Multichannel Sea Surface Temperature Data Research Articles

Retrieval of Sea Surface Skin Temperature from the High Resolution Picture Transmission Data of the National Oceanic and Atmospheric Administration Series Satellites

The High Resolution Picture Transmission (HRPT) data of the National Oceanic and Atmospheric Administration (NOAA) series meteorological satellites had been received by the SeaSpace ground station located at the Ocean University of China (OUC). Based on the atmospheric radiative transfer model, we obtained the NOAA-15/16/17/18/19 Advanced Very High Resolution Radiometer (AVHRR) sea surface skin temperature (SSTskin) data using the Bayesian cloud detection method and the optimal estimation (OE) sea surface temperature (SST) retrieval algorithm. Compared with the NOAA/AVHRR multi-channel SST data, the AVHRR SSTskin data have higher data accuracy. We also compared the AVHRR SSTskin with the buoy SST with spatial and temporal windows of 0.01° and 30 min. The daytime biases ranged from −0.32 °C (NOAA-16) to 0.08 °C (NOAA-17) with standard deviations (SDs) ranging from 0.36 °C (NOAA-18/ NOAA-19) to 0.60 °C (NOAA-16), and the nighttime biases ranged from −0.26 °C (NOAA-16) to −0.02 °C (NOAA-17) with SDs ranging from 0.33 °C (NOAA-19) to 0.60 °C (NOAA-16). The accuracy of all five satellite data during daytime and nighttime was significantly improved. These results show that the AVHRR SSTskin of NOAA series satellites is good and consistent in different periods, and the SSTskin data products with high spatial resolution and accuracy can be used for mesoscale and submesoscale marine applications.

Characteristics of sea surface temperature retrieved from MTSAT-1R and in-situ data

Multi-channel sea surface temperature (MCSST) data were retrieved from the Japanese geostationary satellite, MTSAT-1R, for East Asia in western North Pacific. The coefficients used to calculate the MCSST data were estimated by assuming a linear relationship between the brightness temperatures obtained from the satellite and the in-situ buoy SST. It is important to remove cloud contamination pixels to retrieve meaningful information from infrared data. Therefore, the cloud detection algorithm was improved by using a 10-day maximum or minimum composite map for infrared and visible channels. The RMSE of the MCSST in comparison with the two-year buoy SST was about 0.89oC. The error was the largest at mid-latitudes in summer. Additionally, the error between the two SSTs showed that diurnal variation had a positive bias during daytime and a negative bias during nighttime. Furthermore, in 2007, both SSTs showed seasonal and spatial diurnal variation. The magnitude of the daily variation in the MCSST was two times larger than that in the buoy SST, and this was attributed to diurnal heating with a weak surface wind speed.

The optimal spatial scale for the analysis of elephant seal foraging as determined by geo-location in relation to sea surface temperatures

There is increasing emphasis put on the correlation of marine predator behaviour and foraging performance with the bio-physical properties of the ocean environment. However, spatial error in the estimated position of animals and the accuracy of interpolated, physical oceanographic data require the assessment of the appropriate spatial resolution at which to assess relationships. We recorded surface temperature data from 17 archival tags attached to female southern elephant seals at Macquarie Island during the post-lactation foraging trip of 1999–2000. Archival-tag temperature data were associated with twice-daily, at-sea positions derived from light levels (i.e., ‘‘geo-location’’). We compared these surface temperatures and their associated spatial error to satellite-derived Multi-Channel Sea Surface Temperature (MCSST) data to assess at what spatial scale the agreement between the two data sources was highest. We considered scales from 5050 km through to 500500 km grid cells, at 5050 km increments. Averaged over all individuals and assessed in fortnightly time periods, we found a peak in agreement between the mean surface temperature recorded by the archival tags and the MCSST data at 350350 km grid cells (122 500 km 2 ). We used logistic regression model selection to examine the effects of spatial scale, time period, latitude, longitude, and MCSST variance per grid cell on the agreement between the data sources. The most parsimonious model included all main effects, and a significant interaction between time period and latitude. There was also a trend for reduced agreement at higher latitudes, and there was a peak in temperature agreement in the general region around 180 longitude. There were two peaks in agreement during the early and latter parts of the post-lactation foraging trip, resulting probably from the more northerly latitudes travelled during these times and the improved MCSST coverage in the summer months. The 350350 km scale recommended by this study should be appropriate for the study of coarse-scale associations between marine predators located using geo-location and physical oceanographic data during summer. However, the approach remains valid for predators located at sea using other positioning methods.

A Data-Assimilative Numerical Model of the Northern Indian Ocean

Abstract A primitive equation, three-dimensional, baroclinic circulation model has been configured for use in the North Indian Ocean. After having been spun up by climatological winds, the model was used to generate a hindcast for 1993–95 under synoptic forcing, both with and without assimilation of multichannel sea surface temperature (MCSST) and altimetric sea surface height (SSH) anomaly data. Without data constraints, the model captures many of the salient oceanographic features in this region including equatorial surface and subsurface currents, the Laccadive High Eddy, the Great Whirl, and the reversing Somali Current. However, assimilation of altimetric data enables it to depict these features more accurately. MCSST data enable the near-surface layers to be simulated more accurately. The National Aeronautics and Space Administration TOPEX precision altimeter has provided oceanographers with an important tool to study the variability in the circulation of the world’s oceans. The availability of SSH ...

Seasonal and spatial SST variability in the Agulhas retroflection and Agulhas return current

Multichannel sea surface temperature data from the NOAA advanced very high-resolution radiometer for the three-year period October 1991–September 1994 were analysed to characterise spatial and temporal sea surface temperature (SST) variability in the Agulhas retroflection and Agulhas return current regions south and east of Africa. Empirical orthogonal function (EOF) analysis revealed an unusual, coherent, large-scale pattern of SST variability associated with a dominant annual cycle, with interannual variability. The first EOF mode accounted for approximately 75% of the variability in the three-year record. The structure of the fronts and their role in determining the SST variability were explored. The major characteristics of the seasonal expression were found to be (1) annually varying SST anomalies with a maximum range of 10.6°C, (2) stronger horizontal sea surface temperature gradients across the subtropical front in winter than in summer, and (3) an unexpectedly patchy spatial SST variability of the Agulhas return current. The SST results have been discussed in comparison with recent sea level variability measured from TOPEX/POSEIDON in 1993.

Applying MCSST to coral reef bleaching

In the 1980s and early 1990s, coral reef bleaching events of unprecedented frequency and global extent were observed. Elevated water temperature is suspected as the primary causal stress of mass bleaching events from this period. The relationship between sea surface temperatures (SSTs) and coral bleaching events was investigated using National Oceanic and Atmospheric Administration (NOAA) Multi-Channel Sea Surface Temperature (MCSST) satellite imagery from 1982–1992. Nighttime MCSST weekly averages were compared with moored-buoy temperatures for sea-truthing the satellite. Average errors from 11 individual buoy comparisons throughout the tropics were found to be approximately 0.5°C. Confirmed satellite SST data were applied to bleaching events at Bermuda (1988, 1991), Tahiti (1984, 1987, 1991), and Jamaica (1987, 1989, 1990), with a non-bleached site off Belize selected as control. MCSST data showed elevated SSTs coincided with bleaching events both in onset and duration. Bleaching thresholds were developed. An MCSST Degree Heating Weeks (DHW) bleaching index was developed for the Belizean and Jamaican reef sites. A cumulative heating stress of 26 DHW is proposed as the threshold for mass reef bleaching at Belize and Jamaica.

Evaluation of multichannel sea surface temperature product quality for climate monitoring: 1982–1988

Satellite‐derived multichannel sea surface temperature (MCSST) data are evaluated for the period 1982–1988 relative to in situ data from the comprehensive ocean‐atmosphere data set (COADS). A number of statistical methods are used to assess the quality of the MCSST data. Temporal anomaly cross correlations are significant at the 95% confidence level for all basins and subregions examined, except for the North Atlantic Ocean. Biases in the MCSST product, caused by volcanic aerosols and by use of the wrong satellite calibration tables, cause problems in the utility of the MCSST data for climate monitoring. Signal‐to‐noise variance ratios range from 0.49 in the North Atlantic Ocean to above 4 in the equatorial Pacific. Mean differences between MCSST and COADS show that the MCSST values are lower than COADS by 0.19° to 0.64°C. The mean differences are found to have an annual cycle that varies with latitude and season. The mean MCSST standard deviations are always lower than those of COADS (except again for the North Atlantic Ocean), ranging from 0.03° to 0.45°C. Accurate monitoring of SST variability in the southern oceans is not possible without the use of satellite data. Because of the stringent accuracy requirements of SST data for climate monitoring, however, the utility of MCSST data alone for this purpose is only marginal.

A Simple Method for Combining MCSST Data and In Situ Data in the Eastern Near-Equatorial Pacific

Abstract Ordinary least-squares is used to estimate the accuracy of monthly averaged sea surface temperature products in the eastern near-equatorial Pacific. Daytime multichannel sea surface temperature (MCSST) data, nighttime MCSST data, Climate Analysis Center (CAC) in situ temperatures, and CAC blended temperatures are all compared to monthly averaged, equatorial, 1 m moored-buoy temperatures at 110°W, 124°W, and 140°W. In addition, reduced least-squares (RLS) is used to develop regression equations between the CAC in situ temperature and the MCSST data. Bootstrap methods are used to estimate the RLS regression statistics. new regression equations are used to convert the MCSST to equivalent in situ temperatures prior to combining the two datasets with a one over distance-squared gridding algorithm. Data used in this study are from the period January 1983 to December 1985. When data from the 1982/1983 El Niho are excluded from analysis, the MCSST data are not significantly different from the moored-buoy...