Oceanology

, Volume 57, Issue 2, pp 232–238 | Cite as

Estimating the reproduction quality of precipitation over the north atlantic and influence of the hydrostatic approximation in the WRF–ARW atmospheric model

Marine Physics
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Abstract

The Weather Research and Forecast numerical model (WRF) with the dynamic Advanced Research WRF (ARW) solver was used to simulate the winter (January 2016) and summer (July 2015) atmospheric state over the North Atlantic with a high (15 km) spatial resolution. The quality of precipitation modeling was validated by remote sensing Global Precipitation Measurements (GPM) data and atmospheric ERA-Interim reanalysis. Nonhydrostatic and hydrostatic equations for the vertical velocity were additionally used to investigate their influence on the accuracy of the precipitation modeling results. It was shown that the model in this configuration satisfactorily reproduces the precipitation field. No evidence of hydrostatic approximation was revealed (over a simulation domain with a resolution of 15 km, simplified topography, and parameterizations of convection and microphysical processes).

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References

  1. 1.
    A. V. Gavrikov and A. Y. Ivanov, “Anomalously strong bora over the Black Sea: observations from space and numerical modeling,” Izv. Atmos. Ocean. Phys. 51, 546–556 (2015).CrossRefGoogle Scholar
  2. 2.
    M. Yu. Markina and A. V. Gavrikov, “Wave climate variability in the North Atlantic in recent decades in the winter period using numerical modeling,” Oceanology (Engl. Transl.) 56, 320–325 (2016).Google Scholar
  3. 3.
    Recommendations for Hydrometeorological Stations and Posts (Gidrometeoizdat, Leningrad, 1985), No. 3, Part 1.Google Scholar
  4. 4.
    W. D. Collins, P. J. Rasch, B. A. Boville, et al., Description of the NCAR Community Atmosphere Model (CAM 3.0) (National Center for Atmospheric Research, Boulder, CO, 2004), No. 226.Google Scholar
  5. 5.
    S.-Y. Hong, J. Dudhia, and S.-H. Chen, “A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation,” Mon. Weather Rev. 132 (1), 103–120 (2004).CrossRefGoogle Scholar
  6. 6.
    A. Y. Hou, R. K. Kakar, S. Neeck, et al., “The global precipitation measurement mission,” Bull. Am. Meteorol. Soc. 95, 701–722 (2014). doi 10.1175/BAMS-D- 13-00164.1CrossRefGoogle Scholar
  7. 7.
    G. Huffman, D. Bolvin, and E. J. Nelkin, Integrated multi-satellite retrievals for GPM (IMERG), version 4.4. ftp://arthurhou.pps.eosdis.nasa.gov/gpmdata/. Accessed March 31, 2015.Google Scholar
  8. 8.
    Z. I. Janjic, “The step-mountain eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes,” Mon. Weather Rev. 122 (5), 927–945 (1994).CrossRefGoogle Scholar
  9. 9.
    Z. I. Janjic, Nonsingular Implementation of the Mellor- Yamada Level 2.5 Scheme in NCEP Meso-Model (National Center for Environmental Prediction, College Park, MD, 2001), No. 437.Google Scholar
  10. 10.
    J. S. Kain, “The Kain–Fritsch convective parameterization: an update,” J. Appl. Meteorol. 43 (1), 170–181 (2004).CrossRefGoogle Scholar
  11. 11.
    M. Nakanishi and H. Niino, “Development of an improved turbulence closure model for the atmospheric boundary layer,” J. Meteorol. Soc. Jpn. 87, 895–912 (2009).CrossRefGoogle Scholar
  12. 12.
    E. Sharifi, R. Steinacker, and B. Saghafian, “Assessment of GPM-IMERG and other precipitation products against Gauge data under different topographic and climatic conditions in Iran: preliminary results,” Remote Sens. 8 (135), (2016). doi 10.3390/rs8020135Google Scholar
  13. 13.
    A. Simmons, S. Uppala, D. Dee, et al., ERA-Interim: New ECMWF Reanalysis Products from 1989 Onwards. ECMWF Newsletter No. 110 (European Centre for Medium-Range Weather Forecasts, Reading, 2007), pp. 25–35.Google Scholar
  14. 14.
    W. C. Skamarock, J. B. Klemp, J. Dudhia, et al., A Description of the Advanced Research WRF Version 3 (National Center for Atmospheric Research, Boulder, CO, 2008), No. 125.Google Scholar
  15. 15.
    S. S. Zilitinkevich, “Non-local turbulent transport pollution dispersion aspects of coherent structure of convective flows,” in Air Pollution III, Vol. 1: Air Pollution Theory and Simulation, Ed. by H Power, N. Moussiopoulos and C. A. Brebbia (Computational Mechanics, Boston, 1995), pp. 53–60.Google Scholar

Copyright information

© Pleiades Publishing, Inc. 2017

Authors and Affiliations

  1. 1.Shirshov Institute of OceanologyRussian Academy of SciencesMoscowRussia

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