Miyeong Jo , Kim Juyeong, Kim Yujeong, Haemi Nohi, …1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 f h e date 가축폐사 일최고기온

Fig. 11 The stational distribution of cold-realated patients in the last 6 years(2013~2018year)

In Busan, Ulsan and Gyeongnam

According to the IPCC report, greenhouse gas emissions continue to increase, resulting in frequent extremeweather phenomena worldwide. In particular, heat waves are often strong during extreme weather events. In Korea,the heat wave has been included in natural disasters since 2018 as not only social damage but also humancasualties have occurred. In this study, weather observation data related to the summer(June to August) heat wave inBusan, Ulsan and Gyeongsangnam-do were analyzed to identify the weather conditions for the heat wave. Inaddition, the effects of heat wave by sector were analyzed in relation to the heat wave impact forecast currentlybeing implemented by the Korea Meteorological Administration. Meanwhile, from 2018, cold waves will also beincluded in natural disasters, and research will be needed to match local characteristics. Weather conditions of coldwave occurrence were identified by dividing cold file water in Busan, Ulsan, and Gyeongsangnam-do into threetemperature ranges depending on the time of increase of cold-related patient. In preparation for the cold waveimpact forecast service that will take effect in December, the government plans to investigate cases of damagefrom cold wave in different areas and analyze the impact from each vulnerable area to set the critical values forcold wave impact forecast in Busan, Ulsan and Gyeongsangnam-do.

Introduction

Miyeong Jo , Kim Juyeong, Kim Yujeong, Haemi Nohi, Jaedong Jang

Forecast Division in Busan Regional Office of Meteorology

[email protected]

It shows twice the tendency of the outbreak of cold patients. 1st soaring section: 5~7℃ 2st soaring section: 0℃

This study analyzed the weather conditions of heat and cold weather in Busan, Ulsan and Gyeongsangnam-do using high-rise temperature, ground temperature, wind direction, and wind speed. The heat wave appeared to be related to the temperature of 21 KST at 850 hPa and the correlation between the temperature of 15 KST at 925 hPa and the maximum temperature(Changnyeong) at R2=0.42 and R2=0.61, respectively. The risk levels of the heat wave impact forecast for each sector were about the same based on the health sector, but they differed by 1 to 2℃. Cold waves appeared to be linked a day earlier with the correlation between 21 KST temperatures of 850 hPa and 925 hPa and the lowest temperature(Geochang) of R2=0.42 and R2=0.56, respectively. The possibility of cold wave was high when wind direction was close to north wind and wind speed was weak. Cold-related patient numbers surged when the cold spell lasted 10 days in a row. Cold-related patients tended to develop in Ulsan and Gyeongsangnam-do from 0℃ and in Busan from -2℃.

Result

y = 0.6092x + 23.773

R² = 0.4238

30

32

34

36

38

40

10 15 20 25 30

Th

e m

axim

um

tem

per

atu

re

in 850hPa temperature

y = 0.7693x + 17.642

R² = 0.6092

30

32

34

36

38

40

10 12 14 16 18 20 22 24 26 28 30

Th

e m

axim

um

tem

per

atu

re


Correlation Analysis between High-Rise Temperature and Ground Maximum Temperature

(a) (b)

Weather Conditions in areas with frequent heat and cold waves in Busan, Ulsan and Gyeongsangnam-do

Fig. 1 The maximum temperature correlation. (a) in 850hPa, 21KST, (b) in 925hPa, 15KST

850hPa Tem. – Ground Max. Tem.– 21KST > 15KST > 09KST

925hPa Tem. – Ground Max. Tem.– 15KST > 21KST > 09KST

30

28

26

24

22

20

10

12

14

16

18

850hPa(09KST)) 850hPa(15KST)) 850hPa(21KST)) 925hPa(09KST)) 925hPa(15KST)) 925hPa(21KST)

tem

pera

ture

30

28

26

24

22

20

10

12

14

16

18

850hPa(09KST) 850hPa(15KST) 850hPa(21KST) 925hPa(09KST) 925hPa(15KST) 925hPa(21KST)

30

28

26

24

22

20

10

12

14

16

18

30

28

26

24

22

20

10

12

14

16

18

850hPa(09KST)) 850hPa(15KST)) 850hPa(21KST)) 925hPa(09KST)) 925hPa(15KST)) 925hPa(21KST) 850hPa(09KST) 850hPa(15KST) 850hPa(21KST) 925hPa(09KST) 925hPa(15KST) 925hPa(21KST)

Fig. 2 The case Analysis of the upper temperature in 850hPa and 925hPa) in Changwon about the maximum temperature

(( a) 31℃, (b) 33℃, (c) 35℃, (d) 38℃) in Changnyeong

(a) (b)

(c) (d)

TheMaximum

Temperature(Risk Level)

Critical Value(%ile)

Impact850hPa Tem.(℃)

925hPaTem.(℃)

38℃(Take Action)

50 High probability 22 26

25 Possible occurrence 21 25

35℃(Be

Prepared)



33℃(Be Aware)



31℃(No Severe Weather)



Table. 1 Risk table of heat wave levels

tem

pera

ture

y = 0.7767x + 3.5351

R² = 0.5565

-20

-15

-10

-5

0

5

10

15

20

-20 -10 0 10 20

min

imu

m t

emp

eratu

re


y = 0.7483x + 0.3601

R² = 0.416

-25

-20

-15

-10

-5

0

5

10

15

-20 -10 0 10 20

min

imu

m t

emp

eratu

re


(a) (b) Correlation Analysis between High-Rise

Temperature and Ground Maximum Temperature

850hPa Tem. – Ground Max. Tem. – Day before 21KST > 09KST > 21KST

925hPa Tem. – Ground Max. Tem.– Day before 21KST > 09KST > 21KST

Fig. 3 Correlation with minimum temperature

(a) in 850hPa and the previous day 21KST, (b) in 925hPa and the previous day 21KST

tem

pera

ture

tem

pera

ture

The LowestTemperature

Critical Value(%ile)

ImpactDay before

850hPa Tem.(℃)

Day before925hPa Tem.(℃)

6℃50%ile High probability -4 -0.7

25%ile Possible occurrence -0.4 1.6

0℃50%ile High probability -4.2 0.8

25%ile Possible occurrence -0.7 1.6

-12℃50%ile High probability -10.9 -9.1

25%ile Possible occurrence -7.1 -5.1

Fig. 4 Geochang Correlation with minimum temperature

(a) in 850hPa and the previous day 21KST, (b) in 925hPa and the previous day 21KST

Fig. 5 Geochang windrose of temperature

(a)below 6℃, (b) below0℃ and, (c) below -12 ℃

Analysis of wind direction and wind speed characteristics by minimum temperature range Less than 6℃, Less than 0℃(fig 12 reference)

– NNW > W > NW

Less than -12℃(special weather report)

– N > NNW > NNE

Analysis of heat wave and cold wave characteristics Analysis of the Temperature Correlation between High-Rise Observation Data and Ground Meteorological

Observation Data in the Last 4 Years(2015-2018)

[High-rise] Temperature data from Changwon point 925hPa, 850hPa(09, 15, 21KST)

[ground] maximum temperature at Changnyeong point, lowest temperature at Geochang point, wind direction

and wind speed(excluding measured value)

Analysis of the impact of Heat Wave Vulnerabilities Analysis of impact by sector(health, livestock, fisheries, agriculture and industry) in the last 7 years(2012-2018)

risk level of heat wave impact forecast

[No Severe Weather] 31℃, [Be Aware] 33℃, [Be Prepared] 35℃ [Take Action] 38℃

the Number of heat-related patient, the number of livestock died, heat damage to fish-farming, heat damage by

crops

(a) (b)

The stronger the cold wave, the weaker the wind speed

0

10

20

30

40

50

60

70

80

90

100

0

50

100

150

200

250

300

350

400

450

500

20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Cum

ula

tive p

erc

enta

ge

Num

ber

of

warm

-heate

d p

atients

The maximum temperature

온열질환자수 누적백분율(%)

The number of heat-related patients Cumulative percentage

Cumulative Percentage of Heat-related Patients by Maximum Temperature A tendency to increase rapidly in four sections

- 31℃(cumulative 10%), 33℃(cumulative 20%),35℃(cumulative 50%), 37℃(cumulative 90%)

Same as the risk level for heat wave impact forecast except for the ‘Take Action‘

Fig. 6 The number of patients with the maximum temperature

of each day for 7 years(2012~2018year) in Busan, Ulsan and Gyeongnam

Fig. 8 The number of domestic animals death and with the maximum temperature

of each day in the last 7 years(2012~2018year)

0

10

20

30

40

50

60

70

80

90

100

0

50000

100000

150000

200000

250000

300000

20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Cum

ula

tive p

erc

enta

ge

Dom

est

ic a

nim

als

death

The maximum temperature

가축폐사수 누적백분율(%)

Cumulative Percentage of number of livestock deaths by Maximum Temperature Similar trend to the field of health- 30℃(cumulative 5%), 33℃(cumulative 20%),

35℃(cumulative 50%), 36℃(cumulative 80%) Compared to Forecast of heat wave impact

Standard temperature by risk level, ‘No Severe Weather’is 1 degree lower and ‘Take Action’ is 2 degree lower

[The field of health]

[The field of livestock] 0

20

40

60

80

100

0

5

10

15

20

25

30

35

40

45

15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -13 -14 -15

Cum

ula

tive p

erc

enta

ge

Cold

-rela

ted p

atient

The minimum temperature

한랭질환자수 누적백분율(%)

Fig. 12 The number of cold-related patients by temperature for the last 6 years(2013~2018year)


0

10

20

30

40

50

60

70

80

90

100

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Cum

ula

tive p

erc

enta

ge

Cold

-rela

ted p

atient

The minimum temperature

평균 한랭질환자수 누적백분율(%)

Fig. 13 The number of cold-related patients and number of files(below 0℃)

in the past 6 years(2013~2018year) In Busan, Ulsan and GyeongnamFig. 14 critical temperature for cold-related patients in Busan, Ulsan and Gyeongnam

Maximum temperature(Busan)

-15

Maximum temperature(Ulsan) Maximum tempurature(Gyeongnam)

-13

-11

-9

-7

-5

-3

-1

1

3

5

7

9

11

13

15

tem

pera

ture

The number of cold disease patients according to the duration of the cold wave

When the number of cold files continued for 10 days in a row, the number of cold disease increased rapidly

Minimum temperature threshold of cold disease outbreak(25%ile) Gyeongnam, Ulsan: 0℃ Busan: -2℃

0

5

10

15

20

25

30

35

40

45

0

5

10

15

20

25

30

35

40

20180601

20180604

20180607

20180610

20180613

20180616

20180619

20180622

20180625

20180628

20180701

20180704

20180707

20180710

20180713

20180716

20180719

20180722

20180725

20180728

20180731

20180803

20180806

20180809

20180812

20180815

20180818

20180821

20180824

20180827

20180830

20180902

20180905

20180908

20180911

20180914

20180917

20180920

20180923

20180926

20180929

Num

ber

of

warm

-rela

ted p

atients

Continuous

day o

f heatw

ave

Date

온열질환자수

일최고기온

연속(31)

연속(33)

연속(35)

연속(38)

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

0

5

10

15

20

25

30

35

40

20180601

20180603

20180605

20180607

20180609

20180611

20180613

20180615

20180617

20180619

20180621

20180623

20180625

20180627

20180629

20180701

20180703

20180705

20180707

20180709

20180711

20180713

20180715

20180717

20180719

20180721

20180723

20180725

20180727

20180729

20180731

20180802

20180804

20180806

20180808

20180810

20180812

20180814

20180816

20180818

20180820

20180822

20180824

20180826

20180828

20180830

Dom

est

ic a

nim

als

death

Continuous

days

of

heatw

ave

date

가축폐사

일최고기온

연속(31)

연속(33)

연속(35)

연속(38)

Fig. 7 The number of heat-related patients during continuous days of heatwave in 2018


Fig. 9 The number of domestic animals death during continuous days of heatwave

in 2018 in Busan, Ulsan and Gyeongnam

Risk Level 기준

관심 Daily maximum temperature 31 or more for 3 consecutive days

주의 Daily maximum temperature 33 or more for 2 consecutive days

경고 Daily maximum temperature 35 or more for 2 consecutive days

위험 Daily maximum temperature 38 or more for 2 consecutive days

table. 3 Criteria for determination of heat wave impact prediction by risk level

21

46

1

22

12

1 1

33

14

50

12

8

1 12 2

12

1

0

10

20

30

40

50

60

Cold

-rela

ted p

atient

Stational distribution

47

81

7269

80

39

0

10

20

30

40

50

60

70

80

90

2013 2014 2015 2016 2017 2018

Cold

-reala

ted

patient

year

Fig. 10 The annual number of cold-related patients for the last 6 years (2013~2018year)


Table. 2 Risk table of cold wave levels

1st surge

2nd surge

3rd surge

4th surge

1st surge2nd surge

3rd surge

4th surge

0도 이하 영상 6도 이하 영하 12도 이하0도 이하 영상 6도 이하 영하 12도 이하-20

-10

0

10

-15

-10

0

10

5

-5

-5

-15

15

Data and Method

(c)

The relationship between the number of consecutive heat waves and the occurrence of heat-related diseases Highly correlated with the incidence of

thermal illness when consecutive days over 35 degrees are continuous

The relationship between the number ofconsecutive heat waves and the death of livestock It tends to increase when consecutive days over

35 degrees are continuous, but it is difficult tofind distinct features.

1st surge

2nd surge

1st surge

Average cold disease Cumulative percentage(%)

Average cold disease Cumulative percentage(%)

Fig. 11 The stational distribution of cold-realated patients in the last 6 years(2013~2018year)


Thermal illness

Maximum temperature

Thermal illness

Maximum temperature

31(continuity)

33(continuity)

35(continuity)

38(continuity)

31(continuity)

33(continuity)

35(continuity)

38(continuity)

Livestock died

Analysis of the Heat Wave Impact in Busan, Ulsan and Gyeongsangnam-do

Analysis of the Cold Wave Impact in Busan, Ulsan and Gyeongsangnam-do

Documents

Miyeong Jo , Kim Juyeong, Kim Yujeong, Haemi Nohi, …1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 f h e date 가축폐사 일최고기온