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

Abstract

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.

Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway

“…At lower elevations, precipitation was less important but factors such as wind speed or surface roughness increased in importance. Quéno et al (2017) used satellite products of incoming solar and long-wave radiation to force the SURFEX/Crocus model; however they concluded that improved meteorological forcing does not always lead to more accurate snow- Figure 12. The fraction of accumulated snowfall from total accumulated precipitation computed for the period 1 September 2014 to 31 May 2015 .…”

“…7 for the GridObs-Crocus experiment. Underestimated melting was also found by Quéno et al (2016Quéno et al ( , 2017 and Vionnet et al (2016), and complementary studies are needed to investigate the cause of this issue. Lafaysse et al (2017) developed an ensemble snowpack model using SURFEX/Crocus called ESCROC (Ensemble System Crocus) to address modelling errors.…”

Black carbon and dust alter the response of mountain snow cover under climate change

“…While air temperature biases have not been reported compared to observations at different locations, radiatif and precipitation biases have been observed. For instance, Queno et al (57) reported a bias of the shortwave radiation reaching 25 %/ in the Pyrenees. Vionnet et al (65) showed and under-estimation of precipitation amount in the Alps at high elevation.…”

57 years (1960–2017) of snow and meteorological observations from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.)

“…This correction, although ranging in the uncertainty values provided by sensor manufacturor, is of large significance for snowpack modelling considering the high sensitivity of the snowpack to this variable (e.g. Raleigh et al, 2015;Sauter and Obleitner, 2015;Quéno et al, 2017). The sensors have been installed for the WMO SPICE project and are used in this study only to complement the dataset if a problem exists for the reference sensor.…”