Investigation of Road Visibility Conditions
using Precipitation Intensity measured
by Weather Radar during Winter Seasons
September 5th, 2023
○Yuki NAKAMURA [Hokkaido university] (Corresponding author)○Toru HAGIWARA [Hokkaido university]○Sho TAKAHASHI [Hokkaido university] (Presenter)○Yasuhiro NAGATA [Hokkaido Development Engineering Center]○Ryoichi TSURUMAKI [Hokkaido Weather Technology Center]○Naoki MATSUOKA [Hokkaido Weather Technology Center]1IntroductionIn Hokkaido, Japan, poor road visibilityoccurs during winter season.
Hokkaido

The risk of traffic restriction and crashincreases. [1], [2]Poor road visibility suddenly occurs andchanges rapidly.
Quantitative evaluation is difficult.

Clear visibility condition

Korea
Tokyo
Osaka

Poor road visibility condition[1] Kajiya, Y., Kaneda, Y. and Tanji, K.: Factors Inducing Multi-vehicular Collisions During Visibility Reduced by Blowing snow, Transportation Research Record: Journal of the Transportation
Research Board, Vol. 1745, pp. 61-66, 2001.
[2] Matsuzawa, M., Kajiya, Y. and Takeuchi, M.: The development and validation of a method to estimate visibility during snowfall and blowing snow, Cold Regions Science and Technology, Vol.
41, Issue 2, pp. 91-109, 2005.

Introduction

2

By utilizing the image/signal processing technologies such as value of the Weighted
Intensity of Power Spectra (WIPS),
visibility inspection system was developed. [3]Visibility based on WIPS is measured from the Closed-circuit Television (CCTV)
Camera, actually. [4]－ For improve －By using snowfall information, the realization of more accurate/effective evaluation inreal-time is expected.
Precipitation intensity by weather radar might be available as snowfall information.
XRAIN (Japanese weather radar by MLIT[5]) is available to observe precipitation withhigh spatio-temporal resolution: 250-meter square grid mesh, every 1-minutes[3] Nagata, Y., Hagiwara, T., Kaneda, Y., Araki, K. and Murakami, K. (2006) Simple way to use closed-circuit television road images for poor-visibility information. Transportation Research
Record: Journal of the Transportation Research Board, Vol.1980, pp. 105-116.
[4] Hagiwara, T., Ota, Y., Kaneda, Y. and Araki, K. (2006) Method of processing closed-circuit television digital images for poor visibility identification. Transportation Research Record: Journal ofthe Transportation Research Board, Vol.1973, pp. 95-104.
[5] Kawasaki, A., Yamamoto, A., Koudelova, P., Acierto, R. A., Nemoto, T., Kitsuregawa, M., and Koike, T. (2017) Data Integration and Analysis System (DIAS) Contributing to Climate Change
Analysis and Disaster Risk Reduction. Data Science Journal, 16: 41, pp. 1-17.

3

ObjectiveTo verify the applicability of precipitation intensity by XRAIN as snowfall informationfor poor road visibility evaluation.
- The relationship between precipitation intensity and value of the WIPS isinvestigated by utilizing cross-correlation.
Considering snowstorm due to strong wind, the weather information by AMeDAS areused as support data for understanding and consideration.
Snow cloud

Precipitation intensity(mm/h)
Weather data(AMeDAS)

Snowfall information along the road
CCTVcamera

Poor roadvisibility

Snowstorm

Still images
on the serverTransmit video images toroad maintenance office

Fiber-optic network
for road management

Collecting road visibility informationfrom CCTV camera

SnowfallWeather radar(XRAIN)

4

Investigate Road Section and Weather Condition
MM/DD/YY

Average
Average
No. Time Period Temperature wind velocity
(℃)
(m/s)

Major
wind direction

Dec. 26, 2020.

1

12:00-16:00

-5.7

10.5

West-Northwest

Dec. 27, 2020.

2

11:00-15:00

-4.5

8.0

West or West-Northwest

West roadside：ocean

Dec. 29, 2020.

3

10:00-14:00

-5.4

9.7

West

East roadside：high cliff

4

12:00-13:30

-7.2

3.3

South-Southeast

Dec. 31, 2020.
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.

911:00-15:00

-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

Investigate Road Section (22 km)
 NR232 [K.P. 56.0～K.P. 78.0]

 Snowstorm tend to occur
by West wind.

Dec. 28, 2021.

Weather condition (11 time periods)
 Major wind direction: West Wind velocity: over 6.0（m/s）

Feb. 3, 2022.

Wakkanai

HaboroSyosanbetsu town

SapporoHaboro town

5

Obtained data: Precipitation intensity by XRAIN
Elevation angle (Hakodake Radar) In Hokkaido, elevation angle in summer
and winter are different.
 From November 2021, winter elevation
angle is adopted for observation.

Precipitation intensity by XRAIN
 We used precipitation intensity per 1kilometer square grid mesh.
 We adopted average 10-minute valueof it at the current time.

Reflection of snowfall in the air Altitude of Weather radar：1,149(m) Distance (Haboro⇔Hakodake)：60（km）
 Height for observing snow clouds:
700～1,600(m) Falling velocity (snow particles):
1.0～4.0 (m/s)
Angle of Elevation

 Until snowfall reflected on the ground,time lag might be occur.
5 ～15 minute

Altitude

Height from Groundθ0

θ1

Hakodake：1,129(m)1,251(m)：0.1°

0.1°

0.4°

Weather Radar：
1,149(m)1,569(m)：0.4°(-0.4°)(0.1°)

729(m)：-0.4°

Obtained data: Visibility Information – CCTV camera images

6Still images captured by the CCTV cameraIn the 22-km/section, there are seven CCTV cameras. (SD quality: 720×480)
CCTV captures still image every 5-minutes.
We used still images captured by two CCTV cameras (K.P. 67.8 and K.P. 72.0) forroad visibility evaluation.
Other five cameras sometimes occurred heavy adhesion of snow/ice, and changed
the shooting direction in winter season.

CCTV camera (K.P. 67.8)

CCTV camera (K.P. 72.0)

Specification of CCTV camera
Image Sensor
Valid Pixels
Definition
S/N
Minimum Illuminance Level

Veneer CCD (Color)
About 380,000 pixels or more
Horizontal: 480 or more
47dB or more
Electronic Sensitivity OFF: 0.4 lux or lessElectronic Sensitivity ON: 0.007 lux or less

Camera Case

Installed Wiper and DefrosterObtained data: Visibility Information – WIPS values

7

Value of the WIPSWIPS is image/signal processing-based criterion.
WIPS represents road visibility condition.
Clear road visibility: Large value
Poor road visibility: Small value

Value of the WIPS focused on subjective visibility by human eyes.
It doesn’t classify the factors of poor road visibility. (such as dense or fog)

WIPS is actually used for measuring road management as visibility assessment value.

Related data: Weather Information
Weather data by Haboro AMeDAS
Haboro AMeDAS is located near Haboro town.
(AMeDAS: Automated Meteorological Data Acquisition System)

We collected followings (every 10-minutes):Temperature
Wind velocity and direction

89Evaluation method: Cross-Correlation
between Precipitation Intensity (XRAIN) and Value of the WIPS
Calculation and unit of time intervalCross-correlation calculated from equation (1).
Value of the WIPS as 𝑦 and precipitation intensity as 𝑥 .

Sign of cross-correlation was negative:
WIPS changes with a time lag in response to changes in precipitation intensity.
Unit of time interval: 5-minute.

Covariance𝑥𝑥̅𝑦Calculate𝑦𝐶 𝜏𝑥̅𝑦PI WIPSPI WIPS

1x1

y1

x1

y1x1

y1

11:40

2x2

y2x2

y2x2

y2

11:45

3x3

y3x3

y3x3

y3

11:50

4x4

y4x4

y4x4

y4

y5(1)
𝑥Time N(i) PI WIPS
11:35

𝑦〜

…

…

…

12:30 12 x12

y12x12

12:35 13 x13

y13x13y5…

…

…
x12y13x13

y13

Slide the data in 5-minute.

Standard deviation

𝐶 𝜏 ：Cross-correlation，𝜏：Time lag (min.)，𝑁：Number of data（𝑖 1, … , 𝑁），
𝑥 ：Precipitation intensity (mm/h)，𝑦 ：Value of the WIPS，𝑥̅ ：Average value of the precipitation intensity，𝑦：Average value of the WIPS

ResultCross-correlation

 This case shows WIPS changes with a time lag (5-minute) in response to changes in precipitation
intensity.
τ(min.)
0 (min.)5 (min.)10 (min.)
1.0
0.5

CC-1(11:35-12:35)

0.0
-0.5
-1.0
-60 -50 -40 -30 -20 -100Time lag (min.)
The highest cross-correlation
was about -0.80.
(Time lag was 5 minute)10

Results of Cross-Correlation: Three categories
Results of 11 time periods could be classified into three categories depending on
the cross-correlation value and time lag.
Category 1 and Category 2[Category-1]
Precipitation intensity:almost 0.0 (mm/h)
Clear road visibility condition[Category-2]
Visibility and weather were related.
The sign of cross-correlation:
Negative
The highest Cross-correlation value:Larger than 0.4
Time lag was relatively small.

K.P. 67.8
DateTime Period
No.

Time Period
12:00-13:00

Dec. 26, 2020.

No. 1

The Highest
Time lag
CategoryCross-Correlation(min.)-0.79
52

12:15-13:15

-0.74

52

12:55-13:55-0.64

02

14:55-15:55

-0.74

02

No precipitation

Dec. 27, 2020.

No. 2

Dec. 29, 2020.

No. 3

10:00-11:00

-0.57

52

No. 4 and No. 5

13:10-14:10

-0.52

10

2

No. 5

14:40-15:40

0.50

25

3

No. 6

10:25-11:25

0.47

10

3

No. 6 and No. 7

10:45-11:45

-0.60

02

8:00-9:00

-0.35

35

3

8:25-9:25

0.34

35

3

11:00-12:00

-0.68

10

2

12:25-13:25

-0.75

02

13:35-14:35-0.78

52No. 10

12:20-13:20

-0.70

52

No. 11

14:35-15:35

0.53

03

Dec. 31, 2020.

Dec. 28, 2021.

Dec. 30, 2021.

Jan. 29, 2022.

Feb. 3, 2022.

No. 8

No. 91

11

Results of Cross-Correlation: Three categories
Results of 11 time periods could be classified into three categories depending on
the cross-correlation value and time lag.
Category 3
Visibility and weather were complex.

K.P. 72.0
Date Advection of snow particle

The Highest
Time lag
Category
Cross-Correlation(min.)-0.80
52

12:55-13:55

-0.59

02

14:55-15:55

-0.72

5

2

11:35-12:35

-0.84

52

10:00-11:00

-0.71

02

11:05-12:05-0.72

02No. 4

12:55-13:55-0.81

153No. 5

14:35-15:35

0.59

25

3

Dec. 28, 2021.

No. 6

10:15-11:15

0.60

103

Dec. 30, 2021.

No. 8

8:50-9:50

-0.70

02

11:00-12:00

-0.84

102

Jan. 29, 2022.

No. 9

12:25-13:25-0.79

102

13:35-14:35

-0.59

02

No. 10

12:20-13:20

-0.71

02

No. 11

14:35-15:35

0.47

153

No. 1

Dec. 27, 2020.

No. 2

Dec. 29, 2020.

No. 3

Dec. 31, 2020.

- Falling speed and non-vertical fall
 Time lag occur until snowfall

Time Period12:15-13:15

Dec. 26, 2020.

The sign of cross-correlation:
PositiveTime lag was large.
Over 15.0 (min.)[Cause of these results]

Time PeriodNo.

Feb. 3, 2022.

reflected on the ground. [6][6] Derin, Y., Anagnostou, E., Anagnostou, M. and Kalogi-ros, J.: Evaluation of X-Band Dual-Polarization
Radar-Rainfall Estimates from OLYMPEX, Journal of Hydro-meteorology, Vol. 20 (9), pp. 1941-1959, 2019.

Results of comparing with XRAIN, WIPS and Weather data: 12Category 1 [No.2 time period]
Dec. 27, 2020. [K.P. 67.8][Category-1]
 Precipitation intensity was slight.
Almost 0.0 (mm/h)

 Wind velocity was strong.
Around 8.0 (m/s)

 In the day time, there was a low
pressure around Hokkaido.
 Poor road visibility might be occur due
to snowstorm.
 However, snowstorm didn’t occur.
 Clear road visibility conditions
 Value of the WIPS and precipitation
intensity were related.

Clear road visibility

Results of comparing with XRAIN, WIPS and Weather data: 13
Category 2 [No.9 time period]
Jan. 29, 2022. [K.P. 67.8]

24[Category-2]
 Low pressure around Hokkaido.
 Precipitation intensity increased.
 Poor road visibility occurred.
[Two cross-correlations] The sign of cross-correlation:
Negative The highest value:Large Time lag:Small (0.0 or 5.0 (min.)) Value of the WIPS and precipitation
intensity were related.
 Poor road visibility due to snowfall.

0 min

5 min

Results of comparing with XRAIN, WIPS and Weather data: 14
Category 3 [No.4 and No.5 time periods]
Dec. 31, 2020. [K.P. 72.0]

13[Category-3]
 Low pressure around Hokkaido.
 Interval of isobars was narrow.
[13:00 – 14:00]
 Precipitation intensity increased.
However, clear road visibility conditions.

 After, precipitation intensity decreased.
Poor road visibility due to strong wind.

[14:30 – 15:30]
 Precipitation intensity increased again.
From 14:30, poor road visibility continued.

 Visibility and XRAIN were different.
Visibility changed due
caused by strong wind.

tosnowstorm

The possibilities of the advection ofsnow particle is expected.

15 minPositive sign

Conclusions

15Objectives of the present studyTo verify the applicability of precipitation intensity by XRAIN as snowfall information
for poor road visibility evaluation.
- The relationship between precipitation intensity and value of the WIPS isinvestigated by utilizing cross-correlation.
Results in Category-1 and Category-2
 Both conditions (visibility and weather) were related.
 Applicability of road visibility estimation by precipitation intensity was indicated.
Results in Category-3
 Both conditions were different.
 Under strong wind, we should consider the advection of snow particles in the air.
－ For future works and to develop road monitoring system －In order to use the precipitation intensity by XRAIN as snowfall information forevaluating snow condition, followings should be considered.
 Accuracy of snow particle observation by weather radar
 Falling velocity and advection of snow particles

Faculty of Engineering, Hokkaido University
Sho Takahashi
Tel: +81-11-706-6215
E-mail: stakahashi@eng.hokudai.ac.jp

1Outline
 Introduction
 Objectives
 Investigate Road Section and Time Period
 Observation data
 Cross-correlation
 Results of Cross-Correlation [Category 1 to Category 3]
 Conclusion