Using artificial neural networks to predict riming from Doppler cloud radar observations

Riming, i.e., the accretion and freezing of super-cooled liquid water (SLW) on ice particles in mixed-phase clouds, is an important pathway for precipitation formation. Detecting and quantifying riming using ground-based cloud radar observations is of great interest; however, approaches based on measurements of the mean Doppler velocity (MDV) are unfeasible in convective and orographically influenced cloud systems. Here, we show how artificial neural networks (ANNs) can be used to predict riming using ground-based, zenith-pointing cloud radar variables as input features. ANNs are a versatile means to extract relations from labeled data sets, which contain input features along with the expected target values. Training data are extracted from a data set acquired during winter 2014 in Finland, containing both Ka-and W-band cloud radar and in situ observations of snow-fall by a Precipitation Imaging Package from which the rime mass fraction (FRPIP) is retrieved. ANNs are trained separately either on the Ka-band radar or the W-band radar data set to predict the rime fraction FRANN. We focus on two configurations of input variables. ANN 1 uses the equivalent radar reflectivity factor (Ze), MDV, the width from left to right edge of the spectrum above the noise floor (spectrum edge width - SEW), and the skewness as input features. ANN 2 only uses Ze, SEW, and skewness. The application of these two ANN configurations to case studies from different data sets demonstrates that both are able to predict strong riming (FRANN > 0.7) and yield low values (FRANNPeer reviewe

Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks

In mixed-phase clouds, the variable mass ratio between liquid water and ice as well as the spatial distribution within the cloud plays an important role in cloud lifetime, precipitation processes, and the radiation budget. Data sets of vertically pointing Doppler cloud radars and lidars provide insights into cloud properties at high temporal and spatial resolution. Cloud radars are able to penetrate multiple liquid layers and can potentially be used to expand the identification of cloud phase to the entire vertical column beyond the lidar signal attenuation height, by exploiting morphological features in cloud radar Doppler spectra that relate to the existence of supercooled liquid. We present VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn), a retrieval based on deep convolutional neural networks (CNNs) mapping radar Doppler spectra to the probability of the presence of cloud droplets (CD). The training of the CNN was realized using the Cloudnet processing suite as supervisor. Once trained, VOODOO yields the probability for CD directly at Cloudnet grid resolution. Long-term predictions of 18 months in total from two mid-latitudinal locations, i.e., Punta Arenas, Chile (53.1 S, 70.9 W), in the Southern Hemisphere and Leipzig, Germany (51.3 N, 12.4 E), in the Northern Hemisphere, are evaluated. Temporal and spatial agreement in cloud-droplet-bearing pixels is found for the Cloudnet classification to the VOODOO prediction. Two suitable case studies were selected, where stratiform, multi-layer, and deep mixed-phase clouds were observed. Performance analysis of VOODOO via classification-evaluating metrics reveals precision > 0.7, recall ≈ 0.7, and accuracy ≈ 0.8. Additionally, independent measurements of liquid water path (LWP) retrieved by a collocated microwave radiometer (MWR) are correlated to the adiabatic LWP, which is estimated using the temporal and spatial locations of cloud droplets from VOODOO and Cloudnet in connection with a cloud parcel model. This comparison resulted in stronger correlation for VOODOO (≈ 0.45) compared to Cloudnet (≈ 0.22) and indicates the availability of VOODOO to identify CD beyond lidar attenuation. Furthermore, the long-term statistics for 18 months of observations are presented, analyzing the performance as a function of MWR-LWP and confirming VOODOO's ability to identify cloud droplets reliably for clouds with LWP > 100 g m-2. The influence of turbulence on the predictive performance of VOODOO was also analyzed and found to be minor. A synergy of the novel approach VOODOO and Cloudnet would complement each other perfectly and is planned to be incorporated into the Cloudnet algorithm chain in the near future

SN 2019ewu: A Peculiar Supernova with Early Strong Carbon and Weak Oxygen Features from a New Sample of Young SN Ic Spectra

With the advent of high cadence, all-sky automated surveys, supernovae (SNe) are now discovered closer than ever to their dates of explosion. However, young pre-maximum light follow-up spectra of Type Ic supernovae (SNe Ic), probably arising from the most stripped massive stars, remain rare despite their importance. In this paper we present a set of 49 optical spectra observed with the Las Cumbres Observatory through the Global Supernova Project for 6 SNe Ic, including a total of 17 pre-maximum spectra, of which 8 are observed more than a week before V-band maximum light. This dataset increases the total number of publicly available pre-maximum light SN Ic spectra by 25% and we provide publicly available SNID templates that will significantly aid in the fast identification of young SNe Ic in the future. We present detailed analysis of these spectra, including Fe II 5169 velocity measurements, O I 7774 line strengths, and continuum shapes. We compare our results to published samples of stripped supernovae in the literature and find one SN in our sample that stands out. SN 2019ewu has a unique combination of features for a SN Ic: an extremely blue continuum, high absorption velocities, a P-cygni shaped feature almost 2 weeks before maximum light that TARDIS radiative transfer modeling attributes to C II rather than H$\alpha$, and weak or non-existent O I 7774 absorption feature until maximum light.Comment: Submitted to the Astrophysical Journal. 15 pages, 6 figure

New particle formation and its effect on cloud condensation nuclei abundance in the summer Arctic : a case study in the Fram Strait and Barents Sea

In a warming Arctic the increased occurrence of new particle formation (NPF) is believed to originate from the declining ice coverage during summertime. Understanding the physico-chemical properties of newly formed particles, as well as mechanisms that control both particle formation and growth in this pristine environment, is important for interpreting aerosol-cloud interactions, to which the Arctic climate can be highly sensitive. In this investigation, we present the analysis of NPF and growth in the high summer Arctic. The measurements were made on-board research vessel Polarstern during the PS106 Arctic expedition. Four distinctive NPF and subsequent particle growth events were observed, during which particle (diameter in a range 10-50 nm) number concentrations increased from background values of approx. 40 up to 4000 cm(-3). Based on particle formation and growth rates, as well as hygroscopicity of nucleation and the Aitken mode particles, we distinguished two different types of NPF events. First, some NPF events were favored by negative ions, resulting in more-hygroscopic nucleation mode particles and suggesting sulfuric acid as a precursor gas. Second, other NPF events resulted in less-hygroscopic particles, indicating the influence of organic vapors on particle formation and growth. To test the climatic relevance of NPF and its influence on the cloud condensation nuclei (CCN) budget in the Arctic, we applied a zero-dimensional, adiabatic cloud parcel model. At an updraft velocity of 0.1 m s(-1), the particle number size distribution (PNSD) generated during nucleation processes resulted in an increase in the CCN number concentration by a factor of 2 to 5 compared to the background CCN concentrations. This result was confirmed by the directly measured CCN number concentrations. Although particles did not grow beyond 50 nm in diameter and the activated fraction of 15-50 nm particles was on average below 10 %, it could be shown that the sheer number of particles produced by the nucleation process is enough to significantly influence the background CCN number concentration. This implies that NPF can be an important source of CCN in the Arctic. However, more studies should be conducted in the future to understand mechanisms of NPF, sources of precursor gases and condensable vapors, as well as the role of the aged nucleation mode particles in Arctic cloud formation.Peer reviewe

práticas artísticas no ensino básico e secundário

A Educação Artística joga-se em muito mais locais que no ensino formal. As oportunidades formativas têm sido aproveitadas por museus, bibliotecas, centros culturais, exposições, festivais, associações e plataformas culturais, eventos, plataformas de disseminação artística, edições. Os artistas individuais têm vindo a integrar as dinâmicas relacionais e de criação de públicos nas suas obras, ao convocarem as audiências e implicarem o espectador. O terreno é limitado apenas pela imaginação, e as oportunidades de convocação alargam-se aos novos conteúdos e plataformas digitais, a par com a valorização do que é local e identitário: a revolução pode fazer-se pela cidadania.info:eu-repo/semantics/publishedVersio

Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm

In many types of clouds, multiple hydrometeor populations can be present at the same time and height. Studying the evolution of these different hydrometeors in a time-height perspective can give valuable information on cloud particle composition and microphysical growth processes. However, as a prerequisite, the number of different hydrometeor types in a certain cloud volume needs to be quantified. This can be accomplished using cloud radar Doppler velocity spectra from profiling cloud radars if the different hydrometeor types have sufficiently different terminal fall velocities to produce individual Doppler spectrum peaks. Here we present a newly developed supervised machine learning radar Doppler spectra peak-finding algorithm (named PEAKO). In this approach, three adjustable parameters (spectrum smoothing span, prominence threshold, and minimum peak width at half-height) are varied to obtain the set of parameters which yields the best agreement of user-classified and machine-marked peaks. The algorithm was developed for Ka-band ARM zenith-pointing radar (KAZR) observations obtained in thick snowfall systems during the Atmospheric Radiation Measurement Program (ARM) mobile facility AMF2 deployment at Hyytiälä, Finland, during the Biogenic Aerosols - Effects on Clouds and Climate (BAECC) field campaign. The performance of PEAKO is evaluated by comparing its results to existing Doppler peak-finding algorithms. The new algorithm consistently identifies Doppler spectra peaks and outperforms other algorithms by reducing noise and increasing temporal and height consistency in detected features. In the future, the PEAKO algorithm will be adapted to other cloud radars and other types of clouds consisting of multiple hydrometeors in the same cloud volume. © 2019 Copernicus GmbH. All rights reserved