Résumé : Depuis les années 1990, environ 12 personnes par année sont tuées des suites d’une avalanche, ce qui en fait maintenant la principale cause de décès liés aux catastrophes hivernales au Canada (Stethem, 2003). Comme l'intérêt pour les activités de plein air telles que la randonnée, la motoneige et le ski de randonnée dans les régions éloignées augmente, l’amélioration de la prévision des avalanches à l'échelle régionale est nécessaire afin d’assurer la sécurité des usagers de l’arrière-pays. La logistique et les mesures de sécurité étant importantes lors de déplacements dans l’arrière-pays, les observations du couvert neigeux (observations d'avalanches, profils stratigraphiques et tests de stabilité) en terrain avalancheux ne sont pas toujours possibles pour les praticiens et prévisionnistes du milieu. Une alternative intéressante est d'analyser le manteau neigeux à distance en utilisant les sorties de modèles physiques de simulation du couvert neigeux. SNOWPACK, un modèle développé par l'Institut WSL sur la neige et les avalanches (SLF) en Suisse, est actuellement utilisé de manière opérationnelle pour la prévision d'avalanches et la recherche dans les Alpes suisses. Le projet vise à adapter SNOWPACK aux différentes conditions météorologiques dans les montagnes canadiennes (climat parfois côtier, transitionnel ou continental) et à l’utiliser dans le contexte de gestion de la prévention d’avalanches d’Avalanche Canada afin d’améliorer les prévisions à l’échelle régionale de la stabilité du couvert neigeux. Ce mémoire présente les traitements et analyses qui ont été menés pour évaluer le potentiel d'utilisation du modèle SNOWPACK forcé à la fois avec des données météorologiques in-situ et des données météorologiques de réanalyses. La validation des données de réanalyses avec les données in-situ pour les hivers de 2013-2014 et de 2014-2015 montre que le modèle météorologique GEM-LAM (Global Environmental Multiscale Limited Area Model) du Centre Météorologique Canadien (CMC) est le plus précis pour les trois contextes climatiques du projet. Un biais sur les données de précipitation proportionnel à l’intensité de celles-ci a toutefois également été identifié. Les sorties des simulations forcées avec GEM-LAM sont les plus proches des mesures observées sur le terrain en ce qui a trait aux indices de densité et de température relative moyenne, montrant des R² supérieurs et des valeurs de RMSE plus faibles. Finalement, l’analyse qualitative de la présence de couches faibles persistantes à l'aide de la plate-forme InfoEX d’Avalanche Canada montre un accord entre les dates de formation de croûte de regel et de givre de surface et les sorties du modèle SNOWPACK, confirmant son potentiel pour une adaptation canadienne. / Abstract : Since the 1990s, approximately 12 people per year are killed on average by avalanches, which are now the primary cause of death related to winter disasters in Canada (Stethem, 2003). As interest in outdoor activities, such as hiking, sledding and ski touring, in remote areas is increasing, there is a strong need for improved avalanche forecasting at the regional scale. Due to important logistical and safety matters, avalanche terrain measurements are not always possible for practitioners/forecasters (avalanche observations, snowpack profiles and stability tests). An interesting alternative is to analyze the snowpack without these challenges by using multilayered snow model outputs. SNOWPACK, a model developed by the WSL Institute for Snow and Avalanche Research (SLF) in Switzerland, is currently used operationally for avalanche prediction and research (Lehning, 1999) in the Swiss Alps. This projects aims to improve largescale predictions of snow stability and avalanches in a Canadian context using SNOWPACK. Thus, this documents presents the analyses that have been conducted to assess the potential of using SNOWPACK driven with both in-situ and forecasted meteorological data. A comparison of meteorological data from in-situ and predicted datasets for the winters of 2013-2014 and 2014-2015 shows that the GEM-LAM model is the most accurate for the three climatic contexts in this project, but also showed a precipitation bias proportional to its intensity/rate. Snow simulations forced with GEM-LAM are the closest to field measurements, showing a higher R² and lower RMSE values. Finally, predictions of persistent weak layers have also been validated using the InfoEx platform from Avalanche Canada. Crust and surface hoar formation dates simulated by SNOWPACK agree with the information reported in InfoEx highlighting the potential for a Canadian implementation.

No description available.

This work focuses on a series of studies that contribute to the development and test of advanced large-scale snow data assimilation methodologies. Compared to the existing snow data assimilation methods and strategies, which are limited in the domain size and landscape coverage, the number of satellite sensors, and the accuracy and reliability of the product, the present work covers the continental domain, compares single- and multi-sensor data assimilations, and explores uncertainties in parameter and model structure. In the first study a continental-scale snow water equivalent (SWE) data assimilation experiment is presented, which incorporates Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) data to Community Land Model (CLM) estimates via the ensemble Kalman filter (EnKF). The greatest improvements of the EnKF approach are centered in the mountainous West, the northern Great Plains, and the west and east coast regions, with the magnitude of corrections (compared to the use of model only) greater than one standard deviation (calculated from SWE climatology) at given areas. Relatively poor performance of the EnKF, however, is found in the boreal forest region. In the second study, snowpack related parameter and model structure errors were explicitly considered through a group of synthetic EnKF simulations which integrate synthetic datasets with model estimates. The inclusion of a new parameter estimation scheme augments the EnKF performance, for example, increasing the Nash-Sutcliffe efficiency of season-long SWE estimates from 0.22 (without parameter estimation) to 0.96. In this study, the model structure error is found to significantly impact the robustness of parameter estimation. In the third study, a multi-sensor snow data assimilation system over North America was developed and evaluated. It integrates both Gravity Recovery and Climate Experiment (GRACE) Terrestrial water storage (TWS) and MODIS SCF information into CLM using the ensemble Kalman filter (EnKF) and smoother (EnKS). This GRACE/MODIS data assimilation run achieves a significantly better performance over the MODIS only run in Saint Lawrence, Fraser, Mackenzie, Churchill & Nelson, and Yukon river basins. These improvements demonstrate the value of integrating complementary information for continental-scale snow estimation. / text

No description available.

Snow initialization is crucial for weather and seasonal prediction, but the National Centers for Environmental Prediction (NCEP) operational models have been found to produce too little snow water equivalent, partly because they assume a constant and unrealistically low snow density for the snowpack. One possible solution is to use the snow density formulation from the Noah land model used in NCEP operational forecast models. While this solution is better than the constant density assumption, the seasonal evolution of snow density in Noah is still found to be unrealistic, through the evaluation of both the offline Noah model output and the Noah snow density formulation itself. A physically based snow density parameterization is then developed, which performs considerably better than the Noah parameterization based on the measurements from the SNOTEL network over the western United States and Alaska. It also performs better than the snow density schemes used in three other models. This parameterization could be easily implemented in NCEP operational snow initialization. With the consideration of up to 10 snow layers, this parameterization can also be applied to multilayer snowpack initiation or to estimate snow water equivalent from in situ and airborne snow depth measurements.

Thesis (Master of Engineering) in Engineering Physics--University of Maine, 2004. / Includes vita. Includes bibliographical references (leaves 81-83).

Résumé: L’Institut pour l'étude de la neige et des avalanches en Suisse (SLF) a développé SNOWPACK, un modèle thermodynamique multi-couches de neige permettant de simuler les propriétés géophysiques du manteau neigeux (densité, température, taille de grain, teneur en eau, etc.) à partir desquelles un indice de stabilité est calculé. Il a été démontré qu’un ajustement de la microstructure serait nécessaire pour une implantation au Canada. L'objectif principal de la présente étude est de permettre au modèle SNOWPACK de modéliser de manière plus réaliste la taille de grain de neige et ainsi obtenir une prédiction plus précise de la stabilité du manteau neigeux à l’aide de l’indice basé sur la taille de grain, le Structural Stability Index (SSI). Pour ce faire, l’erreur modélisée (biais) par le modèle a été analysée à l’aide de données précises sur le terrain de la taille de grain à l’aide de l’instrument IRIS (InfraRed Integrated Sphere). Les données ont été recueillies durant l’hiver 2014 à deux sites différents au Canada : parc National des Glaciers, en Colombie-Britannique ainsi qu’au parc National de Jasper. Le site de Fidelity était généralement soumis à un métamorphisme à l'équilibre tandis que celui de Jasper à un métamorphisme cinétique plus prononcé. Sur chacun des sites, la stratigraphie des profils de densités ainsi des profils de taille de grain (IRIS) ont été complétés. Les profils de Fidelity ont été complétés avec des mesures de micropénétromètre (SMP). L’analyse des profils de densité a démontré une bonne concordance avec les densités modélisées (R[indice supérieur 2]=0.76) et donc la résistance simulée pour le SSI a été jugée adéquate. Les couches d’instabilités prédites par SNOWPACK ont été identifiées à l’aide de la variation de la résistance dans les mesures de SMP. L’analyse de la taille de grain optique a révélé une surestimation systématique du modèle ce qui est en accord avec la littérature. L’erreur de taille de grain optique dans un environnement à l’équilibre était assez constante tandis que l’erreur en milieux cinétique était plus variable. Finalement, une approche orientée sur le type de climat représenterait le meilleur moyen pour effectuer une correction de la taille de grain pour une évaluation de la stabilité au Canada. / Abstract : The snow thermodynamic multi-layer model SNOWPACK was developed in order to address the risk of avalanches by simulating the vertical geophysical and thermophysical properties of snow. Risk and stability assessments are based on the simulation of the vertical variability of snow microstructure (grain size, sphericity, dendricity and bond size), as well as snow cohesion parameters. Previous research has shown a systematic error in the grain size simulations (equivalent optical grain size) over several areas in northern Canada. In order to quantify the simulated errors in snow grain size and associated uncertainties in stability, snow specific surface area (SSA), was measured using a laser-based system measuring snow albedo through an integrating sphere (InfraRed Integrating Sphere, IRIS) at 1310 nm. Optical grain size was retrieved from the IRIS SSA measurements in order to validate the optical equivalent grain radius from simulated SNOWPACK outputs. Measurements occurred during a field campaign conducted during the 2013-2014 winter season in the Canadian Rockies. The two study plots selected are located at Glacier National Park, BC and Jasper National Park, AB. Profiles of density and stratigraphic analysis were completed as well as grain size (IRIS) profiles, combine with snow micropenetrometer (SMP) measurements. Density analysis showed good agreement for the simulated values (R[superscript 2]=0.76) and thus the simulated resistance for the SSI was assumed of reasonable precision. Snow instabilities predicted by SNOWPACK were observed by SMP resistance variation. The optical grain size analysis showed systematic overestimation of the modeled values, in agreement with the current literature. Error in SSA evolution in a rounding environment was mostly constant whereas error in conditions driven by temperature gradient was variable. Finally, it is suggested that a climate-oriented parametrization of the microstructure could represent an improvement for stability assessment in Canada given the variability and size of avalanche terrain.

No description available.

This document is a summary of research conducted to develop and apply climate analysis tools toward a better understanding of the past and future of hydroclimate variability in the state of Utah. Two pilot studies developed data management and climate analysis tools subsequently applied to our region of interest. The first investigated the role of natural atmospheric forcing in the inter-annual variability of precipitation of the Sahel region in Africa, and found a previously undocumented link with the East Atlantic mode, which explains 29% of variance in regional precipitation. An analysis of output from an operational seasonal climate forecast model revealed a failure in the model to reproduce this linkage, thus highlighting a shortcoming in model performance. The second pilot study studied long-term trends in the strength of the Great Plains low-level jet, an driver of storm development in the region’s wet spring season. Our analysis showed that since 1979 the low-level jet has strengthened as shifted the timing of peak activity, resulting in shifts both in time and location for peak precipitation, possibly the result of anthropogenic forcing. Our third study used a unique tree-ring dataset to create a reconstruction of April 1 snow water equivalent, an important measure of water supply in the Intermountain West, for the state of Utah to 1850. Analysis of the reconstruction shows the majority of snowpack variability occurs monotonically over the whole state at decadal to multidecadal frequencies. The final study evaluated decadal prediction performance of climate models participating in the Coupled Model Intercomparison Project 5. We found that the analyzed models exhibit modest skill in prediction of the Pacific Decadal Oscillation and better skill in prediction of global temperature trends post 1960.

Multiple reconstructions of April 1st snow water equivalent (SWE) are generated for the Wind River Range (WRR), located in west-central Wyoming, to determine the most accurate predictors. Predictors included climate signal data (Southern Oscillation Index), traditional predictors (tree-ring chronologies), and non-spatially biased Pacific Ocean sea surface temperatures (SSTs). Incorporation of Pacific Ocean SSTs as a whole provides a more comprehensive representation of oceanic-atmospheric variability. Rotated principal component analysis (PCA) was used to regionalize April 1st snowpack data (1961 – 1999) from snow telemetry stations (SNOTEL stations). Tree-ring chronologies that were stable across the period of overlapping records (1961 – 1999) and that were positively correlated with regional snowpack at 99% confidence levels or higher were retained. Singular value decomposition (SVD) was performed on Pacific Ocean SSTs and regional snowpack data to identify coupled regions of climate (SSTs) and hydrology (SWE). Stepwise regressions were performed across the calibration period to identify the best predictor combinations. When data from the instrumental based SST regions identified by SVD were included in the pool of predictors, an increase in reconstruction skill was observed. Further regressions were performed using tree based and coral based SST data. Reconstruction equations were obtained from these regressions and regional April 1st snowpack was reconstructed for the WRR for all three types of SST data. A higher degree of snowpack variance is explained by reconstructions utilizing tree based, coral based, and instrumental based data for the Pacific Ocean SST region identified by SVD than is possible utilizing only tree-ring and SOI data, indicating that non-spatially biased SSTs are excellent predictors for snowpack reconstruction in the WRR.