Multi-scale Ultra-high Resolution Research Articles

Fine resolution satellite sea surface temperatures capture the conditions experienced by corals at monthly but not daily timescales

Abstract Water temperature is a strong driver of growth, survival, and local adaptation in corals, but our knowledge of the temperatures experienced by corals on reefs worldwide remains limited. While in situ temperature loggers can provide high quality data, they are relatively expensive to place and retrieve. Alternatively, remotely sensed sea surface temperature data are globally available but may be a biased representation of the temperatures experienced by corals. Here, we compared data from 314 temperature loggers on coral reefs to the ~ 1 km2 resolution remotely sensed Multi-scale Ultra-high Resolution Sea Surface Temperature (MUR) product from NASA. We found good agreement (Pearson’s r = 0.95) between maximum monthly mean temperatures calculated from remote and in situ data, with 84% of temperatures within 0.5 °C of each other. However, remotely sensed temperature did not effectively capture sub-diel temperature fluctuations and the highest peak temperatures that may be most dangerous for corals. Predictions of in situ temperatures were significantly but weakly improved by a consideration of reef geomorphology. Ultimately, we found that remotely sensed temperatures can accurately represent the monthly conditions experienced by most corals but should be used with caution at finer temporal scales.

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.

Assessing the accuracy of MUR high resolution satellite sea surface temperature data

Marine bivalve mollusks are valuable proxies for El Niño-Southern Oscillation (ENSO) events in paleoclimate research, reflecting changes in sea surface temperature (SST) and other ambient marine conditions in their shell chemistry. However, the use of several Peruvian species of bivalves as paleoENSO proxies needs to be established by comparing their shells’ δ18O-derived SST records with instrumental SST measurements. Along the Peruvian coast, the limited spatial coverage of in-situ coastal monitoring stations hinders such comparisons. This study aims to assess the accuracy of the Multi-scale Ultra-high Resolution (MUR) satellite-derived SST dataset as an alternative source of SST information for paleoENSO studies in Peru, which could advance research on Peruvian bivalve species. This study compares MUR satellite data with in-situ measured SST from ten coastal monitoring stations operated by the Instituto del Mar del Perú (IMARPE). The correlation between the datasets is evaluated, and systematic and non-systematic variations are examined. Environmental factors that may impact the accuracy of satellite SST measurements, such as clouds and aerosols, are also investigated. Results show that MUR data generally correlate with overall trends in SST variability captured by in-situ measurements. However, there are regional variations in the accuracy of MUR data, with some areas showing consistent temperature offsets. Differences in measurement methodologies and environmental variables are explored as potential causes of variations between the datasets. Overall, the study demonstrates the potential of using high-resolution satellite SST data for assessing δ18O SST records from bivalves in locations without local SST records. It provides insights into the accuracy and limitations of using MUR data as an alternative source of SST information for paleoENSO studies in coastal Peru.

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.

Tropical coral reefs in Sri Lanka are threatened due to the fluctuation of seasonal and interannual sea surface temperature.

The thermal stress of oceans causes coral bleaching, which induces loss of life in coral reefs and makes them exposed to other threats which directly and indirectly affect millions of other species that inhabit the reef. However, studies focusing on how those thermal stresses affect Sri Lankan fringing reef ecosystems are scarce. Hence, the patterns of long-term and short-term fluctuations of sea surface temperature (SST) over the shallow reefs around the country were studied by separating them into different zones as the eastern coast (Passikudha, Kayankerni, Adukkuparu, Parrot Rock, and Pigeon Island), the southern coast (Beruwala Barbarian, Hikkaduwa, Unawatuna, Ahangama, Mirissa, Madiha, Polhena, and Devundara), and northern-northwestern coasts (Valiththoondal, Palk Bay, Mannar, Kalpitiya, Thalwila, and Uswatakeiyawa). The 1km Multiscale Ultrahigh Resolution (MUR) Level 4 SST dataset was used to analyze seasonal and interannual SST variability from 2005 to 2021. The data were correlated with the Indian Ocean Dipole (IOD), Ekman velocity, and wind stress curl. The annual, seasonal, and monthly variability of SST on different coasts is significantly different. Higher increasing trends of SST from 0.0324 to 0.0411 ℃/year are observed on different coasts, and most of the time higher positive anomalies are recorded after 2014. The First Inter Monsoon (IM-1) and the month of April are more critical since they reach the maximum SST, and the minimum is in the North West Monsoon (NWM) and January. Significant positive relationships are recorded between the Indian Ocean Dipole (IOD) index and the monthly average SST on different coasts, which was robust on the southern coast. Therefore, tropical coral reefs in Sri Lanka are severely threatened due elevation of SST by global warming and climate variability.

Why Are the High Frequency Structures of the Sea Surface Temperature in the Brazil–Malvinas Confluence Area Difficult to Predict? An Explanation Based on Multiscale Imagery and Fractal Geometry

The Brazil–Malvinas Confluence (BMC) is one of the most complex oceanic areas in the Earth’s oceans and the prediction of high frequency structures tends to fail. The authors studied the BMC using Multiscale Ultrahigh Resolution (MUR) imagery for the Sea Surface Temperature (SST) to address why the predictions are not as good as expected. The studies were carried out by means of two approaches. The first approach is the non-linear fitting of a harmonic model keeping the frequencies as parameters pixel by pixel. The second approach is from fractal geometry. The three first q-order Rényi dimensions were computed. At the same time, an inverse fractal interpolation was carried out to compute the contraction factor. Both of them are related to the chaotic behavior of nature. This work has three relevant contributions. The correlation between the harmonic models and the SST data is quite poor in general, implying the low harmonicity, and low harmonic predictability, of the pixel-by-pixel time series. It is verified that the quasi-annual and quasi-semiannual waves have periods of about 420 and 210 days, respectively. The second one is the confirmation of the high complexity of the BMC area because the three Rényi dimensions are equal. This has the strong finding of the monofractality of the dynamic of the SST in the BMC. Finally, the contraction factor, one of the parameters of the fractal interpolation, is relatively high, implying the presence of highly complex internal structures in the SST temporal evolution.

Validation of NASA Sea Surface Temperature Satellite Products Using Saildrone Data

Sea Surface Temperature (SST) is at the core of many processes in the oceans. Various remote sensing platforms have been used to obtain SST products of different scales, but their validation remains a topic of ongoing research. One promising platform is an uncrewed surface vehicle called Saildrone. We use the data from eight Saildrone deployments of the USA West Coast 2019 campaign to validate MODIS level-2 and Multi-scale Ultra-high Resolution (MUR) level-4 satellite SST products at 1 km spatial resolution and to assess the robustness of the quality levels of MODIS level-2 products over the California Coast. Pixel-based SST comparisons between Saildrone and the satellite products were performed, as well as thermal gradient comparisons computed both at the pixel-base level and using kriging interpolation. The results generally showed better accuracies for the MUR products. The characterization of the MODIS quality level proved to be valid in areas covered by bad-quality MODIS pixels but less valid in areas covered by lower-quality pixels. The latter implies possible errors in the MODIS quality level characterization and MUR interpolation processes. We have demonstrated the ability of the Saildrones to accurately validate near-shore satellite SST products and provide important information for the quality assessment of satellite products.

Identification of Sea Surface Temperature and Sea Surface Salinity Fronts along the California Coast: Application Using Saildrone and Satellite Derived Products

Coastal upwelling regions are one of the most dynamic areas of the world’s oceans. The California and Baja California Coasts are impacted by both coastal upwelling and the California Current, leading to frontal activity that is captured by gradients in both Sea Surface Temperature (SST) and Sea Surface Salinity (SSS). Satellite data are a great source of spatial data to study fronts. However, biases near coastal areas and coarse resolutions can impair its usefulness in upwelling areas. In this work gradients in SST from NASA Multi-Scale Ultra-High Resolution (MUR) and in two SSS products derived from the Soil Moisture Active Passive (SMAP) NASA mission are compared directly with gradients derived from the Saildrone uncrewed vehicles to validate the gradients as well as to assess their ability to detect known frontal features. The three remotely sensed data sets (MURSST/JPL, SMAP SSS/RSS, SMAP SSS) were co-located with the Saildrone data prior to the calculation of the gradients. Wavelet analysis is used to determine how well the satellite derived SST and SSS products are reproducing the Saildrone derived gradients. Overall results indicate the remote sensing products are reproducing features of known areas of coastal upwelling. Differences between the SST and SSS gradients are mainly associated with the limitations of the microwave derived SSS coverage near land and its reduced spatial resolution. The results are promising for using remote sensing data sets to monitor frontal structure along the California Coast and the application to long term changes in coastal upwelling and dynamics.

Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic

Abstract. As offshore wind farm development expands, accurate wind resource forecasting over the ocean is needed. One important yet relatively unexplored aspect of offshore wind resource assessment is the role of sea surface temperature (SST). Models are generally forced with reanalysis data sets, which employ daily SST products. Compared with observations, significant variations in SSTs that occur on finer timescales are often not captured. Consequently, shorter-lived events such as sea breezes and low-level jets (among others), which are influenced by SSTs, may not be correctly represented in model results. The use of hourly SST products may improve the forecasting of these events. In this study, we examine the sensitivity of model output from the Weather Research and Forecasting model (WRF) 4.2.1 to different SST products. We first evaluate three different data sets: the Multiscale Ultrahigh Resolution (MUR25) SST analysis, a daily, 0.25∘ × 0.25∘ resolution product; the Operational Sea Surface Temperature and Ice Analysis (OSTIA), a daily, 0.054∘ × 0.054∘ resolution product; and SSTs from the Geostationary Operational Environmental Satellite 16 (GOES-16), an hourly, 0.02∘ × 0.02∘ resolution product. GOES-16 is not processed at the same level as OSTIA and MUR25; therefore, the product requires gap-filling using an interpolation method to create a complete map with no missing data points. OSTIA and GOES-16 SSTs validate markedly better against buoy observations than MUR25, so these two products are selected for use with model simulations, while MUR25 is at this point removed from consideration. We run the model for June and July of 2020 and find that for this time period, in the Mid-Atlantic, although OSTIA SSTs overall validate better against in situ observations taken via a buoy array in the area, the two products result in comparable hub-height (140 m) wind characterization performance on monthly timescales. Additionally, during hours-long flagged events (< 30 h each) that show statistically significant wind speed deviations between the two simulations, both simulations once again demonstrate similar validation performance (differences in bias, earth mover's distance, correlation, and root mean square error on the order of 10−1 or less), with GOES-16 winds validating nominally better than OSTIA winds. With a more refined GOES-16 product, which has been not only gap-filled but also assimilated with in situ SST measurements in the region, it is likely that hub-height winds characterized by GOES-16-informed simulations would definitively validate better than those informed by OSTIA SSTs.

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.

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.

Ocean Front Detection with Glider and Satellite-Derived SST Data in the Southern California Current System

This study proposes a method to detect ocean fronts from in situ temperature and density glider measurements. This method is applied to data collected along the CalCOFI Line 90, south of the California Current System (CCS), over the 2006–2013 period. It is based on image-processing techniques commonly applied to sea surface temperature (SST) satellite data. Front detection results using glider data are consistent with those obtained in other studies carried out in the CCS. SST images of the Multi-scale Ultra-high Resolution (MUR) dataset were also used to compare the probability of occurrence or front frequency (FF) obtained with the two datasets. Glider and MUR temperatures are highly correlated. Along Line 90, frontal frequency exhibited the same maxima near the transition zone (~130 km offshore) as derived from MUR and glider datasets. However, marked differences were found in the bimonthly FF probability with high (low) front frequency in spring-summer for glider (MUR) data. Methodological differences explaining these contrasting results are investigated. Thermohaline-compensated fronts are more abundant towards the oceanic zone, although most fronts are detected using both temperature and density criteria, indicating a significant contribution of temperature to density in this region.

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.

Seasonal Variability of SST Fronts in the Inner Sea of Chiloé and Its Adjacent Coastal Ocean, Northern Patagonia

Surface oceanic fronts are regions characterized by high biological activity. Here, Sea Surface Temperature (SST) fronts are analyzed for the period 2003–2019 using the Multi-scale Ultra-high Resolution (MUR) SST product in northern Patagonia, a coastal region with high environmental variability through river discharges and coastal upwelling events. SST gradient magnitudes were maximum off Chiloé Island in summer and fall, coherent with the highest frontal probability in the coastal oceanic area, which would correspond to the formation of a coastal upwelling front in the meridional direction. Increased gradient magnitudes in the Inner Sea of Chiloé (ISC) were found primarily in spring and summer. The frontal probability analysis revealed the highest occurrences were confined to the northern area (north of Desertores Islands) and around the southern border of Boca del Guafo. An Empirical Orthogonal Function analysis was performed to clarify the dominant modes of variability in SST gradient magnitudes. The meridional coastal fronts explained the dominant mode (78% of the variance) off Chiloé Island, which dominates in summer, whereas the SST fronts inside the ISC (second mode; 15.8%) were found to dominate in spring and early summer (October–January). Future efforts are suggested focusing on high frontal probability areas to study the vertical structure and variability of the coastal fronts in the ISC and its adjacent coastal ocean.

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.

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.

Monitoring pearl farming lagoon temperature with global high resolution satellite-derived products: An evaluation using Raroia Atoll, French Polynesia

Temperature is important for pearl oyster reproduction, pelagic larval duration, and growth in the context of pearl farming, but has seldom been monitored over long periods in remote atolls. To test if satellite-derived Sea Surface Temperature (SST) could provide a solution, two daily global SST products were compared with 18 high-precision loggers deployed during 10-months in the wide Raroia atoll (Tuamotu Archipelago, French Polynesia). The Multi-scale-Ultra-high-Resolution (MUR) SST was better correlated with lagoon temperature (r > 0.97) than the Global-Foundation-Sea-Surface-Temperature-Analysis (G1SST) SST (r < 0.94). Differences between observations and MUR SST ranged between −0.75 °C and + 1.12 °C and were influenced by seasons and locations, depth, and hours of measurements. Within this uncertainty range, simulations using a Dynamic Energy Budget model predicted similar life traits of oysters. Therefore, MUR SST appears suitable to monitor lagoon temperature in wide atolls, model oyster population dynamics and assist pearl oyster research and management.