Advanced Very High Resolution Radiometer Sea Surface Temperature Research Articles

A split-window method to retrieving sea surface temperature from landsat 8 thermal infrared remote sensing data in offshore waters

Sea surface temperature (SST) is an important parameter used to describe the air-sea interaction and the state of marine structures. Atmospheric water vapor has a significant attenuation effect on thermal infrared information, which will reduce the accuracy of SST inversion. However, the effect of atmospheric water vapor on the inversion accuracy is not considered carefully in the existing SST inversion algorithm. In this study, a new method is proposed for retrieving SST from Landsat 8 Thermal Infrared Remote Sensing (TIRS) data based on the variation of atmospheric water vapor content (wvc). First, simulating atmospheric conditions by using moderate resolution atmospheric transmission (MODTRAN) based on atmospheric profiles data (air temperature and pressure). Second, calculating the bright temperature based on the radiation transfer equation and Planck's law. Then, constructing the SST retrieval model of thermal infrared remote sensing based on wvc. Finally, evaluating the accuracy of the proposed model by using simulation data. The root mean square errors (RMSEs) are within 0.5 K, indicating that the accuracy of the model is good in theory. In addition, taking the Zhoushan sea area as the research area, SST is retrieved by Landsat 8 TIRS data. The accuracy of inversion results is evaluated by advanced very high-resolution radiometer (AVHRR) SST products. The bias and RMSE based on the AVHRR SST products are within 1 K and 2 K, respectively. The results show that the accurate SST with high spatial resolution was successfully obtained by using the method. The study is of great significance to the acquisition of marine structural parameters, the exploitation of marine resources, and the monitoring of marine disasters.

Evaluating operational AVHRR sea surface temperature data at the coastline using surfers

Sea surface temperature (SST) is an essential climate variable that can be measured routinely from Earth Observation (EO) with high temporal and spatial coverage. To evaluate its suitability for an application, it is critical to know the accuracy and precision (performance) of the EO SST data. This requires comparisons with co-located and concomitant in situ data. Owing to a relatively large network of in situ platforms there is a good understanding of the performance of EO SST data in the open ocean. However, at the coastline this performance is not well known, impeded by a lack of in situ data. Here, we used in situ SST measurements collected by a group of surfers over a three year period in the coastal waters of the UK and Ireland, to improve our understanding of the performance of EO SST data at the coastline. At two beaches near the city of Plymouth, UK, the in situ SST measurements collected by the surfers were compared with in situ SST collected from two autonomous buoys located ∼7 km and ∼33 km from the coastline, and showed good agreement, with discrepancies consistent with the spatial separation of the sites. The in situ SST measurements collected by the surfers around the coastline, and those collected offshore by the two autonomous buoys, were used to evaluate the performance of operational Advanced Very High Resolution Radiometer (AVHRR) EO SST data. Results indicate: (i) a significant reduction in the performance of AVHRR at retrieving SST at the coastline, with root mean square errors in the range of 1.0 to 2.0 °C depending on the temporal difference between match-ups, significantly higher than those at the two offshore stations (0.4 to 0.6 °C); (ii) a systematic negative bias in the AVHRR retrievals of approximately 1 °C at the coastline, not observed at the two offshore stations; and (iii) an increase in the root mean square error at the coastline when the temporal difference between match-ups exceeded three hours. Harnessing new solutions to improve in situ sampling coverage at the coastline, such as tagging surfers with sensors, can improve our understanding of the performance of EO SST data in coastal regions, helping inform users interested in EO SST products for coastal applications. Yet, validating EO SST products using in situ SST data at the coastline is challenged by difficulties reconciling the two measurements, which are provided at different spatial scales in a dynamic and complex environment.

위성자료를 이용한 동해안 냉수대의 시공간적 변화 분석 연구

이 연구는 여름철 동해안 냉수대의 시공간적 이동에 따른 변화 분석에 관한 것이다. 2013년 여름에 위성 및 현장자료(바람, 기온, 수온)를 이용하여 다양한 환경 요인들에 기반을 둔 자료 분석을 시도하였다. 냉수대의 생성과 소멸의 영상을 분석하기 위해 AVISO 지형류 및 하루 동안의 천리안 해양관측위성(Geostationary Ocean Color Imager; GOCI) 엽력소 농도 자료를 사용하였다. 또한, 냉수대의 이동을 추적하기 위해 Advanced Very High Resolution Radiometer-Sea Surface Temperature (AVHRR-SST) 자료를 사용하였다. 초여름에 냉수대가 발현하여 늦여름에 소멸한다는 것과 이 기간 동안 냉수대의 생성과 소멸이 지속적으로 반복되었다는 것을 확인할 수 있었다. 추가적으로 냉수대의 후속 영향으로 인해 늦여름에 엽력소 농도가 증가되었다는 것을 확인할 수 있었다. We investigated the changes with temporal and spatial movements of cold water events in summer season around the East Sea of Korea. Several data analyses were performed based on the various environmental factors using satellite and in-situ (winds, air/sea surface temperatures) data in the summer season during 2013. For analyzing the influence of cold water life cycle we employed AVISO geostrophic current and daily Geostationary Ocean Color Imager (GOCI) chlorophyll concentration (chl) data. Also, we used daily Advanced Very High Resolution Radiometer-Sea Surface Temperature (AVHRR-SST) data to trace the movements of cold water events. We found out the cold water events occurred in the early summer season and disappeared in the late summer season, and the cold water life cycle is repeated in this period. Additionally, we could show that the chl were increased in late summer season due to the inertial influence of cold water zone.

Seasonal patterns of SST diurnal variation over the Tropical Warm Pool region

Five-year (2010 through 2014) Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature (SST) data produced by the Australian Bureau of Meteorology have been validated against drifting buoy data and then used to study the seasonal patterns of the SST diurnal variation (DV) events over the Tropical Warm Pool region (TWP, 25°S–15°N, 90°E–170°E). The in-situ validation results illustrate the overall good quality of the AVHRR SST data set, although an average 0.19 K underestimation of the daytime measurements has been observed. The night-time observations are in good agreement with in-situ buoys with an average bias of 0.03 K and a 0.30 K standard deviation of the biases. This SST data set is then used to characterise the SST DV seasonal patterns, together with wind speeds, daily maximum solar shortwave insolation (SSImax) and latent heat flux (LHF). A double-peak seasonal pattern of SST DV is observed over the study region: the strongest DVs are found in March and October and the weakest in June. Sensitivity tests of DV to wind, SSImax, and LHF are conducted. The results indicate: (1) different morning and early afternoon winds (7 am to 2 pm local time, LT) affect DV by as much as 0.73 K when the half-daily (defined as 2 am to 2 pm LT in this study) average winds are fixed between 2-3 ms−1; (2) SSImax levels regulate DV less significantly (< 0.68 K) under fixed winds; and (3) LHF effects on DV are relatively weak (< 0.35 K). This article is protected by copyright. All rights reserved.

Assessing the impact of brightness temperature simulation adjustment conditions in correcting Metop-A SST over the Mediterranean Sea

Multispectral sea surface temperature (SST) algorithms applied to infrared (IR) radiometer data exhibit regional biases due to the intrinsic inability of the SST algorithm to cope with the vast range of atmospheric types, mainly influenced by water vapor and temperature profiles. Deriving a SST correction from simulated brightness temperatures (BTs), obtained by applying a Radiative Transfer Model (RTM) to Numerical Weather Prediction (NWP) atmospheric profiles and first guess SST, is one of the solutions to reduce regional biases. This solution is envisaged in the particular case of Metop-A Advanced Very High Resolution Radiometer (AVHRR) derived SST. Simulated BTs show errors, linked to RTM, atmospheric profiles or guess field errors. We investigated the conditions of adjusting simulated to observed BTs in the particular case of the Mediterranean Sea over almost one year. Our study led to define optimal spatio/temporal averaging parameters of the simulation observation differences, both during day and night, summer and colder season and for two simulation modes: operational (with reduced vertical resolution – 15 levels – NWP atmospheric profiles and two days old analysis used as first guess SST) and delayed (full vertical resolution – 91 levels – and concurrent analysis used as first guess SST). Each BT adjustment has been evaluated by comparing the corresponding corrected AVHRR SST to the AATSR SST that we adopted as validation reference. We obtained an optimized result across all defined conditions and modes for a spatial smoothing of 15deg and a temporal averaging between 3 and 5days. Specifically, analyses based on 10day averages showed that a standard deviation based criterion favors spatial smoothing above 10deg for all temporal averaging, while a bias based criterion favors shorter temporal averaging during daytime (< 5days) and higher spatial smoothing (>10deg) for nighttime. This study has shown also the impact of diurnal warming both in deriving BT adjustment and in validation results.

Comparisons of regional satellite sea surface temperature gradients derived from MODIS and AVHRR sensors

Regional sea surface temperature (SST) gradients were examined for a 6-year (2003–2008) period using data from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and the Advanced Very High Resolution Radiometer (AVHRR) on two NOAA satellite platforms. Two regions, one in the California Current System and the other in the Gulf Stream, representing an eastern boundary upwelling region and strong western boundary current, respectively, were chosen to investigate the seasonal variability, statistical differences and similarities, and correlations with respect to the two sets of SST gradients. Results indicated higher gradient magnitudes using MODIS SST in relative comparison to those derived from AVHRR that are attributed to instrument and algorithm differences. These observed differences are important for any studies that employ SST gradients, such as fisheries investigations that have traditionally relied on AVHRR SST gradients only.

Towards a bias correction of the AVHRR Pathfinder SST data from 1985 to 1998 using ATSR

There are many sea surface temperature (SST) datasets derived from various satellite instruments with different characteristics in terms of orbit, coverage, retrieval, calibration, accuracy and measurement type. This paper presents the results of a project to combine SSTs from two instruments: the Along-Track Scanning Radiometer (ATSR) SSTs and the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder SSTs. The dual-view nature of the (A)ATSR series of radiometers enables high-quality sea surface temperatures to be retrieved. These SSTs are ideal for using as a reference in order to bias-adjust the AVHRR series of instruments. In addition a method to correct for aerosol effects in the AVHRR SSTs is employed using aerosol optical depth from the Total Ozone Mapping Spectrometer. The method enables positive characteristics of both datasets to be combined to provide a dataset of potentially high-quality, long time-series, large data coverage satellite sea surface temperatures for climate monitoring purposes.An AVHRR SST dataset at ¼ degree spatial resolution with associated systematic biases and uncertainties is produced for climate research covering the period 1985 to 1998. The method is used to produce improved AVHRR SSTs for the period 1991 to the present (when the ATSRs are available) and additionally the method can be used to estimate biases on AVHRR SSTs prior to the first ATSR launch (1985 to 1991).The bias-correction has the effect of reducing an observed cool bias in the AVHRR SSTs, relative to the Hadley Centre Sea-Ice and Sea Surface Temperature dataset (HadISST), especially in cases of high volcanic and Saharan dust aerosol. The time-series indicates that the bias correction scheme is effective during the 1990s at decreasing the cool bias in the AVHRR SSTs. The scheme is especially effective in times of high atmospheric volcanic aerosol levels such as the period following the Mount Pinatubo eruption in 1991.

Intraseasonal signals in the daily high resolution blended Reynolds sea surface temperature product over the tropical Indian Ocean and their validation

National Oceanic and Atmospheric Administration daily sea surface temperature (SST) products based on Advanced Microwave Scanning Radiometer (AMSR) and Advanced Very High Resolution Radiometer (AVHRR) have been used to understand the variability in the tropical Indian Ocean SST. These products are comparable with the deep sea moored buoy observations and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) SST in the tropical Indian Ocean. However considerable difference is noticed between these satellite SST products and deep sea buoys, especially at the intraseasonal time scale. Further the first Complex Empirical Orthogonal Function (CEOF) mode of TMI and AVHRR SST explains respectively 46.49% and 46.19% of the total variance. The second CEOF mode of TMI and AVHRR SST explains respectively 23.19% and 18.94% of the total SST variance in the tropical Indian Ocean. The AVHRR SST product is important because this daily product has been available since 1985. The analysis shows that AMSR measurements are contributing considerably to the understanding of the tropical Indian Ocean SST variability. Though satellite SST products are able to capture the observed intraseasonal variability reasonably well, more accurate satellite SST products are therefore necessary to understand the climatologically important Indian Ocean region and its air–sea interaction processes.

Relationship between swordfish swimming behaviour and sea surface temperature in the central Mediterranean Sea during the reproductive period

Swordfish is the target species for harpoon fishery in the Central Mediterranean Sea (southern Tyrrhenian Sea and Strait of Messina). This is a seasonal fishing activity, carried out in daylight during the swordfish's reproductive period. The relationship between sea surface temperature (SST), catches, Catch per Unit Effort (CPUE) and fish swimming behaviour, recorded by direct observation, was investigated between 1999 and 2008. Data from AVHRR-SST (Advanced Very High Resolution Radiometer–Sea Surface Temperature), the meteorological station and the swordfish daily catch were recorded. A low presence of pairs was observed in the 2003 summer season, corresponding to an anomalous sea surface temperature recorded in the study area. After 2003, an early presence of pairs (in May rather than in June) was recorded. ANOVA and Bonferroni correction showed significant differences of CPUE of pairs between May and June, and June and August (F3=8.07, P<0.0001). The relationship between immature and mature adult specimens, single and in pairs, four ranges of SST, seven swimming behaviours and five maturity stages was analysed by the multiple correspondence analysis (MCA). Fish in pairs were mainly observed at temperatures over 24°C, showing a well-defined courtship behaviour, i.e. a female route. At lower temperatures, the dominant behaviour was basking in immature fish and breaching in mature fish. A significant relationship between SST and fish behaviour was detected in both immature and mature adult specimens.

On the consistency in variations of the South China Sea Warm Pool as revealed by three sea surface temperature datasets

The areal and intensity indices of the South China Sea Warm Pool (SCSWP) derived from three datasets, the Advanced Very High Resolution Radiometer (AVHRR), Tropical Rainfall Measuring Mission's Microwave Imager (TMI) and Optimum Interpolation Version 2 (OI.v2) sea surface temperature (SST), are generally consistent with each other at monthly, seasonal and interannual scales. However, the three records are different in some cases. First, minor differences among the monthly records of intensity index are observed in the period July to September. Secondly, the interannual records of SCSWP intensity derived from AVHRR and OI.v2 are different in autumn during the period 1990–1996. The reason is not yet clear and nor is it clear which record best represents fluctuations in SCSWP intensity. These suggest that various drawbacks of the three datasets, such as low resolution of OI.v2, and cloud and rain contamination on AVHRR and TMI data, would be serious enough to allow deviation from each other to appear. Merging AVHRR and TMI SST data might be the way leading to a more convincing time series of SCSWP. In addition, changes of areal and intensity indices are not always consistent with each other, for example, they have different monthly patterns. Although the three interannual records of intensity index in three seasons all capture the main Multivariate ENSO Index (MEI) signals at a half-year lag, only those which are in the summer significantly correlated with MEI.

The Measurement of the Sea Surface Temperature by Satellites from 1991 to 2005

AbstractA near-continuous series of global retrievals of sea surface temperature (SST) has been made from the Along-Track Scanning Radiometer (ATSR) series of instruments from 1991 to 2005. To analyze possible long-term trends in the global or regional SST throughout the period daily anomalies are computed using a 1961–90 daily climatology, averaged into global monthly means, and plotted as a global time series. To evaluate any biases in these anomalies they are compared with other satellite SST datasets that have been computed and compared over the same time period. Global infrared satellite SST data have been received from the Advanced Very High Resolution Radiometer (AVHRR) series, microwave SST data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and global microwave SST data from the Advanced Microwave Sounding Radiometer (AMSR)-E on Aqua. Additionally, the anomalies have also been compared with the Hadley Centre Global Sea Ice Coverage and Sea Surface Temperature (HadISST1) anomalies. HadISST1 is a globally complete 1° SST analysis compiled from in situ and bias-corrected AVHRR SSTs at the Met Office (UK).The results of the study show the high accuracy of the Advanced Along Track Scanning Radiometer (AATSR) SSTs, but there are concerns with the NOAA-14 AVHRR data (1996–2000) being biased cold, especially in the Northern Hemisphere, and the AMSR-E SSTs (version 4), which show unexplained biases. Since 1999 TMI SSTs appear to have a consistently warm (∼0.2 K) bias relative to the infrared sensors and HadISST1.The time series in (A)ATSR SSTs indicate the possibility of warming trends between 0.1 and 0.2 K decade−1, but the remaining ATSR-1 data are required to confirm this.

Inter‐comparison of NOAA‐AVHRR and IRS‐P4 (MSMR) derived sea surface temperatures

Due to the limitations of infrared remote sensing, sea surface temperature (SST) can be derived only under clear sky conditions from the infrared channels like those in National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR), where as microwave radiometers can provide SST even under cloudy conditions. However, the accuracy of SST derived from the microwave sensor is less with a poor spatial resolution. In this study, SSTs over the Arabian Sea derived from NOAA‐AVHRR and Indian Remote Sensing Satellite (IRS‐P4) multi‐frequency scanning microwave radiometer (MSMR) observations have been compared on weekly basis with a view to blend these two observations, so that SST can be provided continuously even under cloudy conditions. The NOAA‐AVHRR derived pathfinder SSTs with spatial resolution of 18 km were averaged to 1.5°×1.5° grid resolutions to match with MSMR observations. The analysis was carried out during 2000. Statistical analysis of the NOAA‐AVHRR SST shows that the spatial variation of SST within 1.5° grid is negligible compared with the MSMR accuracy of 1.52°C. Thus the comparison of SST from these two sources with different spatial resolution is reasonable. The RMS difference is 1.55°C with a correlation coefficient of 0.73. After removing the seasonal bias, the RMS difference reduced to 0.66°C and the coefficient of correlation improved to 0.89. The correlation coefficient between the two observations has further improved to 0.90 and the RMS difference reduced to 0.53°C when the averaging was done using 5°×5° grid resolution. The accuracy of satellite derived SSTs are also evaluated with the moored buoy observations over the Arabian Sea. The accuracy of MSMR SST observations have improved if 95% confidence level data is considered. The results indicate the possibility of replacing the data gaps in AVHRR SSTs with MSMR estimations after adjusting for the seasonal biases.

Classifying and Forecasting Coastal Upwellings in Lake Michigan Using Satellite Derived Temperature Images and Buoy Data

Coastal upwellings are common in the Great Lakes but have lacked enumeration and systematic classification of spatial extent, frequency, duration, and magnitude. Near real-time sea surface temperature (SST) images derived from the Advanced Very High Resolution Radiometer (AVHRR) provide indices of upwelling events, but visual inspection of daily images can be tedious. Moreover, the definition of what constitutes an upwelling from AVHRR data is subjective. We developed a semi-automated method to classify upwellings during the period of thermal stratification using daily, cloud-free surface temperature charts from AVHRR SST data. Then we statistically evaluated the location, frequency, magnitude, extent, and duration of upwelling events in Lake Michigan from 1992–2000. Further, we analyzed meteorological data from the National Data Buoy Center buoys in an attempt to improve the reliability of the classification and to provide a means for future forecast of coastal upwelling. Although variable, upwelling events along the western shoreline were preceded by 4 days of southerly and west-to-northwesterly winds, while upwelling events occurring along the eastern shore were preceded by 4 days of northerly winds. Probability of an upwelling event occurring was a function of the direction-weighted wind speed, reaching a 100% probability at direction weighted wind speeds of 11 m s −1 for the western shore. Probability of an upwelling occurrence along the east coast reached 73% at 11 m s −1 and 100% at 13 m s −1. Continuous measurements of wind data with a sufficient temporal resolution are required during the entire upwelling season to improve the predictability of upwellings.

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.

A case study of sea surface temperature–cooling dynamics near the Indian tip during May 1997

The sea surface temperature (SST) off the southwest coast of India is important to the Indian summer monsoon onset, which generally occurs in late May off this coast. It is known that both locally and remotely forced coastal processes contribute to the SST cooling. In this paper we focus on the latter by studying the SST‐cooling dynamics near the Indian tip (IT) during 1–15 May, using advanced very high resolution radiometer (AVHRR) SST, NASA scatterometer (NSCAT) winds, National Centers for Environmental Prediction/National Center for Atmospheric Research surface meteorological variables, and the Princeton Ocean Model. High spatial resolution (9 km) daily mean AVHRR SST maps indicate that the SST cooling near the IT is a recurrent phenomenon, wherein the cold SST water occupies the southwest India shelf and extends southward of the IT. During the 15 day period the NSCAT‐observed winds increased from near zero, the net surface heat flux into the ocean changed by ∼62 W/m2, and the SST decreased by 1.8°C near the IT. Under these conditions the model integration is carried out as an initial value problem with realistic bottom topography, climatological density stratification, and observed NSCAT winds. The results show that the Ekman dynamics along the west India shelf promote upwelling and initiate SST cooling. Shallow and spatially variable shelf topography near the IT enhance wind mixing, while sharp gradients in the isobaths at the shelf break and stratification enhance upwelling/downwelling intensity through cross‐isobath flow. It is inferred that the SST cooling near the IT is a locally forced process that is dictated by shelf topography.

Mechanism for Sea Surface Temperature Cooling In the Gulf of Oman during Winter

We report on sea surface temperature (SST) cooling in the Gulf of Oman during winter monsoon 1993 using evidence from the advanced very high resolution radiometer (AVHRR). A combination of the AVHRR SST and scatterometer‐based momentum and heat fluxes is used to discuss the SST‐cooling mechanism. Cold SST evolves in the gulf during an intense outbreak of wintertime winds. The turbulence generated by these winds in the surface atmospheric boundary layer depletes large heat from the sea surface and cause 1–2°C SST cooling during January–March. Additionally, the alongshore component of the wind stress promotes Ekman dynamics and further enhances SST cooling along the periphery of the gulf.

Observations of oceanic mesoscale features and variability in the Canary Islands area from ERS-1 altimeter data, satellite infrared imagery and hydrographic measurements

The main purposes of this paper are to identify and evaluate the oceanic mesoscale features that appear in the Atlantic Ocean eastern boundary, 20-40° N, 19-9° W, using ERS-1 (1992-1993) satellite altimeter data. The sea surface height anomalies and the eddy kinetic energy fields are calculated. High energy values, between 0.03 and 0.05 m 2 s -2, are observed with the altimeter data in the Canary region mainly in summer. These maximum values are associated with eddies located downstream of Gran Canaria and Tenerife (Canary Islands). Comparison with in situ measurements provided by the hydrographic surveys from a cruise in summer 1993 around the Canary Islands showed a good agreement. Dynamic heights relative to 300 dbar for August and the altimetric heights from ERS-1 data were averaged over 0.125° boxes for the duration of the cruise. The correlation coefficient was 0.7. Negative anomalies of the sea level calculated from ERS-1 between Cape Yubi and Cape Bojador (26.0°-27.5° N) were obtained in August 1993. Possibly these correspond with filaments from the north-west African upwelling coast. Also, the results of the altimetric data were compared with NOAA AVHRR (Advanced Very High Resolution Radiometer) sea surface temperature (SST) satellite images. A cyclonic and an anticyclonic eddy to the south-west of the island of Gran Canaria were identified during the same period in the SST images.

Impact of the AVHRR sea surface temperature on atmospheric forcing in the Japan/East Sea

The impact of high resolution sea surface temperature (SST) data on the atmospheric forcing in the Japan/East Sea (JES) is investigated using a high‐resolution mesoscale atmospheric model. We use the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder SST (PFSST) and the operational NCEP (National Center for Environmental Prediction) global SST analysis (NCEPSST) as the lower boundary conditions for two numerical experiments. A sharp SST gradient associated with the subpolar front in JES, well‐resolved by PFSST, has a significant influence on both synoptic scale and monthly mean surface winds and heat fluxes in JES. These results have an important implication for research related to coastal atmosphere and ocean circulation and air‐sea interactions.

Measurements of an Oceanic Front Using a Front-Following Algorithm for AVHRR SST Imagery

The Subtropical Front (STF) is a global front which extends around the Southern Ocean and is the boundary where the Subantarctic Surface Water mass (SAW) converges with the Subtropical Surface Water mass (STW). The Southland Front (SF) is part of the STF, which lies off the east coast of the South Island, New Zealand. The SF is narrow, approximately 8 km, with a temperature difference of approximately 1.8°C which can be detected using remote sensing Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature (SST) data. The work presented here is an application of remote sensing for the first detailed study of the surface spatial and temporal variability of the SF. The variability of the SF was quantified using an algorithm developed to follow the Front using AVHRR SST imagery. The algorithm used a new approach to determine the position, mean SST, SST difference, width, and gradient across the Front. Three time scales of variability were examined: long-term (3 years), annual, and seasonal. The algorithm efficiently followed the SF and consistently showed the 3-year mean position was stable and constrained by the bathymetry of the continental slope. Seasonally, the front moved inshore during summer. The temperature gradient across the front was strongest and the front narrowest in winter. The decrease in SST gradient during the 3-year data set coincided with the decrease in Southern Oscillation Index (SOI).

A Front-Following Algorithm for AVHRR SST Imagery

A Front-Following Algorithm provides a new approach to determining the position and characteristics of thermal oceanic fronts using Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature (SST) imagery. This algorithm differs from standard line enhancement, threshold edge detection, or classical contour techniques. Instead it utilizes a hyperbolic tangent function in a surface-fitting technique to follow an oceanic front. It has the advantage of describing the characteristics of an oceanic thermal front (including mean SST, SST difference, width and gradient across the front) and extracting information on the position and characteristics of the front into parameter form. Thus, the algorithm has the added benefit of recording the changes in the characteristics of the thermal front as it tracks along the front. The algorithm was applied to AVHRR SST imagery on part of the Subtropical Front known as the Southland Front (SF) off the east coast of the South Island of New Zealand, where subantarctic surface waters and subtropical surface waters converge. The algorithm was tested on examples of the SF and also compared with a standard gradient operator. The results showed that the algorithm performed well when following the SF, with low standard errors of parameter estimates, good visual verification of tracking, and consistent standards of accepted data. The algorithm estimated gradients better than a gradient operator.