Deep convective clouds (DCCs) play a crucial role in redistributing latent heat, hydrological cycle and in the radiative budget of our climate system. Therefore, their complex evolution processes are in focus of many studies. Changes in the structure of DCCs can delay the onset of precipitation and alter the albedo of clouds. Knowing where in the cloud and under what circumstances the cloud liquid water droplets start to freeze is an important step to improve climate and weather forecast models. The purpose of this planned study is to characterize the impact of aerosol and thermodynamic conditions on the cloud particle growth. Therefore, ground-based cloud side observation of the reflected solar spectral radiation (near infrared) using an imaging spectroradiometer and measurements of the emitted thermal radiation using an infrared camera will be combined. These measurements will be taken at the Amazon Tall Tower Observatory, in the Amazon forest, Brazil. Here, the campaign will be introduced. / Hochreichend konvektive Bewölkung (deep convective clouds, DCCs) spielt eine entscheidende Rolle bei der Umverteilung latenter Wärme, sowie für den Wasserkreislauf und dem Strahlungshaushalt unseres Klimasystems. Aus diesem Grund stehen ihre komplexen Wolkenbildungsprozesse im Fokus vieler Untersuchungen. Veränderungen in der mikrophysikalischen Struktur der DCCs können das Einsetzen der Niederschlagsbildung verzögern. Darüber hinaus verändern sie die Albedo der Wolke. Das Wissen darüber, wo in der Wolke und unter welchen Umständen die Wolkentropfen beginnen zu gefrieren, ist ein wichtiger Schritt zur Verbesserung von Klima- und Wettervorhersagemodellen. Das Ziel der geplanten Untersuchungen besteht in der Charakterisierung des Einflusses von Aerosolpartikeln und thermodynamischer Bedingungen auf den Partikelwachstum und der Phasenumwandlung in Wolken. Hierzu werden bodengebundene Wolkenseitenbeobachtungen der reflektierten solaren Strahlung (nahes infrarot), aufgezeichnet mit Hilfe eines abbildenden Spektrometers, sowie Messungen der emittierten thermischen Strahlung, detektiert mit einer Infrarotkamera, kombiniert. Die entsprechenden Messungen werden am „Amazon Tall Tower Observatory“ im Amazonas Regenwald in Brasilien durchgeführt. Im folgendem wird die zugehörige Kampagne vorgestellt.

We describe an ultraviolet spectroscopic survey of interstellar high-velocity cloud (HVC) absorption in the strong λ1206.500 line of Si III using the Space Telescope Imaging Spectrograph aboard the Hubble Space Telescope. Because the Si III line is 4-5 times stronger than O VI λ1031.926, it provides a sensitive probe of ionized gas down to column densities N Si III 5 × 1011 cm-2 at Si III equivalent width 10 m. We detect high-velocity Si III over 91% 4% of the sky (53 of 58 sight lines); 59% of the HVCs show negative local standard of rest velocities. The mean HVC column density per sight line is 〈log N Si III 〉 = 13.19±0.45, while the mean for all 90 velocity components is 12.92±0.46. Lower limits due to Si III line saturation are included in this average, so the actual mean/median values are even higher. The Si III appears to trace an extensive ionized component of Galactic halo gas at temperatures 104.0-4.5 K indicative of a cooling flow. Photoionization models suggest that typical Si III absorbers with 12.5 < log N Si III < 13.5 have total hydrogen column densities N H 1018-1019 cm-2 for gas of hydrogen density n H 0.1 cm-3 and 10% solar metallicity. With typical neutral fractions N H I/N H 0.01, these HVCs may elude even long-duration 21 cm observations at Arecibo, the EVLA, and other radio facilities. However, if Si III is associated with higher density gas, n H ≥ 1 cm-3, the corresponding neutral hydrogen could be visible in deep observations. This reservoir of ionized gas may contain 10 8M and produce a mass infall rate of 1 M yr-1 to the Galactic disk.

We present HST Goddard High Resolution Spectrograph and Space Telescope Imaging Spectrograph observations of five quasi stellar objects that probe the prominent high-velocity cloud (HVC) Complex C, covering ∼10% of the northern sky. Based upon a single sight-line measurement (Mrk 290), a metallicity [S/H] = -1.05 ± 0.12 has been associated with Complex C by Wakker et al. When coupled with its inferred distance (5 ≲ d ≲ 30 kpc) and line-of-sight velocity (v ∼ -100 to -200 km s-1), Complex C appeared to represent the first direct evidence for infalling low-metallicity gas onto the Milky Way, which could provide the bulk of the fuel for star formation in the Galaxy. We have extended the abundance analysis of Complex C to encompass five sight lines. We detect S n absorption in three targets (Mrk 290, 817, and 279); the resulting [S II/H I] values range from -0.36 (Mrk 279) to -0.48 (Mrk 817) to -1.10 (Mrk 290). Our preliminary O I FUSE analysis of the Mrk 817 sight line also supports the conclusion that metallicities as high as 0.3 times solar are encountered within Complex C. These results complicate an interpretation of Complex C as infalling low-metallicity Galactic fuel. Ionization corrections for H II and S III cannot easily reconcile the higher apparent metallicities along the Mrk 817 and Mrk 279 sight lines with that seen toward Mrk 290, since Hα emission measures preclude the existence of sufficient H II. If gas along the other lines of sight has a similar pressure and temperature to that sampled toward Mrk 290, the predicted Hα emission measures would be ∼900 mR. It may be necessary to reclassify Complex C as mildly enriched Galactic waste from the Milky Way or processed gas torn from a disrupted neighboring dwarf, as opposed to low-metallicity Galactic fuel.

A longstanding source of uncertainty within the climate system is our understanding of clouds and their response to aerosols. The resulting cloud optical property changes constitute the largest uncertainty in our understanding of 20th century climate change. Central to being able to monitor and better understand the effects aerosols composition, size and concentration have on cloud reflectivity are accurate observations of the cloud droplet number concentration. Cloud droplet number concentrations couple aerosol properties to changes in cloud brightness. In the first portion of this dissertation, I present the development and evaluation of two techniques for observing cloud properties. The first is a new method of observing cloud droplet number concentration that uses polarimetric measurements and requires relatively few assumptions. The theoretical derivation is first presented followed by a method of implementation using NASA’s airborne Research Scanning Polarimeter (RSP). I use data obtained during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). Comparing cloud droplet number concentration retrievals with in situ measurements made by a cloud droplet probe during NAAMES shows strong agreement between measurements over a range of meteorological conditions and cloud types. Multilayered clouds are ubiquitous within Earth’s atmosphere, yet detecting their presence and height has been a longstanding challenge for passive remote sensing instruments. Retrieving the cloud top height is also an important part of the droplet concentration retrieval, and detecting the presence of multilayered clouds supports interpreting results. For this second technique, I present an assessment of RSP cloud top height retrievals, which are based on the concept of parallax. By comparing RSP cloud top height retrievals to the Cloud Physics Lidar (CPL), the technique is found to be capable of determining the presence and heights of up to three cloud layers, which is innovative for a passive remote sensing instrument. A second element essential to addressing the uncertainty in cloud’s response to aerosols is to better understand processes and drivers of cloud properties. Air-campaign studies offer opportunities to study high temporal and spatial resolution measurements that are needed to better understand the complex processes between aerosols, clouds and meteorological properties. My final investigation uses the two developed cloud property retrievals, in conjunction with other in situ and remotely sensed data, to undertake a broad investigation quantifying connections observed between aerosols, clouds and meteorology. I find a well- defined link between cloud microphysical property changes and marine biogenic aerosol concentrations. Changes in cloud properties are consistent with the Twomey effect, whereby an increase in cloud condensation nuclei is associated with increases in droplet concentrations and decreased droplet sizes. I also observe complex, non-linear secondary effects of aerosols on clouds such as cloud thinning and decreased droplet distribution width. I conclude this study by integrating my findings and discussing plausible linkages between aerosol, cloud and meteorological properties within the context of existing concepts.

Thesis (Sc.D.)--Massachusetts Institute of Technology, Dept. of Meteorology and Physical Oceanography, 1981. / Microfiche copy available in Archives and Science. / Vita. / Bibliography: leaves 140-145. / by Frank Decatur Marks, Jr. / Sc.D.

Thesis. 1977. Ph.D.--Massachusetts Institute of Technology. Dept. of Meteorology. / Microfiche copy available in Archives and Science. / Vita. / Bibliography : leaves 290-293. / Ph.D.

This work addresses the problem of rendering clouds. The task of rendering clouds is important to film and video game directors who want to use clouds to further the story or create a specific atmosphere for the audience. While there has been significant progress in this area, other solutions to this problem are inadequate because they focus on speed instead of accuracy, or focus only on a few specific properties of rendered clouds while ignoring others. Another common shortcoming with other methods is that they are not integrated into existing rendering pipelines. We propose a solution to this problem based on creating a point cloud to represent the cloud volume, then calculating light scattering events between the points. The key insight is blending isotropic and anisotropic scattering events to mimic realistic light scattering of anisotropic participating media. Rendered images are visually plausible representations of how light interacts with clouds.

Four regions of massive star formation in the Large Magellanic Cloud (LMC) were observed for water and methanol maser emission and radio continuum emission. A total of 42 radio detections were made including 27 new radio sources, four water masers, and eight compact HII regions. The lobes of a radio galaxy were resolved for the first time, and the host galaxy identified. Seven sources were associated with known massive young stellar objects (YSOs). A multi-wavelength analysis using both the infrared and radio spectrum was used to characterize the sources. Mid-infrared color-magnitude selection criteria for ultracompact HII (UCHII) regions in the LMC are presented, yielding 136 UCHII region candidates throughout that galaxy. New maser detections identified two previously unknown massive YSOs. No methanol masers were detected, consistent with previous studies and supporting the hypothesis that the LMC may be deficient in these molecules. These discoveries contribute to the history of star formation in the LMC, which will lead to a better understanding of star formation in the Milky Way and throughout the universe.

No description available.

Autonomous vehicles are complex robotic and artificial intelligence systems working together to achieve safe operation in unstructured environments. The objective of this work is to provide a foundation to develop more advanced algorithms for off-road autonomy. The project explores the sensors used for off-road autonomy and the data capture process. Additionally, the point cloud data captured from lidar sensors is processed to restore some of the geometric information lost during sensor sampling. Because ground truth values are needed for quantitative comparison, the MAVS was leveraged to generate a large off-road dataset in a variety of ecosystems. The results demonstrate data capture from the sensor suite and successful reconstruction of the selected geometric information. Using this geometric information, the point cloud data is more accurately segmented using the SqueezeSeg network.