Sensed Sea Surface Temperature Data Research Articles

The influence of the Gulf Stream induced SST gradients on the US East Coast winter storm of 24–25 January 2000

This study presents an investigation of the influence of remotely sensed high resolution sea surface temperature (SST) and the SST gradient on the formation and evolution of the 24–25 January 2000 East Coast winter storm. A numerical model was employed for experimental simulation replaced SST analysis with a 1.1 km gridded data set. The most significant improvements were seen in the forecast deepening rate and track. Reduced development of the storm in the control simulation, as compared to the experimental simulation, appears to be due to the coarse grid SST representation, which fails to capture key thermal gradient features of the Gulf Stream. The simulations suggest that the high resolution remotely sensed SST data affect the track by changing the location of lower‐tropospheric frontal boundaries through thermally‐induced near‐surface convergence and differential turbulent heat flux. Enhanced vortex stretching associated with the convergence along the lower frontal boundary appears to contribute to a stronger storm in the experimental simulations.

TurtleWatch: a tool to aid in the bycatch reduction of loggerhead turtles Caretta caretta in the Hawaii-based pelagic longline fishery

Operational longline fishery characteristics, bycatch information, and loggerhead turtle satellite tracks were all used in conjunction with remotely sensed sea surface temperature data to identify the environmental area where the majority of loggerhead turtle bycatch occurred in the Hawaii-based longline fishery during 1994 to 2006. In the first quarter of each calendar year from 1994 to 2006, the majority of shallow longline sets and associated loggerhead turtle bycatch were above 28° N, which corresponds to the area near the North Pacific Subtropical Frontal Zone. Based on the thermal ranges of bycatch, sets and the satellite-tagged turtles, it was recommended that shal- low sets should only be deployed in waters south of the 18.5°C (~65.5°F) isotherm to decrease logger- head turtle bycatch. This recommendation formed the basis for the TurtleWatch tool, a map provid- ing up-to-date information about the thermal habitat of loggerhead sea turtles in the Pacific Ocean north of the Hawaiin Islands. TurtleWatch was released to fishers and managers in electronic and paper formats on December 26, 2006, to assist in decision making during the first quarter of 2007. Fishery information from 2007 was later compared with data for the years 2005 to 2006 to assess the response of the fishery to TurtleWatch. The observed fleet movement during the first quarter of 2007 was to the north of the 18.5°C (~65.5°F) isotherm (i.e. in the area recommended for avoidance by the TurtleWatch product) with increased effort and lower bycatch rates. We discuss possible reasons for this decrease in turtle bycatch north of the frontal zone together with future research directions which may lead to refinement of the TurtleWatch product.

Analysis and modeling of the seasonal South China Sea temperature cycle using remote sensing

The present paper describes the analysis and modeling of the South China Sea (SCS) temperature cycle on a seasonal scale. It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is motivated by the possibility to significantly increase the model’s computational efficiency when neglecting tides. The goal is to develop a flexible and efficient tool for seasonal scenario analysis and to generate transport boundary forcing for local models. Given the significant spatial extent of the SCS basin and the focus on seasonal time scales, synoptic remote sensing is an ideal tool in this analysis. Remote sensing is used to assess the seasonal temperature cycle to identify the relevant driving forces and is a valuable source of input data for modeling. Model simulations are performed using a three-dimensional baroclinic-reduced depth model, driven by monthly mean sea surface anomaly boundary forcing, monthly mean lateral temperature, and salinity forcing obtained from the World Ocean Atlas 2001 climatology, six hourly meteorological forcing from the European Center for Medium range Weather Forecasting ERA-40 dataset, and remotely sensed sea surface temperature (SST) data. A sensitivity analysis of model forcing and coefficients is performed. The model results are quantitatively assessed against climatological temperature profiles using a goodness-of-fit norm. In the deep regions, the model results are in good agreement with this validation data. In the shallow regions, discrepancies are found. To improve the agreement there, we apply a SST nudging method at the free water surface. This considerably improves the model’s vertical temperature representation in the shallow regions. Based on the model validation against climatological in situ and SST data, we conclude that the seasonal temperature cycle for the deep SCS basin can be represented to a good degree. For shallow regions, the absence of tidal mixing and exchange has a clear impact on the model’s temperature representation. This effect on the large-scale temperature cycle can be compensated to a good degree by SST nudging for diagnostic applications.

An Evaluation of the Use of Remotely Sensed Parameters for Prediction of Incidence and Risk Associated with Vibrio parahaemolyticus in Gulf Coast Oysters (Crassostrea virginica)

The U.S. Food and Drug Administration recently published a Vibrio parahaemolyticus risk assessment for consumption of raw oysters that predicts V. parahaemolyticus densities at harvest based on water temperature. We retrospectively compared archived remotely sensed measurements (sea surface temperature, chlorophyll, and turbidity) with previously published data from an environmental study of V. parahaemolyticus in Alabama oysters to assess the utility of the former data for predicting V. parahaemolyticus densities in oysters. Remotely sensed sea surface temperature correlated well with previous in situ measurements (R2 = 0.86) of bottom water temperature, supporting the notion that remotely sensed sea surface temperature data are a sufficiently accurate substitute for direct measurement. Turbidity and chlorophyll levels were not determined in the previous study, but in comparison with the V. parahaemolyticus data, remotely sensed values for these parameters may explain some of the variation in V. parahaemolyticus levels. More accurate determination of these effects and the temporal and spatial variability of these parameters may further improve the accuracy of prediction models. To illustrate the utility of remotely sensed data as a basis for risk management, predictions based on the U.S. Food and Drug Administration V. parahaemolyticus risk assessment model were integrated with remotely sensed sea surface temperature data to display graphically variations in V. parahaemolyticus density in oysters associated with spatial variations in water temperature. We believe images such as these could be posted in near real time, and that the availability of such information in a user-friendly format could be the basis for timely and informed risk management decisions.

A Simple Method of Deriving Three-Dimensional Temperature Fields Using Remotely Sensed and In Situ Data for Application to Numerical Hydrodynamic Models of Estuaries and Bays

Abstract This paper describes the subjective interpolation method (SIM) for generating three-dimensional temperature distributions from remotely sensed sea surface temperature (SST) fields. SIM incorporates MATLAB-based cloud removal software and a method of generating synthetic temperature profiles based on observations. This approach depends on the human facility for recognizing patterns in complex images. Three-dimensional temperature fields produced by SIM are compared to analogous fields based on optimal interpolation (OI) methods by using temperature fields interpolated by the two methods to initialize a baroclinic coastal ocean circulation model. The initial SST surface fields from both methods have a bias of less than −0.5°C and rms errors of less than 1.5°C. After running for 48 h, the bias and rms errors for the OI simulations are 0.3° and 1.2°C, respectively, whereas the same errors for the SIM run are 0.7° and 0.9°C. The OI and SIM approaches can be combined to allow preprocessing of SST data ...

Seasonal and interannual variability of oceanographic processes in the Gulf of Guinea: An investigation using AVHRR sea surface temperature data

The Gulf of Guinea is situated in a critical position for understanding Atlantic equatorial dynamics. This study investigates seasonal and interannual variability in sea surface temperature (SST) throughout this region, focusing on dynamical ocean processes. A 10.5-year time series of remotely sensed SST data with 4 km spatial resolution from the Advanced Very High Resolution Radiometer (AVHRR) were used for this investigation, as they are sufficient to resolve shelf processes. Firstly, patterns of cloud cover were assessed, then spatio-temporal variability in SST patterns was investigated. Features identified in climatological SST images were the Senegalese upwelling influence, coastal upwelling, tropical surface water, river run-off and fronts. Of particular interest is a shelf-edge cooling along the coast of Liberia and Sierra Leone in February. Interannual variability, assessed using annual mean images, time series decomposition and spectral analysis, showed a quasi-cyclic pattern of warm and cool years, perhaps related to El Niño-type forcing. The results of this study show the usefulness of infrared remote sensing for tropical oceanography, despite high levels of cloud cover and atmospheric water vapour contamination, and they provide evidence for theories of westward movement of the upwelling against the Guinea current and remote forcing of the upwelling.

The use of AVHRR data in a pilot study for investigating and modelling regional ocean circulation

Regional oceanographic studies rely on in situ data. However, sampling design strategies for the collection of oceanographic data are not always straightforward, particularly in regions of complex bathymetry. Here we present a pilot study methodology that uses remotely sensed sea surface temperature data to generate coarse resolution surface temperature fields suitable for forcing a regional numerical circulation model. The model is then used to help identify the major surface and interior oceanographic features in the region and hence determine the most suitable locations to deploy instrumentation. Such pilot studies are uncommon in regional oceanographic research and here we demonstrate an effective, low-cost pilot study methodology.

Gulf Stream Simulations and the Dynamics of Ring and Meander Processes

We present here a regional, eddy resolving, numerical study of the dynamics of Gulf Stream Meander and Ring (GSMR) interaction processes. We initialize the Harvard quasi-geostrophic open-boundary model with realistic meander and ring locations as indicated by remotely sensed sea surface temperature (SST) data and predict the flow evolution for the period 23 November to 19 December 1984. The methodology of Feature-Model initialization is introduced to extend the surface information to the thermocline and deep levels in terms of climatological structures, which are then dynamically adjusted by the model. Six numerical simulators are carried out to explore the influence of initial and boundary conditions on the flow evolution. All of the major events observed in the SST data are simulated, including the birth of new warm and cold core rings. The results show the relevance of quasi-geostrophic dynamics for the GSMR region on these time scales in the thermocline. A set of parameter and sensitivity experiments then elucidate the dependence on physical parameters; ring births are nonlinear baroclinic processes. The dynamics of these realistic cold and warm core formation events are quantified via local energy and vorticity budget analyses (EVA). The cold core case involves a process of nonlinear baroclinic cascades that convert available gravitational energy to kinetic energy and vice versa. The warm core case involves a differential horizontal advection process.