Ultra-high Resolution Sea Surface Temperature Research Articles

A New Insight on the Upwelling along the Atlantic Iberian Coasts and Warm Water Outflow in the Gulf of Cadiz from Multiscale Ultrahigh Resolution Sea Surface Temperature Imagery

The ATLAZUL project is an Interreg effort among 18 partners from Spain and Portugal along the Atlantic Iberian coasts. One of its objectives is the development of new methods and data processing for oceanic information to produce useful products for private and public stakeholders. This study proposes a new insight on the sea surface dynamic of the ATLAZUL area based on almost two years of multiscale high resolution sea surface temperature imagery. The use of techniques such as the Karhunen–Loève transform (Empirical Orthogonal Function) and the Maximum Entropy Spectral Analysis were applied to study long- and short-term features in the sea surface temperature imagery. Mathematical Morphology and the Geometrical Theory of Measure are utilized to compute the Medial Axis Transform and the Hausdorff dimension. The results can be summarized as follows: (i) the tow upwelling areas are identified along the Galician–Portugal coast as indicated in the second and third modes of KLT/EOF analysis, and they are partially affected by wind; (ii) the tow warm water outflows from the Bay of Cádiz to the Gulf of Cádiz are identified as the second and third modes of KLT/EOF analysis, which are also influenced by wind; (iii) the skeletons of the surface signature of the upwelling and of the warmer water outflow, along with their fractal dimensions, indicate a chaotic pattern of spatial distribution and (iv) the harmonic prediction model should be combined with the wind prediction.

Automated ocean front feature mapping using Sentinel-1 with examples from the Gulf Stream

ABSTRACT This study assessed the ability of Sentinel-1 radial velocity (RVL) products to mark the position of ocean current front features, using the Gulf Stream (GS) as a case study. RVL-derived front features were compared to fronts derived from Multi-scale Ultra-high Resolution Sea Surface Temperature Analysis (MURSST) data. A ridge filter was used to find fronts in both the Sea Surface Temperature (SST) and RVL data, and the similarity between each pair of fronts was measured using the discrete Hausdorff Distance (HD) and Mean Hausdorff Distance (MHD). Front features were correctly identified in concurrent SST and RVL data pairs in 65% of cases. The automatic front extraction sometimes failed by misclassifying an eddy or similar ocean feature as the ocean current in either the RVL or SST image, and sometimes failed to extract the entire length of the front visible within the image. SST and RVL fronts were classified manually to determine the success rate of the automatic front extraction, and to exclude front extraction errors from further analysis. The results demonstrated that RVL products were effective at determining the location of ocean fronts where the angle between the front’s normal vector and the sensor’s azimuthal heading is less than ~ 40°. A mean HD of 25.5 km and a mean MHD of 10.8 km was calculated for all front pairs in the study area.

Short-Term (7 Day) Beaufort Sea Ice Extent Forecasting with Deep Learning

Abstract Ships inside the Arctic basin require high-resolution (1–5 km), near-term (days to semimonthly) forecasts for guidance on scales of interest to their operations where forecast model predictions are insufficient due to their coarse spatial and temporal resolutions. Deep learning techniques offer the capability of rapid assimilation and analysis of multiple sources of information for improved forecasting. Data from the National Oceanographic and Atmospheric Administration’s Global Forecast System, Multi-scale Ultra-high Resolution Sea Surface Temperature (MEaSUREs), and the National Snow and Ice Data Center’s Multisensor Analyzed Sea ice Extent (MASIE) were used to develop the sea ice extent deep learning forecast model, over the freeze-up periods of 2016, 2018, 2019, and 2020 in the Beaufort Sea. Sea ice extent forecasts were produced for 1–7 days in the future. The approach was novel for sea ice extent forecasting in using forecast data as model input to aid in the prediction of sea ice extent. Model accuracy was assessed against a persistence model. While the average accuracy of the persistence model dropped from 97% to 90% for forecast days 1–7, the deep learning model accuracy dropped only to 93%. A k-fold (fourfold) cross-validation study found that on all except the first day, the deep learning model, which includes a U-Net architecture with an 18-layer residual neural network (Resnet-18) backbone, does better than the persistence model. Skill scores improve the farther out in time to 0.27. The model demonstrated success in predicting changes in ice extent of significance for navigation in the Amundsen Gulf. Extensions to other Arctic seas, seasons, and sea ice parameters are under development. Significance Statement Ships traversing the Arctic require timely, accurate sea ice location information to successfully complete their transits. After testing several potential candidates, we have developed a short-term (7 day) forecast process using existing observations of ice extent and sea surface temperature, with operational forecasts of weather and oceanographic variables, and appropriate machine learning models. The process included using forecasts of atmospheric and oceanographic conditions, as a human forecaster/analyst would. The models were trained for the Beaufort Sea north of Alaska using data and forecasts from 2016 combined with 2018–20. The results showed improvement in short-term forecasts of ice locations over current methods and also demonstrated correctly predicted changes in the sea ice that are important for navigation.

Development of a multigrid operational forecast system for the oceanic region off Rio de Janeiro State

We propose the development of a multigrid open-source operational forecast system, with the oceanic region off Rio de Janeiro state as our first case study. The system is composed by three nested ROMS domains, employing Mercator Ocean at the lateral open boundaries of the outermost grid, and GFS (Global Forecast System) at the surface open boundary of all grids. Two schemes were built for the operational forecast workflow, free (without Data Assimilation) and assimilated (with data assimilation). For evaluation, we performed hindcast simulations using Mercator and ERA5 as input data for different parts of the Southeastern Brazilian oceanic region, covering various regimes of the unique Brazil Current system. The modelled data was compared against velocity components from three oceanic buoys, SST from MUR (Ultra-high Resolution Sea Surface Temperature), and SSH from AVISO. The comparison against the buoys showed a good representation of the velocity field by ROMS, performing better than Mercator for most scenarios. ROMS also presented good performances for SST and SSH. For the three-nested forecast system, the velocity fields presented comparable magnitudes against Mercator, without the presence of jet-like instabilities, indicating a stable system. We also implemented the I4D-Var method in a 2-day analysis for the simulation of 7-day forecasts, finding an improvement in the modelled forecasts initialized from the assimilated analysis for both velocity components, with an overall reduction in the RMSE and bias, and an increase in correlation.

Investigating Sea Surface Temperature and Coral Bleaching in the Coastal Area of Khanh Hoa Province

Khanh Hoa Coastal area is considered the area with the most diverse coral reefs in the west of the East Sea. With the trend of increasing global temperature, the coral reefs here are affected, including the phenomenon of bleaching. This paper uses a Multi-scale Ultra-high Resolution Sea Surface Temperature (MUR SST) data source to evaluate the possible relationship between sea surface temperature and coral bleaching in the period 2010-2019 in Khanh Hoa coastal area is based on two parameters: Hot Spot (HS) and Degree Heating Months (DHM). Research results show that in the past 10 years, corals in Khanh Hoa coastal area may experience heat stress in 6 years, including 2010, 2013 and the years from 2016 to 2019. The phenomenon of heat stress starts to occur in May of each year, and the level of heat stress in 2010 was stronger than in other years. Within 6 years, there have been heat stress, coral bleaching events due to temperature only occurred in 2010; while in 2013, 2016, 2017, 2018 and 2019, corals suffered from heat stress in the watch level, meaning an increase in temperature is not yet capable of causing coral bleaching. The cause of coral bleaching in 2010 was the combined effect of the increase in sea surface temperature and the suppression of upwelling during the southwest monsoon.

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.

Potential impact assessment between sst and extreme precipitation: an observational study in Southeastern Brazil

This work aims to identify, by means of statistical analysis, the relationship between extreme precipitation events and SST anomalies (SSTA) in the municipalities surrounding the Ilha Grande (IGB) and Sepetiba (SB) bays in the state of Rio de Janeiro evaluating its annual distributions using measured data from rainfall stations and relating these events to potential SST anomalies found in this region. Data from 10 rainfall stations distributed around the bays and SST dataset obtained from the Multi-scale Ultra-high Resolution Sea Surface Temperature (MUR) from 2003 to 2014 were used. SSTA and their average were generated for each of the three subareas delimited in the study area. The obtained time series of each subarea define the thresholds for significant SSTA, based on their respective standard deviation. The number of days with SSTA were obtained from these significant anomaly values, and days with positive or negative SSTA, for each subarea, were computed. For SSTA analysis, only events with significant anomalies were considered. Only SSTA occurring on the days of intense precipitation highlighted in P95 and up to 3 days before the extreme event date were analyzed. The results had shown that about 35% of extreme precipitation events were potentially related to the presence of significant SST anomalies in the studied area. Finally, it is pointed that regional anomalies in SST, such as those described in this study, can also contribute to the formation of extreme rain events.

Impacts of the Kuroshio Intrusion through the Luzon Strait on the Local Precipitation Anomaly

The Kuroshio Current has its origin in the northwestern Pacific, flowing northward to the east of Taiwan and the northern part of Luzon Island. As the Kuroshio Current flows northward, it quasi-periodically intrudes (hereafter referred to as Kuroshio intrusion (KI)) into the northern South China Sea (SCS) basin through the Luzon Strait. Despite the complex generation mechanisms of KI, the purpose of this study is to improve our understanding of the effects of KI through the Luzon Strait on the regional atmospheric and weather variations. Long-term multiple satellite observations, including absolute dynamic topography, absolute geostrophic currents, sea surface winds by ASCAT, multi-scale ultra-high resolution sea surface temperature (MURSST) level-four analysis, and research-quality three-hourly TRMM multi-satellite precipitation analysis (TMPA), was used to systematically examine the aforementioned scientific problem. Analysis indicates that the KI is interlinked with the consequential anomalous precipitation off southwestern Taiwan. This anomalous precipitation would lead to ~560 million tons of freshwater influx during each KI event. Subsequently, independent moisture budget analysis suggests that moisture, mainly from vertical advection, is the possible source of the precipitation anomaly. Additionally, a bulk formula analysis was applied to understand how KI can trigger the precipitation anomaly through vertical advection of moisture without causing an evident change in the low-level flows. These new research findings might reconcile the divisiveness on why winds are not showing a synchronous response during the KI and consequential anomalous precipitation events.

Late 19th century navigational uncertainties and their influence on sea surface temperature estimates

Accurate estimates of historical changes in sea surface temperatures (SSTs) and their uncertainties are important for documenting and understanding historical changes in climate. A source of uncertainty that has not previously been quantified in historical SST estimates stems from position errors. A Bayesian inference framework is proposed for quantifying errors in reported positions and their implications on SST estimates. The analysis framework is applied to data from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS3.0) in 1885, a time when astronomical and chronometer estimation of position was common but predated the use of radio signals. Focus is upon a subset of 943 ship tracks from ICOADS3.0 that report their position every two hours to a precision of 0.01∘ longitude and latitude. These data are interpreted as positions determined by dead reckoning that are periodically updated by celestial correction techniques. The posterior medians of uncertainties in celestial correction are 33.1 km (0.30∘ on the equator) in longitude and 24.4 km (0.22∘) in latitude, respectively. Celestial navigation uncertainties being smaller in latitude than longitude is qualitatively consistent with the relative difficulty of obtaining astronomical estimates. The posterior medians for two-hourly dead reckoning uncertainties are 19.2% for ship speed and 13.2∘ for ship heading, leading to random position uncertainties with median 0.18∘ (20 km on the equator) in longitude and 0.15∘ (17 km) in latitude. Reported ship tracks also contain systematic position uncertainties relating to precursor dead-reckoning positions not being updated after obtaining celestial position estimates, indicating that more accurate positions can be provided for SST observations. Finally, we translate position errors into SST uncertainties by sampling an ensemble of SSTs from the Multiscale Ultrahigh Resolution Sea Surface Temperature (MURSST) data set. Evolving technology for determining ship position, heterogeneous reporting and archiving of position information, and seasonal and spatial changes in navigational uncertainty and SST gradients together imply that accounting for positional error in SST estimates over the span of the instrumental record will require substantial additional effort.

Spatio - temporal variations of sea surface temperature in coastal waters of Khanh Hoa province (South Viet Nam) during the period of 2010-2019

Sea Surface Temperature (SST) is considered to be the most important parameter in oceanography. SST is not only an important physical parameter for studying the exchange of water vapor and heat between sea surface and atmosphere but also provides an useful index for oceanographic studies such as ocean circulation, water mass, ocean front, upwelling current, seawater mixing and ocean ecological environment. Empirical orthogonal function (EOF) and time series analysis was used to assess spatio-temporal variability of sea surface temperature in coastal waters of Khanh Hoa province (south Viet Nam) from Multi-scale Ultra-high Resolution Sea Surface Temperature (MUR SST) data for a period of 10 years (2010-2019). The first 4 EOF modes of SST explained 99.80% of the spatio-temporal variance in total variabilities. The first EOF mode explained 98.10% of the total variation of SST, representing the distribution and variability of SST in the summer with a decreasing trend from north to south. The second EOF mode explained 1,10% of the total variation of SST, representing the variation of the SST distribution in winter and spring with an increasing trend from shore to offshore. SST had a seasonal variation, it was highest in summer and lowest in winter. Trend analysis of SST time series in the past 10 years showed that SST increased 0,03oC/year in the period 1/2010-12/2019. The trend and the speed of increase/decrease of SST varied with each different period. The results of the study are useful documents to support the management and exploitation of marine ecosystems.

Summer season sea surface temperature changes in the Aegean Sea based on 30years (1989-2019) of Landsat thermal infrared data.

In this research, sea surface temperature (SST) variability for the summer season in the Aegean Sea was analysed over a period of 30years by using the Landsat thermal infrared bands. A total of 88 Landsat 5, 7, and 8 thermal infrared satellite images from June, July, and August were used for each year from 1989 to 2019. To estimate SST from the thermal infrared band data, thermal infrared sensor at-sensor spectral radiance, and thermal infrared sensor top-of-atmosphere brightness temperatures were used. SST pixel values were extracted from thermal images for the 3-month summer season of each year. In order to validate the findings, regression analysis was performed between the Mediterranean Sea Ultra High Resolution SST L4 data and Landsat data for the 2008-2019 period. Regression constant R-squared values were found to be 0.9672 for June, 0.9550 for July, 0.9634 for August, and 0.9634 for all summer seasons. It was calculated that the minimum value of the average SST was 18.44 ± 2.87°C in 1992, and the maximum value of the average SST was 23.45 ± 0.70°C in 2018. According to the Landsat data, over the past 30years, the annual average SST changes were estimated to be 0.11°C, and the total changes of average SST were estimated to be 3.19 ± 1.26°C. As a result of the analysis and the validation, we can see that there is a rising trend in sea surface temperature in the Aegean Sea. However, it is difficult to determine whether this upward trend is related to global climate change.