Table:
Name

Dates

Peak intensity

Areas affected

Damage
(USD)

Deaths

Refs

Category

Wind speed

Pressure

01W
May 25 – 27
Tropical depression
Not specified
1000 hPa (29.53 inHg)
South China
$8.7 million
None
[21]

TD
June 22 – 23
Tropical depression
55 km/h (35 mph)
1006 hPa (29.71 inHg)
Vietnam
None
None

Ambo
June 25 – 28
Tropical depression
55 km/h (35 mph)
1004 hPa (29.65 inHg)
Philippines, South China
None
None

Nepartak (Butchoy)
July 2 – 10
Typhoon
205 km/h (125 mph)
900 hPa (26.58 inHg)
Philippines, Ryukyu Islands
Taiwan, East China
$1.89 billion
111
[376][21]

03W
July 15 – 20
Tropical depression
Not specified
1006 hPa (29.71 inHg)
Ryukyu Islands
None
None

Lupit
July 22 – 24
Tropical storm
75 km/h (45 mph)
1000 hPa (29.53 inHg)
None
None
None

Mirinae
July 25 – 28
Severe tropical storm
100 km/h (65 mph)
980 hPa (28.94 inHg)
South China, Indochina
$346 million
7
[61][21]

Nida (Carina)
July 29 – August 3
Severe tropical storm
110 km/h (70 mph)
975 hPa (28.79 inHg)
Philippines, Taiwan
South China, Vietnam
$316 million
6
[61][21][377]

Omais
August 2 – 9
Severe tropical storm
110 km/h (70 mph)
975 hPa (28.79 inHg)
Mariana Islands, Japan, Russia
None
None

TD
August 6 – 9
Tropical depression
Not specified
998 hPa (29.47 inHg)
Ryukyu Islands, East China
None
None

Conson
August 7 – 14
Tropical storm
85 km/h (50 mph)
985 hPa (29.09 inHg)
Japan, Russia
None
None

TD
August 10 – 13
Tropical depression
Not specified
996 hPa (29.41 inHg)
Ryukyu Islands, Taiwan, East China
None
None

TD
August 10 – 12
Tropical depression
Not specified
1010 hPa (29.83 inHg)
Midway Atoll
None
None

Chanthu
August 12 – 17
Severe tropical storm
100 km/h (65 mph)
980 hPa (28.94 inHg)
Japan, Russian Far East
$94.7 million
None

TD
August 12 – 13
Tropical depression
Not specified
1002 hPa (29.59 inHg)
Taiwan
None
None

Dianmu
August 15 – 19
Tropical storm
75 km/h (45 mph)
980 hPa (28.94 inHg)
South China, Indochina, Bangladesh, India
$481 million
22
[378][21]

TD
August 15 – 16
Tropical depression
Not specified
996 hPa (29.41 inHg)
South China, Vietnam
None
None

Mindulle
August 17 – 23
Typhoon
120 km/h (75 mph)
975 hPa (28.79 inHg)
Mariana Islands, Japan
$448 million
2
[377]

Lionrock (Dindo)
August 17 – 30
Typhoon
165 km/h (105 mph)
940 hPa (27.76 inHg)
Japan, Russia,
Northeast China, North Korea
$3.84 billion
550
[379][376]

Kompasu
August 18 – 21
Tropical storm
65 km/h (40 mph)
994 hPa (29.35 inHg)
Japan, Russian Far East
None
1

TD
August 23 – 24
Tropical depression
Not specified
1000 hPa (29.53 inHg)
Philippines
None
None

14W
August 24
Tropical depression
55 km/h (35 mph)
1002 hPa (29.59 inHg)
Mariana Islands
None
None

TD
August 24
Tropical depression
Not specified
1000 hPa (29.53 inHg)
None
None
None

TD
August 27
Tropical depression
Not specified
1004 hPa (29.65 inHg)
None
None
None

TD
August 30 – 31
Tropical depression
Not specified
1004 hPa (29.65 inHg)
None
None
None

Namtheun (Enteng)
August 31 –
September 5
Typhoon
130 km/h (80 mph)
955 hPa (28.20 inHg)
Taiwan, South Korea, Japan
None
None

Malou
September 5 – 7
Tropical storm
75 km/h (45 mph)
1000 hPa (29.53 inHg)
Japan
None
None

TD
September 7 – 8
Tropical depression
Not specified
998 hPa (29.47 inHg)
Japan
None
None

Meranti (Ferdie)
September 8 – 16
Typhoon
220 km/h (140 mph)
890 hPa (26.28 inHg)
Mariana Islands, Philippines,
Taiwan, China, South Korea
$4.79 billion
47
[380][381][21]

TD
September 9 – 10
Tropical depression
Not specified
1008 hPa (29.77 inHg)
None
None
None

TD
September 10
Tropical depression
Not specified
1008 hPa (29.77 inHg)
Taiwan
None
None

17W
September 10 – 12
Tropical depression
55 km/h (35 mph)
1000 hPa (29.53 inHg)
None
None
None

Rai
September 11 – 13
Tropical storm
65 km/h (40 mph)
996 hPa (29.41 inHg)
Indochina
$37 million
12

Malakas (Gener)
September 11 – 20
Typhoon
175 km/h (110 mph)
930 hPa (27.46 inHg)
Mariana Islands, Taiwan, Japan
$300 million
2
[382][383]

Megi (Helen)
September 22 – 29
Typhoon
155 km/h (100 mph)
945 hPa (27.91 inHg)
Caroline Islands, Ryukyu Islands,
Taiwan, China
$1.56 billion
52
[384][21]

Chaba (Igme)
September 24 – October 5
Typhoon
215 km/h (130 mph)
905 hPa (26.73 inHg)
Mariana Islands, Japan, South Korea, Russian Far East
$2.61 billion
10
[385][386]

Songda
October 4 – 13
Typhoon
185 km/h (115 mph)
925 hPa (27.32 inHg)
Pacific Northwest
None
None

Aere (Julian)
October 4 – 14
Severe tropical storm
110 km/h (70 mph)
975 hPa (28.79 inHg)
Philippines, Taiwan, South China, Indochina
$112 million
35
[15]

Sarika (Karen)
October 13 – 19
Typhoon
175 km/h (110 mph)
935 hPa (27.61 inHg)
Philippines, South China, Vietnam
$866 million
37
[387][21][388][389]

Haima (Lawin)
October 14 – 21
Typhoon
215 km/h (130 mph)
900 hPa (26.58 inHg)
Caroline Islands, Philippines,
Taiwan, China, Japan
$972 million
19
[390][391][392][21]

TD
October 15
Tropical depression
Not specified
1008 hPa (29.77 inHg)
None
None
None

Meari
October 30 – November 7
Typhoon
140 km/h (80 mph)
960 hPa (28.35 inHg)
Mariana Islands
None
None

TD
October 31
Tropical depression
Not specified
1008 hPa (29.47 inHg)
Mariana Islands
None
None

TD
November 1 – 5
Tropical depression
55 km/h (35 mph)
998 hPa (29.47 inHg)
None
None
None

TD
November 2 – 6
Tropical depression
55 km/h (35 mph)
1004 hPa (29.65 inHg)
Borneo, Indochina
$48.1 million
15
[302][393]

Ma-on
November 8 – 13
Tropical storm
65 km/h (40 mph)
1002 hPa (29.59 inHg)
None
None
None

28W
November 9 – 12
Tropical depression
Not specified
1008 hPa (29.77 inHg)
Marshall Islands
None
None

Tokage (Marce)
November 24 – 28
Severe tropical storm
95 km/h (60 mph)
992 hPa (29.29 inHg)
Philippines, Vietnam
$30,000
1
[394]

TD
December 10 – 13
Tropical depression
55 km/h (35 mph)
1004 hPa (29.65 inHg)
Vietnam
$53.4 million
30
[395][367]

Nock-ten (Nina)
December 20 – 28
Typhoon
195 km/h (120 mph)
915 hPa (27.02 inHg)
Caroline Islands, Philippines, Vietnam
$123 million
13
[396]

TD
December 27
Tropical depression
Not specified
1004 hPa (29.65 inHg)
None
None
None

Season aggregates

51 systems
May 25 –
December 28

220 km/h (140 mph)
890 hPa (26.28 inHg)

$18.9 billion
972

Given a table image and a set of target cells, your task is to identify their positions. Represent each cell's location as JSON in the format {'value': '<cell value>', 'location': (row index, column index)}, with both row and column indices begin from 1.
Refer to the values below:
(1) 'Deaths'
(2) 'Philippines, South China'
(3) '52'

If a cell does not exist in the table, denote its location as 'DOES NOT EXIST'. 

The coordinates of target cells are listed below.
(1) {'value': 'Deaths', 'location': (1, 8)}
(2) {'value': 'Philippines, South China', 'location': (4, 6)}
(3) {'value': '52', 'location': (36, 8)}