G band atmospheric radars: new frontiers in cloud physics

Clouds and associated precipitation are the largest source of uncertainty in current weather and future climate simulations. Observations of the microphysical, dynamical and radiative processes that act at cloud scales are needed to improve our understanding of clouds. The rapid expansion of ground-based super-sites and the availability of continuous profiling and scanning multi-frequency radar observations at 35 and 94 GHz have significantly improved our ability to probe the internal structure of clouds in high temporal-spatial resolution, and to retrieve quantitative cloud and precipitation properties. However, there are still gaps in our ability to probe clouds due to large uncertainties in the retrievals. The present work discusses the potential of G band (frequency between 110 and 300 GHz) Doppler radars in combination with lower frequencies to further improve the retrievals of microphysical properties. Our results show that, thanks to a larger dynamic range in dual-wavelength reflectivity, dual-wavelength attenuation and dual-wavelength Doppler velocity (with respect to a Rayleigh reference), the inclusion of frequencies in the G band can significantly improve current profiling capabilities in three key areas: boundary layer clouds, cirrus and mid-level ice clouds, and precipitating snow

Fractal geometry of aggregate snowflakes revealed by triple wavelength radar measurements

Radar reflectivity measurements from three different wavelengths are used to retrieve information about the shape of aggregate snowflakes in deep stratiform ice clouds. Dual-wavelength ratios are calculated for different shape models and compared to observations at 3, 35 and 94 GHz. It is demonstrated that many scattering models, including spherical and spheroidal models, do not adequately describe the aggregate snowflakes that are observed. The observations are consistent with fractal aggregate geometries generated by a physically-based aggregation model. It is demonstrated that the fractal dimension of large aggregates can be inferred directly from the radar data. Fractal dimensions close to 2 are retrieved, consistent with previous theoretical models and in-situ observations

Proteomic mapping of differentially vulnerable pre-synaptic populations identifies regulators of neuronal stability in vivo.

Synapses are an early pathological target in many neurodegenerative diseases ranging from well-known adult onset conditions such as Alzheimer and Parkinson disease to neurodegenerative conditions of childhood such as spinal muscular atrophy (SMA) and neuronal ceroid lipofuscinosis (NCLs). However, the reasons why synapses are particularly vulnerable to such a broad range of neurodegeneration inducing stimuli remains unknown. To identify molecular modulators of synaptic stability and degeneration, we have used the Cln3-/- 33 mouse model of a juvenile form of NCL. We profiled and compared the molecular composition of anatomically-distinct, differentially-affected pre-synaptic populations from the Cln3-/- 35 mouse brain using proteomics followed by bioinformatic analyses. Identified protein candidates were then tested using a Drosophila CLN3 model to study their ability to modify the CLN3-neurodegenerative phenotype in vivo. We identified differential perturbations in a range of molecular cascades correlating with synaptic vulnerability, including valine catabolism and rho signalling pathways. Genetic and pharmacological targeting of key ‘hub’ proteins in such pathways was sufficient to modulate phenotypic presentation in a Drosophila CLN3 model. We propose that such a workflow provides a target rich method for the identification of novel disease regulators which could be applicable to the study of other conditions where appropriate models exist