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[ACC28-08] A study on the contribution of graupel to the snowfall in Hokkaido
Keywords:cloud microphysics, snowfall, graupel
Information about hydrometeor type is important for avalanche prediction, because particular hydrometeors, such as a large graupel and non-rimed crystal may trigger slab avalanches1.
This study investigated the contribution of graupel to snowfall by numerical simulations using a meteorological model SCALE23 targeting on Hokkaido. To investigate the contribution, a numerical experiment and observation with a disdrometer4 at Sapporo were conducted from December 2017 to February of 2018. The contribution of the graupel defined as the graupel particle number ratio to the total snowfall particle number was derived from the simulated results with 2-moment bulk cloud microphysical scheme5. To derive the graupel ratio from the measured data, we first estimated the mixing probability distribution that explains the observed results of particle size and terminal velocity from the observed data using an expectation maximization algorithm6. Based on the estimated results, the snowfall particles were classified to several dominant patterns of snow and graupel by using a self-organizing map7.
A comparison between simulated graupel ratio and observed one showed that the simulated graupel ratio is consistent with the observed one. In addition, our analysis of the simulated results elucidated that the large contribution of the graupel to the total snowfall is originated from the existence of liquid water over the height where the temperature range of is -5 °C to -15 °C, which is suitable for riming8,9.
Reference
1. McClung, D. & Schaerer, P. The avalanche handbook. 3rd Edn., The Mountaineers Books, Seattle, 342 pp., (2006).
2. Nishizawa, S., Yashiro, H., Sato, Y., Miyamoto, Y. & Tomita, H. Influence of grid aspect ratio on planetary boundary layer turbulence in large-eddy simulations. Geosci. Model Dev. 8, 3393–3419 (2015).
3. Sato, Y. S. Nishizawa, H. Yashiro, Y. Miyamoto, Y. Kajikawa, & H. Tomita. Impacts of cloud microphysics on trade wind cumulus: which cloud microphysics processes contribute to the diversity in a large eddy simulation? Prog. Earth Planet. Sci. 2, (2015).
4. Katsuyama, Y. & Inatsu, M. Advantage of volume scanning video disdrometer in solid-precipitation observation. Sci. Online Lett. Atmos. 17, 35–40 (2021).
5. Seiki, T. & Nakajima, T. Aerosol effects of the condensation process on a convective cloud simulation. J. Atmos. Sci. 71, 833–853 (2014).
6. Katsuyama, Y. & Inatsu, M. Fitting precipitation particle size–velocity data to mixed joint probability density function with an expectation maximization algorithm. J. Atmos. Ocean. Technol. 37, 911–925 (2020).
7. Kohonen, T. Self-Organizing Maps. Springer Series in Information Sciences. Vol. 30, Springer-Verlag, 362p (1995).
8. Pflaum, J. & H. R. Pruppacher. A wind tunnel investigation of the growth of graupel initiated from frozen drops. J. Atmos. Sci. 36.4, 680–689 (1979).
9. Von Blohn, N., Diehl, K., Mitra, S. K. & Borrmann, S. Riming of graupel: wind tunnel investigations of collection kernels and growth regimes. J. Atmos. Sci. 66, 2359–2366 (2009).
This study investigated the contribution of graupel to snowfall by numerical simulations using a meteorological model SCALE23 targeting on Hokkaido. To investigate the contribution, a numerical experiment and observation with a disdrometer4 at Sapporo were conducted from December 2017 to February of 2018. The contribution of the graupel defined as the graupel particle number ratio to the total snowfall particle number was derived from the simulated results with 2-moment bulk cloud microphysical scheme5. To derive the graupel ratio from the measured data, we first estimated the mixing probability distribution that explains the observed results of particle size and terminal velocity from the observed data using an expectation maximization algorithm6. Based on the estimated results, the snowfall particles were classified to several dominant patterns of snow and graupel by using a self-organizing map7.
A comparison between simulated graupel ratio and observed one showed that the simulated graupel ratio is consistent with the observed one. In addition, our analysis of the simulated results elucidated that the large contribution of the graupel to the total snowfall is originated from the existence of liquid water over the height where the temperature range of is -5 °C to -15 °C, which is suitable for riming8,9.
Reference
1. McClung, D. & Schaerer, P. The avalanche handbook. 3rd Edn., The Mountaineers Books, Seattle, 342 pp., (2006).
2. Nishizawa, S., Yashiro, H., Sato, Y., Miyamoto, Y. & Tomita, H. Influence of grid aspect ratio on planetary boundary layer turbulence in large-eddy simulations. Geosci. Model Dev. 8, 3393–3419 (2015).
3. Sato, Y. S. Nishizawa, H. Yashiro, Y. Miyamoto, Y. Kajikawa, & H. Tomita. Impacts of cloud microphysics on trade wind cumulus: which cloud microphysics processes contribute to the diversity in a large eddy simulation? Prog. Earth Planet. Sci. 2, (2015).
4. Katsuyama, Y. & Inatsu, M. Advantage of volume scanning video disdrometer in solid-precipitation observation. Sci. Online Lett. Atmos. 17, 35–40 (2021).
5. Seiki, T. & Nakajima, T. Aerosol effects of the condensation process on a convective cloud simulation. J. Atmos. Sci. 71, 833–853 (2014).
6. Katsuyama, Y. & Inatsu, M. Fitting precipitation particle size–velocity data to mixed joint probability density function with an expectation maximization algorithm. J. Atmos. Ocean. Technol. 37, 911–925 (2020).
7. Kohonen, T. Self-Organizing Maps. Springer Series in Information Sciences. Vol. 30, Springer-Verlag, 362p (1995).
8. Pflaum, J. & H. R. Pruppacher. A wind tunnel investigation of the growth of graupel initiated from frozen drops. J. Atmos. Sci. 36.4, 680–689 (1979).
9. Von Blohn, N., Diehl, K., Mitra, S. K. & Borrmann, S. Riming of graupel: wind tunnel investigations of collection kernels and growth regimes. J. Atmos. Sci. 66, 2359–2366 (2009).