No description available.

The Cloud Imaging and Particle Size (CIPS) instrument on the Aeronomy of Ice in the Mesosphere satellite images in the nadir at the UV wavelength of 265 nm. The camera array has an approximately 120° along track (2000 km) by 80° cross track (1000 km) field of view at a horizontal resolution of 1 by 2 km in the nadir. The satellite is in a sun synchronous orbit with an approximately noon local time equator crossing. The observed albedo is due to Rayleigh scattered sun light from an altitude of approximately 50 km and sunlight scattered from Polar Mesospheric Clouds (PMC) which occur in the summer mesosphere at 83 km. The goal of the CIPS instrument is to retrieve high horizontal resolution maps of PMC albedo and the mode radius of the particle size distribution. The first manuscript analyzes the uncertainties involved in the retrieval. The ability to infer mode radius from the PMC signal is made significantly harder by the presence of the Rayleigh signal. Much of the difference between PMC signals of different mode radii is also consistent with possible changes in the Rayleigh signal. The signal is decomposed into components which isolate the portion of the PMC signal's dependence on radius which is not consistent with changes in the Rayleigh signal. This isolated component is compared with the measurement noise to estimate and understand the uncertainties in the CIPS retrieval. The presence of the Rayleigh signal is a difficulty in the PMC retrieval, but it is also a valuable data product. The second manuscript highlights the initial findings of a new gravity wave data set developed by the author. The data set provides relative ozone variations at the stratopause with a horizontal resolution of 20 by 20 km. An abundance of gravity wave signatures can be seen in the data which appear to emanate from weather events like thunderstorms and hurricanes as well as orographic sources such as the Andes and the Antarctic Peninsula. The data set fills a gap that presently exists in our observational coverage of gravity waves, so the data set should help significantly in constraining Global Climate Models. / Ph. D.

Light Detection And Ranging (LiDAR), as well as many other applications and sensors, involve segmenting sparse sets of points (point clouds) for which point density is the only discriminating feature. The segmentation of these point clouds is challenging for several reasons, including the fact that the points are not associated with a regular grid. Moreover, the presence of noise, particularly impulsive noise with varying density, can make it difficult to obtain a good segmentation using traditional techniques, including the algorithms that had been developed to process LiDAR data. This dissertation introduces novel algorithms and frameworks based on statistical techniques and image analysis in order to segment and extract surfaces from sparse noisy point clouds. We introduce an adaptive method for mapping point clouds onto an image grid followed by a contour detection approach that is based on an enhanced version of region-based Active Contours Without Edges (ACWE). We also proposed a noise reduction method using Bayesian approach and incorporated it, along with other noise reduction approaches, into a joint framework that produces robust results. We combined the aforementioned techniques with a statistical surface refinement method to introduce a novel framework to detect ground and canopy surfaces in micropulse photon-counting LiDAR data. The algorithm is fully automatic and uses no prior elevation or geographic information to extract surfaces. Moreover, we propose a novel segmentation framework for noisy point clouds in the plane based on a Markov random field (MRF) optimization that we call Point Cloud Densitybased Segmentation (PCDS). We also developed a large synthetic dataset of in plane point clouds that includes either a set of randomly placed, sized and oriented primitive objects (circle, rectangle and triangle) or an arbitrary shape that forms a simple approximation for the LiDAR point clouds. The experiment performed on a large number of real LiDAR and synthetic point clouds showed that our proposed frameworks and algorithms outperforms the state-of-the-art algorithms in terms of segmentation accuracy and surface RMSE. / Ph. D.

Object detection and seafloor segmentation for conventional 2D side-scan sonar imagery is a well-investigated problem. However, due to recent advances in sensing technology, the side-scan sonar now produces a true 3D point cloud representation of the seafloor embedded with echo intensity. This creates a need to develop algorithms to process the incoming 3D data for applications such as object detection and segmentation, and an opportunity to leverage advances in 3D point cloud processing developed for terrestrial applications using optical sensors (e.g. LiDAR). A bottleneck in deploying 3D side-scan sonar sensors for online applications is attributed to the complexity in handling large amounts of data which requires higher memory for storing and processing data on embedded computers. The present research aims to improve data processing capabilities on-board autonomous underwater vehicles (AUVs). A supervoxel-based framework for over-segmentation and object detection is proposed which reduces a dense point cloud into clusters of similar points in a neighborhood. Supervoxels extracted from the point cloud are then described using feature vectors which are computed using geometry, echo intensity and depth attributes of the constituent points. Unsupervised density based clustering is applied on the feature space to detect objects which appear as outliers. / Master of Science / Acoustic imaging using side-scan sonar sensors has proven to be useful for tasks like seafloor mapping, mine countermeasures and habitat mapping. Due to advancements in sensing technology, a novel type of side-scan sonar sensor is developed which provides true 3D representation of the seafloor along with the echo intensity image. To improve the usability of the novel sensors on-board the carrying vehicles, efficient algorithms needs to be developed. In underwater robotics, limited computational and data storage capabilities are available which poses additional challenges in online perception applications like object detection and segmentation. In this project, I investigate a clustering based approach followed by an unsupervised machine learning method to perform detection of objects on the seafloor using the novel side scan sonar. I also show the usability of the approach for performing segmentation of the seafloor.

Remote sensing offers many advantages to previous forest measurements, such as limiting costs and time in the field. Light detection and ranging (lidar) has been shown to enable accurate estimates of forest height. Lidar does produce precise measurements for ground elevation and forest height, where and when it is available. However, it is expensive to collect and does not have wall-to-wall coverage in the United States. In this study, we estimated height using the National Agricultural Imagery Program (NAIP) photogrammetric point clouds to create a predicted height map for managed loblolly pine stands in the southeastern United States. Recent studies have investigated the ability of digital aerial photogrammetry (DAP), and more specifically NAIP, as an alternative to lidar as a means of estimating forest height due to its lower costs, frequency of acquisition, and wall-to-wall coverage across the United States. Field-collected canopy height for 534 plots in Virginia and North Carolina were regressed against the 90th percentile derived from NAIP point clouds. The model for predicted pine height using the 90th percentile of height (P90) is predicted pine height = 1.09(P90) – 0.43. The adjusted R^2 is 0.93, and the RMSE is 1.44 m. This model is being used to produce a 5 m x 5 m canopy height model for all pine stands across Virginia, North Carolina, and Tennessee. NAIP-derived point clouds are thus a viable means of predicting canopy height in southern pines. / M.S. / Collecting accurate measurements of pine plantations is essential to managing them to maximize various ecosystem goods and services. However, it can be difficult and time-consuming to collect these measurements by hand. This study demonstrates that point clouds derived from digital stereo aerial photograms enable calculating forest height to an accuracy sufficient for pine plantation management. We developed a simple linear regression model to predict forest canopy height using the 90th percentile of the photo-derived heights above the ground in a given area. With this model, we created a map of pine plantation canopy heights (consisting of 5 m x 5 m grid cells, each containing a canopy height estimate) for pine forests in Virginia, North Carolina, and Tennessee. Digital aerial photography from the National Agricultural Imagery Program (NAIP) is repeated every three years for a given state, allowing growth to be mapped over time. Photography collected by NAIP and similar programs also has uniform acquisition parameters in a given year applicable over large regions. State- and national photography programs like NAIP are also less expensive than other data sets, like airborne laser scanning data, that enable estimation of tree height.

A climatology of thermodynamic phase of precipitating cloud is presented derived from global—land and ocean—, retrievals from Cloudsat, CALIPSO, and Moderate Resolution Imaging Spectroradiometer. Like precipitation rate, precipitation frequency is dominated by warm rain, defined as rain produced via the liquid phase only, over the tropical oceans outside the Intertropical Convergence Zone and by cold rain, produced via the ice phase, over the midlatitude oceans and continents. Warm rain is very infrequent over the continents, with significant warm rain found only in onshore flow in the tropics, and over India, China, and Indochina. Comparison of the properties of precipitating and nonprecipitating warm clouds shows that the scarcity of warm rain over land can be explained by smaller effective radii in continental clouds that delay the onset of precipitation. The results highlight the importance of ice-phase processes for the global hydrological cycle and may lead to an improved parameterization of precipitation in general circulation models.

Small water ice crystals are the main component of cold tropospheric clouds such as cirrus. Because these clouds cover large areas of our planet, their role in the radiation budget of incoming and outgoing radiation to the planet's surface is important. At present, the representation of these clouds in climate and weather models is subject to improvements: a large part of the uncertainty error stems from the lack of precise micro-physical and radiation model schemes for ice crystal clouds. To improve the cloud representations, a better understanding of the life time dynamics of the clouds and their composition is necessary, comprising a detailed understanding of the ice particle genesis, and development over their lifetime. It is especially important to understand how the development of ice crystals over time is linked to the changes in observable variables such as water vapour content and temperature and how they change the light scattering properties of the crystals. Recent remote and aircraft based in-situ measurements have shown that many ice particles show a light scattering behaviour typical for crystals having rough surfaces or being of complex geometrical shapes. The aim of this thesis was to develop the experimental setup and experiments to investigate this further by studying the surface morphology of small water ice crystals using scanning electron microscopy (SEM). The experiments I developed study the growth of water ice crystals inside an SEM chamber under controlled environmental conditions. The influence of water vapour supersaturation, pressure and temperature is investigated. I demonstrate how to retrieve the surface topology from observed crystals for use as input to computational light scattering codes to derive light scattering phase functions and asymmetry parameters, which can be used as input into atmospheric models. Difficulties with the method for studying the growth of water ice crystals, such as the effect of the electron beam-gas ionization and charging effects, the problem of facilitating repeated and localized ice growth, and the effect of radiative influences on the crystal growth are discussed. A broad set of nucleation target materials is studied. In a conclusion, I demonstrate that the method is suitable to study the surface morphologies, but is experimentally very challenging and many precautions must be taken, such as imaging only once and preventing radiative heat exchange between the chamber walls and the crystals to avoid unwanted effects on the crystal morphology. It is also left as a question if a laboratory experiment, where crystals will need to be grown in connection to a substrate, can represent the real world well enough. Deriving the required light scattering data in-situ might be an alternative, easier way to collect data for modelling use.

Cirrus clouds play an important role in radiative transfer, and thus have impact on the energy balance of the atmosphere and the climate of the Earth. Furthermore, they occur often and cover large areas globally at any time. Nevertheless, cirrus clouds are poorly studied, especially in the polar regions. Cirrus clouds are present in a large amount of the 14 years of data produced by the lidar at the Swedish Institude of Space Physics (IRF), but has not been studied to a large extent. A lidar is an active remote sensing instrument using a laser. This master's thesis develops and improves programs for analysis of cirrus clouds from this lidar data. It also performs analysis of six case studies chosen from the available data, and statistics of these six cases. The parameters calculated for each date are the cloud top, base and mean altitude, the geometrical thickness, the depolarisation ratio, the backscatter ratio (BSR), the backscatter coefficient, the extinction coefficient, the optical thickness and the number of cloud layers. No clear correlation between the optical thickness and the cloud top, base or mean altitude was found. There seems to be a weak correlation between increased optical thickness and increased geometrical thickness, which is not unreasonable. The mean cloud layer top altitude was 11.82 km and the mean cloud base was 10.36 km. The mean optical thickness for a cloud layer was 1.46 km, and the average of the cloud layer mean altitude was 11.09 km. It should be noted that the statistical analysis is based on only six cases with a total observation time of no more than 37 hours. A far larger dataset is needed in order to obtain any statistically signicant conclusions. The effect of averaging is studied, and it is concluded that averaging over altitude reduced the noise and facilitated the interpolation more than averaging over time did. Different approaches to obtain the molecular backscatter coefficient are compared, as well as the effect on the simulated molecular signal. Two of these approaches calculate the molecular backscatter coeffcient with input of the temperature and pressure either as continuously measured ground vales from the weather station at IRF or as radiosonde profiles for a specific time. In the other two, the molecular backscatter coeffcient is obtained from ECMWF data and from the standard atmosphere. Differences in the range 12-35% between the methods are found. Different approaches to calculate the backscatter ratio (BSR) are also compared. At cirrus altitudes, the decrease in the signal due to the molecular cloudfree part of the atmosphere is still strong, and finding the top and base separately by comparison with the standard deviation of the signal is proven a better method than interpolating between the point where the signal starts to increase and the point where it reaches the same signal value again. Height-normalising the signal provides a more robust method. For thin cirrus, the signal is not significantly attenuated above the cloud layer, and it is found that a method based on the ratios between the measured signal and the simulated molecular signal at cloud top and base did not produce reliable results for the optical thickness. In addition to analysing data and data processing methods, new data processing tools in MATLAB have been developed and existing functions have been improved. These will be valuable for continued studies with the IRF lidar, for cirrus as well as PSCs and thick and/or low-altitude clouds.

Due to the unique environmental conditions and different feedback mechanisms, the Arctic region is especially sensitive to climate changes. The influence of clouds on the radiation budget is substantial, but difficult to quantify and parameterize in models. In the framework of the PhD, elastic backscatter and depolarization lidar observations of Arctic clouds were performed during the international Arctic Study of Tropospheric Aerosol, Clouds and Radiation (ASTAR) from Svalbard in March and April 2007. Clouds were probed above the inaccessible Arctic Ocean with a combination of airborne instruments: The Airborne Mobile Aerosol Lidar (AMALi) of the Alfred Wegener Institute for Polar and Marine Research provided information on the vertical and horizontal extent of clouds along the flight track, optical properties (backscatter coefficient), and cloud thermodynamic phase. From the data obtained by the spectral albedometer (University of Mainz), the cloud phase and cloud optical thickness was deduced. Furthermore, in situ observations with the Polar Nephelometer, Cloud Particle Imager and Forward Scattering Spectrometer Probe (Laboratoire de Météorologie Physique, France) provided information on the microphysical properties, cloud particle size and shape, concentration, extinction, liquid and ice water content. In the thesis, a data set of four flights is analyzed and interpreted. The lidar observations served to detect atmospheric structures of interest, which were then probed by in situ technique. With this method, an optically subvisible ice cloud was characterized by the ensemble of instruments (10 April 2007). Radiative transfer simulations based on the lidar, radiation and in situ measurements allowed the calculation of the cloud forcing, amounting to -0.4 W m-2. This slight surface cooling is negligible on a local scale. However, thin Arctic clouds have been reported more frequently in winter time, when the clouds' effect on longwave radiation (a surface warming of 2.8 W m-2) is not balanced by the reduced shortwave radiation (surface cooling). Boundary layer mixed-phase clouds were analyzed for two days (8 and 9 April 2007). The typical structure consisting of a predominantly liquid water layer on cloud top and ice crystals below were confirmed by all instruments. The lidar observations were compared to European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological analyses. A change of air masses along the flight track was evidenced in the airborne data by a small completely glaciated cloud part within the mixed-phase cloud system. This indicates that the updraft necessary for the formation of new cloud droplets at cloud top is disturbed by the mixing processes. The measurements served to quantify the shortcomings of the ECMWF model to describe mixed-phase clouds. As the partitioning of cloud condensate into liquid and ice water is done by a diagnostic equation based on temperature, the cloud structures consisting of a liquid cloud top layer and ice below could not be reproduced correctly. A small amount of liquid water was calculated for the lowest (and warmest) part of the cloud only. Further, the liquid water content was underestimated by an order of magnitude compared to in situ observations. The airborne lidar observations of 9 April 2007 were compared to space borne lidar data on board of the satellite Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The systems agreed about the increase of cloud top height along the same flight track. However, during the time delay of 1 h between the lidar measurements, advection and cloud processing took place, and a detailed comparison of small-scale cloud structures was not possible. A double layer cloud at an altitude of 4 km was observed with lidar at the West coast in the direct vicinity of Svalbard (14 April 2007). The cloud system consisted of two geometrically thin liquid cloud layers (each 150 m thick) with ice below each layer. While the upper one was possibly formed by orographic lifting under the influence of westerly winds, or by the vertical wind shear shown by ECMWF analyses, the lower one might be the result of evaporating precipitation out of the upper layer. The existence of ice precipitation between the two layers supports the hypothesis that humidity released from evaporating precipitation was cooled and consequently condensed as it experienced the radiative cooling from the upper layer. In summary, a unique data set characterizing tropospheric Arctic clouds was collected with lidar, in situ and radiation instruments. The joint evaluation with meteorological analyses allowed a detailed insight in cloud properties, cloud evolution processes and radiative effects. / Die Arktis mit ihren speziellen Umweltbedingungen ist besonders empfindlich gegenüber Klimaveränderungen. Dabei spielen Wolken eine große Rolle im Strahlungsgleichgewicht, die aber nur schwer genau bestimmt und in Klimamodellen dargestellt werden kann. Die Daten für die Promotionsarbeit wurden im Frühjahr 2007 bei Flugzeug-Messungen von Wolken über dem Arktischen Ozean von Spitzbergen aus erhoben. Das dafür verwendete Lidar (Licht-Radar) des Alfred-Wegener-Instituts lieferte ein höhenaufgelöstes Bild der Wolkenstrukturen und ihrer Streu-Eigenschaften, andere Messgeräte ergänzten optische sowie mikrophysikalische Eigenschaften der Wolkenteilchen (Extinktion, Größenverteilung, Form, Konzentration, Flüssigwasser- und Eisgehalt, Messgeräte vom Laboratoire de Météorologie Physique, France) und Strahlungsmessungen (Uni Mainz). Während der Messkampagne herrschte Nordwind vor. Die untersuchten Luftmassen mit Ursprung fern von menschlichen Verschmutzungsquellen war daher sehr sauber. Beim Überströmen der kalten Luft über den offenen warmen Arktischen Ozean bildeten sich in der Grenzschicht (ca. 0-1500 m Höhe) Mischphasenwolken, die aus unterkühlten Wassertröpfchen im oberen Bereich und Eis im unteren Bereich der Wolken bestehen. Mit den Flugzeug-Messungen und numerischen Simulationen des Strahlungstransports wurde der Effekt einer dünnen Eiswolke auf den Strahlungshaushalt bestimmt. Die Wolke hatte lokal eine geringe Abkühlung der Erdoberfläche zur Folge. Ähnliche Wolken würden jedoch im Winter, wenn keine Sonnenstrahlung die Arktis erreicht, durch den Treibhauseffekt eine nicht vernachlässigbare Erwärmung der Oberfläche verursachen. Die Messungen der Mischphasenwolken wurden mit einem Wettervorhersagemodell (ECMWF) verglichen. Für die ständig neue Bildung von flüssigen Wassertropfen im oberen Teil der Wolke ist das Aufsteigen von feuchten Luftpaketen nötig. Während einer Messung wurden entlang der Flugstrecke verschiedene Luftmassen durchflogen. An der Luftmassengrenze wurde eine reine Eiswolke inmitten eines Mischphasen-Systems beobachtet. Die Messungen zeigen, dass das Mischen von Luftmassen den Nachschub an feuchter Luft blockiert, was unmittelbare Auswirkungen auf die thermodynamische Phase des Wolkenwassers hat. Weiterhin wurde bestimmt, wie groß die Abweichungen der Modellrechnungen von den Messungen bezüglich Wassergehalt und der Verteilung von Flüssigwasser und Eis waren. Durch die vereinfachte Wolken-Parameterisierung wurde die typische vertikale Struktur von Mischphasenwolken im Modell nicht wiedergegeben. Die flugzeuggetragenen Lidar-Messungen vom 9. April 2007 wurden mit Lidar-Messungen an Bord des Satelliten CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) verglichen. Die Messungen zeigten beide eine ansteigende Wolkenobergrenze entlang desselben Flugwegs. Da die Messungen jedoch nicht genau gleichzeitig durchgeführt wurden, war wegen Advektion und Prozessen in den Wolken kein genauer Vergleich der kleinskaligen Wolkenstrukturen möglich. Außerdem wurde eine doppelte Wolkenschicht in der freien Troposphäre (4 km Höhe) analysiert. Die Wolke bestand aus zwei separaten dünnen Schichten aus flüssigem Wasser (je 150 m dick) mit jeweils Eis darunter. Die untere Schicht entstand wahrscheinlich aus verdunstetem Eis-Niederschlag. Diese feuchte Schicht wurde durch die Abstrahlung der oberen Wolkenschicht gekühlt, so dass sie wieder kondensierte. Solche Wolkenformationen sind in der Arktis bisher vor allem in der Grenzschicht bekannt. Ein einzigartiger Datensatz von arktischen Wolken wurde mit einer Kombination verschiedener Flugzeug-Messgeräte erhoben. Zusammen mit meteorologischen Analysen konnten für verschiedene Fallstudien Wolkeneigenschaften, Entwicklungsprozesse und Auswirkungen auf den Strahlungshaushalt bestimmt werden.

Cirrus clouds cover between 20% - 50% of the globe and are an essential component in the climate. The improved understanding of ice cloud microphysical properties is contingent on acquiring and analyzing in-situ and remote sensing data from cirrus clouds. In ??u observations of microphysical properties of ice and mixed-phase clouds using the mini-Video Ice Particle Sizer (mini-VIPS) aboard robotic unmanned aerial vehicles (UAVs) provide a promising and powerful platform for obtaining valuable data in a cost-effective, safe, and long-term manner. The purpose of this study is to better understand cirrus microphysical properties by analyzing the effectiveness of the mini-VIPS/UAV in-situ platform. The specific goals include: (1) To validate the mini-VIPS performance by comparing the mini-VIPS data retrieved during an Artic UAV mission with data retrieved from the millimeterwavelength cloud radar (MMCR) at the Barrow ARM/CART site. (2) To analyze mini-VIPS data to survey the properties of high latitude mixedphase clouds The intercomparison between in-situ and remote sensing measurements was carried out by comparing reflectivity values calculated from in-situ measurements with observations from the MMCR facility. Good agreement between observations and measurements is obtained during the time frame where the sampled volume was saturated with respect to ice. We also have 1 2 shown that the degree of closure between calculated and observed reflectivity strongly correlates with the assumption of ice crystal geometry observed in the mini-VIPS images. The good correlation increases the confidence in mini-VIPS and MMCR measurements. Finally, the size distribution and ice crystal geometry obtained from the data analysis is consistent with published literature for similar conditions of temperature and ice supersaturation.