Breaking of the Bancroft rule for multiple emulsions stabilized by a single stimulable polymer

International audienceWe investigated emulsions of water and toluene stabilized by (co)polymers consisting of styrene (S) and 2-(dimethylamino)ethyl methacrylate (DMAEMA) monomer units with different compositions and structures such as a PDMAEMA homopolymer, a P(S-co-DMAEMA) random copolymer and various PS-b-PDMAEMA and PS-b-(S-co-DMAEMA) block copolymers. The model system is used to study the fundamental conditions under which the different kinds of polymer-stabilized emulsions (direct oil in water, inverse water in oil and multiple emulsions) are stabilized or destabilized by pH change (at constant temperature). Polymer properties like chain conformation at the toluene-water interface as probed by SANS and neutron reflectivity at the liquid-liquid interface, the oil-water partitioning of the polymer chains (Bancroft's rule of thumb) as determined by UV spectroscopy and interfacial tensions measured by the rising and spinning drop techniques are determined. Overall, results evidence that the curvature sign, as defined by positive and negative values as the chain segments occupy preferentially the water and toluene sides of the interface respectively, reliably predicts the emulsion kind. In contrast, the Bancroft rule failed at foreseeing the emulsion type. In the region of near zero curvature the crossover from direct to inverse emulsions occurs through the formation of either unstable coexisting direct and inverse emulsions (i) or multiple emulsions (ii). The high compact adsorption of the chains at the interface as shown by low interfacial tension values does not allow to discriminate between both cases. However, the toluene-water partitioning of the polymeric emulsifier is still a key factor driving the formation of (i) or (ii) emulsions. Interestingly, the stabilization of the multiple emulsions can be tuned to a large extent as the toluene-water polymer partitioning can be adjusted using quite a large number of physico-chemical parameters linked to polymer architecture like diblock length ratio or polymer total molar mass, for example. Moreover, we show that monitoring the oil-water partitioning aspect of the emulsion system can also be used to lower the interfacial tension at low pH to values slightly higher than 0.01 mN m-1, irrespective of the curvature sign

A Radar-Based Hail Climatology of Australia

In Australia, hailstorms present considerable public safety and economic risks, where they are considered the most damaging natural hazard in terms of annual insured losses. Despite these impacts, the current climatological distribution of hailfall across the continent is still comparatively poorly understood. This study aims to supplement previous national hail climatologies, such as those based on environmental proxies or satellite radiometer data, with more direct radar-based hail observations. The heterogeneous and incomplete nature of the Australian radar network complicates this task and prompts the introduction of some novel methodological elements. We introduce an empirical correction technique to account for hail reflectivity biases at C-band, derived by comparing overlapping C- and S-band observations. Furthermore, we demonstrate how object-based hail swath analysis may be used to produce resolution-invariant hail frequencies, and describe an interpolation method used to create a spatially continuous hail climatology. The Maximum Estimated Size of Hail (MESH) parameter is then applied to a mixture of over fifty operational radars in the Australian radar archive, resulting in the first nationwide, radar-based hail climatology. The spatiotemporal distribution of hailstorms is examined, including their physical characteristics, seasonal and diurnal frequency, and regional variations of such properties across the continent.Comment: Revision 1 of manuscript submitted to Monthly Weather Revie

The Effects of Spatial Interpolation on a Novel, Dual-Doppler 3D Wind Retrieval Technique

Three-dimensional wind retrievals from ground-based Doppler radars have played an important role in meteorological research and nowcasting over the past four decades. However, in recent years, the proliferation of open-source software and increased demands from applications such as convective parameterizations in numerical weather prediction models has led to a renewed interest in these analyses. In this study, we analyze how a major, yet often-overlooked, error source effects the quality of retrieved 3D wind fields. Namely, we investigate the effects of spatial interpolation, and show how the common practice of pre-gridding radial velocity data can degrade the accuracy of the results. Alternatively, we show that assimilating radar data directly at their observation locations improves the retrieval of important dynamic features such as the rear flank downdraft and mesocyclone within a simulated supercell, while also reducing errors in vertical vorticity, horizontal divergence, and all three velocity components.Comment: Revised version submitted to JTECH. Includes new section with a real data cas

The variability of tropical ice cloud properties as a function of the large-scale context from ground-based radar-lidar observations over Darwin, Australia

The high complexity of cloud parameterizations now held in models puts more pressure on observational studies to provide useful means to evaluate them. One approach to the problem put forth in the modelling community is to evaluate under what atmospheric conditions the parameterizations fail to simulate the cloud properties and under what conditions they do a good job. It is the ambition of this paper to characterize the variability of the statistical properties of tropical ice clouds in different tropical "regimes" recently identified in the literature to aid the development of better process-oriented parameterizations in models. For this purpose, the statistical properties of non-precipitating tropical ice clouds over Darwin, Australia are characterized using ground-based radar-lidar observations from the Atmospheric Radiation Measurement (ARM) Program. The ice cloud properties analysed are the frequency of ice cloud occurrence, the morphological properties (cloud top height and thickness), and the microphysical and radiative properties (ice water content, visible extinction, effective radius, and total concentration). The variability of these tropical ice cloud properties is then studied as a function of the large-scale cloud regimes derived from the International Satellite Cloud Climatology Project (ISCCP), the amplitude and phase of the Madden-Julian Oscillation (MJO), and the large-scale atmospheric regime as derived from a long-term record of radiosonde observations over Darwin. The vertical variability of ice cloud occurrence and microphysical properties is largest in all regimes (1.5 order of magnitude for ice water content and extinction, a factor 3 in effective radius, and three orders of magnitude in concentration, typically). 98 % of ice clouds in our dataset are characterized by either a small cloud fraction (smaller than 0.3) or a very large cloud fraction (larger than 0.9). In the ice part of the troposphere three distinct layers characterized by different statistically-dominant microphysical processes are identified. The variability of the ice cloud properties as a function of the large-scale atmospheric regime, cloud regime, and MJO phase is large, producing mean differences of up to a factor 8 in the frequency of ice cloud occurrence between large-scale atmospheric regimes and mean differences of a factor 2 typically in all microphysical properties. Finally, the diurnal cycle of the frequency of occurrence of ice clouds is also very different between regimes and MJO phases, with diurnal amplitudes of the vertically-integrated frequency of ice cloud occurrence ranging from as low as 0.2 (weak diurnal amplitude) to values in excess of 2.0 (very large diurnal amplitude). Modellers should now use these results to check if their model cloud parameterizations are capable of translating a given atmospheric forcing into the correct statistical ice cloud properties

Segmentation of polarimetric radar imagery using statistical texture

Weather radars are increasingly being used to study the interaction between wildfires and the atmosphere, owing to the enhanced spatio-temporal resolution of radar data compared to conventional measurements, such as satellite imagery and in situ sensing. An important requirement for the continued proliferation of radar data for this application is the automatic identification of fire-generated particle returns (pyrometeors) from a scene containing a diverse range of echo sources, including clear air, ground and sea clutter, and precipitation. The classification of such particles is a challenging problem for common image segmentation approaches (e.g. fuzzy logic or unsupervised machine learning) due to the strong overlap in radar variable distributions between each echo type. Here, we propose the following two-step method to address these challenges: (1) the introduction of secondary, texture-based fields, calculated using statistical properties of gray-level co-occurrence matrices (GLCMs), and (2) a Gaussian mixture model (GMM), used to classify echo sources by combining radar variables with texture-based fields from (1). Importantly, we retain all information from the original measurements by performing calculations in the radar's native spherical coordinate system and introduce a range-varying-window methodology for our GLCM calculations to avoid range-dependent biases. We show that our method can accurately classify pyrometeors' plumes, clear air, sea clutter, and precipitation using radar data from recent wildfire events in Australia and find that the contrast of the radar correlation coefficient is the most skilful variable for the classification. The technique we propose enables the automated detection of pyrometeors' plumes from operational weather radar networks, which may be used by fire agencies for emergency management purposes or by scientists for case study analyses or historical-event identification.</p

Coastal Observations of Weather Features in Senegal during the AMMA SOP-3 Period

During 15 August through 30 September 2006, ground and aircraft measurements were obtained from a multi-national group of students and scientists in Senegal. Key measurements were aimed at investigating and understanding precipitation processes, thermodynamic and dynamic environmental conditions, cloud, aerosol and microphysical processes and spaceborne sensors (TRMM, CloudSat/Calipso) validation. Ground and aircraft instruments include: ground based polarimetric radar, disdrometer measurements, a course and a high-density rain gauge network, surface chemical measurements, a 10 m flux tower, broadband IR, solar and microwave measurements, rawinsonde and radiosonde measurements, FA-20 dropsonde, in situ microphysics and cloud radar measurements. Highlights during SOP3 include ground and aircraft measurements of squall lines, African Easterly Waves (AEWs), Saharan Air Layer advances into Senegal, and aircraft measurements of AEWs -- including the perturbation that became Hurricane Isaac

Toward understanding of differences in current cloud retrievals of ARM ground-based measurements

Accurate observations of cloud microphysical properties are needed for evaluating and improving the representation of cloud processes in climate models and better estimate of the Earth radiative budget. However, large differences are found in current cloud products retrieved from ground-based remote sensing measurements using various retrieval algorithms. Understanding the differences is an important step to address uncertainties in the cloud retrievals. In this study, an in-depth analysis of nine existing ground-based cloud retrievals using ARM remote sensing measurements is carried out. We place emphasis on boundary layer overcast clouds and high level ice clouds, which are the focus of many current retrieval development efforts due to their radiative importance and relatively simple structure. Large systematic discrepancies in cloud microphysical properties are found in these two types of clouds among the nine cloud retrieval products, particularly for the cloud liquid and ice particle effective radius. Note that the differences among some retrieval products are even larger than the prescribed uncertainties reported by the retrieval algorithm developers. It is shown that most of these large differences have their roots in the retrieval theoretical bases, assumptions, as well as input and constraint parameters. This study suggests the need to further validate current retrieval theories and assumptions and even the development of new retrieval algorithms with more observations under different cloud regimes

SIRTA, a ground-based atmospheric observatory for cloud and aerosol research

Ground-based remote sensing observatories have a crucial role to play in providing data to improve our understanding of atmospheric processes, to test the performance of atmospheric models, and to develop new methods for future space-borne observations. Institut Pierre Simon Laplace, a French research institute in environmental sciences, created the Site Instrumental de Recherche par T&amp;#233;l&amp;#233;d&amp;#233;tection Atmosph&amp;#233;rique (SIRTA), an atmospheric observatory with these goals in mind. Today SIRTA, located 20km south of Paris, operates a suite a state-of-the-art active and passive remote sensing instruments dedicated to routine monitoring of cloud and aerosol properties, and key atmospheric parameters. Detailed description of the state of the atmospheric column is progressively archived and made accessible to the scientific community. This paper describes the SIRTA infrastructure and database, and provides an overview of the scientific research associated with the observatory. Researchers using SIRTA data conduct research on atmospheric processes involving complex interactions between clouds, aerosols and radiative and dynamic processes in the atmospheric column. Atmospheric modellers working with SIRTA observations develop new methods to test their models and innovative analyses to improve parametric representations of sub-grid processes that must be accounted for in the model. SIRTA provides the means to develop data interpretation tools for future active remote sensing missions in space (e.g. CloudSat and CALIPSO). SIRTA observation and research activities take place in networks of atmospheric observatories that allow scientists to access consistent data sets from diverse regions on the globe