2018-4-3, Li, Qiang, Rapp, Markus, Stober, Gunter, Latteck, Ralph

The Middle Atmosphere Alomar Radar System (MAARSY) installed at the island of Andøya has been run for continuous probing of atmospheric winds in the upper troposphere and lower stratosphere (UTLS) region. In the current study, we present high-resolution wind measurements during the period between 2010 and 2013 with MAARSY. The spectral analysis applying the Lomb–Scargle periodogram method has been carried out to determine the frequency spectra of vertical wind velocity. From a total of 522 days of observations, the statistics of the spectral slope have been derived and show a dependence on the background wind conditions. It is a general feature that the observed spectra of vertical velocity during active periods (with wind velocity > 10 m s−1) are much steeper than during quiet periods (with wind velocity < 10 m s−1). The distribution of spectral slopes is roughly symmetric with a maximum at −5/3 during active periods, whereas a very asymmetric distribution with a maximum at around −1 is observed during quiet periods. The slope profiles along altitudes reveal a significant height dependence for both conditions, i.e., the spectra become shallower with increasing altitudes in the upper troposphere and maintain roughly a constant slope in the lower stratosphere. With both wind conditions considered together the general spectra are obtained and their slopes are compared with the background horizontal winds. The comparisons show that the observed spectra become steeper with increasing wind velocities under quiet conditions, approach a spectral slope of −5/3 at a wind velocity of 10 m s−1 and then roughly maintain this slope (−5/3) for even stronger winds. Our findings show an overall agreement with previous studies; furthermore, they provide a more complete climatology of frequency spectra of vertical wind velocities under different wind conditions.

2021, Wang, Wanli, Pang, Jinbo, Su, Jie, Li, Fujiang, Li, Qiang, Wang, Xiaoxiong, Wang, Jingang, Ibarlucea, Bergoi, Liu, Xiaoyan, Li, Yufen, Zhou, Weijia, Wang, Kai, Han, Qingfang, Liu, Lei, Zang, Ruohan, Rümmeli, Mark H., Li, Yang, Liu, Hong, Hu, Han, Cuniberti, Gianaurelio

The dream of human beings for long living has stimulated the rapid development of biomedical and healthcare equipment. However, conventional biomedical and healthcare devices have shortcomings such as short service life, large equipment size, and high potential safety hazards. Indeed, the power supply for conventional implantable device remains predominantly batteries. The emerging nanogenerators, which harvest micro/nanomechanical energy and thermal energy from human beings and convert into electrical energy, provide an ideal solution for self‐powering of biomedical devices. The combination of nanogenerators and biomedicine has been accelerating the development of self‐powered biomedical equipment. This article first introduces the operating principle of nanogenerators and then reviews the progress of nanogenerators in biomedical applications, including power supply, smart sensing, and effective treatment. Besides, the microbial disinfection and biodegradation performances of nanogenerators have been updated. Next, the protection devices have been discussed such as face mask with air filtering function together with real‐time monitoring of human health from the respiration and heat emission. Besides, the nanogenerator devices have been categorized by the types of mechanical energy from human beings, such as the body movement, tissue and organ activities, energy from chemical reactions, and gravitational potential energy. Eventually, the challenges and future opportunities in the applications of nanogenerators are delivered in the conclusive remarks. The combination of nanogenerator and biomedicine have been accelerating the development of self‐powered biomedical devices, which show a bright future in biomedicine and healthcare such as smart sensing, and therapy.

2016, Li, Qiang, Rapp, Markus, Schrön, Anne, Schneider, Andreas, Stober, Gunter

We present the derivation of turbulent energy dissipation rate ε from a total of 522 days of observations with the Middle Atmosphere Alomar Radar SYstem (MAARSY) mesosphere–stratosphere–troposphere (MST) radar running tropospheric experiments during the period of 2010–2013 as well as with balloon-borne radiosondes based on a campaign in the summer 2013. Spectral widths are converted to ε after the removal of the broadening effects due to the finite beam width of the radar. With the simultaneous in situ measurements of ε with balloon-borne radiosondes at the MAARSY radar site, we compare the ε values derived from both techniques and reach an encouraging agreement between them. Using all the radar data available, we present a preliminary climatology of atmospheric turbulence in the UTLS (upper troposphere and lower stratosphere) region above the MAARSY site showing a variability of more than 5 orders of magnitude inherent in turbulent energy dissipation rates. The derived ε values reveal a log-normal distribution with a negative skewness, and the ε profiles show an increase with height which is also the case for each individual month. Atmospheric turbulence based on our radar measurements reveals a seasonal variation but no clear diurnal variation in the UTLS region. Comparison of ε with the gradient Richardson number Ri shows that only 1.7 % of all the data with turbulence occur under the condition of Ri < 1 and that the values of ε under the condition of Ri < 1 are significantly larger than those under Ri > 1. Further, there is a roughly negative correlation between ε and Ri that is independent of the scale dependence of Ri. Turbulence under active dynamical conditions (velocity of horizontal wind U > 10 m s−1) is significantly stronger than under quiet conditions (U < 10 m s−1). Last but not least, the derived ε values are compared with the corresponding vertical shears of background wind velocity showing a linear relation with a corresponding correlation coefficient r = 58 % well above the 99.9 % significance level. This implies that wind shears play an important role in the turbulence generation in the troposphere and lower stratosphere (through the Kelvin–Helmholtz instability).

2021, Stevens, Bjorn, Bony, Sandrine, Farrell, David, Ament, Felix, Blyth, Alan, Fairall, Christopher, Karstensen, Johannes, Quinn, Patricia K., Speich, Sabrina, Acquistapace, Claudia, Aemisegger, Franziska, Crewell, Susanne, Cronin, Timothy, Cui, Zhiqiang, Cuypers, Yannis, Daley, Alton, Damerell, Gillian M., Dauhut, Thibaut, Deneke, Hartwig, Desbios, Jean-Philippe, Dörner, Steffen, Albright, Anna Lea, Donner, Sebastian, Douet, Vincent, Drushka, Kyla, Dütsch, Marina, Ehrlich, André, Emanuel, Kerry, Emmanouilidis, Alexandros, Etienne, Jean-Claude, Etienne-Leblanc, Sheryl, Faure, Ghislain, Bellenger, Hugo, Feingold, Graham, Ferrero, Luca, Fix, Andreas, Flamant, Cyrille, Flatau, Piotr Jacek, Foltz, Gregory R., Forster, Linda, Furtuna, Iulian, Gadian, Alan, Galewsky, Joseph, Bodenschatz, Eberhard, Gallagher, Martin, Gallimore, Peter, Gaston, Cassandra, Gentemann, Chelle, Geyskens, Nicolas, Giez, Andreas, Gollop, John, Gouirand, Isabelle, Gourbeyre, Christophe, de Graaf, Dörte, Caesar, Kathy-Ann, de Groot, Geiske E., Grosz, Robert, Güttler, Johannes, Gutleben, Manuel, Hall, Kashawn, Harris, George, Helfer, Kevin C., Henze, Dean, Herbert, Calvert, Holanda, Bruna, Chewitt-Lucas, Rebecca, Ibanez-Landeta, Antonio, Intrieri, Janet, Iyer, Suneil, Julien, Fabrice, Kalesse, Heike, Kazil, Jan, Kellman, Alexander, Kidane, Abiel T., Kirchner, Ulrike, Klingebiel, Marcus, de Boer, Gijs, Körner, Mareike, Kremper, Leslie Ann, Kretzschmar, Jan, Krüger, Ovid, Kumala, Wojciech, Kurz, Armin, L'Hégaret, Pierre, Labaste, Matthieu, Lachlan-Cope, Tom, Laing, Arlene, Delanoë, Julien, Landschützer, Peter, Lang, Theresa, Lange, Diego, Lange, Ingo, Laplace, Clément, Lavik, Gauke, Laxenaire, Rémi, Le Bihan, Caroline, Leandro, Mason, Lefevre, Nathalie, Denby, Leif, Lena, Marius, Lenschow, Donald, Li, Qiang, Lloyd, Gary, Los, Sebastian, Losi, Niccolò, Lovell, Oscar, Luneau, Christopher, Makuch, Przemyslaw, Malinowski, Szymon, Ewald, Florian, Manta, Gaston, Marinou, Eleni, Marsden, Nicholas, Masson, Sebastien, Maury, Nicolas, Mayer, Bernhard, Mayers-Als, Margarette, Mazel, Christophe, McGeary, Wayne, McWilliams, James C., Fildier, Benjamin, Mech, Mario, Mehlmann, Melina, Meroni, Agostino Niyonkuru, Mieslinger, Theresa, Minikin, Andreas, Minnett, Peter, Möller, Gregor, Morfa Avalos, Yanmichel, Muller, Caroline, Musat, Ionela, Forde, Marvin, Napoli, Anna, Neuberger, Almuth, Noisel, Christophe, Noone, David, Nordsiek, Freja, Nowak, Jakub L., Oswald, Lothar, Parker, Douglas J., Peck, Carolyn, Person, Renaud, George, Geet, Philippi, Miriam, Plueddemann, Albert, Pöhlker, Christopher, Pörtge, Veronika, Pöschl, Ulrich, Pologne, Lawrence, Posyniak, Michał, Prange, Marc, Quiñones Meléndez, Estefanía, Radtke, Jule, Gross, Silke, Ramage, Karim, Reimann, Jens, Renault, Lionel, Reus, Klaus, Reyes, Ashford, Ribbe, Joachim, Ringel, Maximilian, Ritschel, Markus, Rocha, Cesar B., Rochetin, Nicolas, Hagen, Martin, Röttenbacher, Johannes, Rollo, Callum, Royer, Haley, Sadoulet, Pauline, Saffin, Leo, Sandiford, Sanola, Sandu, Irina, Schäfer, Michael, Schemann, Vera, Schirmacher, Imke, Hausold, Andrea, Schlenczek, Oliver, Schmidt, Jerome, Schröder, Marcel, Schwarzenboeck, Alfons, Sealy, Andrea, Senff, Christoph J., Serikov, Ilya, Shohan, Samkeyat, Siddle, Elizabeth, Smirnov, Alexander, Heywood, Karen J., Späth, Florian, Spooner, Branden, Stolla, M. Katharina, Szkółka, Wojciech, de Szoeke, Simon P., Tarot, Stéphane, Tetoni, Eleni, Thompson, Elizabeth, Thomson, Jim, Tomassini, Lorenzo, Hirsch, Lutz, Totems, Julien, Ubele, Alma Anna, Villiger, Leonie, von Arx, Jan, Wagner, Thomas, Walther, Andi, Webber, Ben, Wendisch, Manfred, Whitehall, Shanice, Wiltshire, Anton, Jacob, Marek, Wing, Allison A., Wirth, Martin, Wiskandt, Jonathan, Wolf, Kevin, Worbes, Ludwig, Wright, Ethan, Wulfmeyer, Volker, Young, Shanea, Zhang, Chidong, Zhang, Dongxiao, Jansen, Friedhelm, Ziemen, Florian, Zinner, Tobias, Zöger, Martin, Kinne, Stefan, Klocke, Daniel, Kölling, Tobias, Konow, Heike, Lothon, Marie, Mohr, Wiebke, Naumann, Ann Kristin, Nuijens, Louise, Olivier, Léa, Pincus, Robert, Pöhlker, Mira, Reverdin, Gilles, Roberts, Gregory, Schnitt, Sabrina, Schulz, Hauke, Siebesma, A. Pier, Stephan, Claudia Christine, Sullivan, Peter, Touzé-Peiffer, Ludovic, Vial, Jessica, Vogel, Raphaela, Zuidema, Paquita, Alexander, Nicola, Alves, Lyndon, Arixi, Sophian, Asmath, Hamish, Bagheri, Gholamhossein, Baier, Katharina, Bailey, Adriana, Baranowski, Dariusz, Baron, Alexandre, Barrau, Sébastien, Barrett, Paul A., Batier, Frédéric, Behrendt, Andreas, Bendinger, Arne, Beucher, Florent, Bigorre, Sebastien, Blades, Edmund, Blossey, Peter, Bock, Olivier, Böing, Steven, Bosser, Pierre, Bourras, Denis, Bouruet-Aubertot, Pascale, Bower, Keith, Branellec, Pierre, Branger, Hubert, Brennek, Michal, Brewer, Alan, Brilouet, Pierre-Etienne, Brügmann, Björn, Buehler, Stefan A., Burke, Elmo, Burton, Ralph, Calmer, Radiance, Canonici, Jean-Christophe, Carton, Xavier, Cato Jr., Gregory, Charles, Jude Andre, Chazette, Patrick, Chen, Yanxu, Chilinski, Michal T., Choularton, Thomas, Chuang, Patrick, Clarke, Shamal, Coe, Hugh, Cornet, Céline, Coutris, Pierre, Couvreux, Fleur

The science guiding the EUREC4A campaign and its measurements is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC4A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC4A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC4A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement.

2022, Li, Qiang, Larosz, Kelly C., Han, Dingding, Ji, Peng, Kurths, Jürgen

Networks of identical coupled oscillators display a remarkable spatiotemporal pattern, the chimera state, where coherent oscillations coexist with incoherent ones. In this paper we show quantitatively in terms of basin stability that stable and breathing chimera states in the original two coupled networks typically have very small basins of attraction. In fact, the original system is dominated by periodic and quasi-periodic chimera states, in strong contrast to the model after reduction, which can not be uncovered by the Ott-Antonsen ansatz. Moreover, we demonstrate that the curve of the basin stability behaves bimodally after the system being subjected to even large perturbations. Finally, we investigate the emergence of chimera states in brain network, through inducing perturbations by stimulating brain regions. The emerged chimera states are quantified by Kuramoto order parameter and chimera index, and results show a weak and negative correlation between these two metrics.

2023, Facio, Jorge I., Nocerino, Elisabetta, Fulga, Ion Cosma, Wawrzynczak, Rafal, Brown, Joanna, Gu, Genda, Li, Qiang, Mansson, Martin, Sassa, Yasmine, Ivashko, Oleh, von Zimmermann, Martin, Mende, Felix, Gooth, Johannes, Galeski, Stanislaw, van den Brink, Jeroen, Meng, Tobias

Real-world topological semimetals typically exhibit Dirac and Weyl nodes that coexist with trivial Fermi pockets. This tends to mask the physics of the relativistic quasiparticles. Using the example of ZrTe5, we show that strain provides a powerful tool for in-situ tuning of the band structure such that all trivial pockets are pushed far away from the Fermi energy, but only for a certain range of Van der Waals gaps. Our results naturally reconcile contradicting reports on the presence or absence of additional pockets in ZrTe5, and provide a clear map of where to find a pure three-dimensional Dirac semimetallic phase in the structural parameter space of the material.