Magister Scientiae (Biodiversity and Conservation Biology) / Coastal upwelling is one of the most important oceanographic processes relating to ecosystem function at local and global spatial scales. To better understand how changes in upwelling trends may occur in the face of ongoing anthropogenically induced climate change it is important to quantify historical trends in climatic factors responsible for enabling coastal upwelling. However, a paucity of conclusive knowledge relating to patterns concerning changes in upwelling across the world’s oceans over time makes such analyses difficult. In this study I aimed to quantify these patterns by first identifying when upwelling events occur using a novel method for predictingthe behaviours of coastal upwelling systems over time. By using remotely sensed SST data of differing resolutions as well as several wind variables I was able to identify and quantify upwelling signals at several distances away from the coastline of various upwelling systems. Using this novel method of determining upwelling, I then compared upwelling patterns within all Eastern Boundary Upwelling Systems (EBUS) over a period of 37 years, with the assumption that climate change was likely to have driven variable wind patterns leading to a more intense upwelling over time. Overall, upwelling patterns and wind variables did not intensify overtime. This method of identifying upwelling may allow for the development of predictive capabilities to investigate investigate investigate upwelling trends in the future.

The Special Sensor Microwave/Imager (SSM/I) radiometer is a useful tool for monitoring snow conditions and estimating snow water equivalent and wetness because it is sensitive to the changes in the physical and dielectric properties of snow. Development and improvement of SSM/I snow-related algorithms is hampered generally by the lack of quantitative snow wetness data and the restriction of a fixed uniform footprint. Currently, there is a need for snow classification algorithms for terrain where forests overlie snow cover. A field experiment was conducted to examine the relationship between snow wetness and meteorological variables. Based on the relationship, snow wetness was estimated concurrently with SSM/I local crossing time at selected footprints to develop an SSM/I snow wetness algorithm. For the improvement of existing algorithms, SSM/I observations were linked with concurrent ground-based snow data over a study area containing both sparse- and medium-vegetated regions. Unsupervised cluster analysis was applied to separate SSM/I brightness temperature (Tb) data into groups. Six typical SSM/I Tb signatures, based on cluster means of desired snow classes, were identified. An artificial neural network (ANN) classifier was designed to learn the typical Tb patterns Ill for land-surface snow cover classification. An ANN approximator was trained with the relations between inputs of SSM/I Tb observations and outputs of ground-based snow water equivalent and wetness. Results indicated that snow wetness estimated from concurrent air temperature could provide the ground-based data needed for the development of SSM/I algorithms. The use of cluster means might be sufficient in ANN supervised learning for snow classification, and the ANN has the potential to be trained for retrieving different snow parameters simultaneously from SSM/I data. It is concluded that the ANN approach may overcome the drawbacks and limitations of the existing SSM/I algorithms for land-surface snow classification and parameter estimation over varied terrain. This study demonstrated a nonlinear retrieval method towards making the inferences of snow conditions and parameters from SSM/I data over varied terrain operational.

Retarding Potential Analyzers (RPA) have a rich flight heritage. These instruments are largely popular since a single current-voltage (I-V) profile can provide in-situ measurements of ion temperature, velocity and composition. The estimation of parameters from an RPA I-V curve is affected by grid geometries and non-ideal biasing which have been studied in the past. In this dissertation, we explore the uncertainties associated with estimated ion parameters from an RPA in the presence of instrument noise. Simulated noisy I-V curves representative of those expected from a mid-inclination low Earth orbit are fitted with standard curve fitting techniques to reveal the degree of uncertainty and inter-dependence between expected errors, with varying levels of additive noise. The main motive is to provide experimenters working with RPA data with a measure of error scalable for different geometries. In subsequent work, we develop a statistics based bootstrap technique designed to mitigate the large inter-dependency between spacecraft potential and ion velocity errors, which were seen to be highly correlated when estimated using a standard algorithm. The new algorithm - BATFORD, acronym for "Bootstrap-based Algorithm with Two-stage Fit for Orbital RPA Data analysis" - was applied to a simulated dataset treated with noise from a laboratory calibration based realistic noise model, and also tested on real in-flight data from the C/NOFS mission. BATFORD outperforms a traditional algorithm in simulation and also provides realistic in-situ estimates from a section of a C/NOFS orbit when the satellite passed through a plasma bubble. The low signal-to-noise ratios (SNR) of measured I-Vs in these bubbles make autonomous parameter estimation notoriously difficult. We thus propose a method for robust autonomous analysis of RPA data that is reliable in low SNR environments, and is applicable for all RPA designs. / Doctor of Philosophy / The plasma environment in Earth’s upper atmosphere is dynamic and diverse. Of particular interest is the ionosphere - a region of dense ionized gases that directly affects the variability in weather in space and the communication of radio wave signals across Earth. Retarding potential analyzers (RPA) are instruments that can directly measure the characteristics of this environment in flight. With the growing popularity of small satellites, these probes need to be studied in greater detail to exploit their ability to understand how ions - the positively charged particles- behave in this region. In this dissertation, we aim to understand how the RPA measurements, obtained as current-voltage relationships, are affected by electronic noise. We propose a methodology to understand the associated uncertainties in the estimated parameters through a simulation study. The results show that a statistics based algorithm can help to interpret RPA data in the presence of noise, and can make autonomous, robust and more accurate measurements compared to a traditional non-linear curve-fitting routine. The dissertation presents the challenges in analyzing RPA data that is affected by noise and proposes a new method to better interpret measurements in the ionosphere that can enable further scientific progress in the space physics community.

Philosophiae Doctor - PhD (Biodiversity and Conservation Biology) / Seawater temperature from regional to global scale is central to many measures of biodi- versity and continues to aid our understanding of the evolution and ecology of biolog- ical assemblages. Therefore, a clear understanding of the relationship between marine biodiversity and thermal structures is critical for effective conservation planning. In the an- thropocene, an epoch characterised by anthropogenic forcing on the climate system, future patterns in biodiversity and ecological functioning may be estimated from projected climate scenarios however; absent from many of these scenarios is the inclusion of extreme thermal events, known as marine heatwaves (MHWs). There is also a conspicuous absence in knowl- edge of the drivers for all but the most notorious of these events. Before the drivers of MHWs along the coast of South Africa could be determined, it was first necessary to validate the 129 in situ coastal seawater temperature time series that could be used to this end. In doing so it was found that time series created with older (longer), lower precision (0.5 Degrees Celsius) instruments were more useful than newer (shorter) time series produced with high precision (0.001 Degrees Celsius) instruments. With the in situ data validated, a history of the occurrence of MHWs along the coastline (nearshore) was created and compared against MHWs detected by remotely sensed data (offshore). This comparison showed that the forcing of offshore temperatures onto the nearshore was much lower than anticipated, with the rates of co-occurrence for events between the datasets along the coast ranging from 0.2 to 0.5. To accommodate this lack of consistency between datasets, a much larger mesoscale area was then taken around southern Africa when attempting to determine potential mesoscale drivers of MHWs along the coast. Using a self organising-map (SOM), it was possible to organise the synoptic scale oceanographic and atmospheric states during coastal MHWs into discernible groupings. It was found that the most common synoptic oceanographic pattern during coastal MHWs was Agulhas Leakage, and the most common atmospheric pattern was anomalously warmoverland air temperatures.With these patterns known it is now necessary to calculate how often they occur when no MHW has been detected. This work may then allow for the development of predictive capabilities that could help mitigate the damage caused by MHWs.

No description available.

Analisamos aproximadamente três anos de dados correntográficos, em toda a coluna d\'água, e de temperatura junto ao fundo, a fim de avaliar o comportamento hidrodinâmico e termal nas proximidades da Quebra da Plataforma Continental (QPC) de Cabo Frio (CF, Rio de Janeiro, Brasil - 23° 20\'S). A Corrente do Brasil (CB) força movimentos apontando para sudoeste, paralelos à isóbata, em todos os níveis verticais, com variação sazonal das intensidades: médias máximas para o verão (58,7 cm/s) e primavera (41,4 cm/s) e mínimas para inverno (31,0 cm/s) e outono (22,8 cm/s). Foram obtidos máximos significativos de correlação entre as correntes paralelas à isóbata, em toda a coluna d\'água, e o vento na mesma direção, com defasagem na resposta das correntes da ordem do período inercial local (31 h). Também foram obtidos máximos significativos de correlação entre as correntes de fundo normais à isóbata, e a componente paralela do vento, concordantes com mecanismos de intrusões de Água Central do Atlântico Sul (ACAS) na plataforma continental, as quais antecedem a conhecida ressurgência costeira de CF. Sazonalmente, os resultados concordaram principalmente com as variações de posicionamento da frente da CB na QPC e, secundariamente, com a variabilidade dos ventos. Os dados de temperatura indicaram presença quase permanente da ACAS no fundo, e os máximos de correlação obtidos com as correntes paralelas à isóbata indicam que águas mais quentes, oriundas do núcleo da CB (Água Tropical), se aproximam do fundo da QPC, principalmente durante o verão. As correntes de maré se mostraram fracas em todo o período avaliado, com importância decrescendo da superfície para o fundo (20 e 10% da variância, respectivamente) / Current data throughout the water column and temperatures at the bottom from about three years have been analyzed in order to evaluate the hydrodynamic and thermal behavior near the Cabo Frio (CF) continental shelf break (23° 20\'S - Rio de Janeiro, Brazil). The Brazil Current (BC) forces movements pointing to SW, isobath-aligned, on all vertical levels. The speed varies seasonally, with surface mean currents maximum on summer (58.7 cm/s) and spring (41.4 cm/s), and minimum on winter (31.0 cm/s) and autumn (22.8 cm/s). Significant maximum correlation was found between subinertial winds and driven-wind currents, both isobath-aligned, with a delay next to the local inertial period (31 hours). Significant maximum correlation were also obtained between bottom cross-isobath currents and the isobath-aligned component of the wind, consistent with the South Atlantic Central Water (SACW) transport towards the continental shelf, which antedates the well-known coastal CF upwelling. Seasonally, the results agreed mainly with the positioning variations of the BC to the shelf break and, secondly, with the local winds variability. The temperature values below the 18ºC (SACW thermohaline index) was almost permanent on the bottom of the shelf break, and the maximum correlation obtained with current along the isobath indicates that the warmer water of the BC nucleus (Tropical Water) approaches to the bottom of the shelf break, especially during the summer. Tidal currents were weak during the entire sampling period, decreasing the relative strength from the surface to the bottom (20% and 10% of the variance, respectively)

Analisamos aproximadamente três anos de dados correntográficos, em toda a coluna d\'água, e de temperatura junto ao fundo, a fim de avaliar o comportamento hidrodinâmico e termal nas proximidades da Quebra da Plataforma Continental (QPC) de Cabo Frio (CF, Rio de Janeiro, Brasil - 23° 20\'S). A Corrente do Brasil (CB) força movimentos apontando para sudoeste, paralelos à isóbata, em todos os níveis verticais, com variação sazonal das intensidades: médias máximas para o verão (58,7 cm/s) e primavera (41,4 cm/s) e mínimas para inverno (31,0 cm/s) e outono (22,8 cm/s). Foram obtidos máximos significativos de correlação entre as correntes paralelas à isóbata, em toda a coluna d\'água, e o vento na mesma direção, com defasagem na resposta das correntes da ordem do período inercial local (31 h). Também foram obtidos máximos significativos de correlação entre as correntes de fundo normais à isóbata, e a componente paralela do vento, concordantes com mecanismos de intrusões de Água Central do Atlântico Sul (ACAS) na plataforma continental, as quais antecedem a conhecida ressurgência costeira de CF. Sazonalmente, os resultados concordaram principalmente com as variações de posicionamento da frente da CB na QPC e, secundariamente, com a variabilidade dos ventos. Os dados de temperatura indicaram presença quase permanente da ACAS no fundo, e os máximos de correlação obtidos com as correntes paralelas à isóbata indicam que águas mais quentes, oriundas do núcleo da CB (Água Tropical), se aproximam do fundo da QPC, principalmente durante o verão. As correntes de maré se mostraram fracas em todo o período avaliado, com importância decrescendo da superfície para o fundo (20 e 10% da variância, respectivamente) / Current data throughout the water column and temperatures at the bottom from about three years have been analyzed in order to evaluate the hydrodynamic and thermal behavior near the Cabo Frio (CF) continental shelf break (23° 20\'S - Rio de Janeiro, Brazil). The Brazil Current (BC) forces movements pointing to SW, isobath-aligned, on all vertical levels. The speed varies seasonally, with surface mean currents maximum on summer (58.7 cm/s) and spring (41.4 cm/s), and minimum on winter (31.0 cm/s) and autumn (22.8 cm/s). Significant maximum correlation was found between subinertial winds and driven-wind currents, both isobath-aligned, with a delay next to the local inertial period (31 hours). Significant maximum correlation were also obtained between bottom cross-isobath currents and the isobath-aligned component of the wind, consistent with the South Atlantic Central Water (SACW) transport towards the continental shelf, which antedates the well-known coastal CF upwelling. Seasonally, the results agreed mainly with the positioning variations of the BC to the shelf break and, secondly, with the local winds variability. The temperature values below the 18ºC (SACW thermohaline index) was almost permanent on the bottom of the shelf break, and the maximum correlation obtained with current along the isobath indicates that the warmer water of the BC nucleus (Tropical Water) approaches to the bottom of the shelf break, especially during the summer. Tidal currents were weak during the entire sampling period, decreasing the relative strength from the surface to the bottom (20% and 10% of the variance, respectively)

The Tibetan Plateau with an extent of about 2,5 million square kilometers at an average altitude higher than 4,700 meters has a significant impact on the Asian monsoon and regulates with its snow and ice reserves the upstream headwaters of seven major south-east Asian rivers. Upon the water supply of these rivers depend over 1,4 billion people, the agriculture, the economics, and the entire ecosystem in this region. As the increasing number of floods and droughts show, these seasonal water reserves however are likely to be influenced by climate change, with negative effects for the downstream water supply and subsequently the food security. The international cooperation project CEOP-AEGIS – funded by the European Commission under the Seventh Framework Program – aims as a result to improve the knowledge of the hydrology and meteorology of the Qinghai-Tibetan Plateau to further understand its role in climate, monsoon and increasing extreme meteorological events. Within the framework of this project, a large variety of earth observation datasets from remote sensing products, model outputs and in-situ ground station measurements are collected and evaluated. Any foreground products of CEOP-AEGIS will have to be made available to the scientific community by an online data repository which is a contribution to the Global Earth Observation System of Systems (GEOSS). The back-end of the CEOP-AEGIS Data Portal relies on a Dapper OPeNDAP web server that serves data stored in the NetCDF file format to a DChart client front-end as web-based user interface. Data from project partners are heterogeneous in its content, and also in its type of storage and metadata description. However NetCDF project output data and metadata has to be standardized and must follow international conventions to achieve a high level of interoperability. Out of these needs, the capabilities of NetCDF, OPeNDAP, Dapper and DChart were profoundly evaluated in order to take correct decisions for implementing a suitable and interoperable NetCDF data model for CEOP-AEGIS data that allows a maximum of compatibility and functionality to OPeNDAP and Dapper / DChart as well. This NetCDF implementation is part of a newly developed upstream data interface that converts and aggregates heterogeneous input data of project partners to standardized NetCDF datasets, so that they can be feed via OPeNDAP to the CEOP-AEGIS Data Portal based on the Dapper / DChart technology. A particular focus in the design of this data interface was set to an intermediate data and metadata representation that easily allows to modify its elements with the scope of achieving standardized NetCDF files in a simple way. Considering the extensive variety and amount of data within this project, it was essential to properly design a data interface that converts heterogeneous input data of project partners to standardized and aggregated NetCDF output files in order to ensure maximum compatibility and functionality within the CEOP-AEGIS Data Portal and subsequently interoperability within the scientific community. / Das Hochplateau von Tibet mit einer Ausdehnung von 2.5 Millionen Quadratkilometer und einer durchschnittlichen Höhe von über 4 700 Meter beeinflusst wesentlich den asiatischen Monsun und reguliert mit seinen Schnee- und Eisreserven den Wasserhaushalt der Oberläufe der sieben wichtigsten Flüsse Südostasiens. Von diesem Wasserzufluss leben 1.4 Milliarden Menschen und hängt neben dem Ackerbau und der Wirtschaft das gesamte Ökosystem in dieser Gegend ab. Wie die zunehmende Zahl an Dürren und Überschwemmungen zeigt, sind diese jahreszeitlich beeinflussten Wasserreserven allen Anscheins nach vom Klimawandel betroffen, mit negativen Auswirkungen für die flussabwärts liegenden Stromgebiete und demzufolge die dortige Nahrungsmittelsicherheit. Das internationale Kooperationsprojekt CEOP-AEGIS – finanziert von der Europäischen Kommission unter dem Siebten Rahmenprogramm – hat sich deshalb zum Ziel gesetzt, die Hydrologie und Meteorologie dieses Hochplateaus weiter zu erforschen, um daraus seine Rolle in Bezug auf das Klima, den Monsun und den zunehmenden extremen Wetterereignissen tiefgreifender verstehen zu können. Im Rahmen dieses Projektes werden verschiedenartigste Erdbeobachtungsdaten von Fernerkundungssystemen, numerischen Simulationen und Bodenstationsmessungen gesammelt und ausgewertet. Sämtliche Endprodukte des CEOP-AEGIS Projektes werden der wissenschaftlichen Gemeinschaft auf Grundlage einer über das Internet erreichbaren Datenbank zugänglich gemacht, welche eine Zuarbeit zur Initiative GEOSS (Global Earth Observing System of Systems) ist. Hintergründig basiert das CEOP-AEGIS Datenportal auf einem Dapper OPeNDAP Internetserver, welcher die im NetCDF Dateiformat gespeicherten Daten der vordergründigen internetbasierten DChart Benutzerschnittstelle auf Grundlage des OPeNDAP Protokolls bereit stellt. Eingangsdaten von Partnern dieses Projektes sind heterogen nicht nur in Bezug ihres Dateninhalts, sondern auch in Anbetracht ihrer Datenhaltung und Metadatenbeschreibung. Die Daten- und Metadatenhaltung der im NetCDF Dateiformat gespeicherten Endprodukte dieses Projektes müssen jedoch auf einer standardisierten Basis internationalen Konventionen folgen, damit ein hoher Grad an Interoperabilität erreicht werden kann. In Anbetracht dieser Qualitätsanforderungen wurden die technischen Möglichkeiten von NetCDF, OPeNDAP, Dapper und DChart in dieser Diplomarbeit gründlich untersucht, damit auf Grundlage dieser Erkenntnisse eine korrekte Entscheidung bezüglich der Implementierung eines für CEOP-AEGIS Daten passenden und interoperablen NetCDF Datenmodels abgeleitet werden kann, das eine maximale Kompatibilität und Funktionalität mit OPeNDAP und Dapper / DChart sicher stellen soll. Diese NetCDF Implementierung ist Bestandteil einer neu entwickelten Datenschnittstelle, welche heterogene Daten von Projektpartnern in standardisierte NetCDF Datensätze konvertiert und aggregiert, sodass diese mittels OPeNDAP dem auf der Dapper / DChart Technologie basierendem Datenportal von CEOP-AEGIS zugeführt werden können. Einen besonderen Schwerpunkt bei der Entwicklung dieser Datenschnittstelle wurde auf eine intermediäre Daten- und Metadatenhaltung gelegt, welche mit der Zielsetzung von geringem Arbeitsaufwand die Modifizierung ihrer Elemente und somit die Erzeugung von standardisierten NetCDF Dateien auf eine einfache Art und Weise erlaubt. In Anbetracht der beträchtlichen und verschiedenartigsten Geodaten dieses Projektes war es schlussendlich wesentlich, eine hochwertige Datenschnittstelle zur Überführung heterogener Eingangsdaten von Projektpartnern in standardisierte und aggregierte NetCDF Ausgansdateien zu entwickeln, um damit eine maximale Kompatibilität und Funktionalität mit dem CEOP-AEGIS Datenportal und daraus folgend ein hohes Maß an Interoperabilität innerhalb der wissenschaftlichen Gemeinschaft erzielen zu können.

The Tibetan Plateau with an extent of about 2,5 million square kilometers at an average altitude higher than 4,700 meters has a significant impact on the Asian monsoon and regulates with its snow and ice reserves the upstream headwaters of seven major south-east Asian rivers. Upon the water supply of these rivers depend over 1,4 billion people, the agriculture, the economics, and the entire ecosystem in this region. As the increasing number of floods and droughts show, these seasonal water reserves however are likely to be influenced by climate change, with negative effects for the downstream water supply and subsequently the food security. The international cooperation project CEOP-AEGIS – funded by the European Commission under the Seventh Framework Program – aims as a result to improve the knowledge of the hydrology and meteorology of the Qinghai-Tibetan Plateau to further understand its role in climate, monsoon and increasing extreme meteorological events. Within the framework of this project, a large variety of earth observation datasets from remote sensing products, model outputs and in-situ ground station measurements are collected and evaluated. Any foreground products of CEOP-AEGIS will have to be made available to the scientific community by an online data repository which is a contribution to the Global Earth Observation System of Systems (GEOSS). The back-end of the CEOP-AEGIS Data Portal relies on a Dapper OPeNDAP web server that serves data stored in the NetCDF file format to a DChart client front-end as web-based user interface. Data from project partners are heterogeneous in its content, and also in its type of storage and metadata description. However NetCDF project output data and metadata has to be standardized and must follow international conventions to achieve a high level of interoperability. Out of these needs, the capabilities of NetCDF, OPeNDAP, Dapper and DChart were profoundly evaluated in order to take correct decisions for implementing a suitable and interoperable NetCDF data model for CEOP-AEGIS data that allows a maximum of compatibility and functionality to OPeNDAP and Dapper / DChart as well. This NetCDF implementation is part of a newly developed upstream data interface that converts and aggregates heterogeneous input data of project partners to standardized NetCDF datasets, so that they can be feed via OPeNDAP to the CEOP-AEGIS Data Portal based on the Dapper / DChart technology. A particular focus in the design of this data interface was set to an intermediate data and metadata representation that easily allows to modify its elements with the scope of achieving standardized NetCDF files in a simple way. Considering the extensive variety and amount of data within this project, it was essential to properly design a data interface that converts heterogeneous input data of project partners to standardized and aggregated NetCDF output files in order to ensure maximum compatibility and functionality within the CEOP-AEGIS Data Portal and subsequently interoperability within the scientific community.:Task of Diploma Thesis ii Declaration of academic honesty vii Abstract ix Acknowledgments xiii Dedication xv Table of Contents xvii List of Figures xxi List of Tables xxiii List of Listings xxv Nomenclature xxvii 1 Introduction 1 1.1 CEOP-AEGIS project . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Objective of this thesis . . . . . . . . . . . . . . . . . . . . . . 8 1.4 Structure of this work . . . . . . . . . . . . . . . . . . . . . . 10 2 Theoretical foundations 13 2.1 NetCDF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.1.1 Data models . . . . . . . . . . . . . . . . . . . . . . . . 16 2.1.2 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.3 Dimensions . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.4 Variables . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.1.5 Attributes . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.1.6 NetCDF 3 . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.1.7 NetCDF 4 . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.1.8 Common Data Model . . . . . . . . . . . . . . . . . . . 31 2.1.9 NetCDF libraries and APIs . . . . . . . . . . . . . . . 33 2.1.10 NetCDF utilities . . . . . . . . . . . . . . . . . . . . . 34 2.1.11 NetCDF textual representations . . . . . . . . . . . . . 35 2.1.12 NetCDF conventions . . . . . . . . . . . . . . . . . . . 36 2.2 OPeNDAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.2.1 Architecture . . . . . . . . . . . . . . . . . . . . . . . . 41 2.2.2 OPeNDAP servers . . . . . . . . . . . . . . . . . . . . 42 2.2.3 OPeNDAP clients . . . . . . . . . . . . . . . . . . . . . 47 2.2.4 Data Access Protocol . . . . . . . . . . . . . . . . . . . 48 2.2.5 OPeNDAP data models and data types . . . . . . . . . 49 2.2.6 OPeNDAP and NetCDF . . . . . . . . . . . . . . . . . 53 2.3 Dapper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 2.3.1 Climate Data Portal . . . . . . . . . . . . . . . . . . . 57 2.3.2 System architecture and Dapper services . . . . . . . . 58 2.3.3 Data aggregation . . . . . . . . . . . . . . . . . . . . . 60 2.3.4 Supported conventions of Dapper . . . . . . . . . . . . 61 2.4 DChart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 2.4.1 Design goals . . . . . . . . . . . . . . . . . . . . . . . . 63 2.4.2 Functionality . . . . . . . . . . . . . . . . . . . . . . . 63 2.4.3 System architecture . . . . . . . . . . . . . . . . . . . . 64 2.5 Dapper and DChart configuration . . . . . . . . . . . . . . . . 66 2.5.1 License and release notes . . . . . . . . . . . . . . . . . 67 2.5.2 Dapper and DChart system requirements . . . . . . . . 67 3 Implementation 69 3.1 Scientific data types . . . . . . . . . . . . . . . . . . . . . . . 69 3.1.1 Gridded data . . . . . . . . . . . . . . . . . . . . . . . 70 3.1.2 In-situ data . . . . . . . . . . . . . . . . . . . . . . . . 71 3.2 NetCDF for CEOP-AEGIS . . . . . . . . . . . . . . . . . . . . 71 3.2.1 CF Climate and Forecast Convention . . . . . . . . . . 73 3.2.2 Dapper In-situ Convention . . . . . . . . . . . . . . . . 80 3.2.3 NetCDF implementation for CEOP-AEGIS . . . . . . 89 3.3 CEOP-AEGIS Data Interface . . . . . . . . . . . . . . . . . . 93 3.3.1 Intermediate data model . . . . . . . . . . . . . . . . . 95 3.3.2 Data Interface dependencies . . . . . . . . . . . . . . . 98 3.3.3 Data Interface usage . . . . . . . . . . . . . . . . . . . 98 3.3.4 Data Interface modules . . . . . . . . . . . . . . . . . . 105 3.4 Final products . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4 Conclusion 111 4.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 4.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 4.3 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 A Appendix 119 A.1 CD-ROM of project data . . . . . . . . . . . . . . . . . . . . . 119 A.2 Flood occurrence maps . . . . . . . . . . . . . . . . . . . . . . 121 A.2.1 Flood occurrence May . . . . . . . . . . . . . . . . . . 122 A.2.2 Flood occurrence August . . . . . . . . . . . . . . . . . 123 A.3 CEOP-AEGIS Data Portal . . . . . . . . . . . . . . . . . . . . 124 A.3.1 Capture image of CEOP-AEGIS Data Portal . . . . . . 125 A.3.2 Dapper configuration file . . . . . . . . . . . . . . . . . 126 A.3.3 DChart configuration file . . . . . . . . . . . . . . . . . 127 A.4 NetCDF data models for CEOP-AEGIS . . . . . . . . . . . . 130 A.4.1 Data model for gridded data . . . . . . . . . . . . . . . 131 A.4.2 Data model for in-situ data . . . . . . . . . . . . . . . 132 A.5 Upstream data interface . . . . . . . . . . . . . . . . . . . . . 133 A.5.1 Data Interface and service chain . . . . . . . . . . . . . 134 A.5.2 Data Interface data flow . . . . . . . . . . . . . . . . . 135 A.5.3 Data Interface data flow 2 . . . . . . . . . . . . . . . . 136 A.5.4 Data Interface modules and classes . . . . . . . . . . . 137 A.5.5 Data Interface NetCDF metadata file for gridded data 138 A.5.6 Data Interface NetCDF metadata file for in-situ data . 139 A.5.7 Data Interface coordinate metadata file for gridded data140 A.5.8 Data Interface coordinate metadata file for in-situ data 140 A.5.9 Data Interface UI main program . . . . . . . . . . . . . 141 A.5.10 Data Interface UI GrADS component . . . . . . . . . . 142 A.5.11 Data Interface UI GDAL component . . . . . . . . . . 143 A.5.12 Data Interface UI CSV component . . . . . . . . . . . 144 A.5.13 Data Interface settings file for gridded data . . . . . . . 145 A.5.14 Data Interface settings file for in-situ data . . . . . . . 146 A.5.15 Data Interface batch file for data conversion via GrADS146 A.5.16 Data Interface batch file for data conversion via GDAL 147 A.5.17 Data Interface batch file for data conversion via CSV . 148 A.6 Pydoc documentation for upstream data interface . . . . . . . 149 A.6.1 grads_2Interface.py . . . . . . . . . . . . . . . . . . . . 150 A.6.2 gdal_2Interface.py . . . . . . . . . . . . . . . . . . . . 155 A.6.3 csv_2Interface.py . . . . . . . . . . . . . . . . . . . . . 162 A.6.4 interface_Main.py . . . . . . . . . . . . . . . . . . . . 167 A.6.5 interface_Settings.py . . . . . . . . . . . . . . . . . . . 172 A.6.6 interface_Control.py . . . . . . . . . . . . . . . . . . . 175 A.6.7 interface_Model.py . . . . . . . . . . . . . . . . . . . . 179 A.6.8 interface_ModelUtilities.py . . . . . . . . . . . . . . . 185 A.6.9 interface_Data.py . . . . . . . . . . . . . . . . . . . . . 189 A.6.10 interface_ProcessingTools.py . . . . . . . . . . . . . . 191 Bibliography 197 Index 205 / Das Hochplateau von Tibet mit einer Ausdehnung von 2.5 Millionen Quadratkilometer und einer durchschnittlichen Höhe von über 4 700 Meter beeinflusst wesentlich den asiatischen Monsun und reguliert mit seinen Schnee- und Eisreserven den Wasserhaushalt der Oberläufe der sieben wichtigsten Flüsse Südostasiens. Von diesem Wasserzufluss leben 1.4 Milliarden Menschen und hängt neben dem Ackerbau und der Wirtschaft das gesamte Ökosystem in dieser Gegend ab. Wie die zunehmende Zahl an Dürren und Überschwemmungen zeigt, sind diese jahreszeitlich beeinflussten Wasserreserven allen Anscheins nach vom Klimawandel betroffen, mit negativen Auswirkungen für die flussabwärts liegenden Stromgebiete und demzufolge die dortige Nahrungsmittelsicherheit. Das internationale Kooperationsprojekt CEOP-AEGIS – finanziert von der Europäischen Kommission unter dem Siebten Rahmenprogramm – hat sich deshalb zum Ziel gesetzt, die Hydrologie und Meteorologie dieses Hochplateaus weiter zu erforschen, um daraus seine Rolle in Bezug auf das Klima, den Monsun und den zunehmenden extremen Wetterereignissen tiefgreifender verstehen zu können. Im Rahmen dieses Projektes werden verschiedenartigste Erdbeobachtungsdaten von Fernerkundungssystemen, numerischen Simulationen und Bodenstationsmessungen gesammelt und ausgewertet. Sämtliche Endprodukte des CEOP-AEGIS Projektes werden der wissenschaftlichen Gemeinschaft auf Grundlage einer über das Internet erreichbaren Datenbank zugänglich gemacht, welche eine Zuarbeit zur Initiative GEOSS (Global Earth Observing System of Systems) ist. Hintergründig basiert das CEOP-AEGIS Datenportal auf einem Dapper OPeNDAP Internetserver, welcher die im NetCDF Dateiformat gespeicherten Daten der vordergründigen internetbasierten DChart Benutzerschnittstelle auf Grundlage des OPeNDAP Protokolls bereit stellt. Eingangsdaten von Partnern dieses Projektes sind heterogen nicht nur in Bezug ihres Dateninhalts, sondern auch in Anbetracht ihrer Datenhaltung und Metadatenbeschreibung. Die Daten- und Metadatenhaltung der im NetCDF Dateiformat gespeicherten Endprodukte dieses Projektes müssen jedoch auf einer standardisierten Basis internationalen Konventionen folgen, damit ein hoher Grad an Interoperabilität erreicht werden kann. In Anbetracht dieser Qualitätsanforderungen wurden die technischen Möglichkeiten von NetCDF, OPeNDAP, Dapper und DChart in dieser Diplomarbeit gründlich untersucht, damit auf Grundlage dieser Erkenntnisse eine korrekte Entscheidung bezüglich der Implementierung eines für CEOP-AEGIS Daten passenden und interoperablen NetCDF Datenmodels abgeleitet werden kann, das eine maximale Kompatibilität und Funktionalität mit OPeNDAP und Dapper / DChart sicher stellen soll. Diese NetCDF Implementierung ist Bestandteil einer neu entwickelten Datenschnittstelle, welche heterogene Daten von Projektpartnern in standardisierte NetCDF Datensätze konvertiert und aggregiert, sodass diese mittels OPeNDAP dem auf der Dapper / DChart Technologie basierendem Datenportal von CEOP-AEGIS zugeführt werden können. Einen besonderen Schwerpunkt bei der Entwicklung dieser Datenschnittstelle wurde auf eine intermediäre Daten- und Metadatenhaltung gelegt, welche mit der Zielsetzung von geringem Arbeitsaufwand die Modifizierung ihrer Elemente und somit die Erzeugung von standardisierten NetCDF Dateien auf eine einfache Art und Weise erlaubt. In Anbetracht der beträchtlichen und verschiedenartigsten Geodaten dieses Projektes war es schlussendlich wesentlich, eine hochwertige Datenschnittstelle zur Überführung heterogener Eingangsdaten von Projektpartnern in standardisierte und aggregierte NetCDF Ausgansdateien zu entwickeln, um damit eine maximale Kompatibilität und Funktionalität mit dem CEOP-AEGIS Datenportal und daraus folgend ein hohes Maß an Interoperabilität innerhalb der wissenschaftlichen Gemeinschaft erzielen zu können.:Task of Diploma Thesis ii Declaration of academic honesty vii Abstract ix Acknowledgments xiii Dedication xv Table of Contents xvii List of Figures xxi List of Tables xxiii List of Listings xxv Nomenclature xxvii 1 Introduction 1 1.1 CEOP-AEGIS project . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Objective of this thesis . . . . . . . . . . . . . . . . . . . . . . 8 1.4 Structure of this work . . . . . . . . . . . . . . . . . . . . . . 10 2 Theoretical foundations 13 2.1 NetCDF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.1.1 Data models . . . . . . . . . . . . . . . . . . . . . . . . 16 2.1.2 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.3 Dimensions . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.4 Variables . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.1.5 Attributes . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.1.6 NetCDF 3 . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.1.7 NetCDF 4 . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.1.8 Common Data Model . . . . . . . . . . . . . . . . . . . 31 2.1.9 NetCDF libraries and APIs . . . . . . . . . . . . . . . 33 2.1.10 NetCDF utilities . . . . . . . . . . . . . . . . . . . . . 34 2.1.11 NetCDF textual representations . . . . . . . . . . . . . 35 2.1.12 NetCDF conventions . . . . . . . . . . . . . . . . . . . 36 2.2 OPeNDAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.2.1 Architecture . . . . . . . . . . . . . . . . . . . . . . . . 41 2.2.2 OPeNDAP servers . . . . . . . . . . . . . . . . . . . . 42 2.2.3 OPeNDAP clients . . . . . . . . . . . . . . . . . . . . . 47 2.2.4 Data Access Protocol . . . . . . . . . . . . . . . . . . . 48 2.2.5 OPeNDAP data models and data types . . . . . . . . . 49 2.2.6 OPeNDAP and NetCDF . . . . . . . . . . . . . . . . . 53 2.3 Dapper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 2.3.1 Climate Data Portal . . . . . . . . . . . . . . . . . . . 57 2.3.2 System architecture and Dapper services . . . . . . . . 58 2.3.3 Data aggregation . . . . . . . . . . . . . . . . . . . . . 60 2.3.4 Supported conventions of Dapper . . . . . . . . . . . . 61 2.4 DChart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 2.4.1 Design goals . . . . . . . . . . . . . . . . . . . . . . . . 63 2.4.2 Functionality . . . . . . . . . . . . . . . . . . . . . . . 63 2.4.3 System architecture . . . . . . . . . . . . . . . . . . . . 64 2.5 Dapper and DChart configuration . . . . . . . . . . . . . . . . 66 2.5.1 License and release notes . . . . . . . . . . . . . . . . . 67 2.5.2 Dapper and DChart system requirements . . . . . . . . 67 3 Implementation 69 3.1 Scientific data types . . . . . . . . . . . . . . . . . . . . . . . 69 3.1.1 Gridded data . . . . . . . . . . . . . . . . . . . . . . . 70 3.1.2 In-situ data . . . . . . . . . . . . . . . . . . . . . . . . 71 3.2 NetCDF for CEOP-AEGIS . . . . . . . . . . . . . . . . . . . . 71 3.2.1 CF Climate and Forecast Convention . . . . . . . . . . 73 3.2.2 Dapper In-situ Convention . . . . . . . . . . . . . . . . 80 3.2.3 NetCDF implementation for CEOP-AEGIS . . . . . . 89 3.3 CEOP-AEGIS Data Interface . . . . . . . . . . . . . . . . . . 93 3.3.1 Intermediate data model . . . . . . . . . . . . . . . . . 95 3.3.2 Data Interface dependencies . . . . . . . . . . . . . . . 98 3.3.3 Data Interface usage . . . . . . . . . . . . . . . . . . . 98 3.3.4 Data Interface modules . . . . . . . . . . . . . . . . . . 105 3.4 Final products . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4 Conclusion 111 4.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 4.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 4.3 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 A Appendix 119 A.1 CD-ROM of project data . . . . . . . . . . . . . . . . . . . . . 119 A.2 Flood occurrence maps . . . . . . . . . . . . . . . . . . . . . . 121 A.2.1 Flood occurrence May . . . . . . . . . . . . . . . . . . 122 A.2.2 Flood occurrence August . . . . . . . . . . . . . . . . . 123 A.3 CEOP-AEGIS Data Portal . . . . . . . . . . . . . . . . . . . . 124 A.3.1 Capture image of CEOP-AEGIS Data Portal . . . . . . 125 A.3.2 Dapper configuration file . . . . . . . . . . . . . . . . . 126 A.3.3 DChart configuration file . . . . . . . . . . . . . . . . . 127 A.4 NetCDF data models for CEOP-AEGIS . . . . . . . . . . . . 130 A.4.1 Data model for gridded data . . . . . . . . . . . . . . . 131 A.4.2 Data model for in-situ data . . . . . . . . . . . . . . . 132 A.5 Upstream data interface . . . . . . . . . . . . . . . . . . . . . 133 A.5.1 Data Interface and service chain . . . . . . . . . . . . . 134 A.5.2 Data Interface data flow . . . . . . . . . . . . . . . . . 135 A.5.3 Data Interface data flow 2 . . . . . . . . . . . . . . . . 136 A.5.4 Data Interface modules and classes . . . . . . . . . . . 137 A.5.5 Data Interface NetCDF metadata file for gridded data 138 A.5.6 Data Interface NetCDF metadata file for in-situ data . 139 A.5.7 Data Interface coordinate metadata file for gridded data140 A.5.8 Data Interface coordinate metadata file for in-situ data 140 A.5.9 Data Interface UI main program . . . . . . . . . . . . . 141 A.5.10 Data Interface UI GrADS component . . . . . . . . . . 142 A.5.11 Data Interface UI GDAL component . . . . . . . . . . 143 A.5.12 Data Interface UI CSV component . . . . . . . . . . . 144 A.5.13 Data Interface settings file for gridded data . . . . . . . 145 A.5.14 Data Interface settings file for in-situ data . . . . . . . 146 A.5.15 Data Interface batch file for data conversion via GrADS146 A.5.16 Data Interface batch file for data conversion via GDAL 147 A.5.17 Data Interface batch file for data conversion via CSV . 148 A.6 Pydoc documentation for upstream data interface . . . . . . . 149 A.6.1 grads_2Interface.py . . . . . . . . . . . . . . . . . . . . 150 A.6.2 gdal_2Interface.py . . . . . . . . . . . . . . . . . . . . 155 A.6.3 csv_2Interface.py . . . . . . . . . . . . . . . . . . . . . 162 A.6.4 interface_Main.py . . . . . . . . . . . . . . . . . . . . 167 A.6.5 interface_Settings.py . . . . . . . . . . . . . . . . . . . 172 A.6.6 interface_Control.py . . . . . . . . . . . . . . . . . . . 175 A.6.7 interface_Model.py . . . . . . . . . . . . . . . . . . . . 179 A.6.8 interface_ModelUtilities.py . . . . . . . . . . . . . . . 185 A.6.9 interface_Data.py . . . . . . . . . . . . . . . . . . . . . 189 A.6.10 interface_ProcessingTools.py . . . . . . . . . . . . . . 191 Bibliography 197 Index 205