Arctic surface temperatures from Metop AVHRR compared to in situ ocean and land data

Dybkjær, Gorm; Tonboe, Rasmus; Høyer, Jacob L.

doi:10.5194/os-8-959-2012

Cited by 21 publications

(34 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…T 2 m could have been utilized here. This type of measurement has been used in other studies, such as Hall et al (2004) and Dybkjaer et al (2012), to validate satellite observations as, although measured at a different height to satellite T s , these temperatures are highly correlated (Comiso, 2003). But comparing different types of temperature is not ideal, in particular when the relationship between them can be complex (Adolph et al, 2018;Good et al, 2017;Vihma & Pirazzini, 2005).…”

Section: 1029/2019jd030262mentioning

confidence: 99%

Toward a Combined Surface Temperature Data Set for the Arctic From the Along‐Track Scanning Radiometers

et al. 2019

View full text Add to dashboard Cite

Surface temperature data sets for, or including, the Arctic have been derived from various thermal infrared sensors. However, a combined, all surface temperature data set for the Arctic has not been generated previously. Here we present the first combined land, ocean, and ice surface temperature data set for the Arctic produced from Along‐Track Scanning Radiometer ‐ 2 and the Advanced Along‐Track Scanning Radiometer satellite sensors: the Along‐Track Scanning Radiometer Arctic combined Surface Temperature data set. Separate products, produced independently for each sensor and containing quantified uncertainties, together cover the period August 1995 to April 2012. Product validation, utilizing a more extensive in situ database than previous studies, shows that Along‐Track Scanning Radiometer Arctic combined Surface Temperature surface temperatures generally agree with in situ data and are similar to previous validation of input surface temperature retrievals. Biases range from −1.74 to 0.23 K over open ocean, sea ice, snow over land, and the Greenland ice sheet with higher variability over snow/ice. However, there are noticeable outliers in the validation results, particularly over Arctic land in boreal summer for Along‐Track Scanning Radiometer ‐ 2, which are likely due to cloud contamination resulting from a climatologically static snow field being used for that sensor. This study suggests that the Along‐Track Scanning Radiometer Arctic combined Surface Temperature data set presented here is a useful tool for assessment of models in the Arctic. Further work would have clear benefits including improvements to snow cover and cloud clearing to achieve a fully consistently processed, climate quality combined surface temperature data set for the Arctic region.

show abstract

Section: 1029/2019jd030262mentioning

confidence: 99%

Toward a Combined Surface Temperature Data Set for the Arctic From the Along‐Track Scanning Radiometers

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The determination of high latitude surface temperatures is an important factor in assessing climate change in polar environments [1][2][3][4][5][6]. This is particularly true in the Arctic where surface air temperatures are increasing more rapidly than the global average [7,8].…”

Section: Introductionmentioning

confidence: 99%

The Case for a Single Channel Composite Arctic Sea Surface Temperature Algorithm

Vincent

2019

Remote Sensing

View full text Add to dashboard Cite

Surface temperatures derived from satellite thermal infrared (TIR) data are critical inputs for assessing climate change in polar environments. Sea and ice surface temperature (SST, IST) are commonly determined with split window algorithms that use the brightness temperature from the 11 µm channel (BT 11 ) as the main estimator and the difference between BT 11 and the 12 µm channel (BTD 11-12 ) to correct for atmospheric water vapor absorption. An issue with this paradigm in the Arctic maritime environment is the occurrence of high BTD 11-12 that is not indicative of atmospheric absorption of BT 11 energy. The Composite Arctic Sea Surface Temperature Algorithm (CASSTA) considers three regimes based on BT 11 pixel value: seawater, ice, and marginal ice zones. A single channel (BT 11 ) estimator is used for SST and a split window algorithm for IST. Marginal ice zone temperature is determined with a weighted average between the SST and IST. This study replaces the CASSTA split window IST with a single channel (BT 11 ) estimator to reduce errors associated with BTD 11-12 in the split window algorithm. The single channel IST returned improved results in the CASSTA dataset with a mean average error for ice and marginal ice zones of 0.142 K and 0.128 K, respectively.

show abstract

“…However, as Hori et al (2006) demonstrated by in situ observations, snow emissivity varies depending on wavelength, snow type and exitance angle. Thus, the dependence of snow emissivity on snow type and exitance angle has to be taken into account in an analysis of the Earthʼs radiative balances over the polar region (Dybkjaer et al, 2012). Snow emissivity is also an indispensable boundary condition for estimating the thermal emissive properties of the ground surface for polar-night cloud detection (Yamanouchi et al, 1987;Kadosaki et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Dependence of thermal infrared emissive behaviors of snow cover on the surface snow type

Hori

Aoki²,

Tanikawa

et al. 2014

Bulletin of Glacier Research

View full text Add to dashboard Cite

The potential of the thermal infrared (TIR) remote sensing for discriminating surface snow types was examined by analyzing TIR radiances acquired from space over the Greenland ice sheet. The brightness temperature difference (BTD) between TIR wavelengths of 11 and 12 m was found to increase in accordance with in situ observed evolutions of surface snow type. Spatial and temporal distributions of BTD over the entire ice sheet indicated that BTD has a sensitivity of about 1.2 K for variations of the possible snow types. The observed behaviors of BTD were coincident with those predicted by a radiative transfer calculation using previous in situ measured snow emissivities, although some biases on the order of 0.1-0.3 K remain. The dependence of BTD on the surface snow type was also consistent with the behaviors of snow reflectance at the shortwave infrared (SWIR) wavelength 1.6 m, which is a measure of snow grain size, except for the case of melting wet snow. The inconsistency in the wet snow case was considered to be due to the different optical responses of the TIR and SWIR signals to wet snow, which suggested the possibility of using TIR signals to discriminate wet/dry conditions of snow cover in an old stage. As a result, it is determined that TIR remote sensing has potential not only as an approach supplementary to the SWIR method for assessing surface snow types in daytime but also as the only method for simultaneous retrieval of snow type and surface temperature in nighttime.

show abstract

Arctic surface temperatures from Metop AVHRR compared to in situ ocean and land data

Cited by 21 publications

References 26 publications

Toward a Combined Surface Temperature Data Set for the Arctic From the Along‐Track Scanning Radiometers

Toward a Combined Surface Temperature Data Set for the Arctic From the Along‐Track Scanning Radiometers

The Case for a Single Channel Composite Arctic Sea Surface Temperature Algorithm

Dependence of thermal infrared emissive behaviors of snow cover on the surface snow type

Contact Info

Product

Resources

About