Ocean And Sea Ice Satellite Application Facility Research Articles

Polar Sea Ice Monitoring Using HY-2B Satellite Scatterometer and Scanning Microwave Radiometer Measurements

The Ku band microwave scatterometer (SCA) and scanning microwave radiometer (SMR) onboard HaiYang-2B (HY-2B) can simultaneously supply active and passive microwave observations over the polar region. In this paper, a polar ice water discrimination model and Arctic sea-ice-type classification model based on the support vector machine (SVM) method were established and used to produce a daily sea ice extent dataset from 2019 to 2021 with data from SCA and SMR. First, suitable scattering and radiation parameters are chosen as input data for the discriminant model. Then, the sea ice extent was obtained based on the monthly ice water discrimination model, and finally, the ice over the Arctic was classified into multiyear ice (MYI) and first-year ice (FYI). The 3-year ice extent and MYI extent products were consistent with the similar results of the National Snow and Ice Data Center (NSIDC) and Ocean and Sea Ice Satellite Application Facility (OSISAF). Using the OSISAF similar product as validation data, the overall accuracies (OAs) of ice/water discrimination and FYI/MYI discrimination are 99% and 97%, respectively. Compared with the high spatial resolution classification results of the Moderate Resolution Imaging Spectroradiometer (MODIS) and SAR, the OAs of ice/water discrimination and FYI/MYI discrimination are 96% and 86%, respectively. In conclusion, the SAC and SMR of HY-2B have been verified for monitoring polar sea ice, and the sea ice extent and sea-ice-type products are promising for integration into long-term sea ice records.

Polar Sea Ice Monitoring Using HY-2B Satellite Scatterometer and Scanning Microwave Radiometer Measurements

The Ku band microwave scatterometer (SCA) and scanning microwave radiometer (SMR) onboard HaiYang-2B (HY-2B) can simultaneously supply active and passive microwave observations over the polar region. In this paper, a polar ice water discrimination model and Arctic sea-ice-type classification model based on the support vector machine (SVM) method were established and used to produce a daily sea ice extent dataset from 2019 to 2021 with data from SCA and SMR. First, suitable scattering and radiation parameters are chosen as input data for the discriminant model. Then, the sea ice extent was obtained based on the monthly ice water discrimination model, and finally, the ice over the Arctic was classified into multiyear ice (MYI) and first-year ice (FYI). The 3-year ice extent and MYI extent products were consistent with the similar results of the National Snow and Ice Data Center (NSIDC) and Ocean and Sea Ice Satellite Application Facility (OSISAF). Using the OSISAF similar product as validation data, the overall accuracies (OAs) of ice/water discrimination and FYI/MYI discrimination are 99% and 97%, respectively. Compared with the high spatial resolution classification results of the Moderate Resolution Imaging Spectroradiometer (MODIS) and SAR, the OAs of ice/water discrimination and FYI/MYI discrimination are 96% and 86%, respectively. In conclusion, the SAC and SMR of HY-2B have been verified for monitoring polar sea ice, and the sea ice extent and sea-ice-type products are promising for integration into long-term sea ice records.

Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020

Abstract. Advanced data assimilation (DA) methods, widely used in geophysical and climate studies to merge observations with numerical models, can improve state estimates and consequent forecasts. We interface the deterministic ensemble Kalman filter (DEnKF) to the Lagrangian neXt generation Sea Ice Model, neXtSIM. The ensemble is generated by perturbing the atmospheric and oceanic forcing throughout the simulations and randomly initialized ice cohesion. Our ensemble–DA system assimilates sea ice concentration (SIC) from the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) and sea ice thickness (SIT) from the merged CryoSat-2 and SMOS datasets (CS2SMOS). Because neXtSIM is computationally solved on a time-dependent evolving mesh, it is a challenging application for ensemble–DA. As a solution, we perform the DEnKF analysis on a fixed and regular reference mesh, on which model variables are interpolated before the DA and then back to each member's mesh after the DA. We evaluate the impact of assimilating different types of sea ice observations on the model's forecast skills of the Arctic sea ice by comparing satellite observations and a free-run ensemble in an Arctic winter period, 2019–2020. Significant improvements in modeled SIT indicate the importance of assimilating weekly CS2SMOS SIT, while the improvements of SIC and ice extent are moderate but benefit from daily ingestion of the OSI-SAF SIC. For most of the winter, the correlation between SIT and SIC is weaker, which results in little cross-inference between the two variables in the assimilation step. Overall, the ensemble–DA system based on the stand-alone sea ice model demonstrates the feasibility of winter Arctic sea ice prediction with good computational efficiency. These results open the path toward operational implementation and the extension to multi-year assimilation.

Multiple Sea Ice Type Retrieval Using the HaiYang-2B Scatterometer in the Arctic

Sea ice type classification is of great significance for the exploration of waterways, fisheries, and offshore operations in the Arctic. However, to date, there is no multiple remote sensing method to detect sea ice type in the Arctic. This study develops a multiple sea ice type algorithm using the HaiYang-2B Scatterometer (HY-2B SCA). First, the parameters most applicable to classify sea ice type are selected through feature extraction, and a stacking model is established for the first time, which integrates decision tree and image segmentation algorithms. Finally, multiple sea ice types are classified in the Arctic, comprising Nilas, Young Ice, First Year Ice, Old Ice, and Fast Ice. Comparing the results with the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) Sea Ice Type dataset (SIT) indicates that the sea ice type classified by HY-2B SCA (Stacking-HY2B) is similar to OSI-SAF SIT with regard to the changing trends in extent of sea ice. We use the Copernicus Marine Environment Monitoring Service (CMEMS) high-resolution sea ice type data and EM-Bird ice thickness data to validate the result, and accuracies of 87% and 88% are obtained, respectively. This indicates that the algorithm in this work is comparable with the performance of OSI-SAF dataset, while providing information of multiple sea ice types.

Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers

The knowledge of Arctic Sea ice coverage is of particular importance in studies of climate change. This study develops a new sea ice classification approach based on machine learning (ML) classifiers through analyzing spaceborne GNSS-R features derived from the TechDemoSat-1 (TDS-1) data collected over open water (OW), first-year ice (FYI), and multi-year ice (MYI). A total of eight features extracted from GNSS-R observables collected in five months are applied to classify OW, FYI, and MYI using the ML classifiers of random forest (RF) and support vector machine (SVM) in a two-step strategy. Firstly, randomly selected 30% of samples of the whole dataset are used as a training set to build classifiers for discriminating OW from sea ice. The performance is evaluated using the remaining 70% of samples through validating with the sea ice type from the Special Sensor Microwave Imager Sounder (SSMIS) data provided by the Ocean and Sea Ice Satellite Application Facility (OSISAF). The overall accuracy of RF and SVM classifiers are 98.83% and 98.60% respectively for distinguishing OW from sea ice. Then, samples of sea ice, including FYI and MYI, are randomly split into training and test dataset. The features of the training set are used as input variables to train the FYI-MYI classifiers, which achieve an overall accuracy of 84.82% and 71.71% respectively by RF and SVM classifiers. Finally, the features in every month are used as training and testing set in turn to cross-validate the performance of the proposed classifier. The results indicate the strong sensitivity of GNSS signals to sea ice types and the great potential of ML classifiers for GNSS-R applications.

Prediction of Categorized Sea Ice Concentration From Sentinel-1 SAR Images Based on a Fully Convolutional Network

The consistent and long-term spaceborne synthetic aperture radar (SAR) missions such as Sentinel-1 (S-1) provide high-quality dual-polarized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C$</tex-math></inline-formula> -band images particularly suited to sea ice monitoring. SAR data are currently the primary source of information for sea ice charting made by human ice experts as a result of its integration with multiple information sources at different scales (mainly radiometers). The rise of deep learning now opens the prospect of automatic sea ice mapping. In this article, we investigate the potential of a fully convolutional network (FCN) for the automatic estimation of sea ice concentration (SIC). With input data down-sampled at 200 m and an FCN architecture (depth and receptive field) duly parameterized, our approach is to mimic the work of an analyst who considers general context and does not necessarily use the highest possible resolution but speckle-noise polluted data. A comprehensive database is generated with 1320 dual-polarized S-1 scenes collocated with ice charts produced by MET Norway. A dedicated attention is paid to seasonal representativeness to ensure the adequate performance for all sea ice types. Even if the FCN output is modeled as a categorical problem, the proposed architecture accounts for the semantic distances between SIC classes by introducing an auxiliary loss. A comparative benchmark with Ocean and Sea Ice Satellite Application Facility (OSISAF) and MET Norway SIC products is carried out, showing an overall accuracy of 78.2% for our 6-class classification approach. The FCN model is shown to be evenly robust to sea ice seasonal variability and incidence angle.

Validation of remote-sensing products of sea-ice motion: a case study in the western Arctic Ocean

AbstractThe accuracy of sea-ice motion products provided by the National Snow and Ice Data Center (NSIDC) and the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) was validated with data collected by ice drifters that were deployed in the western Arctic Ocean in 2014 and 2016. Data from both NSIDC and OSI-SAF products exhibited statistically significant (p&lt; 0.001) correlation with drifter data. The OSI-SAF product tended to overestimate ice speed, while underestimation was demonstrated for the NSIDC product, especially for the melt season and the marginal ice zone. Monthly Lagrangian trajectories of ice floes were reconstructed using the products. Larger spatial variability in the deviation between NSIDC and drifter trajectories was observed than that of OSI-SAF, and seasonal variability in the deviation for NSIDC was observed. Furthermore, trajectories reconstructed using the NSIDC product were sensitive to variations in sea-ice concentration. The feasibility of using remote-sensing products to characterize sea-ice deformation was assessed by evaluating the distance between two arbitrary positions as estimated by the products. Compared with the OSI-SAF product, relative errors are lower (&lt;11.6%), and spatial-temporal resolutions are higher in the NSIDC product, which makes it more suitable for estimating sea-ice deformation.

A Sea Surface Temperature data record (2004–2012) from Meteosat Second Generation satellites

The Ocean and Sea-Ice Satellite Application Facility (OSI-SAF) of the EUropean Organisation for the Exploitation of METeorological Satellites (EUMETSAT) has performed a reprocessing of Sea Surface Temperature (SST) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) archive (2004–2012).The retrieval method consists of a non-linear split-window algorithm and an algorithm correction relying on simulations of infrared brightness temperatures performed using atmospheric profiles of water vapour and temperature from a Numerical Weather Prediction model, and a radiative transfer model. The cloud mask used is the Climate SAF reprocessing of the MSG/SEVIRI archive which is consistent over the period considered. Atmospheric Saharan dust has a strong impact on the retrieved SST in the Atlantic and Mediterranean regions, they are taken into consideration through the computation of the Saharan Dust Index (SDI) which is then used to determine an empirical correction applied to SST.The reprocessing has benefited from the experience of the OSI SAF team in operational near real time processing of MSG/SEVIRI data, and the methods have been improved to provide a higher quality SST. The MSG/SEVIRI SST reprocessing dataset consists of hourly level 3 composites of sub-skin temperature projected onto a regular 0.05° grid over the region delimited by 60N,60S and 60W,60E. It has been thoroughly validated against drifting buoys and moored buoys measurements.Results of this validation have shown that the reprocessed data record is of significantly better quality than the OSI SAF operational processing (for instance the day-time robust standard deviation is 0.45 K for the operational processing and 0.35 K for the reprocessed dataset). The data record has been used to characterize the diurnal variability of SST over large temporal and spatial scales.

Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea

Abstract. We assess the impact of assimilating the satellite sea surface temperature (SST) data on the Baltic forecast, particularly on the forecast of ocean variables related to SST. For this purpose, a multivariable data assimilation (DA) system has been developed based on a Nordic version of the Nucleus for European Modelling of the Ocean (NEMO-Nordic). We use Kalman-type filtering to assimilate the observations in the coastal regions. Further, a low-rank approximation of the stationary background error covariance metrics is used at the analysis steps. High-resolution SST from the Ocean and Sea Ice Satellite Application Facility (OSISAF) is assimilated to verify the performance of the DA system. The assimilation run shows very stable improvements of the model simulation as compared with both independent and dependent observations. The SST prediction of NEMO-Nordic is significantly enhanced by the DA forecast. Temperatures are also closer to observations in the DA forecast than the model results in the water above 100 m in the Baltic Sea. In the deeper layers, salinity is also slightly improved. In addition, we find that sea level anomaly (SLA) is improved with the SST assimilation. Comparisons with independent tide gauge data show that the overall root mean square error (RMSE) is reduced by 1.8 % and the overall correlation coefficient is slightly increased. Moreover, the sea-ice concentration forecast is improved considerably in the Baltic Proper, the Gulf of Finland and the Bothnian Sea during the sea-ice formation period, respectively.

Evaluation of summer passive microwave sea ice concentrations in the Chukchi Sea based on KOMPSAT-5 SAR and numerical weather prediction data

Satellite passive microwave (PM) sensors have observed sea ice in Polar Regions and provided sea ice concentration (SIC) data since the 1970s. SIC has been used as a primary data source for climate change prediction and ship navigation. However, the accuracy of PM SIC is typically low and biased in summer. To provide more accurate information for climatic research and ship navigation, it is necessary to evaluate quantitatively the accuracy of PM SIC and to account for its errors. In this research, we evaluated the SIC data derived from PM measurements using four representative sea ice algorithms: NASA Team (NT), Bootstrap (BT), Ocean and Sea Ice Satellite Application Facility (OSISAF) hybrid, and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI). Analyses were performed for the Chukchi Sea in summer using KOrean Multi-Purpose SATellite-5 (KOMPSAT-5) Enhanced Wide-swath synthetic aperture radar (SAR) images. Ice/water maps were generated by binary classification of texture features in the SAR images based on Random Forest, a rule-based machine learning approach. SIC values estimated from the sea ice algorithms showed good correlation with those calculated from the KOMPSAT-5 ice/water maps, but the root mean square error was larger than 10%. SIC values estimated from the algorithms showed different error trends according to the KOMPSAT-5 SIC range. All algorithms overestimated SIC values in open drift ice zones (KOMPSAT-5 SICs ranged from 0% to 15%). In marginal ice zones (SICs ranged from 15% to 80%), the OSISAF SIC values were the least biased compared to those from KOMPSAT-5. The NT algorithm largely underestimated SIC values in marginal ice zones, while the BT and ASI algorithms overestimated them considerably. All algorithms, except for BT, underestimated SIC in consolidated pack ice zones (SICs ranged from 80% to 100%). By analyzing the correlations of biases of SIC from the algorithms with the numerical weather prediction (NWP) data from the European Reanalysis Agency Interim reanalysis, it was found that the overestimation of NT and ASI SICs was largely influenced by atmospheric water vapor content, while the underestimation of NT and OSISAF SICs was owing to ice surface melting. The overestimation of BT SICs was not significantly correlated with the NWP data. The underestimated SIC from the BT and ASI algorithms for high SIC regions might be compensated by the atmospheric water vapor content. The differences in SIC values estimated from each algorithm were due to different sensitivities to atmospheric water vapor content in the regions with KOMPSAT-5 SIC lower than 40% and to ice surface melting in the regions with higher KOMPSAT-5 SIC.

The EUMETSAT sea ice concentration climate data record

Abstract. An Arctic and Antarctic sea ice area and extent dataset has been generated by EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSISAF) using the record of microwave radiometer data from NASA's Nimbus 7 Scanning Multichannel Microwave radiometer (SMMR) and the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager and Sounder (SSMIS) satellite sensors. The dataset covers the period from October 1978 to April 2015 and updates and further developments are planned for the next phase of the project. The methodology for computing the sea ice concentration uses (1) numerical weather prediction (NWP) data input to a radiative transfer model for reduction of the impact of weather conditions on the measured brightness temperatures; (2) dynamical algorithm tie points to mitigate trends in residual atmospheric, sea ice, and water emission characteristics and inter-sensor differences/biases; and (3) a hybrid sea ice concentration algorithm using the Bristol algorithm over ice and the Bootstrap algorithm in frequency mode over open water. A new sea ice concentration uncertainty algorithm has been developed to estimate the spatial and temporal variability in sea ice concentration retrieval accuracy. A comparison to US National Ice Center sea ice charts from the Arctic and the Antarctic shows that ice concentrations are higher in the ice charts than estimated from the radiometer data at intermediate sea ice concentrations between open water and 100 % ice. The sea ice concentration climate data record is available for download at www.osi-saf.org, including documentation.

The implementation of sea ice model on a regional high-resolution scale

The availability of high-resolution atmospheric/ocean forecast models, satellite data and access to high-performance computing clusters have provided capability to build high-resolution models for regional ice condition simulation. The paper describes the implementation of the Los Alamos sea ice model (CICE) on a regional scale at high resolution. The advantage of the model is its ability to include oceanographic parameters (e.g., currents) to provide accurate results. The sea ice simulation was performed over Baffin Bay and the Labrador Sea to retrieve important parameters such as ice concentration, thickness, ridging, and drift. Two different forcing models, one with low resolution and another with a high resolution, were used for the estimation of sensitivity of model results. Sea ice behavior over 7 years was simulated to analyze ice formation, melting, and conditions in the region. Validation was based on comparing model results with remote sensing data. The simulated ice concentration correlated well with Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and Ocean and Sea Ice Satellite Application Facility (OSI-SAF) data. Visual comparison of ice thickness trends estimated from the Soil Moisture and Ocean Salinity satellite (SMOS) agreed with the simulation for year 2010–2011.

Relationship between sea ice concentration and sea ice albedo over Antarctica

Abstract : Sea ice is a key parameter for understanding the climate change in cryosphere. In this study,we investigated the correlation with the factors that influenced change of the sea ice extent. We used the SeaIce Concentration (SIC) from Ocean and Sea Ice Satellite Application Facility (OSI-SAF), and surface albedoprovided by The Satellite Application Facility on Climate Monitoring (CM SAF). We converted the sametemporal and spatial resolution of the data and detected the sea ice using SIC data. We performed therelationship analysis between SIC and sea ice albedo. As a result, we found they have a strong positivecorrelation. We performed the linear regression between SIC and sea ice albedo, and found they have high-level coefficient of determination. It shows using either SIC or sea ice albedo is possible to estimate the sea iceproducts. Key Words : Antarctica, sea ice concentration, sea ice albedo Korean Journal of Remote Sensing, Vol.31, No.4, 2015, pp.347~351http://dx.doi.org/10.7780/kjrs.2015.31.4.7

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.

Night time detection of Saharan dust using infrared window channels: Application to NPP/VIIRS

A Saharan Dust Index (SDI) has been produced operationally since 2006 from Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) data. This SDI, based on brightness temperatures (BTs) at 3.9, 8.7, 10.8 and 12.0μm, has been used in the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) Sea Surface Temperature (SST) chain to detect Saharan dust and correct for its effect on SST calculations. OSI-SAF has developed a processing chain to derive Sea Surface Temperature (SST) from the Suomi National Polar-orbiting partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) data. In this framework, an SDI algorithm adapted to VIIRS data has been developed. Consistency between various SDI derived from various sensors is essential in the OSI-SAF context. Without absolute definition of this parameter, we have adopted the Meteosat-9 nominal SDI values as reference. In this paper we present a new method to define SDI algorithms, based on the use of simulated BTs contaminated by aerosols at various concentrations and altitudes. This method has been applied to VIIRS data. It shows consistent results with the original 2006 method and a simple regression against Meteosat-9 SDI values when compared to the operational Meteosat-9 SDI. This new method can be used to prepare SDI equations for any sensor with adequate channels once their radiometric characteristics are known. In addition, it ensures consistency with our reference SDI.

A combined optimal interpolation and nudging scheme to assimilate OSISAF sea-ice concentration into ROMS

AbstractThe Ocean and Sea Ice Satellite Application Facility (OSISAF) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) distributes satellite observations operationally. In this paper, we describe the development of a combined optimal interpolation and nudging (COIN) scheme in the Norwegian Meteorological Institute that assimilates OSISAF sea-ice concentration (SIC) into a coupled sea-ice–ocean Regional Ocean Modeling System (ROMS). The scheme modifies the modeled SIC at every time-step, based on the difference between observation and forecast as well as on the ratio of observation error to model error. Snow and sea-ice thickness are diagnostically modified to match the ice concentration field. Hindcast experiments of Arctic sea ice are performed for the whole of 2009. The results are compared with OSISAF and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) SIC, showing a significant improvement in the analyzed sea-ice edge and summer SIC.

The accuracy of SST retrievals from Metop-A IASI and AVHRR using the EUMETSAT OSI-SAF matchup dataset

Skin sea surface temperatures (SST) from the infra-red atmospheric sounding interferometer (IASI) have been available in the Group for High Resolution Sea Surface Temperatures (GHRSST) L2Pcore format from the EUMETSAT data centre since March 2011. IASI SSTs, produced from version 5.0 of the IASI Product Processing Facility (PPF), have been validated against drifting buoy SSTs and compared to AVHRR SSTs contained with the EUMETSAT Ocean and Sea-Ice Satellite Application Facility (OSI-SAF) matchup dataset, over the period October 2010 to July 2011. The IASI SSTs were found to be of good quality with a slight cool bias compared to drifting buoys (bias −0.32K, σ 0.32K) and AVHRR SSTs (bias −0.35K, σ 0.30K). The highest quality IASI SSTs had a bias compared to drifting buoys of −0.16K (σ 0.33K). Using three-way collocations of differences between IASI, AVHRR, and drifting buoy SSTs gave IASI SSTs to have a standard deviation of error of 0.28K, AVHRR 0.13K, and drifting buoys 0.20K. IASI and AVHRR SSTs were found to be complementary to each other, giving benefit in different locations. Some residual cloud contamination was observed in the IASI SSTs, contributing to the slight cool bias. For collocations deemed likely to be 100% cloud free, the IASI bias reduces to −0.25K (σ 0.39K) using quality levels 2 to 5, and a bias of −0.09K (σ 0.34K) for observations with quality level 5. In addition, some evidence of increased uncertainties due to comparing point buoy observations with areal satellite observations was seen in the daily statistics for all observation types.