Validation Of Sea Surface Temperature Research Articles

Retrieval and validation of sea surface temperature from HY-1D COCTS

Retrieval and validation of sea surface temperature from HY-1D COCTS

An Efficient Oceanic Eddy Identification Method With XBT Data Using Transformer

Oceanic mesoscale eddies are relatively small, short-lived circulation patterns that are approximately in geostrophic balance. Meanwhile, eddies are omnipresent and can be characterized by dynamic sea level anomalies and temperature anomalies. This makes the eddy identification mainstream with Sea Level Anomaly (SLA). Unfortunately, nearly 90% of sea level dynamic anomalies caused by oceanic eddies cannot be observed due to insufficient resolution of satellite altimeters. Combining in-situ Expendable Bathythermograph (XBT) profiles data, and sea surface temperature data calibrated by the altimeter, this paper proposes a deep neural network to identify subsurface oceanic eddies and inverse the corresponding sea surface eddy properties. First, the eddies identified by SLA are purified to match the corresponding vertical profile dataset. Then, a neural network with a self-attention mechanism is constructed by combining the eddy vertical profile structure with temporal and spatial characteristics and external features to effectively identify the eddy. Furthermore, the eddy properties including radius, amplitude, and energy are inversion with XBT profile and SST features. Finally, the experimental results show that the accuracy of eddy classification can reach 98.22%, which demonstrates that vertical profiles can be used to classify eddies effectively. Subsequent reclassification of the outside altimeter-identified eddies recaptured about 36% of the eddies. The authenticity of the newly identified eddies can be demonstrated by statistical model validation as well as validation of sea surface temperature anomaly (SSTA). These results indicate that the subsurface eddies identification can be implemented by vertical profiles with deep learning.

Development of SST Validation Methodology for Upcoming SSTM/EOS-06 Sensor: A Prelude Experiment on SLSTR/Sentinel-3A

Radiometers aboard satellites provide an essential Sea Surface Temperature (SST) data archive, which is one of the key indicators of climate change. Since new radiometers are planned to fill the gap in the temporal data coverage, it is important to quantify the retrieval accuracy to utilize the data and seamlessly merge it into an existing SST data-archive. In this regard, we validate SST product from SLSTR/Sentinel-3A with in-situ measurements to create a reference frame for evaluating the new sensors products. We used NOAA <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> Quam SST dataset for validating satellite-retrievals, since it’s easily obtainable unlike skin-SST measurements. Several criteria have yielded a total of 30,353 high-quality cloud-free collocated data points between SLSTR and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> Quam SST datasets for 2017. Detailed evaluation of biases globally under different wind speeds and precipitable water vapor conditions indicates the fundamental difference between the satellite and in-situ SST, and recommendations are made for how datasets should be handled. SLSTR-retrieved skin-SST agrees with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> Quam subsurface-SST, irrespective of region and time of measurement, within -0.17±0.04K for all-day, -0.11±0.06K for daytime and -0.21±0.05K for nighttime datasets, with respective RMSE values of 0.62±0.10K, 0.58±0.09K and 0.65±0.13K. These small differences are partly due to the difference between ocean skin-temperature and subsurface-temperature, measurement uncertainties and sampling mismatch. Comprehensive validation carried out for SLSTR-retrieved SST has direct implications for future validation studies and future blended infrared, microwave and in-situ SST products.

Validation of Sea Surface Temperature from GCOM-C Satellite Using iQuam Datasets and MUR-SST in Indonesian Waters

Sea surface temperature (SST) is an important variable in oceanography. One of the SST data can be obtained from the Global Observation Mission-Climate (GCOM-C) satellite. Therefore, this data needs to be validated before being applied in various fields. This study aimed to validate SST data from the GCOM-C satellite in the Indonesian Seas. Validation was performed using the data of Multi-sensor Ultra-high Resolution sea surface temperature (MUR-SST) and in situ sea surface temperature Quality Monitor (iQuam). The data used are the daily GCOM-C SST dataset from January to December 2018, as well as the daily dataset from MUR-SST and iQuam in the same period. The validation process was carried out using the three-way error analysis method. The results showed that the accuracy of the GCOM-C SST was 0.37oC.

Validation of Sea Surface Temperature Derived From Himawari-8 by JAXA

Hourly sea surface temperature (SST) retrieved from Himawari-8 by the Japan Aerospace Exploration Agency (H8-SST/JAXA, latest version 1.2) is becoming an important data source for data merging as well as for resolving diurnal variation (DV). However, the spatial and temporal variation of the errors for the full disk is still unclear. In this article, two years of H8-SSTs/JAXA are validated against in situ measurements from iQuam2. In general, H8-SSTs/JAXA shows small biases between −0.11 and −0.03 K with root mean square error (RMSE) between 0.58 and 0.73 K. The spatial distributions of the errors reveal the following patterns: 1) a small median bias close to 0.1 K and RMSE of 0.4–0.6 K comparing to drifters are found at satellite zenith angle (SZA) 0°–35°; 2) negative biases (∼−0.3 K) are detected at SZA s 35°–58°; and 3) larger positive biases exceeding 0.3 K are also found along the viewing boundaries. The temporal variations of the errors show that 1) there is no prominent seasonal variation; 2) the amplitude of the DV of the errors is only ∼0.1 K for the statistical of all matchups, and 3) the maximum errors appears in the morning rather than in the noon. The statistics will be used in future work for DV analysis and merging purposes.

Surface Heat Budget over the North Sea in Climate Change Simulations

An ensemble of regional climate change scenarios for the North Sea is validated and analyzed. Five Coupled Model Intercomparison Project Phase 5 (CMIP5) General Circulation Models (GCMs) using three different Representative Concentration Pathways (RCPs) have been downscaled with the coupled atmosphere–ice–ocean model RCA4-NEMO. Validation of sea surface temperature (SST) against different datasets suggests that the model results are well within the spread of observational datasets. The ensemble mean SST with a bias of less than 1 ∘ C is the solution that fits the observations best and underlines the importance of ensemble modeling. The exchange of momentum, heat, and freshwater between atmosphere and ocean in the regional, coupled model compares well with available datasets. The climatological seasonal cycles of these fluxes are within the 95% confidence limits of the datasets. Towards the end of the 21st century the projected North Sea SST increases by 1.5 ∘ C (RCP 2.6), 2 ∘ C (RCP 4.5), and 4 ∘ C (RCP 8.5), respectively. Under this change the North Sea develops a specific pattern of the climate change signal for the air–sea temperature difference and latent heat flux in the RCP 4.5 and 8.5 scenarios. In the RCP 8.5 scenario the amplitude of the spatial heat flux anomaly increases to 5 W/m 2 at the end of the century. Different hypotheses are discussed that could contribute to the spatially non-uniform change in air–sea interaction. The most likely cause for an increased latent heat loss in the central western North Sea is a drier atmosphere towards the end of the century. Drier air in the lee of the British Isles affects the balance of the surface heat budget of the North Sea. This effect is an example of how regional characteristics modulate global climate change. For climate change projections on regional scales it is important to resolve processes and feedbacks at regional scales.

Validation of Sea Surface Temperature (SST) from Satellite Passive Microwave Sensor (GPM/GMI) and Causes of SST Errors in the Northwest Pacific

Validation of Sea Surface Temperature (SST) from Satellite Passive Microwave Sensor (GPM/GMI) and Causes of SST Errors in the Northwest Pacific

INSAT-3D and MODIS retrieved sea surface temperature validation and assessment over waters surrounding the Indian subcontinent

ABSTRACTThe INSAT-3D imager (4 km) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on-board Aqua and Terra space-platforms level-2 (1 km) sea surface temperature (SSTskin) product accuracy has been analysed over waters surrounding the Indian subcontinent by indirect comparison method using collocated bulk in-situ measurements (SSTdepth) for 3 years (October 2013–October 2016). Statistical results show that root mean square error of all the three satellites is in range of around 0.60–0.70°C. Retrieval error is found to be slightly more in case of validation against iQuam data set. INSAT-3D is showing more underestimation with bias ranging from about −0.16°C to −0.20°C than MODIS sensor having bias in range of about 0.06°C to −0.12°C. All the three missions are slightly underestimating over open-ocean with bias ranging in 0–0.17°C. INSAT-3D is significantly underestimating in-situ observations over the Arabian Sea (approximate bias = 0.27°C). Seasonal validation analysis reveals relatively high retrieval error during monsoon season than pre-monsoon and post-monsoon seasons. MODIS sensor is showing significant underestimation during monsoon with bias ranging from approximately −0.29°C to −0.58°C. Overall, all the three missions are performing similarly well over the study area.

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.

In situ validation of sea surface temperatures from the GCOM‐W1 AMSR2 RSS calibrated brightness temperatures

AbstractRemote Sensing Systems AMSR2 v7.2 data from 25 July 2012 to 9 October 2014 are collocated with in situ sea surface temperature (SST) data. The RSS SST algorithm uses AMSR2 brightness temperatures calibrated using a methodology developed at RSS rather than using the standard JAXA AMSR2 product, which includes the JAXA calibration. The new RFI exclusion methodology used for the AMSR2 v7.2 data is described. Buoy data are quality controlled using an internal quality indicator. Daytime collocations with wind speeds of less than 6 m s−1 are excluded to avoid diurnal contamination of the results. A mean bias (AMSR2 minus in situ) of −0.04 K and standard deviation 0.55 K with 109,350 collocations is found. The geographical distribution of biases is investigated, with a small increase in biases found at higher latitudes. At lower SST the uncertainty increases and the bias. The dependencies of the bias and uncertainties on other geophysical variables are shown to be negligible. The time series of the bias and uncertainty show little variability, but a small seasonal dependence is determined to be related to a seasonal shift in wind speeds. Overall, the AMSR2 SSTs are of comparable quality to the AMSR‐E SSTs and continue the climate microwave SST record that started in 1997.

Evaluation of the MODIS-Aqua Sea-Surface Temperature product in the inner and mid-shelves of southwest Buenos Aires Province, Argentina

Validation of sea-surface temperature (SST) provided by the MODIS-Aqua sensor (Moderate Resolution Imaging Spectroradiometer) for the inner and mid-shelves of the southwest of Buenos Aires Province (Argentina), is presented for the first time. In situ data obtained with a multi-parametric sonde YSI-6600 and a CTD SBE91 between 2002 and 2011 are used for comparison with the satellite SST product. The match-up exercise was established after comparing different spatial boxes, time difference windows, wind speeds, and also a coefficient of variation. The comparison exercise was made in the coastal zone and the rest of the inner and mid-shelves separately. In the coastal zone, applying a 3 × 2 pixel box and a time window of ±3 hours led to the most accurate results, with a coefficient of determination (R2) of 0.99, a bias of 0.62°C, and a root-mean-square-error (RMSE) of 0.79°C. In the inner-mid-shelves when applying a coefficient of variability <0.3, a time window of ±3 hours, and taking only values of wind speed > 6 m s−1, R2 is 0.97, bias is 0.46°C, and RMSE is 0.95°C. Wind speed plays a major role in the inner-mid-shelves as the SST product is affected by stratification and formation of a diurnal thermocline in the ‘skin and sub-skin layer’ when wind speed is below 6 m s−1. The results for the two shelves are very similar. Finally, the spatial and temporal variability of the SST satellite product was analysed in the study area for the period August 2002–December 2010. The results show that inter-annual variability is not significant and that there is no positive or negative trend for the 9 years of the study. Seasonality is the main component of temporal variability, with variation in amplitude signal depending on bathymetry changes, physical forcing, stability of the water column, and presence of flood plains.

VARIABILITY AND VALIDATION OF SEA SURFACE TEMPERATURE ESTIMATED BY PATHFINDER ALGORITHM OF NOAA-AVHRR SATELLITE IN THE NORTH PAPUA WATERS

Variability and validation of sea surface temperatures (SST) in north Papua waters were conducted using SST estimated by Pathfinder algorithm of NOAA AVHRR satellite and SST measurements from TAO buoy in 2001-2009. Satellite data (SST Pathfinder) were daily, weekly, and monthly composite with 4x4 km2 resolution and downloaded from http://poet.jpl.nasa.gov. In situ data (SST measurement from buoy TAO) were measured at a depth of 1.5 m and recorded every hour (http://www.pmel.noaa.gov/tao_deliv). The in situ data then converted into daily, weekly, and monthly average data. In general, the SST values of both satellite and in situ SST in the north Papua waters ranged between 27.10 - 31.90 °C. During the east season (June-September), SST values (27.90-31.90 °C) were generally higher than the SST values ( 27.10-30.13 °C) during the west season (December-February). In general, the SST values both day-time and night-time from in situ and the satellite measurements showed no significant differences except in waters close to the shore. The results also showed that the coefficient of determination values (R2) between the satellite and the in situ SST measurements were relatively low (65%) and up to 5% of RMSE. The relatively low correlation between in situ dan satellite SST measurements may be due to high cloud coverage (90-96%) in the north Papua waters so that SST satellite data become less representative of the in situ data. These results also indicated that the Pathfinder algorithm can not be used as a valid estimate of SST NOAA AVHRR satellite for the north Papua waters.&#x0D; Keywords: SST Pathfinder, NOAA AVHRR, Validation, TAO buoy, North Papua Waters

Evaluation of the relative performance of sea surface temperature measurements from different types of drifting and moored buoys using satellite‐derived reference products

Sea surface temperature (SST) measurements from drifting and moored buoys have been used extensively to validate satellite SST products. Previous efforts explored overall uncertainties in the buoy measurements, but did not quantify differences in the performance of different buoy types. Here, drifting and moored buoys stratified into open‐ocean and coastal, were evaluated by manufacturer (drifters) and buoy program (moorings) to identify significant deviations in their suitability for satellite validation. Biases and standard deviations were computed for differences between the buoy SST measurements and two satellite references: the Advanced Microwave Scanning Radiometer SST product from Remote Sensing Systems and the U.S. Naval Oceanographic Office K10 analysis. No significant differences were found among the various drifting and tropical moored buoys, regardless of the manufacturer/program. The largest deviations were observed for the non‐tropical moored buoys possibly in part due to calibration differences. In general, coastal buoys exhibited greater standard deviations associated with increased physical variability within the coastal regions. Tropical moored buoys showed smaller standard deviations than for the drifters resulting from better quality measurements or reduced variability in the tropics. Large deviations between the coastal moorings and the SST references appeared linked to coastal currents with upwelling, rather than buoy performance. Coastal moorings provided sampling of small‐scale coastal variability, not resolved in satellite products, offering the potential for improving poorly parameterized coastal SSTs. Differences due to diurnal warming were largely eliminated by constraining collocations between the satellite and the buoy to ±1 h. Overall, most buoy types appear suitable for satellite SST validation.

Validation of Sea Surface Temperature from ERS-1/ATSR in the Tropical and Northwest Pacific

Validation of Sea Surface Temperature from ERS-1/ATSR in the Tropical and Northwest Pacific

Improved techniques for the derivation of sea surface temperatures from ATSR data

A new technique is developed for the analysis of satellite data to provide accurate measurements of seasurface temperature (SST). Satellite data sets are partitioned into subsets depending on the value of a selected parameter (for example, latitude, total water vapor, and water vapor content of an atmospheric layer) to provide a suite of algorithm coefficients that reduces the errors associated with the derivation of SST. For data sets obtained with the along‐track scanning radiometer (ATSR) the data themselves can be used to modify the coefficients used in the SST algorithm, but for other instruments it may be necessary to use additional data sets to select a correct set of algorithm coefficients. A simulated set of ATSR data is used to develop algorithm coefficients for different atmospheric conditions, and the improvement in SST derivation is demonstrated. For ATSR, when all the data for three infrared channels in both the nadir and forward views are available, the improvement is marginal, but for situations when there are limited data the improvement is considerable. The analysis suggests that in the future the best SST analyses will be obtained by developing an interactive system, where the satellite data are ingested into a numerical weather forecast model so that algorithms can be selected and applied with a forecast or analysis of the atmospheric state. The new techniques are tested by application to a large global data set of ATSR brightness temperatures. This analysis highlights the future need for large SST validation data sets that include coincident satellite and surface‐based measurements.