) of the western Pacific Ocean, we first used the North–South and zonal SST variation analyses for the data set named A Group for High-Resolution Sea-Surface Temperature (GHRSST) Level 4 Multiscale Ultrahigh Resolution (MUR). Second, we applied a localized correlation analysis to the SST data set and rainfall data (Integrated Multi-satellitE Retrievals for Global precipitation measurement (IMERG)) in order to determine the boundaries and characteristics of the complex interactions between SST and rainfall variations. The zonal SST variation analysis result suggested that the warm pool in the North of western Pacific zones close to SEA (120 °E–150 °E) was more sustained and less varied than that in the South zones. In terms of the western Pacific SST impact on rainfall in SEA, the correlation analysis confirmed the well-established concept that the SST of the ocean part close to the SEA land, in general, has more influence on SEA rainfall than does the SST of the ocean part far away from SEA. Still, there were some parts of SEA whose rainfall was not governed by the sea–land juxtaposition.

On the understanding of very severe cyclone storm Ockhi with the WRF-ARW model

Understanding the dynamics of tropical cyclones (TCs) in terms of intensity, and their trajectory is essential for an adequate early warning and mitigation. The present study attempts to understand the synoptic features of the recent TC Ockhi through a simulation-based approach with the Weather Research and Forecasting (WRF, version 3.8.1) model. Ockhi is considered as a unique TC that originated from depression in the Bay of Bengal on 29 November 2017, recurved towards the Arabian Sea, where it intensified into a very severe cyclone storm and weakened on 5 December 2017. WRF model forced with initial condition from (global forecasting system) GFS data and sea surface temperature (SST) from Group for High-Resolution SST (GHRSST) product for different lead times to test the potential sensitivity of the model. One with an extended period from 20 November to 20 December 2017 (WRF1) and another initiated from 27 November to 6 December 2017 (WRF2). Comparison of the simulated track with the best track estimates from the Indian Meteorological Department indicated an overall track deviation greater than 100 km for both the simulations. The analysis with the extended lead time simulation indicates that the WRF simulated sea level pressure and wind intensity are close to that observed by Arabian sea buoys; CB02, AD08, AD10, and AD07. Daily averaged wind estimate comparison of WRF1 with Scatsat-1 and ERA-5 indicates that the model is slightly overestimating, whereas comparison of peak wind intensity with the time instantaneous swath product of Scatsat-1 leads to underestimation. Analysis of various simulated synoptic features of the cyclone, as discussed in this paper, indicates that the model is skillful in capturing the various stages of cyclone Ockhi.

Atmosphere-Ocean Coupled Variability in the Arabian/Persian Gulf

The Arabian Gulf comprises one of the world's most unique and fragile marine ecosystems; it is susceptible to the adverse effects of climate change due to its shallow depth and its location within an arid region that witnesses frequent severe atmospheric events. To reproduce these effects in numerical models, it is important to obtain a better understanding of the region's sea surface temperature (SST) variability patterns, as SST is a major driver of circulation in shallow environments. To this end, here, empirical orthogonal function (EOF) decomposition analysis was conducted to investigate interannual to multi-decadal SST variability in the Gulf from 1982 to 2020, using daily Level 4 Group for High Resolution SST (GHRSST) data. In this way, three dominant EOF modes were identified to contribute the Gulf's SST variability. Significant spatial and temporal correlations were found suggesting that throughout the 39-year study period, SST variability could be attributed to atmospheric changes driven by the El Nio-Southern Oscillation (ENSO), Atlantic Multi-decadal Oscillation (AMO), and Indian Ocean Dipole (IOD) climate modes. Spatial and temporal analyses of the dataset revealed that the average SST was 26.7°C, and that the warming rate from 1982 to 2020 reached up to 0.59°C/decade. A detailed examination of SST changes associated with heat exchange at the air-sea interface was conducted using surface heat fluxes from fifth generation (ERA5) European Centre for Medium-Range Weather Forecasts (ECMWF). Despite the SST warming trend, the accumulation of heat during the study period is suggesting that there was an overall loss of heat (cooling). This cooling reverted into heating in 2003 and has since been increasing.

Sea Surface Temperature Intercomparison in the Framework of the Copernicus Climate Change Service (C3S)

AbstractA joint effort between the Copernicus Climate Change Service (C3S) and the Group for High Resolution Sea Surface Temperature (GHRSST) has been dedicated to an intercomparison study of eight global gap-free sea surface temperature (SST) products to assess their accurate representation of the SST relevant to climate analysis. In general, all SST products show consistent spatial patterns and temporal variability during the overlapping time period (2003–18). The main differences between each product are located in the western boundary current and Antarctic Circumpolar Current regions. Linear trends display consistent SST spatial patterns among all products and exhibit a strong warming trend from 2012 to 2018 with the Pacific Ocean basin as the main contributor. The SST discrepancy between all SST products is very small compared to the significant warming trend. Spatial power spectral density shows that the interpolation into 1° spatial resolution has negligible impacts on our results. The global mean SST time series reveals larger differences among all SST products during the early period of the satellite era (1982–2002) when there were fewer observations, indicating that the observation frequency is the main constraint of the SST climatology. The maturity matrix scores, which present the maturity of each product in terms of documentation, storage, and dissemination but not the scientific quality, demonstrate that ESA-CCI and OSTIA SST are well documented for users’ convenience. Improvements could be made for MGDSST and BoM SST. Finally, we have recommended that these SST products can be used for fundamental climate applications and climate studies (e.g., El Niño).

Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1

AbstractThe NOAA/NESDIS/NCEI Daily Optimum Interpolation Sea Surface Temperature (SST), version 2.0, dataset (DOISST v2.0) is a blend of in situ ship and buoy SSTs with satellite SSTs derived from the Advanced Very High Resolution Radiometer (AVHRR). DOISST v2.0 exhibited a cold bias in the Indian, South Pacific, and South Atlantic Oceans that is due to a lack of ingested drifting-buoy SSTs in the system, which resulted from a gradual data format change from the traditional alphanumeric codes (TAC) to the binary universal form for the representation of meteorological data (BUFR). The cold bias against Argo was about −0.14°C on global average and −0.28°C in the Indian Ocean from January 2016 to August 2019. We explored the reasons for these cold biases through six progressive experiments. These experiments showed that the cold biases can be effectively reduced by adjusting ship SSTs with available buoy SSTs, using the latest available ICOADS R3.0.2 derived from merging BUFR and TAC, as well as by including Argo observations above 5-m depth. The impact of using the satellite MetOp-B instead of NOAA-19 was notable for high-latitude oceans but small on global average, since their biases are adjusted using in situ SSTs. In addition, the warm SSTs in the Arctic were improved by applying a freezing point instead of regressed ice-SST proxy. This paper describes an upgraded version, DOISST v2.1, which addresses biases in v2.0. Overall, by updating v2.0 to v2.1, the biases are reduced to −0.07° and −0.14°C in the global ocean and Indian Ocean, respectively, when compared with independent Argo observations and are reduced to −0.04° and −0.08°C in the global ocean and Indian Ocean, respectively, when compared with dependent Argo observations. The difference against the Group for High Resolution SST (GHRSST) Multiproduct Ensemble (GMPE) product is reduced from −0.09° to −0.01°C in the global oceans and from −0.20° to −0.04°C in the Indian Ocean.

A machine learning approach to estimating the error in satellite sea surface temperature retrievals

Global, repeated, and accurate measurements of Sea Surface Temperature (SST) are critical for weather and climate projections. While thermometers on buoys measure SST relatively accurately, only sensors aboard satellites give global and repeated SST measurements necessary for many applications, including climate modeling. For satellite-based thermal infrared sensors, an atmospheric correction converts calibrated brightness temperatures measured at orbital height into an SST estimate, but imperfect assumptions in the correction algorithm coupled with variability in atmospheric conditions and viewing geometries can lead to a wide range of errors and uncertainties in the satellite-derived SST retrievals. Estimates of the resulting errors are imperative for satellite-derived SST assimilation into climate models. This paper evaluates the use of machine learning Decision Tree algorithms to predict the central tendency and dispersion of errors in satellite-derived SST retrievals. First, using records from the NASA R2014.1 MODIS Aqua SST Matchup Database, which includes matched-up satellite and in situ SST measurements, a set of seven variables was derived that addresses the assumptions and known issues in the satellite SST retrieval process. Then, both Random Forest and Cubist Decision Trees were used to predict the SST residual (satellite SST minus skin-corrected buoy SST) for each matchup. While both Decision Tree methods performed similarly well, the Cubist model is more easily interpreted and yields better predictions of errors for relatively infrequent conditions. Various characteristics of the groups of matchups identified by Cubist were explored, and uncertainty values for the error estimates were derived for each group. Overall, the Cubist model predicted the skin SST residual with a root-mean-squared-error of 0.380 °C across nighttime cloud-filtered domains, demonstrating that a Cubist model is viable for quantitatively and accurately predicting Single Sensor Error Statistics per pixel as required by the Group for High Resolution SST. In this paper, we present the training and testing of both Decision Tree models. Because of its interpretability, we explore in detail the characteristics of the Cubist-derived groups to gain new geophysical insight into the satellite-derived SST retrieval error across different measurement conditions.

Variational Based Estimation of Sea Surface Temperature from Split-Window Observations of INSAT-3D/3DR Imager

Infrared (IR) radiometers from geostationary (GEO) satellites have an advantage over low-earth orbiting (LEO) satellites as they provide continuous observations to monitor the diurnal variations in the sea surface temperature (SST), typically better than 30-minute interval. However, GEO satellite observations suffer from significant diurnal and seasonal biases arising due to varying sun-earth-satellite geometry, leading to biases in SST estimates from conventional non-linear regression-based algorithms (NLSST). The midnight calibration issue occurring in GEO sensors poses a different challenge altogether. To mitigate these issues, we propose SST estimation from split-window IR observations of INSAT-3D and 3DR Imagers using One-Dimensional Variational (1DVAR) scheme. Prior to SST estimation, the bias correction in Imager observations is carried out using cumulative density function (CDF) matching. Then NLSST and 1DVAR algorithms were applied on six months of INSAT-3D/3DR observations to retrieve the SST. For the assessment of the developed algorithms, the retrieved SST was validated against in-situ SST measurements available from in-situ SST Quality Monitor (iQuam) for the study period. The quantitative assessment confirms the superiority of the 1DVAR technique over the NLSST algorithm. However, both the schemes under-estimate the SST as compared to in-situ SST, which may be primarily due to the differences in the retrieved skin SST versus bulk in-situ SST. The 1DVAR scheme gives similar accuracy of SST for both INSAT-3D and 3DR with a bias of −0.36 K and standard deviation (Std) of 0.63 K. However, the NLSST algorithm provides slightly less accurate SST with bias (Std) of −0.18 K (0.87 K) for INSAT-3DR and −0.27 K (0.95 K) for INSAT-3D. Both the NLSST and 1DVAR algorithms are capable of producing the accurate thermal gradients from the retrieved SST as compared to the gradients calculated from daily Multiscale Ultrahigh Resolution (MUR) level-4 analysis SST acquired from Group for High-Resolution Sea Surface Temperature (GHRSST). Based on these spatial gradients, thermal fronts can be generated that are very useful for predicting potential fishery zones (PFZ), which is available from GEO satellites, INSAT-3D/3DR, in near real-time at 15-minute intervals. Results from the proposed 1DVAR and NLSST algorithms suggest a marked improvement in the SST estimates with reduced diurnal/seasonal biases as compared to the operational NLSST algorithm.

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.

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 modelling 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.

Significance of high resolution GHRSST on prediction of Indian Summer Monsoon

In this study, the Weather Research and Forecasting (WRF) model was used to assess the importance of very high resolution sea surface temperature (SST) on seasonal rainfall prediction. Two different SST datasets available from the National Centers for Environmental Prediction (NCEP) global model analysis and merged satellite product from Group for High Resolution SST (GHRSST) are used as a lower boundary condition in the WRF model for the Indian Summer Monsoon (ISM) 2010. Before using NCEP SST and GHRSST for model simulation, an initial verification of NCEP SST and GHRSST are performed with buoy measurements. It is found that approximately 0.4K root mean square difference (RMSD) in GHRSST and NCEP SST when compared with buoy observations available over the Indian Ocean during 01 May to 30 September 2010. Our analyses suggest that use of GHRSST as lower boundary conditions in the WRF model improve the low level temperature, moisture, wind speed and rainfall prediction over ISM region. Moreover, temporal evolution of surface parameters such as temperature, moisture and wind speed forecasts associated with monsoon is also improved with GHRSST forcing as a lower boundary condition. Interestingly, rainfall prediction is improved with the use of GHRSST over the Western Ghats, which mostly not simulated in the NCEP SST based experiment.

Study of the Temporal and Spatial Analysis by using SST Satellite Data

It is studied the sea surface temperature(SST) variation around Korean Peninsula by using Group for High Resolution Sea Surface Temperature(GHRSST) images for 22-year data from 1993 January 1 to 2014 December 31. For the accuracy of GHRSST data, near observation station were collected to correlate GHRSST and water temperature data. Mean correlation in Yellow Sea was 0.968, mean correlation in South Sea was 0.963, and mean correlation in East Sea was 0.962. The result of harmonic analysis showed that the amplitude of SST in Yellow Sea was 10~16℃ and the amplitude of SST in East Sea and South Sea was 8~12℃. The phase of SST in Yellow Sea was 230~235°, and the phase of SST in South Sea and East Sea was 240~244. EOF analysis resulted that the first mode of coefficient was 97.59%. When it is used with various climate data, for example, sea level, wind, air temperature, it will be represented the specific variation of SST.

HYCOM 재분석 자료가 재현한 동해 수온 및 염분 평가

We evaluate the temperature and salinity fields in the East Sea reproduced by the global ocean reanalysis data using HYbrid Coordinate Ocean Model (HYCOM for short). Temporal correlation of Sea Surface Temperature (SST) change between HYCOM and the Group for High Resolution Sea Surface Temperature (GHRSST) are higher in summer than winter. Though distributions of temperature and salinity in the HYCOM are similar to those from historical data (World Ocean Atlas 2013 V2), salinity in the HYCOM is lower (highter) in the region where the salinity is high (low). Temperature fields in the Ulleung basin of HYCOM are quite similar to those derived from Pressure-recording Inverted Echo Sounder (PIES), such as the correlation coefficient is higher than 0.7. This indicates that the HYCOM represents well the circulation and meso-scale phenomena in the Ulleung basin.

Validation of satellite sea surface temperature analyses in the Beaufort Sea using UpTempO buoys

Many different blended sea surface temperature (SST) analyses are currently available and exhibit significant differences in the high latitude regions. It is challenging for users to determine which of these products is most accurate and best suited for their applications. Nine different SST analyses and two single sensor satellite products are compared with independent observations from Upper Temperature of the polar Oceans (UpTempO) buoys deployed in the Beaufort Sea in 2012 and 2013 during the Marginal Ice Zone Processes Experiment (MIZOPEX). The relative skill of the different SST products is evaluated using a combination of Taylor diagrams and two different verification scores that weight different statistical measures. Skill thresholds based on satellite accuracy requirements are chosen to map products with similar performance into three discrete skill categories: excellent, good, and poor. Results are presented for three subsets of the buoys corresponding to different regimes: coastal waters, northerly waters, and extreme weather. The presence of strong thermal gradients and cloudiness posed problems for the SST products, while in more homogeneous regions the performance was improved and more similar among products. The impact of variations in the ice mask between the SST products was mostly inconsequential. While the relative performance of the analyses varied with regime, overall, the best performing analyses for this region and period included the NOAA Optimal Interpolation SST (OISST), the Canadian Meteorological Centre (CMC) SST, and the Group for High Resolution SST (GHRSST) Multi-Product Ensemble (GMPE).

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).

Generation of high resolution sea surface temperature using multi-satellite data for operational oceanography

In the present article, we introduce a high resolution sea surface temperature (SST) product generated daily by Korea Institute of Ocean Science and Technology (KIOST). The SST product is comprised of four sets of data including eight-hour and daily average SST data of 1 km resolution, and is based on the four infrared (IR) satellite SST data acquired by advanced very high resolution radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), Multifunctional Transport Satellites-2 (MTSAT-2) Imager and Meteorological Imager (MI), two microwave radiometer SSTs acquired by Advanced Microwave Scanning Radiometer 2 (AMSR2), and WindSAT with in-situ temperature data. These input satellite and in-situ SST data are merged by using the optimal interpolation (OI) algorithm. The root-mean-square-errors (RMSEs) of satellite and in-situ data are used as a weighting value in the OI algorithm. As a pilot product, four SST data sets were generated daily from January to December 2013. In the comparison between the SSTs measured by moored buoys and the daily mean KIOST SSTs, the estimated RMSE was 0.71°C and the bias value was–0.08°C. The largest RMSE and bias were 0.86 and–0.26°C respectively, observed at a buoy site in the boundary region of warm and cold waters with increased physical variability in the Sea of Japan/East Sea. Other site near the coasts shows a lower RMSE value of 0.60°C than those at the open waters. To investigate the spatial distributions of SST, the Group for High Resolution Sea Surface Temperature (GHRSST) product was used in the comparison of temperature gradients, and it was shown that the KIOST SST product represents well the water mass structures around the Korean Peninsula. The KIOST SST product generated from both satellite and buoy data is expected to make substantial contribution to the Korea Operational Oceanographic System (KOOS) as an input parameter for data assimilation.

Group for High Resolution Sea Surface temperature (GHRSST) analysis fields inter-comparisons. Part 1: A GHRSST multi-product ensemble (GMPE)

Many sea surface temperature (SST) gap-free gridded analysis (Level 4, or L4) fields are produced by various groups in different countries. The Group for High Resolution SST (GHRSST) is an international collaboration body which has formed the inter-comparison technical advisory group (IC-TAG), to advise SST producers and users on the relative performance of these SST fields. This two-part paper describes two of the three major systems developed under GHRSST coordination towards this goal. Part one (this paper) describes the GHRSST Multi-Product Ensemble (GMPE) system, which runs on a daily basis at the UK Met Office, taking various L4 analyses as inputs, transferring them onto a common grid, and producing an ensemble median and standard deviation. The various analysis systems contributing to the GHRSST inter-comparisons are discussed, highlighting areas of commonality between the systems as well as those parts of the systems where there is less agreement on the appropriate algorithmic or parametric choices. The characteristics of the contributing L4 analyses are demonstrated by comparing them to near-surface Argo profile temperature data, which provide an independent measurement of SST and have been shown to provide a good estimate of foundation SST (the SST free of diurnal warming). The feature resolution characteristics of the L4 analyses are demonstrated by calculating horizontal gradients of the SST fields (on their original grid). The accuracy and resolution of the GMPE median are compared with those of the input analyses using the same metrics, showing that the GMPE median is more accurate than any of the contributing analyses with a standard deviation error of 0.40K globally with respect to near-surface Argo data. For use in climate applications such as trend analysis or assimilation into climate models, it is important to have a good measure of uncertainty, so the suitability of the GMPE standard deviation as a measure of uncertainty is explored. This assessment shows that, over large spatial and temporal scales, the spread in the ensemble does have a strong relationship with the error in the median, although it underestimates the error by about one third.

Group for High Resolution Sea Surface Temperature (GHRSST) analysis fields inter-comparisons—Part 2: Near real time web-based level 4 SST Quality Monitor (L4-SQUAM)

There are a growing number of level 4 (L4; gap-free gridded) sea surface temperature (SST) products generated by blending SST data from various sources which are available for use in a wide variety of operational and scientific applications. In most cases, each product has been developed for a specific user community with specific requirements guiding the design of the product. Consequently differences between products are implicit. In addition, anomalous atmospheric conditions, satellite operations and production anomalies may occur which can introduce additional differences. This paper describes a new web-based system called the L4 SST Quality Monitor (L4-SQUAM) developed to monitor the quality of L4 SST products.L4-SQUAM intercompares thirteen L4 products with 1-day latency in an operational environment serving the needs of both L4 SST product users and producers. Relative differences between products are computed and visualized using maps, histograms, time series plots and Hovmöller diagrams, for all combinations of products. In addition, products are compared to quality controlled in situ SST data (available from the in situ SST Quality Monitor, iQUAM, companion system) in a consistent manner. A full history of products statistics is retained in L4-SQUAM for time series analysis. L4-SQUAM complements the two other Group for High Resolution SST (GHRSST) tools, the GHRSST Multi Product Ensemble (GMPE) and the High Resolution Diagnostic Data Set (HRDDS) systems, documented in part 1 of this paper and elsewhere, respectively.Our results reveal significant differences between SST products in coastal and open ocean areas. Differences of >2°C are often observed at high latitudes partly due to different treatment of the sea-ice transition zone. Thus when an ice flag is available, the intercomparisons are performed in two ways: including and excluding ice-flagged grid points. Such differences are significant and call for a community effort to understand their root cause and ensure consistency between SST products. Future work focuses on including the remaining daily L4 SST products, accommodating for newer L4 SSTs which resolve the diurnal variability and evaluating retrospectively regenerated L4 SSTs to support satellite data reprocessing efforts aimed at generating improved SST Climate Data Records.