Low-level stratiform clouds and dynamical features observed within the southern West African monsoon

During the boreal summer, the monsoon season that takes place in West Africa is accompanied by low stratus clouds over land that stretch from the Guinean coast several hundred kilometers inland. Numerical climate and weather models need finer description and knowledge of cloud macrophysical characteristics and of the dynamical and thermodynamical structures occupying the lowest troposphere, in order to be properly evaluated in this region. The Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa (DACCIWA) field experiment, which took place in summer 2016, addresses this knowledge gap. Low-level atmospheric dynamics and stratiform low-level cloud macrophysical properties are analyzed using in situ and remote sensing measurements continuously collected from 20 June to 30 July at Savè, Benin, roughly 180 km from the coast. The macrophysical characteristics of the stratus clouds are deduced from a ceilometer, an infrared cloud camera, and cloud radar. Onset times, evolution, dissipation times, base heights, and thickness are evaluated. The data from an ultra-high-frequency (UHF) wind profiler, a microwave radiometer, and an energy balance station are used to quantify the occurrence and characteristics of the monsoon flow, the nocturnal low-level jet, and the cold air mass inflow propagating northward from the coast of the Gulf of Guinea. The results show that these dynamical structures are very regularly observed during the entire 41 d documented period. Monsoon flow is observed every day during our study period. The so-called “maritime inflow” and the nocturnal low-level jet are also systematic features in this area. According to synoptic atmospheric conditions, the maritime inflow reaches Savè around 18:00–19:00 UTC on average. This timing is correlated with the strength of the monsoon flow. This time of arrival is close to the time range of the nocturnal low-level jet settlement. As a result, these phenomena are difficult to distinguish at the Savè site. The low-level jet occurs every night, except during rain events, and is associated 65 % of the time with low stratus clouds. Stratus clouds form between 22:00 and 06:00 UTC at an elevation close to the nocturnal low-level jet core height. The cloud base height, 310±30 m above ground level (a.g.l.), is rather stationary during the night and remains below the jet core height. The cloud top height, at 640±100 m a.g.l., is typically found above the jet core. The nocturnal low-level jet, low-level stratiform clouds, monsoon flow, and maritime inflow reveal significant day-to-day and intra-seasonal variability during the summer given the importance of the different monsoon phases and synoptic atmospheric conditions. Distributions of strength, depth, onset time, breakup time, etc. are quantified here. These results contribute to satisfy the main goals of DACCIWA and allow a conceptual model of the dynamical structures in the lowest troposphere over the southern part of West Africa

Raman LIDARs for the atmospheric calibrationalong the line-of-sight of CTA

The Cherenkov Telescope Array (CTA) is the next generation ground-based observatory forgamma-ray astronomy at very-high energies. Employing more than 100 Imaging AtmosphericCherenkov Telescopes in the northern and southern hemispheres, it was designed to reach un-precedented sensitivity and energy resolution. Understanding and correcting for systematic bi-ases on the absolute energy scale and instrument response functions will be a crucial issue forthe performance of CTA. The LUPM group and the Spanish/Italian/Slovenian collaboration arecurrently building two Raman LIDAR prototypes for the online atmospheric calibration alongthe line-of-sight of the CTA. Requirements for such a solution include the ability to characterizeaerosol extinction at two wavelengths to distances of 30 km with an accuracy better than 5%,within time scales of about a minute, steering capabilities and close interaction with the CTAarray control and data acquisition system as well as other auxiliary instruments. Our Raman LI-DARs have design features that make them different from those used in atmospheric science andare characterized by large collecting mirrors (∼2.5 m2), liquid light-guides that collect the light atthe focal plane and transport it to the readout system, reduced acquisition time and highly preciseRaman spectrometers. The Raman LIDARs will participate in a cross-calibration and character-ization campaign of the atmosphere at the CTA North site at La Palma, together with other sitecharacterization instruments. After a one-year test period there, an in-depth evaluation of the so-lutions adopted by the two projects will lead to a final Raman LIDAR design proposal for bothCTA sites

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Design and Development of a Raman Lidar for Cherenkov Gamma Array Experiments

Future Cherenkov Gamma Ray (CGR) experiments will reach a sensitivity and energy resolution never obtained until now by any other high energy gamma–ray experiment. It is well known that atmospheric conditions contribute particularly in this aspect. Raman lidars can help reduce the systematic uncertainties of the molecular and aerosol components of the atmosphere so these performances can be reached. The motivation for Raman lidars and the design and development of the Montpellier Raman lidar system is described. It provides both multiple elastic and Raman readout channels and custom-made optics design. Preliminary lidar tests and signals show the actual performance of the lidar in consistency with the desired goals

Design and Development of a Raman Lidar for Cherenkov Gamma Array Experiments

Future Cherenkov Gamma Ray (CGR) experiments will reach a sensitivity and energy resolution never obtained until now by any other high energy gamma–ray experiment. It is well known that atmospheric conditions contribute particularly in this aspect. Raman lidars can help reduce the systematic uncertainties of the molecular and aerosol components of the atmosphere so these performances can be reached. The motivation for Raman lidars and the design and development of the Montpellier Raman lidar system is described. It provides both multiple elastic and Raman readout channels and custom-made optics design. Preliminary lidar tests and signals show the actual performance of the lidar in consistency with the desired goals

ELIFAN, an algorithm for the estimation of cloud cover from sky imagers

International audienceIn the context of a network of sky cameras installed on atmospheric multi-instrumented sites, we present an algorithm named ELIFAN, which aims to estimate the cloud cover amount from full-sky visible daytime images with a common principle and procedure. ELIFAN was initially developed for a self-made full-sky image system presented in this article and adapted to a set of other systems in the network. It is based on red-to-blue ratio thresholding for the distinction of cloudy and cloud-free pixels of the image and on the use of a cloud-free sky library, without taking account of aerosol loading. Both an absolute (without the use of a cloud-free reference image) and a differential (based on a cloud-free reference image) red-to-blue ratio thresholding are used. An evaluation of the algorithm based on a 1-year-long series of images shows that the proposed algorithm is very convincing for most of the images, with about 97 % of relevance in the process, outside the sunrise and sunset transitions. During those latter periods, however, ELIFAN has large difficulties in appropriately processing the image due to a large difference in color composition and potential confusion between cloud-free and cloudy sky at that time. This issue also impacts the library of cloud-free images. Beside this, the library also reveals some limitations during daytime, with the possible presence of very small and/or thin clouds. However, the latter have only a small impact on the cloud cover estimate. The two thresholding methodologies, the absolute and the differential red-to-blue ratio thresholding processes, agree very well, with departure usually below 8 % except in sunrise-sunset periods and in some specific conditions. The use of the cloud-free image library gives generally better results than the absolute process. It particularly better detects thin cirrus clouds. But the absolute thresholding process turns out to be better sometimes, for example in some cases in which the sun is hidden by a cloud

Raman LIDARs for the atmospheric calibration along the line-of-sight of CTA

The Cherenkov Telescope Array (CTA) is the next generation ground based observatory for gamma ray astronomy at very high energies. Employing more than 100 Imaging Atmospheric Cherenkov Telescopes in the northern and southern hemispheres, it was designed to reach unprecedented sensitivity and energy resolution. Understanding and correcting for systematic biases on the absolute energy scale and instrument response functions will be a crucial issue for the performance of CTA. The LUPM group and the Spanish/Italian/Slovenian collaboration are currently building two Raman LIDAR prototypes for the online atmospheric calibration along the line of sight of the CTA. Requirements for such a solution include the ability to characterize aerosol extinction at two wavelengths to distances of 30 km with an accuracy better than 5%, within time scales of about a minute, steering capabilities and close interaction with the CTA array control and data acquisition system as well as other auxiliary instruments. Our Raman LIDARs have design features that make them different from those used in atmospheric science and are characterized by large collecting mirrors (2.5 m2), liquid light guides that collect the light at the focal plane and transport it to the readout system, reduced acquisition time and highly precise Raman spectrometers. The Raman LIDARs will participate in a cross calibration and characterization campaign of the atmosphere at the CTA North site at La Palma, together with other site characterization instruments. After a one year test period there, an in depth evaluation of the solutions adopted by the two projects will lead to a final Raman LIDAR design proposal for both CTA sites