Methodologies for transforming data to information and advancing the understanding of water resources systems towards integrated water resources management

2017 Summer.Includes bibliographical references.The majority of river basins in the world, have undergone a great deal of transformations in terms of infrastructure and water management practices in order to meet increasing water needs due to population growth and socio-economic development. Surface water and groundwater systems are interwoven with environmental and socio-economic ones. The systems' dynamic nature, their complex interlinkages and interdependencies are inducing challenges for integrated water resources management. Informed decision-making process in water resources is deriving from a systematic analysis of the available data with the utilization of tools and models, by examining viable alternatives and their associated tradeoffs under the prism of a set of prudent priorities and expert knowledge. In an era of increasing volume and variety of data about natural and anthropogenic systems, opportunities arise for further enhancing data integration in problem-solving approaches and thus support decision-making for water resources planning and management. Although there is a plethora of variables monitored in various spatial and temporal scales, particularly in the United States, in real life, for water resources applications there are rarely, if ever, perfect data. Developing more systematic procedures to integrate the available data and harness their full potential of generating information, will improve the understanding of water resources systems and assist at the same time integrated water resources management efforts. The overarching objective of this study is to develop tools and approaches to overcome data obstacles in water resources management. This required the development of methodologies that utilize a wide range of water and environmental datasets in order to transform them into reliable and valuable information, which would address unanswered questions about water systems and water management practices, contributing to implementable efforts of integrated water resources management. More specifically, the objectives of this research are targeted in three complementary topics: drought, water demand, and groundwater supply. In this regard, their unified thread is the common quest for integrated river basin management (IRBM) under changing water resources conditions. All proposed methodologies have a common area of application namely the South Platte basin, located within Colorado. The area is characterized by limited water resources with frequent drought intervals. A system's vulnerability to drought due to the different manifestations of the phenomenon (meteorological, agricultural, hydrological, socio-economic and ecological) and the plethora of factors affecting it (precipitation patterns, the supply and demand trends, the socioeconomic background etc.) necessitates an integrated approach for delineating its magnitude and spatiotemporal extent and impacts. Thus, the first objective was to develop an implementable drought management policy tool based on the standardized drought vulnerability index framework and expanding it in order to capture more of drought's multifaceted effects. This study illustrated the advantages of a more transparent data rigorous methodology, which minimizes the need for qualitative information replacing it with a more quantitative one. It is believed that such approach may convey drought information to decision makers in a holistic manner and at the same time avoid the existing practices of broken linkages and fragmentation of reported drought impacts. Secondly, a multi-scale (well, HUC-12, and county level) comparative analysis framework was developed to identify the characteristics of the emergent water demand for unconventional oil and gas development. This effort revealed the importance of local conditions in well development patterns that influence water demand, the magnitude of water consumption in local scales in comparison to other water uses, the strategies of handling flowback water, and the need for additional data, and improved data collection methods for a detailed water life-cycle analysis including the associated tradeoffs. Finally, a novel, easy to implement, and computationally low cost methodology was developed for filling gaps in groundwater level time series. The proposed framework consists of four main components, namely: groundwater level time series; data (groundwater level, recharge and pumping) from a regional physically-based groundwater flow model; autoregressive integrated moving average with external inputs modeling; and the Ensemble Smoother (ES) technique. The methodology's efficacy to predict accurately groundwater levels was tested by conducting three numerical experiments at eighteen alluvial wells. The results suggest that the framework could serve as a valuable tool in gaining further insight of alluvium aquifer dynamics by filling missing groundwater level data in an intermittent or continuous (with relative short span) fashion. Overall, it is believed that this research has important implications in water resources decision making by developing implementable frameworks which advance further the understanding of water systems and may aid in integrated river basin management efforts

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

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

The WWRP Polar Prediction Project (PPP)

Mission statement: “Promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on time scales from hours to seasonal”. Increased economic, transportation and research activities in polar regions are leading to more demands for sustained and improved availability of predictive weather and climate information to support decision-making. However, partly as a result of a strong emphasis of previous international efforts on lower and middle latitudes, many gaps in weather, sub-seasonal and seasonal forecasting in polar regions hamper reliable decision making in the Arctic, Antarctic and possibly the middle latitudes as well. In order to advance polar prediction capabilities, the WWRP Polar Prediction Project (PPP) has been established as one of three THORPEX (THe Observing System Research and Predictability EXperiment) legacy activities. The aim of PPP, a ten year endeavour (2013-2022), is to promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on hourly to seasonal time scales. In order to achieve its goals, PPP will enhance international and interdisciplinary collaboration through the development of strong linkages with related initiatives; strengthen linkages between academia, research institutions and operational forecasting centres; promote interactions and communication between research and stakeholders; and foster education and outreach. Flagship research activities of PPP include sea ice prediction, polar-lower latitude linkages and the Year of Polar Prediction (YOPP) - an intensive observational, coupled modelling, service-oriented research and educational effort in the period mid-2017 to mid-2019

Aeronautical engineering: A continuing bibliography with indexes (supplement 247)

This bibliography lists 437 reports, articles, and other documents introduced into the NASA scientific and technical information system in December, 1989. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Proceedings of the NASA Symposium on Global Wind Measurements

This Proceedings contains a collection of the papers which were presented at the Symposium and Workshop on Global Wind Measurements. The objectives and agenda for the Symposium and Workshop were decided during a planning meeting held in Washington, DC, on 5 February 1985. Invited papers were presented at the Symposium by meteorologists and leading experts in wind sensing technology from the United States and Europe on: (1) the meteorological uses and requirements for wind measurements; (2) the latest developments in wind sensing technology; and (3) the status of our understanding of the atmospheric aerosol distribution. A special session was also held on the latest development in wind sensing technology by the United States Air Force