9 research outputs found

    Microwave remote sensing of snow and environment

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    Hemispheric snow extent and snow mass are two important parameters affecting the water cycle, carbon cycle and the radiation balance in particular at the high latitudes. In this dissertation these topics have been investigated focusing on the mapping of snow clearance day (melt-off day) and Snow Water Equivalent (SWE) by applying spaceborne microwave radiometer instruments. New algorithms have been developed and existing ones have been further advanced. Specific attention has been paid to estimate snow in boreal forests. This work has resulted in Climate Data Records (CDRs) of snow clearance day and daily values of SWE. Data are available for the entire Northern Hemisphere covering more than three decades. The developed CDRs are relevant for climate research, for example concerning the modeling of Earth System processes. CDR on snow clearance day can be used to map the CO2 balance between the biosphere and atmosphere in the case of boreal forests, which is demonstrated in the thesis. Further, methodologies to assess snow mass in terms of SWE for hemispherical and regional scales have been developed. The developed methodologies have also resulted in the establishment of new Near-Real-Time (NRT) satellite data services for hydrological end-use. In hydrology SWE data are used to enhance the performance of river discharge forecasts, which is highly important for hydropower industry and flood prevention activities

    Monitoring Snow Cover and Snowmelt Dynamics and Assessing their Influences on Inland Water Resources

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    Snow is one of the most vital cryospheric components owing to its wide coverage as well as its unique physical characteristics. It not only affects the balance of numerous natural systems but also influences various socio-economic activities of human beings. Notably, the importance of snowmelt water to global water resources is outstanding, as millions of populations rely on snowmelt water for daily consumption and agricultural use. Nevertheless, due to the unprecedented temperature rise resulting from the deterioration of climate change, global snow cover extent (SCE) has been shrinking significantly, which endangers the sustainability and availability of inland water resources. Therefore, in order to understand cryo-hydrosphere interactions under a warming climate, (1) monitoring SCE dynamics and snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced waterbodies, and (3) assessing the causal effect of snowmelt conditions on inland water resources are indispensable. However, for each point, there exist many research questions that need to be answered. Consequently, in this thesis, five objectives are proposed accordingly. Objective 1: Reviewing the characteristics of SAR and its interactions with snow, and exploring the trends, difficulties, and opportunities of existing SAR-based SCE mapping studies; Objective 2: Proposing a novel total and wet SCE mapping strategy based on freely accessible SAR imagery with all land cover classes applicability and global transferability; Objective 3: Enhancing total SCE mapping accuracy by fusing SAR- and multi-spectral sensor-based information, and providing total SCE mapping reliability map information; Objective 4: Proposing a cloud-free and illumination-independent inland waterbody dynamics tracking strategy using freely accessible datasets and services; Objective 5: Assessing the influence of snowmelt conditions on inland water resources

    Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

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    The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary signiïŹcantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an eïŹƒcient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments

    DĂ©veloppement d’un systĂšme d’assimilation de mesures satellites micro-ondes passives dans un modĂšle de neige pour la prĂ©vision hydrologique au QuĂ©bec

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    Dans le contexte quĂ©bĂ©cois (Est du Canada), une bonne gestion de la ressource en eau est devenue un enjeu Ă©conomique majeur et permet Ă©galement d’éviter d’importantes catastrophes naturelles lors des crues printaniĂšres. La plus grande incertitude des modĂšles de prĂ©vision hydrologique rĂ©sulte de la mĂ©connaissance de la quantitĂ© de neige au sol accumulĂ©e durant l’hiver. Pour optimiser la gestion de ses barrages hydroĂ©lectriques, l'entreprise Hydro-QuĂ©bec veut pouvoir mieux quantifier et anticiper l'apport en eau que reprĂ©sentera la fonte des neiges au printemps. Cet apport est estimĂ© Ă  partir de l’équivalent en eau de la neige (‘ÉEN’, ou Snow Water Equivalent, ‘SWE’) extrapolĂ© sur l’ensemble d’un territoire. Cette Ă©tude se concentre sur la zone subarctique et borĂ©ale du QuĂ©bec (58° - 45°N) incluant les bassins hydrographiques du complexe de la Baie James et du sud du QuĂ©bec. Ces territoires reprĂ©sentent des rĂ©gions immenses et hĂ©tĂ©rogĂšnes difficiles d’accĂšs. Le faible nombre de stations mĂ©tĂ©orologiques permanentes et de relevĂ©s nivomĂ©triques entrainent de fortes incertitudes dans l’extrapolation de l’équivalent en eau de la neige, que ce soit Ă  partir de mesures au sol ou de modĂšles de neige pilotĂ©s par des forçages mĂ©tĂ©orologiques. La couverture quasi - quotidienne et globale des observations satellitaires est donc une source d’information au potentiel certain, mais encore peu utilisĂ©e pour ajuster les estimations de l’ÉEN dans les modĂšles hydrologiques. Utilisant les observations satellitaires micro-ondes passives (MOP) et des mesures de hauteurs de neige au sol pour ajuster les cartes de neige interpolĂ©es, le produit ÉEN GlobSnow2 est actuellement considĂ©rĂ© comme un des plus performants Ă  l’échelle globale. En comparant ce produit Ă  une sĂ©rie temporelle de 30 ans de donnĂ©es au sol sur l’Est du Canada (1980 – 2009, avec un total de 38 990 mesures d’ÉEN), nous avons montrĂ© que sa prĂ©cision n'Ă©tait pas adaptĂ©e pour les besoins d'Hydro-QuĂ©bec, avec une erreur quadratique moyenne (RMSE) relative de l'ordre de 36%. Une partie des incertitudes provient de la non reprĂ©sentativitĂ© des mesures de hauteur de neige au sol. Ce travail de thĂšse s'est donc concentrĂ© sur l'amĂ©lioration de la prĂ©diction du couvert nival au QuĂ©bec par l’assimilation des observations satellitaires MOP sans utilisation de relevĂ©s au sol. Les observations, dĂ©crites comme des tempĂ©ratures de brillance (TB), sont fournies par les radiomĂštres AMSR-2 (Advanced Microwave Scanning Radiometer – 2) embarquĂ©s sur le satellite Jaxa (10 x 10 km2). L’approche dĂ©veloppĂ©e propose de coupler un modĂšle de neige (Crocus de MĂ©tĂ©o-France) avec un modĂšle de transfert radiatif (DMRT-ML du LGGE, Grenoble) pour simuler l’émission du manteau neigeux modĂ©lisĂ©. Des modĂšles de transfert radiatifs de vĂ©gĂ©tation, de sol et d’atmosphĂšre sont ajoutĂ©s et calibrĂ©s pour reprĂ©senter le signal MOP au niveau des capteurs satellitaires. Les observations MOP d’AMSR-2 sont alors assimilĂ©es en rĂ©ajustant directement les forçages atmosphĂ©riques pilotant le modĂšle de neige. Ces forçages sont dĂ©rivĂ©s du modĂšle de prĂ©vision atmosphĂ©rique canadien GEM Ă  10 km de rĂ©solution spatiale. Le systĂšme d’assimilation implĂ©mentĂ© est un filtre particulaire par rĂ©Ă©chantillonnage d’importance. La chaĂźne de modĂšles a Ă©tĂ© calibrĂ©e et validĂ©e avec des mesures au sol de radiomĂ©trie micro-onde et des relevĂ©s continus d’ÉEN et de hauteurs de neige. L’assimilation des TB montre d'excellents rĂ©sultats avec des observations synthĂ©tiques simulĂ©es, amĂ©liorant la RMSE sur l’ÉEN de 82% comparĂ© aux simulations d’ÉEN sans assimilation. Les experiences prĂ©liminaires de l’assimilation des observations satellitaires d’AMSR-2 en 11, 19 et 37 GHz (verticale polarization) montrent une amĂ©lioration significative des biais sur les ÉEN simulĂ©s sur un important jeu de donnĂ©es ponctuelles (12 stations de mesures d’ÉEN continues sur 4 annĂ©es). La moyenne des biais inversĂ©s des valeurs d’ÉEN moyens et maximums sont rĂ©duits respectivement de 71 % et 32 % par rapport aux simulations d’ÉEN sans assimilation. Avec l’assimilation des observations d’AMSR-2 et pour les sites avec moins de 75 % de couverts forestiers, le pourcentage d'erreur relative sur l’ÉEN par rapport aux observations est de 15 % (contre 20 % sans assimilation), soit une prĂ©cision significativement amĂ©liorĂ©e pour des applications hydrologiques. Ce travail ouvre de nouvelles perspectives trĂšs prometteuses pour la cartographie d’ÉEN Ă  des fins hydrologiques sur une base journaliĂšre

    Satellite and in situ observations for advancing global Earth surface modelling: a review

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    In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort

    Snow Properties Retrieval Using Passive Microwave Observations

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    Seasonal snow cover, the second-largest component of the cryosphere, is crucial in controlling the climate system, through its important role in modifying Earth’s albedo. The temporal variability of snow extent and its physical properties in the seasonal cycle also make up a significant element to the cryospheric energy balance. Thus, seasonal snowcover should be monitored not only for its climatological impacts but also for its rolein the surface-water supply, ground-water recharge, and its insolation properties at local scales. Snowpack physical properties strongly influence the emissions from the substratum, making feasible snow property retrieval by means of the surface brightness temperature observed by passive microwave sensors. Depending on the observing spatial resolution, the time series records of daily snow coverage and a snowpacks most-critical properties such as the snow depth and snow water equivalent (SWE) could be helpful in applications ranging from modeling snow variations in a small catchment to global climatologic studies. However, the challenge of including spaceborne snow water equivalent (SWE) products in operational hydrological and hydroclimate modeling applications is very demanding with limited uptake by these systems. Various causes have been attributed to this lack of up-take but most stem from insufficient SWE accuracy. The root causes of this challenge includes the coarse spatial resolution of passive microwave (PM) observations that observe highly aggregated snowpack properties at the spaceborne scale, and inadequacies during the retrieval process that are caused by uncertainties with the forward emission modeling of snow and challenges to find robust parameterizations of the models. While the spatial resolution problem is largely in the realm of engineering design and constrained by physical restrictions, a better understanding of the whole range of retrieval methodologies can provide the clarity needed to move the thinking forward in this important field. Following a review on snow depth and SWE retrieval methods using passive microwave remote sensing observations, this research employs a forward emission model to simulate snowpacks emission and compare the results to the PM airborne observations. Airborne radiometer observations coordinated with ground-based in-situ snow measurements were acquired in the Canadian high Arctic near Eureka, NT, in April 2011. The observed brightness temperatures (Tb) at 37 GHz from typical moderate density dry snow in mid-latitudes decreases with increasing snow water equivalent (SWE) due to the volume scattering of the ground emissions by the overlying snow. At a certain point, however, as SWE increases, the emission from the snowpack offsets the scattering of the sub-nivean emission. In tundra snow, the Tb slope reversal occurs at shallower snow thicknesses. While it has been postulated that the inflection point in the seasonal time series of observed Tb V 37 GHz of tundra snow is controlled by the formation of a thick wind slab layer, the simulation of this effect has yet to be confirmed. Therefore, the Dense Media Radiative Transfer Theory forMulti Layered (DMRT-ML) snowpack is used to predict the passive microwave response from airborne observations over shallow, dense, slab-layered tundra snow. The DMRT-ML was parameterized with the in-situ snow measurements using a two-layer snowpack and run in two configurations: a depth hoar and a wind slab dominated pack. Snow depth retrieval from passive microwave observations without a-priori information is a highly underdetermined system. An accurate estimate of snow depth necessitates a-priori information of snowpack properties, such as grain size, density, physical temperature and stratigraphy, and, very importantly, a minimization of this a prior information requirement. In previous studies, a Bayesian Algorithm for Snow Water Equivalent (SWE) Estimation (BASE) have been developed, which uses the Monte Carlo Markov Chain (MCMC) method to estimate SWE for taiga and alpine snow from 4-frequency ground-based radiometer Tb. In our study, BASE is used in tundra snow for datasets of 464 footprints inthe Eureka region coupled with airborne passive microwave observations—the same fieldstudy that forward modelling was evaluated. The algorithm searches optimum posterior probability distribution of snow properties using a cost function between physically based emission simulations and Tb observations. A two-layer snowpack based on local snow cover knowledge is assumed to simulate emission using the Dense Media Radiative Transfer-Multi Layered (DMRT-ML) model. Overall, the results of this thesis reinforce the applicability of a physics-based emission model in SWE retrievals. This research highlights the necessity to consider the two-part emission characteristics of a slab-dominated tundra snowpack and suggests performing inversion in a Bayesian framework

    Status of the Global Observing System for Climate

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    Status of the Global Observing System for Climat
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