Direct observations of skin‐bulk SST variability

Murray, M. J.; Allen, Myles; Merchant, Christopher J.; Harris, Andrew R.; Donlon, C.

doi:10.1029/1999gl011133

Cited by 58 publications

(42 citation statements)

References 17 publications

(4 reference statements)

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“…For wind speeds up to 12 m s Ϫ1 , the numbers of points per bin are sufficient to make statistically significant statements about variation of the SST residual against wind speed. For the range 3-12 m s Ϫ1 the mean residuals are, to within the standard errors, constant against wind speed at Ϫ0.23 K. For the Ͻ3 m s Ϫ1 range the residual is 0.12 K cooler at Ϫ0.35 K. This is a modest difference, but it is statistically significant at the 99.9% confidence level, it is geophysically plausible, and it matches the results observed in completely independent data (Murray et al 2000). We therefore cautiously attribute this cooling at low wind speed to thickening of the skin layer.…”

Section: Filtering With Respect To Wind Speedsupporting

confidence: 71%

“…There may also be diurnal warm-layer effect (Fairall et al 1996) causing temperature stratification in the upper meters of the ocean, as a result of near-surface heating by solar irradiance during daytime. These differences are detectable in comparisons of accurate satellite SSTs and high quality in situ measurements (Murray et al 2000).…”

Section: Offset Adjustment: General Commentsmentioning

confidence: 99%

“…A strategy for performing the bias-correction (or ''offset adjustment'') step is discussed in section 4, taking into account issues of skinbulk difference and diurnal SST variability (Murray et al 2000;Gentemann et al 2003). This is illustrated by a particular example in section 5.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Retrieval of Sea Surface Temperature from Space, Based on Modeling of Infrared Radiative Transfer: Capabilities and Limitations

Merchant

Borgne

2004

Journal of Atmospheric and Oceanic Technology

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The retrieval (estimation) of sea surface temperatures (SSTs) from space-based infrared observations is increasingly performed using retrieval coefficients derived from radiative transfer simulations of top-of-atmosphere brightness temperatures (BTs). Typically, an estimate of SST is formed from a weighted combination of BTs at a few wavelengths, plus an offset. This paper addresses two questions about the radiative transfer modeling approach to deriving these weighting and offset coefficients. How precisely specified do the coefficients need to be in order to obtain the required SST accuracy (e.g., scatter <0.3 K in week-average SST, bias <0.1 K)? And how precisely is it actually possible to specify them using current forward models? The conclusions are that weighting coefficients can be obtained with adequate precision, while the offset coefficient will often require an empirical adjustment of the order of a few tenths of a kelvin against validation data. Thus, a rational approach to defining retrieval coefficients is one of radiative transfer modeling followed by offset adjustment. The need for this approach is illustrated from experience in defining SST retrieval schemes for operational meteorological satellites. A strategy is described for obtaining the required offset adjustment, and the paper highlights some of the subtler aspects involved with reference to the example of SST retrievals from the imager on the geostationary satellite GOES-8.

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Section: Filtering With Respect To Wind Speedsupporting

confidence: 71%

Section: Offset Adjustment: General Commentsmentioning

confidence: 99%

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Retrieval of Sea Surface Temperature from Space, Based on Modeling of Infrared Radiative Transfer: Capabilities and Limitations

Merchant

Borgne

2004

Journal of Atmospheric and Oceanic Technology

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“…During periods of low wind speeds (weak wind condition; < 6 m/s), there are significant skin deviations. If wind is strong (> 6 m/s), the SST can be corrected by considering the bias (skin-subsurface) of 0.17 K. If wind speed is low, however, the deviation between skin and sub-skin is much larger, up to 1.5 K with high solar radiation (Donlon et al 1999(Donlon et al , 2002Barton 2001;Murray et al 2000). During the observation periods in 2008, there were low wind speeds (therefore, a lower mixing effect) on several days.…”

Section: Ngsst-o Vs In-situ Sstsmentioning

confidence: 98%

Satellite-derived SST validation based on in-situ data during summer in the East China Sea and western North Pacific

Kim¹,

Kang²,

Jang³

et al. 2010

Ocean Sci. J.

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“…These corrections are still subject to active research. For example, using almost 6000 skin and bulk temperature measurements collected over four-year period Murray et al (2000) found the mean skin-bulk SST difference of -0.2±0.46K at night, and +0.05±0.51K during daytime; for the low wind conditions (prevailing in summer months over the Adriatic Sea) Murray et al found the mean skin-bulk daytime difference of approximately 0.8 K. On the other hand, Donlon and Robinson (1998) report less than 0.05K in situ skin-bulk temperature difference ascribing it to the high wind speeds that dominate their dataset. In an effort to avoid introduction of two more still researched models, which themselves require additional data and introduce their own uncertainties, we have kept the ERA40 solutions uncorrected, and focused more on the scatter in residuals.…”

Section: Such An Error Exhibits Regional and Seasonal Variability (O'mentioning

confidence: 99%

ERA-40-aided assessment of the atmospheric influence on satellite retrieval of Adriatic Sea surface temperature

Tomažić

Kuzmić

2008

Meteorol Atmos Phys

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SummaryThe aim of this work has been assessment of regional atmospheric influence on satellite derivation of the Adriatic Sea surface temperature (SST). To that end the ECMWF ERA-40 reanalysis dataset has been employed to provide the temperature and humidity profiles and surface data, while the RTTOV 8.7 radiative transfer model was used to calculate the top of atmosphere brightness temperatures for the AVHRR channels. Ten ERA-40 grid points over the Adriatic Sea were used in the analysis, providing 29590, 00 UTC and 12 UTC, clear-sky profiles.Climatological analysis of the ERA-40 profiles demonstrated a distinct seasonal variability over the Adriatic. Seasonality noted in the temperature and specific humidity profiles also evinced in the atmospheric transmittance, thermal channels temperature deficit, and derived γ and ρ parameters. A multivariate analysis was applied to relate the simulated top of the atmosphere (TOA) brightness temperatures (BT) to the Adriatic SSTs, in order to generate exploratory sets of SST retrieval coefficients. All 10 derived coefficient sets exhibited smaller noise amplification factor than the global counterpart. A test comparison of satellite-derived SST with an eleven-month in situ SST series showed than locally derived coefficients provide smaller scatter (improved precision), and bias that requires empirical adjustment before operational use. Almost identical SST residual and metrics was obtained with seasonally adjusted "classical" split-window coefficients and with coefficients explicitly accommodating water vapour dependence. The comparison to data has reinforced the notion that the over-the-Adraitic atmosphere may exhibit variability which globally adjusted correction can not fully accommodate.

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Direct observations of skin‐bulk SST variability

Cited by 58 publications

References 17 publications

Retrieval of Sea Surface Temperature from Space, Based on Modeling of Infrared Radiative Transfer: Capabilities and Limitations

Retrieval of Sea Surface Temperature from Space, Based on Modeling of Infrared Radiative Transfer: Capabilities and Limitations

Satellite-derived SST validation based on in-situ data during summer in the East China Sea and western North Pacific

ERA-40-aided assessment of the atmospheric influence on satellite retrieval of Adriatic Sea surface temperature

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