Journal of Natural Disaster Science, Volume 30, Number 1, 2008, pp35-43

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Forecasting Experiments Using the Regional Meteorological Model andthe Numerical Snow Cover Model in the Snow Disaster Forecasting System
Katsushi IWAMOTO, Satoru YAMAGUCHI, Sento NAKAI and Atsushi SATO
Snow and Ice Research Center, National Research Institute for Earth Science and Disaster
Prevention, Suyoshi, Nagaoka 940-0821, Japan(Received for 24 May, 2007 and in revised from 6 June, 2008)

ABSTRACTThe 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 withslight prediction errors. The snow depth tends to be underestimated when NHM does not suitably reproduce theprecipitation. To apply the system to a wet snow region, such as Niigata, accurate predictions of temperature andprecipitation are needed.

1. INTRODUCTION
Japan receives some of the heaviest snowfall in the world.
The snowfall occurs mainly on the Japan Sea side of Japan and in
Hokkaido, which account for more than one half of the country s
landmass. Many snow disasters occur in these areas every winter.
They are caused by various snow-related phenomena, such asavalanches, blizzards, and the accompanying poor visibility, frozen
road surfaces, snow accretions, and snow on roofs. To preventthese disasters, various facilities have been constructed. These
hardware countermeasures are effective for preventing snow disasters. However, since hardware methods are expensive to build andmaintain, building them throughout a snowfall region is difficult.
Hence, software management is important to mitigate the damageof snow disasters as an alternative or collaborative method.
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 for predicting snow disasters, as shown in Fig. 1 (Sato etal., 2003, 2004a). This system could provide the software management necessary to deal effectively with snow disasters. A similarsystem for avalanche prediction is currently being used in
Switzerland (Lehning et al., 2002a). In that system, snow coverproperties are nowcasted with a numerical snow cover model topredict avalanches. The system of NIED-SIRC is, on the other
hand, a forecasting system that combines numerical weather prediction (NWP) using a regional meteorological model with anumerical snow cover model and diagnostic models of the probability of snow disasters, aiming at prediction within a few days
before their occurrence. A prototype of the system has alreadybeen completed, and its performance is currently being improved.
In the system, the accuracy of NWP and the snow cover model,
which supply the basic data for the disaster models, could significantly influence the performance of the system.
In this paper, by conducting a forecasting experiment using
the regional meteorological model and a nowcasting experimentFig. 1 Schematic structure of the snow disaster forecasting system. The
arrows indicate the data flow (after Sato et al., 2004a).

KEY WORDS: snow disaster forecast, numerical weather prediction, snow cover prediction, NHM, SNOWPACK36

K. IWAMOTO, S. YAMAGUCHI, S. NAKAI AND A. SATO

using the snow cover model, the performance of each model andviews for improvement of the models are investigated. In the next
section, we give a brief outline of the snow disaster forecasting
system of NIED-SIRC. Experiments involving NWP and snow
cover properties are then performed, and the results are discussed.
In addition, forecasting experiments of the snow cover properties
using a combination of a snow cover model with NWP are conducted in order to discuss the influence of the prediction inaccuracy of NWP on the prediction of snow cover.
2. THE SNOW DISASTER FORECASTING SYSTEM
Fig. 1 illustrates the schematic structure of the snow disaster
forecasting system. The system consists of a regional meteorological model, a numerical snow cover model, and diagnostic models
of snow disasters. Since the models operate independently, amodel can be selected arbitrarily and treated as a module. In addition, the snow cover model and the snow disaster models can beused both for a forecast based on the prediction of the regional
meteorological model and for a nowcast based on the observed
data.
First, a numerical weather prediction (NWP) is carried out
with a regional meteorological model. The predicted meteorological data, such as air temperature, wind direction/speed, snowfall
amount, and downward shortwave and longwave radiation at target
points, are inputted into the snow cover model, the blowing snow
model, and the road surface model. For the prediction of snow disasters, it is required for the NWP to resolve a snow cloud that has ahorizontal scale of less than a few kilometers. Therefore, a modelthat explicitly represents convections of the atmosphere is necessary for the NWP. Then, the numerical snow cover model diagnoses the physical properties of the snow cover, such as the snow
type, temperature, density, and water content, using the meteorological data from the beginning of the winter to the present time.
These data are then inputted into the blowing snow model and theavalanche model. Finally, the mass flux of blowing snow and its
related visibility, the occurrence probability of an avalanche, andthe road surface conditions and friction are diagnosed by each disaster model based on the meteorological data and the physicalproperties of the snow cover. The dataflow between the models inthe system is basically one way, that is, feedback is not considered,
except between the snow cover model and the blowing snow
model. The calculated amount of snow redistribution is transferredfrom the blowing snow model to the snow cover model as feedback.
At present, we use the nonhydrostatic model (NHM), developed by the Japan Meteorological Agency (JMA) (Ikawa andSaito, 1991; Saito et al., 2006), as the regional meteorologicalmodel, and SNOWPACK, developed by the Swiss Federal Institutefor Snow and Avalanche Research (Bartelt and Lehning, 2002;
Lehning et al., 2002a, 2002b), as the numerical snow cover model.
SNOWPACK is also used as the avalanche model. The avalanche
model estimates avalanche danger based on a stability index (SI),which is the ratio of the shear strength and shear stress of the snow
cover. The properties of the snow cover and the angle of the target
slope are used to estimate the shear strength and shear stress. To
apply SNOWPACK, which was developed for the prediction of drysnow avalanches in the Swiss Alps, to the wet snow region in

Japan, the melting processes (Yamaguchi et al., 2004) and the formulation of the shear strength (Hirashima et al., 2006) have been
improved. The blowing snow model and the road surface model
have been developed by SIRC. The blowing snow model diagnoses visibility at a height of 1.2 m, which represents the heightfrom which a typical vehicle driver views the road, and estimates
the depth of the snow drift (Sato et al., 2004b, 2004c). The roadsurface model diagnoses the surface temperature and the road surface conditions based on a heat budget method (Nishimura et al.,2004). The conditions are categorized into dry, wet, slush, andcompacted snow.
3. FORCASTING EXPERIMENT OF NHM
NHM, used as the regional meteorological model in the system, is a nonhydrostatic mesoscale model. Since atmospheric vertical motion is explicitly treated in the governing equations, convections and convective clouds are well represented in this model.
Therefore, this model is suitable for forecasting convective snow
clouds in winter. NHM uses three classes of precipitation particles(rain, snow, and graupel) and a bulk method with empirical formulae as cloud microphysics. In the snow disaster forecasting system,
only the regional meteorological model is a forecasting model,
while others are diagnostic models. The accuracy of the regional
meteorological model, therefore, could be a critical factor in thesystem’s performance. In this section, conducting the forecasting
experiment throughout a month, the performance of NHM is evaluated.
3.1 Setting of NHM and experimental design
Heavy snowfall and related snow disasters were frequent inJapan in December 2005. We selected this month as an experimental case to investigate the performance of NHM. The initialand lateral boundary data of NHM are given by the grid point value(GPV) of the regional spectrum model (RSM) provided by JMA.
RSM-GPV consists of an atmospheric analysis and three-hourly
51-hour forecasts of the geopotential height, the horizontal and vertical wind components, the potential temperature, and the mixing
ratio of water vapor. The horizontal resolution is about 20 km at
the surface and about 40 km above the surface. For resolving convective snow clouds in NWP, a horizontal grid interval of 2 km is
employed as the inner NHM (NHM02km) using a one-way dualnesting method from RSM-GPV through the outer NHM with agrid interval of 10 km (NHM10km). The experimental area of
NHM10km and NHM02km is shown in Fig. 2. The domain of
NHM02km extends 200 km × 200 km and roughly corresponds to
the Niigata area, the central part of the Japan Sea side of Japan.
The vertical grid interval varies from 40 m at a lower boundary to
1,180 m at an upper boundary with a height of about 20 km.
The analysis and forecasts of RSM-GPV are interpolated linearly to the grid points of NHM10km as the initial and the lateralboundary data, respectively. NHM10km forecasts the mixing ratioof the cloud water and cloud ice, the mixing ratio of the precipitation particles, and the amount of the precipitation as well as themeteorological data. The initial and the boundary data of
NHM02km are also made by interpolation of the output data of
NHM10km. NHM02km outputs the same data as NHM10km. In
lower-boundary conditions, classification of the sea surface and theFORECASTING EXPERIMENTS USING THE REGIONAL METEOROLOGICAL MODEL AND THE NUMERICAL SNOW COVER MODEL IN THE SNOW DISASTER FORECASTING SYSTEM

37

Fig. 3 Hourly atmospheric data at SIRC. (a): precipitation simulated by
NHM02km (bar) and temperature difference between the simulationand the observation (line), and (b): observed precipitation (bar) andtemperature (line). The output of NHM02km at the nearest grid
point to SIRC is selected in (a).

Fig. 2 Experimental domain of NHM. (a): location map of NHM10km andNHM02km and (b): topography of NHM02km. The cross mark in(b) indicates the position of the Snow and Ice Research Center(SIRC).

land surface is considered; it is assumed that the land surface iscovered exclusively by grass. In the snowfall region, whether theland surface is covered by snow or not is thought to be importantfrom the viewpoint of the energy budget at the surface. Feedback
from the snow cover model to NHM is necessary to treat the snow
cover as the lower boundary in NHM. This improvement will bemade in future work.
First, a 33-hour forecast run of NHM10km was carried out
twice daily at the initial times of 0000 and 1200 UTC (Coordinated
Universal Time), i.e., 0900 and 2100 JST (Japan Standard Time),
respectively. The end times of each forecast run were 0900 UTC(1800 JST on the next day) and 2100 UTC on the next day (0600
JST on the day after the next day). Then, a 30-hour forecast run of
NHM02km was conducted. The initial data of NHM02km were
supplied by the output of NHM10km at forecast time 03 (that is, 3
hours after the initial time; hereafter, FT=03). Therefore, the initialtimes of NHM02km were 0300 UTC (1200 JST) and 1500 UTC(0000 JST on the next day). The end time was the same as that of
NHM10km. Taking the spin-up time for 7 hours into account,
forecast data for 24 hours from FT=10 to 33 were used for the
analysis. The output interval of the predicted data was one hour.
In addition, continuous data from 0100 JST 1 December 2005 to
0000 JST 1 January 2006 were obtained by successively connecting twice-daily 12-hour forecasts from FT=10 to 21.

3.2 Results
Fig. 3 shows the precipitation and the temperature at SIRC for
one month. Following the manner in which the snow disaster forecasting system is utilized for forecasting at a target point, the values at the nearest grid point of NHM02km from SIRC were plottedas the simulated results. NHM reproduces, to some extent, the timing of precipitation. However, the precipitation amount of NHM ismuch smaller than that of the observation throughout the period,
with few exceptions. The total precipitation simulated by NHM is
225 mm, whereas the observed precipitation is 583 mm. The simulated temperature variation shows a tendency towards overestimation (Fig. 3a). In our experiment, the forecasting result of NHM
has a tendency to underestimate the cloud amount (figures notshown), which results in an underestimate of surface precipitation
and an overestimate of surface temperature.
The daily precipitation amount simulated by NHM on 18
December (27 mm) was the largest in the experiment and relatively
close to the observed daily precipitation (37 mm). At this time, thesimulated temperature also corresponded well to the observed temperature (the daily-averaged temperature difference was about 0.1
K on 18 December). The horizontal distributions of the precipitation at 0300 JST on 18 December are shown in Fig. 4. The corresponding observed precipitation is retrieved from a radar-echo
composite map obtained from the JMA radar network (for the sakeof simplicity, the JMA radar network is referred to as radar, andthe radar-echo composite map, as a radar map ). Both in the
NHM prediction and the radar map (Figs. 4a, 4b, respectively), theprecipitation areas are aligned from northwest to southeast over thesea, and precipitation, occurring primarily over land, is dominant in
the mountains. NHM successfully predicts both the distribution
and the amount of precipitation at this time. It should be noted thatNHM predicts heavy precipitation around 36.8°N, 138.6°E, which

38

K. IWAMOTO, S. YAMAGUCHI, S. NAKAI AND A. SATO

Fig. 4 Horizontal distribution of the preceding hourly precipitation at 0300 JST on 18 December 2005. (a): result of NHM02km
and (b): radar map.

Fig. 5 Same as Fig. 4 but at 2100 JST on 24 December 2005.

does not appear on the radar map. This disagreement is thought tobe caused by a radar problem. In general, the radar cannot observe
clouds over the higher mountains because the radar beam passes
over the top of the clouds. Therefore, precipitation derived by theradar might be underestimated in the mountains.
NHM did not exactly reproduce the horizontal distribution of
precipitation at 2100 JST on 24 December (Fig. 5). In this case,the basic structure of the snowfall area, which is linear with a widthof about 20 km, is in good agreement with the observed structure;
however, a heavy snowfall area (around 37.3°N, 138.4°E) does
not appear in the simulation. In addition, since the running direction of the simulated precipitation is from northwest to southeast
but from west to east on the radar map, the snowfall area over land
shifts southward compared with that on the radar map. The precipitation amount of NHM at SIRC was found to be smaller than thatof the observation at around this time. Even if the basic structureof snow clouds is reproduced in NHM, the occurrence of inconsis-

tency in the spatial pattern might result in a large difference at a
certain point.
Fig. 6 shows the total precipitation from 1 to 31 December.
NHM underestimates the amount of precipitation near the coastfrom 37.0°N to 37.4°N and from 138.2°E to 138.7°E. The
monthly precipitation of NHM averaged in the domain is about 240
mm, which is small compared with the observed precipitation of
430 mm. The underestimation may be attributed to problems related to sea and land surface processes, cloud parameterization, andinitial and boundary data. These are the most important points for
improving NHM. It is noteworthy that the radar cannot be used toobserve the mountainous region of Niigata. Therefore, theobserved precipitation might be underestimated near the southernand southeastern edge of the domain. Considering a validation
method for the prediction results is also important for improving
NHM. Using surface observation would be the most appropriate
method of validation at present.

FORECASTING EXPERIMENTS USING THE REGIONAL METEOROLOGICAL MODEL AND THE NUMERICAL SNOW COVER MODEL IN THE SNOW DISASTER FORECASTING SYSTEM

39

Fig. 6 Accumulated precipitation from 0100JST on 1 December 2005 to 0000JST on 1 January 2006. (a): simulation of NHM02km
and (b): radar map.

Fig. 7 Snow depth at SIRC in December 2005. The solid line denotes the simulated snow depth
by SNOWPACK, whereas the dashed line denotes the observed snow depth.

4. NOWCASTING EXPERIMENT OF THE PHYSICAL PROPERTIES OF SNOW COVER BYSNOWPACKSNOWPACK is a one-dimensional snow cover model.
SNOWPACK includes the realistic dynamics and thermodynamics
of snow cover in the governing equations, diagnosing the time evolution of the physical properties of the snow cover, such as snow
temperature, density, water content, and type, using meteorological
data. The snow type is categorized by the diagnosed dendricity,sphericity, and grain size of each layer in the snow cover. By using
the finite-element method, SNOWPACK can represent even finestructures in the snow cover, such as a thin ice layer.
4.1 Experimental design
A SNOWPACK run is based on the meteorological data
observed every one hour from the snow and weather observationsite at SIRC. The input data of SNOWPACK are the temperature,
relative humidity, wind speed, precipitation, and downward shortwave and longwave radiation at the surface. All of the input data,
except precipitation, are instant values, whereas precipitation is an
accumulated value for the preceding one hour. Considering thatthe catch efficiency of the precipitation gauge declines in winter,the observed precipitation was corrected with the formulation of
Yokoyama et al. (2003) using the observed wind speed. It is presumed that precipitation occurs as snow below 0.5℃ and as rain
above this temperature (Yamaguchi et al., 2006). The density of
new snow is set at 130 kg m−3. The calculation period is from0100 JST 1 December 2005 to 0000 JST 1 January 2006. The output interval of the data is three hours. The snow depth automatically observed from the SIRC snow and weather observation site isused for validation of the simulated snow depth. The simulated
snow type and snow density are compared with the results of snow
pit observations at SIRC on 20, 25, and 30 December 2005.
4.2 Results
The time evolution of the snow depths at SIRC is shown in
Fig. 7. SNOWPACK reproduces the observed snow depth withslight differences, particularly at rapid increases and at the diminishing stage. Diminishing snow depth is generally caused by melting and/or densification, and these are connected with the snow
type of the snow cover. Fig. 8 depicts the simulated snow types asfour categories. Metamorphism of snow is generally categorized
into three types: equitemperature, wet snow, and temperature gra-40

K. IWAMOTO, S. YAMAGUCHI, S. NAKAI AND A. SATO

Fig. 8 Simulated snow type at SIRC by SNOWPACK in December 2005.

Fig. 9 Simulated (CTL, left bar) and observed (OBS, right bar) snow type on (a): 20 December,(b): 25 December, and (c): 30 December 2005. Because of the difficulty in distinguishing
between lightly compacted snow and compacted snow on the basis of the observation,
they are both shown as compacted snow.

dient. The wet grain layer and compacted snow layers are formed
by wet snow metamorphism and equitemperature metamorphism,
respectively. The process of equitemperature metamorphism iswell simulated in SNOWPACK. For example, new snow near thesurface on 14 December undergoes metamorphic development into
lightly compacted snow at 45 cm on 16 December and into compacted snow at 25 cm on 24 December. This layer contains liquidwater (figures not shown) because it is exposed to high air temperature at the snow surface on 15 December (indicated by the downward arrow in Fig. 3b). Therefore, the layer finally changes into
wet grain at 20 cm on 27 December after the grain size of the layer
becomes larger.
Vertical profiles of snow types are compared in Fig. 9.
Obviously, the simulated snow types are different from thoseobserved. The wet grain layers in the middle of the snow cover do
not appear in the simulation. In addition, the simulation shows
fewer layers of compacted snow than the observation. In SNOWPACK, calculation of shear strength at a layer is based on the typeof metamorphism at the target layer by the formulation of Jamieson

and Johnson (2001) and Yamanoi and Endo (2002). Therefore,
failure in determining the type of metamorphism could be a problem in the prediction of avalanches. In particular, methods of eliminating underestimates of wet grain layers, which are potentiallyweak layers in surface avalanches, need to be developed. For theformation of a wet grain layer in the snow cover, it is necessarythat meltwater is supplied to the layer by penetration and/or by lateral transport. The penetration process can be improved by adopting the concept of unsaturated hydraulic conductivity in the snow
cover (that is, the process in which water penetrates from the upper
layer to the lower layer through an unsaturated layer). Suitably
expressing lateral transport in SNOWPACK is difficult because
SNOWPACK is a one-dimensional model. To represent lateraltransport, a two- or three-dimensional model must be developed.
On the other hand, underestimating compacted snow seems to
be an observational problem. New snow and compacted snow in
SNOWPACK are classified according to their sphericity and dendricity; the criteria used for observations are different. Moreover,
the observed snow type is subjective and varies from one observer

FORECASTING EXPERIMENTS USING THE REGIONAL METEOROLOGICAL MODEL AND THE NUMERICAL SNOW COVER MODEL IN THE SNOW DISASTER FORECASTING SYSTEM

to another. This is, however, not a serious problem for the prediction of avalanches, because the snow type change from new snow
to compacted snow occurs under the same metamorphic process(equitemperature metamorphism). The shear strength at a layer isdetermined only by the density of the layer in the cases of newsnow and compacted snow.
5. FORCASTING OF PHYSICAL PROPERTIESOF SNOW COVER BY A COMBINATION OF
NHM AND SNOWPACKIn this section, the influences of the prediction error of NHM
on the result of SNOWPACK are discussed. Two cases were chosen for the experiment in December 2005: from 1900 JST on 17 to1900 JST on 18 December (case 1) and from 1900 JST on 24 to1900 JST on 25 December (case 2) (see Fig. 3). First, a SNOWPACK run was performed on the basis of the observation data at
SIRC from 1 December to the end time of each case (hereafter, thisrun is called a control run). Then, using the output of the control
run at the initial time of each case as the initial conditions, the
SNOWPACK run was performed on the basis of the forecast dataat the nearest grid point of NHM02km from SIRC from the initialtime to the end time of the case (this run is called a forecast run).

41

The diagnosed snow depth and snow density of the forecast run
were compared with those of the control run.
The snow depth and the snow density simulated by the forecast run are shown in Fig. 10. In case 1, the simulated depth for 24hours is within a difference of 10 cm compared with the result of
the control run. The vertical profile of the density of the forecasting run also reproduces the result of the control run with slight differences at around 40 cm. Both the precipitation and temperature
simulated by NHM are in good agreement with the observation inthis case (see Figs. 3a, 4). The forecasted results, therefore, correspond to the control run as a whole. The slight differences between
the density profiles are caused by the erroneous melting water at
the surface due to an overestimate of the downward shortwave
radiation of NHM in this case (figures not shown here).
In case 2, the depth increase until 04 JST on 25 December is
not suitably reproduced, although the depth decrease after 04 JST
is consistent with the control run (Fig. 10b). The underestimate ofthe depth increase is caused by an underestimate of the precipitation at the former part in case 2 (Fig. 3). The underestimate occurs
as a result of inconsistency of the horizontal distribution of thecloud, as previously mentioned (see Fig. 5). The snow density of
the forecasting run below 85 cm has a similar profile to that of thecontrol run, but the local minima at around 60 cm and 70 cm in theFig. 10 Variation in snow depth (left panels) and snow densities at the final state in
the SNOWPACK forecast run (right panels) of (a): case 1 and (b): case 2. The
solid and dotted lines denote the results of the control run (CTL) and the forecast run (FCT), respectively.

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K. IWAMOTO, S. YAMAGUCHI, S. NAKAI AND A. SATO

control run are not reproduced. As indicated by the upward arrow
in Fig. 3a, the air temperature simulated by NHM on 25 December
is much higher than the observed temperature (〜7℃). As a result,the erroneous melting water due to the overestimate of the air temperature of the forecasting run seems to smooth the density profilein this case.
According to the results of cases 1 and 2, prediction error of
the precipitation affects the layer near the snow surface, while
overestimate of the air temperature could influence the deeper layer
in the snow cover through erroneous melting water, especially inthe warmer temperature range (〜0℃). Yamaguchi et al. (2007)
revealed that much snowfall occurs at about 0℃ in the Niigata
region. Therefore, accuracy of the temperature as well as of the
precipitation must be considered for improving NHM.
6. CONCLUSIONS
A snow disaster forecasting system has been developed at
SIRC. The system was designed to predict various types of snow
disasters a few days before their occurrence. In this study, weinvestigated the performance of numerical weather prediction by
NHM and the numerical snow cover model SNOWPACK in the
Niigata region, selecting the events of December 2005 as the casefor the experiment.
NHM reproduces snowfall events at suitable timing but reproduces less precipitation in most of the events. In particular, it tends
to underestimate precipitation near coastal areas. Due to the highpopulation density near the coast, there is more infrastructure in thearea; thus, it is important to correct underestimate of the precipitation. The snowfall processes of NHM should be improved for better prediction. Moreover, the validation method of prediction also
poses certain problems. The JMA radar network cannot reach overand behind higher mountainous areas in the Niigata region.
Surface observation is useful for validation, but there are few
observatories in mountainous areas. Methods of evaluating theresults of simulation need to be examined.
SNOWPACK suitably reproduced the snow depth inDecember 2005, but the snow type was not consistent with thatshown by snow pit observation. In particular, underestimating the
wet grain layer could be a significant problem in avalanche prediction. The penetration and/or the lateral transport processes of meltwater should be represented suitably in the snow cover model.
Moreover, there are two problems to be considered.
1. In this study, the observed precipitation is assumed to be rainwhen the air temperature is above 0.5℃ and snow below thistemperature. However, this assumption should change with various atmospheric conditions, including those in the upper air. It
is unrealistic to fix this value, but it is difficult to evaluate thethreshold temperature at present. The heat energy applied tosnow cover is different for rain and snow even if their temperatures are the same. The threshold temperature of 0.5℃ is also
related to correction of precipitation because it is assumed thatthe catch efficiency of the precipitation gauge changes at thistemperature (Yokoyama et al., 2003) and thus, the total precipitation itself can fluctuate depending on the threshold.
2. New snow density is assumed to be 130 kg m−3 in this study.
However, the density depends strongly on the atmospheric conditions. According to Kajikawa (1989), it depends on the airtemperature and the wind speed. In reality, the value is changeable and difficult to observe. New snow density is closely connected to the densification process.
NHM separately outputs the amounts of rain, snow, and graupel.
By combining the precipitation information in NHM with theobservation, the former issue might be resolved. As for the latterissue, distributions of temperature and wind predicted by NHM
might be useful.
The forecasting experiment of the physical properties of thesnow cover was performed by combining NHM and SNOWPACK.
The results of the forecast run indicated that the snow depth wasunderestimated when NHM did not suitably reproduce the snow
cloud and that the density profile was smoothed when NHM overestimated the air temperature. Taking into consideration the factthat much snowfall occurs at about 0℃ in the Niigata region(Yamaguchi et al., 2007), an accurate prediction of temperatureand precipitation is required for prediction by SNOWPACK. Dataassimilation techniques, such as nudging, the Kalman filter
method, and three- or four-dimensional variational analyses, arepotential solutions for improving the prediction of NHM.
ACKNOWLEGEMENTS
We are grateful to the staff of the Numerical Prediction
Division and Meteorological Research Institute of JMA for the utilization of NHM in this study. We are also grateful to Michael
Lehning, the Swiss Federal Institute for Snow and Avalanche
Research, for his useful advice with regard to the application of
SNOWPACK in Japan. Two anonymous reviewers also gave us
valuable comments. The snow disaster forecasting system wasdeveloped by all the researchers at the Snow and Ice ResearchCenter. This study is supported by a research project of the
National Research Institute for Earth Science and Disaster
Prevention entitled Development of a snow disaster forecasting
system.
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