Satellite Satellite Sea Surface Temperature Research Articles

Applying satellite sea surface temperature as Dirichlet-type surface thermal boundary condition in an ocean model

Following the Dirichlet-type boundary condition that specifies value at boundary of a system in mathematics and physics, this study suggests applying satellite sea surface temperature (SST) as Dirichlet-type surface thermal boundary condition (STBC) in ocean models. Numerical experiments with Dirichlet-type and Combined-type STBC with different configurations of satellite SST, for the period of January to April 2019 in the Northwest Pacific Ocean, were carried out based on the Princeton Ocean Model. The experiments were assessed using satellite and in situ observations of temperature, GOFS3.1 analysis and GLORYS12v1 reanalysis. Results show that applying satellite SST as Dirichlet-type STBC could constrain the modeled SST and near-surface temperature in upper 50 m depth well, which is better than the scheme that uses satellite SST as the relaxation terms of Combined-type STBC. The temperature section along the 137°E resulted from the Dirichlet-type STBC is comparable with GOFS3.1 analysis and better than GLORYS12v1 reanalysis in upper 50 m depth. These results suggest that applying high-accuracy satellite SST as Dirichlet-type STBC in ocean models has a promising prospect in numerical simulation.

Improving sea surface temperature in a regional ocean model through refined sea surface temperature assimilation

Abstract. Infrared (IR) and passive microwave (PMW) satellite sea surface temperature (SST) retrievals are valuable to assimilate into high-resolution regional ocean forecast models. Still, there are issues related to these SSTs that need to be addressed to achieve improved ocean forecasts. Firstly, satellite SST products tend to be biased. Assimilating SSTs from different providers can thus cause the ocean model to receive inconsistent information. Secondly, while PMW SSTs are valuable for constraining models during cloudy conditions, the spatial resolution of these retrievals is rather coarse. Assimilating PMW SSTs into high-resolution ocean models will spatially smooth the modeled SST and consequently remove finer SST structures. In this study, we implement a bias correction scheme that corrects satellite SSTs before assimilation. We also introduce a special observation operator, called the supermod operator, into the Regional Ocean Modeling System (ROMS) four-dimensional variational data assimilation algorithm. This supermod operator handles the resolution mismatch between the coarse observations and the finer model. We test the bias correction scheme and the supermod operator using a setup of ROMS covering the shelf seas and shelf break off Norway. The results show that the validation statistics in the modeled SST improve if we apply the bias correction scheme. We also find improvements in the validation statistics when we assimilate PMW SSTs in conjunction with the IR SSTs. However, our supermod operator must be activated to avoid smoothing the modeled SST structures on spatial scales smaller than twice the PMW SST footprint. Both the bias correction scheme and the supermod operator are easy to apply, and the supermod operator can easily be adapted for other observation variables.

Stacking of EAC Eddies Observed From Argo

AbstractUsing data from Argo floats, satellite altimetry, and satellite sea surface temperature (SST), we investigate the merging of two anti‐cyclonic eddies in the Tasman Sea. The eddies are of different size and different density. Once the distance between the eddies falls below a critical separation distance, water from the smaller, denser eddy is observed to flow around the larger, lighter eddy, sink to greater than 400 m depth, and slowly spiral toward the eddy‐center. The merging event is characterized by filaments around the eddy perimeter, evident in high‐resolution, satellite SST data. The merged eddy develops multiple well‐mixed layers, each almost 400 m thick, with large anomalies in temperature and salinity that penetrate to about 1,000 m depth. After the merging is complete, water from the lighter eddy is stacked on top of water from the denser eddy. Using a simple conceptual model, we show that the merged profiles can be simply explained by subduction of surface waters from the denser eddy, and mixing with subsurface waters from the lighter eddy. The stacked eddy has a distinct signature in TS‐space. Using this TS‐signature, together with altimetry for context, we identify over 20 other examples when Argo floats sample stacked eddies in the Tasman Sea. We speculate that merged eddies may be more common than previously thought, with implications for ocean productivity, underwater acoustics, and the transfer of energy across scales.

An Observation-Based Study of Gulf Stream Meander Kinematics Offshore of Cape Hatteras

Gulf Stream (GS) meander structure and propagation offshore of Cape Hatteras are investigated by integrating current measurements from a bottom-moored Acoustic Doppler Current Profiler (ADCP) with high-frequency radar (HFR) surface current measurements and satellite Sea Surface Temperature (SST) images during November 2014. The ADCP measurements provide well-resolved current observations throughout most of the water column, while hourly surface current measurements from HF radars and available satellite SST images provide spatial context to the GS orientation, meander propagation, circulation, and shear structure in the region of the ADCP mooring. The observations provide new insights about meander propagation and evolution in this important transition region. ADCP measurements observed that the increase and deepening intervals of the downstream current with approaching meander crests were typically longer than those for the decrease and shoaling of downstream current, consistent with prominent skewed crests near the surface. The transition time from trough to crest is much greater than that from crest to trough, reflecting the asymmetry in the downstream velocity structure. Vertical shears in the downstream and cross-stream velocity components are indicative of a cold dome centered downslope and offshore of the ADCP. Local maxima in downstream current and bottom temperature at the ADCP occur simultaneously, are accompanied by large vertical velocities, and are led by offshore currents in the upper water column. The mean meander phase speed estimated with HFRs is 48 km/day. Meander periods during the month are about 5-6 days. Where the maxima are seen in the water column, downstream currents reach 2.5 m/s, with current reversals sometimes in excess of 0.5 m/s. Downstream currents occupy an increasing portion of the water column as a crest approaches, and a decreasing fraction as a trough approaches. The deepening increase in downstream velocities with approaching crests is often accompanied by an increase in upstream velocities near the bottom.

Comparison of a Smartfin with an Infrared Sea Surface Temperature Radiometer in the Atlantic Ocean

The accuracy and precision of satellite sea surface temperature (SST) products in nearshore coastal waters are not well known, owing to a lack of in-situ data available for validation. It has been suggested that recreational watersports enthusiasts, who immerse themselves in nearshore coastal waters, be used as a platform to improve sampling and fill this gap. One tool that has been used worldwide by surfers is the Smartfin, which contains a temperature sensor integrated into a surfboard fin. If tools such as the Smartfin are to be considered for satellite validation work, they must be carefully evaluated against state-of-the-art techniques to quantify data quality. In this study, we developed a Simple Oceanographic floating Device (SOD), designed to float on the ocean surface, and deployed it during the 28th Atlantic Meridional Transect (AMT28) research cruise (September and October 2018). We attached a Smartfin to the underside of the SOD, which measured temperature at a depth of ∼0.1 m, in a manner consistent with how it collects data on a surfboard. Additional temperature sensors (an iButton and a TidbiT v2), shaded and positioned a depth of ∼1 m, were also attached to the SOD at some of the stations. Four laboratory comparisons of the SOD sensors (Smartfin, iButton and TidbiT v2) with an accurate temperature probe (±0.0043 K over a range of 273.15 to 323.15 K) were also conducted during the AMT28 voyage, over a temperature range of 290–309 K in a recirculating water bath. Mean differences (δ), referenced to the temperature probe, were removed from the iButton (δ=0.292 K) and a TidbiT v2 sensors (δ=0.089 K), but not from the Smartfin, as it was found to be in excellent agreement with the temperature probe (δ=0.005 K). The SOD was deployed for 20 min periods at 62 stations (predawn and noon) spanning 100 degrees latitude and a gradient in SST of 19 K. Simultaneous measurements of skin SST were collected using an Infrared Sea surface temperature Autonomous Radiometer (ISAR), a state-of-the-art instrument used for satellite validation. Additionally, we extracted simultaneous SST measurements, collected at slightly different depths, from an underway conductivity, temperature and depth (CTD) system. Over all 62 stations, the mean difference (δ) and mean absolute difference (ϵ) between Smartfin and the underway CTD were −0.01 and 0.06 K respectively (similar results obtained from comparisons between Smartfin and iButton and Smartfin and TidbiT v2), and the δ and ϵ between Smartfin and ISAR were 0.09 and 0.12 K respectively. In both comparisons, statistics varied between noon and predawn stations, with differences related to environmental variability (wind speed and sea-air temperature differences) and depth of sampling. Our results add confidence to the use of Smartfin as a citizen science tool for evaluating satellite SST data, and data collected using the SOD and ISAR were shown to be useful for quantifying near-surface temperature gradients.

A hybrid ensemble adjustment Kalman filter based high‐resolution data assimilation system for the Red Sea: Implementation and evaluation

AbstractA new Hybrid ensemble data assimilation system is implemented with a Massachusetts Institute of Technology general circulation model (MITgcm) of the Red Sea. The system is based on the Data Assimilation Research Testbed (DART) and combines a time‐varying ensemble generated by the Ensemble Adjustment Kalman Filter (EAKF) with a pre‐selected quasi‐static (monthly varying) ensemble as used in an Ensemble Optimal Interpolation (EnOI) scheme. The goal is to develop an efficient system that enhances the state estimate and model forecasting skill in the Red Sea with reduced computational load compared to the EAKF. Observations of satellite sea‐surface temperature (SST), altimeter sea‐surface height (SSH), and in situ temperature and salinity profiles are assimilated to evaluate the new system. The performance of the Hybrid scheme (hereafter Hybrid‐EAKF) is assessed with respect to the EnOI and the EAKF results. The comparisons are based on the daily averaged forecasts against satellite SST and SSH measurements and independent in situ temperature and salinity profiles. Hybrid‐EAKF yields significant improvements in terms of ocean state estimates compared to both EnOI and EAKF, in particular mitigating for dynamical imbalances that affect EnOI. Hybrid‐EAKF improves the estimation of SST and SSH root‐mean‐square differences by up to 20% compared to EAKF. High‐resolution mesoscale eddy features, which dominate the Red Sea circulation, are further better represented in Hybrid‐EAKF. Important reduction, by about 75%, in computational cost is also achieved with the Hybrid‐EAKF system compared to the EAKF. These significant improvements were obtained with the Hybrid‐EAKF after accounting for uncertainties in the atmospheric forcing and internal model physics in the time‐varying ensemble.

A Regional Operational Model for the North East Atlantic: Model Configuration and Validation

An operational model for an area of the northeast Atlantic that encompasses all of Ireland’s territorial waters has been developed. The model is an implementation of the Regional Ocean Modelling System (ROMS) and uses operationally available atmospheric and boundary forcing, and a global tide solution for tidal forcing. River forcing is provided by climatological daily discharge rates for 29 rivers across Ireland, west Britain, and west France. It is run in an operational framework to produce 7-day hindcasts once a week, and daily 3-day forecasts which are published in a number of formats. We evaluated the model skill by comparing with measured data and calculating statistics such as mean error, root mean square error (RMSE), and correlation coefficient. The observations consist of satellite Sea Surface Temperature (SST), total surface velocity fields from satellite, water level time series from around the Irish coast, and temperature and salinity data from Array for Real-Time Geostrophic Oceanography (ARGO) and Conductivity Temperature Depth (CTD) profiles. The validation period is from 1 January 2016 until 31 December 2019. The correlation coefficient between the model and satellite SST is 0.97 and recorded in March and April 2018. The model error is about 5% of the total M2 amplitude in the Celtic Sea recorded at Dunmore East tide gauge station. The maximum RMSE between the model and the CTD temperature profiles is 0.8 °C while it is 0.17 PSU for salinity. The model correctly defines the shelf water masses around Ireland. In 2019 the Irish Coastal Current (ICC) was very strong and well defined along most of the western Irish coast. The model results have well reproduced the ICC front for the whole simulation period.

Sensitivity Analysis of Bias in Satellite Sea Surface Temperature Measurements

The satellite sea surface temperature (SST) measurement is based on the detection of ocean radiation using microwave or infrared wavelengths within the electromagnetic spectrum. The radiance of individual wavelengths can be converted into brightness temperatures for using in SST determination. The calibration and validation of the determined SST data require reference measurements from in-situ observations. These in-situ observations are from various platforms such as ships, drifters, floats and mooring buoys and require a high measurement accuracy. This paper presents an investigation about the possibility of using a glider as in-situ platform. A glider is a type of autonomous underwater vehicle (AUV) which can log oceanographic data over a period of up to one year by following predetermined routes. In contrast to buoys, a glider allows a targeted investigation of regional anomalies in SST circulations. To assess the quality of SST observations from a glider, logged data from a glider mission in the Atlantic Ocean from 2018 to 2019 and corresponding satellite SST data were used. The influence of variables (e.g. measurement depth, latitude, view zenith angle, local solar time) of the bias between satellite and glider SST data was investigated using sensitivity analysis. A new and efficient distribution-based method for global sensitivity analyzes, called PAWN, was used successfully. Interested readers will find information about its operation principle and the usage for passive observations where only “given-data” are available.

Combining Satellite Imagery and Numerical Modelling to Study the Occurrence of Warm Upwellings in the Southern Baltic Sea in Winter

Coastal upwelling involves an upward movement of deeper, usually colder, water to the surface. Satellite sea surface temperature (SST) observations and simulations with a hydrodynamic model show, however, that the coastal upwelling in the Baltic Sea in winter can bring warmer water to the surface. In this study, the satellite SST data collected by the advanced very high resolution radiometer (AVHRR) and the moderate-resolution imaging spectroradiometer (MODIS), as well as simulations with the Parallel Model 3D (PM3D) were used to identify upwelling events in the southern Baltic Sea during the 2010–2017 winter seasons. The PM3D is a three-dimensional hydrodynamic model of the Baltic Sea developed at the Institute of Oceanography, University of Gdańsk, Poland, in which parallel calculations enable high-resolution modelling. A validation of the model results with in situ observations and satellite-derived SST data showed the PM3D to adequately represent thermal conditions in upwelling areas in winter (91.5% agreement). Analysis of the frequency of warm upwellings in 12 areas of the southern Baltic Sea showed a high variability in January and February. In those months, the upwelling was most frequent, both in satellite imagery and in model results, off the Hel Peninsula (38% and 43% frequency, respectively). Upwelling was also frequent off the Vistula Spit, west of the Island of Rügen, and off the eastern coast of Skåne, where the upwelling frequency estimated from satellite images exceeded 26%. As determined by the PM3D, the upwelling frequency off VS and R was at least 25%, while off the eastern coast of Skåne, it reached 17%. The faithful simulation of SST variability in the winters of 2010–2017 by the high-resolution model used was shown to be a reliable tool with which to identify warm upwellings in the southern Baltic Sea.

Evolution of surface cold patches in the North Yellow Sea based on satellite SST data

Ten years (from 2005 to 2014) of satellite sea surface temperature (SST) data from the Advanced Very High Resolution Radiometer (AVHRR) are analyzed to reveal the monthly changes in surface cold patches (SCPs) in the main areas of the Northern Yellow Sea (NYS). The Canny edge detection algorithm is used to identify the edges of the patches. The monthly changes are described in terms of location, temperature and area. The inter-annual variations, including changes in the location and area of the SCPs from 2010 to 2014, are briefly discussed. The formation mechanisms of the SCPs in different periods are systematically analyzed using both in situ data and numerical simulation. The results show that from May to October, the location and area of the SCPs remain stable, with a north-south orientation. The SCPs altogether cover about 1° of longitude (124°E–125°E) in width and 2° of latitude (37.5°N–39.5°N) in length. In November, the SCP separates from the Jangsan Cape and forms a closed, isolated, and approximately circular cold patch in the central NYS. From May to October, the upwelling that leads to the formation of the SCP is mainly triggered by the headland residual current, wind field, climbing movement of the current and secondary circulation at the tide front. In November, cyclonic circulation in the NYS is primarily responsible for generating the upwelling that leads to the formation of the closed and isolated SCP.

Suitability of satellite sea surface salinity data for use in assessing and correcting ocean forecasts

Abstract Near-surface salinity data from the Forecasting Ocean Assimilation Model (FOAM) system are used to understand various characteristics of satellite sea surface salinity (SSS) data from SMOS. The errors in the model fields are first estimated by comparing them to near-surface Argo salinity measurements, with RMS errors of less than 0.2 pss over most of the global oceans, except for regions of high variability in SSS such as boundary current regions and areas of large precipitation or river run-off. Regional biases are generally less than 0.05 pss but some regions such as the Antarctic Circumpolar Current and the region to the north of the Gulf Stream extension have larger biases. Various different processing versions of the SMOS data are assessed, including different temporal and spatial averaging, and the daily 1° resolution SMOS differences to FOAM are approximately 3–10 times larger than Argo-FOAM differences. The spatial information in the SMOS data is also assessed by comparing spatial gradients in the satellite SSS data with those calculated from other datasets including satellite sea surface temperature (SST) and satellite altimeter sea surface height (SSH) data, as well as with the model's gradients. The SMOS data contain information about the underlying ocean dynamics in the summer months, in agreement with the SSH data, which are not present in the satellite SST data or in the model's SSS fields. This demonstrates that the data contains useful information which could be used to correct the model through data assimilation.

Sea‐surface temperature and salinity product comparison against external in situ data in the Arctic Ocean

AbstractSea‐surface temperature and salinity (SST/S) in the Arctic Ocean (AO) are largely governed by sea‐ice and continental runoff rather than evaporation and precipitation as in lower latitude oceans, and global satellite analyses and models which incorporate remotely observed SST/S may be inaccurate in the AO due to lack of direct measurements for calibrating satellite data. For this reason, we are motivated to validate several satellite sea‐surface temperature (SST) data products and SST/S models by comparing gridded data in the AO with oceanographic records from 2006 to 2013. Statistical analysis of product‐minus‐observation differences reveals that the satellite SST products considered have a temperature bias magnitude of less than 0.5 C compared to ship‐based CTD measurements, and most of these biases are negative in sign. SST/S models also show an overall negative temperature bias, but no common sign or magnitude of salinity bias against CTD data. Ice tethered profiler (ITP) near‐surface data span the seasons of several years, and these measurements reflect a sea‐ice dominated region where the ocean surface cannot be remotely observed. Against this data, many of the considered models and products show large errors with detectable seasonal differences in SST bias. Possible sources of these errors are discussed, and two adjustments of product SST on the basis of sea‐ice concentration are suggested for reducing bias to within less than 0.01 C of ITP near‐surface temperatures.

Upwelling Parameters From Bias-Corrected Composite Satellite SST Maps in the Gulf of Finland (Baltic Sea)

This letter proposes a method for using the operational ship of opportunity temperature data at a fixed depth for bias correction of satellite sea surface temperature (SST) images. The bias-corrected SST imagery from MODerate Resolution Imaging Spectroradiometer (MODIS) and Advanced Along-Track Scanning Radiometer (AATSR) sensors were used to calculate mean upwelling characteristics in the Gulf of Finland (GoF, Baltic Sea). First, we determined that the operational flow through temperature data at a 4-m depth can be used for validation and bias correction of satellite SST images in cases of wind speed over 5 m · s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . The composite sea temperature maps were calculated from bias-corrected images collected during upwelling events in the GoF, in 2000-2009. Mean upwelling characteristics were estimated from composite maps for both the northern and southern coasts of the gulf.

A bias correction method for Arctic satellite sea surface temperature observations

A multi-sensor bias correction method has been developed using satellite sea surface temperature (SST) products from one microwave and five infrared sensors that cover the Arctic Ocean. The correction method has been used to construct improved single sensor and multi-sensor, merged and interpolated satellite SST products from January to December 2008.The validation of the satellite products ingested in the level 4 production shows that large biases can persist for months in this region. The SST products from the AATSR sensor on ENVISAT and the NAVOCEANO AVHRR GAC are the most stable and reliable products for the Arctic region. These products have therefore been used to construct the reference product against which the other satellite products have been corrected. The bias correction method has been developed using detailed error characteristics and thorough time-space analysis, and the bias corrected fields are validated against in situ observations from drifting buoys. All the individual satellite products show improvement in both bias and standard deviation after correction. Largest improvements are found for the Modis sensor on the Terra satellite, where biases are improved from −0.46K to 0.02K with the correction method. Temporal validation statistics reveal that extended periods with significant biases are also removed by the bias correction method.A significant improvement is seen when the corrected satellite products are used for the SST analysis. When compared against drifting buoys, not included in the analysis, the corrected level 4 satellite SST product shows a bias of −0.04K and standard deviation of 0.54K, compared to an original bias of −0.28K and standard deviation of 0.61K. The effect of a missing reference sensor is assessed for the full period. Level 4 test runs using only one reference sensor demonstrates that improvements can be obtained with a single sensor bias correction method, and that the AATSR sensor gives the largest improvements. However, using both reference sensors in the bias calculation gives significantly better performance than using just one.

Blending multi-resolution satellite sea surface temperature (SST) products using Bayesian maximum entropy method

Integrating multiple satellite sea surface temperature (SST) products is one of the ways to improve the accuracy, spatial resolution and completeness of satellite SST products. In this paper, The Bayesian Maximum Entropy (BME), a nonlinear geostatistical methodology, is used for blending the satellite SST datasets (SSTs) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) on EOS Aqua satellite. An error model is developed to link the MODIS SST and the AMSR-E SST at different resolutions. The AMSR-E SSTs are processed as probability soft data by this error model to take into account the uncertainty associated with coarser resolution pixels. The MODIS SSTs are taken as hard data. The soft AMSR-E SSTs and hard MODIS SSTs are then merged in the BME paradigm to produce 8-day average and spatially continuous SSTs with 4km spatial resolution. The Merged SSTs are validated by using the drifting buoy SST records. The local variance is used to evaluate the ability of the BME method in preserving the original fine spatial structure of the 4km resolution MODIS SSTs in the blended SSTs. The validation results show that the RMSE and Bias for the BME blended SSTs over the whole study area in 2003 are 0.653°C and −0.146°C, respectively. These values are a bit larger than those for AMSR-E SST (the RMSE and Bias are 0.504°C and 0.0392°C, respectively) and MODIS SST (the RMSE and Bias are 0.635°C and −0.102°C, respectively). The estimations of SST pixels where both of MODIS and AMSR-E have missing data have a Bias of −0.255°C and an RMSE of 0.826°C. The difference in local variance between the Merged SSTs and MODIS SSTs is less than 0.01°C2, and the difference between the Merged SSTs and AMSR-E SSTs is about 0.19°C2.The blended 4-km SST data set is equal to MODIS SST in revealing the fine scale structures of SST spatial variation. In addition, the blended SST dataset has the complete spatial coverage. The results demonstrate the blending potential of BME for multiple scales satellite derived products integration.

Intensity of tropical cyclones during pre- and post-monsoon seasons in relation to accumulated tropical cyclone heat potential over Bay of Bengal

The aim of the present study is to understand the impact of oceanic heat potential in relation to the intensity of tropical cyclones (TC) in the Bay of Bengal during the pre-monsoon (April–May) and post-monsoon (October–November) cyclones for the period 2006–2010. To accomplish this, the two-layer gravity model (TLGM) is employed to estimate daily tropical cyclone heat potential (TCHP) utilizing satellite altimeter data, satellite sea surface temperature (SST), and a high-resolution comprehensive ocean atlas developed for Indian Ocean, subsequently validated with in situ ARGO profiles. Accumulated TCHP (ATCHP) is estimated from genesis to the maximum intensity of cyclone in terms of minimum central pressure along their track of all the cyclones for the study period using TLGM generated TCHP and six-hourly National Centre for Environmental Prediction Climate Forecast System Reanalysis data. Similarly, accumulated sea surface heat content (ASSHC) is estimated using satellite SST. In this study, the relationship between ATCHP and ASSHC with the central pressure (CP) which is a function of TC intensity is developed. Results reveal a distinct relationship between ATCHP and CP during both the seasons. Interestingly, it is seen that requirement of higher ATCHP during pre-monsoon cyclones is required to attain higher intensity compared to post-monsoon cyclones. It is mainly attributed to the presence of thick barrier layer (BL) resulting in higher enthalpy fluxes during post-monsoon period, where as such BL is non-existent during pre-monsoon period.

Incorporating sea-surface temperature to the light-based geolocation model TrackIt

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 419:71-84 (2010) - DOI: https://doi.org/10.3354/meps08862 Incorporating sea-surface temperature to the light-based geolocation model TrackIt Chi H. Lam1,*, Anders Nielsen2, John R. Sibert3 1University of Southern California, Marine Environmental Biology, 3616 Trousdale Pkwy, AHF 107, Los Angeles, California 90089, USA 2Technical University of Denmark, National Institute of Aquatic Resources, Jaegersborg Allé 1, 2920 Charlottenlund, Denmark 3Pelagic Fisheries Research Program, Joint Institute for Marine and Atmospheric Research, University of Hawai’i at Mãnoa, 1000 Pope Road, MSB 312, Honolulu, Hawaii 96822, USA *Email: chihinl@usc.edu ABSTRACT: Archival and pop-up satellite archival tags have been widely used to study the movement dynamics in many marine pelagic species. Recent advances in light-based geolocation models have enabled better estimation of the geographical positions of tagged animals. In particular, TrackIt, a state-space model with the Kalman filter, uses only light data from a tag for estimating positions and does so independently of manufacturer calculations. This approach is a complete break from previous geolocation methodologies, which rely on manufacturer-processed positions as an input. In this paper, a unified model is presented to extend TrackIt to incorporate satellite sea-surface temperature (SST) matching, an approach that has been demonstrated to improve accuracy. The performance of various satellite SST imagery products is also evaluated by the comparison of SST-inclusive models against the basic TrackIt model using only light information. Three new model parameters (bias, error and smoothing radius) are introduced for handling SST observations. Analyses based on double-tagging comparisons show that the overall accuracy of TrackIt increases with the incorporation of SST, even when the resolution of the matching satellite product is rather coarse. At the same time, the model accuracy can decrease when the SST observations do not exhibit any strong trends, rendering SST matching less informative. The incorporation of SST within this generic and statistically sound modeling framework illustrates how TrackIt can readily be extended to utilize new data streams, such as geomagnetic data, which will become available with the next generation of archival tags. KEY WORDS: Light-based geolocation · Sea-surface temperature · TrackIt · Archival tag · Pop-up satellite archival tag · State–space model Full text in pdf format Supplementary material PreviousNextCite this article as: Lam CH, Nielsen A, Sibert JR (2010) Incorporating sea-surface temperature to the light-based geolocation model TrackIt. Mar Ecol Prog Ser 419:71-84. https://doi.org/10.3354/meps08862Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 419. Online publication date: November 30, 2010 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2010 Inter-Research.

Application of an edge detection method to satellite images for distinguishing sea surface temperature fronts near the Japanese coast

This study applies an edge detection method to satellite sea surface temperature (SST) images in order to distinguish SST fronts frequently occurring near the Japanese coast. First, we develop a new set of edge detection algorithms. The method is based on an entropic approach using Jensen–Shannon divergence, which is certified as a robust technique for detecting edge pixel candidates. Additionally, we incorporate ridge-line extraction morphological filter to obtain edge pixels, which is suited to delineate intricate frontal features. The methodology allows us to detect reasonable amount of possible fronts to the fullest extent according to the original image resolution. Therefore, it enables us to analyze finer-scale fronts near the coast as well as larger-scale fronts associated with major currents. Then, we present four case study examples of SST fronts near the Japanese coast by applying the method to Advanced Very High Resolution Radiometer (AVHRR) SST images with a grid size of 0.01°. We describe the configuration and seasonal variability of the SST frontal systems from the viewpoint of occurrence probability, SST gradient magnitude, and orientation to the local bathymetry as compared with the previous observational studies. Consequently, it is demonstrated that application of the method can derive new views of finer-scale and highly variable SST fronts. Moreover, this study first verifies the capability of comprehensive and statistical description of SST front configurations and variations near the Japanese coast by applying edge detection method to sequences of satellite SST images.

Impacts of in situ and additional satellite data on the accuracy of a sea‐surface temperature analysis for climate

AbstractAdditional in situ and satellite data improve the accuracy of a blended (in situ and satellite) sea‐surface temperature (SST) analysis using optimum interpolation (OI). Two studies were conducted to evaluate the impacts of in situ and additional satellite data. One study evaluated the adequacy of the recent in situ network. Because of the high coverage of satellite data, in situ data used in the analysis tends to be overwhelmed by satellite data. Thus, the most important role of the in situ data in the analysis is to correct large‐scale satellite biases. Simulations with different buoy densities showed the need for at least two buoys on a 10° spatial grid. This will ensure that satellite biases do not exceed 0.5 °C. Using this criterion, regions were identified where additional buoys are needed.A second study evaluated the impact of satellite SST retrievals from the tropical rainfall measuring mission microwave imager (TMI) on the OI analysis. The present version only uses infrared satellite data from the advanced very high resolution radiometer (AVHRR) instrument. The results of the intercomparisons showed that both AVHRR and TMI data have biases that must be corrected for climate studies. The addition of TMI data clearly improved the OI analysis accuracy without bias correction, but was less significant when bias correction was used. However, there are areas of the ocean with limited in situ data and restricted AVHRR coverage due to cloud cover, and the use of both TMI and AVHRR should improve the accuracy of the analysis in those areas. Copyright © 2005 Royal Meteorological Society

Eastern Mediterranean Transient studied with Lagrangian diagnostics applied to a Mediterranean OGCM forced by satellite SST and ECMWF wind stress for the years 1988–1993

In this work, we estimate deep and intermediate water mass dispersal in the different phases of the Eastern Mediterranean Transient by means of the Lagrangian analysis of a Mediterranean ocean general circulation model (OGCM) run, forced by daily European Centre for Medium‐Range Weather Forecasts (ECMWF) wind stress and satellite sea surface temperature (SST) over the years from 1988 to 1993. While in a first phase (1989–1991) major modifications of the intermediate salty waters path are observed, during 1992 and 1993 the general mechanism of the transient is reproduced in the deep Ionian basin, where the Cretan overflow replaces Adriatic deep water that uplifting of several hundred meters freshens in the intermediate layers. Analysis of the water mass formation rates shows that dense water production during 1993 is twice the climatological values in the eastern subbasins of the Mediterranean and that the densest water is formed in the Cretan Sea. From 1988 to 1993, about 1.3 × 1014 m3 of water flows over the Cretan Arcs (see the caption of Figure 1 for a definition of the Cretan Arcs) to sink deeper than 600 m in the eastern Mediterranean, which is roughly half of the value estimated by Roether et al. [1996]. About 60% of this deep outflow (∼0.8 × 1014 m3) occurs in a few months, during the cold winters of 1992 and 1993, when the overflow of Cretan dense water in the Antikithera Strait reaches 1.5 Sv and rapidly propagates in the deep Ionian basin, developing very energetic coherent structures. We study the characteristic times of the relaxation of the system toward the climatology, and finally, we estimate the rate and the geographical distribution of the upwelling in the intermediate and subsurface layers.