Journal metrics

Journal metrics

  • IF value: 4.252 IF 4.252
  • IF 5-year value: 4.890 IF 5-year 4.890
  • CiteScore value: 4.49 CiteScore 4.49
  • SNIP value: 1.539 SNIP 1.539
  • SJR value: 2.404 SJR 2.404
  • IPP value: 4.28 IPP 4.28
  • h5-index value: 40 h5-index 40
  • Scimago H index value: 51 Scimago H index 51
Volume 10, issue 3 | Copyright
Geosci. Model Dev., 10, 1363-1381, 2017
https://doi.org/10.5194/gmd-10-1363-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Model experiment description paper 31 Mar 2017

Model experiment description paper | 31 Mar 2017

Global 7 km mesh nonhydrostatic Model Intercomparison Project for improving TYphoon forecast (TYMIP-G7): experimental design and preliminary results

Masuo Nakano et al.
Related authors
Turbulent enhancement of radar reflectivity factor for polydisperse cloud droplets
Keigo Matsuda and Ryo Onishi
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-480,https://doi.org/10.5194/acp-2018-480, 2018
Manuscript under review for ACP
Direct Lagrangian tracking simulation of droplet growth in vertically developing cloud
Yuichi Kunishima and Ryo Onishi
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-328,https://doi.org/10.5194/acp-2018-328, 2018
Revised manuscript under review for ACP
Stratospheric tropical warming event and its impact on the polar and tropical troposphere
Kunihiko Kodera, Nawo Eguchi, Hitoshi Mukougawa, Tomoe Nasuno, and Toshihiko Hirooka
Atmos. Chem. Phys., 17, 615-625, https://doi.org/10.5194/acp-17-615-2017,https://doi.org/10.5194/acp-17-615-2017, 2017
Reynolds-number dependence of turbulence enhancement on collision growth
Ryo Onishi and Axel Seifert
Atmos. Chem. Phys., 16, 12441-12455, https://doi.org/10.5194/acp-16-12441-2016,https://doi.org/10.5194/acp-16-12441-2016, 2016
Turbulence effects on warm-rain formation in precipitating shallow convection revisited
Axel Seifert and Ryo Onishi
Atmos. Chem. Phys., 16, 12127-12141, https://doi.org/10.5194/acp-16-12127-2016,https://doi.org/10.5194/acp-16-12127-2016, 2016
Related subject area
Atmospheric Sciences
Assimilating compact phase space retrievals (CPSRs): comparison with independent observations (MOZAIC in situ and IASI retrievals) and extension to assimilation of truncated retrieval profiles
Arthur P. Mizzi, David P. Edwards, and Jeffrey L. Anderson
Geosci. Model Dev., 11, 3727-3745, https://doi.org/10.5194/gmd-11-3727-2018,https://doi.org/10.5194/gmd-11-3727-2018, 2018
libcloudph++ 2.0: aqueous-phase chemistry extension of the particle-based cloud microphysics scheme
Anna Jaruga and Hanna Pawlowska
Geosci. Model Dev., 11, 3623-3645, https://doi.org/10.5194/gmd-11-3623-2018,https://doi.org/10.5194/gmd-11-3623-2018, 2018
CTDAS-Lagrange v1.0: a high-resolution data assimilation system for regional carbon dioxide observations
Wei He, Ivar R. van der Velde, Arlyn E. Andrews, Colm Sweeney, John Miller, Pieter Tans, Ingrid T. van der Laan-Luijkx, Thomas Nehrkorn, Marikate Mountain, Weimin Ju, Wouter Peters, and Huilin Chen
Geosci. Model Dev., 11, 3515-3536, https://doi.org/10.5194/gmd-11-3515-2018,https://doi.org/10.5194/gmd-11-3515-2018, 2018
Implementation of a simple thermodynamic sea ice scheme, SICE version 1.0-38h1, within the ALADIN–HIRLAM numerical weather prediction system version 38h1
Yurii Batrak, Ekaterina Kourzeneva, and Mariken Homleid
Geosci. Model Dev., 11, 3347-3368, https://doi.org/10.5194/gmd-11-3347-2018,https://doi.org/10.5194/gmd-11-3347-2018, 2018
ORACLE 2-D (v2.0): an efficient module to compute the volatility and oxygen content of organic aerosol with a global chemistry–climate model
Alexandra P. Tsimpidi, Vlassis A. Karydis, Andrea Pozzer, Spyros N. Pandis, and Jos Lelieveld
Geosci. Model Dev., 11, 3369-3389, https://doi.org/10.5194/gmd-11-3369-2018,https://doi.org/10.5194/gmd-11-3369-2018, 2018
Cited articles
Baba, Y., Takahashi, K., Sugimura, T., and Goto, K.: Dynamical core of an atmospheric general circulation model on a yin–yang grid, Mon. Weather Rev., 138, 3988–4005, https://doi.org/10.1175/2010MWR3375.1, 2010.
Bénard, P., Vivoda, J., Mašek, J., Smolíková, P., Yessad, K., Smith, Ch., Brožková, R., and Geleyn, J.-F.: Dynamical kernel of the Aladin-NH spectral limited-area model: Revised formulation and sensitivity experiments, Q. J. Roy. Meteor. Soc., 136, 155–169, https://doi.org/10.1002/qj.522, 2010.
Bernardet, L., Tallapragada, V., Bao, S., Trahan, S., Kwon, Y., Liu, Q., Tong, M., Biswas, M., Brown, T., Stark, D., Carson, L., Yablonsky, R., Uhlhorn, E., Gopalakrishnan, S., Zhang, X., Marchok, T., Kuo, B., and Gall, R.: Community support and transition of research to operations for the hurricane weather research and Forecasting model, B. Am. Meteorol. Soc., 96, 953–960, https://doi.org/10.1175/BAMS-D-13-00093.1, 2015.
Braun, S. A. and Tao, W.-K.: Sensitivity of high-resolution simulations of Hurricane Bob (1991) to planetary boundary layer parameterizations, Mon. Weather Rev., 128, 3941–3961, 2000.
Bubnová, R., Hello, G., Bénard, P., and Geleyn, J.-F.: Integration of the fully elastic equations cast in the hydrostatic pressure terrain-following coordinate in the framework of the ARPEGE/Aladin NWP system, Mon. Weather Rev., 123, 515–535, 1995.
Publications Copernicus
Download
Short summary
Three 7 km mesh next-generation global models and a 20 km mesh conventional global model were run to improve tropical cyclone (TC) prediction. The 7 km mesh models reduce systematic errors in the TC track, intensity and wind radii predictions. However, the simulated TC structures and their intensities in each case are very different for each model. These results suggest that the development of more sophisticated initialization techniques and model physics is needed to further improvement.
Three 7 km mesh next-generation global models and a 20 km mesh conventional global model were...
Citation
Share