The Mars climate has been explored using two reanalysis datasets based on combining spacecraft observations of temperature and dust with the UK version of the LMD Mars GCM. The semiannual oscillation (SAO) of zonal-mean zonal wind was studied using the existing Mars Analysis Correction Data Assimilation reanalysis during Mars Years (MYs) 24-27. The SAO of zonal-mean zonal wind was shown to exist and extend over a wide range of latitudes. The dynamical driving processes of the SAO in the tropics were investigated, and the forcing due to meridional advection appeared to be the main contributor to the SAO. The study also highlighted some phenomena associated with perturbations of the global circulation during the MY 25 global dust storm (GDS). The meridional advection term was shown to be weaker in the first half of GDS year MY 25 than in the following year, but the forcing due to meridional advection and westward thermal tides both appeared to intensify during the MY 25 GDS. The capabilities of the Mars data assimilation system were also extended in this thesis, 1) to represent dynamic dust lifting and dust transport during the assimilation and 2) to assimilate measurements of the dust vertical distribution. The updated reanalysis was then used to study several major dust events during MY28-29. It proved able to reproduce a southward-moving regional dust storm without the overwhelming assistance of the assimilation. Dust devil lifting was found to at least partly provide the initial pattern of dust of this moving dust storm. The cold anomaly of the cooling zone beneath this dust storm could be as large as ∼ 2 K similar to the magnitude of what was found during the MY 25 GDS. Using the reanalysis, the life cycle of the planet-encircling global dust storm in MY28 was also studied. The Noachis dust storm that occurred just before the MY 28 GDS was found to be the joint result of a travelling Chryse storm, enhanced by dust lifting along its path and local dust lifting in Noachis itself. The adiabatic heating associated with the north polar warming that occurred during MY 28 GDS was up to ∼ 3 times as large as that found during the non-GDS year MY 29. The wind stress dust lifting was shown to in strong correlation with the global average dust loadings, and significantly decreased when the GDS decayed.

Cette thèse présente des travaux en lien avec l’utilisation de données cliniques dans la construction de modèles appliqués à l’oncologie. Les modèles actuels visant à intégrer plusieurs mécanismes biologiques liés à la croissance tumorale comportent trop de paramètres et ne sont pas calibrables sur des cas cliniques. A l’inverse, les modèles plus simples ne parviennent pas à prédire précisément l’évolution tumorale pour chaque patient. La multitude et la variété des données acquises par les médecins sont de nouvelles sources d’information qui peuvent permettre de rendre les estimations des modèles plus précises. A travers deux projets différents, nous avons intégré des données dans le processus de modélisation afin d’en tirer le maximum d’information. Dans la première partie, des données d’imagerie et de génétique de patients atteints de gliomes sont combinées à l’aide de méthodes d’apprentissage automatique. L’objectif est de différencier les patients qui rechutent rapidement au traitement de ceux qui ont une rechute plus lente. Les résultats montrent que la stratification obtenue est plus efficace que celles utilisées actuellement par les cliniciens. Cela permettrait donc d’adapter le traitement de manière plus spécifique pour chaque patient. Dans la seconde partie, l’utilisation des données est cette fois destinée à corriger un modèle simple de croissance tumorale. Même si ce modèle est efficace pour prédire le volume d’une tumeur, sa simplicité ne permet pas de rendre compte de l’évolution de forme. Or pouvoir anticiper la future forme d’une tumeur peut permettre au clinicien de mieux planifier une éventuelle chirurgie. Les techniques d’assimilation de données permettent d’adapter le modèle et de reconstruire l’environnement de la tumeur qui engendre ces changements de forme. La prédiction sur des cas de métastases cérébrales est alors plus précise. / This thesis deals with the use of clinical data in the construction of models applied to oncology. Existing models which take into account many biological mechanisms of tumor growth have too many parameters and cannot be calibrated on clinical cases. On the contrary, too simple models are not able to precisely predict tumor evolution for each patient. The diversity of data acquired by clinicians is a source of information that can make model estimations more precise. Through two different projets, we integrated data in the modeling process in order to extract more information from it. In the first part, clinical imaging and biopsy data are combined with machine learning methods. Our aim is to distinguish fast recurrent patients from slow ones. Results show that the obtained stratification is more efficient than the stratification used by cliniciens. It could help physicians to adapt treatment in a patient-specific way. In the second part, data is used to correct a simple tumor growth model. Even though this model is efficient to predict the volume of a tumor, its simplicity prevents it from accounting for shape evolution. Yet, an estimation of the tumor shape enables clinician to better plan surgery. Data assimilation methods aim at adapting the model and rebuilding the tumor environment which is responsible for these shape changes. The prediction of the growth of brain metastases is then more accurate.

Neste trabalho é apresentado um esquema de assimilação de dados a ser realizado com o Modelo Oceânico de Coordenadas Híbridas HYCOM ao largo da costa sudeste brasileira. O HYCOM utiliza 3 diferentes coordenadas verticais, a saber: coordenada-z na camada de mistura, coordenada isopicnal no oceano profundo estratificado e coordenada sigma-z nas regiões mais rasas e costeiras. Entretanto, como os perfis verticais das principais variáveis oceânicas, como temperatura, salinidade e densidade, são observados e disponibilizados em coordenadas-z, a assimilação desses dados não é tão trivial. Por esse motivo, uma técnica de transformação de coordenadas verticais de isopicnal para z é aqui proposta como uma alternativa para a realização da assimilação de dados no HYCOM. Essa técnica utiliza multiplicadores de Lagrange juntamente com um processo de otimização que garante a conservação do fluxo de massa barotrópico. A técnica de transformação é aplicada juntamente com o método de assimilação de dados proposto por Ezer & Mellor (1997). Esse método utiliza interpolação estatística e correlações, calculadas a priori com resultados do modelo, entre dados de superfície - temperatura (TSM) e /ou altura (ASM) - e a estrutura de subsuperfície de temperatura e densidade potenciais. Com base nos experimentos numéricos realizados, pode-se verificar que o esquema de assimilação de dados foi capaz de reproduzir eficientemente a circulação oceânica do domínio proposto e com os melhores resultados quando utilizando conjuntamente ASM e TSM nas correlações. / The present work presents a data assimilation scheme customized to work with the Hybrid Coordinate Ocean Model (HYCOM) for the Southeastern Brazilian Bights. HYCOM uses hybrid vertical coordinates, i.e., it uses z coordinates in the mixed layer, isopycnal coordinates in the deep ocean and sigma-z coordinates in the continental shelf. However, since vertical profiles of the main ocean variables, like temperature, density and salinity, are observed in z -coordinates, the assimilation of these data into HYCOM is not trivial. For this reason, a technique to transform vertical profiles from isopycnal coordinates to z -coordinates is here proposed as an alternative to realize data assimilation in HYCOM. This technique uses Lagrangian multipliers with a optmization process that guarantees the conservation of the barotropic mass ux. The technique of transformation is applied with the data assimilation method proposed by Ezer & Mellor (1997). The method uses statistical interpolation and correlations, a priori calculated with the models output, between the sea surface data - temperature (SST) and/or height (SSH) - and subsurface potential temperature and density structures. Numerical experiments showed that the data assimilation scheme is able to reproduce eficiently the local ocean circulation. The best performance scheme included the correlation with both SST and SSH.

abstract: This thesis outlines the development of a vector retrieval technique, based on data assimilation, for a coherent Doppler LIDAR (Light Detection and Ranging). A detailed analysis of the Optimal Interpolation (OI) technique for vector retrieval is presented. Through several modifications to the OI technique, it is shown that the modified technique results in significant improvement in velocity retrieval accuracy. These modifications include changes to innovation covariance portioning, covariance binning, and analysis increment calculation. It is observed that the modified technique is able to make retrievals with better accuracy, preserves local information better, and compares well with tower measurements. In order to study the error of representativeness and vector retrieval error, a lidar simulator was constructed. Using the lidar simulator a thorough sensitivity analysis of the lidar measurement process and vector retrieval is carried out. The error of representativeness as a function of scales of motion and sensitivity of vector retrieval to look angle is quantified. Using the modified OI technique, study of nocturnal flow in Owens' Valley, CA was carried out to identify and understand uncharacteristic events on the night of March 27th 2006. Observations from 1030 UTC to 1230 UTC (0230 hr local time to 0430 hr local time) on March 27 2006 are presented. Lidar observations show complex and uncharacteristic flows such as sudden bursts of westerly cross-valley wind mixing with the dominant up-valley wind. Model results from Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) and other in-situ instrumentations are used to corroborate and complement these observations. The modified OI technique is used to identify uncharacteristic and extreme flow events at a wind development site. Estimates of turbulence and shear from this technique are compared to tower measurements. A formulation for equivalent wind speed in the presence of variations in wind speed and direction, combined with shear is developed and used to determine wind energy content in presence of turbulence. / Dissertation/Thesis / Ph.D. Mechanical Engineering 2013

Better understanding and prediction of tree growth is important because of the many ecosystem services provided by forests and the uncertainty surrounding how forests will respond to anthropogenic climate change. With the ultimate goal of improving models of forest dynamics, here we construct a statistical model that combines complementary data sources, tree-ring and forest inventory data. A Bayesian hierarchical model was used to gain inference on the effects of many factors on tree growth-individual tree size, climate, biophysical conditions, stand-level competitive environment, tree-level canopy status, and forest management treatments-using both diameter at breast height (dbh) and tree-ring data. The model consists of two multiple regression models, one each for the two data sources, linked via a constant of proportionality between coefficients that are found in parallel in the two regressions. This model was applied to a data set of similar to 130 increment cores and similar to 500 repeat measurements of dbh at a single site in the Jemez Mountains of north-central New Mexico, USA. The tree-ring data serve as the only source of information on how annual growth responds to climate variation, whereas both data types inform non-climatic effects on growth. Inferences from the model included positive effects on growth of seasonal precipitation, wetness index, and height ratio, and negative effects of dbh, seasonal temperature, southerly aspect and radiation, and plot basal area. Climatic effects inferred by the model were confirmed by a den-droclimatic analysis. Combining the two data sources substantially reduced uncertainty about non-climate fixed effects on radial increments. This demonstrates that forest inventory data measured on many trees, combined with tree-ring data developed for a small number of trees, can be used to quantify and parse multiple influences on absolute tree growth. We highlight the kinds of research questions that can be addressed by combining the high-resolution information on climate effects contained in tree rings with the rich tree-and stand-level information found in forest inventories, including projection of tree growth under future climate scenarios, carbon accounting, and investigation of management actions aimed at increasing forest resilience.

La prévision des crues et des inondations reste aujourd’hui un défi pour anticiper et assurer la sécurité des biens et des personnes. En France, le SCHAPI, qui dépend du MEDDE, assure ce rôle. Les niveaux et les débits d’un cours d’eau dépendent étroitement des interactions à différentes échelles entre les précipitations, les caractéristiques géométriques du cours d’eau et les propriétés topographiques, géologiques et pédologiques du bassin versant. Les modèles hydrauliques, utilisés dans le cadre de la prévision des crues, sont entachés d’incertitudes qu’il est nécessaire de quantifier et de corriger afin de mieux anticiper l’évolution hydrodynamique du cours d’eau en temps réel. L’objectif de ces travaux de thèse est d’améliorer les prévisions de hauteurs d’eau et de débits, sur le bassin de la Marne, issues des modèles hydrauliques utilisés dans le cadre opérationnel de la prévision des crues à partir de méthodes d’assimilation de données. Ces prévisions reposent sur une modélisation mono-dimensionnelle (1D) de l’hydrodynamique du cours d’eau à partir du code hydraulique 1D Mascaret basé sur la résolution des équations de Saint-Venant, enrichie par une méthode d’assimilation de données in situ utilisant un Filtre de Kalman Étendu (EKF). Ce mémoire de thèse s’articule en cinq chapitres, trois dédiés à la recherche et les deux derniers à l’application opérationnelle. Le chapitre 1 présente les données et les outils utilisés pour caractériser le risque inondation dans le cadre de la prévision des crues, ainsi que les modèles hydrauliques Marne Amont Global (MAG) et Marne Moyenne (MM), sujets d’application des méthodes d’assimilation de données développées dans cette étude. Le chapitre 2 est dédié à la méthodologie : il traite des différentes sources d’incertitudes liées à la modélisation hydraulique et présente les approches d’assimilation de données de type EKF appliquées dans cette étude à travers la maquette DAMP pour les réduire. Dans le chapitre 3, cette approche est appliquée aux modèles MAG et MM en mode réanalyse pour un ensemble de crues ayant touché le bassin de la Marne par le passé. Deux publications ont été insérées dans ce chapitre "étude". Dans le chapitre 4, les corrections appliquées dans le chapitre 3, sont validées à partir du rejeu de la crue de 1983 en condition opérationnelle avec le modèle MM. La quantification des incertitudes de prévision et la réalisation de cartes de zones inondées potentielles y sont aussi abordées. L’application de ces méthodes d’assimilation de données pour les modèles MAG et MM en opérationnel au SCHAPI au niveau national et au SPC SAMA au niveau local est présentée dans le chapitre 5. Cette thèse s’inscrit dans un contexte collaboratif où chacun apporte son expertise : la modélisation hydraulique pour le LNHE, les méthodes numériques pour le CERFACS et la prévision opérationnelle pour le SCHAPI. L’ensemble de ces travaux de thèse a permis de démontrer les bénéfices et la complémentarité de l’estimation des paramètres et de l’état hydraulique par assimilation de données sur les hauteurs d’eau et les débits prévus par un modèle hydraulique 1D, ce qui constitue un enjeu d’importance pour l’anticipation du risque hydrologique. Ces méthodes ont été intégrées dans la chaîne opérationnelle de prévision du SCHAPI et du SPC SAMA. / Flood forecasting remains a challenge to anticipate and insure security of people. In France, the SCHAPI, wich depends on the MEDDE, takes this function. Water levels and discharges are highly dependent on interactions at different scales between rainfall, geometric characteristics of rivers and topographic, geological and soil properties of the watershed. Hydraulic models, used in the context of flood forecasting, are tainted by uncertainties which necessist to be quantified and corrected in order to better anticipate flow evolution in real time. The work carried out for this PhD thesis aims to improve water level and discharge forecasts on the Marne watershed, from hydraulic models used in the operational framework of flood forecasting using data assimilation methods. These forecasts come from a mono-dimensional (1D) hydraulic model Mascaret based on the resolution of Saint-Venant equations, improved by data assimilation methods using an Extended Kalman Filter (EKF). This thesis consists of five chapters, three dedicated to research and the two last to the operational application. The first presents data, tools and methods used to characterize the flood risk in the context of flood forecasting, as well as the Marne Amont Global (MAG) and Marne Moyenne (MM) models, subjects of application of data assimilation methods developed in this study. The second chapter covers hydraulic model uncertainties and data assimilation methodology (Kalman filter) applied in this thesis through DAMP in order to reduce them. In the third chapter, this approach is applied to the MAG and MM models for different flood events. In the fourth chapter, the April 1983 flood event allows to validate the corrections applied in the previous chapter for the MM model in an operational context. The uncertainties evaluations and the mapping of potential flooded zones are also reported. The real-time application of these data assimilation methods for MAG and MM models by SCHAPI and SPC SAMA is presented in the fifth chapter. This thesis takes place in a collaborative work where each member brings his own expertise : the hydraulic modeling for LNHE, the numeric methods for the CERFACS and operational forecasting for the SCHAPI. This thesis shows the benefits and complementarity of the evaluation of parameters and hydraulic state using data assimilation on water levels and discharges forcasted by a 1D hydraulic model, which is an important issue for the anticipation of hydrologic risk. These methods have already been integrated to the operational chain of flood forecasting of the SCHAPI and the SPC SAMA.

Understanding changes in the burden and growth rate of atmospheric methane (CH4) has been the focus of several recent studies but still lacks scientific consensus. Here we investigate the role of decreasing anthropogenic carbon monoxide (CO) emissions since 2002 on hydroxyl radical (OH) sinks and tropospheric CH4 loss. We quantify this impact by contrasting two model simulations for 2002-2013: (1) a Measurement of the Pollution in the Troposphere (MOPITT) CO reanalysis and (2) a Control-Run without CO assimilation. These simulations are performed with the Community Atmosphere Model with Chemistry of the Community Earth System Model fully coupled chemistry climate model with prescribed CH4 surface concentrations. The assimilation of MOPITT observations constrains the global CO burden, which significantly decreased over this period by similar to 20%. We find that this decrease results to (a) increase in CO chemical production, (b) higher CH4 oxidation by OH, and (c) similar to 8% shorter CH4 lifetime. We elucidate this coupling by a surrogate mechanism for CO-OH-CH4 that is quantified from the full chemistry simulations.

Numerical models often fail to accurately simulate and forecast a hydrological state in operation due to its inherent uncertainties. Data Assimilation (DA) is a promising technology that uses real-time observations to modify a model's parameters and internal variables to make it more representative of the actual state of the system it describes. In this thesis, hydrological DA is first reviewed from the perspective of its objective, scope, applications and the challenges it faces. Special attention is then given to nonlinear Kalman filters such as the Extended Kalman Filter (EKF). Based on a review of the existing studies, it is found that the potential of EKF has not been fully exploited. The Soil and Water Assessment Tool (SWAT) is a semi-distributed rainfall-runoff model that is widely used in agricultural water management and flood forecasting. However, studies of hydrological DA that are based on distributed models are relatively rare because hydrological DA is still in its infancy, with many issues to be resolved, and linear statistical models and lumped rainfall-runoff models are often used for the sake of simplicity. This study aims to fill this gap by assimilating streamflow and surface soil moisture observations into the SWAT model to improve its state simulation and forecasting capability. Unless specifically defined, all ‘forecasts’ in Italic font are based on the assumption of a perfect knowledge of the meteorological forecast. EKF is chosen as the DA method for its solid theoretical basis and parsimonious implementation procedures. Given the large number of parameters and storage variables in SWAT, only the watershed scale variables are included in the state vector, and the Hydrological Response Unit (HRU) scale variables are updated with the a posteriori/a priori ratio of their watershed scale counterparts. The Jacobian matrix is calculated numerically by perturbing the state variables. Two case studies are carried out with real observation data in order to verify the effectiveness of EKF assimilation. The upstream section of the Senegal River (above Bakel station) in western Africa is chosen for the streamflow assimilation, and the USDA ARS Little Washita experimental watershed is chosen to examine surface soil moisture assimilation. In the case of streamflow assimilation, a spinoff study is conducted to compare EKF state-parameter assimilation with a linear autoregressive (AR) output assimilation to improve SWAT’s flood forecasting capability. The influence of precipitation forecast uncertainty on the effectiveness of EKF assimilation is discussed in the context of surface soil moisture assimilation. In streamflow assimilation, EKF was found to be effective mostly in the wet season due to the weak connection between runoff, soil moisture and the curve number (CN2) in dry seasons. Both soil moisture and CN2 were significantly updated in the wet season despite having opposite update patterns. The flood forecast is moderately improved for up to seven days, especially in the flood period by applying the EKF subsequent open loop (EKFsOL) scheme. The forecast is further improved with a newly designed quasi-error update scheme. Comparison between EKF and AR output assimilation in flood forecasting reveals that while both methods can improve forecast accuracy, their performance is influenced by the hydrological regime of the particular year. EKF outperformed the AR model in dry years, while AR outperformed the EKF in wet years. Compared to AR, EKF is more robust and less sensitive to the length of the forecast lead time. A combined EKF-AR method provides satisfying results in both dry and wet years. The assimilation of surface soil moisture is proved effective in improving the full profile soil moisture and streamflow estimate. The setting of state and observation vector has a great impact on the assimilation results. The state vector with streamflow and all-layer soil moisture outperforms other, more complicated state vectors, including those augmented with intermediate variables and model parameters. The joint assimilation of surface soil moisture and streamflow observation provides a much better estimate of soil moisture compared to assimilating the streamflow only. The updated SWAT model is sufficiently robust to issue improved forecasts of soil moisture and streamflow after the assimilation is ‘unplugged’. The error quantification is found to be critical to the performance of EKF assimilation. Nevertheless, the application of an adaptive EKF shows no advantages over using the trial and error method in determining time-invariant model errors. The robustness of EKF assimilation is further verified by explicitly perturbing the precipitation ‘forecast’ in the EKF subsequent forecasts. The open loop model without previous EKF update is more vulnerable to erroneous precipitation estimates. Compared to streamflow forecasting, soil moisture forecasting is found to be more resilient to erroneous precipitation input.

Cette thèse s’intéresse à l’analyse de séries temporelles d’images satellites à moyenne résolution spatiale. L’intérêt principal de telles données est leur haute répétitivité qui autorise des analyses de l’usage des sols. Cependant, deux problèmes principaux subsistent avec de telles données. En premier lieu, en raison de la couverture nuageuse, des mauvaises conditions d’acquisition, ..., ces données sont souvent très bruitées. Deuxièmement, les pixels associés à la moyenne résolution spatiale sont souvent “mixtes” dans la mesure où leur réponse spectrale est une combinaison de la réponse de plusieurs éléments “purs”. Ces deux problèmes sont abordés dans cette thèse. Premièrement, nous proposons une technique d’assimilation de données capable de recouvrer des séries temporelles cohérentes de LAI (Leaf Area Index) à partir de séquences d’images MODIS bruitées. Pour cela, le modèle de croissance de plantes GreenLab estutilisé. En second lieu, nous proposons une technique originale de démélangeage, qui s’appuie notamment sur des noyaux “élastiques” capables de gérer les spécificités des séries temporelles (séries de taille différentes, décalées dans le temps, ...)Les résultats expérimentaux, sur des données synthétiques et réelles, montrent de bonnes performances des méthodologies proposées. / This PhD dissertation is concerned with time series analysis for medium spatial resolution (MSR) remote sensing images. The main advantage of MSR data is their high temporal rate which allows to monitor land use. However, two main problems arise with such data. First, because of cloud coverage and bad acquisition conditions, the resulting time series are often corrupted and not directly exploitable. Secondly, pixels in medium spatial resolution images are often “mixed” in the sense that the spectral response is a combination of the response of “pure” elements.These two problems are addressed in this PhD. First, we propose a data assimilation technique able to recover consistent time series of Leaf Area Index from corrupted MODIS sequences. To this end, a plant growth model, namely GreenLab, is used as a dynamical constraint. Second, we propose a new and efficient unmixing technique for time series. It is in particular based on the use of “elastic” kernels able to properly compare time series shifted in time or of various lengths.Experimental results are shown both on synthetic and real data and demonstrate the efficiency of the proposed methodologies.

We develop a stochastic parametrization, based on a 'simple' deterministic model for the dynamics of steady longshore currents, that produces ensembles that are statistically consistent with field observations of these currents. Unlike deterministic models, stochastic parameterization incorporates randomness and hence can only match the observations in a statistical sense. Unlike statistical emulators, in which the model is tuned to the statistical structure of the observation, stochastic parametrization are not directly tuned to match the statistics of the observations. Rather, stochastic parameterization combines deterministic, i.e physics based models with stochastic models for the "missing physics" to create hybrid models, that are stochastic, but yet can be used for making predictions, especially in the context of data assimilation. We introduce a novel measure of the utility of stochastic models of complex processes, that we call consistency of sensitivity. A model with poor consistency of sensitivity requires a great deal of tuning of parameters and has a very narrow range of realistic parameters leading to outcomes consistent with a reasonable spectrum of physical outcomes. We apply this metric to our stochastic parametrization and show that, the loss of certainty inherent in model due to its stochastic nature is offset by the model's resulting consistency of sensitivity. In particular, the stochastic model still retains the forward sensitivity of the deterministic model and hence respects important structural/physical constraints, yet has a broader range of parameters capable of producing outcomes consistent with the field data used in evaluating the model. This leads to an expanded range of model applicability. We show, in the context of data assimilation, the stochastic parametrization of longshore currents achieves good results in capturing the statistics of observation that were not used in tuning the model.