Spatial modeling of extreme snow depth

Blanchet, Juliette; Davison, A. C.

doi:10.1214/11-aoas464

Cited by 156 publications

(211 citation statements)

References 59 publications

(71 reference statements)

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“…The rootmean-square error was then calculated, providing the expected RMS error of the in situ data according to the number of snow depths measured. This method assumes a Poisson distribution, which has been used in snow depth modeling [Blanchet and Davidson, 2011] and in W99. For an average of 16 in situ measurements per snow radar footprint, the expected error was 1.4 cm.…”

Section: In Situ Datamentioning

confidence: 99%

Interdecadal changes in snow depth on Arctic sea ice

et al. 2014

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Snow plays a key role in the growth and decay of Arctic sea ice. In winter, it insulates sea ice from cold air temperatures, slowing sea ice growth. From spring to summer, the albedo of snow determines how much insolation is absorbed by the sea ice and underlying ocean, impacting ice melt processes. Knowledge of the contemporary snow depth distribution is essential for estimating sea ice thickness and volume, and for understanding and modeling sea ice thermodynamics in the changing Arctic. This study assesses spring snow depth distribution on Arctic sea ice using airborne radar observations from Operation IceBridge for 2009-2013. Data were validated using coordinated in situ measurements taken in March 2012 during the Bromine, Ozone, and Mercury Experiment (BROMEX) field campaign. We find a correlation of 0.59 and root-mean-square error of 5.8 cm between the airborne and in situ data. Using this relationship and IceBridge snow thickness products, we compared the recent results with data from the 1937, 1954-1991 Soviet drifting ice stations. The comparison shows thinning of the snowpack, from 35.1 6 9.4 to 22.2 6 1.9 cm in the western Arctic, and from 32.8 6 9.4 to 14.5 6 1.9 cm in the Beaufort and Chukchi seas. These changes suggest a snow depth decline of 37 6 29% in the western Arctic and 56 6 33% in the Beaufort and Chukchi seas. Thinning is negatively correlated with the delayed onset of sea ice freezeup during autumn.

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Section: In Situ Datamentioning

confidence: 99%

Interdecadal changes in snow depth on Arctic sea ice

et al. 2014

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“…xi(s) distributes identical independent random [5]. Z(s) is said max-stable if and only if follows distribution of GEV which constitutes distribution for extreme happening data.…”

Section: Max-stable Processmentioning

confidence: 99%

Parameter Estimation of Smith Model Max-Stable Process Spatial Extreme Value (Case-Study: Extreme Rainfall Modelling in Ngawi Regency)

Azizah

Sutikno

Purhadi

2017

IJOS

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“…Looking at recent applications in extreme geostatistics (Blanchet and Davison (2011) , Davison and Gholamrezaee (2011)), a new kind of stochastic model suits the need of extreme spatial modelling. They are based on the so-called max-stable processes.…”

Section: Spatial Extreme Modellingmentioning

confidence: 99%

“…But max-stable processes overtake them by bringing information continuously over the area studied, even where no observation is available. The performance of such modelling has been shown in applications in other environmental contexts, like for instance the study of heavy snow events in Blanchet and Davison (2011) or heatwaves in Davison and Gholamrezaee (2011). Some investigations on significant waves has been produced by Raillard et al (2014).…”

Section: Introductionmentioning

confidence: 99%

Spatial Assessment of Extreme Significant Waves Heights in the Gulf of Lions

Chailan

Toulemonde²,

Bouchette

et al. 2014

Int. Conf. Coastal. Eng.

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In the analysis of coastal hazards, the features of extreme waves are determining information to question the impact of storms to the coast. The spatial behaviour of extreme waves is even more valuable especially since it is sparsely provided. Regarding recent applications in other contexts, a kind of statistical models called max-stable processes is relevant for modelling spatial extreme events. Max-stable processes are extensions of the well-known Generalised Extreme Value (GEV) distribution. Unlike univariate approaches, max-stable processes consider spatial dependence of a phenomenon. Such a modelling also overtakes a standard multivariate approach by providing information continuously over the area studied, even where no observation is available. Relying on such a stochastic modelling, the aim of this study is to discuss the extreme waves hazards in the Gulf of Lions, focusing on their spatial behaviour.

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Spatial modeling of extreme snow depth

Cited by 156 publications

References 59 publications

Interdecadal changes in snow depth on Arctic sea ice

Interdecadal changes in snow depth on Arctic sea ice

Parameter Estimation of Smith Model Max-Stable Process Spatial Extreme Value (Case-Study: Extreme Rainfall Modelling in Ngawi Regency)

Spatial Assessment of Extreme Significant Waves Heights in the Gulf of Lions

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