Accurate forecasts of tropical cyclone surface wind fields are essential for decisions involving evacuation preparation and damage potential. Towards addressing these actions, a comparison of the CFAN tropical cyclone surface wind field model with the H*Wind wind field reanalyzes is done to assess the accuracy of the CFAN algorithm and to determine potential limitations of its use. 16 tropical cyclones were assessed through correlation coefficient, mean bias, and root mean square error. The resolution of initial conditions to be ingested into the model was also analyzed, along with storm type and whether or not wind shear was a limiting factor. Results suggest that the CFAN wind model accurately predicts the H*Wind analyses in most regions of the TC. The center of circulation has the highest error due to the CFAN wind model treating the center of circulation as a point rather than having finite lateral extent. Results from the sensitivity analysis based on input resolution show that the minimum input resolution for the CFAN wind model to produce fine spatial resolutions with high fidelity is 0.25°. It is shown that the reproductions of weaker tropical cyclones have lower accuracy due to wind field asymmetries within these systems, while stronger TCs are better reproduced, as these systems are usually better organized. Finally, through the wind shear analysis, it is shown that the accuracy of reconstruction is not dependent on the magnitude of vertical wind shear.

Predictive anisotropy is a phenomenon referring to unequal predictability of surface wind components in different directions. This study addresses the question of whether predictive anisotropy resulting from statistical prediction is influenced by physical factors or by types of regression methods (linear vs nonlinear) used to construct the statistical prediction. A systematic study of statistical predictability of surface wind components at 2109 land stations across the globe is carried out. The results show that predictive anisotropy is a common characteristic for both linear and nonlinear statistical prediction, which suggests that the type of regression method is not a major influential factor. Both strong predictive anisotropy and poor predictability are more likely to be associated with wind components characterized by relatively weak and non-Gaussian variability and in areas characterized by surface heterogeneity. An idealized mathematical model is developed separating predictive signal and noise between large-scale (predictable) and local (unpredictable) contributions to the variability of surface wind, such that small signal-to-noise ratio (SNR) corresponds to low and anisotropic predictability associated with non-Gaussian local variability. The comparison of observed and simulated statistical predictability by Regional Climate models (RCM) and reanalysis in the Northern Hemisphere indicates that small-scale processes that cannot be captured well by RCMs contribute to poor predictability and strong predictive anisotropy in observations. A second idealized mathematical model shows that spatial variability in specifically the minimum directional predictability, resulting from local processes, is the major contributor to predictive anisotropy. / Graduate

This dissertation presents new methods for detecting dynamic and thermodynamic characteristics of Arctic sea ice using radar remote sensing. A new technique for sea ice motion detection from sequential satellite synthetic aperture radar (SAR) images was developed and thoroughly validated. The accuracy of the system is 0.43 km obtained from a comparison between SAR-derived ice motion vectors and in-situ sea ice beacon trajectories. For the first time, we evaluated ice motion tracking results derived from co-polarization (HH) and cross-polarization (HV) channels of RADARSAT-2 ScanSAR imagery and formulated a condition where the HV channel is more reliable than the HH channel for ice motion tracking. Sea ice motion is substantially controlled by surface winds. Two new models for ocean surface wind speed retrieval from C-band SAR data have been developed and validated based on a large body of statistics on buoy observations collocated and coincided with RADARSAT-1 and -2 ScanSAR images. The proposed models without wind direction input demonstrated a better accuracy than conventionally used algorithms. As a combination of the developed methods we designed a wind speed-ice motion product which can be a useful tool for studying sea ice dynamics processes in the marginal ice zone. To effectively asses the thermodynamic properties of sea ice advanced tools for modeling electromagnetic (EM) wave scattering from rough natural surfaces are required. In this dissertation we present a new analytical formulation for EM wave scattering from rough boundaries interfacing inhomogeneous media based on the first-order approximation of the small perturbation method. Available solutions in the literature represent special cases of our general solution. The developed scattering theory was applied to experimental data collected at three stations (with different snow thicknesses) in the Beaufort Sea from the research icebreaker Amundsen during the Circumpolar Flaw Lead system study. Good agreement between the model and experimental data were observed for all three case studies. Both model and experimental radar backscatter coefficients were considerably higher for thin snow cover (4 cm) compared to the thick snow cover case (16 cm). Our findings suggest that, winter snow thickness retrieval may be possible from radar observations under particular scattering conditions.

<div> <div> <p>Global Navigation Satellite System Reflectometry (GNSS-R) has presented a great potential as an important approach for ocean remote sensing. Numerous studies have demonstrated that the shape of a code-correlation waveform of forward-scattered Global Positioning System (GPS) signals may be used to measure ocean surface roughness and related geophysical parameters such as wind speed. Recent experiments have extended the reflectometry technique to transmissions from communication satellites. Due to the high power and frequencies of these signals, they are more sensitive to smaller scale ocean surface features, which makes communication satellites a promising signal of opportunity (SoOp) for ocean remote sensing. Recent advancements in fundamental physics are represented by the new scattering model and bistatic radar function developed by Voronovich and Zavorotny based on the SSA (Small Slope Approximation). This new model allows the partially coherent scattering in low wind conditions to be correctly described, which overcomes the limitations of diffuse scattering inherited in the conventional KA-GO (Kirchhoff Approximation-Geometric Optics) model. Furthermore, exploration and practice using spaceborne platforms have become a primary research focus, which is highlighted by the launch of CYGNSS (Cyclone Global Navigation Satellite System) in 2016. CYGNSS is a NASA (National Aeronautics and Space Administration) Earth Venture Mission consisting of an 8 micro-satellite constellation of GNSS-R instruments designed to observe tropical cyclones.</p><p>However, in spite of the significant achievements made in the past 10 years, there are still a variety of challenges to be addressed currently in the ocean reflectometry field. To begin with, the airborne demonstration experiments conducted previously for S-band reflectometry provided neither sufficient amount of data nor the desired scenarios to assess high wind retrieval performance of S-band signals. The current L-band empirical model function theoretically does not also apply to S-band reflectometry. With respect to scattering models, there have been no results of actual data processing so far to verify the performance of the SSA model, especially on low wind retrievals. Lastly, the conventional model fitting methods for ocean wind retrievals were proposed for airborne missions, and new approaches will need to be developed to satisfy the requirement of spaceborne systems.<br></p><p>The research described in this thesis is mainly focused on the development, application and evaluation of new models and algorithms for ocean wind remote sensing. The first part of the thesis studies the extension of reflectometry methods to the general class of SoOps. The airborne reception of commercial satellite S-band transmissions is demonstrated under both low and high wind speed conditions. As part of this effort, a new S-band geophysical model function (GMF) is developed for ocean wind remote sensing using S-band data collected in the 2014 NOAA (National Oceanic and Atmospheric Administration) hurricane campaign. The second part introduces a dual polarization L- and S-band reflectometry experiment, performed in collaboration with Naval Research Lab (NRL), to retrieve and analyze surface winds and compare the results with CYGNSS satellite retrievals and NOAA data buoy measurements. The problems associated with low wind speed retrieval arising from near specular surface reflections are studied. Results have shown improved wind speed retrieval accuracy using bistatic radar cross section (BRCS) modeled by the SSA when compared with KA-GO, in the cases of low to medium diffuse scattering. The last part focuses on the contributions to the NASA-funded spaceborne CYGNSS project. It shows that the accuracy of CYGNSS ocean wind retrieval is improved by an Extended Kalman Filter (EKF) algorithm. Compared with the baseline observable methods, preliminary results showed promising accuracy improvement when the EKF was applied to actual CYGNSS data.<br><br></p></div></div>

Les erreurs des modèles de prévision numérique du temps (PNT) peuvent être réduites par des méthodes de post-traitement (dites d'adaptation statistique ou AS) construisant une relation statistique entre les observations et les prévisions. L'objectif de cette thèse est de construire des AS de prévisions de vent pour la France sur la grille de plusieurs modèles de PNT, pour les applications opérationnelles de Météo-France en traitant deux problèmes principaux. Construire des AS sur la grille de modèles de PNT, soit plusieurs milliers de points de grille sur la France, demande de développer des méthodes rapides pour un traitement en conditions opérationnelles. Deuxièmement, les modifications fréquentes des modèles de PNT nécessitent de mettre à jour les AS, mais l'apprentissage des AS requiert un modèle de PNT inchangé sur plusieurs années, ce qui n'est pas possible dans la majorité des cas.Une nouvelle analyse du vent moyen à 10 m a été construite sur la grille du modèle local de haute résolution (2,5 km) de Météo-France, AROME. Cette analyse se compose de deux termes: une spline fonction de la prévision la plus récente d'AROME plus une correction par une spline fonction des coordonnées du point considéré. La nouvelle analyse obtient de meilleurs scores que l'analyse existante, et présente des structures spatio-temporelles réalistes. Cette nouvelle analyse, disponible au pas horaire sur 4 ans, sert ensuite d'observation en points de grille pour construire des AS.Des AS de vent sur la France ont été construites pour ARPEGE, le modèle global de Météo-France. Un banc d'essai comparatif désigne les forêts aléatoires comme meilleure méthode. Cette AS requiert un long temps de chargement en mémoire de l'information nécessaire pour effectuer une prévision. Ce temps de chargement est divisé par 10 en entraînant les AS sur des points de grille contigü et en les élaguant au maximum. Cette optimisation ne déteriore pas les performances de prévision. Cette approche d'AS par blocs est en cours de mise en opérationnel.Une étude préalable de l'estimation du « continuous ranked probability score » (CRPS) conduit à des recommandations pour son estimation et généralise des résultats théoriques existants. Ensuite, 6 AS de 4 modèles d'ensemble de PNT de la base TIGGE sont combinées avec les modèles bruts selon plusieurs méthodes statistiques. La meilleure combinaison s'appuie sur la théorie de la prévision avec avis d'experts, qui assure de bonnes performances par rapport à une prévision de référence. Elle ajuste rapidement les poids de la combinaison, un avantage lors du changement de performance des prévisions combinées. Cette étude a soulevé des contradictions entre deux critères de choix de la meilleure méthode de combinaison : la minimisation du CRPS et la platitude des histogrammes de rang selon les tests de Jolliffe-Primo. Il est proposé de choisir un modèle en imposant d'abord la platitude des histogrammes des rangs. / Errors of numerical weather prediction (NWP) models can be reduced thanks to post-processing methods (model output statistics, MOS) that build a statistical relationship between the observations and associated forecasts. The objective of the present thesis is to build MOS for windspeed forecasts over France on the grid of several NWP models, to be applied on operations at Météo-France, while addressing the two main issues. First, building MOS on the grid of some NWP model, with thousands of grid points over France, requires to develop methods fast enough for operational delays. Second, requent updates of NWP models require updating MOS, but training MOS requires an NWP model unchanged for years, which is usually not possible.A new windspeed analysis for the 10 m windspeed has been built over the grid of Météo-France's local area, high resolution (2,5km) NWP model, AROME. The new analysis is the sum of two terms: a spline with AROME most recent forecast as input plus a correction with a spline with the location coordinates as input. The new analysis outperforms the existing analysis, while displaying realistic spatio-temporal patterns. This new analysis, now available at an hourly rate over 4, is used as a gridded observation to build MOS in the remaining of this thesis.MOS for windspeed over France have been built for ARPEGE, Météo-France's global NWP model. A test-bed designs random forests as the most efficient MOS. The loading times is reduced by a factor 10 by training random forests over block of nearby grid points and pruning them as much as possible. This time optimisation goes without reducing the forecast performances. This block MOS approach is currently being made operational.A preliminary study about the estimation of the continuous ranked probability score (CRPS) leads to recommendations to efficiently estimate it and to generalizations of existing theoretical results. Then 4 ensemble NWP models from the TIGGE database are post-processed with 6 methods and combined with the corresponding raw ensembles thanks to several statistical methods. The best combination method is based on the theory of prediction with expert advice, which ensures good forecast performances relatively to some reference forecast. This method quickly adapts its combination weighs, which constitutes an asset in case of performances changes of the combined forecasts. This part of the work highlighted contradictions between two criteria to select the best combination methods: the minimization of the CRPS and the flatness of the rank histogram according to the Jolliffe-Primo tests. It is proposed to choose a model by first imposing the flatness of the rank histogram.

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