Snowpack modelling in the Pyrenees driven by kilometric-resolution meteorological forecasts

Quéno, Louis; Vionnet, Vincent; Dombrowski-Etchevers, Ingrid; Lafaysse, Matthieu; Dumont, Marie; Karbou, Fatima

doi:10.5194/tc-10-1571-2016

Cited by 66 publications

(96 citation statements)

References 50 publications

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“…A strong negative bias for SAFRAN 325 can be noticed in the South-Western massifs of the Spanish Pyrenees (Fig. 6g), highlighting a poor representation of the orographic blocking as already noticed in Quéno et al (2016).…”

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confidence: 96%

“…On the contrary, the use of SAFRAN irradiances was found to decrease the positive bias obtained with AROME-Crocus. Vionnet et al (2016) and Quéno et al (2016) already showed an overestimation of snow depth by AROME-Crocus in the French Alps and the Pyrenees respec-…”

Section: Sensitivity Of Snowpack Simulations To the Radiative Forcingmentioning

confidence: 99%

“…2.1, Fig. 1), similarly to Quéno et al (2016) and Vionnet et al (2016). Besides incoming shortwave and longwave radiations, 2 m temperature and humidity, as well as 10 m wind speed and precipitation (amount of rainfall and snowfall) are part of the AROME forcing.…”

Section: Irradiance Datasetsmentioning

confidence: 99%

“…as described in details in Vionnet et al (2016) for the French Alps and in Quéno et al (2016) for the French and Spanish Pyrenees.…”

mentioning

confidence: 99%

“…• grid of the AROME forcing, within SAFRAN massifs, similarly to Quéno et al (2016) and Vionnet et al (2016), following the method described in Vionnet et al (2012). ary satellite (Schmetz et al, 2002).…”

mentioning

confidence: 99%

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Satellite products of incoming solar and longwave radiations used for snowpack modelling in mountainous terrain

Quéno¹,

Karbou²,

Vionnet³

et al. 2017

Preprint

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Abstract. In mountainous terrain, the snowpack is strongly affected by incoming shortwave and longwave radiations. In this study, a thorough evaluation of the incoming solar and longwave radiation products (DSSF and DSLF) derived from the Meteosat Second Generation satellite was undertaken in the French Alps and the Pyrenees. The satellite products were compared with forecast fields from the meteorological model AROME and with analyses fields from the SAFRAN system. 5A new satellite-derived product (DSLFnew) was developed by combining satellite observations and AROME forecasts. An evaluation against in situ measurements showed lower errors for DSSF than AROME and SAFRAN in terms of solar irradiances. For longwave irradiances, contrasted results falling in the range of uncertainty of sensors did not enable us to select the best product. Spatial comparisons of the different datasets over the Alpine and Pyrenean domains highlighted a better 10 representation of the spatial variability of solar fluxes by DSSF and AROME than SAFRAN. We also showed that the altitudinal gradient of longwave irradiance is too strong for DSLFnew and too weak for SAFRAN. These datasets were then used as radiative forcing together with AROME near-surface forecasts to drive distributed snowpack simulations by the model Crocus in the French Alps and the Pyrenees. An evaluation against in-situ snow depth measurements showed higher bi-15 ases when using satellite-derived products, despite their quality. This effect is attributed to some error compensations in the atmospheric forcing and the snowpack model. However, satellite-derived radiation products are judged beneficial for snowpack modelling in mountains, when the error compensations are solved.

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mentioning

confidence: 96%

Section: Sensitivity Of Snowpack Simulations To the Radiative Forcingmentioning

confidence: 99%

Section: Irradiance Datasetsmentioning

confidence: 99%

“…as described in details in Vionnet et al (2016) for the French Alps and in Quéno et al (2016) for the French and Spanish Pyrenees.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Satellite products of incoming solar and longwave radiations used for snowpack modelling in mountainous terrain

Quéno¹,

Karbou²,

Vionnet³

et al. 2017

Preprint

Self Cite

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Snow climatology for the mountains in the Iberian Peninsula using satellite imagery and simulations with dynamically downscaled reanalysis data

Alonso‐González

López‐Moreno

Navarro-Serrano

et al. 2019

Intl Journal of Climatology

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The presence of a seasonal snowpack determines the hydrology, geomorphology and ecology of wide parts of the Iberian Peninsula, with strong implications for the economy, transport and risk management. Thus, reliable information on snow is necessary from a scientific and operational point of view. This is the case of the Iberian Peninsula where, lack of observation has impeded proper analysis of snowpack duration, magnitude and interannual variability. In this study, we present the first snow climatology of the entire Iberian Peninsula. The scarcity of in situ observations has been overcome, using a newly developed remote sensing snow database from MODIS satellite sensors for the period 2000–2014 and a physically based snow model (Factorial Snow Model—FSM), driven by a regional atmospheric model (Weather Research and Forecast model—WRF) over the Iberian Peninsula for the period 1980–2014. The snowpack of the main mountain areas (Pyrenees, Cantabrian, Central, Iberian range and Sierra Nevada) are described, estimated from the generated databases. The information has been processed using a k‐means cluster algorithm, looking for similarities in snow indices at different elevation bands. Results show four different types of snowpack in terms of depth, duration and interannual variability, lying over different elevation bands in the different ranges, proving the variability of the snowpack over Iberia. Analyses reveal areas characterized by ephemeral snowpacks, while in some sectors snowpack lasts, on average, 198 days per year with 3.02 m of peak snow depth. The coefficient of variation of interannual peak snow depth oscillated between 35.2 and 162.4%. All the analysed indices show that at common elevations the Cantabrian range and the Pyrenees host the deepest and longest lasting snowpacks, followed by the Central and Iberian ranges. The Sierra Nevada exhibits the shortest, shallowest snowpack and more year‐to‐year variability.

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HIM-STRAT: a neural network-based model for snow cover simulation and avalanche hazard prediction over North-West Himalaya

et al. 2020

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Snowpack modelling in the Pyrenees driven by kilometric-resolution meteorological forecasts

Cited by 66 publications

References 50 publications

Satellite products of incoming solar and longwave radiations used for snowpack modelling in mountainous terrain

Satellite products of incoming solar and longwave radiations used for snowpack modelling in mountainous terrain

Snow climatology for the mountains in the Iberian Peninsula using satellite imagery and simulations with dynamically downscaled reanalysis data

HIM-STRAT: a neural network-based model for snow cover simulation and avalanche hazard prediction over North-West Himalaya

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