TRB Annual Meeting
Investigation of Relationship between Weather Conditions and Visibility Conditions on
the Road in Winter--Manuscript Draft-Full Title:

Investigation of Relationship between Weather Conditions and Visibility Conditions on
the Road in Winter

Abstract:

In Hokkaido, Japan, in winter, poor visibility frequently occurs due to snowstorm andthe risk of traffic restriction and crash increases. Estimation of poor visibility on the roaddue to snowstorm is necessary to contribute to traffic safety. Aim of the present study
is to explore possibility of precipitation intensity measured by the X-band multiparameter radar network (XRAIN) for estimating visibility conditions along the road in
winter. XRAIN could observe precipitation per 250-meter square grid mesh every 1minutes, visibility evaluation along the road in detail would be possible. We
investigated relationship between precipitation intensity and value of the Weighted
Intensity of Power Spectra (hereinafter WIPS) measured by the Closed-circuit
Television (hereinafter CCTV) Camera which shows visibility conditions on the road.
Cross-correlation analysis was used to see relationship between precipitation intensityand value of the WIPS. Results of this study revealed the possibility to evaluate
visibility conditions on the road using precipitation intensity measured by XRAIN.
Meanwhile, there were several causes of poor visibility conditions, then it is difficult for
us to estimate visibility conditions using precipitation intensity only. It is supposed that
integration of weather conditions including precipitation intensity by XRAIN and groundconditions on the road are required to estimate the visibility conditions on the road.

Manuscript Classifications:

Infrastructure; Highway Maintenance; Winter Maintenance AKR40; Automated vehicle
location systems; Management tools and practices; Snow and ice

Manuscript Number:
Article Type:

Presentation and Publication

Order of Authors:

Yuki Nakamura, M.S.
Toru Hagiwara, Ph.D.
Yasuhiro Nagata, Ph.D.
Sho Takahashi, Ph.D.
Ryoichi Tsurumaki, Ph.D.
Naoki Matsuoka, B.E.

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Investigation of Relationship between Weather Conditions and Visibility Conditions on the
Road in WinterYuki Nakamura, M/S.
Student
Hokkaido University
Sapporo, Japan, 060-8628
Email: yuki-nakamura@frontier.hokudai.ac.jp
Toru Hagiwara, Ph.D.
Professor
Hokkaido University
Sapporo, Japan, 060-8628
Email: hagiwara@eng.hokudai.ac.jp
Yasuhiro Nagata, Ph.D.
Principal Researcher
Hokkaido Development Engineering Center
Sapporo, Japan, 001-0011
Email: nagata@decnet.or.jp
Sho Takahashi, Ph.D.
Associate Professor
Hokkaido University
Sapporo, Japan, 060-8628
Email: stakahashi@eng.hokudai.ac.jp
Ryoichi Tsurumaki, Ph.D.
Chief Engineer
Hokkaido Weather Technology Center
Sapporo, Japan, 064-8555
Email: ryoichi.tsurumaki@howtecc.jp
Naoki Matsuoka, B.E.
Managing Director
Hokkaido Weather Technology Center
Sapporo, Japan, 064-8555
Email: naoki.matsuoka@howtecc.jp
Word Count: 4,778 words + 3 table (250 words per table) = 5,528 words

Submission date: August 1, 2022.

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ABSTRACTIn Hokkaido, Japan, in winter, poor visibility frequently occurs due to snowstorm and the risk of traffic
restriction and crash increases. Estimation of poor visibility on the road due to snowstorm is necessary tocontribute to traffic safety. Aim of the present study is to explore possibility of precipitation intensity
measured by the X-band multi-parameter radar network (XRAIN) for estimating visibility conditions along
the road in winter. XRAIN could observe precipitation per 250-meter square grid mesh every 1-minutes,
visibility evaluation along the road in detail would be possible. We investigated relationship between
precipitation intensity and value of the Weighted Intensity of Power Spectra (hereinafter WIPS) measured
by the Closed-circuit Television (hereinafter CCTV) Camera which shows visibility conditions on the road.
Cross-correlation analysis was used to see relationship between precipitation intensity and value of the
WIPS. Results of this study revealed the possibility to evaluate visibility conditions on the road using
precipitation intensity measured by XRAIN. Meanwhile, there were several causes of poor visibility
conditions, then it is difficult for us to estimate visibility conditions using precipitation intensity only. It is
supposed that integration of weather conditions including precipitation intensity by XRAIN and groundconditions on the road are required to estimate the visibility conditions on the road.
Keywords: Poor visibility, Snowstorm, Precipitation intensity, XRAIN, WIPS, Cross-correlation

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INTRODUCTION
In Hokkaido, Japan, every winter, poor visibility frequently occurs due to snowstorm and the riskof traffic restriction and crash increases (1). Estimation of poor visibility due to snowstorm is necessary tocontribute to traffic safety (2). Nagata and Hagiwara et al. evaluated visibility conditions in winter used byvalue of the Weighted Intensity of Power Spectra (hereinafter WIPS) measured by the Closed-circuit
Television (hereinafter CCTV) Camera, and developed visibility inspection system for road administrator(3).
Hagiwara et al. suggested that value of the WIPS may be appropriate for identifying poor visibility
using digital images (4). Nakamura et al. detected poor visibility on the road using value of the WIPS
measured by the CCTV camera installed along National Route (hereinafter NR) 244 and NR 272 in
Hokkaido, Japan. Still images recorded by CCTV camera evaluated poor visibility conditions occurred bystrong wind despite no snowfall (5). Visibility conditions on the road could be evaluated by the CCTV
camera. Nakamura et al. developed visibility estimation model using value of the WIPS using images taken
by on-board camera. Under severe poor visibility conditions, the model could not estimate visibility
conditions precisely due to missing snowfall information along the road (6). Then, snowfall information isrequired to estimate occurrence of poor visibility conditions on the road.
Precipitation intensity observed by weather radar might be available to observe snowfall on theroad in winter. Weather radar could observe precipitation intensity frequently in high spatial density. In the
United States of America and Japan, precipitation measured by weather radar has been used. In the UnitedStates of America, in 1988, NEXt-Generation RADar network (hereinafter NEXRAD) was developed toobserve precipitation and wind velocity all over the United States of America (7). In the state of Alaska,
NEXRAD is used to observe scattered material by tornado and precipitation quantitively (8). In Japan, since2016, the X-band multi-parameter radar network (hereinafter XRAIN) for river maintenance and prevention
activities such as landslide developed by the Ministry of Land, Infrastructure, Transport and Tourism(hereinafter MLIT) (9). XRAIN could observe precipitation per 250-meter square grid mesh every 1minutes, visibility evaluation along the road in detail would be possible. In Hokkaido, Japan, Omiya et al.
discussed whether it is possible to spatially obtain real-time information on surface snowstorm based ondata observed in the air by XRAIN (10).
Aim of the present study is to explore possibility of precipitation intensity measured by the X-band
multi-parameter radar network (XRAIN) for estimating visibility conditions along the road in winter.
Relationship precipitation intensity measured by XRAIN and value of the WIPS using images recorded by
the CCTV cameras along the road was examined. Using these two time series variables, we tried to evaluate
the situation of occurrence poor visibility.

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METHODS
Investigate Road Section and time period
A 22-km/section of National Route 232 (hereinafter NR 232) from 56.0 kilometer post (hereinafterK.P.) to K.P. 78.0 was selected for investigation. K.P. in Japan represents milepost in the United States ofAmerica. Figure 1 shows a map of the 22-km/section on NR 232 and shows the locations of K.P., CCTV
cameras, and Automated Meteorological Data Acquisition System (AMeDAS). The 22-km/section
potentially tends to occur poor visibility due to snowstorm in winter. TABLE 1 shows average airtemperature, average wind velocity, and major wind direction of each of 11 time periods during two winterseasons. These 11 time periods have a greater chance of occurring poor visibility caused by snowstorm/s.
TABLE 1 Time Periods under Poor visibility was Likely to Occur due to Snowstorm.
MM/DD/YYNo.

Time
PeriodDec. 26, 2020.

1

12:00-16:00

Average
Temperature
(℃)
-5.7

Dec. 27, 2020.

2

11:00-15:00

-4.5

8.0

West or West-Northwest

Dec. 29, 2020.

3

10:00-14:00

-5.4

9.7

West

4

12:00-13:30

-7.2

3.3

South-Southeast

5

13:40-16:00

-6.6

11.9

West-Northwest

6

10:00-11:00

-9.4

1.8

East-Southeast

7

11:10-14:00

-4.7

7.6

West-Northwest

Dec. 30, 2021.

88:00-12:00

-2.5

6.0

West-Northwest

Jan. 29, 2022.

9

11:00-15:00

-4.8

7.6

West or West-Southwest

10

12:00-13:20

-4.1

3.0

Nort or North-Northeast

11

13:30-16:00

-3.6

6.8

Northwest

Dec. 31, 2020.

Dec. 28, 2021.

Feb. 3, 2022.

Averagewind velocity
(m/s)
10.5

West-Northwest

Majorwind direction

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Figure 1 Map of the 22-km/section in Haboro area included the 22 km/section of NR 232

Figure 2 Map of the radar station and 10-km/square grid mesh in Haboro area including the 22
km/section of NR 232

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Data Collection
Precipitation intensity of XRAIN (mm/h)
MLIT developed XRAIN as precipitation observation system. The present study, we downloaded“MLIT XRAIN CXMP composite dataset” from Data Integration and Analysis System (hereinafter DIAS)(9). “MLIT XRAIN CXMP composite dataset” consists of “MLIT X-band multi-parameter radar” and “Cband multi-parameter radar”. “MLIT XRAIN CXMP composite dataset” provides precipitation intensity
per 250-meter square grid mesh every 1-minutes. The present study, we used precipitation intensity per 1kilometer square grid mesh instead of 250-meter squire grid mesh. Figure 2 shows the location of the
Hakodake radar station close to the 22-km/section on NR 232 and 10-kilometer square grid mesh in Haboro
area included the 22-km/section of NR 232. The 10-kilimeter square grid mesh (shown in Figure 2) consistsof 100 1-kilometer square grid meshes to provide precipitation intensity.
Precipitation intensity measured by XRAIN is based on observations of rain particle in the air.
Time lag may occur between precipitation intensity and amount of precipitation on the ground (11). In
winter, most rain particles in the air are snow particles. Then, value of time lag increases caused by the nonvertical fall of snow particle in winter (10)(12). Then, average value from precipitation intensity before 10minutes to the current time is used as represented precipitation intensity at the current time.
Still images captured by the CCTV camera
In the 22-km/section of NR 232, seven CCTV cameras are installed. The video images recorded by
CCTV cameras are continuously transmitted by means of a fiber-optic network to the road maintenanceoffice, and still images are provided to drivers via the Internet (3)(4). CCTV camera captures still image
every 5-minutes. We used still images captured by CCTV cameras at K.P. 67.8 and K.P. 72.0 for visibility
evaluation on the road. These two CCTV cameras almost never changed the shooting direction. Also, there
were few cases for ice and snow on the camera lens due to the relationship between major wind direction
and their shooting direction.
Temperature, wind velocity, and wind direction measured by Haboro AMeDAS
Haboro AMeDAS is located near Haboro town. Haboro AMeDAS has been observed airtemperature, wind velocity and wind direction every 10-minutes. Table 1 shows average value of air
temperature and wind velocity during each of 11 time period, and shows major wind direction during eachof 11 time period.
Value of the Weighted Intensity of the Power Spectrum (WIPS)
Hagiwara et al. developed value of the WIPS measured by the CCTV camera to evaluate visibility(4). The present study, value of the WIPS has been adopted as an assessment value of visibility conditions
on the winter road. The value of the WIPS represents the difference in spatial frequencies within the still
image based on the human contrast sensitivity function, and it can assess visibility level road ahead (13).
The followings are image processing method for calculation value of the WIPS (3)(4). First, still image wascaptured from CCTV camera every 5-minutes. Second, a two-dimensional image of 256×256 pixels was
cropped from/still image. Third, the grayscale intensity of each pixel was calculated from the intensities of
the red-green-blue (RGB) components recorded in the two-dimensional image of 256×256 pixels. The
grayscale intensity ranges from 0 to 255. Fourth, the image was broken into sinusoidal gratings of different
spatial frequencies using the two-dimensional fast Fourier transform (hereinafter FFT). The power spectrum
value computed by FFT corresponds to the amplitude of the spatial frequency for each cycle per degree. At
the final step, the power spectrum intensities in the range of 1.5 to 18 cycles per degree were summarized.
Under clear conditions, the power spectra for each spatial frequency component of the still image on theroad are great and value of the WIPS is large. Under poor visibility conditions, such as those in fog or snow,
the power spectra are small. As a result, value of the WIPS is small.

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Cross-Correlation between Precipitation Intensity and Value of the WIPS
Cross-correlation is used to know the closeness of the relationship between two variables in theform of time series data (14). In the relationship between two time series (𝑦𝑖 and 𝑥𝑖 ), the series 𝑥𝑖 may berelated to past time lag ( 𝜏 ) of the x-series. We calculated the cross-correlation between two time series
variables, precipitation intensity and value of the WIPS, where 𝜏 = −𝜏𝑚𝑎𝑥 , … , 𝜏𝑚𝑎𝑥 represents the timelag. Cross-correlation is ranged from -1.0 to +1.0, where zero indicates no relationship between the twovariables. In the present study, we adopted value of the WIPS as 𝑦𝑖 and precipitation intensity as 𝑥𝑖 . Crosscorrelation was calculated by Equation 1. When one or more 𝑥𝑖+𝜏 , with 𝜏 negative, are predictors of 𝑦𝑖 , itis that 𝑥 leads 𝑦. When one or more 𝑥𝑖+𝜏 , with 𝜏 positive, are predictors of 𝑦𝑖 , it is that 𝑥 lags 𝑦.
𝑁−𝜏

𝑥𝑖 − 𝑥𝑦𝑖+𝜏 − 𝑦

𝑖=1

𝐶 𝜏 =

𝑁

𝑥𝑖 − 𝑥 2

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𝑖=1

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎(1)𝑁𝑦𝑖 − 𝑦 2
𝑖=1

𝐶 𝜏 ：Cross-correlation
𝜏 ：Time lag (min.)𝑁：i=1 to N, Number of data
𝑥𝑖 ：Precipitation intensity (mm/h）
𝑦𝑖 ：Value of the WIPS
𝑥 ：Average value of the precipitation intensity
𝑦：Average value of the WIPSIn the present study, the time period for calculating the cross-correlation was set to 1 hour. Numberof data: N in Equation 1 was 12. We explored time zone of each of 11 time periods when the precipitation
intensity became strong, and determined time when the precipitation intensity became peak within the timezone. One hour period for calculating cross-correlation was set from 30 minutes before the peak time to 30
minutes after the peak time. Then, −𝜏𝑚𝑎𝑥 is -60 (min.) and 𝜏𝑚𝑎𝑥 is 60 (min.). If there were multiple timezones of the precipitation intensity, the cross-correlation was calculated per each of multiple time zones.
For calculating the cross-correlation, unit of the time lag between precipitation intensity and valueof WIPS was set to 5-minutes. Value of the WIPS was calculated every 5-minutes corresponded to the still
image collect by the CCTV cameras. Precipitation intensity every 5-minutes was set to an average of 10minutes precipitation intensity before the current time period. There is the time lag caused by the nonvertical fall of rain particle from the cloud shot by radar to the ground (10). Then, average value fromprecipitation intensity before 10-minutes to the current time is used as represented precipitation intensity at
the current time.

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RELATIONSHIP BETWEEN WEATHER CONDITIONS AND VISIBILITY CONDITIONS
Cross-correlation was applied to investigate relationship between precipitation intensity measured
by XRAIN and value of the WIPS measured by the CCTV camera. We tried to reveal the effect ofprecipitation intensity for snowstorm/s on the road. This section shows detailed results for the four time
periods which are No.2 time period on December 27, 2020, No.4 and No.5 time periods on December 31,
2020, and No.9 time period on January 29, 2022. Results shown from Figure 3 to Figure 8 for each of thefour time periods contain the following three graphs.
1) Two Line charts: Above line chart in Figure 3 (a) to Figure 8 (a) shows the time series of wind velocity,and major wind direction. Below line chart in Figure 3 (a) to Figure 8 (a) shows the time series ofprecipitation intensity and value of the WIPS. The large value of the vertical axis shows strong windvelocity and large precipitation intensity. It should be noted that large value of the WIPS means poor
visibility condition. Red points on line of value of the WIPS show the worse visibility conditions.
2) Still images: To understand actual visibility condition on the road, Figure 3 (b) to Figure 8 (b) show
still images captured by the CCTV camera corresponded to red points on line chart of value of the WIPS.
Red boxed frame on the still image shows a two-dimensional image of 256×256 pixels cropped from/still
image.
3) Bar charts: Bar charts shown in Figure 4 (c) to Figure 8 (c) are results of cross-correlation between
precipitation intensity and value of the WIPS. The vertical axis shows the cross-correlation, the horizontal
axis shows time lag. The white bar shown in Figure 4 (c) to Figure 8 (c) was the highest cross-correlation.
No 2. Time period on December 27, 2020
In this time period, there were slight precipitation intensity and strong wind. At K.P. 67.8, timeseries of precipitation intensity was almost 0.0 (mm/h). Visibility conditions from four still images at No.1
point to No.4 point in Figure 3 (a) were good shown in Figure 3 (b). Time series of value of the WIPS wasflat and continuously showed clear visibility conditions.
At K.P. 72.0, from 11:00 to 12:30, precipitation intensity was under 0.5 (mm/h). Visibility
condition of still image at No.2 point in Figure 4 (a) was slightly poor in Figure 4 (b). The white bar shownin Figure 4 (c) was the highest cross-correlation. The sign of the highest cross-correlation was negative.
The value of the WIPS moved in synchronization with precipitation intensity.

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Figure 3 Time series of weather and visibility conditions at K.P. 67.8 on December 27, 2020.

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Figure 4 Time series of weather and visibility conditions at K.P. 72.0 on December 27, 2020.

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No.4 and No. 5 time periods on December 31, 2020 at K.P.67.8
In No.4 time period, wind velocity wasn’t strong and wind direction was South. At K.P. 67.8, from12:00 to 13:20, precipitation intensity was almost 0.0 (mm/h). The time series of value of the WIPS wasflat and showed clear visibility conditions. In No.5 time period, wind velocity increased in two times andwind direction changed from West.
From 13:30 to 14:00, precipitation intensity was about 1.5 (mm/h). Visibility condition of still
image at No.2 point in Figure 5 (a) was very poor in Figure 5 (b). From 14:00 to 15:00, the time series ofprecipitation intensity and value of the WIPS didn’t move in synchronization. At 14:30, precipitation
intensity was almost 0.0 (mm/h), the value of the WIPS was 6.0. Visibility condition of the still image at
No.3 point was very poor in Figure 5 (b). Wind velocity was over 12.0 (m/s). There were much fresh snow
particles on the ground. Poor visibility occurred due to snowstorm. From 13:10 to 14:10, the sign of thehighest cross-correlation was negative, and time lag was 5.0 (min.). The value of the WIPS moved insynchronization with precipitation intensity.
From 15:00 to 15:30, precipitation intensity increased again, and its value was 1.5 (mm/h).
Visibility condition of the still image at No.4 point in Figure 5 (a) was poor in Figure 5 (b). Poor visibility
occurred due to snowstorm. The cross-correlation was shown in Figure 5 (c). White bar shown in Figure
5 (c) was the highest cross-correlation. From 14:40 to 15:40, the sign of cross-correlation value was positive.
The value of the WIPS did not move in synchronization with precipitation intensity.
No.4 and No. 5 time periods on December 31, 2020 at K.P.72.0
In No.4 time period, wind velocity wasn’t strong and wind direction was South. At K.P. 72.0, from13:00 to 13:30, precipitation intensity increased and its value was about 1.5 (mm/h). The time series ofvalue of the WIPS was flat and showed clear visibility conditions. Visibility condition of the still image at
No.1 point in Figure 6 (a) was good in Figure 6 (b). From 12:45 to 13:45, the sign of the highest crosscorrelation was negative, and time lag was 25.0 (min.).
In No.5 time period, wind velocity increased until around 14.0 (m/s) and wind direction changed
from West. From 13:30 to 14:30, precipitation intensity decreased, and its value was under 0.5 (mm/h). At
13:45, precipitation intensity was almost 0.5 (mm/h), the value of the WIPS was 6.6. At 14:30, precipitation
intensity was almost 0.3 (mm/h), the value of the WIPS was 4.3. Visibility conditions of still images at
No.2 point and No.3 point in Figure 6 (a) were very poor in Figure 6 (b). Wind velocity was over 12.0(m/s). There were much fresh snow particles on the ground. Poor visibility occurred due to snowstorm.
From 14:30 to 15:30, precipitation intensity increased again, and its value was almost 0.5 (mm/h).
Visibility condition of the still image at No.4 point in Figure 6 (a) was poor in Figure 6 (b). Poor visibility
occurred due to snowstorm. After 15:30, precipitation intensity decreased and its value was almost 0.0(mm/h). The value of the WIPS moved in synchronization with precipitation intensity. From 14:20 to 15:20,
the sign of the highest cross-correlation was negative, and time lag was 0.0 (min.).

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Figure 5 time series of weather and visibility conditions at K.P. 67.8 on December 31, 2020.

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Figure 6 Time series of weather and visibility conditions at K.P. 72.0 on December 31, 2020.

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No.9 time period on January 29, 2022.
In No.9 time period, wind velocity was slightly strong and wind direction was West. At K.P. 67.8,
from 12:30 to 14:30, precipitation intensity increased and its value was 2.0 (mm/h). Wind velocity wasslightly strong, and wind direction was West. Visibility conditions of still images at No.2 point and No.4
point in Figure 7 (a) were poor in Figure 7 (b). Poor visibility occurred due to snowfall. The highest crosscorrelation was 0.7, and time lag was 0.0 (min.) in Figure 7 (c). From 12:30 to 13:30, and from 13:35 to
14:35, the sign of the highest cross-correlation was negative and large. Time lag was 5.0 (min.). Value ofthe WIPS moved in synchronization with precipitation intensity.
At K.P. 72.0, from 12:00 to 13:00, precipitation intensity increased and its value was about1.0(mm/h). From 12:00 to 13:00, wind velocity was slightly strong, and wind direction was West. Visibility
condition of the still image at No.2 point in Figure 8 (a) was poor in Figure 8 (b). From 12:00 to 13:00,
the highest cross-correlation was 0.8. However, time lag was 15.0 (min.) in Figure 8 (c). From 12:30 to
13:00, precipitation intensity decreased and wind velocity increased. Poor visibility occurred due tosnowstorm. From 13:00 to 14:00, precipitation intensity increased again and its value was about 1.0 (mm/h).
From 13:00 to 14:00, wind velocity was slightly strong, and wind direction was West. Visibility conditionsof still images at No.3 point and No.4 point in Figure 8 (a) were very poor in Figure 8 (b). From 13:00 to
14:00, the highest cross-correlation was 0.4 and small. From 13:30 to 14:00, precipitation intensity
decreased and its value was 0.5 (mm/h). Wind velocity was strong. Poor visibility occurred due tosnowstorm.

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Figure 7 Time series of weather and visibility conditions at K.P. 67.8 on January 29, 2022.

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Figure 8 Time series of weather and visibility conditions at K.P. 72.0 on January 29, 2022.

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RESULTS OF CROSS-CORRELATION
We calculated the cross-correlation and time lag between two time series variables, precipitation
intensity and value of the WIPS. Results of 11 time periods could be classified into three categories
depending on the cross-correlation value and time lag. Table 2 shows the number of time period in Table
1, time period of calculating cross-correlation, the highest cross-correlation, time lag, and Category at K.P.
67.8. Table 3 shows the number of time period in Table 1, time period of calculating cross-correlation, thehighest cross-correlation, time lag, and Category at K.P. 72.0.
In Category-1, precipitation intensity was almost 0.0 (mm/h) and visibility condition on the roadwas good. In Category-2, value of the WIPS moved in synchronization with precipitation intensity. Crosscorrelation was larger than 0.4, and time lag between two time series variables was relatively small. In
Category-3, the relationship between precipitation intensity and value of the WIPS were complex. The signof cross-correlation was positive, cross-correlation was weak less than 0.4, and time lag between two timeseries variables was large (over 20.0 (min.)).
Category-1 shows a time period that value of the WIPS shows good visibility under none
precipitation intensity. It should be noted that snowstorm on the road might occur poor under none
precipitation if there are fine and fresh snow particles on the ground around the CCTV location. No.2 time
period is classified into Category-1. Wind velocity was over 8.0 (m/s) and there was little precipitation
intensity during No.2 time period. The time series of value of the WIPS shown good visibility conditions
during No.2 time period. In this case, snow particles did not pile on the ground due to strong wind from theprevious day. As a result, on December 27, snowstorm didn't occur despite strong wind.
Cetegory-2 shows a time period that value of the WIPS shows poor visibility under precipitation
intensity. Major wind direction was West. Time lag between precipitation intensity and value of the WIPS
was relatively small. The sign of the highest cross-correlation was negative. Value of the WIPS moved insynchronization with precipitation intensity. In Category-2, there are two causes of poor visibility under thesimilar cross-correlation. One is poor visibility due to snowstorm. Under poor visibility due to snowstorm,
time lag was ranged from 5.0 to 15.0 (min.). Poor visibility affected by not only precipitation but strong
wind from West. The other is poor visibility due to snowfall. Under poor visibility due to snowfall, timelags were ranged from 0.0 to 5.0 (min.). Wind velocity was weak, and major wind direction wasn't West. It
was considered snowfall made visibility worse directly.
Category-3 shows a time period that value of the WIPS did not move in synchronization with
precipitation intensity. One of the reasons is that snowstorm due to strong wind didn’t occur when winddirection wasn’t West. Geographical features of the road affected occurrence of poor visibility along theroad (15). East side of NR232 is high cliff. Then, wind from east side becomes weak. Another reason isthat wind velocity and wind direction were not stable, visibility on the road has been affected by them andbecome unstable. In these cases, as a result, cross-correlation was positive and weak. Also, time lag between
two time series variables tends to large.

Nakamura, Hagiwara, Nagata, Takahashi, Tsurumaki and Matsuoka
1
2

TABLE 2 Results of Cross-correlation and Time Lag. [K.P. 67.8]
K.P. 67.8
DD/MM/YY
Dec. 26, 2020.

Dec. 27, 2020.

No.2

Dec. 29, 2020.

No.3

Dec. 31, 2020.

Dec. 28, 2021.

Dec. 30, 2021.

Jan. 29, 2022.

Feb. 3, 2022.
3

Time Period
in TABLE 1
No.1

Time
Period
12:00-13:00

The Highest
Cross-correlation-0.85

Time lag(min.)
5

Category

12:15-13:15

-0.77

5

2

12:50-13:50

-0.67

5

2

15:00-16:00

-0.73

0

2

2

No precipitation
10:00-11:00

-0.55

5

2

10:30-11:30

-0.79

5

2

No.4

13:10-14:10

-0.62

5

2

No.5

14:40-15:40

0.50

20

3

No.6

10:20-11:20

0.46

20

3

No.7

10:45-11:45

0.54

20

3

No.8

8:00-9:00

-0.38

30

3

8:30-9:30

-0.32

5

3

11:00-12:00

-0.71

5

2

12:30-13:30

-0.71

0

2

13:35-14:35

-0.81

5

2

No.10

12:20-13:20

-0.80

0

2

No.11

14:40-15:40

0.20

0

3

No.9

Nakamura, Hagiwara, Nagata, Takahashi, Tsurumaki and Matsuoka
1

TABLE 3 Results of Cross-correlation and Time Lag. [K.P. 72.0]
K.P. 72.0
DD/MM/YY
Dec. 26, 2020.

Time
Period
12:10-13:10

The Highest
Cross-correlation-0.80

Time lag(min.)
10

Category

12:50-13:50

-0.49

5

2

14:50-15:50

-0.61

10

2

2

Dec. 27, 2020.

No.2

11:30-12:30

-0.76

10

2

Dec. 29, 2020.

No.3

10:00-11:00

-0.67

5

2

11:00-12:00

-0.79

5

2

No.4

12:45-13:45

-0.78

25

3

No.5

14:20-15:20

-0.68

0

2

Dec. 28, 2021.

No.6

10:10-11:10

-0.68

10

2

Dec. 30, 2021.

No.8

8:30-9:30

-0.71

0

2

Jan. 29, 2022.

No.9

12:00-13:00

-0.83

15

2

13:20-14:20

-0.40

10

2

No.10

12:00-13:00

-0.57

10

2

No.11

14:40-15:40

-0.77

0

2

Dec. 31, 2020.

Feb. 3, 2022.
2

Time Period
in TABLE 1
No.1

Nakamura, Hagiwara, Nagata, Takahashi, Tsurumaki and Matsuoka
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25

DISCUSSIONIn Category-3, at K.P. 72.0 on December 31, 2020, time lag was negative and large. At 13:00,
precipitation intensity was almost 0.0 (mm/h), wind velocity was 4.0 (m/s), and wind direction was SouthSoutheast. From 13:00 to 13:15, precipitation intensity increased rapidly. However, at 13:15, visibility
condition from the still image was clear despite precipitation intensity was large. After that, wind directionchanged to West-Northwest, and wind became strong, its velocity increased 6.0 to 14.0 (m/s). The strong
wind from West-Northwest occurred and visibility became poor on the road shown in Figure 6 (a) and
Figure 6 (b).
At 13:30, snowstorm occurred on the ground. From 13:30 to 14:00, visibility became poor despite
precipitation intensity decreased. It is required to know the reasons why the time lag was large in case of
No.4 time period on December 31, 2020. We considered effects of wind-drift on snow particles in the air.
Steven A. Lack. et al. mentioned to the effect of wind-drift on radar-derived surface rainfall estimations(16). Six 1-kilometer square grid meshes shown in Figure 8 located in North-West from the square gridmesh including K.P. 72.0 on NR 232. These 1-kirometer square grid mesh are painted by green. Figure 9(a) shows the time series of precipitation intensity on six 1-kilometer square grid meshes and value of the
WIPS. Figure 9 (b) shows the time series of precipitation intensity on two 1-kilometer square grid meshes,
which are 66414588 square grid mesh above K.P. 72.0 and 66415514 square grid mesh. Time lag between
precipitation intensity at 66415514 and value of the WIPS was 10.0 (min.). Under strong wind, snow
particles did not fall into the road directly below.
It is supposed that snow particles are falling on the road around K.P.72 might be coming from 5kilometer approximately in the afternoon on December 31, 2020 according to Figure 9 (b). It might beconsidered that time series of precipitation intensity should be measured on upstream 1-kirometer squaregrid mesh reflected by wind velocity and wind direction. When we catch visibility conditions on the roadby precipitation intensity by XRAIN in winter, it is required to adjust location of the 1-kirometer squaregrid mesh according to wind velocity and wind direction.

Nakamura, Hagiwara, Nagata, Takahashi, Tsurumaki and Matsuoka

1
2
3

Figure 8 Map of the 1-kilometer square grid mesh West-Northwest side from K.P. 72.0.

Nakamura, Hagiwara, Nagata, Takahashi, Tsurumaki and Matsuoka

1
2
3

Figure 9 Time series of precipitation intensity at six 1-kirometer grid meshes on December 31, 2020.

Nakamura, Hagiwara, Nagata, Takahashi, Tsurumaki and Matsuoka
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25

CONCLUSIONSThe present study examined relationship between precipitation intensity measured by XRAIN andvalue of the WIPS using images recorded by the CCTV cameras along the road. As a result, we found casesthat value of the WIPS moved in synchronization with precipitation intensity. Value of the WIPS changed
with time lag (from 5 to 10 minutes) in response to precipitation intensity. The situations that result in theseoutcomes are limited to when the maximum precipitation intensity was between 0.5 and 2.0 (mm/h), windvelocity was not so strong, and wind direction was West. In these cases, visibility conditions on the road in
winter could be estimated by precipitation intensity by XRAIN. Meanwhile, in several other cases, valueof the WIPS did not move in synchronization with precipitation intensity. One case was discussed, whenwind velocity was too strong, it is required to adjust location of the 1-kirometer square grid mesh according
to wind velocity and wind direction. Then, for estimating visibility conditions on the road in winter, as
future research, we will propose a model that should integrate precipitation intensity by XRAIN, windvelocity, wind direction, volume of snow particles on the ground, and so on.
ACKNOWLEDGMENTS
The present study, we used "the XRAIN CXMP composite dataset" provided by the Ministry ofLand, Infrastructure, Transport and Tourism of Japan (MLIT). This dataset was provided by Data
Integration and Analysis System (DIAS). DIAS was developed and operated under the auspices of the
Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT).
AUTHOR CONTRIBUTIONS
The authors confirm contribution to the paper as follows: study conception and design: Y. Nakamura, T.
Hagiwara; collection still images captured by CCTV cameras: Y. Nagata, S. Takahashi; interpretation of
weather conditions: R. Tsurumaki, N. Matsuoka. All authors reviewed the results and approved the final
version of the manuscript.

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