RETRIEVAL OF ICE CLOUD PARAMETERS USING DMSP SPECIAL SENSOR MICROWAVE IMAGER/SOUNDER

Clouds exert a profound influence on both the water balance of the atmosphere and the earth's radiation budget (Stephens 2005; Stephens and Kummerow 2007). Among the global distribution, 30% of them are ice clouds (Riedi et al. 2000). It is important to improve our knowledge of the ice cloud properties in order to determine their influence to the global ecosystem. For ice clouds with millimeter-size ice particles, which are generally found in anvil cirrus and deep convections, microwave and millimeter wave length satellite measurements are suitable for the ice cloud microphysical property retrieval because of its strong ability to penetrate deeper into dense ice clouds. For these types of ice clouds, brightness temperatures at the top of the atmosphere are analytically derived as a function of vertically integrated ice water content (i.e. ice water path), effective particle diameter, and bulk volume density. In general, three brightness temperature measurements are needed to retrieve the three ice cloud microphysical parameters. A two-stream radiative transfer theory was applied to data from the Advanced Microwave Sounding Unit (AMSU) and the Moisture Humidity Sensor (MHS) in order to generate global ice water paths operationally. This research further applied the model and theory to derive ice water path (IWP) from the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite Program (DMSP) F-16 satellite. Compared to AMSU/MHS, which have field of views (FOV) varying with scan position, SSMIS scans the Earth's atmosphere at a constant viewing angle of 53o and therefore offers a uniform FOV within each scan. This unique feature allows for improved global mapping and monitoring of ice clouds so that a more accurate and realistic IWP and ice particle effective diameter distribution is expected. A direct application of SSMIS-derived ice water path is its relationship with surface rain rate as derived previously for AMSU and MHS instruments. Here, SSMIS-derived rain rate was compared to the AMSU and MHS rainfall products and hourly synthetic precipitation observations from rain gauges and surface radar. Results show that SSMIS surface precipitation distribution is spatially consistent and does not have apparent artificial boundary near coastal zones as previously seen in other algorithms. Also, the ice water path associated with a severe storm reasonably delineates the strong convective precipitation areas and has a spatial variation consistent with surface precipitation. From retrieved instantaneous surface precipitation, a tropical and subtropical oceanic precipitation anomaly time series is constructed from 5 year's worth (2005-2009) of SSMIS data. This data record is also linked to the previous constructed SSM/I 15-year (1992-2006) data record to provide a longer term climate study by satellite observations. In future studies, refined algorithms for the estimate of ice cloud base temperature and ice particle bulk volume density are going to be developed to improve the accuracy of IWP retrieval under various cloud vertical distributions. Meanwhile, a better inter-sensor cross calibration scheme is the key to make satellite measurements more useful in climate change study

Characterizing the radiative effect of rain using a global ensemble of cloud resolving simulations

The effect of rain on radiative fluxes and heating rates is a process that is neglected in most of the large scale atmospheric models used for weather forecasting or climate prediction. Yet, to our knowledge, the magnitude of the resulting radiative bias remains unquantified. This study aims to quantify the rain radiative effect (RRE) at a range of temporal and spatial scales, as a step towards determining whether the radiation schemes in these models should include rain. Using offline radiative transfer calculations with input from an ensemble of cloud resolving model simulations, we find that rain has a negligible effect on global mean radiative fluxes (less than 0.2 W m‐2). Weekly mean RREs at specific locations may be larger (less than 4 W m−2). At the finest temporal and spatial resolutions, the RRE can occasionally be much larger again (greater than 100 W m‐2), but values exceeding 10 W m‐2 occur in less than 0.1% of cases. Using detailed analysis of case studies we demonstrate that the magnitude and direction of the RRE depend on the rain water path, its vertical location with respect to cloud and, for longwave radiation, the temperature at which it occurs. Large RREs generally only occur when the rain water path is large and the cloud water path is small. These cases are infrequent and intermittent. As the RREs are generally small, we conclude that this missing process is unlikely to be important for large scale atmospheric models

Polarization studies in electromagnetic scattering by small Solar-system particles

In remote-sensing studies, particles that are comparable to the wavelength exhibit characteristic features in electromagnetic scattering, especially in the degree of linear polarization. These features vary with the physical properties of the particles, such as shape, size, refractive index, and orientation. In the thesis, the direct problem of computing the unknown scattered quantities using the known properties of the particles and the incident radiation is solved at both optical and radar spectral regions in a unique way. The internal electromagnetic fields of wavelength-scale particles are analyzed by using both novel and established methods to show how the internal fields are related to the scattered fields in the far zone. This is achieved by using the tools and methods that were developed specifically to reveal the internal field structure of particles and to study the mechanisms that relate the structure to the scattering characteristics of those particles. It is shown that, for spherical particles, the internal field is a combination of a forward propagating wave with the apparent wavelength determined by the refractive index of the particle, and a standing wave pattern with the apparent wavelength the same as for the incident wave. Due to the surface curvature and dielectric nature of the particle, the incident wave front undergoes a phase shift, and the resulting internal wave is focused mostly at the forward part of the particle similar to an optical lens. This focusing is also seen for irregular particles. It is concluded that, for both spherical and nonspherical particles, the interference at the far field between the partial waves that originate from these concentrated areas in the particle interior, is responsible for the specific polarization features that are common for wavelength-scale particles, such as negative values and local extrema in the degree of linear polarization, asymmetry of the phase function, and enhancement of intensity near the backscattering direction. The papers presented in this thesis solve the direct problem for particles with both simple and irregular shapes to demonstrate that these interference mechanisms are common for all dielectric wavelength-scale particles. Furthermore, it is shown that these mechanisms can be applied to both regolith particles in the optical wavelengths and hydrometeors at microwave frequencies. An advantage from this kind of study is that it does not matter whether the observation is active (e.g., polarimetric radar) or passive (e.g., optical telescope). In both cases, the internal field is computed for two mutually perpendicular incident polarizations, so that the polarization characteristics can then be analyzed according to the relation between these fields and the scattered far field.Kaukokartoitustutkimuksissa aallonpituusluokkaa olevat hiukkaset aiheuttavat niille luonteenomaisia piirteitä sähkömagnettisessa säteilyssä, varsinkin lineaarisen polarisaation asteessa. Nämä piirteet vaihtelevat hiukkasen fyysisten ominaisuuksien, kuten muodon, koon, taitekertoimen ja orientaation myötä. Tässä väitöskirjassa ratkaistaan sähkömagneettisen sironnan suora ongelma uudella tavalla, samalla kun hiukkasten ominaisuudet oletetaan tunnetuiksi. Aallonpituusluokkaa olevien hiukkasten sisäisiä sähkökenttiä analysoidaan sekä uusilla että vakiintuneilla menetelmillä, jotta voidaan osoittaa, mikä on sisäisten kenttien suhde sironneisiin kenttiin kauko-alueessa. Tämä on saavutettu käyttämällä työkaluja ja menetelmiä, jotka on kehitetty paljastamaan sirottajien sisäisen kentän rakenne ja joilla voidaan tutkia mekanismeja, jotka liittävät näiden sirottajien rakenteen niiden sirontaominaisuuksiin. Tutkimuksessa näytetään, että pallomaisten hiukkasten sisäinen kenttä on yhdistelmä eteenpäin etenevää aaltoa, jonka allonpituus määräytyy hiukkasen taitekertoimen mukaan, ja seisovaa aaltoa, jonka aallonpituus on sama kuin tulevan aallon. Koska hiukkanen on eriste ja sen pinta on kaareva, tuleva aaltorintama kokee vaihesiirron ja tuloksena oleva sisäinen aalto fokusoituu pääasiassa hiukkasen etupuolelle optisen linssin tavoin. Tämä fokusointi havaitaan myös epäsäännöllisillä hiukkasilla. Johtopäätöksenä on, sekä pallomaisille että ei-pallomaisille hiukkasille, että kaukokentässä tapahtuva interferenssi osittaisten aaltojen välillä, jotka ovat peräisin näistä fokusoituneista alueista hiukkasen sisällä, on vastuussa tietyistä, aallonpituusluokkaa oleville hiukkasille ominaisista piirteistä lineaarisessa polarisaatiossa, kuten negatiiviset arvot ja paikalliset maksimit, vaihefunktion asymmetria, ja intensiteetin kasvaminen lähellä takaisinsirontasuuntaa. Tässä väitöskirjassa esitellyt paperit ratkaisevat suoran ongelman, sekä yksinkertaisille, että epäsäännöllisille hiukkasille osoittaakseen, että nämä interferenssimekanismit ovat yhteisiä kaikille aallonpituusluokkaa oleville, eristäville sirottajille. Lisäksi näytetään, että näitä mekanismeja voidaan soveltaa sekä regoliittihiukkasille näkyvän valon alueella että hydrometeoriiteille mikroaaltoalueessa. Yksi tällaisen tutkimuksen eduista on, että ei ole merkitystä, onko havaitsija aktiivinen (esim. polarisaatiotutka) vai passiivinen (esim. optinen teleskooppi). Molemmissa tapauksissa sisäinen kenttä lasketaan kahdelle keskenään kohtisuorasti polarisoituneelle tulevalle kentälle, jotta polarisaatiossa havaitut piirteet voidaan analysoida näiden kenttien ja sironneen kentän suhteen avulla

Improving the Understanding and Simulation of Precipitation Forming Processes through Combined Analysis of Microphysical Models and Multi-Frequency Doppler Radar Observations

The society is strongly influenced by precipitation, which forms by cloud microphysical processes, e.g., sedimentation and aggregation. These processes determine where and how clouds precipitate relevant for the global water cycle, freshwater availability, and flooding. However, the precipitation forming processes are poorly understood and pose a significant challenge to earth system modeling. Challenges arise from the difficulties of deriving parameterizations from laboratory experiments or observations. Even if accurate process parameterizations could be derived, implementing them into numerical models poses additional challenges due to computational cost and unresolved scales. In the last decades, rapid progress has been made in modeling and observing microphysical processes, which enables or even necessitates further studies that exploit the synergy between both fields. In this thesis, microphysical models are employed that either resolve the microphysical processes up to the single particle level (3D snowflake model and Lagrangian particle model) or are computationally efficient (bulk scheme). The explicit models are used to derive parameterizations and provide detailed insights into the processes that can be used in the less explicit models. Improving the less explicit but computationally efficient bulk schemes is particularly important, as they are indispensable for weather and climate prediction. Output from all models is compared to observations that provide information either on individual particle properties (in situ particle observations) or average properties of large particle ensembles (multi-frequency Doppler radar observations). These model-observation combinations are used to improve the knowledge about the microphysical processes and their representation in the microphysical models. 3D snowflake models simulate the complex shape of ice particles, the representation of which presents a major difficulty for microphysical schemes. In Study I, such a 3D snowflake model is used to derive parameterizations of particle properties, such as mass as a function of size, monomer number and shape. Hydrodynamic models are used to additionally derive the particle velocity. The most detailed parameterizations are used to assess the effect of aggregate composition on the particle properties, which is challenging to do with observations alone. It is found that aggregate properties change smoothly with increasing monomer number but differ substantially depending on the monomer shapes that constitute the aggregates. Other, less detailed parameterizations can be readily applied in bulk microphysical schemes to improve the physical consistency of these schemes. In simulations with a Lagrangian particle model, it can be shown that these less detailed parameterisations are very accurate even if they only distinguish between the two classes of monomers and aggregates. Comparing the parameterization with in situ observations ensures that they are physically realistic in size ranges where observations are available. In addition, the physical principles of the 3D snowflake and hydrodynamic models help to ensure that the parameterizations are realistic even in size ranges for which it is difficult to obtain observations. In Study II, parameters that are important for the microphysical description of sedimentation and aggregation in a two-moment scheme bulk microphysics scheme are constrained by observations. Traditionally, microphysical parameterizations are tuned to improve the prediction of few variables of interest, such as the precipitation rate. This procedure likely introduces compensating errors, since adjusting one parameter may improve the prediction of these variables even if that change leads away from the most physically meaningful value of the parameters. Therefore, a different approach is used in this study that uses several variables from multi-frequency Doppler radars simultaneously and focuses on single or few processes to avoid this issue of underdetermined parameters. First, the observed statistics are used to evaluate microphysical parameters in an idealized 1D model, which allows efficient testing of all key parameters. These simulations reveal that the simulation of aggregation is most sensitive to the aggregate particle properties, the aggregation kernel formulation and the size distribution width and less sensitive to the monomer habit and the sticking efficiency. A statistical comparison between 3D large-eddy simulations with the default and the new scheme setup and the observations show that previously existing large biases of too fast and too large particles in the scheme could be substantially reduced. This bias reduction can be attributed to the improved simulation of sedimentation and aggregation. Since a large portion of precipitation reaches the ground as rain but forms in the ice phase, processes in the melting layer are an essential part of precipitation modeling. In Study III, an approach is used to infer the dominance of growth or shrinkage processes through the relationship of reflectivity flux at the melting layer boundaries. In addition, radar Doppler spectra and multi-frequency observations are used to evaluate assumptions of the approach and to classify profiles according to the degree of riming. For unrimed profiles, growth processes increase the mean mass only slightly. For rimed profiles, shrinking processes lead to a substantial decrease the mean mass probably caused by particle breakup. Simulations using a Lagrangian particle model reveal that breakup processes for which parameterizations are available can not reproduce the observed decrease of the mean mass for rimed profiles and suggest that further laboratory studies of collisional breakup of melting particles are needed

Numerical simulation of scavenging processes in explosive volcanic eruption clouds

The scavenging of gases and particles in an explosive volcanic eruption plume has been studied by numerical simulations with the plume model ATHAM (Active Tracer High Resolution Atrnospheric Model). We identified relevant factors that determine the fraction of volcanic material eventually being injected into the stratosphere. An extended version of the microphysics has been formulated: predicting both the specific rnass content and the number concentration it de- scribes the interaction of hydrometeors and voicanic ash in the plume, which leads to particle growth and efficient sedimentation. In addition, we developed a mod- ule for the calculation of volcanic gas scavenging by liquid and solid hydrometeors in the plume. This study reveals the dominant role of hydrometeors in controlling many pro- cesses in the plume. The coating of volcanic ash with liquid water or ice results in highly efficient growth of particles, which strongly enhances the fallout velocity of ash. Precipitation of aggregates results in efficient gas-particle separation, which increases the injection of volcanic gases into the stratosphere. In addition, it strongly influences the stream pattern, which in turn influences the microphysics in the plume by lowering the supersaturation in the ascent zone. By far the highest portion of condensed water freezes to ice in the eruption colurnn. The fast plurne rise to regions, which are too cold for even supercooled liquid water to exist causes rnost particles to occur as ice-ash aggregates. We examined the scavenging of the most important volcanic gases, HCl, SO2 and H2S, by liquid and solid hydrometeors and by aggregates in the plume. The scavenging efficiency is determined by the amount of condensed water or ice. HC1 is almost completely removed from the gas phase by dissolution in liquid water occurring in the lower central plurne. These ash-containing drops quickly freeze to graupel aggregates that precipitate efficiently, thus also removing HCl from higher altitudes. On the other hand, a large extent of SO2 and HzS stays at high levels in the umbrella region. The sulphur species are only slightly soluble in liquid water, hence, they are not removed by liquid water drops. However, they are scavenged by frozen hydrorneteors via direct gas incorporation during diffusional growth of ice. This causes a reduction by - 25% of the potential input of an inert volcanic gas, indicating the great relevance of gas trapping in ice. Low relative humidity in the troposphere in our simulations caused precipitation to reevaporate before it could reach the ground. As a consequence, no evidence of hydrometeor-ash interaction or gas scavenging could be found in the fallout of the eruption simulated here, although these processes occurred to a significant degree in upper parts of the plume

Arctic mixed-phase clouds : Macro- and microphysical insights with a numerical model

This work provides new insights into macro- and microphysical properties of Arctic mixed-phase clouds: first, by comparing semi-idealized large eddy simulations with observations; second, by dissecting the influences of different surface types and boundary layer structures on Arctic mixed- phase clouds; third, by elucidating the dissipation process; and finally by analyzing the main microphysical processes inside Arctic mixed-phase clouds

Polarimetric weather radar:from signal processing to microphysical retrievals

Accurate modelling of liquid, solid and mixed-phase precipitation requires a thorough understanding of phenomena occurring at various spatial and temporal scales. At the smallest scales, precipitation microphysics defines all the processes occurring at the level where precipitation is a discrete process. The knowledge of these microphysical processes originates from the interpretation of snowfall and rainfall measurements collected with various sensors. Direct sampling, performed with in-situ instruments, provides data of superior quality. However, the development of remote sensing (and dual-polarization radar in particular) offers a noteworthy alternative: large domains can in fact be sampled in real time and with a single instrument. The drawback is obviously the fact that radars measure precipitation indirectly. Only through appropriate interpretation radar data can be translated into physical mechanisms of precipitation. This thesis contributes to the effort to decode polarimetric radar measurements into microphysical processes or microphysical quantities that characterize precipitation. The first part of the work is devoted to radar data processing. In particular, it focuses on how to obtain high resolution estimates of the specific differential phase shift, a very important polarimetric variable with significant meteorological importance. Then, hydrometeor classification, i.e. the first qualitative microphysical aspect that may come to mind, is tackled and two hydrometeor classification methods are proposed. One is designed for polarimetric radars and one for an in-situ instrument: the two-dimensional video disdrometer. These methods illustrate the potential that supervised and unsupervised techniques can have for the interpretation of meteorological measurements. The combination of in-situ measurements and polarimetric data (including hydrometeor classification) is exploited in the last part of the thesis, devoted to the microphysics of snowfall and in particular of rimed precipitation. Riming is shown to be an important factor leading to significant accumulation of snowfall in the alpine environment. Additionally, the vertical structure of rimed precipitation is examined and interpreted

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

Characterization of clouds and their radiative effects using ground-based instrumentation at a low-mountain site

The interaction of clouds with radiation and aerosols is the greatest source of uncertainty in future climate projections. Part of the reason is the limited amount of observations of clouds and hence the limited knowledge of cloud macro- and microphysical statistics in connection to their effects on the radiative budget and on the vertical redistribution of energy within the atmosphere. In 2007, the Atmospheric Radiation Measurement program�s (ARM) Mobile Facility (AMF) was operated for a nine-month period in the Murg Valley, Black Forest, Germany, in support of the Convective and Orographically-induced Precipitation Study (COPS). Based on the measurements of the AMF and COPS partner instrumentation, the present study aims at improving the data basis of cloud macro- and microphysical statistics and to assess the potential of the derived cloud properties to estimate the radiative effects of clouds. The synergy of various instruments is exploited to derive a data set of high quality thermodynamic and cloud property profiles with a temporal resolution of 30 s. While quality filters in the cloud microphysical retrieval techniques mostly affect the representativity of ice and mixed clouds in the data sample, water clouds are very well represented in the derived 364,850 atmospheric profiles. In total, clouds are present 72% of the time with multi-layer mixed phase (28.4%) and single-layer water clouds (11.3%) occurring most frequently. In order to evaluate the derived thermodynamic and cloud property profiles,radiative closure studies are performed with independent radiation measurements. In clear sky, average differences between calculated and observed surface fluxes are less than 2.1% and 3.6% for the shortwave and longwave, respectively. In cloudy situations, differences, in particular in the shortwave, are much larger, but most of these can be related to broken cloud situations. The cloud radiative effect (CRE), i.e. the difference of cloudy and clear-sky net fluxes, has been analyzed for the whole nine-month period. The largest surface (SFC) net CRE has been found for multi-layer water (-110 Wm-2) and mixed clouds (-116 Wm-2). The estimated uncertainties in the modeled SFC and top of atmopshere (TOA) net CRE are up to 39% and 26%, respectively. For overcast, single-layer water clouds, sensitivity studies reveal that the SW CRE uncertainty at the SFC and TOA is likewise determined by uncertainties in liquid water path (LWP) and effective radius, if the LWP is larger than 100 gm-2. For low LWP values, uncertainties in SFC and TOA shortwave CRE are dominated by the uncertainty in LWP. Uncertainties in CRE due to uncertainties in the shape of the liquid water content (LWC) profile are typically smaller by a factor of two compared to LWP uncertainties. For the difference between the cloudy and clear-sky net heating rates, i.e. the cloud radiative forcing (CRF), of water clouds, the LWP and its vertical distribution within the cloud boundaries are the most important factors. In order to increase the accuracy of LWC profiles and consequentially of the estimates of CRE and CRF, advanced LWC retrieval techniques, such as the Integrated Profiling Technique (IPT), are needed. The accuracy of a LWC profile retrieval using typical microwave radiometer brightness temperatures and/or cloud radar reflectivities is investigated for two realistic cloud profiles. The interplay of the errors of the a priori profile, measurements and forward model on the retrieved LWC error and on the information content of the measurements is analyzed in detail. It is shown that the inclusion of the microwave radiometer observations in the LWC retrieval increases the number of degrees of freedom, i.e. the independent pieces of information in the measurements, by about 1 compared to a retrieval using measuremets from the cloud radar alone. Assuming realistic measurement and forward model errors, it is further demonstrated, that the error in the retrieved LWC is 60% or larger, if no a priori information is available, and that a priori information is essential for a better accuracy. The results of the present work strongly suggest to improve the LWC a priori profile and the corresponding error estimates in the IPT. However, there are few observational datasets available to construct accurate a priori profiles of LWC, and thus more observational data are needed to improve the knowledge of the a priori profile and the corresponding error covariance matrix

Effects of spatial resolution on radar-based precipitation estimation using sub-kilometer X-band radar measurements

Known for the ability to observe precipitation at spatial resolution higher than rain gauge networks and satellite products, weather radars allow us to measure precipitation at spatial resolutions of 1 kilometer (typical resolution for operational radars) and a few hundred meters (often used in research activities). In principle, we can operate a weather radar at resolution higher than 100m and the expectation is that radar data at higher spatial resolution can provide more information. However, there is no systematic research about whether the additional information is noise or useful data contributing to the quantitative precipitation estimation. In order to quantitatively investigate the changes, as either benefits or drawbacks, caused by increasing the spatial resolution of radar measurements, we set up an X-band radar field experiment from May to October in 2017 in the Stuttgart metropolitan region. The scan strategy consists of two quasi-simultaneous scans with a 75-m and a 250-m radial resolution respectively. They are named as the fine scan and the coarse scan, respectively. Both scans are compared to each other in terms of the radar data quality and their radar-based precipitation estimates. The primary results from these comparisons between the radar data of these two scans show that, in contrast to the coarse scan, the fine scan data are characterized with losses of weak echoes, are more subjected to external signals and second-trip echoes (drawback), are more effective in removing non-meteorological echoes (benefit), are more skillful in delineating convective storms (benefit), and show a better agreement with the external reference data (benefit)