On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model

International audienceThis paper examines the ability of optical re-flectance data assimilation to improve snow depth and snow water equivalent simulations from a chain of models with the SAFRAN meteorological model driving the detailed multi-layer snowpack model Crocus now including a two-stream radiative transfer model for snow, TARTES. The direct use of reflectance data, allowed by TARTES, instead of higher level snow products, mitigates uncertainties due to commonly used retrieval algorithms. Data assimilation is performed with an ensemble-based method, the Sequential Importance Resampling Particle filter , to represent simulation uncertainties. In snowpack mod-eling, uncertainties of simulations are primarily assigned to meteorological forcings. Here, a method of stochastic perturbation based on an autoregressive model is implemented to explicitly simulate the consequences of these uncertainties on the snowpack estimates. Through twin experiments, the assimilation of synthetic spectral reflectances matching the MODerate resolution Imaging Spectroradiometer (MODIS) spectral bands is examined over five seasons at the Col du Lautaret, located in the French Alps. Overall, the assimilation of MODIS-like data reduces by 45 % the root mean square errors (RMSE) on snow depth and snow water equivalent. At this study site, the lack of MODIS data on cloudy days does not affect the assimilation performance significantly. The combined assimilation of MODIS-like reflectances and a few snow depth measurements throughout the 2010/2011 season further reduces RMSEs by roughly 70 %. This work suggests that the assimilation of optical reflectances has the potential to become an essential component of spatialized snowpack simulation and forecast systems. The assimilation of real MODIS data will be investigated in future works

Evolution des propriétés physiques de neige de surface sur le plateau Antarctique. Observations et modélisation du transfert radiatif et du métamorphisme

The surface energy balance of the Antarctic Plateau is mainly governed by the physical properties of the snowpack in the topmost centimeters, whose evolution is driven by intricated processes such as: snow metamorphism, temperature profiles variations, solar radiation penetration, precipitation, snow drift, etc. This thesis focuses on the interactions between all these components and aims at simulating the evolution of snow density and snow grain size (specific surface area) on the Antarctic Plateau. To physically model the absorption of solar radiation within the snowpack, a radiative transfer model with high spectral resolution (TARTES) is implemented in the detailed snowpack model Crocus. TARTES calculates the vertical profile of absorbed radiation in a layered snowpack whose characteristics are given. These characteristics include snow grain shape, a parameter that has been seldom studied. For this reason, an experimental method to estimate the optical grain shape is proposed and applied to a large number of snow samples. This method, which combines optical measurements, TARTES simulations and Bayesian inference, is used to estimate the optimal shape to be used in snow optical models. In addition, it highlights that representing snow as a collection of spherical particles results in overestimation of the penetration depth of solar radiation. The influence of the penetration of solar radiation on the snow temperature profiles is then investigated with analytical and numerical tools. The results point out the high sensitivity of the temperature profiles to surface snow physical properties. In particular, the density of the topmost centimeters of the snowpack is critical for the energy budget of the snowpack because it impacts both the effective thermal conductivity and the penetration depth of light. To simulate the evolution of snow physical properties at Dome C by taking into account their interdependence with snow optical properties, the model Crocus is used, driven by meteorological data. These simulations are evaluated against a set of data collected during field missions as well as automatic measurements of snow spectral albedo and penetration depth. These observations highlight the influence of weather conditions on the temporal variability of surface snow properties. They show the existence of a slow decrease of snow grain size at the surface during summer. Rapid changes are also observed, essentially due to precipitation. These variations are well simulated by Crocus when forced by an appropriate atmospheric forcing. In particular, the impact of wind on the evolution of the snowpack is crucial because it controls the surface density through snow transport. This transport is also responsible for the spatial variability of snow properties observed at Dome C. That is why a stochastic representation of snow erosion and transport in Crocus is proposed. It explains well the observations of the spatial variability of density and grain size, and reproduces the variability of the annual accumulation as well as rapid changes in snow height resulting from drift events. This study improves our understanding of the physical processes which drive the properties of snow close to the surface on the Antarctic Plateau, and also points out the critical role of wind, the impact of which is very difficult to account for in models yet.Le bilan d'énergie de surface du Plateau Antarctique est essentiellement contrôlé par les propriétés physiques des premiers centimètres du manteau neigeux. Or l'évolution de cette neige de surface est complexe car elle dépend de processus fondamentalement imbriqués: vitesse de métamorphisme, profils de température, pénétration du rayonnement solaire, précipitations, transport de neige par le vent, etc. L'objectif de ces travaux de thèse est d'étudier ces diverses composantes et leur couplage afin de simuler l'évolution de la densité de la neige et de la taille de grain (surface spécifique) sur le Plateau Antarctique. Pour représenter de manière physique l'absorption de l'énergie solaire à l'intérieur du manteau, un modèle de transfert radiatif à fine résolution spectrale (TARTES) a été implémenté dans le modèle de manteau neigeux détaillé Crocus. TARTES permet de calculer le profil vertical d'absorption d'énergie dans un manteau stratifié dont les caractéristiques sont connues. Parmi elles, la forme des grains, explicitement prise en compte dans TARTES, a été peu étudiée jusqu'à présent. C'est pourquoi une méthode de détermination expérimentale de la forme optique des grains est proposée et appliquée à un grand nombre d'échantillons de neige. Cette méthode, basée sur des mesures optiques, des simulations TARTES, et l'inférence bayésienne, a permis de déterminer la forme la plus adéquate pour simuler les propriétés optiques de la neige, et a mis en évidence le fait que représenter la neige par un ensemble de particules sphériques conduisait à surestimer la profondeur de pénétration du rayonnement d'environ 30%. L'impact de l'absorption en profondeur du rayonnement sur les profils de température dans le manteau et son métamorphisme est ensuite étudié par des approches analytique et numérique, mettant en valeur la sensibilité des profils aux propriétés de la neige proche de la surface. En particulier, la densité de la neige sur les premiers centimètres est cruciale pour le bilan d'énergie du manteau car elle impacte à la fois la profondeur de pénétration du rayonnement et la conductivité thermique du manteau. Puisque le modèle Crocus tient compte de ce couplage entre propriétés optiques et physiques du manteau, il est utilisé pour estimer l'influence des conditions météorologiques sur la variabilité temporelle des propriétés physiques de la neige de surface à Dôme C. Ces simulations sont évaluées au regard d'un jeu de données collectées lors de missions de terrain et de mesures automatiques de l'albédo spectral et de la pénétration du rayonnement dans la neige. Ces observations mettent en évidence le rôle primordial des précipitations dans les variations rapides de taille de grain en surface et l'existence d'un cycle saisonnier de cette taille de grain. Ces variations sont bien simulées par Crocus lorsque le forçage atmosphérique qui lui est imposé est adéquat. En particulier, l'impact du vent sur l'évolution du manteau est fondamental car il contrôle la densité de surface par le biais du transport de neige. Ce transport est aussi à l'origine de la variabilité spatiale des propriétés de la neige observée à Dôme C. C'est pourquoi une modélisation stochastique de l'érosion et du transport de neige dans Crocus est proposée. En plus d'expliquer la variabilité spatiale de la densité et de la taille de grain, elle permet de reproduire celle de l'accumulation annuelle ainsi que les variations rapides de hauteur de neige liées à des épisodes de vent. Ces travaux ont permis une meilleure représentation des processus physiques qui contrôlent les variations des propriétés de la neige de surface à Dôme C, tout en soulignant le rôle primordial du vent, dont l'impact sur le manteau est particulièrement complexe à simuler

A microbolometer-based far infrared radiometer to study thin ice clouds in the Arctic

A far infrared radiometer (FIRR) dedicated to measuring radiation emitted by clear and cloudy atmospheres was developed in the framework of the Thin Ice Clouds in Far InfraRed Experiment (TICFIRE) technology demonstration satellite project. The FIRR detector is an array of 80 × 60 uncooled microbolometers coated with gold black to enhance the absorptivity and responsivity. A filter wheel is used to select atmospheric radiation in nine spectral bands ranging from 8 to 50 µm. Calibrated radiances are obtained using two well-calibrated blackbodies. Images are acquired at a frame rate of 120 Hz, and temporally averaged to reduce electronic noise. A complete measurement sequence takes about 120 s. With a field of view of 6°, the FIRR is not intended to be an imager. Hence spatial average is computed over 193 illuminated pixels to increase the signal-to-noise ratio and consequently the detector resolution. This results in an improvement by a factor of 5 compared to individual pixel measurements. Another threefold increase in resolution is obtained using 193 non-illuminated pixels to remove correlated electronic noise, leading an overall resolution of approximately 0.015 W m−2 sr−1. Laboratory measurements performed on well-known targets suggest an absolute accuracy close to 0.02 W m−2 sr−1, which ensures atmospheric radiance is retrieved with an accuracy better than 1 %. Preliminary in situ experiments performed from the ground in winter and in summer on clear and cloudy atmospheres are compared to radiative transfer simulations. They point out the FIRR ability to detect clouds and changes in relative humidity of a few percent in various atmospheric conditions, paving the way for the development of new algorithms dedicated to ice cloud characterization and water vapor retrieval

Simulation of Direct normal Irradiance (DNI) in Portugal with the Meso-NH Model. Several Case Studies

Simulations of short-term forecasts of DNI with the Meso-NH model.COMPETE 2020 (Operational Program Competitiveness and Internationalization) through the ICT (UID/GEO/04683/2013, POCI-01-0145-FEDER-007690), DNI-A (ALT20-03-0145-FEDER-000011) and ALOP (ALT20-03-0145-FEDER-000004) project