Overview

Dataset statistics

Number of variables11
Number of observations98
Missing cells34
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.0 KiB
Average record size in memory94.3 B

Variable types

Categorical8
Numeric3

Dataset

DescriptionSample
Author해봄데이터㈜
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT02HBM001

Alerts

LW_WTEM_ISSUED_STEP_NM has constant value ""Constant
LW_WTEM_ISSUED_AREA_NM is highly overall correlated with LW_WTEM_ISSUED_SAR_NM and 3 other fieldsHigh correlation
LW_WTEM_ISSUED_SAR_NM is highly overall correlated with LW_WTEM_WTCH_YMD and 4 other fieldsHigh correlation
LW_WTEM_ISSUED_TM is highly overall correlated with LW_WTEM_ISSUED_AREA_NM and 5 other fieldsHigh correlation
LW_WTEM_ISSUED_YMD is highly overall correlated with LW_WTEM_WTCH_YMD and 4 other fieldsHigh correlation
LW_WTEM_OBVP_NM is highly overall correlated with LW_WTEM_WTCH_YMD and 4 other fieldsHigh correlation
LW_WTEM_ISSUED_SITTN_NM is highly overall correlated with LW_WTEM_ISSUED_TM and 1 other fieldsHigh correlation
THDT_LWET_WTEM is highly overall correlated with NMYR_WTEMHigh correlation
NMYR_WTEM is highly overall correlated with THDT_LWET_WTEMHigh correlation
LW_WTEM_WTCH_YMD is highly overall correlated with LW_WTEM_ISSUED_SAR_NM and 3 other fieldsHigh correlation
LW_WTEM_NBRK_DOC_NM is highly overall correlated with LW_WTEM_WTCH_YMD and 2 other fieldsHigh correlation
LW_WTEM_ISSUED_TM is highly imbalanced (72.1%)Imbalance
LW_WTEM_ISSUED_SITTN_NM is highly imbalanced (62.9%)Imbalance
NMYR_WTEM has 34 (34.7%) missing valuesMissing

Reproduction

Analysis started2024-03-13 12:52:43.191100
Analysis finished2024-03-13 12:52:45.959955
Duration2.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

LW_WTEM_ISSUED_AREA_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size916.0 B
충남
64 
전남
26 
경남
8 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충남
2nd row충남
3rd row충남
4th row충남
5th row충남

Common Values

ValueCountFrequency (%)
충남 64
65.3%
전남 26
26.5%
경남 8
 
8.2%

Length

2024-03-13T21:52:46.100048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:52:46.254623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충남 64
65.3%
전남 26
26.5%
경남 8
 
8.2%

LW_WTEM_ISSUED_SAR_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size916.0 B
가로림만
32 
천수만
32 
서남해
18 
가막만
8 
강진만,사천만
8 

Length

Max length7
Median length3
Mean length3.6530612
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가로림만
2nd row가로림만
3rd row가로림만
4th row가로림만
5th row가로림만

Common Values

ValueCountFrequency (%)
가로림만 32
32.7%
천수만 32
32.7%
서남해 18
18.4%
가막만 8
 
8.2%
강진만,사천만 8
 
8.2%

Length

2024-03-13T21:52:46.438654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:52:46.615953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가로림만 32
32.7%
천수만 32
32.7%
서남해 18
18.4%
가막만 8
 
8.2%
강진만,사천만 8
 
8.2%

LW_WTEM_OBVP_NM
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size916.0 B
서산 지곡
32 
서산 창리
16 
보령 효자
16 
신안 압해
9 
해남 화산
9 
Other values (2)
16 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서산 지곡
2nd row서산 지곡
3rd row서산 지곡
4th row서산 지곡
5th row서산 지곡

Common Values

ValueCountFrequency (%)
서산 지곡 32
32.7%
서산 창리 16
16.3%
보령 효자 16
16.3%
신안 압해 9
 
9.2%
해남 화산 9
 
9.2%
여수 신월 8
 
8.2%
남해 강진 8
 
8.2%

Length

2024-03-13T21:52:46.786296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:52:46.984010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서산 48
24.5%
지곡 32
16.3%
창리 16
 
8.2%
보령 16
 
8.2%
효자 16
 
8.2%
신안 9
 
4.6%
압해 9
 
4.6%
해남 9
 
4.6%
화산 9
 
4.6%
여수 8
 
4.1%
Other values (3) 24
12.2%

LW_WTEM_ISSUED_YMD
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size916.0 B
20180112
34 
20171214
32 
20180105
32 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20171214
2nd row20171214
3rd row20171214
4th row20171214
5th row20171214

Common Values

ValueCountFrequency (%)
20180112 34
34.7%
20171214 32
32.7%
20180105 32
32.7%

Length

2024-03-13T21:52:47.185682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:52:47.334537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20180112 34
34.7%
20171214 32
32.7%
20180105 32
32.7%

LW_WTEM_ISSUED_TM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size916.0 B
<NA>
91 
100000
 
4
110000
 
3

Length

Max length6
Median length4
Mean length4.1428571
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row110000
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 91
92.9%
100000 4
 
4.1%
110000 3
 
3.1%

Length

2024-03-13T21:52:47.505766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:52:47.664252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 91
92.9%
100000 4
 
4.1%
110000 3
 
3.1%

LW_WTEM_ISSUED_STEP_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
저수온 주의보
98 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row저수온 주의보
2nd row저수온 주의보
3rd row저수온 주의보
4th row저수온 주의보
5th row저수온 주의보

Common Values

ValueCountFrequency (%)
저수온 주의보 98
100.0%

Length

2024-03-13T21:52:47.807113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:52:47.952497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
저수온 98
50.0%
주의보 98
50.0%

LW_WTEM_ISSUED_SITTN_NM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
발령
91 
신규발령
 
7

Length

Max length4
Median length2
Mean length2.1428571
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신규발령
2nd row발령
3rd row발령
4th row발령
5th row발령

Common Values

ValueCountFrequency (%)
발령 91
92.9%
신규발령 7
 
7.1%

Length

2024-03-13T21:52:48.103589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:52:48.292454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
발령 91
92.9%
신규발령 7
 
7.1%

THDT_LWET_WTEM
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3183673
Minimum0.9
Maximum6.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.0 B
2024-03-13T21:52:48.497757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile2.54
Q13.725
median4.2
Q35.2
95-th percentile5.915
Maximum6.2
Range5.3
Interquartile range (IQR)1.475

Descriptive statistics

Standard deviation1.0845063
Coefficient of variation (CV)0.25113805
Kurtosis0.73454968
Mean4.3183673
Median Absolute Deviation (MAD)0.65
Skewness-0.57126524
Sum423.2
Variance1.176154
MonotonicityNot monotonic
2024-03-13T21:52:48.692036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3.9 8
 
8.2%
5.6 6
 
6.1%
4.2 6
 
6.1%
5.2 5
 
5.1%
4.1 5
 
5.1%
4.0 5
 
5.1%
4.3 4
 
4.1%
4.7 4
 
4.1%
3.7 4
 
4.1%
3.1 4
 
4.1%
Other values (28) 47
48.0%
ValueCountFrequency (%)
0.9 1
 
1.0%
1.2 1
 
1.0%
1.4 1
 
1.0%
2.1 1
 
1.0%
2.2 1
 
1.0%
2.6 1
 
1.0%
3.0 2
2.0%
3.1 4
4.1%
3.3 2
2.0%
3.4 2
2.0%
ValueCountFrequency (%)
6.2 1
 
1.0%
6.1 1
 
1.0%
6.0 3
3.1%
5.9 2
 
2.0%
5.8 2
 
2.0%
5.7 3
3.1%
5.6 6
6.1%
5.5 1
 
1.0%
5.4 1
 
1.0%
5.3 2
 
2.0%

NMYR_WTEM
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)39.1%
Missing34
Missing (%)34.7%
Infinite0
Infinite (%)0.0%
Mean5.478125
Minimum3.7
Maximum7.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.0 B
2024-03-13T21:52:48.851602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile4.015
Q14.4
median5.6
Q36.2
95-th percentile6.585
Maximum7.7
Range4
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation0.96961271
Coefficient of variation (CV)0.17699719
Kurtosis-0.45695807
Mean5.478125
Median Absolute Deviation (MAD)0.65
Skewness-0.059034948
Sum350.6
Variance0.94014881
MonotonicityNot monotonic
2024-03-13T21:52:49.049852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
6.3 6
 
6.1%
5.6 6
 
6.1%
4.2 6
 
6.1%
5.5 6
 
6.1%
6.2 5
 
5.1%
6.0 4
 
4.1%
5.9 3
 
3.1%
4.3 3
 
3.1%
4.1 2
 
2.0%
3.7 2
 
2.0%
Other values (15) 21
21.4%
(Missing) 34
34.7%
ValueCountFrequency (%)
3.7 2
 
2.0%
4.0 2
 
2.0%
4.1 2
 
2.0%
4.2 6
6.1%
4.3 3
3.1%
4.4 2
 
2.0%
4.6 1
 
1.0%
4.9 1
 
1.0%
5.0 1
 
1.0%
5.2 1
 
1.0%
ValueCountFrequency (%)
7.7 2
 
2.0%
7.5 1
 
1.0%
6.6 1
 
1.0%
6.5 1
 
1.0%
6.4 2
 
2.0%
6.3 6
6.1%
6.2 5
5.1%
6.1 1
 
1.0%
6.0 4
4.1%
5.9 3
3.1%

LW_WTEM_WTCH_YMD
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20178934
Minimum20171214
Maximum20180122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.0 B
2024-03-13T21:52:49.258051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20171214
5-th percentile20171220
Q120180107
median20180113
Q320180117
95-th percentile20180119
Maximum20180122
Range8908
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation3031.7678
Coefficient of variation (CV)0.0001502442
Kurtosis2.897742
Mean20178934
Median Absolute Deviation (MAD)4
Skewness-2.1997706
Sum1.9775355 × 109
Variance9191615.7
MonotonicityNot monotonic
2024-03-13T21:52:49.892086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20180114 7
 
7.1%
20180119 7
 
7.1%
20180118 7
 
7.1%
20180117 7
 
7.1%
20180116 7
 
7.1%
20180115 7
 
7.1%
20180113 7
 
7.1%
20180112 7
 
7.1%
20180122 5
 
5.1%
20180110 3
 
3.1%
Other values (21) 34
34.7%
ValueCountFrequency (%)
20171214 1
1.0%
20171215 1
1.0%
20171216 1
1.0%
20171218 1
1.0%
20171219 1
1.0%
20171220 1
1.0%
20171221 1
1.0%
20171222 1
1.0%
20171224 1
1.0%
20171226 2
2.0%
ValueCountFrequency (%)
20180122 5
5.1%
20180119 7
7.1%
20180118 7
7.1%
20180117 7
7.1%
20180116 7
7.1%
20180115 7
7.1%
20180114 7
7.1%
20180113 7
7.1%
20180112 7
7.1%
20180111 3
3.1%

LW_WTEM_NBRK_DOC_NM
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Memory size916.0 B
2018.01.19(제31호)
7 
2018.01.17(제29호)
7 
2018.01.16(제28호)
7 
2018.01.15(제27호)
7 
2018.01.14(제26호)
7 
Other values (27)
63 

Length

Max length16
Median length16
Mean length15.72449
Min length15

Unique

Unique16 ?
Unique (%)16.3%

Sample

1st row2017.12.14(제1호)
2nd row2017.12.15(제2호)
3rd row2017.12.16(제3호)
4th row2017.12.18(제4호)
5th row2017.12.19(제5호)

Common Values

ValueCountFrequency (%)
2018.01.19(제31호) 7
 
7.1%
2018.01.17(제29호) 7
 
7.1%
2018.01.16(제28호) 7
 
7.1%
2018.01.15(제27호) 7
 
7.1%
2018.01.14(제26호) 7
 
7.1%
2018.01.13(제25호) 7
 
7.1%
2018.01.12(제24호) 7
 
7.1%
2018.01.18(제30호) 7
 
7.1%
2018.01.22(제32호) 5
 
5.1%
2018.01.8(제20호) 3
 
3.1%
Other values (22) 34
34.7%

Length

2024-03-13T21:52:50.096424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018.01.19(제31호 7
 
7.1%
2018.01.16(제28호 7
 
7.1%
2018.01.15(제27호 7
 
7.1%
2018.01.14(제26호 7
 
7.1%
2018.01.13(제25호 7
 
7.1%
2018.01.12(제24호 7
 
7.1%
2018.01.18(제30호 7
 
7.1%
2018.01.17(제29호 7
 
7.1%
2018.01.22(제32호 5
 
5.1%
2018.01.9(제21호 3
 
3.1%
Other values (22) 34
34.7%

Interactions

2024-03-13T21:52:45.063676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:44.190929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:44.595114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:45.186654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:44.313092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:44.754550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:45.332393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:44.465058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:44.922483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:52:50.226694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LW_WTEM_ISSUED_AREA_NMLW_WTEM_ISSUED_SAR_NMLW_WTEM_OBVP_NMLW_WTEM_ISSUED_YMDLW_WTEM_ISSUED_TMLW_WTEM_ISSUED_SITTN_NMTHDT_LWET_WTEMNMYR_WTEMLW_WTEM_WTCH_YMDLW_WTEM_NBRK_DOC_NM
LW_WTEM_ISSUED_AREA_NM1.0001.0001.0000.9401.0000.0000.6690.7400.1440.000
LW_WTEM_ISSUED_SAR_NM1.0001.0001.0001.0001.0000.0000.5130.6520.4270.000
LW_WTEM_OBVP_NM1.0001.0001.0001.0001.0000.0000.5970.6520.4700.000
LW_WTEM_ISSUED_YMD0.9401.0001.0001.0001.0000.0000.7770.7670.3340.096
LW_WTEM_ISSUED_TM1.0001.0001.0001.0001.000NaN0.7740.000NaN1.000
LW_WTEM_ISSUED_SITTN_NM0.0000.0000.0000.000NaN1.0000.0000.0000.0000.820
THDT_LWET_WTEM0.6690.5130.5970.7770.7740.0001.0000.6640.1650.000
NMYR_WTEM0.7400.6520.6520.7670.0000.0000.6641.0000.5050.820
LW_WTEM_WTCH_YMD0.1440.4270.4700.334NaN0.0000.1650.5051.0001.000
LW_WTEM_NBRK_DOC_NM0.0000.0000.0000.0961.0000.8200.0000.8201.0001.000
2024-03-13T21:52:50.436378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LW_WTEM_ISSUED_AREA_NMLW_WTEM_ISSUED_SAR_NMLW_WTEM_ISSUED_TMLW_WTEM_ISSUED_YMDLW_WTEM_OBVP_NMLW_WTEM_NBRK_DOC_NMLW_WTEM_ISSUED_SITTN_NM
LW_WTEM_ISSUED_AREA_NM1.0000.9890.8940.7000.9790.0000.000
LW_WTEM_ISSUED_SAR_NM0.9891.0000.6320.9890.9890.0000.000
LW_WTEM_ISSUED_TM0.8940.6321.0000.8941.0000.8941.000
LW_WTEM_ISSUED_YMD0.7000.9890.8941.0000.9790.0000.000
LW_WTEM_OBVP_NM0.9790.9891.0000.9791.0000.0000.000
LW_WTEM_NBRK_DOC_NM0.0000.0000.8940.0000.0001.0000.563
LW_WTEM_ISSUED_SITTN_NM0.0000.0001.0000.0000.0000.5631.000
2024-03-13T21:52:50.693038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
THDT_LWET_WTEMNMYR_WTEMLW_WTEM_WTCH_YMDLW_WTEM_ISSUED_AREA_NMLW_WTEM_ISSUED_SAR_NMLW_WTEM_OBVP_NMLW_WTEM_ISSUED_YMDLW_WTEM_ISSUED_TMLW_WTEM_ISSUED_SITTN_NMLW_WTEM_NBRK_DOC_NM
THDT_LWET_WTEM1.0000.5650.2260.3680.3190.3640.4660.3650.0000.000
NMYR_WTEM0.5651.000-0.3500.3560.4400.4400.4600.0000.0000.336
LW_WTEM_WTCH_YMD0.226-0.3501.0000.2480.5260.5060.5460.0000.0000.829
LW_WTEM_ISSUED_AREA_NM0.3680.3560.2481.0000.9890.9790.7000.8940.0000.000
LW_WTEM_ISSUED_SAR_NM0.3190.4400.5260.9891.0000.9890.9890.6320.0000.000
LW_WTEM_OBVP_NM0.3640.4400.5060.9790.9891.0000.9791.0000.0000.000
LW_WTEM_ISSUED_YMD0.4660.4600.5460.7000.9890.9791.0000.8940.0000.000
LW_WTEM_ISSUED_TM0.3650.0000.0000.8940.6321.0000.8941.0001.0000.894
LW_WTEM_ISSUED_SITTN_NM0.0000.0000.0000.0000.0000.0000.0001.0001.0000.563
LW_WTEM_NBRK_DOC_NM0.0000.3360.8290.0000.0000.0000.0000.8940.5631.000

Missing values

2024-03-13T21:52:45.538306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:52:45.812163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LW_WTEM_ISSUED_AREA_NMLW_WTEM_ISSUED_SAR_NMLW_WTEM_OBVP_NMLW_WTEM_ISSUED_YMDLW_WTEM_ISSUED_TMLW_WTEM_ISSUED_STEP_NMLW_WTEM_ISSUED_SITTN_NMTHDT_LWET_WTEMNMYR_WTEMLW_WTEM_WTCH_YMDLW_WTEM_NBRK_DOC_NM
0충남가로림만서산 지곡20171214110000저수온 주의보신규발령3.97.7201712142017.12.14(제1호)
1충남가로림만서산 지곡20171214<NA>저수온 주의보발령4.27.7201712152017.12.15(제2호)
2충남가로림만서산 지곡20171214<NA>저수온 주의보발령4.07.5201712162017.12.16(제3호)
3충남가로림만서산 지곡20171214<NA>저수온 주의보발령4.16.4201712182017.12.18(제4호)
4충남가로림만서산 지곡20171214<NA>저수온 주의보발령4.16.2201712192017.12.19(제5호)
5충남가로림만서산 지곡20171214<NA>저수온 주의보발령4.16.0201712202017.12.20(제6호)
6충남가로림만서산 지곡20171214<NA>저수온 주의보발령4.36.6201712212017.12.21(제7호)
7충남가로림만서산 지곡20171214<NA>저수온 주의보발령4.76.5201712222017.12.22(제8호)
8충남가로림만서산 지곡20171214<NA>저수온 주의보발령5.66.3201712242017.12.24(제9호)
9충남가로림만서산 지곡20171214<NA>저수온 주의보발령4.65.9201712262017.12.26(제10호)
LW_WTEM_ISSUED_AREA_NMLW_WTEM_ISSUED_SAR_NMLW_WTEM_OBVP_NMLW_WTEM_ISSUED_YMDLW_WTEM_ISSUED_TMLW_WTEM_ISSUED_STEP_NMLW_WTEM_ISSUED_SITTN_NMTHDT_LWET_WTEMNMYR_WTEMLW_WTEM_WTCH_YMDLW_WTEM_NBRK_DOC_NM
88충남천수만보령 효자20180105<NA>저수온 주의보발령4.85.2201801192018.01.19(제31호)
89전남서남해신안 압해20180112<NA>저수온 주의보발령5.8<NA>201801192018.01.19(제31호)
90전남서남해해남 화산20180112<NA>저수온 주의보발령6.0<NA>201801192018.01.19(제31호)
91전남가막만여수 신월20180112<NA>저수온 주의보발령5.75.6201801192018.01.19(제31호)
92경남강진만,사천만남해 강진20180112<NA>저수온 주의보발령6.26.0201801192018.01.19(제31호)
93충남가로림만서산 지곡20171214<NA>저수온 주의보발령3.73.7201801222018.01.22(제32호)
94충남천수만서산 창리20180105<NA>저수온 주의보발령3.6<NA>201801222018.01.22(제32호)
95충남천수만보령 효자20180105<NA>저수온 주의보발령5.05.0201801222018.01.22(제32호)
96전남서남해신안 압해20180112<NA>저수온 주의보발령6.0<NA>201801222018.01.22(제32호)
97전남서남해해남 화산20180112<NA>저수온 주의보발령6.1<NA>201801222018.01.22(제32호)