On the Discrimination and Interaction of Droplets and Ice in Mixed-Phase Clouds (Phasendiskriminierung und Interaktion von Wassertropfen und Eispartikeln in Mischphasenwolken)

Mixed-phase clouds, consisting of both supercooled liquid droplets and ice particles, play a major role in the life cycle of clouds and the radiative balance of the Earth. However, mixed-phase cloud processes are still rather poorly understood and represent a great source of uncertainty for climate predictions. The main reason for this is the insufficient understanding of the microphysical properties of mixed-phase cloud particles. The biggest challenge is the correct discrimination of droplets and ice particles. In this work, the Particle Habit Imaging and Polar Scattering (PHIPS) probe, an airborne in situ cloud instrument, is used to investigate the composition and microphysical properties of mixed-phase clouds. It combines optical microscopy with polar nephelometry to simultaneously measure the angular scattering behaviour while acquiring stereo-microscopic images of single cloud particles. Based on PHIPS data, a novel method to determine the phase of individual cloud particles based on their angular light scattering behaviour is presented. Comparisons with manually classified in situ data show that the algorithm is able to confidently discriminate spherical droplets and aspherical ice particles with a 98% accuracy. Furthermore, a sizing method based on single particle scattering data is presented. Combined, this allows the determination of phase discriminated particle size distributions in a size range of $50 \leq D \leq 700 \, \text{µm}$ and $20 \leq D \leq 700 \, \text{µm}$ for droplets and ice, respectively. This fills the gap between the commonly used forward scattering instruments and optical array probes. The PHIPS probe was deployed during three in situ aircraft field campaigns in the Southern Ocean, the Arctic and the US east coast. In over 250 flight hours, an extensive data set of single particle microphysical data over a wide range of ambient cloud conditions was acquired. Using the aforementioned newly developed methods, the phase composition of the sampled clouds is analysed and the difference between clouds in high latitudes of the northern and southern hemisphere is discussed. Furthermore, riming, the accretion of droplets by ice particles, is investigated based on manual classification of PHIPS\u27 stereo-micrographs. Riming is observed on over 30% of the investigated ice particles in a size range from $20 \leq D \leq 700 \, \text{µm}$ in clouds between -10$^\circ$C$\,\leq\,$T$\,\leq\,$0$^\circ$C. The meteorological conditions of riming are investigated and the correlation of ambient parameters with riming state and riming degree are discussed. It is shown that riming increases the light scattering in the angular range from $\theta = 42^\circ\,\text{and}\,170^\circ$ by up to 135% compared to unrimed particles. Further, particles with faceted, crystalline build-up which is aligned to the lattice structure of the underlying particle are described. For these particles, which are believed to be the result of vapor deposition during the ageing process of rimed particles, the term "epitaxial riming" is proposed

A polarized discrete ordinate scattering model for radiative transfer simulations in spherical atmospheres with thermal source

The development of the new discrete ordinate scattering algorithm, which is a part of the Atmospheric Radiative Transfer Simulator (ARTS), is described. Furthermore, applications of the algorithm, which was implemented to study for example the influence of cirrus clouds on microwave limb sounding, are presented.The model development requires as a theoretical basis the electromagnetic scattering theory. The basic quantities are defined and different methods to compute single scattering properties of small particles are discussed. In order to represent clouds as scattering media in radiative transfer models, information about their micro-physical state is required as an input for calculating the scattering properties.The micro-physical state of a cloud is defined by the phase of the cloud particles, the particle size and shape distributions, the particle orientation, the ice mass or the liquid water content, and the temperature. The model uses the Discrete OrdinateITerative (DOIT) method to solve the vector radiative transfer equation.The implementation of a discrete ordinate method is challenging due to the spherical geometry of the model atmosphere, which is required for the simulation of limb radiances. The involved numerical issues, grid optimization and interpolation methods, are discussed.The new scattering algorithm was compared to three other models, which were developed during the same time period as the DOIT algorithm. Overall, the agreement between the models was very good, giving confidence in new models. Scattering simulations are presented for limb- and down-looking geometries, for one-dimensional and three-dimensional spherical atmospheres. They were performed for the frequency bands of the Millimeter Wave Acquisitions for Stratosphere/Troposphere Exchange Research (MASTER) instrument, and for selected frequencies of the Earth Observing System Microwave Limb Sounder (EOS MLS)

Polar Stratospheric Clouds Satellite Observations, Processes, and Role in Ozone Depletion

Polar stratospheric clouds (PSCs) play important roles in stratospheric ozone depletion during winter and spring at high latitudes (e.g., the Antarctic ozone hole). PSC particles provide sites for heterogeneous reactions that convert stable chlorine reservoir species to radicals that destroy ozone catalytically. PSCs also prolong ozone depletion by delaying chlorine deactivation through the removal of gas-phase HNO$_{3}$ and H$_{2}$O by sedimentation of large nitric acid trihydrate (NAT) and ice particles. Contemporary observations by the spaceborne instruments Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), Microwave Limb Sounder (MLS), and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) have provided an unprecedented polar vortex-wide climatological view of PSC occurrence and composition in both hemispheres. These data have spurred advances in our understanding of PSC formation and related dynamical processes, especially the firm evidence of widespread heterogeneous nucleation of both NAT and ice PSC particles, perhaps on nuclei of meteoritic origin. Heterogeneous chlorine activation appears to be well understood. Reaction coefficients on/in liquid droplets have been measured accurately, and while uncertainties remain for reactions on solid NAT and ice particles, they are considered relatively unimportant since under most conditions chlorine activation occurs on/in liquid droplets. There have been notable advances in the ability of chemical transport and chemistry-climate models to reproduce PSC temporal/spatial distributions and composition observed from space. Continued spaceborne PSC observations will facilitate further improvements in the representation of PSC processes in global models and enable more accurate projections of the evolution of polar ozone and the global ozone layer as climate changes

Synergy of radar, lidar and infrared spectrometry to retrieve microphysical and radiative properties of cirrus clouds

Clouds are the largest source of uncertainty in climate models. Especially the feedbacks from thin ice clouds (cirrus) have a substantial effect on Earth’s radiation budget. They are semi-transparent for incoming solar radiation (cooling effect), but at the same time they can trap outgoing thermal radiation (warming effect). The level of scientific understanding of how these counteracting effects will change in a future warming climate is still low. This is because of the poorly understood processes involved in modelling of ice formation mechanisms and ice cloud evolution. To narrow down these gaps, the microphysical schemes and radiation parameterisations in current climate models have to be constrained by comparisons with ice cloud observations. Both, active (radar and lidar) and passive (infrared spectrometry) remote sensing observations of ice clouds are available to benchmark the models. While active remote sensing offers comprehensive vertical information content, passive remote sensing provides an integrated measure of the effect of clouds by exploiting radiation emitted from clouds and atmosphere together. The translation from measurements to microphysical cloud properties is accomplished by the usage of ice cloud retrieval algorithms. However, these retrievals are limited in their accuracy by crucial assumptions about microphysical properties like ice crystal shape, and by errors in the used inversion procedure. The goal of this thesis is to use the synergy of co-located active and passive remote sensing observations to derive microphysical properties of ice clouds and to quantify all known sources of uncertainty. To achieve these tasks, a three-instrument retrieval algorithm - SynCirrus - has been developed. In this process, a radar-lidar inversion is used to derive profiles of ice particle size and ice water content. These microphysical profiles are used as input for radiative transfer calculations, to simulate a spectrum that can be compared with the measured spectrum from the infrared spectrometer. In the course of this spectral analysis, the algorithm can iterate among the relevant microphysical assumptions, to find the best matching assumptions minimizing the spectral residuals between simulation and measurement. The SynCirrus retrieval includes consistent microphysical assumptions in the inversion and the forward radiative transfer part of the retrieval. To test the SynCirrus retrieval, three studies were performed. First, sensitivity studies of the spectral residuals identified the required data quality criteria for a successful spectral discrimination and for a characterisation of the errors of the inversion method. Second, a radar-lidar retrieval intercomparison study was conducted. Here, the inversion procedure is tested against an established other retrieval approach (VarCloud) using aircraft research flight data, indicating that for good data quality, both retrievals agreed remarkably well. Finally, in a case study using SynCirrus with all instruments at Mount Zugspitze, it was possible to bring radar, lidar and infrared radiance measurements in accordance within the provided uncertainty estimations, for the majority of the cases. The research presented in this thesis is relevant and important for the goal to improve the microphysical description of ice clouds in climate models. The presented retrieval algorithm SynCirrus can assist to narrow down gaps in the understanding of ice clouds, by providing high resolved and quality flagged microphysical profiles.Wolken sind die größte Unsicherheitsquelle bei Klimamodellvorhersagen. Insbesondere die Rückkopplungen von dünnen Eiswolken (Zirren) haben einen erheblichen Einfluss auf den Strahlungshaushalt der Erde. Sie sind halbtransparent für die einfallende Sonnenstrahlung (kühlende Wirkung), können aber gleichzeitig die ausgehende thermische Strahlung absorbieren (wärmende Wirkung). Der wissenschaftliche Kenntnisstand darüber, wie sich diese gegenläufigen Effekte in einem sich erwärmenden Klima verändern werden, ist noch gering. Dies ist zurückzuführen auf die schlecht verstandenen Prozesse bei der Modellierung der Eiskristallbildungsmechanismen innerhalb der Zirren und der Eiswolkenentstehung. Um diese Lücken zu schließen, müssen die mikrophysikalischen Schemata und Strahlungsparametrisierungen in aktuellen Klimamodellen durch Vergleiche mit Eiswolkenbeobachtungen eingeschränkt werden. Sowohl aktive (Radar und Lidar) als auch passive (Infrarotspektrometrie) Fernerkundungsbeobachtungen von Eiswolken sind für den Vergleich der Modelle verfügbar. Während die aktive Fernerkundung einen umfassenden vertikalen Informationsgehalt bietet, stellt die passive Fernerkundung eine integrierte Messung des Strahlungseffekts von Wolken bereit, indem sie die Strahlung detektiert die von Wolken und Atmosphäre emittiert wurde. Die Übersetzung von Messungen zu mikrophysikalischen Wolkeneigenschaften wird durch die Verwendung von Ableitungsverfahren für Eiswolken erreicht. Allerdings sind diese Algorithmen in ihrer Genauigkeit begrenzt durch entscheidende Annahmen über mikrophysikalische Eigenschaften, wie die Form der Eiskristalle, und durch Fehler im verwendeten Inversionsverfahren. Das Ziel dieser Arbeit ist es, die Synergie von aktiven und passiven Fernerkundungsbeobachtungen zu nutzen, um mikrophysikalische Eigenschaften von Eiswolken abzuleiten und alle bekannten Quellen der Unsicherheit zu quantifizieren. Um diese Aufgaben zu erfüllen, ist ein Drei-Instrumente Ableitungsverfahren - SynCirrus - entwickelt worden. In diesem Prozess wird eine Radar-Lidar-Inversion verwendet, um Profile der Eispartikelgröße und des Eiswassergehalts abzuleiten. Diese mikrophysikalischen Profile werden als Input für Strahlungstransportberechnungen verwendet, um ein Spektrum zu simulieren, das mit dem gemessenen Spektrum des Infrarotspektrometers verglichen werden kann. Im Zuge dieser Spektralanalyse kann der Algorithmus zwischen den relevanten mikrophysikalischen Annahmen iterieren, um die am besten passenden Annahmen zu finden, die die spektralen Residuen zwischen Simulation und Messung minimieren. Das SynCirrus Ableitungsverfahren beinhaltet konsistente mikrophysikalische Annahmen im Inversions- und im Vorwärtsmodell (Strahlungstransport) des Algorithmus. Um das SynCirrus Ableitungsverfahren zu testen, wurden drei Studien durchgeführt. Erstens wurden durch Sensitivitätsstudien der spektralen Residuen die erforderlichen Datenqualitätskriterien für eine erfolgreiche spektrale Unterscheidung identifiziert, und eine Charakterisierung der Fehler der Inversionsmethode wurde erarbeitet. Zweitens wurde eine Radar-Lidar-Vergleichsstudie durchgeführt. Hier wird das Inversionsverfahren mit einem anderen etablierten Ableitungsverfahren (VarCloud) unter Verwendung von Forschungsflugzeugdaten getestet. Das Ergebnis zeigt, dass bei guter Datenqualität beide Ableitungsverfahren bemerkenswert gut übereinstimmen. Letztlich wurde SynCirrus in einer Fallstudie mit allen Instrumenten auf der Zugspitze eingesetzt, es konnten Radar-, Lidar- und Infrarotstrahlungsmessungen innerhalb der angegebenen Unsicherheitsabschätzungen, für die Mehrheit der Fälle, in Einklang gebracht werden. Die in dieser Arbeit vorgestellte Forschung ist relevant und wichtig für das Ziel, die mikrophysikalischen Beschreibung von Eiswolken in Klimamodellen zu verbessern. Das vorgestellte Ableitungsverfahren SynCirrus kann dazu beitragen, Lücken im Verständnis von Eiswolken zu schließen, indem es hochaufgelöste und mit Qualitätsmerkmalen versehene mikrophysikalische Profile bereitstellt

Precipitation formation in low-level mixed-phase clouds: determining relevant processes and drivers based on cloud radar observations from a high Arctic site

Low-level mixed-phase clouds (LLMPCs) shroud large portions of Earth’s surface at high latitudes. They have been shown to dramatically affect the surface energy budget, yet, large uncertainties in their model representation remain, both in climate simulations, and in numerical weather prediction. Both computational limitations and poor understanding of a number of processes taking place in LLMPCs are thought to give rise to such uncertainties. In particular, precipitation formation processes have been relatively understudied in LLMPCs, and reaching a refined understanding is expected to lead to an improvement in model performance, as precipitation determines the cloud’s mass sink, and hence lifetime. In this dissertation, precipitation formation processes are investigated in LLMPCs at the high Arctic site of Ny-Ålesund, based on long-term cloud radar observations. Cloud radars are in fact especially suited for ice microphysical studies, due to the wide spectrum of observational fingerprints of ice microphysical processes that they provide. Doppler radar observations provide information on dynamics, multi-frequency radar observations on ice particle size, and polarimetric radar observations on particle shape and concentration. Radar data are combined with thermodynamic information, which further allows to discriminate between ice microphysical processes, due to their high sensitivity to temperature. In the first part of the dissertation, the relevance of the aggregation process for LLMPCs at Ny-Ålesund is assessed. Aggregation occurs when ice particles collide to form larger ice particles. A long-term dataset of dual-frequency radar observations, as well as thermodynamic information, is used to statistically assess the relevance of aggregation and its sensitivity to varying cloud thermodynamic conditions. The study finds that larger aggregate snowflakes are predominantly produced in LLMPCs whose mixed-phase layer is at temperatures compatible with the growth and subsequent mechanical entanglement of dendritic crystals. Surprisingly, the second enhanced aggregation zone close to the 0°C isotherm, typically observed in deeper cloud systems, is absent. In the second part, a novel state-of-the-art long-term dataset developed within this dissertation is presented. It combines dual-frequency and polarimetric Doppler cloud radar observations, together with thermodynamic information, and other auxiliary variables. After detailing the processing and curation approaches, the results on aggregation are confirmed, and expanded upon. Additionally, temperature regimes where columnar ice particles, riming, i.e., the collection of supercooled liquid droplets by ice crystals, and secondary ice production are likely to occur are identified. In the final part of the dissertation, the developed dataset is used to assess the effect of turbulence on aggregation and riming in LLMPCs at Ny-Ålesund. LLMPCs are in fact inherently turbulent, and maintained by turbulent overturning generated at cloud top. The turbulent kinetic energy dissipation rate (EDR) is retrieved, and the sensitivity of aggregation and riming to varying EDR conditions is investigated. It is shown that higher EDR regimes enhance the aggregation of particles, and are associated with signatures of increased ice particle concentration, possibly caused by fragmentation of ice particles. In temperature regimes more favorable to riming, turbulence dramatically enhances the particles’ fall velocities, denoting higher degrees of riming

Exploiting vertically pointing Doppler radar for advancing snow and ice cloud observations

Ice and snow particles play a key role in the atmosphere of the Earth and affect—among others—cloud physics and radiative properties, precipitation, and surface albedo. As a consequence, ice and snow have major impact on weather and climate. However, in situ observations of ice clouds and snowfall are difficult and sparse. This leads to a great potential of remote sensing, which can provide observations at high temporal and spatial resolutions. Among the various types of remote sensing instruments, ground-based vertically pointing Doppler radars are one of the most promising concepts: Doppler radars are the only instruments which can penetrate also optically thick clouds and, at the same time, are capable of measuring the fall velocity of hydrometeors. However, the observables of Doppler radars are only indirectly linked to cloud and precipitation properties. The required transfer functions are not uniquely defined resulting in substantial uncertainties of radar-based ice cloud and snowfall retrievals. In the context of studying ice and snow with radars, this study investigates two key issues: (I) the need for additional snowfall measurements with radar and (II) the potential of higher moments of the radar Doppler spectrum for observing ice cloud properties. To address Key Issue I, an improved spectral processing scheme for the MRR, a compact precipitation Doppler radar, is introduced. The scheme significantly enhances the radar sensitivity and allows observations of snowfall profiles (Publication I). One year of MRR observations from three polar sites in East Antarctica and Svalbard are investigated with respect to changes of snowfall within the vertical column (Publication II). The transformation found is used for assessing the snowfall measurement uncertainties of the radar onboard the CloudSat satellite which is the only source of global snowfall estimates. However, the lowest 1200 m above the surface are contaminated by ground clutter so that the measurements cannot be exploited (blind zone). The analysis shows that snowfall amount is underestimated when using CloudSat. Also, a blind zone reduced by 50% does not improve the snowfall estimation in all aspects. For Key Issue II, the potential of higher moments for observations of ice cloud properties, an advanced radar simulator capable of simulating the full Doppler radar spectrum is developed (Additional Study I). The radar simulator is used to forward model in situ aircraft observations of stratocumulus ice clouds obtained during the ISDAC campaign in Alaska (Publication III). The combination of in situ data and ground-based radar observations with the 35 GHz MMCR radar in Barrow, Alaska, is used to develop a novel method for deriving temperature-dependent particle mass-size relations. Subsequently, the impact of replacing measurements by various parameterizations is investigated for projected particle area and particle size distribution. For this, moments of the radar Doppler spectrum of the MMCR are statistically compared to forward modeled ISDAC data. It is found that the use of higher moments of the Doppler spectrum such as skewness and kurtosis as well as the slopes of the Doppler peak gives additional information when identifying the parameterization methods which lead to most consistent results. Radar-based ice cloud retrievals are often underdetermined and additional observables are desirable. The potential of increasing the number of observables using higher moments and slopes is evaluated based on the developed forward model, parameterizations, and coefficients (Additional Study II). An idealized retrieval based on simulated measurements is successfully developed for moderate turbulence levels. Retrieved are parameters describing particle mass, area, and size distribution. It is shown that a retrieval including higher moments and the slopes provides a higher number of degrees of freedom for signal than a dual-frequency retrieval based on the conventional moments such as radar reflectivity factor and mean Doppler velocity. This highlights the great potential for enhancing observations of ice clouds with higher radar moments

A new technique for interpreting depolarization measurements using the CRL atmospheric lidar in the Canadian High Arctic

Linear depolarization measurement capabilities were added to the CANDAC Rayleigh-Mie-Raman lidar (CRL) at Eureka, Nunavut, in the Canadian High Arctic. This upgrade enables inferences of the phases (liquid versus ice) of cold and mixed-phase clouds, including during polar winter. A rotating-polarizer module was installed in the lidar, and depolarization measurements were calibrated according to existing methods. An alternate calculation technique, using the lidar\u27s existing visible Rayleigh elastic channel in combination with the new rotating polarizer channel, was developed. A detailed mathematical description of both methods and their calibrations is presented. The new method is superior to the traditional method for the CRL: It has lower uncertainty, and gives depolarization parameter values at higher spatial-temporal resolution

Qualification of the airborne FTIR spectrometer MIPAS-STR and study on denitrification and chlorine deactivation in Arctic winter 2009/10

This work describes the qualification of the airborne Fourier-Transform infrared spectrometer Michelson Interferometer for Passive Atmospheric Sounding-STRatospheric aircraft (MIPAS-STR) and studies on ozone-relevant processes in the Arctic winter stratosphere. Using MIPAS-STR measurements, correlative in-situ measurements and simulations of the Chemical Lagrangian Model of the Stratosphere (CLaMS), the processes denitrification and chlorine deactivation are investigated

PRIMA General Observer Science Book

PRIMA (The PRobe for-Infrared Mission for Astrophysics) is a concept for a far-infrared (IR) observatory. PRIMA features a cryogenically cooled 1.8 m diameter telescope and is designed to carry two science instruments enabling ultra-high sensitivity imaging and spectroscopic studies in the 24 to 235 microns wavelength range. The resulting observatory is a powerful survey and discovery machine, with mapping speeds better by 2 - 4 orders of magnitude with respect to its far-IR predecessors. The bulk of the observing time on PRIMA should be made available to the community through a General Observer (GO) program offering 75% of the mission time over 5 years. In March 2023, the international astronomy community was encouraged to prepare authored contributions articulating scientific cases that are enabled by the telescope massive sensitivity advance and broad spectral coverage, and that could be performed within the context of GO program. This document, the PRIMA General Observer Science Book, is the edited collection of the 76 received contributions.Comment: A. Moullet, T. Kataria, D. Lis, S. Unwin, Y. Hasegawa, E. Mills, C. Battersby, A. Roc, M. Meixner are the editors of the PRIMA General Observer Science Book. The book compiles 76 authored contributions. 399 page