scholarly journals Analisis Korelasi Suhu Muka Laut dan Curah Hujan di Stasiun Meteorologi Maritim Kelas II Kendari Tahun 2005 – 2014

2017 ◽  
Vol 2 ◽  
pp. 192
Author(s):  
Rizka Erwin Lestari ◽  
Ambinari Rachmi Putri ◽  
Imma Redha Nugraheni
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Pearson Correlation ◽  
Lag Time ◽  
Meteorological Station ◽  
Temperature Data ◽  
Sea Surface ◽  
Rainfall Prediction ◽  
Agriculture Sector ◽  
Factors Influencing

<p class="AbstractEnglish"><strong>Abstract:</strong>. Sea surface temperature is one of many factors influencing with the weather pattern in Indonesia. This is caused by evaporation process which in turn is influenced by sea surface temperature then it will form a cloud and trigger to rainfall in the land. Additionally, it needs to be known that rainfall can affect to human activities especially in the agriculture sector. The correlation between rainfall and global sea surface temperature has been researched but the researchers have not taken into consideration yet for local sea surface temperature. Because of that, it needs to research about the correlation between rainfall and local sea surface temperature. In this paper, it uses rainfall data for synoptic observation in Maritime Meteorological Station of Kendari 2005 – 2014 and sea surface temperature data of NOAA. The analyzing used is pearson correlation analyzing to determine correlation coefficient value with lag time 1 month and hypothesis testing. Besides that, the researcher sets groups of domain sample to 8 grids. From the result of data processing, it shows that there is correlation between sea surface temperature and rainfall in Maritime Meteorological Station of Kendari. The strongest correlation happens in grid 6 (120°22’30’’ E - 130°7’30’’ E / 9°52’30’’ S - 5°7’30’’ S) and grid 8 (120°22’30’’ E - 130°7’30’’ E / 4°52’30’’ S - 0°7’30’’ S). Correlation sea surface temperature with a lag time of 1 month shows higher correlation than without lag time. According to this result, rainfall prediction in Maritime Meteorological Station of Kendari can involve sea surface temperature in grid 6 and 8 with a lag time of 1 month.</p><p class="KeywordsEngish"> </p><p class="AbstrakIndonesia"><strong>Abstrak:</strong> Suhu muka laut merupakan salah satu unsur yang berpengaruh terhadap pola cuaca di Indonesia. Hal ini disebabkan karena suhu muka laut berperan penting dalam proses penguapan sehingga mempengaruhi pembentukan awan dan selanjutnya mempengaruhi curah hujan. Pada sisi lain, diketahui bahwa curah hujan berpengaruh terhadap kehidupan manusia seperti contohnya pada sektor pertanian. Hubungan antara curah hujan dengan suhu muka laut global telah banyak diteliti, tetapi untuk suhu muka laut lokal belum banyak diperhitungkan oleh para peneliti. Oleh karena itu, perlu dilakukan kajian tentang hubungan curah hujan dan suhu muka laut disekitar wilayah yang menjadi sampel . Adapun data yang digunakan pada kajian ini adalah data curah hujan Stasiun Meteorologi Maritim Kelas II Kendari tahun 2005 – 2014 dan data suhu muka laut dari NOAA. Analisis yang dilakukan adalah analisis korelasi pearson untuk menentukan nilai koefisien korelasi dengan lag waktu 1 bulan dan uji hipotesis. Selain itu peneliti mengelompokkan wilayah menjadi 8 grid. Dari hasil pengolahan data yang telah dilakukan menunjukkan bahwa terdapat hubungan antara suhu muka laut dan curah hujan di Stasiun Meteorologi Kelas II Kendari. Korelasi terkuat terjadi pada suhu muka laut di wilayah grid 6 (120°22’30’’ BT - 130°7’30’’ BT / 9°52’30’’ LS - 5°7’30’’ LS) dan grid 8 (120°22’30’’ BT - 130°7’30’’ BT / 4°52’30’’ LS - 0°7’30’’ LS). Korelasi suhu muka laut dengan lag 1 bulan menunjukkan nilai yang lebih besar daripada korelasi tanpa lag waktu. Sehingga dalam menentukan curah hujan di wilayah Stasiun Meteorologi Maritim Kelas II Kendari dapat melibatkan unsur suhu muka laut pada grid 6 dan 8 dengan lag waktu 1 bulan.</p>

scholarly journals Prediksi Curah Hujan Bulanan Di Wilayah Lombok Barat Menggunakan Principal Component Regression (PCR)

2020 ◽  
Vol 8 (2) ◽  
pp. 175-182
Author(s):  
Anggitya Pratiwi ◽  
◽  
Rian Mahendra Taruna ◽  
Suci Agustiarini ◽  
Dewo Sulistio Adi Wibowo ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Principal Component Regression ◽  
Principal Component ◽  
Temperature Data ◽  
Sea Surface ◽  
Observation Data ◽  
Rainfall Prediction ◽  
Principal Component Analysis Result ◽  
Climate Parameter

Rainfall is a significant climate parameter in various sectoral of human activities. Despite that, precise rainfall prediction is necessary. One factor which can affect the rainfall is Sea Surface Temperature. We used Sea Surface Temperature data in Indonesia limited by 14° LS - 10° LU and 90° BT - 142° BT to predict monthly rainfall in West Lombok. The method used in this study is Principal Component Regression. Regression is calculated using 1998-2013 data, with Principal Component Analysis result of Indonesian Sea Surface Temperature as the predictor. Validation using contingency table for 2014-2018 rainfall characteristic data in West Lombok showed that rainfall prediction is appropriate with the rainfall pattern observation data.

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>

scholarly journals DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations

2020 ◽  
Vol 13 (3) ◽  
pp. 1609-1622 ◽  
Author(s):  
Alexander Barth ◽  
Aida Alvera-Azcárate ◽  
Matjaz Licer ◽  
Jean-Marie Beckers
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Error Variance ◽  
Temperature Data ◽  
Satellite Observations ◽  
Sea Surface ◽  
Function Decomposition ◽  
Surface Temperature Data

Abstract. A method to reconstruct missing data in sea surface temperature data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. Contrary to standard image reconstruction with neural networks, this application requires a method to handle missing data (or data with variable accuracy) in the training phase. The present work shows a consistent approach which uses the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing the likelihood of the observed value. The approach, called DINCAE (Data INterpolating Convolutional Auto-Encoder), is applied to a 25-year time series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data INterpolating Empirical Orthogonal Functions), a commonly used method to reconstruct missing data based on an EOF (empirical orthogonal function) decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error while showing higher variability than the DINEOF reconstruction.

scholarly journals Can sparse proxy data constrain the strength of the Atlantic meridional overturning circulation?

2014 ◽  
Vol 7 (1) ◽  
pp. 419-432 ◽  
Author(s):  
T. Kurahashi-Nakamura ◽  
M. Losch ◽  
A. Paul
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Atlantic Meridional Overturning Circulation ◽  
Temperature Data ◽  
Meridional Overturning Circulation ◽  
Sea Surface ◽  
Proxy Data ◽  
Overturning Circulation ◽  
Meridional Overturning ◽  
Surface Temperature Data

Abstract. In a feasibility study, the potential of proxy data for the temperature and salinity during the Last Glacial Maximum (LGM, about 19 000 to 23 000 years before present) in constraining the strength of the Atlantic meridional overturning circulation (AMOC) with a general ocean circulation model was explored. The proxy data were simulated by drawing data from four different model simulations at the ocean sediment core locations of the Multiproxy Approach for the Reconstruction of the Glacial Ocean surface (MARGO) project, and perturbing these data with realistic noise estimates. The results suggest that our method has the potential to provide estimates of the past strength of the AMOC even from sparse data, but in general, paleo-sea-surface temperature data without additional prior knowledge about the ocean state during the LGM is not adequate to constrain the model. On the one hand, additional data in the deep-ocean and salinity data are shown to be highly important in estimating the LGM circulation. On the other hand, increasing the amount of surface data alone does not appear to be enough for better estimates. Finally, better initial guesses to start the state estimation procedure would greatly improve the performance of the method. Indeed, with a sufficiently good first guess, just the sea-surface temperature data from the MARGO project promise to be sufficient for reliable estimates of the strength of the AMOC.

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