Sea surface temperature forecasts using on-line local learning algorithm in upwelling regions

2000 ◽  
Vol 30 (1-4) ◽  
pp. 59-63 ◽  
Author(s):  
Michel de Bollivier ◽  
Walter Eifler ◽  
Sylvie Thiria
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Learning Algorithm ◽  
Sea Surface ◽  
Local Learning ◽  
On Line

scholarly journals PREDIKSI CURAH HUJAN BULANAN BERDASARKAN SUHU PERMUKAAN LAUT NINO 3.4 : SUATU PENDEKATAN DENGAN METODE FILTER KALMAN(MONTHLY RAINFALL PREDICTION BASED ON SEA SURFACE TEMPERATURE NINO 3.4 : THE APPROACH WITH KALMAN FILTERING)

Agromet ◽  
2005 ◽  
Vol 19 (2) ◽  
pp. 43
Author(s):  
Woro Estinigtyas ◽  
S. Suciantini ◽  
G. Irianto
Keyword(s):  
Kalman Filter ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Correlation Coefficient ◽  
Kalman Filtering ◽  
Sea Surface ◽  
Deterministic Models ◽  
Rainfall Prediction ◽  
Dependent Variables ◽  
On Line

Many approaches have been applied to forecast climate using statistical and deterministic models using independent and dependent variables empirically. It is more practical to analyze the parameters, but it needs validation anytime and anywhere. Kalman filtering unites physical and statistical model approaches to stochastic model renewable anytime for objective of on line forecasting. Based on research, sea surface temperature Nino 3.4 have high correlation with rainfall in Indonesia, so it is used to forecast rainfall in Cirebon as area study. Rainfall clustering in Cirebon results 6 groups with rainfall average 1400-1500 mm/year for dry area and 3000-3200 mm/year for wet area. Validation have correlation coefficient validation value more than 94%, correlation coefficient model value more than 78% and fit model value more than 38%. The result of regression gives R2 value of more than 0,8. It implies that predicting model using Kalman Filter is feasible to forecast montly rainfall based on sea surface temperature Nino 3.4. The result of rainfall prediction in Cirebon show increasing in rainfall until February 2005, with correlation coeficient value of model more than 90% and fit model more than 40%.

Holocene sea surface temperature variations recorded in corals from Kikai Island, Japan

2017 ◽  
Vol 51 (4) ◽  
pp. e9-e14 ◽  
Author(s):  
Hiroto Kajita ◽  
Atsuko Yamazaki ◽  
Takaaki Watanabe ◽  
Chung-Che Wu ◽  
Chuan-Chou Shen ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Temperature Variations

PENENTUAN DAERAH POTENSIAL BUDIDAYA RUMPUT LAUT KAPPAPHYCUS ALVAREZII DI PROVINSI NUSA TENGGARA TIMUR

2019 ◽  
Vol 3 ◽  
pp. 929
Author(s):  
Marianus Filipe Logo ◽  
N M. R. R. Cahya Perbani ◽  
Bayu Priyono
Keyword(s):  
Dissolved Oxygen ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Kappaphycus Alvarezii ◽  
Sea Surface

Provinsi Nusa Tenggara Timur (NTT) merupakan penghasil rumput laut kappaphycus alvarezii kedua terbesar di Indonesia berdasarkan data Badan Pusat Statistik (2016). Oleh karena itu diperlukan zonasi daerah potensial budidaya rumput laut kappaphycus alvarezii untuk pengembangan lebih lanjut. Penelitian ini bertujuan untuk menentukan daerah yang potensial untuk budidaya rumput laut kappaphycus alvarezii di Provinsi NTT berdasarkan parameter sea surface temperature (SST), salinitas, kedalaman, arus, dissolved oxygen (DO), nitrat, fosfat, klorofil-a, dan muara sungai. Penentuan kesesuaian lokasi budidaya dilakukan dengan memberikan bobot dan skor bagi setiap parameter untuk budidaya rumput laut kappaphycus alvarezii menggunakan sistem informasi geografis melalui overlay peta tematik setiap parameter. Dari penelitian ini diperoleh bahwa kadar nitrat, arus, kedalaman, dan lokasi muara sungai menjadi parameter penentu utama. Jarak maksimum dari bibir pantai adalah sekitar 10 km. Potensial budidaya rumput laut kappaphycus alvarezii ditemukan di Pulau Flores bagian barat, kepulauan di Kabupaten Flores Timur dan Alor, selatan Pulau Sumba, Pulau Rote, dan Teluk Kupang.

EXTREME VALUES OF SEA SURFACE TEMPERATURE ASSOCIATED WITH LONG-PERIOD PHENOMENA OCCURRED DURING 1960-2015 IN THE COLOMBIAN PACIFIC OCEAN

2017 ◽  
Author(s):  
Diaz Juan Navia ◽  
Diaz Juan Navia ◽  
Bolaños Nancy Villegas ◽  
Bolaños Nancy Villegas ◽  
Igor Malikov ◽  
...  
Keyword(s):  
Time Series ◽  
Pacific Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Extreme Values ◽  
Sea Surface ◽  
Absolute Maximum ◽  
Data Set ◽  
Arctic Oscillation Index ◽  
Colombian Pacific

Sea Surface Temperature Anomalies (SSTA), in four coastal hydrographic stations of Colombian Pacific Ocean, were analyzed. The selected hydrographic stations were: Tumaco (1°48'N-78°45'W), Gorgona island (2°58'N-78°11'W), Solano Bay (6°13'N-77°24'W) and Malpelo island (4°0'N-81°36'W). SSTA time series for 1960-2015 were calculated from monthly Sea Surface Temperature obtained from International Comprehensive Ocean Atmosphere Data Set (ICOADS). SSTA time series, Oceanic Nino Index (ONI), Pacific Decadal Oscillation index (PDO), Arctic Oscillation index (AO) and sunspots number (associated to solar activity), were compared. It was found that the SSTA absolute minimum has occurred in Tumaco (-3.93°C) in March 2009, in Gorgona (-3.71°C) in October 2007, in Solano Bay (-4.23°C) in April 2014 and Malpelo (-4.21°C) in December 2005. The SSTA absolute maximum was observed in Tumaco (3.45°C) in January 2002, in Gorgona (5.01°C) in July 1978, in Solano Bay (5.27°C) in March 1998 and Malpelo (3.64°C) in July 2015. A high correlation between SST and ONI in large part of study period, followed by a good correlation with PDO, was identified. The AO and SSTA have showed an inverse relationship in some periods. Solar Cycle has showed to be a modulator of behavior of SSTA in the selected stations. It was determined that extreme values of SST are related to the analyzed large scale oscillations.

On the relation between atmospheric CO2 and equatorial sea-surface temperature

Tellus B ◽  
1987 ◽  
Vol 39 (1-2) ◽  
pp. 171-183 ◽  
Author(s):  
William P. Elliott ◽  
James K. Angell
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Atmospheric Co2
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