High-latitude surface temperature estimates from thermal satellite data

Key, Jeffrey R.; Collins, John B.; Fowler, Charles W.; Stone, Robert S.

doi:10.1016/s0034-4257(97)89497-7

Cited by 178 publications

(124 citation statements)

References 28 publications

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“…Surfaces with intermediate T 11 temperatures are considered marginal ice zone and the marginal ice zone temperature is calculated from a linear combination of the MIST and the SST algorithms, scaled by the T 11 temperature. The MIST algorithm and calibration is adopted from Key et al (1997):…”

Section: Metop Arctic Surface Temperature Productmentioning

confidence: 99%

“…The calibration coefficients ad for Metop-AVHRR data are not available at the present and the coefficients from the AVHRR instrument on board NOAA12 were applied for this version of the algorithm. The NOAA12 calibration coefficients are retrieved from RTM modelled brightness temperatures for the AVHRR infrared channels and related to model skin temperatures (Key et al, 1997). The channel centres, width and spectral response functions of the NOAA12 and METOP AVHRR instruments are nearly identical.…”

Section: Metop Arctic Surface Temperature Productmentioning

confidence: 99%

“…We, therefore, considered the applied calibration equally valid for METOP AVHRR data than for NOAA AVHRR data. Different sets of coefficients are used for 3 brightness temperature intervals; see Key et al (1997) and Vincent et al (2008). The T 11 temperature intervals are: −2.2 • C > T 11 > = −13.15 • C; −13.15 • C > T 11 > = −33.15 • C and −33.15 • C > T 11 .…”

Section: Metop Arctic Surface Temperature Productmentioning

confidence: 99%

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

2012

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Abstract. The ice surface temperature (IST) is an important boundary condition for both atmospheric and ocean and sea ice models and for coupled systems. An operational ice surface temperature product using satellite Metop AVHRR infra-red data was developed for MyOcean. The IST can be mapped in clear sky regions using a split window algorithm specially tuned for sea ice. Clear sky conditions prevail during spring in the Arctic, while persistent cloud cover limits data coverage during summer. The cloud covered regions are detected using the EUMETSAT cloud mask. The Metop IST compares to 2 m temperature at the Greenland ice cap Summit within STD error of 3.14 • C and to Arctic drifting buoy temperature data within STD error of 3.69 • C. A case study reveals that the in situ radiometer data versus satellite IST STD error can be much lower (0.73 • C) and that the different in situ measurements complicate the validation. Differences and variability between Metop IST and in situ data are analysed and discussed. An inter-comparison of Metop IST, numerical weather prediction temperatures and in situ observation indicates large biases between the different quantities. Because of the scarcity of conventional surface temperature or surface air temperature data in the Arctic, the satellite IST data with its relatively good coverage can potentially add valuable information to model analysis for the Arctic atmosphere.

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Section: Metop Arctic Surface Temperature Productmentioning

confidence: 99%

Section: Metop Arctic Surface Temperature Productmentioning

confidence: 99%

Section: Metop Arctic Surface Temperature Productmentioning

confidence: 99%

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

2012

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“…To validate the simulation over the entire model domain, satellite observations are also used. They are the skin surface temperature data from the Extended Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder (APP-x) product (Key et al, 1997) and the surface shortwave and long-wave radiation data from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Climate Monitoring Satellite Application Facility (CM SAF) CLoud, Albedo and RAdiation dataset from AVHRR data (CLARA-A1) . Both APP-x and CLARA-A1 have similar spatial resolutions (25 km and 0.25 • , respectively) to that used in our climate simulations.…”

Section: Datamentioning

confidence: 99%

Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

et al. 2016

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Abstract. Sea ice plays an important role in the air-iceocean interaction, but it is often represented simply in many regional atmospheric models. The Noah sea ice scheme, which is the only option in the current Weather Research and Forecasting (WRF) model (version 3.6.1), has a problem of energy imbalance due to its simplification in snow processes and lack of ablation and accretion processes in ice. Validated against the Surface Heat Budget of the Arctic Ocean (SHEBA) in situ observations, Noah underestimates the sea ice temperature which can reach −10 • C in winter. Sensitivity tests show that this bias is mainly attributed to the simulation within the ice when a time-dependent ice thickness is specified. Compared with the Noah sea ice model, the high-resolution thermodynamic snow and ice model (HIGH-TSI) uses more realistic thermodynamics for snow and ice. Most importantly, HIGHTSI includes the ablation and accretion processes of sea ice and uses an interpolation method which can ensure the heat conservation during its integration. These allow the HIGHTSI to better resolve the energy balance in the sea ice, and the bias in sea ice temperature is reduced considerably. When HIGHTSI is coupled with the WRF model, the simulation of sea ice temperature by the original Polar WRF is greatly improved. Considering the bias with reference to SHEBA observations, WRF-HIGHTSI improves the simulation of surface temperature, 2 m air temperature and surface upward long-wave radiation flux in winter by 6, 5 • C and 20 W m −2 , respectively. A discussion on the impact of specifying sea ice thickness in the WRF model is presented. Consistent with previous research, prescribing the sea ice thickness with observational information results in the best simulation among the available methods. If no observational information is available, we present a new method in which the sea ice thickness is initialized from empirical estimation and its further change is predicted by a complex thermodynamic sea ice model. The ice thickness simulated by this method depends much on the quality of the initial guess of the ice thickness and the role of the ice dynamic processes.

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“…In contrast, Sakaida and Kawamura (1992a, b) indicated that NOAA MCSST coefficients are not always applicable to the seas around Japan. Key et al (1997) used an extensive validation analysis for the satellite-derived SSTs at high latitudes and pointed out that their larger errors were attributable to the spatially variable atmospheric/surface conditions of the validation area. Kawai and Kawamura (1997a) investigated the MCSST biases against the in situ SSTs in the seas around Japan, and found seasonal biases in the northern part of the Japan Sea and Sea of Okhotsk.…”

Section: Introductionmentioning

confidence: 99%

Validation of Satellite-Derived Sea Surface Temperatures for Waters around Taiwan

Lee¹,

Chang²,

Sakaida³

et al. 2005

Terr. Atmos. Ocean. Sci.

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High-latitude surface temperature estimates from thermal satellite data

Cited by 178 publications

References 28 publications

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

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

Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

Validation of Satellite-Derived Sea Surface Temperatures for Waters around Taiwan

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