Diurnal signals in satellite sea surface temperature measurements

Gentemann, Chelle; Donlon, Craig; Stuart-Menteth, Alice; Wentz, F. J.

doi:10.1029/2002gl016291

Cited by 258 publications

(252 citation statements)

References 8 publications

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“…This is consistent with a limit between 6 m s -1 and 10 m s -1 found by other authors (Gentemann et al, 2003;Gentemann and Minnett, 2008). The more important surface warming (of almost 2 ºC) occurred for low winds and high solar radiation, in accordance to many studies at the sea (Donlon et al, 1999;Donlon et al, 2002;Kawai and Wada, 2007;Gentemann and Minnett, 2008). The average observed ΔT w (0.55 m) was 0.0 ºC, the maximum observed ΔT w (0.55 m) was 3.0 ºC, and the minimum ΔT w (0.55 m) was -0.7 ºC.…”

Section: Warm Layer Effectsupporting

confidence: 94%

“…The warm layer forms during the day, and is due to surface stratification caused by the absorption of solar radiation in the first meters of the water column (Fairall et al, 1996a). The magnitude of the warm layer effect can attain several degrees (Fairall et al, 1996a;Kawai and Wada, 2007;Gentemann and Minnett, 2008). The 15 cool skin effect is almost always present and arises from the cooling of the first millimetres of the water column because of the joint action of longwave radiation, sensible and latent heat fluxes (Fairall et al, 1996a).…”

Section: Definitions Of Surface Temperaturementioning

confidence: 99%

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LakeSST: Lake Skin Surface Temperatures in French inland water bodies for 1999–2016 from Landsat archives

Prats¹,

Reynaud²,

Rebière³

et al. 2017

Preprint

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Abstract. The spatial and temporal coverage of the Landsat satellite imagery make it an ideal resource for studies on the long term evolution of lake surface temperature and for geographical studies of temperature patterns. The Lake Skin Surface 10 Temperature (LakeSST) data set contains skin surface temperature data for 442 French water bodies (natural lakes, reservoirs, ponds, gravel pit lakes and quarry lakes) for the period 1999-2016 obtained from the thermal band of Landsat 5 and Landsat 7 archive images. The skin temperature measured by satellites differs slightly from water temperature in the first meters of the water column because of cool skin and warm layer effects. Nevertheless surface temperature parameterizations originally developed for the sea can be used to adjust LakeSST to commonly used lake water temperature, e.g. surface 15 temperature or temperature of the first 1~2 m. Moreover, theoretically small differences are to be expected between the freshwater and seawater case for low wind speeds. In fact, at the reservoir of Bimont, the estimated cool skin effect was about -0.3 ºC and -0.6 ºC most of time, while the warm layer effect at 0.55 m was negligible in average, but could occasionally attain several degrees and a cool layer was often observed in the night. The overall accuracy of the satellitederived temperature measurements was about 1.5 ºC, similar to other applications of satellite images to estimate freshwater

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Section: Warm Layer Effectsupporting

confidence: 94%

Section: Definitions Of Surface Temperaturementioning

confidence: 99%

LakeSST: Lake Skin Surface Temperatures in French inland water bodies for 1999–2016 from Landsat archives

Prats¹,

Reynaud²,

Rebière³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…In our case, the amplitude of the satellite-derived temperature daily cycle is up to 3.5 o C. Several methods of removing the effect of diurnal warming have been proposed based on wind and surface heat-flux data (Gentemann et al, 2003;Stuart-Menteth et al, 2004;Nardelli et al, 2004). However, seemingly, there is no well established parameterization of the "skin effect"…”

Section: Filtering With Different Timing and Frequency: Bias Of The Dmentioning

confidence: 99%

Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: Inference about the data

Losa

Danilov

Schröter

et al. 2012

Journal of Marine Systems

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The operational ocean prediction model for the North and Baltic Seas of the German Maritime and Hydrographic Agency (BSH) is augmented with a multivariate data assimilation (DA) system. We report on the implementation and performance of the scheme which is based on ensemble forecasting. 2011. The quality of the predicted fields that were not assimilated (velocities, sea level and salinity) is preserved as is confirmed by independent in situ data.The results have required adjustment of the conditional data error statistics.The experiments conducted with different timing and frequency of data assimilation and variable forecasting periods show that the DA system corrects systematic model uncertainties and, due to memory to the corrections, improves prediction over periods of up to 5 days. The results also explicitly illustrate a lower quality of the AVHRR daytime product and reveal low informative influence of the data on the forecasting system when daytime SSTs are assimilated additionally to midnight observations.

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“…TMI SST (Senan et al 2001, Gentemann et al 2003, Stammer et al 2003, Gentemann et al 2004, and AVHRR and TMI Study region.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the MODIS and AMSR-E-Derived SST Products in joining area of Asia and Indian-Pacific Ocean

Zhu¹,

Bo²,

Cheng³

et al. 2013

AMOJ

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The Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer (AMSR-E) aboard the NASA Earth Observation System's Aqua satellite have been measuring the sea surface temperature (SST) since 2002. Although satellite-derived SST data complement the ground-based SST field data in better understanding the role of SST in coupling the ocean and atmosphere, they need to be validated in order to determine the spatiotemporal variation of their availability, and their error features, and then to decide how they can be used for further applications. In this paper we assess the spatiotemporal coverage and error variation of MODIS and AMSR-E level-3 mapped SSTs during 2003 to 2005 over the joining area of Asia and the Indian-Pacific Ocean. Drifting buoy SST data obtained from the Atlantic Oceanographic and Meteorological Laboratory (AOML) is used as a benchmark for the study. The results show that multi-year and annual average availabilities of MODIS SSTs are lower than those of AMSR-E SSTs due to their different cloud penetration. Weekly mean availability of AMSR-E SSTs is nearly constant during [2003][2004][2005] whereas that of MODIS SSTs shows obvious seasonal variation, i.e. it is low in summer and high in spring and autumn. The intra-annual variation of MODIS and AMSR-E availability is similar for the three year period. The geographical signal of annual mean availability of MODIS is more obvious than that of AMSR-E, i.e. it is high at about latitudes 20°N and 20°S and low around the equatorial regions. For error feature, the overall accuracy and precision of MODIS SSTs are lower than those of AMSR-E SSTs due to their slight differences in atmospheric effects, cloud penetration, spatial resolution, measured depths, and retrieval algorithms. Even so, annual mean biases of these two datasets are both within 0.5 °C. The intraannual variation of the error feature of MODIS is much more obvious than that of AMSR-E due to the wider range of mean bias and standard deviation. The intraannual variations of MODIS and AMSR-E error are similar for the three year period. We also find that the accuracy and precision of MODIS and AMSR-E have an obvious geographical signal for the three year period. That is, the accuracy and precision become higher moving from mid-latitudes of the northern hemisphere through equatorial regions to mid-latitudes of the southern hemisphere.

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Diurnal signals in satellite sea surface temperature measurements

Cited by 258 publications

References 8 publications

LakeSST: Lake Skin Surface Temperatures in French inland water bodies for 1999–2016 from Landsat archives

LakeSST: Lake Skin Surface Temperatures in French inland water bodies for 1999–2016 from Landsat archives

Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: Inference about the data

Assessment of the MODIS and AMSR-E-Derived SST Products in joining area of Asia and Indian-Pacific Ocean

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