HIGH DENSITY SURFACE OBSERVATION NETWORK

FOR LOCALIZED HAZARD DETECTION

AND EARLY WARNING

Bayu Rakhmadyah, Hirotaka Kure, Hisato Iwashita, Shinya Kojima,

Toshiyuki Tsutsui, Takuya Yada, Kae Sato, Koutaro Tsunoda

Meteorological and Disaster Prevention System Headquarters,

Meisei Electric co., ltd., Japan

E-mail: (rbayu, kureh, iwashitah, kojimash, tsutsuit, yadat, satok,

tsunodak)@meisei.co.jp

Abstract and Keywords

Localized weather hazard has been increased recently around the world

and thus early warning system to reduce its extensive losses and damages is

essential. Since the scale of localized weather hazard is relatively smaller

than common reported disasters such as typhoon or tsunami, the most

common used system, AWS network, has a disadvantage in terms of spatial

distribution. This study propose high-density surface observation network as

a solution to provide useful information for early warning system.

POTEKA network, which is high accuracy, low cost, and compact stations

combined with internet services, is installed to monitor weather condition in

Kanto Area. Torrential rain in 2015 and two downbursts in 2015 and 2016

are among the localized weather events captured by POTEKA, which are

also examined in this study.

The results show that high-density surface observation network such as

POTEKA can help in capturing localized heavy rain earlier than AWS and

also detecting propagation of downburst.

Keywords: downburst, torrential rain, compact weather station,

high-density surface observation network, early warning system

Introduction

Recently, localized weather hazard has been increasing all over the world.

Therefore, there is urgency in establishing early warning system to reduce

damages and losses. However, this issue is often given less attention.

Discussed in recent UNISDR publication on disaster risk (2015),

localized hazardis categorized into extensive risk due its high-frequency and

low-severity. It is stated in the report, that in most cases, extensive risk is

excluded from the global risk modelling and report.

The other reason why localized hazard is often overlooked is because it is

relatively smaller in scale compared to typhoons, tsunamis, or other

commonly reported hazards. In many cases, localized weather hazard can

range less than 20km. However, despite this, the damages can increase if

there are factors such as poverty, vulnerable environments, etc. (UNISDR,

2015)

In Japan, current weather system utilizes 1500 AWSs installed in 15 to

20-km interval. Thus, there is a high possibility that the existing system did

not capture small scale extreme weather in Japan. This can cause the risk of

localized weather hazard not fully understood and lead to greater losses in

the future.

Norose et al. (2016) has mentioned the importance of surface weather

monitoring in understanding mechanism of downburst, which is one of small

scale weather events. However, to the authors’ best knowledge, only few

publications can be found discussing high-density surface weather

observation network and how to use its information for disaster early

warning.

This paper will discuss Japan’s localized weather events captured by

high-density surface weather network through several case studies. The case

studies comprised of one torrential rain and two downburst events occurred

in Kanto Area in recent years. Furthermore, the contribution of high-density

surface observation network to localized hazard detection and early warning

will also be evaluated.

High-density Surface Observation Network “POTEKA”

Data for this work is collected from POTEKA network, which is a

densely-distributed weather network consisted of compact weather stations

and cloud services developed by Meisei Electric co., ltd. There are 189

POTEKA weather stations installed in Kanto Area (figure 1).

Each of POTEKA station is equipped with seven types of embedded

sensors (temperature, humidity, air pressure, wind speed, wind direction,

rain detector, and solar radiation) and a separate precipitation sensor (figure

2). These weather elements data is collected every one second, averaged

every 60 seconds, and sent to cloud server in one-minute interval. Data

produced from each POTEKA station is uploaded to POTEKANET website

and is downloadable through any device.

Figure 1. Distribution of POTEKA stations in Kanto Area (excluding islands)

The specification of POTEKA station is as shown in table 1 below.

Temperature, humidity, air pressure, wind speed and direction, solar

radiation, rain detection, and precipitation sensors are certified by Japan

Meteorological Agency (henceforth will be referred as JMA).

The reproducibility of two POTEKAs is as shown in figure 3. The average

reproducibility of temperature, humidity, and air pressure are 0.15ºC, 0.21%,

and 0.10hPa respectively.

Figure 2. POTEKA station, sensor unit, and system. POTEKA station is

designed for high-density surface weather observation. Each station sends

weather information to cloud server through cellular or LAN.

Table 1 Specification of POTEKA station

Figure 3. Reproducibility of temperature, humidity, and air pressure sensor

in two POTEKA stations measuring at the same site for one day.

POTEKA Utilization for Localized Weather Hazard Observation

Flood following heavy rain occurred in around Moriya City, Ibaraki

Prefecture, on 9 September 2015. Approximately 10km to the North of

Moriya City is Joso city, which was hit by unprecedented severe flooding,

resulted in rescue operation of about 100 people.

Figure 4. Rainfall of AWS (A1, A2 etc.) and high-density weather network

POTEKA (P1, P2 etc.) around Moriya City in two different times. Water

mark indicates location of flood. Both figures show significantly higher

rainfall in area uncovered by AWSs which are also indicating localized

torrential rain.

Nine POTEKA stations were installed in Moriya City, surrounded by four

AWSs in four directions. Data of POTEKA installed in the middle of those

AWSs showing indication of localized torrential rain in that area as shown in

figure 4.

According to the figure, in two different times on that day, POTEKA

recorded significantly higher rainfall compared to AWS nearby. At 16:25 JST,

one of POTEKA station recorded 22.0mm/h of rainfall, while the highest

rainfall recorded by nearby AWS was 10.0mm/h. Meanwhile, at 19:10 JST,

POTEKA recorded rainfall up to 35.5mm/h, while the highest rainfall

recorded by surrounding POTEKA was 22.5 mm/h. In this figure, at both

times, three of AWSs (A2, A3, and A4) show small amount of rain which was

less than 10mm/h.

Figure 5. Rainfall in area surrounded by four AWSs in Moriya city (see also

figure 4). High-density weather network POTEKA installed in between

AWSs captured rainfall exceeding 20mm per hour and 30mm per hour up to

1hour 28 minutes earlier than AWS.

The time series of rainfall in all stations is shown in figure 5. From this

figure, one of POTEKA station already detected 20.0mm/h rainfall 55

minutes before nearest AWS, also 30.0mm/h rainfall 1 hour and 28 minutes

in advance. These findings indicate that high-density surface observation

network is effective in capturing real time and pinpoint torrential rain

compared to AWS which is distributed sparsely.

POTEKA Utilization for Localized Weather Hazard Early Warning

In this paper, two occurrences of downburst will be discussed. Figure 6

shows the occurrence of two downbursts, nearby stations, and other stations

which will be used as comparisons. Hereinafter, Area A and B shown in this

figure will be used as references for damaged area by the downbursts.

Downburst Hazard on 15 June 2015

According to JMA report, downburst occurred in several places in Gunma

Prefecture, which are Shibukawa city, Maebashi city, and Isesaki city, on 15

June 2015 from around 15:17 to 19:21 JST. The damage scale caused by the

downburst was reported to be F1 scale (Fujita scale).

Several witness reported arrival of strong winds in Maebashi city at

around 16:00 to 16:15 local time. According to the same report mentioned

before, the damaged area is approximated to be 18km long and 8km wide.

Roofs were blown away and vinyl houses for agriculture were deformed by

the downburst (figure 7).

Figure 6. Map of damaged area by downburst on June 2015 (A) and July

2016 (B). Numbers indicate POTEKA stations. The nearest station of

location A is station 5, while the nearest station of location B is station 6.

Figure 7. Fallen electrical pole (left) and damaged agricultural vinyl houses

(right) caused by downburst.

Figure 8 shows the temperature and air pressure change around

damaged area A through time. According to this figure, when area A was

30ºC in temperature and 1005hPa in pressure, cold air with temperature of

19ºC along with 1008hPa air pressure emerged on the northern part and

moved southward to area A. This abnormality in temperature can be

observed from station 1 which is located approximately 18km away from

station 5 (near area A) 35 minutes before the hazard time.

Figure 8. Temperature and air pressure change through time. Red shows

higher temperature. Numbers in the map indicate POTEKA stations, while

arrows represent wind speed and direction. Bold lines with pressure value

show air pressure. Hazard area A is marked with tornado sign, nearby

station number 5. Cold air moves southward to area A.

Figure 9.Recorded temperature and air pressure data of station 1, 2, 5, and 7

from 15:00 to 17:30 JST on 15 June 2015.Station 5 was the nearest station

from hazard area. Around the estimated time of event, temperature and air

pressure changed abruptly.

The data generated by POTEKA stations are reported in figure 9. In this

figure, stations passed by propagating cold air (shown in figure 8), which are

station 1, 2, and 7, are included.

According to data of POTEKA station 5, before the estimated time of

hazard, temperature decreased rapidly from 15:55 until 16:03 local time. The

average and maximum decreasing rate were 1.4ºC per minute and 3.9ºC per

minute respectively. The total temperature decrease was 11.3ºC.

Two minutes after temperature began to decrease (15:57), air pressure

decreased and reached 1.5hPa in short period, which then followed by rapid

increase up to 4.3hPa in four minutes. The average and maximum increasing

rate were 0.2hPa per minute and 2.2hPa per minute respectively.

A closer look at the data of four stations indicates that abrupt decrease in

temperature and abrupt increase in air pressure happened interchangeably

started from station 1 to the south (station 7) which in between downburst

struck in area A (near station 5). However, in case of air pressure, it declined

gradually before increasing significantly in all station.

Downburst Hazard on 4 July 2016

On 4 July 2016, downburst occurred in Isesaki City, Gunma Prefecture

at around 16:10 local time. The scale of damage was reported to be F0 (Fujita

scale). Several parts of houses and a traffic sign were damaged as the results

(figure 10).

Figure 10. Broken wall (left) and deformed traffic sign (right) caused by

downburst.

Figure 11 shows the change of temperature and air pressure around

damaged area B through time. This time, cold air moved from the west to the

east, until eventually downburst struck in area B. The movement of cold air

can be seen starting at station 3 (approximately 15km from station 6 or

hazard area) from 15:45 local time.

Figure 12 shows data from all four stations shown in figure 11. From

graph of station 6, it can be seen that temperature decreased rapidly and fell

up to 9.9ºC around the estimated time of downburst. The average and

maximum decreasing rate in this station are 0.4ºC per minute and 1.1ºC,

respectively.

At the same graph, it can be seen that there was also rapid increase in

air pressure with two peaks, which are 3.7hPa and 4.8hPa from the

beginning of abrupt increase (16:04 JST). The average and maximum

decreasing rate recorded in this station are 0.2hPa per minute and 0.9hPa

per minute, respectively.

Different from previous case study, temperature change happened

interchangeably from station 3 to station 7, while air pressure did not show

the same pattern. However, the temperature and air pressure trend showing

in station 6 (near damaged area B) showed the same trend as previous case

study.

Figure 11. Temperature and air pressure change in elapsed time. Red shows

higher temperature. Numbers indicate POTEKA stations, while arrows

represent wind speed and direction. Bold lines with pressure value show air

pressure. Tornado mark indicates hazard area B, nearby station 6. Cold air

moves from the west towards hazard area in the east.

Figure 12.Recorded temperature and air pressure data of station 3, 4, 6, and

7 from 15:00 to 17:00 JST on 4 July 2015. Station 6 was the nearest station

from hazard area. Same as figure 9, around the estimated time of event,

temperature and air pressure changed abruptly.

Detection and Early Warning of Localized Extreme Weather

In terms of risk of heavy rain, JMA has classified rainfall and its

generating disaster. According to it, rainfall ranging from 20 to 30mm per

hour can cause overflowing of small rivers or sewages and small scale

landslide, while rainfall ranging from 30 to 50mm per hour can lead to

landslide and overflowing sewage in which evacuation preparation is needed.

The case studies in torrential rain in this paper show that in one day there

were two levels of flooding and landslide risk around study area which were

not immediately detected by AWS.

The result discussed before does not imply that 1 hour and 28 minutes

warning delay of 30mm/h rainfall has caused flooding in Moriya City. The

flooding in Moriya City and other areas in Ibaraki Prefecture was caused by

several days of unusual heavy rain. However, detection of localized heavy

rain can give a significant help in risk mapping, hence locating high flooding

risk or urgent evacuation places can be done more efficiently. In summary,

knowing pinpoint torrential rain will cause significant change in disaster

counter measure.

In terms of strong wind, POTEKA network has been capturing more than

four downbursts from 2013 till present. According to surface observation of

downburst by Sato et al. (2014) and Norose et al. (2016), temperature and air

pressure are among weather elements which are giving clear shifting during

the event. This coincides with results in this paper.

Moreover, Norose et al. (2016) showed that there were two peaks of air

pressure jump observed on 11 August 2013, on around 25 minutes before and

the time of downburst, with a dip in between the peaks. Results of the

downburst on 22 August 2014 discussed by Sato et al. (2014) showed that air

pressures of the stations around the damaged area were relatively steady

with insignificant fluctuation before pressure jump. By comparing those

results and results from this work, it can be concluded that air pressure

trend before time of downburst varies.

On the other hand, the results of this study show that temperature

declines gradually before temperature drop. This is consistent in all reported

downburst captured by POTEKA, including works of Sato et al. (2014) and

Norose et al. (2016).

First crucial thing in early warning system is the warning information

must be meaningful. In generating meaningful information, there should be

clear characteristic or boundary to prevent generation of false alarm. In this

case, air pressure trend before downburst does not provide clear

characteristic for downburst prediction. By contrast, temperature seems to

be more reliable information due to its behavior consistency before arrival of

downburst.

Second crucial thing in warning system is there should be enough time to

communicate the information to those who need it. In this work, the distance

between damaged area and the furthest station detecting temperature drop

is approximately 18km. For downburst early warning, installing weather

station 2 to 5-km interval will enables detection of spatial propagation of

downburst in clear and timely manner, based on temperature change.

Conclusions

This work has highlighted the importance of high-density surface

weather network in detecting localized weather hazard. High-density surface

observation network POTEKA, utilizing compact, certified accuracy, and low

cost weather stations as well as internet services, has been installed in

Kanto Area. Using data from this network, three case studies consisted of

localized torrential rain in Ibaraki Prefecture and two downbursts in Gunma

Prefecture are examined. As the results, POTEKA captured heavy rain up to

1 hour and 28 minutes before nearby AWS. Furthermore, the propagation of

downburst characterized by abrupt change in temperature and air pressure

are recorded POTEKA. The consistency of temperature change suggests that

temperature can be vital information in downburst early warning.

References

1. Norose K, Kobayashi F, Kure H, yada T, Iwasaki H. 2016: Observation of

Downburst Event in Gunma Prefecture on August 11, 2013 Using a

Surface Dense Observation Network. Journal of Atmospheric Electricity,

Vol.35, No.2, 2016, pp.31-41.

2. Sato K, Kure H, Yada T, Maeda R, Kojima H, Morita T, Iwasaki T. 2014:

Surface and Pressure Distributions of Downburst captured by surface

dense observation network POTEKA. Meteor. Soc. Japan. 2014 Spring

Meeting. 105. 223-224pp. (in Japanese)

3. UNISDR. 2015. Making Development Sustainable: The Future of

Disaster Risk Management.Global Assessment Report on Disaster Risk

Reduction 2015. Geneva, Switzerland: United Nations Office for Disaster

Risk Reduction (UNISDR).