An overview of the National Research Institute for Earth Science and Disaster Resilience (NIED) project “Study on Advanced Snow Information and its Application to Disaster Mitigation (ASDIM)” is described here. The Concentrated Snowfall Monitoring System (CSMS) was constructed, and observations of falling snow particles at remote sites of the CSMS were started within the observation range of an X-band multi-parameter radar at the Snow and Ice Research Center (SIRC) in Nagaoka. A parameter for the quantitative description of falling snow particles was derived. Preferential flow within the snowpack was reproduced numerically. State-of-the-art microphysical technologies, such as nuclear magnetic resonance imaging and X-ray computerized tomography, were employed. Advanced snow information, such as center of mass flux distribution, liquid water fraction, specific surface area, and microstructure of the snowpack, were collected for falling and ground snow analyses. A regularly updated Real-time Hazard Map (RHM) displaying the areas affected by various snow and ice-related hazards was developed. The RHM serves as a platform for application of the Snow Disaster Forecasting System to hazards such as avalanches, snow accretion, and blowing snow. The utility of the RHMs was examined through experiments conducted in association with local governments and transport administrators.

Meteorological radars are important for quantitative precipitation estimation (QPE) as they can determine precipitation distribution with high spatiotemporal resolution. However, accurate QPE of solid precipitation remains challenging despite its importance. A precise QPE algorithm requires an appropriate radar reflectivity-precipitation rate (Ze-R) relationship corresponding to the precipitation type, assessment of the change in size and fall velocity of snow particles falling below the radar beam, and validation using accurate precipitation amounts at the surface. In order to address these requirements, the study established an improved snowfall monitoring system, named the Concentrated Snowfall Monitoring System (CSMS) in central Niigata Prefecture. The CSMS was composed of an X-band radar and six ground observation sites. Optical disdrometers were installed at all sites to classify the precipitation type and select the appropriate Ze-R relationship. Vertical profiles of the precipitation particles and thermodynamic environment below the radar beam were assessed using micro rain radars and microwave radiometers. Presently, the precipitation amounts measured using tipping-bucket gauges are underestimated due to wind induced and wetting losses. Therefore, high accuracy weighing gauges were installed at three sites to quantify the underestimation. The CSMS data was used to conduct a preliminary analysis of the heavy snowfall that occurred on January 24 and 25, 2016, in central Niigata Prefecture. The designed CSMS estimated the precipitation distribution and precipitation type successfully. The results indicate that the CSMS can potentially determine an appropriate Ze-R relationship, which can improve the estimation of precipitation rates and contribute to the improved QPE of solid precipitation.

This paper presents simulation schemes, developed by National Research Institute for Earth Science and Disaster Resilience (NIED), for stability indices and liquid water infiltration that may be applied to a range of numerical snowpack models for avalanche prediction. The schemes were originally developed in the SNOWPACK model, and are introduced for wider application using flow charts, equations, and parameter tables for simulation of the natural stability index, shear strength, and water content. Validation of the stability indices was performed through simulations of eight recent surface avalanche accidents. Even though the simulations did not explicitly consider the weak layer formed by brittle precipitation particles that triggered most of the recent avalanches, they show that avalanche risks are high when stability indices are below a threshold of 2. This result supports previous work and demonstrates the wider applicability of the schemes for providing information on snowpack stability. However, estimation of avalanche risk could be improved through incorporation of information on snow crystal type and associated metamorphism parameterization in numerical snowpack models.

Understanding the behavior of water within snow cover, including its content, distribution, and movement, is essential for forecasting avalanches and for predicting the sliding of snow from the roofs of structures. We developed a high-resolution magnetic resonance imaging (MRI) system for non-destructive measurement of the distribution of water in snow cover. Our system is compact, uses a permanent magnet, and is designed for use in temperatures below 0 °C. To adapt the system to cold conditions, it was necessary to correct for inhomogeneities in the static magnetic field because a permanent magnet is strongly affected by the thermal conditions. We designed a single-layer shimming coil for this purpose. To prevent the wet snow sample from melting or freezing during scanning, we also developed a cooling system that uses a combination of liquid and air to maintain the sample at 0 °C. These improvements enabled non-destructive visualization of the water distribution in a wet snow sample, with high resolution. We therefore propose our MRI system as a powerful tool that can contribute to the understanding of wet snow physics.

The operational results for the Cryospheric Environment Simulator (CES) of the National Research Institute for Earth Science and Disaster Resilience (NIED) compiled over a 20-year period from October 1997 to March 2017, during which a total of 598 projects were conducted, are reported herein. These projects were instigated by four types of institutions: official institutes, universities, companies, and the NIED itself. In terms of international cooperative use, 12 institutes or universities from 9 countries have conducted various investigations using the CES. The present specifications, performances, and operations of CES are described herein; some of the scientific results and future considerations are also presented.