Production of global sea surface temperature fields for the Jet Propulsion Laboratory workshop comparisons

Hilland, J.; Chelton, Dudley B.; Njoku, E. G.

doi:10.1029/jc090ic06p11642

Cited by 17 publications

(6 citation statements)

References 5 publications

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“…During the summer period, a similar bias is observed (figure I), as well as a smaller one in the extreme South of both basins. In these areas, the SM M R SST is larger than the EC one, reaching an excess of 5 to 6 deg K. Our comparison confirms the conclusions of the SST Comparison Workshop in 1982, since the same characteristics can be seen on the maps produced for the workshop (Bernstein and Chelton 1985, Milman and Wilheit 1985, Hilland et al 1985.…”

Section: Atlantic Pacific Globalsupporting

confidence: 89%

Validation of microwave radiometer geophysical parameters using meteorological model analyses

Eymard¹,

Bernard²,

Lojou³

1993

International Journal of Remote Sensing

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Data measured by microwave radiometers are brightness temperatures, from which, using algorithms, geophysical parameters arc retrieved. Errors in these parameters can be due either to the algorithms or to instrumental deficiencies affecting the brightness temperatures. Only geophysical data can be validated directly since dedicated meteorological experiments cannot be performed frequently enough or in a sufficient number of places during the satellites lifetime. To achieve such a global validation, we propose to use global meteorological model analyses, which contain the information of all routine observations after checking their quality. In this article, we test this approach by comparing Nimbus-7/SMMR geophysical data with ECMWF model analyses. Data from five months in 1979 are examined, and deviations between both data sets are analysed in terms of satellite or model errors. This comparison reveals SMMR problems in SSTand surface wind data, and local model errors in the precipitable water. Therefore, a good knowledge of model behaviour is necessary for using its analyses in satellite data validation, yet it appears as a useful tool, working at scales ranging from regional to global, and in time, from a particular event to a season or more.

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Section: Atlantic Pacific Globalsupporting

confidence: 89%

Validation of microwave radiometer geophysical parameters using meteorological model analyses

Eymard¹,

Bernard²,

Lojou³

1993

International Journal of Remote Sensing

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“…Furthermore, these satellites have global geographic coverage, unlike merchant vessels that are concentrated into the main shipping routes and in the northern hemisphere (Hilland et al 1985, Folland et al 1993. The most widely used satellite sensor to measure SST is the Advanced Very High Resolution Radiometer (AVHRR) installed in the NOAA series of satellites, which measures the radiation coming from the sea surface in two thermal infrared channels.…”

Section: Introductionmentioning

confidence: 99%

Analysis of sea surface temperature time series of the south-eastern North Atlantic

Borges¹,

Hernández‐Guerra

Nykjær

2004

International Journal of Remote Sensing

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The dominant periods in time series of sea surface temperature (SST) of the south-eastern North Atlantic are determined and related to atmospheric forcing and ocean dynamics. We analyse five-day composite images of a 10.5-year-long (from 10 July 1981 to 31 December 1991) time series of Advanced Very High Resolution Radiometer (AVHRR) onboard NOAA satellites. The dominant signal present in the whole region is the annual cycle. It explains 70% of the SST variance in the northern region and 40% in the southern. The pattern of the annual amplitudes is related to the seasonal cooling and warming cycle in the region. The second dominant period is a semi-annual frequency, estimated by means of periodograms of the residual time series with the annual cycle subtracted. This semi-annual frequency is responsible of making short springs and long autumns. The semi-annual frequency is present in 44% of the time series in the region, contrary to the generalized idea that a time series must always contain it. The geographical distribution of the semi-annual component of SST suggests that it is associated with the curl of the wind stress. The third dominant period is four years, found in three different areas: south of the Canary islands, off the Cape Verde islands and towards the northwest of Lanzarote Island. The main effect of this signal is to increase the maximum temperature every four years and to decrease the minimum temperature two years later. The 4-year signal does not seem to be associated with any atmospheric forcing field. The presence of a signal in the curl of the wind stress with periodicities of 25-30 days located south of the Canary Islands led us to conclude that the curl of the wind stress is important for the generation and shedding of eddies downstream these islands.

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“…Infrared satellite imagery provides one of the best means of mapping changes in global sea surface temperature (SST). A series of workshops [Hilland et al, 1985], held to com pare the various methods of remotely sensing SST, concluded that of the presently available systems, the Advanced Very High Resolution Radiometer (AVHRR), carried on National Oceanic and Atmospheric Administration (NOAA) polar-orbiting weather satellites, yielded the smallest rms error when com pared with ship and buoy in situ data. The widely used Multi Channel SST (MCSST) method, described by McClain et al [1983], was used to calibrate the AVHRR SST for this workshop intercomparison.…”

Section: Introductionmentioning

confidence: 99%

Global differences between skin and bulk sea surface temperatures

Emery

Schluessel

1989

EoS Transactions

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Infrared satellite imagery provides one of the best means of mapping changes in global sea surface temperature (SST). A series of workshops [Hilland et al., 1985], held to compare the various methods of remotely sensing SST, concluded that of the presently available systems, the Advanced Very High Resolution Radiometer (AVHRR), carried on National Oceanic and Atmospheric Administration (NOAA) polar‐orbiting weather satellites, yielded the smallest rms error when compared with ship and buoy in situ data. The widely used Multi Channel SST (MCSST) method, described by McClain et al. [1983], was used to calibrate the AVHRR SST for this workshop intercomparison. This method uses the differences between the radiances of the two AVHRR thermal infrared channels to correct for atmospheric signal attenuation due primarily to water vapor absorption. To provide absolute temperature calibration, the MCSST coefficients are derived by matching the AVHRR observations with simultaneous SSTs measured in situ by freely drifting ocean buoys [McClain et al., 1985]. These drifting buoys typically measure the ocean temperature 0.5–1 m below the sea surface.

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Production of global sea surface temperature fields for the Jet Propulsion Laboratory workshop comparisons

Cited by 17 publications

References 5 publications

Validation of microwave radiometer geophysical parameters using meteorological model analyses

Validation of microwave radiometer geophysical parameters using meteorological model analyses

Analysis of sea surface temperature time series of the south-eastern North Atlantic

Global differences between skin and bulk sea surface temperatures

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