Remote sensing of the diurnal cycle of optically thin cirrus clouds

Eiswolken und insbesondere hohe Zirruswolken bedecken im globalen jährlichen Mittel bis zu 30 % der Erde und haben deshalb einen signifikanten Einfluß auf das Klima. Eine Besonderheit hoher Eiswolken ist, dass sie einen wärmenden Effekt auf das System Erde und Atmosphäre besitzen können. Dieser wärmende Effekt wird u. a. durch tägliche und saisonale Variationen der optischen Eigenschaften beeinflußt. Um genaue Messungen der optischen Eigenschaften von Aerosolen und Zirruswolken zu erhalten, wurde 2006 die "Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations" (CALIPSO) Mission in einen polaren Orbit gestartet. Mit Hilfe des Hauptinstrumentes, des "Cloud- Aerosol Lidar with Orthogonal Polarization" (CALIOP), können nun optische Eigenschaften von Aerosol- und dünnen Wolkenschichten mit bisher unerreichter Genauigkeit und Sensitivität bestimmt werden. Allerdings erlaubt dieser Orbit mit einer Wiederkehrdauer von mehr als zwei Wochen keine Ableitung von Tagesgängen der optischen Eigenschaften und des Bedeckungsgrades von Zirruswolken, weshalb in dieser Arbeit der Wolkensensor "Spinning Enhanced Visible and Infrared Imager" (SEVIRI) auf dem geostationären "METEOSAT Second Generation" (MSG) Satelliten benutzt wird. SEVIRI deckt mit seinen Messungen fast ein Drittel der Erde ab und reicht von 80 N bis 80 S und von 80 W bis 80 E bei einer räumlichen Auflösung von bis zu 3 km x 3 km im Nadir und einer zeitlichen Auflösung von 15 Minuten. Im Rahmen dieser Arbeit wurde ein gänzlich neuer Ansatz verfolgt, um die Vorteile beider Instrumente (die hohe Sensitivität und Genauigkeit von CALIOP und die hohe zeitliche und räumliche Auflösung von SEVIRI) miteinander zu verbinden: Der "Cirrus Optical properties derived from CALIOP and SEVIRI during day and night" (COCS) Algorithmus basiert auf dem Prinzip künstlicher Neuronaler Netze und leitet die optischen Dicken von Zirruswolken und deren Oberkantenhöhen aus Messungen der Infrarotkanäle des Instrumentes SEVIRI ab, was Beobachtungen sowohl in der Nacht als auch am Tage ermöglicht. Dieses Neuronale Netz wurde mit gleichzeitigen Messungen der optischen Dicken und Höhen der Wolkenoberkante von CALIOP trainiert. In dieser Arbeit wird die Entwicklung von COCS und die Validierung mit zwei unterschiedlichen Lidar-Messungen beschrieben, mit denen von CALIOP und mit denen des flugzeuggetragenen "High Spectral Resolution Lidar" (HSRL). Die Validierungen zeigen die hohe Genauigkeit des hier entwickelten Algorithmus in der Ableitung der optischen Dicken und Höhen der Wolkenoberkante von Zirruswolken. Zusätzlich wurden auch Vergleiche der COCS-Ergebnisse mit zwei weiteren auf SEVIRI basierenden Algorithmen durchgeführt: Zum einen mit dem "METEOSAT Cirrus Detection Algorithm 2" (MECiDA-2), welcher ebenfalls die thermischen Infrarotkanäle benutzt, zum anderen mit dem "Algorithm for the Physical Investigation of Clouds with SEVIRI" (APICS), welcher zur Ableitung der optischen Eigenschaften von Wolken sowohl auf den Infrarotkanälen als auch auf Kanälen im sichtbaren Spektralbereich basiert. Die Validierung zeigt hervorragende Ergebnisse für die Erkennung von Zirruswolken mit einer Fehldetektionsrate von unter 5 % und einer Detektionseffizienz von bis zu 99 % ab einer optischen Dicke von 0.1. Ebenfalls wird eine Standardabweichung von 0.25 für die optische Dicke und 0.75 km für die Höhe der Wolkenoberkante nachgewiesen. Basierend auf fünf Jahren prozessierter COCS-Daten werden die Tagesgänge von Zirruswolken in verschiedenen Regionen der Erde analysiert und diskutiert. Die Ergebnisse zeigen ausgeprägte Tagesgänge des Zirrusbedeckungsgrades und der optischen Dicke, welche sich von den Vorhersagen des "European Centre for Medium-range Weather Forecasts" (ECMWF) unterscheiden. Eine Betrachtung des Bedeckungsgrades hoher Wolken, vorhergesagt durch das ECMWF, und der Ergebnisse des COCS Algorithmus zeigt gut übereinstimmende Tagesgänge in konvektiven Regionen, während Unterschiede in nichtkonvektiven Regionen über dem Nord- (NAR) und Südatlantik (SAR) sichtbar werden. Generell wird vor allem in diesen Regionen ein höherer Bedeckungsgrad mit Unterschieden von 3-10 % durch COCS errechnet. Abschließend werden die Unterschiede der NAR und SAR diskutiert, da im Nordatlantik einer der meist frequentierten ozeanischen Flugkorridore liegt. Hier mischen sich die heißen Flugzeugabgase mit kalten Luftmassen und führen zur Bildung von Kondensstreifen. Diese Kondensstreifen verlieren mit der Zeit ihre lineare Form und können anschließend nicht mehr von natürlich entstandenen Zirruswolken unterschieden werden. Grundsätzlich zeigt sich hier eine starke Korrelation des Tagesganges von Bedeckungsgrad und optischer Dicke der Zirruswolken mit der Luftverkehrsdichte. Es werden Unterschiede von bis zu 3 % im Bedeckungsgrad zwischen NAR und SAR detektiert.Aim of this thesis is the retrieval of diurnal variations of cirrus cloud optical properties. Ice clouds and especially cirrus clouds cover on average up to 30 % of the Earth and are therefore important for climate. High ice clouds hold an exceptional position within the large variety of clouds, since they generate positive net forcing and therefore make a contribution to warming of Earth's atmosphere. This heating effect is strongly modified by the diurnal and seasonal variations of the optical properties of cirrus clouds. In order to determine optical properties of aerosols and clouds, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission was launched into a polar orbit in 2006. Equipped with its main instrument, the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), this satellite is able to retrieve optical properties of aerosol layers and thin clouds with unprecedented accuracy and sensitivity from space. With a repeat cycle of more than two weeks it does not provide diurnal variations in cirrus cloud properties and cirrus coverage, therefore the most advanced geostationary cloud sensor, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard METEOSAT Second Generation (MSG) is used in this work. SEVIRI covers almost one third of Earth (from 80 N to 80 S and from 80 W to 80 E) with a high temporal resolution of 15 min and a spatial resolution of 3 km x 3 km at subsatellite point. Within the framework of this thesis a completely new approach was followed to combine the advantages of both instruments (high sensitivity and accuracy of CALIOP with the high temporal resolution and spatial coverage of SEVIRI): The Cirrus Optical properties derived from CALIOP and SEVIRI during day and night (COCS) algorithm is based on an artificial neural network, which retrieves cirrus ice optical thickness (IOT) and top altitude (TOP) from the thermal infrared channels of SEVIRI making day and night observations possible. It is trained by coincident CALIPSO cirrus ice optical thickness and top altitude. This work describes the development of COCS and compares the results of the algorithm with two different lidar measurements, CALIOP and an airborne High Spectral Resolution Lidar (HSRL). The validation with CALIOP and the HSRL proves the accuracy of the retrieved cirrus ice optical thickness and top altitude. Beside this validation the results of the COCS algorithm are further compared with the METEOSAT Cirrus Detection Algorithm 2 (MeCiDA-2), using the thermal infrared channels of SEVIRI to detect cirrus clouds, and the Algorithm for the Physical Investigation of Clouds with SEVIRI (APICS), using a combination of visible and infrared channels to derive optical properties of clouds. The validation shows excellent results for the detection of thin cirrus clouds with false alarm rates lower than 5 % and detection efficiencies up to 99 % at a cirrus ice optical thickness greater or equal than 0.1. Low standard deviations of 0.25 for cirrus ice optical thickness and 756 m for cirrus top altitude are reached. Based on five years of processed COCS data, diurnal cycles of cirrus clouds in different regions of the Earth are analysed and discussed. The results show distinct features in coverage and ice optical thickness, which slightly disagree with the forecasts of the European Center for Medium-range Weather Forecasts (ECMWF). While the ECMWF high cloud coverage shows a diurnal cycle comparable to COCS in convective regions, the diurnal cycle in non-convective regions over the North and South Atlantic disagrees. Furthermore the COCS derives higher cirrus cloud coverage compared to the high cloud coverage of the ECMWF of 3 -10 % for the analysed regions. Finally differences in the North and South Atlantic region, NAR and SAR, are discussed, since the NAR is chosen to cover an area with one of the most frequented air corridors, where hot exhausts of aeroplanes mix with cold air leading to contrail formation. These contrails loose their linear shape with time and then fail to be discriminated from natural formed cirrus clouds. A strong correlation between air traffic density (ATD) and the diurnal cycle of cirrus coverage and ice optical thickness was found over the North Atlantic. Furthermore the differences in cirrus coverage between NAR and SAR follow the diurnal cycle of ATD, with an amplitude of up to 3 %

A novel satellite mission concept for upper air water vapour, aerosol and cloud observations using integrated path differential absorption LiDAR limb sounding

We propose a new satellite mission to deliver high quality measurements of upper air water vapour. The concept centres around a LiDAR in limb sounding by occultation geometry, designed to operate as a very long path system for differential absorption measurements. We present a preliminary performance analysis with a system sized to send 75 mJ pulses at 25 Hz at four wavelengths close to 935 nm, to up to 5 microsatellites in a counter-rotating orbit, carrying retroreflectors characterized by a reflected beam divergence of roughly twice the emitted laser beam divergence of 15 µrad. This provides water vapour profiles with a vertical sampling of 110 m; preliminary calculations suggest that the system could detect concentrations of less than 5 ppm. A secondary payload of a fairly conventional medium resolution multispectral radiometer allows wide-swath cloud and aerosol imaging. The total weight and power of the system are estimated at 3 tons and 2,700 W respectively. This novel concept presents significant challenges, including the performance of the lasers in space, the tracking between the main spacecraft and the retroreflectors, the refractive effects of turbulence, and the design of the telescopes to achieve a high signal-to-noise ratio for the high precision measurements. The mission concept was conceived at the Alpbach Summer School 2010

VADUGS: a neural network for the remote sensing of volcanic ash with MSG/SEVIRI trained with synthetic thermal satellite observations simulated with a radiative transfer model

After the eruption of volcanoes around the world, monitoring of the dispersion of ash in the atmosphere is an important task for satellite remote sensing since ash represents a threat to air traffic. In this work we present a novel method, tailored for Eyjafjallajökull ash but applicable to other eruptions as well, that uses thermal observations of the SEVIRI imager aboard the geostationary Meteosat Second Generation satellite to detect ash clouds and determine their mass column concentration and top height during the day and night. This approach requires the compilation of an extensive data set of synthetic SEVIRI observations to train an artificial neural network. This is done by means of the RTSIM tool that combines atmospheric, surface and ash properties and runs automatically a large number of radiative transfer calculations for the entire SEVIRI disk. The resulting algorithm is called "VADUGS" (Volcanic Ash Detection Using Geostationary Satellites) and has been evaluated against independent radiative transfer simulations. VADUGS detects ash-contaminated pixels with a probability of detection of 0.84 and a false-alarm rate of 0.05. Ash column concentrations are provided by VADUGS with correlations up to 0.5, a scatter up to 0.6 g m-2 for concentrations smaller than 2.0 g m-2 and small overestimations in the range 5 %-50 % for moderate viewing angles 35-65°, but up to 300 % for satellite viewing zenith angles close to 90 or 0°. Ash top heights are mainly underestimated, with the smallest underestimation of -9 % for viewing zenith angles between 40 and 50°. Absolute errors are smaller than 70 % and with high correlation coefficients of up to 0.7 for ash clouds with high mass column concentrations. A comparison with spaceborne lidar observations by CALIPSO/CALIOP confirms these results: For six overpasses over the ash cloud from the Puyehue-Cordón Caulle volcano in June 2011, VADUGS shows similar features as the corresponding lidar data, with a correlation coefficient of 0.49 and an overestimation of ash column concentration by 55 %, although still in the range of uncertainty of CALIOP. A comparison with another ash algorithm shows that both retrievals provide plausible detection results, with VADUGS being able to detect ash further away from the Eyjafjallajökull volcano, but sometimes missing the thick ash clouds close to the vent. VADUGS is run operationally at the German Weather Service and this application is also presented

Interoception and mental health: a roadmap

Interoception refers to the process by which the nervous system senses, interprets, and integrates signals originating from within the body, providing a moment-by moment mapping of the body’s internal landscape across conscious and unconscious levels. Interoceptive signaling has been considered a component process of reflexes, urges, feelings, drives, adaptive responses, and cognitive and emotional experiences, highlighting its contributions to the maintenance of homeostatic functioning, body regulation, and survival. Dysfunction of interoception is increasingly recognized as an important component of different mental health conditions, including anxiety disorders, mood disorders, eating disorders, addictive disorders, and somatic symptom disorders. However, a number of conceptual and methodological challenges have made it difficult for interoceptive constructs to be broadly applied in mental health research and treatment settings. In November 2016, the Laureate Institute for Brain Research organized the first Interoception Summit, a gathering of interoception experts from around the world, with the goal of accelerating progress in understanding the role of interoception in mental health. The discussions at the meeting were organized around four themes: interoceptive assessment, interoceptive integration, interoceptive psychopathology, and the generation of a roadmap that could serve as a guide for future endeavors. This review article presents an overview of the emerging consensus generated by the meeting

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

COCS - Cirrus Optical properties derived from CALIOP and SEVIRI during day and night time

Significant progress in cirrus cloud observation has been achieved with spaceborne active remote sensing techniques, e.g. the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the polar-orbiting Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO). With its high vertical and horizontal resolution CALIOP provides high detailed profiles of optical properties especially of cirrus clouds. But to deliver information on the lifecycle of cirrus clouds high temporal resolution is needed. These observations are performed by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard MSG every 5-15 minutes providing high detailed information (e.g. brightness temperatures of seven infrared channels) making a day and night time observation of clouds possible. In the following, the COCS algorithm (Cirrus Optical properties derived from CALIOP and SEVIRI) combining the advantages of both satellite instruments, some preliminary results, intercomparisons and examples of its application are presented. COCS provides the cloud optical thickness (COT) and the cloud top altitude (CTA) of cirrus clouds

METEOSAT Observations of the Daily Variation of Cirrus

Cirrus clouds have a substantial impact on net radiation and therefore also on climate, but the physical processes involved in cirrus formation and decay are not very well represented in climate and weather prediction models. In-situ formation of natural cirrus clouds is initiated when cooling moist air parcels reach a substantial supersaturation with respect to ice. This happens either due to dynamic lifting of the air or due to radiative cooling. But once ice crystals are formed, they grow until the ambient air becomes sub-saturated either by subsidence of the whole air-mass or sedimentation of the particles into drier air. Thus the pure diagnostic description of clouds, as it is still used in current climate and weather prediction models has to be tuned to match observations at least until a prognostic description of cirrus clouds will be introduced into these models. The decay of cirrus clouds is a process with a typical timescale of hours. Therefore geostationary satellites with their high temporal resolution are an ideal platform for cirrus observations. The data from these satellites offer the possibility to observe the life cycle either by tracking cirrus clouds or by observation of the typical daily and seasonal variation of cirrus coverage. In particular the infrared channels of the METEOSAT satellites, which are independent from day-light and not affected by the different scattering properties of the various ice particle habits are suitable for such observations. For the analysis we use a novel scheme to derive cirrus optical depth and height from the SEVIRI infrared channels. It is based on an artificial neural network trained with the data provided by CALIOP, the lidar system aboard of the CALIPSO satellite

Observing cirrus formation and decay with METEOSAT

Cirrus clouds have a substantial impact on net radiation and therefore also on climate, but the physical processes involved in cirrus formation and decay are not very well represented in climate and weather prediction models. In-situ formation of natural cirrus clouds is initiated when cooling moist air parcels reach a substantial super-saturation with respect to ice. This happens either due to dynamic lifting of the air or due to radiative cooling. But once ice crystals are formed, they grow until the ambient air becomes sub-saturated either by subsidence of the whole air-mass or sedimentation of the particles into drier air. Thus the pure diagnostic description of clouds, as it is used in current climate and weather prediction models has to be tuned to match observations at least until a prognostic description of cirrus clouds will be introduced into these models. The decay of cirrus clouds is a process with a typical timescale of hours. Therefore geostationary satellites with their high temporal resolution are an ideal platform for cirrus observations. The data from these satellites offer the possibility to observe the life cycle either by tracking cirrus clouds or by observation of the typical daily and seasonal variation of cirrus coverage. In particular the infrared channels of the METEOSAT satellites, which are independent from day-light and not affected by the different scattering properties of the various ice particle habits are suitable for such observations. In this article we analyse the daily variation of cirrus coverage in the Southern Atlantic and Indian Ocean region, as this wide area is not affected by deep convection, which dominates the daily variation of cirrus coverage over land

Retrieval of cirrus cloud optical thickness and top altitude from geostationary remote sensing

A novel approach for the detection of cirrus clouds and the retrieval of optical thickness and top altitude based on the measurements of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the geostationary Meteosat Second Generation (MSG) satellite is presented. Trained with 8 000 000 co-incident measurements of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission the new "cirrus optical properties derived from CALIOP and SEVIRI algorithm during day and night" (COCS) algorithm utilizes a backpropagation neural network to provide accurate measurements of cirrus optical depth t at ? = 532 nm and top altitude z every 15 min covering almost one-third of the Earth's atmosphere. The retrieved values are validated with independent measurements of CALIOP and the optical thickness derived by an airborne high spectral resolution lidar