Design of a high performance MAV for atmospheric research

International audienceThis article presents the design of a mini UAV dedicated to atmospheric research with tight operational constraints coming from the end-users, which are the meteorologists associated to the project. Several aspects are covered in addition to the conceptual design of the frame itself and its manufacturing process. This includes the innovative launching system based on water rocket, the design of a 5-hole probe for wind and turbulence measurements, the new version of the on-board autopilot and finally the evaluation of a long range communication system. Preliminary results are presented to conclude the paper

Etude des processus dynamiques et microphysiques dans les nuages convectifs peu profonds : synergie entre simulations numériques et observations par une flotte de drones

Les cumulus, nuages convectifs peu profonds, résultent de convection humide où la vapeur d'eau contenue dans l'atmosphère a pu condenser sur les particules d'aérosols. Ces nuages, de par leurs impacts radiatifs sur le bilan énergétique de la Terre, leur rôle dans la distribution de l'eau dans la troposphère et leur présence importante sur le globe sont des éléments clés du climat. Cependant, leur durée de vie courte (15-30 minutes) et leur échelle spatiale restreinte (de l'ordre de la centaine de mètres voire du kilomètre) rendent leurs observations in situ délicates d'autant plus que ces nuages sont généralement advectés par le vent moyen. Enfin, il est important de comprendre les processus de mélange entre les cumulus et l'environnement car ceux-ci peuvent modifier la thermodynamique de ces nuages et impacter leur cycle de vie. Le projet NEPHELAE (Network for studying Entrainment and microPHysics of cLouds using Adaptive Exploration) a donc pour objectifs d'étudier ces processus de mélange par une caractérisation spatio-temporelle complète de la microphysique et de la dynamique des nuages grâce au développement d'une flotte de drones. L'objectif de cette thèse est donc de mettre au point une stratégie expérimentale afin d'étudier les processus nuageux par plusieurs drones. La méthodologie employée combine études numériques et observations in-situ par des drones. La première partie de cette thèse se focalise sur la caractérisation des processus de mélange dans les nuages, avec l'appui de simulations LES (Large Eddy Simulation). Deux cas de convection, océanique et continentale ont été simulés par le modèle non-hydrostatique de Météo France (Méso-NH) et ont servi de support pour l'étude individuelle des cumulus, via une méthode d'identification des nuages. Les profils des processus d'entraînement (air environnemental pénétrant dans le nuage) sont plus forts à la base des nuages puis diminuent vers leur sommet avec des plus forts taux pour les nuages de petits volumes alors que le détrainement (air sortant du nuage) est quant à lui plus fort au sommet des nuages. Ces processus de mélange restent indépendants du type de convection. Ces résultats ont été confirmés par l'analyse de cumulus lors des campagnes BIO-MAIDO et NEPHELAE, par ballon captif et par drones. La deuxième partie de cette thèse se concentre sur la définition d'une nouvelle stratégie d'observations permettant la caractérisation d'une section horizontale de nuage. Après avoir simulé un champ de cumulus marins à haute résolution et haute fréquence, des trajectoires adaptatives de drones ont été définies et testées dans cette simulation. Ces trajectoires adaptatives ont été jugées suffisantes pour reproduire les champs thermodynamiques horizontaux. La stratégie expérimentale ainsi validée a été déployée en conditions réelles lors de la campagne de terrain EUREC4A. Les statistiques des cumulus traversés ainsi que les suivis adaptatifs ont permis de quantifier les hétérogénéités dans les sections horizontales des cumulus d'alizés.Cumulus, one type of shallow convective clouds, are the result of moist convection where water vapour in the atmosphere has condensed on aerosol particles. These clouds, because of their radiative impacts on the Earth's energy balance, their role in the distribution of water in the troposphere and their ubiquitous presence across the globe are key elements of the climate. However, their short lifetime (15-30 minutes) and their restricted spatial scale (of the order of a hundred meters or even a kilometer) make their in situ observations all the more complex as these clouds are generally advected. It is also important to understand the mixing processes between cumulus clouds and the environment since they can modify the thermodynamics of these clouds and impact their life cycle. The NEPHELAE project (Network for studying Entrainment and microPHysics of cLouds using Adaptive Exploration) aims to study the mixing processes through a complete spatio-temporal characterization of the microphysics and dynamics of clouds through the development of a fleet of RPAs (Remotely Piloted Aircrafts). The objective of this thesis is to develop an experimental strategy to study cloud processes with RPAs. The methodology relies on the combination of numerical studies and in-situ RPAs observations . The first part of the thesis focuses on the characterization of the mixing processes, with the support of LES (Large Eddy Simulation) simulations and in-situ observations. Two cases of convection, oceanic and continental, have been simulated by the non-hydrostatic model of Météo France (Meso-NH). They have allowed an individual study of cumulus clouds, provided by a cloud identification method. Entrainment processes (environmental air entering the cloud) are stronger at the base of the clouds and then decrease towards their top with stronger rates for small volume clouds, whereas the detrainment (air leaving the cloud) is stronger at the top of the clouds. These results were confirmed by the analysis of cumulus layers during the BIO-MAIDO and NEPHELAE campaigns, by tethered balloons and RPAs. The second part of this thesis focuses on the elaboration of a new observation strategy to document a horizontal cloud section. After simulating a marine cumulus field at high resolution and frequency, adaptive RPA trajectories have been defined and tested in this simulation. These adaptive trajectories were judged sufficient to reproduce the horizontal thermodynamic fields, which allowed them to be applied during the EUREC 4 A field campaign. The statistics of the cumulus clouds crossed as well as the adaptive trajectories allowed us to quantify theheterogeneities in the horizontal sections of the trade wind cumulus

Atmospheric convection and air-sea interactions over the tropical oceans: scientific progress, challenges, and opportunities

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hagos, S., Foltz, G. R., Zhang, C., Thompson, E., Seo, H., Chen, S., Capotondi, A., Reed, K. A., DeMott, C., & Protat, A. Atmospheric convection and air-sea interactions over the tropical oceans: scientific progress, challenges, and opportunities. Bulletin of the American Meteorological Society, 101(3), (2020): E253-E258, doi:10.1175/BAMS-D-19-0261.1.Over the past 30 years, the scientific community has made considerable progress in understanding and predicting tropical convection and air–sea interactions, thanks to sustained investments in extensive in situ and remote sensing observations, targeted field experiments, advances in numerical modeling, and vastly improved computational resources and observing technologies. Those investments would not have been fruitful as isolated advancements without the collaborative effort of the atmospheric convection and air–sea interaction research communities. In this spirit, a U.S.- and International CLIVAR–sponsored workshop on “Atmospheric convection and air–sea interactions over the tropical oceans” was held in the spring of 2019 in Boulder, Colorado. The 90 participants were observational and modeling experts from the atmospheric convection and air–sea interactions communities with varying degrees of experience, from early-career researchers and students to senior scientists. The presentations and discussions covered processes over the broad range of spatiotemporal scales (Fig. 1).The workshop was sponsored by the United States and International CLIVAR. Funding was provided by the U.S. Department of Energy, Office of Naval Research, NOAA, NSF, and the World Climate Research Programme. We thank Mike Patterson, Jennie Zhu, and Jeff Becker from the U.S. CLIVAR Project Office for coordinating the workshop

Multiple UAV systems: a survey

Nowadays, Unmanned Aerial Vehicles (UAVs) are used in many different applications. Using systems of multiple UAVs is the next obvious step in the process of applying this technology for variety of tasks. There are few research works that cover the applications of these systems and they are all highly specialized. The goal of this survey is to fill this gap by providing a generic review on different applications of multiple UAV systems that have been developed in recent years. We also present a nomenclature and architecture taxonomy for these systems. In the end, a discussion on current trends and challenges is provided.This work was funded by the Ministry of Economy, Industryand Competitiveness of Spain under Grant Nos. TRA2016-77012-R and BES-2017-079798Peer ReviewedPostprint (published version

Confronting the Challenge of Modeling Cloud and Precipitation Microphysics

In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth\u27s atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle‐based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next‐generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process‐level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle‐based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods

Understanding avian soaring to extend UAV mission endurance through remote detection of thermal updrafts

Thermal updrafts can be utilized to increase an aircraft's flight endurance and range. However, much of the discussion involving thermal updrafts comprise of flight techniques within a known thermal updraft and very little is discussed over the detection of thermals without the influence of the upward or downward velocity component. This paper discusses possible thermal locating methods discovered from looking at thermal detection primarily from an avian viewpoint. An avian study was conducted to gather information on how avian soaring species locate thermal updrafts, in which no definitive answer was found. Of the possible theories of avian thermal location methods, combined with methods used from RC aircraft, a single method was chosen for further study through simulation and experimentation to construct possible applications to unmanned aerial systems. By quantifying wind shifts in the local area, the direction and location of local thermal updrafts was theorized to be able to be calculated. However, due to the high magnitudes of uncertainty found during the experimental approach and then portrayed in additional simulations with added uncertainty, this method is shown to be unfavorable for use in remotely detecting thermal updrafts

Examining the impacts of convective environments on storms using observations and numerical models

2022 Summer.Includes bibliographical references.Convective clouds are significant contributors to both weather and climate. While the basic environments supporting convective clouds are broadly known, there is currently no unifying theory on how joint variations in different environmental properties impact convective cloud properties. The overaching goal of this research is to assess the response of convective clouds to changes in the dynamic, thermodynamic and aerosol properties of the local environment. To achieve our goal, two tools for examining convective cloud properties and their environments are first described, developed and enhanced. This is followed by an examination of the response of convective clouds to changes in the dynamic, thermodynamic and aerosol properties using these enhanced tools. In the first study comprising this dissertation, we assess the performance of small temperature, pressure, and humidity sensors onboard drones used to sample convective environments and convective cloud outflows by comparing them to measurements made from a tethersonde platform suspended at the same height. Using 82 total drone flights, including nine at night, the following determinations about sensor accuracy are made. First, when examining temperature, the nighttime flight temperature errors are found to have a smaller range than the daytime temperature errors, indicating that much of the daytime error arises from exposure to solar radiation. The pressure errors demonstrate a strong dependence on horizontal wind speed with all of the error distributions being multimodal in high wind conditions. Finally, dewpoint temperature errors are found to be larger than temperature errors. We conclude that measurements in field campaigns are more accurate when sensors are placed away from the drone's main body and associated propeller wash and are sufficiently aspirated and shielded from incoming solar radiation. The Tracking and Object-Based Analysis of Clouds (tobac) tracking package is a commonly used tracking package in atmospheric science that allows for tracking of atmospheric phenomena on any variable and on any grid. We have enhanced the tobac tracking package to enable it to be used on more atmospheric phenomena, with a wider variety of atmospheric data and across more diverse platforms than before. New scientific improvements (three spatial dimensions and an internal spectral filtering tool) and procedural improvements (enhanced computational efficiency, internal re-gridding of data, and treatments for periodic boundary conditions) comprising this new version of tobac (v1.5) are described in the second study of this dissertation. These improvements have made tobac one of the most robust, powerful, and flexible identification and tracking tools in our field and expanded its potential use in other fields. In the third study of this dissertation, we examine the relationship between the thermodynamic and dynamic environmental properties and deep convective clouds forming in the tropical atmosphere. To elucidate this relationship, we employ a high-resolution, long-duration, large-area numerical model simulation alongside tobac to build a database of convective clouds and their environments. With this database, we examine differences in the initial environment associated with individual storm strength, organization, and morphology. We find that storm strength, defined here as maximum midlevel updraft velocity, is controlled primarily by Convective Available Potential Energy (CAPE) and Precipitable Water (PW); high CAPE (>2500 J kg-1) and high PW (approximately 63 mm) are both required for midlevel CCC updraft velocities to reach at least 10 m s-1. Of the CCCs with the most vigorous updrafts, 80.9% are in the upper tercile of precipitation rates, with the strongest precipitation rates requiring even higher PW. Furthermore, vertical wind shear is the primary differentiator between organized and isolated convective storms. Within the set of organized storms, we also find that linearly-oriented CCC systems have significantly weaker vertical wind shear than nonlinear CCCs in low- (0-1 km, 0-3 km) and mid-levels (0-5 km, 2-7 km). Overall, these results provide new insights into the joint environmental conditions determining the CCC properties in the tropical atmosphere. Finally, in the fourth study of this dissertation, we build upon the third study by examining the relationship between the aerosol environment and convective precipitation using the same simulations and tracking approaches as in the third study. As the environmental aerosol concentrations are increased, the total domain-wide precipitation decreases (-3.4%). Despite the overall decrease in precipitation, the number of tracked terminal congestus clouds increases (+8%), while the number of tracked cumulonimbus clouds is decreased (-1.26%). This increase in the number of congestus clouds is accompanied by an overall weakening in their rainfall as aerosol concentration increases, with a decrease in overall rain rates and an increase in the number of clouds that do not precipitate (+10.7%). As aerosol particles increase, overall cloud droplet size gets smaller, suppressing the initial generation of rain and leading to clouds evaporating due to entrainment before they are able to precipitate

Long-term Informative Path Planning with Autonomous Soaring

The ability of UAVs to cover large areas efficiently is valuable for information gathering missions. For long-term information gathering, a UAV may extend its endurance by accessing energy sources present in the atmosphere. Thermals are a favourable source of wind energy and thermal soaring is adopted in this thesis to enable long-term information gathering. This thesis proposes energy-constrained path planning algorithms for a gliding UAV to maximise information gain given a mission time that greatly exceeds the UAV's endurance. This thesis is motivated by the problem of probabilistic target-search performed by an energy-constrained UAV, which is tasked to simultaneously search for a lost ground target and explore for thermals to regain energy. This problem is termed informative soaring (IFS) and combines informative path planning (IPP) with energy constraints. IFS is shown to be NP-hard by showing that it has a similar problem structure to the weight-constrained shortest path problem with replenishments. While an optimal solution may not exist in polynomial time, this thesis proposes path planning algorithms based on informed tree search to find high quality plans with low computational cost. This thesis addresses complex probabilistic belief maps and three primary contributions are presented: • First, IFS is formulated as a graph search problem by observing that any feasible long-term plan must alternate between 1) information gathering between thermals and 2) replenishing energy within thermals. This is a first step to reducing the large search state space. • The second contribution is observing that a complex belief map can be viewed as a collection of information clusters and using a divide and conquer approach, cluster tree search (CTS), to efficiently find high-quality plans in the large search state space. In CTS, near-greedy tree search is used to find locally optimal plans and two global planning versions are proposed to combine local plans into a full plan. Monte Carlo simulation studies show that CTS produces similar plans to variations of exhaustive search, but runs five to 20 times faster. The more computationally efficient version, CTSDP, uses dynamic programming (DP) to optimally combine local plans. CTSDP is executed in real time on board a UAV to demonstrate computational feasibility. • The third contribution is an extension of CTS to unknown drifting thermals. A thermal exploration map is created to detect new thermals that will eventually intercept clusters, and therefore be valuable to the mission. Time windows are computed for known thermals and an optimal cluster visit schedule is formed. A tree search algorithm called CTSDrift combines CTS and thermal exploration. Using 2400 Monte Carlo simulations, CTSDrift is evaluated against a Full Knowledge method that has full knowledge of the thermal field and a Greedy method. On average, CTSDrift outperforms Greedy in one-third of trials, and achieves similar performance to Full Knowledge when environmental conditions are favourable

Navigation and autonomy of soaring unmanned aerial vehicles

The use of Unmanned Aerial Vehicles (UAV) has exploded over the last decade with the constant need to reduce costs while maintaining capability. Despite the relentless development of electronics and battery technology there is a sustained need to reduce the size and weight of the on-board systems to free-up payload capacity. One method of reducing the energy storage requirement of UAVs is to utilise naturally occurring sources of energy found in the atmosphere. This thesis explores the use of static and semi-dynamic soaring to extract energy from naturally occurring shallow layer cumulus convection to improve range, endurance and average speed. A simulation model of an X-Models XCalibur electric motor-glider is used in combination with a refined 4D parametric atmospheric model to simulate soaring flight. The parametric atmospheric model builds on previous successful models with refinements to more accurately describe the weather in northern Europe. The implementation of the variation of the MacCready setting is discussed. Methods for generating efficient trajectories are evaluated and recommendations are made regarding implementation. For micro to small UAVs to be able to track the desired trajectories a highly accurate Attitude Heading Reference System (AHRS) is needed. Detailed analysis of the practical implementation of advanced attitude determination is used to enable optimal execution of the trajectories generated. The new attitude determination methods are compared to existing Kalman and complimentary type filters. Analysis shows the methods developed are capable of providing accurate attitude determination with extremely low computational requirements, even during extreme manoeuvring. The new AHRS techniques reduce the need for powerful on-board microprocessors. This new AHRS technique is used as a foundation to develop a robust navigation filter capable of providing improved drift performance, over traditional filters, in the temporary absence of global navigation satellite information. All these algorithms have been verified by flight tests using a mixture of manned and unmanned aerial vehicles and avionics developed specifically for this thesis