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Mozhgan Amiramjadi, Ali R. Mohebalhojeh, Mohammad Mirzaei, Christoph Zülicke, and Riwal Plougonven

1. Introduction Inertia–gravity waves (IGWs) become dominant modes of motion at mesoscales of the atmosphere, i.e., at horizontal scales smaller than about 500 km ( Callies et al. 2014 ; Žagar et al. 2017 ), thereby contributing to the loss of predictability in weather prediction ( Judt 2018 ) and model uncertainty in climate prediction ( Liu 2019 ). For current general circulation models (GCMs), the resolvable scales of atmospheric phenomena are on the order of 100 km. Resolving smaller

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Andreas Dörnbrack

? And I continue citing and using only slight modifications of his wording: Indeed bottomless and no matter to what hazardous lengths we let out our lines of research they still withdraw again, and further, into the depths. Again and further are the right words, for the unresearchable plays, a kind of mocking game with our researching ardors; it offers apparent holds and goals, behind which, when we have gained them, new reaches still open out—as happens to the coastwise voyager, who finds no end to

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Andreas Dörnbrack, Sonja Gisinger, Michael C. Pitts, Lamont R. Poole, and Marion Maturilli

-term forecast of a numerical weather prediction (NWP) model utilizing an unprecedented global resolution of about 8 km (for data sources, see the appendix ). In our days of ceaseless swells of pictures taken everywhere and anytime on the planet, a snapshot taken from a sensor much different than a camera, taken from a perspective so much different than from Earth, and superimposed with numerical predictions reflecting the observed flow features with a remarkable realism elicits wonder and admiration. Fig

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Jannik Wilhelm, T. R. Akylas, Gergely Bölöni, Junhong Wei, Bruno Ribstein, Rupert Klein, and Ulrich Achatz

1. Introduction Internal gravity waves (GWs) play a significant role in atmospheric dynamics on various spatial scales ( Fritts and Alexander 2003 ; Kim et al. 2003 ; Alexander et al. 2010 ; Plougonven and Zhang 2014 ). Already in the lower atmosphere GW effects are manifold. Examples include the triggering of high-impact weather (e.g., Zhang et al. 2001 , 2003 ) and clear-air turbulence ( Koch et al. 2005 ), as well as the effect of small-scale GWs of orographic origin on the predicted

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Gergely Bölöni, Bruno Ribstein, Jewgenija Muraschko, Christine Sgoff, Junhong Wei, and Ulrich Achatz

1. Introduction The parameterization of gravity waves (GWs) is of significant importance in atmospheric global circulation models (GCM), in global numerical weather prediction (NWP) models, and in ocean models. In spite of the increasing available computational power and the corresponding increase of spatial resolution of GCMs and NWP models, for the time being, an important range of GW spatial scales remains unresolved both in climate simulations and in global NWP ( Alexander et al. 2010

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Markus Rapp, Bernd Kaifler, Andreas Dörnbrack, Sonja Gisinger, Tyler Mixa, Robert Reichert, Natalie Kaifler, Stefanie Knobloch, Ramona Eckert, Norman Wildmann, Andreas Giez, Lukas Krasauskas, Peter Preusse, Markus Geldenhuys, Martin Riese, Wolfgang Woiwode, Felix Friedl-Vallon, Björn-Martin Sinnhuber, Alejandro de la Torre, Peter Alexander, Jose Luis Hormaechea, Diego Janches, Markus Garhammer, Jorge L. Chau, J. Federico Conte, Peter Hoor, and Andreas Engel

-cascaded elastic detector channels and one Raman channel. As in the case of ALIMA, density profiles measured with CORAL are converted to temperature profiles using hydrostatic downward integration. CORAL is a portable lidar and commenced operation at EARG in November 2017. CORAL operates autonomously during clear sky conditions in darkness. Weather conditions are continuously and automatically assessed based on local observations and short-term weather forecasts of clouds and precipitation. CORAL lidar data

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Junhong Wei, Gergely Bölöni, and Ulrich Achatz

; Butchart 2014 ). The dynamics of the middle atmosphere can influence the tropospheric circulation by downward control ( Haynes et al. 1991 ), and it can be very important for the forecasting of weather ( Baldwin and Dunkerton 2001 ) and climate ( Scaife et al. 2005 , 2012 ). Despite the increasing computational power, an important range of GW spatial scales remains unresolved in most atmospheric global circulation models (GCM) or in global numerical weather prediction (NWP) models ( Alexander et al

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Claudia Christine Stephan, Cornelia Strube, Daniel Klocke, Manfred Ern, Lars Hoffmann, Peter Preusse, and Hauke Schmidt

fine to capture a major fraction of the GW spectrum (e.g., Beres et al. 2004 ; Choi and Chun 2011 ). GWs in high-resolution (~4 km) simulations of regional mesoscale models, such as the Weather Research and Forecasting (WRF) Model, can have a high degree of realism ( Grimsdell et al. 2010 ; Orr et al. 2015 ; Stephan and Alexander 2015 ; Stephan et al. 2016 ). In the light of ever-increasing computational capabilities, the above challenges have served as a strong motivation to devise global

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