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%.

scholarly journals Data Assimilation of the High-Resolution Sea Surface Temperature Obtained from the Aqua-Terra Satellites (MODIS-SST) Using an Ensemble Kalman Filter

2013 ◽  
Vol 5 (6) ◽  
pp. 3123-3139 ◽  
Author(s):  
Yasumasa Miyazawa ◽  
Hiroshi Murakami ◽  
Toru Miyama ◽  
Sergey Varlamov ◽  
Xinyu Guo ◽  
...  
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Data Assimilation ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ensemble Kalman Filter ◽  
Sea Surface

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 Forecasting experiments of a dynamical–statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

Ocean Science ◽  
2018 ◽  
Vol 14 (2) ◽  
pp. 301-320 ◽  
Author(s):  
Mei Hong ◽  
Xi Chen ◽  
Ren Zhang ◽  
Dong Wang ◽  
Shuanghe Shen ◽  
...  
Keyword(s):  
Statistical Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Correlation Coefficient ◽  
El Niño ◽  
Temperature Anomaly ◽  
El Nino ◽  
Sea Surface ◽  
Sea Surface Temperature Anomaly

Abstract. With the objective of tackling the problem of inaccurate long-term El Niño–Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical–statistical forecast model of the sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamical reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical–statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a Pearson correlation coefficient of approximately 0.80 and a mean absolute percentage error (MAPE) of less than 15 %. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field but also the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The temporal correlation coefficient is 0.8062, and the MAPE value of 19.55 % is small. The difference between forecast results in spring and those in autumn is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.

scholarly journals A Skillful Prediction Model for Winter NAO Based on Atlantic Sea Surface Temperature and Eurasian Snow Cover

2015 ◽  
Vol 30 (1) ◽  
pp. 197-205 ◽  
Author(s):  
Baoqiang Tian ◽  
Ke Fan
Keyword(s):  
Prediction Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Snow Cover ◽  
Correlation Coefficient ◽  
Cross Validation ◽  
Sea Surface ◽  
Interdecadal Variability ◽  
Eurasian Snow Cover ◽  
Eurasian Snow

Abstract A new statistical forecast scheme, referred to as scheme 1, is developed using observed autumn Atlantic sea surface temperature (SST) and Eurasian snow cover in the preceding autumn to predict the upcoming winter North Atlantic Oscillation (NAO) using the year-to-year increment prediction approach (i.e., DY approach). Two predictors for the year-to-year increment are identified that are available in the preceding autumn. Cross-validation tests for the period 1950–2011 and independent hindcasts for the period 1990–2011 are performed to validate the prediction ability of the proposed technique. The cross-validation test results for 1950–2011 reveal a high correlation coefficient of 0.52 (0.58) between the predicted and observed NAO indices (DY of the NAO). The model also successfully predicts the independent hindcasts for the period 1990–2011 with a correlation coefficient of 0.55 (0.74). In addition, scheme 0 (i.e., anomaly approach) is established using the SST and snow cover anomalies during the preceding autumn. Compared with scheme 0, this new prediction model has higher predictive skill in reproducing the interdecadal variability of NAO. Therefore, this study provides an effective climate prediction scheme for the interannual and interdecadal variability of NAO in boreal winter.

scholarly journals Including Uncertainties of Sea Surface Temperature in an Ensemble Kalman Filter: A Case Study of Typhoon Sinlaku (2008)

2012 ◽  
Vol 27 (6) ◽  
pp. 1586-1597 ◽  
Author(s):  
Masaru Kunii ◽  
Takemasa Miyoshi
Keyword(s):  
Kalman Filter ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ensemble Kalman Filter ◽  
Forecast Error ◽  
Ocean Model ◽  
Sea Surface ◽  
Ensemble Spread ◽  
Error Covariance ◽  
The Impact

Abstract Sea surface temperature (SST) plays an important role in tropical cyclone (TC) life cycle evolution, but often the uncertainties in SST estimates are not considered in the ensemble Kalman filter (EnKF). The lack of uncertainties in SST generally results in the lack of ensemble spread in the atmospheric states near the sea surface, particularly for temperature and moisture. In this study, the uncertainties of SST are included by adding ensemble perturbations to the SST field, and the impact of the SST perturbations is investigated using the local ensemble transform Kalman filter (LETKF) with the Weather Research and Forecasting Model (WRF) in the case of Typhoon Sinlaku (2008). In addition to the experiment with the perturbed SST, another experiment with manually inflated ensemble perturbations near the sea surface is performed for comparison. The results indicate that the SST perturbations within EnKF generally improve analyses and their subsequent forecasts, although manually inflating the ensemble spread instead of perturbing SST does not help. Investigations of the ensemble-based forecast error covariance indicate larger scales for low-level temperature and moisture from the SST perturbations, although manual inflation of ensemble spread does not produce such structural effects on the forecast error covariance. This study suggests the importance of considering SST perturbations within ensemble-based data assimilation and promotes further studies with more sophisticated methods of perturbing SST fields such as using a fully coupled atmosphere–ocean model.

scholarly journals Sea Surface Temperature Analysis for Fengyun-3C Data Using Oriented Elliptic Correlation Scales

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8067
Author(s):  
Zhihong Liao ◽  
Bin Xu ◽  
Junxia Gu ◽  
Chunxiang Shi
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Kalman Filtering ◽  
Sea Surface ◽  
Quality Analysis ◽  
Observation Error ◽  
Change Analysis ◽  
Background Error ◽  
Filtering Method ◽  
Correlation Scale

Sea surface temperature (SST) is critical for global climate change analysis and research. In this study, we used visible and infrared scanning radiometer (VIRR) sea surface temperature (SST) data from the Fengyun-3C (FY-3C) satellite for SST analysis, and applied the Kalman filtering methods with oriented elliptic correlation scales to construct SST fields. Firstly, the model for the oriented elliptic correlation scale was established for SST analysis. Secondly, observation errors from each type of SST data source were estimated using the optimal matched datasets, and background field errors were calculated using the model of oriented elliptic correlation scale. Finally, the blended SST analysis product was obtained using the Kalman filtering method, then the SST fields using the optimum interpolation (OI) method were chosen for comparison to validate results. The quality analysis for 2016 revealed that the Kalman analysis with a root-mean-square error (RMSE) of 0.3243 °C had better performance than did the OI analysis with a RMSE of 0.3911 °C, which was closer to the OISST product RMSE of 0.2897 °C. The results demonstrated that the Kalman filtering method with dynamic observation error and background error estimation was significantly superior to the OI method in SST analysis for FY-3C SST data.

scholarly journals Forecasting experiments of a dynamical-statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

2017 ◽  
Author(s):  
Mei Hong ◽  
Xi Chen ◽  
Ren Zhang ◽  
Dong Wang ◽  
Shuanghe Shen ◽  
...  
Keyword(s):  
Statistical Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Correlation Coefficient ◽  
El Niño ◽  
Temperature Anomaly ◽  
El Nino ◽  
Sea Surface ◽  
Sea Surface Temperature Anomaly

Abstract. With the objective of tackling the problem of inaccurate long-term El Niño Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical-statistical forecast model of sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamic reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical-statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a correlation coefficient of approximately 0.80 and a mean absolute percentage error of less than 15 %. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field, but the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The correlation coefficient is 0.8062, and the MAPE value of 19.55 % is small. The difference between forecast results in summer and those in winter is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.

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