Three-Way Error Analysis of Sea Surface Temperature (Sst) Between Himawari-8, Buoy, and Mur SST in Savu Sea

Sukresno, Bambang; Hanintyo, Rizki; Kusuma, Denny Wijaya; Jatisworo, Dinarika; Murdimanto, Ari

doi:10.30536/j.ijreses.2018.v15.a2855

Cited by 8 publications

(10 citation statements)

References 18 publications

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“…The same method was carried out by Xu and Ignatov (2016), who validated SST data from National Oceanic and Atmospheric Administration (NOAA) and Envisat Advanced Along Track Scanning Radiometer (AATSR) satellites using iQuam in situ data. For the Indonesian Seas area, previously Sukresno et al (2018) used this method to validate the Himawari-8 satellite data using in situ data from the Indonesian buoy. ree-way error analysis has also been used to validate diurnal amplitude of SST using in situ mooring buoy data and Satellite SST of Himawari-8 and Geostationary Operational Environmental Satellite (GOES) (Wirasatriya et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Validation of Sea Surface Temperature from GCOM-C Satellite Using iQuam Datasets and MUR-SST in Indonesian Waters

Sukresno¹,

Jatisworo²,

Hanintyo³

2021

IJG

Self Cite

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Sea surface temperature (SST) is an important variable in oceanography. One of the SST data can be obtained from the Global Observation Mission-Climate (GCOM-C) satellite. Therefore, this data needs to be validated before being applied in various fields. This study aimed to validate SST data from the GCOM-C satellite in the Indonesian Seas. Validation was performed using the data of Multi-sensor Ultra-high Resolution sea surface temperature (MUR-SST) and in situ sea surface temperature Quality Monitor (iQuam). The data used are the daily GCOM-C SST dataset from January to December 2018, as well as the daily dataset from MUR-SST and iQuam in the same period. The validation process was carried out using the three-way error analysis method. The results showed that the accuracy of the GCOM-C SST was 0.37oC.

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Section: Introductionmentioning

confidence: 99%

Validation of Sea Surface Temperature from GCOM-C Satellite Using iQuam Datasets and MUR-SST in Indonesian Waters

Sukresno¹,

Jatisworo²,

Hanintyo³

2021

IJG

Self Cite

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“…Sea surface temperature (SST) is a main factor in the interaction between the ocean and the atmosphere (Wiedermann et al, 2017;He et al, 2017;Wu et al, 2019a), and it characterizes the combined results of ocean heat (Buckley et al, 2014;Griffies et al, 2015;Wu et al, 2019b) and dynamic processes (Takakura et al, 2018). It is a very important parameter for climate change and ocean dynamics processes, such as sea-air heat fluxes and water vapor exchange.…”

Section: Introductionmentioning

confidence: 99%

“…Zheng et al, 2016). Therefore, scholars have begun to observe the SST in recent years; the observation of the SST is important (Kumar et al, 2017;Sukresno et al, 2018). Accurate observation and effective prediction of the SST are very important (Hudson et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al, 2015;Kim et al, 2016) and so on. Among them, the back-propagation neural network (BPNN) (Lee, 2004;Jain and Deo, 2006;Savitha and Mamun, 2017;Wang et al, 2018) has certain advantages in dealing with nonlinear problems; it is a basic machine-learning algorithm and its principle is simple and operability is strong, so it has been widely used in ocean science and engineering.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid improved empirical mode decomposition and BP neural network model for the prediction of sea surface temperature

Jiang

Conde

et al. 2019

Ocean Sci.

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Sea surface temperature (SST) is the major factor that affects the ocean-atmosphere interaction, and in turn the accurate prediction of SST is the key to ocean dynamic prediction. In this paper, an SST-predicting method based on empirical mode decomposition (EMD) algorithms and back-propagation neural network (BPNN) is proposed. Two different EMD algorithms have been applied extensively for analyzing time-series SST data and some nonlinear stochastic signals. The ensemble empirical mode decomposition (EEMD) algorithm and complementary ensemble empirical mode decomposition (CEEMD) algorithm are two improved algorithms of EMD, which can effectively handle the mode-mixing problem and decompose the original data into more stationary signals with different frequencies. Each intrinsic mode function (IMF) has been taken as input data to the back-propagation neural network model. The final predicted SST data are obtained by aggregating the predicted data of individual series of IMFs (IMFi). A case study of the monthly mean SST anomaly (SSTA) in the northeastern region of the North Pacific shows that the proposed hybrid CEEMD-BPNN model is much more accurate than the hybrid EEMD-BPNN model, and the prediction accuracy based on a BP neural network is improved by the CEEMD method. Statistical analysis of the case study demonstrates that applying the proposed hybrid CEEMD-BPNN model is effective for the SST prediction. Highlights include the following: Highlights.-An SST-predicting method based on the hybrid EMD algorithms and BP neural network method is proposed in this paper.-SST prediction results based on the hybrid EEMD-BPNN and CEEMD-BPNN models are compared and discussed.-A case study of SST in the North Pacific shows that the proposed hybrid CEEMD-BPNN model can effectively predict the time-series SST.

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“…The Sea Surface Temperature (SST) is a main factor in the interaction between the ocean and the atmosphere (Wiedermann et al, 2017;He et al, 2017), and it characterizes the combined results of ocean heat (Buckley et al, 2014;Griffies et al, 2015), dynamic processes (Takakura et al, 2018). It is a very important parameter for climate change and ocean dynamics process, reflects sea-air heat and water vapor exchange.…”

Section: Introductionmentioning

confidence: 99%

Hybrid improved EMD-BPNN model for the prediction of sea surface temperature

Wu¹,

Jiang²,

Conde³

et al. 2018

Preprint

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Abstract. Sea surface temperature (SST) is the major factor that affects the ocean-atmosphere interaction, and in turn the accurate prediction of SST is the key to ocean dynamic prediction. In this paper, an SST predicting method based on improved empirical mode decomposition (EMD) algorithms and back-propagation neural network (BPNN) is proposed. Two different EMD algorithms have been applied extensively for analyzing time-series SST data and some nonlinear stochastic signals. Ensemble empirical mode decomposition (EEMD) algorithm and Complementary Ensemble Empirical Mode Decomposition (CEEMD) algorithm are two improved algorithms of EMD, which can effectively handle the mode-mixing problem and decompose the original data into more stationary signals with different frequencies. Each Intrinsic Mode Function (IMF) has been taken as an input data to the back-propagation neural network model. The final predicted SST data is obtained by aggregating the predicted data of individual IMF. A case study, of the monthly mean sea surface temperature anomaly (SSTA) in the northeastern region of the North Pacific, shows that the proposed hybrid CEEMD-BPNN model is much more accurate than the hybrid EEMD-BPNN model, and the prediction accuracy based on BP neural network is improved by the CEEMD method. Statistical analysis of the case study demonstrates that applying the proposed hybrid CEEMD-BPNN model is effective for the SST prediction.

show abstract

Three-Way Error Analysis of Sea Surface Temperature (Sst) Between Himawari-8, Buoy, and Mur SST in Savu Sea

Cited by 8 publications

References 18 publications

Validation of Sea Surface Temperature from GCOM-C Satellite Using iQuam Datasets and MUR-SST in Indonesian Waters

Validation of Sea Surface Temperature from GCOM-C Satellite Using iQuam Datasets and MUR-SST in Indonesian Waters

Hybrid improved empirical mode decomposition and BP neural network model for the prediction of sea surface temperature

Hybrid improved EMD-BPNN model for the prediction of sea surface temperature

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