Fundamental remote sensing science research program. Part 1: Scene radiation and atmospheric effects characterization project

Brief articles summarizing the status of research in the scene radiation and atmospheric effect characterization (SRAEC) project are presented. Research conducted within the SRAEC program is focused on the development of empirical characterizations and mathematical process models which relate the electromagnetic energy reflected or emitted from a scene to the biophysical parameters of interest

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

INVESTIGATION OF LIGHT TRANSPORT AND SCATTERING IN TURBULENT CLOUDS: SIMULATIONS AND LABORATORY MEASUREMENTS

A better understanding of light transport and scattering in turbulent clouds is needed for more accurate remote sensing, improved imaging and signal transmission through atmospheric aerosol and fog, and deeper understanding of cloud optical properties relevant to weather and climate. In this study, we investigate the impact of light scattering in clouds on two problems of atmospheric relevance. In the first part, we examine deleterious effects of the atmosphere on remotely acquired images including signal attenuation and potential blurring due to forward-scattered light accepted by the imaging system. A prior proposed aerosol scattering model provides a method for calculating the contrast and spatial detail expected when imaging through atmospheres with significant aerosol optical depth. We compare modulation transfer functions obtained directly from images taken through a cloud chamber to those calculated from theory using measured cloud properties. We find that the significance of scattering-induced optical blurring depends sensitively on the properties of both the particles and the imaging system. The theoretical aerosol expression modulation transfer function capture the basic behavior of the system, with deviations likely a result of not accounting for broad particle size distributions. In the second part, we investigate how clusters and voids in the spatial distributions of particles within a cloud cause light transport to deviate from the exponential extinction law. We explore both perfectly random and correlated scattering media with a Monte Carlo ray tracing program, and find that the degree of non-exponential attenuation can be characterized by the radial distribution function. Our numerical observations regarding direct, diffuse and backward radiative transfer are shown to be consistent with a previous “cloudlet” approach, providing a bridge between the analytical cloudlet model and continuous correlation function approaches. Finally, we numerically explore light propagation through turbulent clouds with polydisperse size distributions calculated by a large eddy simulation of the MTU Pi Chamber. We find that both the mean and standard deviation of direct and diffuse forward flux change when clustering exists, and make suggestions for future laboratory cloud chamber experiments to detect the presence of spatial correlation

Retrieval of Optical and Microphysical Cloud Properties Using Ship-based Spectral Solar Radiation Measurements over the Atlantic Ocean

In this thesis spectral solar zenith radiances are analyzed which were obtained from ship-based measurements over the Atlantic ocean. In combination with high-resolution lidar and microwave remote sensing optical and microphysical cloud properties were retrieved using spectral radiation data. To overcome problems of existing transmissivity-based cloud retrievals, a new retrieval algorithm is introduced which circumvents retrieval ambiguities and reduces the influence of measurement uncertainties. The method matches radiation measurements of ratios of spectral transmissivity at six wavelengths with modeled transmissivities. The new retrieval method is fast and accurate, and thus suitable for operational purposes. It is applied to homogeneous and inhomogeneous liquid water and cirrus clouds. The results from the new algorithm are compared to observations of liquid water path obtained from a microwave radiometer, yielding an overestimation for thick liquid water clouds but a slight underestimation for thin clouds. A statistical analysis of retrieved cloud properties during three Atlantic transects is introduced. Similar characteristics of cloud properties are found in the mid latitudes and northern subtropics but the large variability of meridional distribution in the remaining regions imply the prevailing influence of weather systems compared to typical cloud distributions. With about 63% homogeneous stratocumulus clouds are found to be the prevailing cloud type over ocean, while scattered and inhomogeneous liquid water clouds amount to 16% and 21%, respectively. All analyzed distributions are affected by an increased frequency of small values of cloud properties caused by 3D radiative effects. The comparison with satellite-based and ship-based cloud retrievals along the cruise track show comparable results for the cloud optical thickness with limitations for thick liquid water clouds. The meridional distribution of effective radius agreed within the uncertainties of both methods, however, the satellite-derived values are biased toward larger mean values

Atmospheric remote sensing and radiopropagation: from numerical modeling to spaceborne and terrestrial applications

The remote sensing of electromagnetic wave properties is probably the most viable and fascinating way to observe and study physical media, comprising our planet and its atmosphere, at the same time ensuring a proper continuity in the observations. Applications are manifold and the scientific community has been importantly studying and investing on new technologies, which would let us widen our knowledge of what surrounds us. This thesis aims at showing some novel techniques and corresponding applications in the field of the atmospheric remote sensing and radio-propagation, at both microwave and optical wavelengths. The novel Sun-tracking microwave radiometry technique is shown. The antenna noise temperature of a ground-based microwave radiometer is measured by alternately pointing toward-the-Sun and off-the-Sun while tracking it along its diurnal ecliptic. During clear sky the brightness temperature of the Sun disk emission at K and Ka frequency bands and in the under-explored millimeter-wave V and W bands can be estimated by adopting different techniques. Parametric prediction models for retrieving all-weather atmospheric extinction from ground-based microwave radiometers are tested and their accuracy evaluated. Moreover, a characterization of suspended clouds in terms of atmospheric path attenuation is presented, by exploiting a stochastic approach used to model the time evolution of the cloud contribution. A model chain for the prediction of the tropospheric channel for the downlink of interplanetary missions operating above Ku band is proposed. On top of a detailed description of the approach, the chapter presents the validation results and examples of the model-chain online operation. Online operation has already been tested within a feasibility study applied to the BepiColombo mission to Mercury operated by the European Space Agency (ESA) and by exploiting the Hayabusa-2 mission Ka-band data by the Japan Aerospace Exploration Agency (JAXA), thanks to the ESA cross-support service. A preliminary (and successful) validation of the model-chain has been carried out by comparing the simulated signal-to-noise ratio with the one received from Hayabusa-2. At the next ITU World Radiocommunication Conference 2019, Agenda Item 1.13 will address the identification and the possible additional allocation of radio-frequency spectrum to serve the future development of systems supporting the fifth generation of cellular mobile communications (5G). The potential impact of International Mobile Telecommunications (IMT) deployments is shown in terms of received radio frequency interference by ESA’s telecommunication links. Received interference can derive from several radio-propagation mechanisms, which strongly depend on atmospheric conditions, radio frequency, link availability, distance and path topography; at any time a single mechanism, or more than one may be present. Results are shown in terms of required separation distances, i.e. the minimum distance between the earth station and the IMT station ensuring that the protection criteria for the earth station are met

Optical Thickness Retrievals of Subtropical Cirrus and Arctic Stratus from Ground-Based and Airborne Radiance Observations Using Imaging Spectrometers

The development and application of new cloud retrieval methods from ground–based and airborne measurements of spectral radiance fields above heteorogeneous surfaces is introduced. The potential of imaging spectrometers in remote–sensing applications is evaluated. The analyzed spectral radiance fields were measured during two international field campaigns in the visible wavelength range (400–970 nm) with high spatial (<10m) resolution. From ground–based measurements, high ice clouds were observed and from airborne measurements Arctic stratus. From the measurements, cloud optical thickness is retrieved with high spatial resolution and the horizontal cloud inhomogeneities are investigated. Depending on the measurement configuration, different uncertainties arise for the retrieval of the cloud optical thickness. A reduction of those uncertainties is derived by a specification of the ice crystal shape to improve the retrieval of the optical thickness of high ice clouds. The ice crystal shape is obtained independently from the angular information of the scattering phase function features, imprinted in the radiance fields. A performed sensitivity study reveals uncertainties of up to 90%, when neglecting this information and applying a wrong crystal shape to the retrieval. For remote-sensing of Arctic stratus, the highly variable surface albedo influences the accuracy of the cloud optical thickness retrieval. In cloudy cases the transition of reflected radiance from open water to sea ice is not instantaneous but horizontally smoothed. In general, clouds reduce the reflected radiance above bright surfaces in the vicinity of open water, while it is enhanced above open sea. This results in an overestimation of to up to 90% in retrievals of the optical thickness. This effect is investigated. Using observations and three-dimensional radiative transfer simulations, this effect is quantified to range to up to 2200 m distance to the sea-ice edge (for dark-ocean albedo of αwater = 0.042 and sea-ice albedo of αice = 0.91 at 645 nm wavelength) and to depend on macrophysical cloud and sea-ice properties. The retrieved fields of cloud optical thickness are statistically investigated. Auto–correlation functions and power spectral density analysis reveal that in case of clouds with prevailing directional cloud structures, cloud inhomogeneities cannot be described by a universally valid parameter. They have to be defined along and across the prevailing cloud structures to avoid uncertainties up to 85%.Im folgenden wird die Entwicklung und Anwendung neuer Ableitungsverfahren von Wolkenparametern, basierend auf bodengebundener und flugzeuggetragener spektraler Strahldichtemessungen über heterogenen Untergründen, vorgestellt und das Fernerkundungspotential abbildender Spektrometer evaluiert. Die spektralen Strahldichtefelder wurden während zweier internationaler Feldkampagnen im sichtbaren Wellenlängenbereich (400–970 nm) mit hoher räumlich Auflösung (<10m) gemessen. Bodengebundene Messungen wurden genutzt, um hohe Eiswolken zu beobachten und flugzeuggetragenen um arktischen Stratus zu beobachten. Aus den Messungen werden räumlich hochaufgelöste wolkenoptische Dicken abgeleitet und anschließend horizontale Wolkeninhomogenitäten untersucht. Die Ableitung der wolkenoptischen Dicke birgt je nach Messkonfiguration verschiedene Unsicherheiten. Eine Reduzierung der Unsicherheiten wird durch die Vorgabe einer Eiskristallform zur Verbesserung der Ableitung der optischen Dicke hoher Eiswolken erreicht. Diese werden unabhängig aus den winkelabhängigen, in das gemessene Strahldichtefeld eingeprägten Eigenschaften der Streuphasenfunktion, abgeleitet. Bei Vernachlässigung dieser Information und Wahl der falschen Eiskristallform, treten Fehler in der abgeleiteten optischen Dicke von bis zu 90% auf. Bei der Fernerkundung von arktischem Stratus beeinflusst die sehr variable Bodenalbedo die Genauigkeit der Ableitung der optischen Dicke. Beim Übergang von Meereis zu Wasser, findet die Abnahme der reflektierten Strahldichte im bewölktem Fall nicht direkt über der Eiskante, sondern horizontal geglättet statt. Allgemein reduzieren Wolken die reflektierte Strahldichte über Eisflächen nahe Wasser, während sie über dem Wasser erhöht wird. Dies führt zur Überschätzung der wolkenoptischen Dicke über Wasserflächen nahe Eiskanten von bis zu 90 %. Dieser Effekt wird mit Hilfe von Beobachtungen und dreidimensionalen Strahlungstransferrechnungen untersucht und es wird gezeigt, dass sein Einfluss noch bis zu 2200 m Entfernung zur Eiskante wirkt (für Meeresalbedo 0.042 und Meereisalbedo 0.91 bei 645 nm Wellenlänge) und von den makrophysikalischen Wolken- und Meereiseigenschaften abhängt. Die abgeleiteten Felder der optischen Dicke werden statistisch ausgewertet, um die Inhomogeneität der Wolken zu charakterisieren. Autokorrelationsfunktionen und Leistungsdichtespektren zeigen, dass Inhomogenitäten von Wolken mit vorranging richtungsabhängiger Struktur nicht mit einem allgemeingültigen Parameter beschrieben werden können. Es sind Inhomogenitätsmaße entlang und entgegen der jeweiligen Wolkenstrukturen nötig, um Fehler von bis zu 85% zu vermeiden