Forecasting Experiments Using the Regional Meteorological Model and the Numerical Snow Cover Model in the Snow Disaster Forecasting System

Abstract

The Snow and Ice Research Center (SIRC) of the National Research Institute for Earth Science and Disaster Prevention (NIED) of Japan has been developing a snow disaster forecasting system. This system consists of an atmospheric mesoscale model NHM, the numerical snow cover model SNOWPACK, and three diagnostic models of snow disasters. In this paper, the performance of NHM and SNOWPACK is investigated in the Niigata area, selecting the events of December 2005 as the case for the experiment. NHM reproduces precipitation events but tends to underestimate the amount of precipitation. This is because the positions of snow clouds are not precisely reproduced in the simulation. The predicted snow depth by SNOWPACK is in a good agreement with the observation. However, the snow type has a large prediction error. A snow cover forecasting experiment with a combination of NHM and SNOWPACK reproduces the observed snow depth and snow density well for 24 hours with slight prediction errors. The snow depth tends to be underestimated when NHM does not suitably reproduce the precipitation. To apply the system to a wet snow region, such as Niigata, accurate predictions of temperature and precipitation are needed.

A dynamic snow accumulation simulation approach for forecasting snow distribution over regional‐scale terrain

“…Some prototype systems for snow hazard simulation already exist to project the time-varying snowpack evolution with avalanche forecasting models (Lehning, Löwe, Ryser, & Raderschall, 2008;Lehning et al, 2006). Another system with the same purpose has recently been developed by the Snow and Ice Research Center (SIRC) of the National Research Institute for Earth Science and Disaster Prevention (NIED) of Japan (Iwamoto, Yamaguchi, Nakai, & Sato, 2008). Tominaga et al (2011) have also introduced a notable system aimed at forecasting the snow distribution in densely populated and built-up urban cities.…”

Operational water forecast ability of the iSnobal-HRRR coupling; an evaluation to adapt into production environments

“…An alternative to in-situ stations is using outputs from numerical weather prediction (NWP) models, which are generally spatially and temporally complete and provide all the required forcing variables from a single source. The adaptation of NWP model output to provide forcing data for snow models is ongoing and has been successfully tested as a standalone source for point simulations (Bellaire et al, , 2013Iwamoto et al, 2008) or spatially in 70 combination with data assimilation and filtering techniques (Griessinger et al, 2019;Vernay et al, 2021). Adding NWP as a possible weather measurement input source to iSnobal was first evaluated in (Havens et al, 2019), where downscaled and biascorrected observations from Weather Research and Forecasting (WRF) Model had the best results.…”