approaches of avalanche predictions resulting from … · snow crystals tend to fall near the warm...

APPROACHES OF AVALANCHE PREDICTIONS RESULTING FROM NON-RIMED FALLING SNOW CRYSTALS USING THE SNOWPACK MODEL

Hiroyuki Hirashima1*, Satoru Yamaguchi1, Kazuki Nakamura2 and Akihiro Hashimoto3

1 Snow and Ice Research Center, National Research Institute for Earth Science and Disaster Resilience, Nagaoka, Japan 2 National Research Institute for Earth Science and Disaster Resilience, Tsukuba, Japan

3 Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan

ABSTRACT: On 27 March 2017, an avalanche accident occurred at Nasu, Tochigi Prefecture, Japan, resulting in eight deaths. The avalanche was induced by the collapse of a precipitation particle layer with non-rimed snow crystals. In the current version of the SNOWPACK model, the type of precipita-tion particle is not considered and therefore does not influence the shear strength. The purpose of this study was to predict avalanches induced by a weak layer composed of non-rimed precipitation parti-cles using the numerical snowpack model. To achieve this goal, the following actions were necessary: 1) preparation of information regarding the type of precipitation particle, 2) parameterization of meta-morphism (e.g., densification) depending on the type of precipitation particle, and 3) estimation of the influence of the type of precipitation particle on shear strength. In this study, the implementation of these actions was attempted based on our current knowledge. Although simulations showed a trend toward less density and a lower stability index in the non-rimed snow layer, the simulated difference from other layers was smaller than the observed difference. Parameterization of the initial snow densi-ty, viscosity, and strength by experiments or through observation resulted in accurate prediction of this type of avalanche.

KEYWORDS: stability, precipitation particle, numerical snowpack model

1. INTRODUCTIONIn Japan, avalanches resulting from the collapse of non-rimed snow crystal layers occur often, and sometimes lead to serious accidents. For example, eight people were killed by an ava-lanche due to the collapse of a layer of non-rimed snow during a high-school club activity in March 2017. Japanese researchers have begun to investigate the mechanism of such ava-lanches.

Nakamura et al. (2018) attempted to develop a simple approach to the prediction of this type of avalanche. In their method, avalanche probabil-ity was estimated from the air temperature, pre-cipitation, and wind direction. The prediction was used in practical applications. In the next step, prediction that considers snow layer parameters using the numerical snowpack model SNOW-PACK is necessary. To predict this type of ava-lanche, information regarding snow crystal pre-cipitation is important. However, in the present version of the SNOWPACK model, the type of precipitation particle is not considered and does not influence the stability.

The purpose of this study was to predict ava-

lanches induced by a weak layer of non-rimed precipitation particles using the numerical snow-pack model. To achieve this goal, the following actions were necessary.

1) preparation of information regarding the typeof precipitation particle (snow crystals),

2) parameterization of metamorphism (e.g.,densification) based on the type of precipitation particle, and

3) estimation of the influence of the type of pre-cipitation particle on shear strength.

In this study, currently available parameters were used to reproduce the weak layer and sta-bility indexes of the precipitation particle layer. Information regarding precipitation particle type was estimated using the Japan Meteorological Agency’s Non-Hydrostatic Model (JMA-NHM; Saito et al., 2006) with the option of a double-moment bulk cloud microphysics scheme (Hash-imoto et al. 2017, 2018). Information regarding initial snow density was estimated by referring to Ishizaka et al. (2016). Viscosity depending on snow crystal type was estimated referring to Goto et al. (2006). Although observations indi-cate that non-rimed snow crystals are some-times bonded more weakly than rimed snow crystals, the difference in the relationship be-tween snow density and shear strength depend-ing on the type of precipitation particle was ne-

* Corresponding author address:Hiroyuki Hirashima, Snow and Ice Research Center, NIED, 187-16, Maeyama, Suyoshi-cho, Nagaoka, Niigata, Japan; tel: +81 258-35-8932; fax: +81 258-35-0020 email: [email protected]

Proceedings, International Snow Science Workshop, Innsbruck, Austria, 2018

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glected because parameterized information was available.

2. SNOW PROFILE An avalanche accident occurred in Nasu, Tochigi Prefecture, Japan, on 27 March 2017. The avalanche slope was positioned at 37°07’06.2’’N, 139°58’49’’E, at an altitude of 1350m (Fig. 1). Nakamura et al. (2018) ob-served snow profiles on this avalanche slope on 28 March 2017, i.e., the day following the ava-lanche. Figure 2 shows the profile of grain type, snow density, and hardness at the observation site. A low-density (50–60 kg m-3) precipitation particle layer was present at 17–35 cm depth. Hardness measurement using a push-pull gauge

Fig. 1 Location of the avalanche release area in Nasu, Japan. This figure also shows the area used for the meteorological model, with 1-km mesh simulation (Geospatial Information Au-thority of Japan).

a b c

Fig. 2 Snow profiles observed on the day follow-ing the avalanche release. a: grain type, b: snow density, c: hardness. Profiles from the snow surface to 70 cm depth are shown. (Data from http://www.bosai.go.jp/seppyo/kenkyu_naiyou/seppyousaigai/2017/report_20170328_NasuOnsen_s.pdf).

indicated the presence of a low- hardness layer at a depth of 17–30 cm. The precipitation parti-cle layer at 0–35 cm depth accumulated during the night of 26–27 March 2017. It contained layers of both non-rimed and rimed snow.

Recent studies have shown that non-rimed snow crystals tend to fall near the warm front of an extra-tropical cyclone (Nakamura et al. 2018). The type of snow crystal precipitation is not measured at meteorological stations and not considered in the numerical snowpack model. Therefore, a meteorological model was used to determine the falling snow crystal type. An out-line of this study and the input of snow crystal type into the numerical snowpack model are shown in Fig. 3.

3. ESTIMATION OF THE TYPE OF SNOW CRYSTAL PRECIPITATION USING A METEOROLOGICAL MODEL

The JMA-NHM (Saito et al., 2006) was used to simulate the type of precipitation particle. Hash-imoto et al. (2017, 2018) used a double-moment bulk cloud microphysics scheme to predict the mixing ratios and concentrations of particles of solid hydrometeors (i.e., cloud ice, snow, and graupel) and a single-moment scheme to predict the mixing ratios of particles of liquid hydrome-teors (i.e., cloud water and rain). The numerical prediction was first conducted at 5-km horizontal resolution, covering the whole area of Japan and the Japan Sea. Simulations with 1-km horizontal resolution were performed in smaller domains, i.e., the area shown in Fig. 1.

Figure 4 shows the amounts of hourly precipita-tion and trapped cloud particles. The threshold value distinguishing non-rimed from rimed snow was determined by the ratio of trapped cloud particles to precipitation amount. In this study, when the amount of trapped cloud particles was

Fig. 3 Outline of this study


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less than 2% of the precipitation amount, snow particles were classified as non-rimed.

4. INCLUSION OF PRECIPITATION PAR-TICLE TYPE IN THE NUMERICALSNOWPACK MODEL

4.1 Implementation The crystal type was used as one piece of input data. The value has no physical implication, but was used solely to determine the equation for the initial snow density and viscosity coefficient. In the future, we will associate it with the amount of trapped cloud particles and specific surface area.

4.2 Initial snow density Ishizaka et al. (2016) measured initial snow densities for various snowfall events at the Fall-ing Snow Observatory of the Snow and Ice Re-search Center. In their experience, the initial densities for non-rimed snow ranged from 20 to 30 kg m-3, and those for rimed snow ranged from 50 to 70 kg m-3. No rimed snow associated with a cyclonic snowfall, as in the avalanche consid-ered in this study, had a density exceeding 40 kg m-3. Based on this information, the initial snow densities were set to 40 kg m-3 for non-rimed snow and 60 kg m-3 for rimed snow.

4.3 Viscosity coefficient Goto et al. (2006) and Kajikawa et al. (2006) estimated the compressive viscosity for several precipitation particle types. They sampled the precipitation particle layer and placed the sam-ples in a cold room at a constant air temperature. They then measured changes in snow depth and formulated compressive viscosity for stellar crys-tals, rimed stellar crystals, spatial dendrites and rimed spatial dendrites. In this simulation, stellar crystals were used to represent non-rimed snow and rimed stellar crystals were used to represent rimed snow crystals. The following approxima-tion was used to estimate the viscosity coeffi-cient, η: (Pa s)

η=Cρa (1)

where ρ is snow density, (kg m-3), and C and a are fitting parameters that depend on the type of precipitation particle. Goto et al. (2006) estimat-ed values of C = 15.4, a = 3.39, and C = 0.0682, a = 4.56 for non-rimed and rimed stellar crystals, respectively. In these equations, when the snow

Fig. 4 Amounts of precipitation and trapped cloud particles. The period masked by a gray zone was when non-rimed precipitation particles fell.

density is less than 100 kg m-3, non-rimed snow has greater viscosity and is maintained at a lesser density for a longer time.

4.4 Simulation input The parameters described in Sub-section 4.3 were included in the numerical snowpack model, SNOWPACK. A simulation was performed from 1 January to 31 March 2017. Meteorological data were obtained using the Automated Mete-orological Data Acquisition System (AMeDAS) of the JMA (Nasu; 37°7.4’N, 140°2.1’, 749 m a.s.l.) and used as the input data. Because solar and longwave radiation were not measured, they were estimated using the duration of sunshine following Kondo et al. (1991). Air temperature was corrected considering the difference in ele-vation between the meteorological station and the avalanche slope. Solar radiation was cor-rected by considering the slope angle. The basic procedures used are reported in Hirashima et al. (2008). The type of precipitation particle, esti-mated as described in Section 3, was also in-cluded in the input data. The observed precipita-tion amount from the AMeDAS differed from the amount estimated by the meteorological model; thus, the observed amount was used as the input data. The simulation period used to deter-mine the type of precipitation particle by the meteorological model was 24–29 March 2017. The simulation focused on the layer accumulat-ed during the night of 26–27 March 2017. There-fore, the type of precipitation particle type in other periods was assumed to be rimed snow.


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a

b

c

Fig. 5 Simulated result for (a) grain type, (b) type of precipitation particle, (c) snow density and (d) stability index. Gray masked area is neglected in this study.

d

Fig. 5 Results of simulation using the SNOW-PACK model a: grain type, b: type of precipita-tion particle (blue: rimed. orange: non-rimed), c: snow density, d: natural stability index. Red and blue arrows indicate the times of avalanche re-lease and observation, respectively.

5. RESULTS AND DISCUSSION Simulated SNOWPACK snow profiles are shown in Fig. 5 for (a) grain type, (b) type of precipitation particle, (c) snow density, and (d) the natural stability index for the period of 26–29 March. The simulated snow depth was much lesser than the observed value (305 cm) due to the neglect of drifting snow and correction of the precipitation amount by elevation difference.

Although simulation results showed the for-mation of a faceted crystal layer (Fig. 5a), this layer was not observed in the snow pit (Fig. 2). As this study focus on the precipitation particle layer accumulated during the night of 26–27 March, layers accumulated before this precipita-tion event were neglected and are masked by a gray cover in Fig. 5b–d. The precipitation particle layer contained non-rimed (orange in Fig. 5b) and rimed (blue in Fig. 5b) snow crystals. The layer colored orange represents the snow that fell when the amount of cloud particles was less than 2% of the precipitation amount.

The simulated snow density revealed that the layer of non-rimed snow crystals was less dense than rimed snow. The density of non-rimed snow was about 25% lesser that of rimed snow at the time of avalanche release and about 9% lesser at the time of observation. On the other hand, the observations discussed in Section 2 indicat-ed that the layer of non-rimed snow was about 40% less dense than the rimed snow layer (Fig. 2). This result indicates that the difference be-tween rimed and non-rimed snow can be ex-plained by the initial snow density and viscosity, but that the parameterization used here resulted in underestimation of the difference in snow density. This discrepancy is a shortcoming of the study.

We tried to simulate a stability index for each precipitation particle layer. The weak layer of faceted crystal was masked (Fig. 5d). The stabil-ity index for non-rimed snow was 1.5, which was about 50% less than the value for rimed snow, at the time of avalanche release. Therefore, the simulation reproduced the weak layer of non-rimed precipitation particles well. The difference in the stability index at the time of observation was 25%. This difference was much smaller than the observed difference in hardness (about 67% less). The relationship between snow den-sity and shear strength may differ depending on the type of precipitation particle with some ex-perimental facts showing weaker bonding (e.g., less hardness and a smaller repose angle) of non-rimed snow. Parameterization and the use of these parameters to estimate the stability index were issues identified in this study.

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6. SUMMARY Non-rimed precipitation particles sometimes cause avalanches in Japan. To predict ava-lanches caused by non-rimed precipitation parti-cles, information and parameters regarding dif-ferent precipitation crystal types were used. Re-production of the low snow density and stability index for the non-rimed precipitation particle layer was the target of this study. The simulation qualitatively reproduced the trend of less snow density and a lower stability index. The differ-ences between precipitation particle types were underestimated relative to the observed differ-ences. The parameterization of snow parame-ters based on observations and experiments is necessary to enhance the quantitative accuracy of simulations. We will perform such parameteri-zation in future studies, considering the propor-tion of cloud particle and specific surface area.

ACKNOWLEDGEMENTS This study was part of the project “Research combining risk monitoring and forecasting tech-nologies for the mitigation of diversifying snow disasters”. We would like to acknowledge Hiroki Motoyoshi and Masaaki Ishizaka for their advice regarding the initial density of snow. We are grateful to Mieko Miura, who assisted with our research.

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Experimental studies on the compressive viscosity of new snow. seppyo, 68, 191-198.(in Japanese with Eng-lish abstract).

Hashimoto, A., M. Niwano, T. Aoki, S. H. Motoyoshi, S. Y amaguchi and S. Nakai, 2017: Numerical weather pre-diction experiment in collaboration with research activi-ties in glaciology and snow disaster prevention. CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling, 47, 5.11-5.12.

Hashimoto, A., H. Motoyoshi, R. Misumi, and N. Orikasa, 2018: Multivariable scheme for diagnosing ice particle features in a bulk microphysics model. 15th Conference on Cloud Physics, 1D-94.

Hirashima, H, K. Nishimura, S. Yamaguchi, A. Sato, and M. Lehning, 2008: Avalanche forecasting in a heavy snow-fall area using the SNOWPACK model. Cold Reg. Sci. Technol., 51(2–3), 191–203.

Ishii, Y., S. Nakatsubo, S. Mori, and S. Matoba, 2014: Hydro-logical study of snowmelt flooding during a rain-on-snow event, 2; Comparison of rain simulation experiment in the 3 snowmelt seasons. Hokkaido-no-seppyo, 32, 104-107 (in Japanese).

Saito, K., T. Fujita, Y. Yamada, J. Ishida, Y. Kumagai, K. Aranami, S. Ohmori, R. Nagasawa, S. Kumagai, C. Mu-roi, T. Kato, H. Eito and Y. Yamazaki, 2006: The opera-tional JMA nonhydrostatic mesoscale model. Mon. Wea. Rev., 134, 1266-1298.

Kajikawa, M., N. Sato, Y. Asuma, and K. Kikuchi, 2006: Characteristics of new snow density and compressive viscosity in the Arctic region. seppyo, 68, 277-285.(in Japanese).

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Nakamura, K., S. Yamaguchi, M. Nemoto, H. Motoyoshi and I. Kamiishi, 2018: First attempt at prediction of ava-lanches resulting from no rimed falling snow crystals in Japan. ISSW proceedings, Innsbruck, This Issue.


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