Ukrainian Antarctic Journal

No 2 (2020): Ukrainian Antarctic Journal
Articles

Improving the detailing of atmospheric processes modelling using the Polar WRF model: a case study of a heavy rainfall event at the Akademik Vernadsky station

D. Pishniak
State Institution National Antarctic Scientific Center, Ministry of Education and Science of Ukraine, Kyiv, 01601, Ukraine
B. Beznoshchenko
State Institution National Antarctic Scientific Center, Ministry of Education and Science of Ukraine, Kyiv, 01601, Ukraine
Published December 29, 2020
Keywords
  • Antarctic Peninsula,
  • downscaling,
  • mesoscale atmospheric processes,
  • numerical weather modelling,
  • precipitation amplification effects,
  • statistical evaluation
  • ...More
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How to Cite
Pishniak, D., & Beznoshchenko, B. (2020). Improving the detailing of atmospheric processes modelling using the Polar WRF model: a case study of a heavy rainfall event at the Akademik Vernadsky station. Ukrainian Antarctic Journal, (2), 26-41. https://doi.org/10.33275/1727-7485.2.2020.650

Abstract

The Antarctic Peninsula region is of growing interest due to the regional climate change features and related atmospheric circulation patterns. The regional mesoscale atmospheric model Polar Weather Research and Forecasting (WRF) v4.1.1 was used in this research to study a heavy precipitation event over the Ukrainian Antarctic Akademik Vernadsky station region (Antarctic Peninsula). The passage of the cyclone over the Antarctic Peninsula as a typical synoptic process as well as a case of the daily precipitation maximum amount of 2018 were chosen for investigation in this research. The estimation of the modelling quality and downscaling was done by comparing the obtained results with in-situ observation at the Akademik Vernadsky station and cross-domain tracking of average meteorological values and their deviation. The concept of the nested domains allowed to increase the horizontal resolution of the simulated atmosphere up to 1 km and to reproduce the wind regime of this region with high quality. Comparison with measured data showed a significant improvement in wind simulation with increasing of resolution, but worse representation of surface temperature and humidity. The Polar WRF made a general cooling of near surface temperature of 2 °C during the period of simulation and increased precipitation amount by 4.6–8.4 mm (12–21%) on average over the territory relative to the initial data from Global Data Assimilation System. This can be explained by the contribution of noise and imperfection of the model (including static input data of the terrain description). Based on the modelled results, the interaction of wind flow with the mountainous terrain of the Antarctic Peninsula creates a range of complex dynamic effects in the atmosphere. These effects cause local precipitation maxima both over the Peninsula and over the adjacent ocean. These are, respectively, bay-valley areas of increased precipitation and increased precipitation on the crests of shock waves from orographic
obstacles. Under certain background wind conditions, the influence of the latter effect can reach the Akademik Vernadsky station and cause the formation of heavy precipitation here.

References

  1. Bozkurt, D., Bromwich, D. H., Carrasco, J., Hines, K. M., Maureira, J. C., & Rondanelli, R. (2020). Recent near-surface temperature trends in the Antarctic Peninsula from observed, reanalysis and regional climate model data. Advances in Atmospheric Sciences, 37, 477—493. https://doi.org/10.1007/s00376-020-9183-x
  2. Bromwich, D. H., Monaghan, A. J., Powers, J. G., Cassano, J. J., Wei, H-L., Kuo, Y-H., & Pellegrini, A. (2003). Antarctic Mesoscale Prediction System (AMPS): a case study from the 2000–01 field season. Monthly Weather Review, 131(2), 412–434. https://doi.org/10.1175/1520-0493(2003)131<0412:AMPSAA>2.0.CO;2
  3. Bromwich, D. H., Otieno, F. O., Hines, K. M., Manning, K. W., & Shilo, E. (2013). Comprehensive evaluation of polar weather research and forecasting model performance in the Antarctic. Journal Of Geophysical Research: Atmospheres, 118(2), 274–292. https://doi.org/10.1029/2012JD018139
  4. Deb, P., Orr, A., Hosking, J. S., Phillips, T., Turner, J., Bannister, D., Pope, J. O., & Colwell, S. (2016). An assessment of the Polar Weather Research and Forecasting (WRF) model representation of near-surface meteorological variables over West Antarctica. Journal of Geophysical Research: Atmospheres, 121(4), 1532–1548. https://doi.org/10.1002/2015JD024037
  5. Gómez-Navarro, J. J., Raible, C. C., & Dierer, S. (2015). Sensitivity of the WRF model to PBL parametrisations and nesting techniques: evaluation of wind storms over complex terrain. Geoscientific Model Development, 8, 3349–3363. https://doi.org/10.5194/gmd-8-3349-2015
  6. Hines, K. M., Bromwich, D. H., Bai, L.-S., Barlage, M., & Slater, A. G. (2011). Development and testing of Polar WRF. Part III: Arctic Land. The Journal of Climate, 24(1), 26–48. https://doi.org/10.1175/2010JCLI3460.1
  7. King, J. C., & Comiso, J. C. (2003). The spatial coherence of interannual temperature variations in the Antarctic Peninsula. Geophysical Research Letters, 30(2), Article 1040. https://doi.org/10.1029/2002GL015580
  8. Kirchgaessner, A., King, J., & Gadian, A. (2019). The representation of Föhn Events to the East of the Antarctic Peninsula in simulations by the Antarctic Mesoscale Prediction System. Journal of Geophysical Research: Atmospheres, 124(24), 13663–13679. https://doi.org/10.1029/2019JD030637
  9. Lazzara, M. A., Orendorf, S. A., Norton, T. P., Powers, J. G., Bromvich, D. H., Carpentier, S., Cassano, J. J., Colwell, S. R., Cayette, A. M., & Werner, K. (2020). The 13th and 14th Workshops on Antarctic Meteorology and Climate. Advances in Atmospheric Sciences, 37, 423–430. https://doi.org/10.1007/s00376-019-9215-6
  10. Listowski, C., & Lachlan-Cope, T. (2017). The microphysics of clouds over the Antarctic Peninsula — Part 2: modellingas pects within Polar WRF. Atmospheric Chemistry and Physics, 17, 10195–10221. https://doi.org/10.5194/acp-17-10195-2017
  11. Powers, J. G., Manning, K. W., Bromwich, D. H., Cassano, J. J., & Cayette, A. M. (2012). A decade of Antarctic science support through AMPS. Bulletin of the American Meteorological Society, 93(11), 1699–1712. https://doi.org/10.1175/BAMS-D-11-00186.1
  12. Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J., Gill, D. O., Coen, J. L., Gochis, D. J., Ahmadov, R., Peckham, S. E., Grell, G. A., Michalakes, J., Trahan, S., Benjamin, S. G., Alexander, C. R., Dimego, G. J., Wang, W., Schwartz, C. S., Romine, G. S., … & Duda, M. G. (2017). The Weather Research and Forecasting Model: Overview, system efforts, and future directions. Bulletin of the American Meteorological Society, 98(8), 1717–1737. https://doi.org/10.1175/BAMS-D-15-00308.1
  13. Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., & Huang, X.-Y. (2019). A Description of the Advanced Research WRF Model Version 4 (NCAR Technical Notes NCAR/TN-556+STR). National Center for Atmospheric Research. https://doi.org/10.5065/1dfh-6p97
  14. Thompson, G., Field, P. R., Rasmussen, R. M., & Hall, W. D. (2008). Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization. Monthly Weather Review, 136(12), 5095-5115. https://doi.org/10.1175/2008MWR2387.1
  15. Turner, J., Lu, H., White, I., King, J. C., Phillips, T., Hosking, J. S., Bracegirdle, T. J., Marshall, G. J., Mulvaney, R., & Deb, P. (2016). Absence of 21st century warming on Antarctic Peninsula consistent with natural variability. Nature, 535, 411–415. https://doi.org/10.1038/nature18645
  16. Zhang, C., & Zhang, J. (2018). Modeling Study of Foehn Wind Events in Antarctic Peninsula with WRF Forced by CCSM. Journal of Meteorological Research, 32, 909–922. https://doi.org/10.1007/s13351-018-8067-9