scholarly journals ANALISIS KORELASI CURAH HUJAN DAN SUHU PERMUKAAN LAUT WILAYAH INDONESIA, SERTA IMPLIKASINYA UNTUK PRAKIRAAN CURAH HUJAN (STUDI KASUS KABUPATEN CILACAP) (CORRELATION ANALYSIS OF RAINFALL AND INDONESIA SEA SURFACE TEMPERATURE, AND ITS ...

Agromet ◽  
2007 ◽  
Vol 21 (2) ◽  
pp. 46 ◽  
Author(s):  
W. Estiningtyas ◽  
F. Ramadhani ◽  
E. Aldrian
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Negative Correlation ◽  
Prediction Models ◽  
Time Series Data ◽  
Sea Surface ◽  
Rainfall Pattern ◽  
Series Data ◽  
Rainfall Prediction

<p>Significant decrease in rainfall caused extreme climate has significant impact on agriculture sector, especialy food crops production. It is one of reason and push developing of rainfall prediction models as anticipate from extreme climate events. Rainfall prediction models develop base on time series data, and then it has been included anomaly aspect, like rainfall prediction model with Kalman filtering method. One of global parameter that has been used as climate anomaly indicator is sea surface temperature. Some of research indicate, there are relationship between sea surface temperature and rainfall. Relationship between Indonesian rainfall and global sea surface temperature has been known, but its relationship with Indonesian’s sea surface temperature not know yet, especialy for rainfall in smaller area like district. So, therefore the research about relationship between rainfall in distric area and Indonesian’s sea surface temperature and it application for rainfall prediction is needed. Based on Indonesian’s sea surface temperature time series data Januari 1982 until Mei 2006 show there are zona of Indonesian’s sea surface temperature (with temperature more than 27,6 0C) dominan in Januari-Mei and moved with specific pattern. Highest value of spasial correlation beetwen Cilacap’s rainfall and Indonesian’s sea surface temperature is 0,30 until 0,50 with different zona of Indonesian’s sea surface temperature. Highest positive correlation happened in March and July. Negative correlation is -0,30 until -0,70 with highest negative correlation in May and June. Model validation resulted correlation coeffcient 85,73%, fits model 20,74%, r2 73,49%, RMSE 20,5% and standart deviation 37,96. Rainfall prediction Januari-Desember 2007 period indicated rainfall pattern is near same with average rainfall pattern, rainfall less than 100/month. The result of this research indicate Indonesian’s sea surface temperature can be used as indicator rainfall condition in distric area, that means rainfall in district area can be predicted based on Indonesian’s sea surface temperature in zona with highest correlation in every month.</p><p>------------------------------------------------------------------</p><p>Penurunan curah hujan yang cukup signifikan akibat iklim ekstrim telah membawa dampak yang cukup signifikan pula pada sektor pertanian, terutama produksi tanaman pangan. Hal ini menjadi salah satu alasan yang mendorong semakin berkembangnya model-model prakiraan hujan sebagai upaya antipasi terhadap kejadian iklim ekstrim. Model prakiraan hujan yang pada awalnya hanya berbasis pada data time series, kini telah berkembang dengan memperhitungkan aspek anomali iklim, seperti model prakiraan hujan dengan metode filter Kalman. Salah satu indikator global yang dapat digunakan sebagai indikator anomali iklim adalah suhu permukaan laut. Dari berbagai hasil penelitian diketahui bahwa suhu permukaan laut ini memiliki keterkaitan dengan kejadian curah hujan. Hubungan curah hujan Indonesia dengan suhu permukaan laut global sudah banyak diketahui, tetapi keterkaitannya dengan suhu permukaan laut wilayah Indonesia belum banyak mendapat perhatian, terutama untuk curah hujan pada cakupan yang lebih sempit seperti kabupaten. Oleh karena itu perlu dilakukan penelitian yang mengkaji hubungan kedua parameter tersebut serta mengaplikasikannya untuk prakiraan curah hujan pada wilayah Kabupaten. Hasil penelitian berdasarkan data suhu permukaan laut wilayah Indonesia rata-rata Januari 1982 hingga Mei 2006 menunjukkan zona dengan suhu lebih dari 27,6 0C yang dominan pada bulan Januari-Mei dan bergerak dengan pola yang cukup jelas. Korelasi spasial antara curah hujan kabupaten Cilacap dengan SPL wilayah Indonesia rata-rata bulan Januari-Desember menunjukkan korelasi positip tertinggi antara 0,30 hingga 0,50 dengan zona SPL yang beragam. Korelasi tertinggi terjadi pada bulan Maret dan Juli. Sedangkan korelasi negatip berkisar antara -0,30 hingga -0,70 dengan korelasi negatip tertinggi pada bulan Mei dan Juni. Validasi model prakiraan hujan menghasilkan nilai koefisien korelasi 85,73%, fits model 20,74%, r2 sebesar 73,49%, RMSE 20,5% dan standar deviasi 37,96. Hasil prakiraan hujan bulanan periode Januari-Desember 2007 mengindikasikan pola curah hujan yang tidak jauh berbeda dengan rata-rata selama 19 tahun (1988-2006) dengan jeluk hujan kurang dari 100 mm/bulan. Hasil penelitian mengindikasikan bahwa SPL wilayah Indonesia dapat digunakan sebagai indikator untuk menunjukkan kondisi curah hujan di suatu wilayah (kabupaten), artinya curah hujan dapat diprediksi berdasarkan perubahan SPL pada zona-zona dengan korelasi yang tertinggi pada setiap bulannya.</p>

Pemodelan dan Peramalan Deret Waktu Studi Kasus: Suhu Permukaan Laut di Selatan Jawa Timur

2017 ◽  
Vol 1 (2) ◽  
pp. 187-199
Author(s):  
Hutomo Atman Maulana ◽  
Muliah Muliah ◽  
Maria Zefaya Sampe ◽  
Farrah Hanifah
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sample Size ◽  
Time Series Data ◽  
Temperature Time Series ◽  
Sea Surface ◽  
Series Data ◽  
Forecast Time ◽  
Temperature Time

The sea surface temperature is one of the important components that can determine the potential of the sea. This research aims to model and forecast time series data of sea surface temperature by using a Box-Jenkins method. Data in this research are the sea surface temperatures in the South of East Java (January 1983-December 2013) with sample size of 372. 360 data will be used for modeling which is from January 1983 to December 2012, and data in 2013 will be used for forecasting. Based on the results of analysis time series, the appropriate models is SARIMA(1,0,0) (1,0,1)12 where can be written as Yt = 0,010039 + 0,734220Yt−1 + 0,014893Yt−12 − (0,734220)(0,014893)Yt−13 + 0,940726et−12 with  MSE of 0.07888096.Keywords: Sea surface temperature, time series, Box-Jenkins method

scholarly journals Sea Surface Temperature Variability in the Florida Keys and Its Relationship to Coral Cover

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
I. M. Soto ◽  
F. E. Muller Karger ◽  
P. Hallock ◽  
C. Hu
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Series Data ◽  
Coral Cover ◽  
Monitoring Program ◽  
Florida Keys ◽  
Sea Surface ◽  
Series Data ◽  
Sst Variability

The hypothesis that moderate variability in Sea Surface Temperature (SST) is associated with higher coral cover and slower rates of decline of coral cover within the Florida Keys National Marine Sanctuary (FKNMS) was examined. Synoptic SST time series covering the period 1994–2008 were constructed for the FKNMS with the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer satellite sensors. The SST data were compared with coral-cover time-series data from 36 sites monitored by the Coral Reef and Evaluation Monitoring Program. Sites that experienced moderately high SST variability relative to other sites showed a trend toward higher percentage coral cover in 2008 and relatively slower rates of decline over the 14-year study period. The results suggest that corals at sites that are continuously exposed to moderate variability in temperature are more resilient than corals typically exposed either to low variability or to extremes.

A hybrid CNN-LSTM based model for the prediction of sea surface temperature using time-series satellite data.

2020 ◽  
Author(s):  
Pavan Kumar Jonnakuti ◽  
Udaya Bhaskar Tata Venkata Sai
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Data ◽  
Time Series Data ◽  
Sea Surface ◽  
Machine Learning Techniques ◽  
Global Ocean ◽  
Series Data

&lt;p&gt;Sea surface temperature (SST) is a key variable of the global ocean, which affects air-sea interaction processes. Forecasts based on statistics and machine learning techniques did not succeed in considering the spatial and temporal relationships of the time series data. Therefore, to achieve precision in SST prediction we propose a deep learning-based model, by which we can produce a more realistic and accurate account of SST &amp;#8216;behavior&amp;#8217; as it focuses both on space and time. Our hybrid CNN-LSTM model uses multiple processing layers to learn hierarchical representations by implementing 3D and 2D convolution neural networks as a method to better understand the spatial features and additionally we use LSTM to examine the temporal sequence of relations in SST time-series satellite data. Widespread studies, based on the historical satellite datasets spanning from 1980 - present time, in Indian Ocean region shows that our proposed deep learning-based CNN-LSTM model is extremely capable for short and mid-term daily SST prediction accurately exclusive based on the error estimates (obtained from LSTM) of the forecasted data sets.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords: Deep Learning, Sea Surface Temperature, CNN, LSTM, Prediction.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals RECONSTRUCTION OF SEAWATER d18O SIGNAL FROM CORAL d18O: A RECORD FROM BALI CORAL, INDONESIA

2016 ◽  
Vol 25 (1) ◽  
pp. 23
Author(s):  
Sri Yudawati Cahyarini
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Series Data ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Series Data ◽  
Surface Salinity ◽  
Decadal Scale ◽  
Long Time

Sea surface salinity (SSS) and precipitation are important climate (paleoclimate) parameters. To obtain long time series data of SSS/precipitation one use coral proxy. In this study, seawater d18O is extracted from d18O content in Bali coral using centering method. The result shows more convincing that d18Obali is influenced by both seawater d18O and sea surface temperature (SST). In the interannual/decadal scale the variation d18Obali clearly shows the variation of seawater d18O, it is supposed that highly variation of precipitation contribute to the seawater d18O variation which mirrored by coral d18Obali. Keywords: coral d18O, seawater d18O, precipitation, sea surface salinity, sea surface temperature Salinitas permukaan laut (SSS) dan curah hujan merupakan parameter penting untuk studi iklim maupun paleoiklim (iklim masa lampau). Untuk mendapatkan data dalam urut-urutan waktu (timeseries) yang panjang dari SSS dan curah hujan diperlukan data proksi geokimia dalam koral. Dalam studi ini kandungan d18O dalam air laut dapat di rekonstruksi dari kandungan d18O dalam koral dengan menggunakan metode centering. Hasilnya menunjukkan bahwa d18O dalam koral dipengaruhi oleh kandungan d18O dalam air laut dan SST. Dalam resolusi tahunan dan puluhan tahunan variasi d18Obali dalam koral menunjukkan dengan jelas variasi d18O dalam air laut, hal ini diduga bahwa dalam resolusi tahunan dan puluhan tahunan variasi curah hujan sangat tinggi yang berkontribusi pada tingginya variasi d18Obali dalam air laut sehingga dapat terekam oleh koral. Kata kunci: d18O koral, d18O air laut, curah hujan, salinitas permukaan laut, suhu permukaan laut.

scholarly journals TIME SERIES ANALYSIS FOR MONITORING SEAGRASS HABITAT AND ENVIRONMENT IN BUSUANGA, PHILIPPINES USING GOOGLE EARTH ENGINE

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 109-116
Author(s):  
A. Tamondong ◽  
T. Nakamura ◽  
T. E. A. Quiros ◽  
K. Nadaoka
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Google Earth ◽  
Coastal Ecosystem ◽  
Sea Surface ◽  
Series Data ◽  
Seagrass Habitat ◽  
Percent Cover ◽  
Google Earth Engine

Abstract. Seagrasses are marine flowering plants which are part of a highly productive coastal ecosystem and play key roles in the coastal processes. Unfortunately, they are declining in area coverage globally, and seagrass losses can be attributed to climate change such as sea-level rise, increase in sea surface temperature, and decrease in salinity, as well as human-related activities. The objective of this research is to assess the historical changes in the seagrass habitat and environment of Busuanga, Philippines using time series data available in the Google Earth Engine (GEE) platform. These include satellite data such as MODIS, Landsat 5, 7, and 8, and SeaWIFS. Reanalysis data such as HYCOM was also utilized in this research. Results from HYCOM data show that there has been a 0.0098 °C increase in the sea surface temperature per decade in Busuanga while MODIS data indicates an increase of 0.0045 °C per decade. Moreover, HYCOM data also shows an overall average of 0.76 mm in sea surface elevation anomaly and a decreasing trend in salinity values at 0.0026 psu per decade. Chlorophyll-a concentration has a minimal increase based on results from MODIS and SeaWIFS. Aside from changes in water parameters, changes in the land also affect seagrasses. Forest loss may cause increased siltation in the coastal ecosystem which can lead to seagrass loss. Based on the results of Landsat satellite image processing, there has been forest cover loss in Busuanga with the highest loss occurring in 2013 when super typhoon Yolanda ravaged the island. Lastly, results from the linear spectral unmixing of 778 Landsat images from 1987–2000 show that the average percent cover of seagrasses in Busuanga were declining through the years.

scholarly journals Independent Subspace Analysis of the Sea Surface Temperature Variability: Non-Gaussian Sources and Sensitivity to Sampling and Dimensionality

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-23 ◽  
Author(s):  
Carlos A. L. Pires ◽  
Abdel Hannachi
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Atlantic Multidecadal Oscillation ◽  
Sea Surface ◽  
Series Data ◽  
Central Pacific ◽  
Independent Components ◽  
Non Gaussian

We propose an expansion of multivariate time-series data into maximally independent source subspaces. The search is made among rotations of prewhitened data which maximize non-Gaussianity of candidate sources. We use a tensorial invariant approximation of the multivariate negentropy in terms of a linear combination of squared coskewness and cokurtosis. By solving a high-order singular value decomposition problem, we extract the axes associated with most non-Gaussianity. Moreover, an estimate of the Gaussian subspace is provided by the trailing singular vectors. The independent subspaces are obtained through the search of “quasi-independent” components within the estimated non-Gaussian subspace, followed by the identification of groups with significant joint negentropies. Sources result essentially from the coherency of extremes of the data components. The method is then applied to the global sea surface temperature anomalies, equatorward of 65°, after being tested with non-Gaussian surrogates consistent with the data anomalies. The main emerging independent components and subspaces, supposedly generated by independent forcing, include different variability modes, namely, The East-Pacific, the Central Pacific, and the Atlantic Niños, the Atlantic Multidecadal Oscillation, along with the subtropical dipoles in the Indian, South Pacific, and South-Atlantic oceans. Benefits and usefulness of independent subspaces are then discussed.

scholarly journals ANALISIS HUBUNGAN PRODUKSI PERIKANAN TANGKAP LAUT, CURAH HUJAN, DAN SUHU PERMUKAAN LAUT DENGAN MODEL VECTOR AUTOREGRESSIVE (VAR)

2019 ◽  
Vol 8 (2) ◽  
pp. 155
Author(s):  
RAMADHAN LENGGU RAMLI ◽  
I WAYAN SUMARJAYA ◽  
KARTIKA SARI
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Series Data ◽  
Sea Surface ◽  
Series Data ◽  
Var Model ◽  
Vector Autoregressive Model ◽  
Rainfall Forecasting ◽  
Vector Autoregressive ◽  
Variable Production

NTT Province is one of the provinces with the largest marine catch fisheries production in Indonesia. Rainfall and sea surface temperature are factors that affect marine catch fisheries production. This study aims to analyze relationships between marine catch fisheries production, sea surface temperature and rainfall in NTT based on time series data using a vector autoregressive model (VAR). The result of this study: (i) the best VAR model for forecasting marine catch fisheries, rainfall, and sea surface temperature is a second-order VAR model (VAR (2)), (ii) based on MAPE forecasting criterion, the VAR model obtained classified as good forecasting for variable production of marine catch fisheries, and sea surface temperature, while for rainfall forecasting is inaccurate, (iii) significant sea surface temperatures affect marine catch fisheries production and rainfall in NTT.

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.

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