On the use of radar and aircraft data in Ensemble Data Assimilation of convection for non-hydrostatic numerical weather prediction

The application of Ensemble Data Assimilation (DA) is a method to produce initial states (so-called analyses) for numerical forecasts of thunderstorms. By the means of radar observations of reflectivity and radial winds, the complex inner structure of a convective cell or a mesoscale convective system can be analyzed. At the same time, thunderstorms are embedded in their surrounding air mass and interact dynamically with this convective environment. This thesis aims to answer some questions which link the observational aspect of convective dynamics to the forecasting aspects of the numerical model, and to the practical and intrinsic predictability when applying convective scale DA. Since the atmosphere is a chaotic system, errors in the initial states grow during the forecast and thus limit the predictability. Errors that are small in amplitude and spatial scale intensify with a faster rate than large errors, so that an initial state with relatively large errors may produce, after a few hours, an equivalently good thunderstorm forecast compared to an initial state with smaller errors. To test this hypothesis, simulated radar observations of wind and reflectivity were assimilated into the convection-permitting COSMO model with a resolution of 2~km, by the means of an Ensemble Kalman Filter (EnKF), and under the assumption of a perfect forecast model. The spatial and temporal resolution of the observations was coarsened from 2 to 8~km and from 5 to 20~minutes, and their spatial influence was increased from 8 to 32~km in order to produce analyses that contained less detailed information about the observed storms. After three hours, the resulting forecasts were comparable to such experiments in which the observations had been assimilated with the highest resolution and a close fit in the analysis had been reached. Thus, a recommendable setting for the assimilation of real radar data was found that respects the limited predictability of convection. Due to the input of high resolution data, noise was introduced into the model that disturbed the dynamics and triggered spurious convection in wrong locations. The study showed that this noise consisted of internal gravity waves which caused an over-abundance of variability in the vertical velocity field, both in the storm vicinity and in the environment that was at least 32~km distant to the storms. This noise disturbed the internal dynamics of the convective systems in such a way that the emergence of new updrafts became decoupled from the underlying pools of cold air. This was quantified by applying a spatio-temporal correlation method. In addition to the DA-aspects of mainly storm-interior dynamics, aircraft observations in clear air regions were assimilated into the pre-operational Kilometre-scale Ensemble Data Assimilation (KENDA) system of the German Weather Service (DWD) that couples an EnKF to the COSMO-DE model over Germany with a resolution of 2.8~km. These observations are collected by Mode-S air traffic control radars and contain measurements of wind and temperature along the flight tracks of commercial aircrafts. Their data density is ten times larger than the previously assimilated Aircraft Meteorological Data Relay (AMDAR) observations. Their assimilation was successfully implemented and it was shown that forecasts of three hours lead time benefit from the additional data by exhibiting a smaller error in the tropospheric wind and temperature profiles which, together with humidity, define the convective environment. Conclusions were drawn on how KENDA reacts to the large increase of observation data in terms of ensemble spread and other EnKF parameters, and recommendations on the operational use of Mode-S data were derived. The point-like updraft of a convective storm is coupled to the environment by a divergent circulation that has a wider scale than the core. By assimilating real radar observations in the newly implemented framework of the nested COSMO-MUC-KENDA system, the influence of reflectivity and radial wind observations was investigated. A conceptual model was introduced that relates the scales that are influenced by the assimilation of these two observation types. By applying the noise measures that were developed earlier in the thesis, it was shown that the assimilation of radial wind observations has a damping effect on the noise that is caused by the DA. Reflectivity observations on the other hand are helpful to improve the storm positions for the first tens of minutes into the forecast. It was shown that the EnKF assimilation of radar data produces better forecasts and less noise than the previously used nudging scheme. Combining the assimilation Mode-S data and radar observations with the methods that were developed in this thesis is a promising way to improve analyses and forecasts of severe and damaging convection with the KENDA system.Anfangszustände (sog. Analysen) für die numerische Wettervorhersage von Gewittern und mesoskaligen konvektiven Systemen können mithilfe von Ensemble-Datenassimilation (DA) generiert werden. Reflektivitäts- und Radialwindbeobachtungen von Wetterradaren sind hierzu hilfreich, da sie die Gewitter zeitlich und räumlich hochaufgelöst beobachten. Gewitter besitzen eine komplexe innere Struktur und sind gleichzeitig eingebettet in die umgebende Atmosphäre, die durch das Vertikalprofil von Temperatur, Wind und Feuchte beschrieben wird. Die vorliegende Dissertation setzt es sich zum Ziel, Zusammenhänge zwischen beobachteter Dynamik und numerischer Modellvorhersage aufzudecken. Zudem wird die Frage bearbeitet, inwiefern die begrenzte praktische und intrinsische Vorhersagbarkeit den Anstrengungen der konvektiven DA entgegenwirkt. Im chaotischen System der Atmosphäre wachsen Fehler der Anfangszustände während der Vorhersage rasch an und begrenzen die Vorhersagbarkeit. Kleinskalige Fehler und solche mit kleiner Amplitude intensivieren sich dabei relativ gesehen schneller als größere Fehler. Dementsprechend können Gewittervorhersagen, die von Analysen mit größeren Fehlern gestartet wurden, nach einiger Zeit dieselbe Qualität besitzen wie Vorhersagen von Analysen mit kleineren Fehlern. Um dies zu testen, wurden simulierte Radardaten mithilfe eines Ensemble Kalman Filters (EnKF) und unter der Annahme eines perfekten Modells in das konvektionsauflösende COSMO-Modell assimiliert, bei einer Modellauflösung von 2~km. Die räumliche und zeitliche Auflösung der Beobachtungsdaten wurde von 2 auf 8~km und von 5 auf 20~min variiert, zusammen mit einer Vergrößerung des räumlichen Einflussradius von 8 auf 32~km. Die Vorhersagequalität auf Basis solcher groben Analysen erwies sich nach drei Stunden als vergleichbar mit Vorhersagen auf Basis von Analysen, die mit hochaufgelösten Beobachtungen produziert wurden. Dieser Zeitraum wurde als obere Grenze für die Vorhersagbarkeit in dieser Situation gewertet. Im Falle der hochaufgelösten Beobachtungen waren die Analysen mit Störungen des vertikalen Windfelds (sog. noise) behaftet, die in Form von Schwerewellen die Dynamik der beobachteten Gewitter beeinflussten. Dies führte zum Auftreten von übermäßiger Konvektion an falschen Orten (sog. spurious convection). Zur Beschreibung dieses noise wurde eine Raum-Zeit-Korrelation von verschiedenen Modellfeldern berechnet. Hierbei zeigte sich eine Entkopplung der neu auftretenden spurious convection von den Böenfronten der beobachteten Gewitter, und das gleichzeitige Auftreten von übermäßiger Variabilität des vertikalen Windfelds im nahen und fernen Umfeld der beobachteten Konvektion. Zur Verbesserung des atmosphärischen Vertikalprofils wurden reale Flugzeugbeobachtungen in das vom Deutschen Wetterdienst (DWD) entwickelte, prä-operationelle System Kilometre-scale Ensemble Data Assimilation KENDA assimiliert, welches das deutsche COSMO-DE-Modell mit 2.8~km Auflösung mit einem EnKF verbindet. Diese Flugzeugbeobachtungen stammen aus dem Mode-S-System der Flugsicherung und enthalten Beobachtungen von Wind und Temperatur, wobei die Mode-S-Daten eine zehnmal höhere Datendichte aufweisen als die bisher verwendeten Daten des Systems AMDAR (Aircraft Meteorological Data Relay). Nach Implementierung der Mode-S-Assimilation konnte eine Fehlerverringerung in dreistündigen Vorhersagen des Vertikalprofils erreicht werden, wodurch auch eine bessere Gewittervorhersage erwartbar ist. Um einer operationellen Nutzung der Mode-S-Daten den Weg zu bereiten, wurden die Auswirkungen der stark erhöhten Datenmenge auf Parameter des KENDA-Systems wie Lokalisierung und ensemble spread untersucht. Der Aufwindstrom von Gewitterzellen bewirkt eine divergente und konvergente Horizontalzirkulation in den umgebenden Bereichen, die den Kern des Gewitters umgibt. Um den Einfluss der Radarbeobachtungstypen der Reflektivität und des Radialwinds auf Aufwindstrom und umgebende Zirkulation zu untersuchen, wurden diese in das COSMO-MUC-KENDA-System assimiliert. Dieses wurde im Rahmen dieser Dissertation als Untersystem zu COSMO-DE-KENDA implementiert. Ein konzeptuelles Modell wurde aufgestellt, welches die Einflussskalen der zwei Beobachtungstypen abschätzt. Durch Anwendung der zuvor entwickelten Maße für Modell-noise konnte gezeigt werden, dass eine Assimilation von Radialwinden die Störungen im Vertikalwindfeld dämpft. Reflektivitätsbeobachtungen hingegen verbesserten die Analyse der Gewitterpositionen. Zudem wurde gezeigt, dass die EnKF-Assimilation von Radardaten bessere Vorhersagen liefert und weniger Störungen verursacht als das zuvor genutzte Nudging-Verfahren. Die in dieser Dissertation entwickelte Kombination von Radar- und Flugzeugbeobachtungen stellt einen wichtigen Beitrag zur Verbesserung der Gewittervorhersage mithilfe des KENDA-Systems dar

A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives

Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored

Data Assimilation Fundamentals

This open-access textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts from Bayes theorem and gradually introduces the assumptions and approximations needed to arrive at today's popular data-assimilation methods. This strategy is the opposite of most textbooks and reviews on data assimilation that typically take a bottom-up approach to derive a particular assimilation method. E.g., the derivation of the Kalman Filter from control theory and the derivation of the ensemble Kalman Filter as a low-rank approximation of the standard Kalman Filter. The bottom-up approach derives the assimilation methods from different mathematical principles, making it difficult to compare them. Thus, it is unclear which assumptions are made to derive an assimilation method and sometimes even which problem it aspires to solve. The book's top-down approach allows categorizing data-assimilation methods based on the approximations used. This approach enables the user to choose the most suitable method for a particular problem or application. Have you ever wondered about the difference between the ensemble 4DVar and the "ensemble randomized likelihood" (EnRML) methods? Do you know the differences between the ensemble smoother and the ensemble-Kalman smoother? Would you like to understand how a particle flow is related to a particle filter? In this book, we will provide clear answers to several such questions. The book provides the basis for an advanced course in data assimilation. It focuses on the unified derivation of the methods and illustrates their properties on multiple examples. It is suitable for graduate students, post-docs, scientists, and practitioners working in data assimilation

The path inference filter: model-based low-latency map matching of probe vehicle data

We consider the problem of reconstructing vehicle trajectories from sparse sequences of GPS points, for which the sampling interval is between 10 seconds and 2 minutes. We introduce a new class of algorithms, called altogether path inference filter (PIF), that maps GPS data in real time, for a variety of trade-offs and scenarios, and with a high throughput. Numerous prior approaches in map-matching can be shown to be special cases of the path inference filter presented in this article. We present an efficient procedure for automatically training the filter on new data, with or without ground truth observations. The framework is evaluated on a large San Francisco taxi dataset and is shown to improve upon the current state of the art. This filter also provides insights about driving patterns of drivers. The path inference filter has been deployed at an industrial scale inside the Mobile Millennium traffic information system, and is used to map fleets of data in San Francisco, Sacramento, Stockholm and Porto.Comment: Preprint, 23 pages and 23 figure

Data Assimilation Fundamentals

This open-access textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts from Bayes theorem and gradually introduces the assumptions and approximations needed to arrive at today's popular data-assimilation methods. This strategy is the opposite of most textbooks and reviews on data assimilation that typically take a bottom-up approach to derive a particular assimilation method. E.g., the derivation of the Kalman Filter from control theory and the derivation of the ensemble Kalman Filter as a low-rank approximation of the standard Kalman Filter. The bottom-up approach derives the assimilation methods from different mathematical principles, making it difficult to compare them. Thus, it is unclear which assumptions are made to derive an assimilation method and sometimes even which problem it aspires to solve. The book's top-down approach allows categorizing data-assimilation methods based on the approximations used. This approach enables the user to choose the most suitable method for a particular problem or application. Have you ever wondered about the difference between the ensemble 4DVar and the "ensemble randomized likelihood" (EnRML) methods? Do you know the differences between the ensemble smoother and the ensemble-Kalman smoother? Would you like to understand how a particle flow is related to a particle filter? In this book, we will provide clear answers to several such questions. The book provides the basis for an advanced course in data assimilation. It focuses on the unified derivation of the methods and illustrates their properties on multiple examples. It is suitable for graduate students, post-docs, scientists, and practitioners working in data assimilation

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Linear Estimation in Interconnected Sensor Systems with Information Constraints

A ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed

Linear Estimation in Interconnected Sensor Systems with Information Constraints

A ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed

Intelligent flight control systems

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms