Articles | Volume 15, issue 2
Geosci. Model Dev., 15, 731–744, 2022
https://doi.org/10.5194/gmd-15-731-2022

Special issue: Benchmark datasets and machine learning algorithms for Earth...

Geosci. Model Dev., 15, 731–744, 2022
https://doi.org/10.5194/gmd-15-731-2022
Model evaluation paper
27 Jan 2022
Model evaluation paper | 27 Jan 2022

EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 2: Model application to different datasets

Julian F. Quinting et al.

Related authors

A multi-disciplinary analysis of the exceptional flood event of July 2021 in central Europe. Part 1: Event description and analysis
Susanna Mohr, Uwe Ehret, Michael Kunz, Patrick Ludwig, Alberto Caldas-Alvarez, James E. Daniell, Florian Ehmele, Hendrik Feldmann, Mário J. Franca, Christian Gattke, Marie Hundhausen, Peter Knippertz, Katharina Küpfer, Bernhard Mühr, Joaquim G. Pinto, Julian Quinting, Andreas M. Schäfer, Marc Scheibel, Frank Seidel, and Christina Wisotzky
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-137,https://doi.org/10.5194/nhess-2022-137, 2022
Preprint under review for NHESS
Short summary
EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 1: Development of deep learning model
Julian F. Quinting and Christian M. Grams
Geosci. Model Dev., 15, 715–730, https://doi.org/10.5194/gmd-15-715-2022,https://doi.org/10.5194/gmd-15-715-2022, 2022
Short summary
A new view of heat wave dynamics and predictability over the eastern Mediterranean
Assaf Hochman, Sebastian Scher, Julian Quinting, Joaquim G. Pinto, and Gabriele Messori
Earth Syst. Dynam., 12, 133–149, https://doi.org/10.5194/esd-12-133-2021,https://doi.org/10.5194/esd-12-133-2021, 2021
Short summary
The role of large-scale dynamics in an exceptional sequence of severe thunderstorms in Europe May–June 2018
Susanna Mohr, Jannik Wilhelm, Jan Wandel, Michael Kunz, Raphael Portmann, Heinz Jürgen Punge, Manuel Schmidberger, Julian F. Quinting, and Christian M. Grams
Weather Clim. Dynam., 1, 325–348, https://doi.org/10.5194/wcd-1-325-2020,https://doi.org/10.5194/wcd-1-325-2020, 2020
Short summary
Synoptic-scale controls of fog and low-cloud variability in the Namib Desert
Hendrik Andersen, Jan Cermak, Julia Fuchs, Peter Knippertz, Marco Gaetani, Julian Quinting, Sebastian Sippel, and Roland Vogt
Atmos. Chem. Phys., 20, 3415–3438, https://doi.org/10.5194/acp-20-3415-2020,https://doi.org/10.5194/acp-20-3415-2020, 2020
Short summary

Related subject area

Atmospheric sciences
Simulations of aerosol pH in China using WRF-Chem (v4.0): sensitivities of aerosol pH and its temporal variations during haze episodes
Xueyin Ruan, Chun Zhao, Rahul A. Zaveri, Pengzhen He, Xinming Wang, Jingyuan Shao, and Lei Geng
Geosci. Model Dev., 15, 6143–6164, https://doi.org/10.5194/gmd-15-6143-2022,https://doi.org/10.5194/gmd-15-6143-2022, 2022
Short summary
A daily highest air temperature estimation method and spatial–temporal changes analysis of high temperature in China from 1979 to 2018
Ping Wang, Kebiao Mao, Fei Meng, Zhihao Qin, Shu Fang, and Sayed M. Bateni
Geosci. Model Dev., 15, 6059–6083, https://doi.org/10.5194/gmd-15-6059-2022,https://doi.org/10.5194/gmd-15-6059-2022, 2022
Short summary
TransClim (v1.0): a chemistry–climate response model for assessing the effect of mitigation strategies for road traffic on ozone
Vanessa Simone Rieger and Volker Grewe
Geosci. Model Dev., 15, 5883–5903, https://doi.org/10.5194/gmd-15-5883-2022,https://doi.org/10.5194/gmd-15-5883-2022, 2022
Short summary
A description of the first open-source community release of MISTRA-v9.0: a 0D/1D atmospheric boundary layer chemistry model
Josué Bock, Jan Kaiser, Max Thomas, Andreas Bott, and Roland von Glasow
Geosci. Model Dev., 15, 5807–5828, https://doi.org/10.5194/gmd-15-5807-2022,https://doi.org/10.5194/gmd-15-5807-2022, 2022
Short summary
Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations
Daniel J. Varon, Daniel J. Jacob, Melissa Sulprizio, Lucas A. Estrada, William B. Downs, Lu Shen, Sarah E. Hancock, Hannah Nesser, Zhen Qu, Elise Penn, Zichong Chen, Xiao Lu, Alba Lorente, Ashutosh Tewari, and Cynthia A. Randles
Geosci. Model Dev., 15, 5787–5805, https://doi.org/10.5194/gmd-15-5787-2022,https://doi.org/10.5194/gmd-15-5787-2022, 2022
Short summary

Cited articles

Ahmadi-Givi, F., Graig, G. C., and Plant, R. S.: The Dynamics of a Midlatitude Cyclone with Very Strong Latent-Heat Release, Q. J. Roy. Meteor. Soc., 130, 295–323, https://doi.org/10.1256/qj.02.226, 2004. a
Bechtold, P., Köhler, M., Jung, T., Doblas-Reyes, F., Leutbecher, M., Rodwell, M. J., Vitart, F., and Balsamo, G.: Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales, Q. J. Roy. Meteor. Soc., 134, 1337–1351, https://doi.org/10.1002/qj.289, 2008. a
Binder, H., Boettcher, M., Joos, H., and Wernli, H.: The role of warm conveyor belts for the intensification of extratropical cyclones in Northern Hemisphere winter, J. Atmos. Sci., 73, 3997–4020, https://doi.org/10.1175/JAS-D-15-0302.1, 2016. a, b, c
Bosart, L. F., Moore, B. J., Cordeira, J. M., and Archambault, H. M.: Interactions of north pacific tropical, midlatitude, and polar disturbances resulting in linked extreme weather events over North America in October 2007, Mon. Weather Rev., 145, 1245–1273, https://doi.org/10.1175/MWR-D-16-0230.1, 2017. a
Browning, K. A.: Conceptual models of precipitation systems., ESA Journal, 9, 157–180, https://doi.org/10.1175/1520-0434(1986)001<0023:cmops>2.0.co;2, 1985. a
Short summary
This study applies novel artificial-intelligence-based models that allow the identification of one specific weather system which affects the midlatitude circulation. We show that the models yield similar results as their trajectory-based counterpart, which requires data at higher spatiotemporal resolution and is computationally more expensive. Overall, we aim to show how deep learning methods can be used efficiently to support process understanding of biases in weather prediction models.