Satellite-Based Fog Detection: A Dynamic Retrieval Method for Europe Based on Machine Learning

Fog has many economic as well as ecological impacts and it directly affects human life in many ways. The large number of fog influence factors shows that a comprehensive understanding of its causes and a precise mapping of the spatio-temporal distribution patterns are of great interest. Since there are justifiable concerns about the general applicability of existing fog retrieval methods, this thesis investigates new techniques of satellite based fog detection and the derivation of spatio-temporal information on fog distribution in Europe. The central novelties of this study are: - No static assumptions about microphysical properties were used during fog retrieval. - A novel hybrid approach based on machine learning methods was developed that can be continuously applied 24 hours a day. - The algorithm covers all fog types. Areas of different fog types could also be differentiated indirectly from the generated product due to their typical diurnal and annual frequency cycles. - For the first time, fog frequency maps for Europe could be produced for different general weather situations separately for each fog type

First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib

Fog and low clouds (FLCs) are a typical feature along the southwestern African coast, especially in the central Namib, where fog constitutes a valuable resource of water for many ecosystems. In this study, a novel algorithm is presented to detect FLCs over land from geostationary satellite data using only infrared observations. The algorithm is the first of its kind as it is stationary in time and thus able to reveal a detailed view of the diurnal and spatial patterns of FLCs in the Namib region. A validation against net radiation measurements from a station network in the central Namib reveals a high overall accuracy with a probability of detection of 94%, a false-alarm rate of 12% and an overall correctness of classification of 97%. The average timing and persistence of FLCs seem to depend on the distance to the coast, suggesting that the region is dominated by advection-driven FLCs. While the algorithm is applied to study Namib-region fog and low clouds, it is designed to be transferable to other regions and can be used to retrieve long-term data sets

Satellite-based remote sensing of rainfall in areas with sparse gauge networks and complex topography

Rainfall is an essential parameter in the analysis and research of water resource management. However, the complexity of rainfall combined with the uneven distribution of ground-based gauges and radar in developing countries’ mountainous and semi-arid areas limits its investigation. In this context, satellite-based rainfall products provide area-wide precipitation observations with a high spatio-temporal resolution, engaging them in hydrological management in ungauged basins. Therefore, in this study, I investigated method to establish a satellite-based rainfall algorithm for ungauged basins. The algorithm combines the new Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) rainfall products and second-generation geostationary orbit (GEO) systems developing rainfall retrieval techniques with the high spatio-temporal resolution using machine learning algorithms. For the first step, microwave satellite and Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG SEVIRI) data for Iran were collected to develop a regionally based new rainfall retrieval technique. The method used geostationary multispectral infrared (IR) data to train Random forest (RF) models. I employed the microwave (MW) rainfall information from the IMERG as a reference for RF training. The rainfall area was delineated in the first step, followed by rainfall rate assignment. The validation results showed the new technique’s reliable performance in both rain area delineation and rain estimate, particularly when compared to IR-only IMERG. Multispectral IR data improves rainfall retrieval compared with one single band. In the next step, I investigated the applicability of the developed algorithm in Ecuador with different orography and rainfall regimes compared to Iran. For this aim, I used the Geostationary Operational Environmental Satellite-16 (GOES-16) as the GEO satellite, which covers Ecuador at a suitable angle. The feature selection and algorithm tuning were performed to regionalize the models for Ecuador. The validation results show the reliable performance of the method in both rain area delineation and rain estimation in Ecuador. The results proved the suitability of the developed algorithm with different GEO systems and in different regions. Some inaccuracies at the Andes’ high elevation were evident after the spatial analysis of the validation indices. Evaluating the validation results against a high spatio-temporal radar network showed that the developed algorithm has difficulty capturing drizzles and extreme events dominant in the Andes’ high elevations and needs improvement. In summary, this research presents a new satellite-based technique for rainfall retrieval in a high spatio-temporal resolution for ungauged regions, which can be applied in parts of the world with different rainfall regimes. This findings could be used by planners and water managers regardless of the availability of rain gauges at ground. Furthermore, the research showed, for the very first time, the advantage of using the new generation of GEO satellite combined with microwave satellites integrated in GPM IMERG for estimating rainfall.Der Niederschlag ist ein wesentlicher Parameter bei der Analyse und Erforschung der Bewirtschaftung von Wasserressourcen. Die Komplexität des Niederschlags in Verbindung mit der ungleichmäßigen Verteilung von bodengestützten Messgeräten und Radar in den gebirgigen und halbtrockenen Gebieten von Entwicklungsländern schränkt jedoch seine Untersuchung ein. In diesem Zusammenhang liefern satellitengestützte Produkte flächendeckende Niederschlagsbeobachtungen mit einer hohen räumlich-zeitlichen Auflösung, die für das hydrologische Management in nicht beprobten Einzugsgebieten eingesetzt werden können. Daher konzentriert sich die vorliegende Untersuchung auf die Erstellung eines satellitengestützten Niederschlagsalgorithmus für nicht beprobte Einzugsgebiete. Die neuen IMERG (Integrated Multi-SatEllite Retrieval for Global Precipitation Measurement (GPM)) Satellitenprodukte werden mit geostationären Orbit-Systemen (GEO) der zweiten Generation mittels Algorithmen des maschinellen Lernens zur Niederschlagsermittlung mit hoher räumlicher und zeitlicher Auflösung kombiniert. In einem ersten Schritt wurden Mikrowellensatelliten- und Meteosat-Daten der zweiten Generation des Spinning Enhanced Visible and Infrared Imager (MSG SEVIRI) für den Iran gesammelt, um eine neue, regional basierte Methode zur Niederschlagsermittlung zu entwickeln. Die Methode verwendete geostationäre multispektrale Infrarotdaten (IR), um Random-Forest-Modelle (RF) zu trainieren. Als Referenz für das RF-Training wurden Mikrowellen-Niederschlagsdaten (MW) des IMERG verwendet. Im ersten Schritt wurde das Niederschlagsgebiet abgegrenzt, gefolgt von der Zuordnung der Niederschlagsmenge. Die Validierungsergebnisse zeigen, dass die neue Technik sowohl bei der Abgrenzung des Niederschlagsgebiets als auch bei der Niederschlagsschätzung zuverlässig funktioniert, insbesondere im Vergleich zum IR-only IMERG. Multispektrale IR-Daten verbessern die Niederschlagsermittlung im Vergleich zu einem einzelnen Band. Im nächsten Schritt wurde die Anwendbarkeit des entwickelten Algorithmus in Ecuador untersucht, das sich in Bezug auf die Orographie und das Niederschlagssystem vom Iran unterscheidet. Zu diesem Zweck wurde der Geostationary Operational Environmental Satellite-16 (GOES-16) als GEO-Satellit verwendet, der Ecuador in einem geeigneten Winkel abdeckt. Die Auswahl der Features und das Tuning des Algorithmus wurden durchgeführt, um die Modelle für Ecuador zu regionalisieren. Die Validierungsergebnisse zeigen die zuverlässige Leistung der Methode sowohl bei der Abgrenzung von Regengebieten als auch bei der Schätzung der Niederschlagsmenge in Ecuador. Die Ergebnisse belegen die Eignung des entwickelten Algorithmus für verschiedene GEO-Systeme und verschiedene Regionen. Nach der räumlichen Analyse der Validierungsindizes wurden einige Ungenauigkeiten in denhohen Lagen der Anden deutlich. Die Auswertung der Validierungsergebnisse anhand eines räumlich-zeitlichen Radarnetzes zeigt, dass der entwickelte Algorithmus Schwierigkeiten bei der Erfassung von Nieselregen und extremen Wetterereignissen hat, die in den hohen Lagen der Anden vorherrschen, und dahingehend verbessert werden muss. Diese Forschungsarbeit stellt ein neues satellitengestütztes Verfahren zur Niederschlagsermittlung mit hoher räumlicher und zeitlicher Auflösung vor, das auf Regionen ohne Bodenstationsmessungen und unterschiedliche Niederschlagsregime angewendet werden kann. Dieser Algorithmuskann von Planungs- und Wasserwirtschaftsämtern oder anderen einschlägigen Einrichtungen unabhängig von der Verfügbarkeit von Regenmessern am Boden genutzt werden. Darüber hinaus zeigte die Untersuchung zum ersten Mal den Vorteil der Nutzung der neuen Generation von GEO-Satelliten in Kombination mit den in IMERG integrierten Mikrowellensatelliten für die Bewertung der Niederschlagsmenge

First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib

Fog and low clouds (FLCs) are a typical feature along the southwestern African coast, especially in the central Namib, where fog constitutes a valuable resource of water for many ecosystems. In this study, a novel algorithm is presented to detect FLCs over land from geostationary satellite data using only infrared observations. The algorithm is the first of its kind as it is stationary in time and thus able to reveal a detailed view of the diurnal and spatial patterns of FLCs in the Namib region. A validation against net radiation measurements from a station network in the central Namib reveals a high overall accuracy with a probability of detection of 94&thinsp;%, a false-alarm rate of 12&thinsp;% and an overall correctness of classification of 97&thinsp;%. The average timing and persistence of FLCs seem to depend on the distance to the coast, suggesting that the region is dominated by advection-driven FLCs. While the algorithm is applied to study Namib-region fog and low clouds, it is designed to be transferable to other regions and can be used to retrieve long-term data sets.</p

Reconciling aerosol light extinction measurements from spaceborne lidar observations and in situ measurements in the Arctic

© Author(s) 2014. This work is distributed under the Creative Commons Attribution 3.0 License.In this study we investigate to what degree it is possible to reconcile continuously recorded particle light extinction coefficients derived from dry in situ measurements at Zeppelin station (78.92° N, 11.85° E; 475 m above sea level), Ny-Ålesund, Svalbard, that are recalculated to ambient relative humidity, as well as simultaneous ambient observations with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. To our knowledge, this represents the first study that compares spaceborne lidar measurements to optical aerosol properties from short-term in situ observations (averaged over 5 h) on a case-by-case basis. Finding suitable comparison cases requires an elaborate screening and matching of the CALIOP data with respect to the location of Zeppelin station as well as the selection of temporal and spatial averaging intervals for both the ground-based and spaceborne observations. Reliable reconciliation of these data cannot be achieved with the closest-approach method, which is often used in matching CALIOP observations to those taken at ground sites. This is due to the transport pathways of the air parcels that were sampled. The use of trajectories allowed us to establish a connection between spaceborne and ground-based observations for 57 individual overpasses out of a total of 2018 that occurred in our region of interest around Svalbard (0 to 25° E, 75 to 82° N) in the considered year of 2008. Matches could only be established during winter and spring, since the low aerosol load during summer in connection with the strong solar background and the high occurrence rate of clouds strongly influences the performance and reliability of CALIOP observations. Extinction coefficients in the range of 2 to 130 Mmg-1 at 532 nm were found for successful matches with a difference of a factor of 1.47 (median value for a range from 0.26 to 11.2) between the findings of in situ and spaceborne observations (the latter being generally larger than the former). The remaining difference is likely to be due to the natural variability in aerosol concentration and ambient relative humidity, an insufficient representation of aerosol particle growth, or a misclassification of aerosol type (i.e., choice of lidar ratio) in the CALIPSO retrieval.Peer reviewe

Quantum-inspired computational imaging

Computational imaging combines measurement and computational methods with the aim of forming images even when the measurement conditions are weak, few in number, or highly indirect. The recent surge in quantum-inspired imaging sensors, together with a new wave of algorithms allowing on-chip, scalable and robust data processing, has induced an increase of activity with notable results in the domain of low-light flux imaging and sensing. We provide an overview of the major challenges encountered in low-illumination (e.g., ultrafast) imaging and how these problems have recently been addressed for imaging applications in extreme conditions. These methods provide examples of the future imaging solutions to be developed, for which the best results are expected to arise from an efficient codesign of the sensors and data analysis tools.Y.A. acknowledges support from the UK Royal Academy of Engineering under the Research Fellowship Scheme (RF201617/16/31). S.McL. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grant EP/J015180/1). V.G. acknowledges support from the U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office award W911NF-10-1-0404, the U.S. DARPA REVEAL program through contract HR0011-16-C-0030, and U.S. National Science Foundation through grants 1161413 and 1422034. A.H. acknowledges support from U.S. Army Research Office award W911NF-15-1-0479, U.S. Department of the Air Force grant FA8650-15-D-1845, and U.S. Department of Energy National Nuclear Security Administration grant DE-NA0002534. D.F. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grants EP/M006514/1 and EP/M01326X/1). (RF201617/16/31 - UK Royal Academy of Engineering; EP/J015180/1 - UK Engineering and Physical Sciences Research Council; EP/M006514/1 - UK Engineering and Physical Sciences Research Council; EP/M01326X/1 - UK Engineering and Physical Sciences Research Council; W911NF-10-1-0404 - U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office; HR0011-16-C-0030 - U.S. DARPA REVEAL program; 1161413 - U.S. National Science Foundation; 1422034 - U.S. National Science Foundation; W911NF-15-1-0479 - U.S. Army Research Office; FA8650-15-D-1845 - U.S. Department of the Air Force; DE-NA0002534 - U.S. Department of Energy National Nuclear Security Administration)Accepted manuscrip

Componentes e pontos de quebra em séries temporais na análise de imagens de sensoriamento remoto

Orientador: Ricardo da Silva TorresDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A detecção e caracterização de mudanças temporais são indicadores cruciais no processo de compreensão da maneira como mecanismos complexos funcionam e evoluem. Técnicas e imagens de sensoriamento remoto têm sido amplamente empregadas nas últimas décadas com objetivo de detectar e investigar mudanças temporais na superfície terrestre. Tal detecção em dados de séries temporais é passível de ser refinada ainda mais isolando-se as componentes aditivas de tendência e sazonalidade do ruído subjacente. Este trabalho investiga, em particular, o método Breaks For Additive Season and Trend (BFAST) para a análise, decomposição e detecção de pontos de quebra em séries temporais associadas a dados de sensoriamento remoto. Os outputs do método são, então, utilizados em três distintas ¿ mas altamente interconectadas ¿ linhas de pesquisa: em uma melhor compreensão de fenômenos climáticos; na correlação com dados de distúrbios antropológicos; e em problemas de classificação usando funções de dissimilaridade descobertas por um framework evolucionário baseado em Programação Genética (GP). Experimentos realizados demonstram que a decomposição e pontos de quebra produziram resultados efetivos quando aplicados aos estudos com dados ecológicos, mas não foram capazes de melhorar os resultados de classificação quando comparados ao uso das séries brutas. As realizações nesses três contextos também culminaram na criação de duas ferramentas de análise de séries temporais com código aberto baseadas na web, sendo que uma delas foi tão bem aceita pela comunidade-alvo, que atualmente encontra-se integrada em uma plataforma privada de computação em nuvemAbstract: Detecting and characterizing temporal changes are crucial indicators in the process of understanding how complex mechanisms work and evolve. The use of remote sensing images and techniques has been broadly employed over the past decades in order to detect and investigate temporal changes on the Earth surface. Such change detection in time series data may be even further refined by isolating the additive long-term (trend) and cyclical (seasonal) components from the underlying noise. This work investigates the particular Breaks For Additive Season and Trend (BFAST) method for the analysis, decomposition, and breakpoint detection of time series associated with remote sensing data. The derived outputs from that method are, then, used in three distinct ¿ but highly interconnected ¿ research venues: in a better comprehension of climatic phenomena; in the correlation to human-induced disturbances data; and in data classification problems using time series dissimilarity functions discovered by a Genetic-Programming-(GP)-based evolutionary framework. Performed experiments show that decomposition and breakpoints produced insightful and effective results when applied to the ecological data studies, but could not further improve the classification results when compared to its raw time series counterpart. The achievements in those three contexts also led to the creation of two open-source web-based time series analysis tools. One of those tools was so well received by the target community, that it is currently integrated into a private cloud computing platformMestradoCiência da ComputaçãoMestre em Ciência da Computação132847/2015-92015/02105-0CNPQFAPES

Comparison between active sensor and radiosonde cloud boundaries over the ARM Southern Great Plains site

In order to test the strengths and limitations of cloud boundary retrievals from radiosonde profiles, 4 years of radar, lidar, and ceilometer data collected at the Atmospheric Radiation Measurements Southern Great Plains site from November 1996 through October 2000 are used to assess the retrievals of Wang and Rossow [1995] and Chernykh and Eskridge [1996]. The lidar and ceilometer data yield lowest-level cloud base heights that are, on average, within approximately 125 m of each other when both systems detect a cloud. These quantities are used to assess the accuracy of coincident cloud base heights obtained from radar and the two radiosonde-based methods applied to 200 m resolution profiles obtained at the same site. The lidar/ceilometer and radar cloud base heights agree by 0.156 ± 0.423 km for 85.27% of the observations, while the agreement between the lidar/ceilometer and radiosonde-derived heights is at best −0.044 ± 0.559 km for 74.60% of all cases. Agreement between radar- and radiosonde-derived cloud boundaries is better for cloud base height than for cloud top height, being at best 0.018 ± 0.641 km for 70.91% of the cloud base heights and 0.348 ± 0.729 km for 68.27% of the cloud top heights. The disagreements between radar- and radiosonde-derived boundaries are mainly caused by broken cloud situations when it is difficult to verify that drifting radiosondes and fixed active sensors are observing the same clouds. In the case of the radar the presence of clutter (e.g., vegetal particles or insects) can affect the measurements from the surface up to approximately 3–5 km, preventing comparisons with radiosonde-derived boundaries. Overall, Wang and Rossow [1995] tend to classify moist layers that are not clouds as clouds and both radiosonde techniques report high cloud top heights that are higher than the corresponding heights from radar