Thesis (M.S.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 118-123). Also available on the World Wide Web.

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In this work high-resolution numerical simulation (Large-Eddie Simulation, LES) has been used to study the characteristic factors causing and influencing the development of moist convective clouds. Through this work a 1D cloud-model was derived from first principles to represent the vertical profile of individual convective clouds. A microphysics framework was implemented to ensure identical behaviour in LES and cloud-model integration where the microphysical processes represented are numerically integrated using a novel adaptive step microphysics integration which uses the physical speed at which a process takes place to adjust the integration step size (in space and time). This work also introduces a simple representation of cloud-droplet formation which allows for super-saturation to exist in-cloud and through this provide more physical representation of the in-cloud state. Together with high-resolution simulation of isolated individual and interacting multiple clouds in environmental conditions leading to shallow convection, the 1D cloud-model was used to infer that the principal influence on moist convective clouds is the entrainment of air from a cloud’s immediate environment which is significantly different from the environmental mean state. This suggests that convection parameterisations must represent the influence of moist convective downdrafts to properly predict the vertical structure of convective clouds so as to correctly predict the cloud-top height and vertical transport. Finally it was found that cloud-base radius is not in itself adequate as a means of classification for defining cloud-types as clouds with the same cloud-base radius showed large variation (≈ 600m) in cloud-top height. Based on simulations of individual convective clouds it was found that 3D simulations are necessary to capture the full dynamic behaviour of convective clouds (2D axisymmetric simulations have too little entrainment) and that agreement with the 1D cloud-model could only be found when entrainment was diagnosed from simulation instead of being parameterised by the traditional Morton-Turner model and only for 2D axisymmetric simulations, suggesting that the 1D cloud-model will require further extension or the diagnosis of entrainment improved.

Data from the GARP Atlantic Tropical Experiment (GATE) was analysed in an exploratory manner to discover the characteristics of mesoscale organization of convection and it's relationship to large-scale wind profiles. Automated methods were developed to identify the convective cells and their linear organization. These automated methods use a median high-pass filter to identify enhanced cells and a simple pattern recognition technique to ascertain the linear organization between them. Due to the simplified nature of the algorithm, the whole data set of the 21 day period from the phase 3 of GATE was processed in an economical manner obtaining a large data base which was used in the investigation of clusters and other associated phenomena. The mesoscale organization of convective cells and the widespread areas of lighter precipitation associated with them showed expected characteristics and compared satisfactorily with previous results. A large fraction of the rainfall (64%) fell from the widespread area. The total precipitation had a correlation of 0.94 with the fractional area of the widespread and a correlation of 0.89 with the fractional area of the clusters. The widespread precipitation had a correlation index of 0.97 with it's fractional area and the cluster precipitation had a strong linear relationship to it's area with a correlation of 0.99. These factors argue well for the parameterization of rainfall rate in tropical regions to a high accuracy by the area covered by organized convective cells and widespread areas. It was also seen that there was a good correlation with the number of clusters and number of cores with the total precipitation rate in a given area. These factors create a strong argument for identifying mesoscale systems consisting of convective cells and widespread precipitation as basic units of precipitation in tropical regions, having a characteristic life cycle of their own. The widespread and total precipitation showed very good correlation with upper-level vertical motion. Clusters tended to align parallel with the horizontal low-level wind shear and the degree of alignment appears to depend on the strength of the wind shear. / Graduation date: 1991

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.

The mesoscale organization and structure of convective clouds is thought to be rooted in the thermodynamic properties of the atmosphere and in the turbulent to mesoscale dynamics of the flow. Such structure may contribute to the transition between shallow and deep convection. The thermodynamic state of the boundary layer is forced by the amount of surface fluxes from below. Conversely, landscape patterns and land-cover heterogeneity may equally give rise to focused regions for deep convection triggering, in particular when patch sizes exceed 10 km. Since the convective boundary layer has a mediating function between the surface and deep storm clouds, the connection between surface and upper atmosphere is not straightforward. It is generally believed to involve local erosion of the capping inversion layer, the build-up of a moist energy supply, gradual humidification of the lower-free troposphere that reduces dry air entrainment into burgeoning deeper clouds, and thermal mesoscale circulations that can generate moisture convergence and locally forced ascent. To what extent microscale realistic surface heterogeneity and an interactive surface response matter to shallow and deep convection and its organization remains an open question. In this dissertation, we describe the coupling of a physiology-based vegetation model (HYBRID) and of a sea surface flux algorithm (COARE) to the cloud-resolving Active Tracer High-resolution Atmospheric Model (ATHAM). We investigate the full diurnal cycle of convection based on the example of the Hector storm over Tiwi Islands, notably the well-characterized event on 30th November 2005. The model performs well in terms of timing and cloud dynamics in comparison to a range of available observations. Also, ATHAM-HYBRID seems to do well in terms of flux partitioning. Whilst awaiting more thorough flux validation, we remain confident that the interactive surface response of both HYBRID and COARE is suited for the purpose of simulating convective-scale processes. We find the storm system evolution in 3D simulations to be robust with respect to differences in surface configuration and initialization. Within our 3D sensitivity runs, we could not identify a strong dependence on either realistic surface heterogeneity in the island landscape or on the interactive surface response. We conclude that in our case study at least, atmospheric (turbulent) dynamics likely dominate over surface heterogeneity effects, provided that the bulk magnitude of the surface energy fluxes, and their partitioning into sensible and latent heat (Bowen ratio), remain unaltered. This is consistent with 2D sensitivity studies, where we find model grid-spacing and momentum diffusion, governing the dynamics, to have an important influence on the overall evolution of deep convection. Fine grid-spacing is necessary, as the median width of updraught cores mostly does not exceed 1000 m. We associate this influence with the dry air entrainment rate in the wake of rising parcels, and with how resolution and diffusion act on coherent structures in the flow. In 2D sensitivity studies with differences in realistic heterogeneities of surface properties, we find little evidence for a clear deterministic influence of these properties on the transition between shallow and deep convection, in spite of largely different storm evolutions across the various runs. In these runs, we tentatively ascribe triggering to stochastic features in the flow, without discarding the relevance of convergence lines produced by mesoscale density currents, such as the sea breeze and cold pool storm outflows.

Within this Ph.D. thesis, the optical thickness and particle effective radius of cirrus and deep convective clouds (DCCs) are retrieved using passive remote sensing techniques. For this purpose, airborne and satellite measurements of spectral solar radiation combined with extensive radiative transfer simulations have been conducted. Data analyzed in this study were collected during the ML-CIRRUS and the ACRIDICON-CHUVA campaigns, which aimed to study natural and contrail cirrus over Europe and DCCs over the Amazon rainforest using the German High Altitude and Long Range Research Aircraft (HALO), respectively. During the campaigns, HALO was equipped with a comprehensive set of remote sensing and in situ instruments. In particular flights, closely collocated measurements with the overpasses of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard of the Aqua satellite were carried out. A cirrus located above liquid water clouds and a DCC topped by an anvil cirrus are investigated. In general, the research framework can be divided into four parts. In the first part, the spectral upward radiances measured by the Spectral Modular Airborne Radiation Measurement System (SMART)-Albedometer aboard of HALO are compared with those measured by the MODIS. In the second part, a radiance ratio retrieval assuming a vertically homogeneous cloud is applied to obtain the cloud optical thickness and particle effective radius based on the measurements of SMART-Albedometer and MODIS. Multiple near-infrared wavelengths with different absorption characteristics are utilized in the retrieval in order to study the vertical structure of cloud particle sizes. In the third part, the retrieved cloud properties are compared with those derived from the MODIS cloud products. For the cirrus case, the retrieved values of particle effective radius are further compared to in situ data measured by the Cloud Combination Probe (CCP). To allow this comparison, a vertical weighting method is applied. Although the comparison results in a good agreement, retrievals using this conventional technique only provide information on cloud particle sizes from the upper layers, even if spectral measurements have been employed. The retrieved particle effective radius represents a vertically weighted value, where the upper cloud layers are weighted at most. In the fourth part, an extended technique based on Bayesian optimal estimation has been developed to obtain the full vertical profile of particle effective radius. For this purpose, a parameterization assuming the shape of the vertical profile with respect to a vertical coordinate within the cloud is applied. The information content of SMART-Albedometer measurements is analyzed to identify wavelengths that bring the most information pertaining to each retrieval parameter. The new retrieval technique is applied to the cirrus case to infer the profile of particle effective radius as a function of optical thickness. The comparison between the retrieved and the in situ profiles shows a good agreement with a deviation of about 5 % at the cloud top and increases to values of up to 15 % at the cloud base. The new retrieval technique has shown excellent skill in improving the study of the vertical profile of cloud microphysical properties, which can be applied in the future generation of airborne and satellite retrievals based on the measurements of passive remote sensing.:1. Introduction 2. Definitions 3. Measurements 4. Comparison of upward radiance 5. Retrieval of cloud optical thickness and particle effective radius 6. Comparison of cloud optical thickness and particle effective radius 7. Retrieval of the vertical profile of particle effective radius 8. Summary and conclusion

Weather radars are increasingly used for the study of clouds, understanding the precipitation systems and also for forecasting very short range weather (one hour to a few hours). Now, Doppler Weather Radar (DWR) data are available in India and it is possible to study cloud properties at fine temporal and spatial scales. Radar is a complex system and calibration of a radar is not an easy job. But derived cloud properties strongly depend on the absolute magnitude of the reflectivity. Therefore, there is a need to check how data from two or more radars compare if they measure a common volume. Chennai and SHAR radars are within 66 km from each other, and the data collected during their calibration and intercomparison experiment in 2006 enables the comparison of their reflectivity(Z) values. Individual reflectivity are compared after plotting SHAR versus Chennai in a scatter plot. Fitting a least square linear best fit line shows that the intercept has a value around 6 dBZ and the slope of the line is 1.06. Thus, there is a trend as well, and the difference between the two radars increase with Z, and for Z around 40 dBZ (for SHAR DWR), the difference between the two is around 8.5 dBZ. Visual intercomparison also validated the results. Data from the two radars are compared with Precipitation Radar (PR) data on board TRMM satellite. TRMM radar slightly overestimates compared to Chennai radar above the range of 30 dBZ. After standardized, SHAR data is used for understanding the evolution and propagation of cloud systems. The diurnal variation in convection is strong in the study region, with increase around local evening and morning and weakening around midnight except in December. Average liquid water content in the clouds is about 0.5 gm/m3. There is some seasonal dependence but no clear dependence on cloud size. Smaller systems of May have more liquid water content compared to larger ones. For nowcasting vertically projected maximum reflectivity is taken. A threshold of 30 dBZ is set to identify the cloud systems. Both center of gravity tracking (CG) and cross-correlation (CC) methods are used to track them. Frequent merging and splitting is common in the clouds which makes storm tracking difficult. Tracking by CC is giving better result than that by the CG method in the case of large systems (i.e., clusters). For smaller systems (individual cloud systems), CC method gives better result than CG method but not as good as cluster.