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Dataset Title:  EMODPACE - In situ Sea level (relative to local reference) data - monthly data
from 1993 to 2021
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Institution:  National Marine Data and Information Service,Ministry of Natural Resource   (Dataset ID: EMODPACE_NMDIS_PSMSL_L2A_SLEV_TG_TS)
Information:  Summary ? | License ? | FGDC | ISO 19115 | Metadata | Background (external link) | Data Access Form | Make a graph

Select a subset:      (Current number of distinct combinations of matching data: 173)
Make as many selections as you want, in any order. Each selection changes the other options (and the map and data below) accordingly.

    StationName ?  =  173 options

View:      Map of All Related Data ?      Distinct Data Counts ?     Distinct Data ?      Related Data Counts ?     Related Data ?

 
Map of All Related Data ?   (Refine the map and/or download the image)

To view the map, check View : Map of All Related Data above.

WARNING: This may involve lots of data. For some datasets, this may be slow. Consider using this only when you need it and have selected a small subset of the data.
 


Distinct Data Counts ?

To view the counts of distinct combinations of the variables listed above,
check View : Distinct Data Counts above and select a value for one of the variables above.

 


Distinct Data ?   (Metadata)    (Refine the data subset and/or download the data)  

StationName
 
ANHEUNG
BALANACAN. MARINDUQUE
BALINTANG. QUEZON. PALAWAN
BEIHAI
BINTULU
BITUNG II
BORYEONG
BUKOM
BUSAN NEW PORT
CATICLAN MALAY AKLAN
CEBU
CENDERING
CHABAHAR II
CHARCHANGA
CHENNAI
CHI MA WAN. LANTAU ISLAND
CHITTAGONG A
CHUJADO
COCHIN (WILLINGDON IS.)
COX'S BAZAAR
CURRIMAO ILOCOS NORTE
DAESAN
DALIAN
Dalian
DANANG
DAVAO. DAVAO GULF
DIAMOND HARBOUR
DONGFANG
DONGHAE PORT
EILAT NORTH
EILAT SOUTH
FORT PHRACHULA CHOMKLAO (POM PHRACHUN)
FUKUE
GANGHWA DAEGYO
GANGRA
GARDEN REACH
GEOJEDO
GETING
GULEOBDO
GUNSAN (OUTER PORT)
GWANGYANG
HAIKOU
HEUKSANDO
HIRON POINT
HONDAU
HONNGU
HUPO
INCHEONSONGDO
ISHIGAKI II
IZUHARA II
JANGHANG
JEJU
JINDO
JOHOR BAHRU
JOLO. SULU
JURONG
KANDLA
Kanmen
KARACHI
KARIYA
KEELUNG II
KHEPUPARA
KO LAK
KO MATTAPHON
KO SICHANG
KO TAPHAO NOI
KOTA KINABALU
KUDAT
KUKUP
LABUAN 2
LAHAT DATU
LAMU B
LAOHUTAN
LEGASPI. ALBAY
LIANYUNGANG
LOK ON PAI
LUMUT
LUSI
Lvsi
MACABALAN PORT. CAGAYAN DE ORO
MAKAR. GENERAL SANTOS CITY
MALE-B. HULULE
MAMBAJAO CAMGUIN
MANGALORE (PANAMBURU)
MANILA. S. HARBOR
MASAN
MASIRAH
MATI. DAVAO ORIENTAL
MINA SULMAN
MIRI
MOMBASA II
MOSULPO
MUKHO
MUMBAI / BOMBAY (APOLLO BANDAR)
MUSCAT
MUSCAT B
NAGAPATTINAM
NAGASAKI
NAHA
NAKANO SIMA
NAN SHA
Nansha
NASE III
OCHEONGDO
OKHA
OKINAWA
PADANG B
PAGADIAN CITY
PANWA CAPE
PELABUHAN KELANG
POINT LA RUE
PORT SUDAN
PUERTO PRINCESA. PALAWAN
PULAU LANGKAWI
PULAU PINANG
PULAU TIOMAN
PULUPANDAN. NEGROS OCCIDENTAL
PYEONGTAEK
QINHUANGDAO
QUARRY BAY
QUI NHON II
QUINHON
RAFFLES LIGHT HOUSE
REAL QUEZON
SABANG
SALALAH
SAN JOSE
SANDAKAN
SASEBO II
SEJINGKAT
SEMBAWANG
SEOGWIPO
SEONGSANPO
SHANWEI
SHEK PIK
SHIJIUSUO
SIBOLGA II
SOKCHO
SUBIC ZAMBALES
TAEAN
TAI MIU WAN
TANAH MERAH
TANDAG. SURIGAO DEL SUR
TANGGU
TANJONG CHANGI
TANJONG PAGAR
TANJUNG GELANG
TANJUNG KELING
TANJUNG SEDILI
TRIBENI
TUAS (WEST JURONG)
UBIN
ULSAN
VIRAC. CATANDUANES
VISAKHAPATNAM
VUNG TAU II
VUNGTAU
WAGLAN ISLAND
WANDO
WEST COAST
WEST TUAS
WIDO
XI SHA
XIAMEN
Xisha
YANTAI
YEONGGWANG
YEONGHEUNGDO
YEONGJONG DAEGYO
YEOSU
ZAMBOANGA CITY
ZANZIBAR
Zhapo

In total, there are 173 rows of distinct combinations of the variables listed above. All of the rows are shown above.
To change the maximum number of rows displayed, change View : Distinct Data above.
 


Related Data Counts ?

To view the related data counts,
check View : Related Data Counts above and select a value for one of the variables above.

WARNING: This may involve lots of data. For some datasets, this may be slow. Consider using this only when you need it and have selected a small subset of the data.
 


Related Data ?   (Metadata)    (Refine the data subset and/or download the data)

To view the related data, change View : Related Data above.

WARNING: This may involve lots of data. For some datasets, this may be slow. Consider using this only when you need it and have selected a small subset of the data.


 
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