10 research outputs found

    Integration Frameworks for Merging Satellite Remote Sensing Observations with Hydrological Model Outputs

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    With a growing number of available datasets especially from satellite remote sensing, there is a great opportunity to improve our knowledge of hydrological processes by integrating them with hydrological models. In this regard, data assimilation technique can be used to constrain the dynamic of a model with available observations in order to improve its estimates. In this thesis, a comprehensive data assimilation framework containing multiple stages is proposed and tested over various areas

    Coupled ice-ocean modeling and predictions

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    We review the coupled ice-ocean modeling activities aimed at predictions, both in the near term (days to a week) and in the long term (seasonal to decadal) of the polar oceans. First the state of the knowledge of potential predictability is exposed, then an overview is given of the tools available for carrying out such predictions: the observations that can be used to initialize actual predictions, the coupled ice-ocean–modeling, including the fully-coupled Earth System Models for long-term predictions, and data-assimilation techniques. Finally, the performance of existing prediction systems is reviewed, showing that, although more predictive capability remains than what is presently achieved, both the near- and long-term forecasts show skill over trivial predictors. Parallel efforts should therefore be invested into acquiring more observations of the ocean and sea ice, developing new models both in standalone and coupled mode, and improving the data-assimilation techniques

    Modified shallow water models and idealised satellite data assimilation

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    Satellites constitute an essential source of observations in operational satellite data assimilation (DA). In this thesis, we investigate the impact of assimilating satellite observations at different spatial scales: is there a relative benefit in focussing on small rather than large scales (or vice versa)? In order to address this question without using complex and computationally expensive Numerical Weather Prediction (NWP) models, we conduct a series of idealised satellite DA experiments based on a modified shallow water model able to imitate convection and precipitation. The use of an isopycnal, single-layer version of the model (modRSW) is discussed first. A series of forecast-assimilation experiments are carried out using a Deterministic Ensemble Kalman filter (DEnKF). As a result, the filter performance and the relevance of the modRSW model for convective-scale DA in Numerical Weather Prediction systems are demonstrated and a protocol to extend a similar analysis to other idealised systems is presented. After establishing that the modRSW model is not suitable for satellite DA research, a new isentropic, 1.5-layer model (ismodRSW) is developed. The revised model is equipped with a fluid temperature definition and is therefore a better candidate for satellite DA experiments. The dynamics and the numerics of this model are discussed, and its numerical solver is verified against an analytical solution. In order to imitate closely an operational system, an idealised observing system comprising both ground and satellite observations is created, and pseudo observations mimicking microwave radiation measured by polar-orbiting satellites are generated, with clouds and precipitation implicitly taken into account within the new (and nonlinear) observation operator. Finally, a new series of forecast-assimilation simulations is run to obtain a well-tuned system which is used as a reference in a series of data denial experiments, where satellite observations at small and large scales are selectively excluded from the assimilation to evaluate their impact on the system. Preliminary results show a degradation of both the analysis and the forecasts when only large-scale satellite observations are utilised, although further work is needed to ascertain the robustness of these findings. All in all, this thesis shows that the idea of investigating satellite DA using a modified shallow water model is a viable strategy. By imitating closely several aspects of an operational system and by developing a more realistic model, we have demonstrated that large-scale satellite observations alone can have a negative impact on the quality of a DA system

    A water storage reanalysis over the European continent: assimilation of GRACE data into a high-resolution hydrological model and validation

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    Continental water storage and redistribution within the Earth’s system are key variables of the terrestrial water cycle. Changes in water storage and fluxes may affect resources for drinking water and irrigation, lead to drought or flood conditions, or cause severe changes of ecosystems e.g., through salinification. Hydrological models, which map water storages and fluxes, are being continuously improved and deepen our understanding of geophysical processes related to the water cycle. However, models are built on a simplified representation of reality, which leads to limited predicting skills of the simulation results. Assimilating remotely sensed total water storage variability from the Gravity Recovery and Climate Experiment (GRACE) mission has become a valuable tool for reducing uncertainties of hydrological model simulations. Simultaneously, coarse GRACE observations are disaggregated spatially and temporally through data assimilation. In this thesis, GRACE data are assimilated into the Community Land Model version 3.5 (CLM3.5) yielding a unique daily 12.5 km reanalysis of total water storage evolution over Europe (2003 to 2010). Independent observations are evaluated to identify model deficits and to validate the performance of data assimilation. For the first time, the effect of data assimilation on modeled total water storage is also shown on the level of GRACE K-band observations. Optimal strategies for assimilating GRACE data into a high-resolution hydrological model are investigated through synthetic experiments. These experiments address the choice of the assimilation algorithm, localization, inflation of the ensemble of model states, ensemble size, error model of the observations, and spatial resolution of the observation grid. As the assimilation of GRACE data into CLM3.5 is realized within the Terrestrial Systems Modeling Platform (TerrSysMP), future assimilation experiments can be extended for the groundwater and atmosphere components included in TerrSysMP.Eine Reanalyse des europĂ€ischen Wasserspeichers: Assimilierung von GRACE Daten in ein hochaufgelöstes hydrologisches Modell und Validierung Änderungen im kontinentalen Wasserspeicher und im Transport von Wasser durch das Erdsystem sind wichtige EinflussgrĂ¶ĂŸen fĂŒr die VerfĂŒgbarkeit von Frischwasserresourcen, die Entstehung von DĂŒrren und Überschwemmungen, sowie fĂŒr die Erhaltung von Ökosystemen, welche z.B. durch Versalzung gefĂ€hrdet werden. Hydrologische Modelle, die die Speicherung und den Transport von Wassermassen abbilden, werden stetig verbessert und helfen unser VerstĂ€ndnis von hydrologischen Prozessen zu vertiefen. Allerdings ermöglichen hydrologische Modelle nur eine vereinfachte Abbildung der RealitĂ€t, sodass die Aussagekraft der Simulationsergebnisse beschrĂ€nkt ist. Die Assimilierung von WasserspeicherĂ€nderungen, gemessen von den GRACE (Gravity Recovery and Climate Experiment) Satelliten, kann hydrologische Simulationen verbessern und erlaubt gleichzeitig eine rĂ€umliche und zeitliche Differenzierung der grobaufgelösten GRACE Beobachtungen. In dieser Doktorarbeit werden GRACE Daten in das Land-OberflĂ€chen-Modell CLM3.5 (Community Land Model Version 3.5) assimiliert, um eine neuartige Reanalyse tĂ€glicher WasserspeicherĂ€nderungen (2003 bis 2010) fĂŒr Europa mit 12.5 km Auflösung zu generieren. Durch unabhĂ€ngige Beobachtungen werden Defizite des Modells identifiziert und das Ergebnis der Datenassimilierung beurteilt. Zum ersten Mal wird auch die Auswirkung der Assimilierung direkt auf Basis der GRACE K-Band Beobachtungen untersucht. Mit Hilfe synthetischer Experimente wird die beste Strategie zur Assimilierung von GRACE Daten in ein hochaufgelöstes hydrologisches Modell ermittelt. Dabei wird der Einfluss unterschiedlicher Assimilierungsstrategien untersucht, unter anderem die Wahl des Assimilierungsalgorithmus, die Lokalisierung des Einflussbereichs von Beobachtungen, die Erhöhung der Spannweite der Ensemblemitglieder des Modells, die EnsemblegrĂ¶ĂŸe, das Fehlermodell der Beobachtung und die rĂ€umliche Auflösung des Beobachtungsgitters. Da die Assimilierung von GRACE in das CLM3.5 Modell unter Verwendung von TerrSysMP (Terrestrial Systems Modeling Platform) geschieht, können die Assimilierungsexperimente in Zukunft auf die zusĂ€tzliche Verwendung des in TerrSysMP enthaltenen Grundwasser- und des AtmosphĂ€renmodells erweitert werden.</p

    Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

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    Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

    L’assimilation de donnĂ©es multivariĂ©es par filtre de Kalman d’ensemble pour la prĂ©vision hydrologique

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    Le potentiel de l’assimilation d’un type d’observation pour la prĂ©vision hydrologique a Ă©tĂ© dĂ©montrĂ© dans certaines Ă©tudes. Cependant, le potentiel de l’assimilation simultanĂ©e de plusieurs types d’observations a Ă©tĂ© peu validĂ©, particuliĂšrement pour les donnĂ©es comprenant une information sur la neige au sol. De plus, l’amplitude et la durĂ©e de l’impact de l’assimilation de donnĂ©es peuvent dĂ©pendre du type de donnĂ©es assimilĂ©, ainsi que du contenu du vecteur d’état employĂ© pour mettre Ă  jour les variables ou les paramĂštres du modĂšle hydrologique. La prĂ©sente thĂšse examine l’impact de l’assimilation de donnĂ©es multivariĂ©es en fonction du type de donnĂ©es assimilĂ© et du contenu du vecteur d’état pour la prĂ©vision hydrologique Ă  court terme (horizon de prĂ©vision jusqu’à 5 jours) et moyen terme (horizon de prĂ©vision entre 25 et 50 jours). Le filtre de Kalman d’ensemble est employĂ© pour assimiler les observations de l’équivalent en eau de la neige Ă  trois endroits sur le bassin versant de la riviĂšre Nechako, ainsi que le dĂ©bit Ă  l’exutoire, dans le modĂšle hydrologique CEQUEAU. Les scĂ©narios d’assimilation sont premiĂšrement testĂ©s dans un cadre synthĂ©tique afin d’identifier les variables les plus susceptibles Ă  l’assimilation des donnĂ©es pour la prĂ©vision hydrologique. La robustesse des scĂ©narios d’assimilation de donnĂ©es est ensuite testĂ©e en introduisant un biais sur les prĂ©cipitations solides. Finalement, les observations rĂ©elles sont assimilĂ©es pour vĂ©rifier l’impact rĂ©el des scĂ©narios pour la prĂ©vision hydrologique. Les rĂ©sultats montrent une amĂ©lioration variable des prĂ©visions hydrologiques en fonction des scĂ©narios selon plusieurs critĂšres de performance mesurant l’exactitude, le biais et la reprĂ©sentativitĂ© de l’incertitude reprĂ©sentĂ©e par les prĂ©visions d’ensemble. L’assimilation du dĂ©bit pour la mise Ă  jour des variables amĂ©liore principalement les prĂ©visions Ă  court terme, tandis que l’impact de la mise Ă  jour de certains paramĂštres persiste Ă  moyen terme. L’assimilation de l’équivalent en eau de la neige amĂ©liore les prĂ©visions Ă  court et moyen terme, principalement pendant la pĂ©riode de fonte de neige. Pour la plupart des scĂ©narios, l’assimilation simultanĂ©e du dĂ©bit et de l’équivalent en eau de la neige surpasse l’assimilation des donnĂ©es individuellement. Ces rĂ©sultats sont cohĂ©rents entre les cadres synthĂ©tique et rĂ©el

    Improving Data Assimilation Algorithms for Enhanced Environmental Predictions

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    Data Assimilation (DA) methods provide a means of combining model output with observations based on their respective uncertainties. They are considered an invaluable tool in a wide variety of disciplines, particularly in hydrologic and meteorological forecasting. There is significant potential to improve existing DA methods, which have predominantly been developed in an ad-hoc manner to enhance their applicability to complex real world problems. In particular, relatively little attention has been devoted to one of the most fundamental aspects of DA: Model uncertainty quantification. This thesis aims to develop improved DA based methods for highly non-Gaussian/non-linear systems, with a particular focus on hydrologic and atmospheric systems. It also examines how DA methods can be enhanced to solve problems outside of their traditional application domain. Specifically, two overarching aims are investigated: 1) the development of DA based methods for estimating time varying model parameters, with the ultimate goal of improving hydrologic predictions in dynamic catchments; and 2) the development of objective model uncertainty quantification techniques for use in state-estimation DA. Firstly, a DA based method for sequentially estimating time varying model parameters is investigated. Two new methods for proposing prior parameter distributions are developed, which can be utilised depending on the amount of a priori information available regarding the form of temporal variations in model parameters. The methods are verified against synthetic data and applied to a number of real catchments with land use change, without relying on prior information of such changes. This approach represents a promising modelling paradigm for hydrologists faced with providing predictions in rapidly changing catchments. In addressing the second objective, two model uncertainty quantification methods are developed for DA in partially observed systems with highly non-Gaussian uncertainties. The methods proposed in this thesis address some of the major shortcomings in existing methods related to objectivity and ability to characterise non-Gaussian errors. Their efficacy is demonstrated through application to flood forecasting problems, and also for state estimation in a partially observed multi-scale atmospheric toy model. In all cases, the proposed methods are shown to provide improved forecasts and updates compared to standard approaches

    Retrieval of soil physical properties:Field investigations, microwave remote sensing and data assimilation

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    DEnKF–Variational Hybrid Snow Cover Fraction Data Assimilation for Improving Snow Simulations with the Common Land Model

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    This study assesses the analysis performance of a hybrid DEnKF-variational data assimilation (DA) method (DEnVar) for assimilating the MODIS snow cover fraction (SCF) into the Common Land Model (CoLM). Coupling a deterministic ensemble Kalman filter (DEnKF) with a one-dimensional variational DA method (1DVar), DEnVar without observation perturbations is a two-step DA method. That is, the analysis ensemble mean and analysis error covariance of DEnKF are introduced into the 1DVar hybrid cost function, and the analysis mean of DEnKF is replaced by the 1DVar analysis. The analysis performance of DEnVar was experimentally compared with DEnKF, 1DVar, and EnVar (hybrid ensemble-variational DA) at five sites in the Altay region of China from November 2008 to March 2009. From our results, it is shown that the four DA experiments can improve snow simulations at most sites when the available MODIS SCF is assimilated. The DEnVar experiment using the hybrid error covariance shows the best analysis performance among the four DA experiments at most sites. Furthermore, sensitivity tests show that DEnVar is slightly sensitive to the weighting coefficient, which controls the respective weights of ensemble- and (National Meteorological Center) NMC-based error covariances, but is highly sensitive to the observation error. DEnVar obtains better analysis performance when using the ensemble-based analysis error covariance rather than the hybrid error covariance coupling ensemble-based analysis and static NMC-based error covariances. The inaccurate distribution of observation error may invalidate the DEnVar method
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