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
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
in 925hPa temperature
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
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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)
50 High probability 21 24
25 Possible occurrence 20 23
33℃(Be Aware)
50 High probability 20 23
25 Possible occurrence 18 22
31℃(No Severe Weather)
50 High probability 18 22
25 Possible occurrence 16 20
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
in 925hPa temperature
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
in 850hPa temperature
(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)
In Busan, Ulsan and Gyeongnam
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
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20180704
20180707
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20180827
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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
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20180721
20180723
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20180731
20180802
20180804
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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
In Busan, Ulsan and Gyeongnam
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)
In Busan, Ulsan and Gyeongnam
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)
In Busan, Ulsan and Gyeongnam
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