Добірка наукової літератури з теми "El-Nino Southern Oscillation-index"

At present, most of the studies on the relationship between El Nino Southern Oscillation and agricultural futures focus on perceptual analyses and directly data analysis, and these discussions are usually limited to futures price. This article uses EMD algorithm to decompose Wheat futures prices and denoised ENSO index, and finally gets the negative relationship between El Nino Southern Oscillation and wheat Futures prices. Then, this article conducts the comparative analysis of operation performance based on El Niño Southern Oscillation, finding that this mode of operation can greatly increase yields, which further explains the practical significance of ENSO. In order to explore the impact of El Nino Southern Oscillation on wheat futures yields, use classic GARCH models, transform ENSO index into virtual variables, respectively introduce them into the mean value equation and conditional variance equation. After analysis, the conclusion shows that the impact is mainly on the volatility of return rate, which reminds traders of considering risk management first.

IntisariBeberapa lembaga riset dunia dan badan-badan meteorologi beberapa negara di dunia menyatakan adanya kejadian El Nino Tahun 2015 terus berlanjut hingga tahun 2016. Adanya kejadian El Nino tersebut secara umum akan mempengarui intensitas curah hujan di sebagian besar wilayah Indonesia termasuk wilayah Jabodetabek. Analisis kejadian El Nino Tahun 2015/2016 dilakukan dengan menganalisis nilai NINO 3.4 SST Index, Southern Oscillation Index (SOI), Indian Ocean Dipole (IOD), pola sebaran suhu permukaan laut (Sea Surface Temperature) dan juga gradient wind di Samudra Pasifik Tropis. Sedangkan Analisis Curah Hujan dilakukan dengan menggunakan data TRMM (Tropical Rainfall Measuring Mission). Dari penelitian ini dapat diketahui bahwa berdasarkan parameter NINO 3.4 SST Index dan Southern Oscillation Index (SOI) pada pertengahan Tahun 2015 hingga awal Tahun 2016 telah terjadi fenomana El Nino pada level kuat, adanya peningkatan suhu permukaan laut di sebagian besar wilayah Indonesia sejak Bulan November 2015 yang diikuti dengan penurunan indeks Dipole Mode hingga menjadi bernilai negatif (-) sejak awal Tahun 2016 serta dengan adanya peralihan Angin Muson Timur ke Angin Muson Barat di wilayah Indonesia telah menyebabkan peningkatan curah hujan yang cukup signifikan dalam batas normal di wilayah Jabodetabek pada puncak musim hujan Tahun 2015/2016 (November 2015 - Februari 2016) walaupun pada Bulan November 2015 hingga Februari 2016 tersebut masih berada pada level El Nino kuat. AbstractVarious research institutions in the world that work in the field of Meteorology and Climatology predicted an El Nino events in 2015 continued into 2016. The El Nino events phenomenon in general will affect to intensity of the rainfall in most parts of Indonesia, including the Greater Jakarta area. El Nino events phenomenon Analysis by Nino 3.4 SST index, Southern Oscillation Index (SOI), Indian Ocean Dipole (IOD), Sea Surface Temperature (SST) and gradient wind in the Tropical Pacific Ocean. While rainfall intensity analysis using TRMM (Tropical Rainfall Measuring Mission) data. From this research it is known that based on the parameters NINO 3.4 SST index and the Southern Oscillation Index (SOI), it is known that there was a strong El Nino event occurred in mid-2015 to early 2016, the increase of sea surface temperature in most parts of Indonesia since November 2015 followed by declines Dipole Mode Index to be negative (-) since the beginning 2016 as well as the shift East monsoon to West monsoon in Indonesia has led to significant rainfall increased within normal limits in the Greater Jakarta area at the peak period of the rainy season 2015/2016 (November 2015 - February 2016) although in November 2015 until February 2016 El Nino event is still at the strong level.

Abstract El Nino-Southern Oscillation (ENSO) contributes to the regional climates, such as precipitation and droughts. The objectives of the present work were to: (1) identify the severity index; (2) analyze the correlation of SPI and RDI, and; (3) identify the response of SPI and RDI towards ENSO. SPI and RDI were calculated for time scales (3, 6, and 12 months), and these represented the seasonal and annual drought. The identification of the responses of the drought severity index, based on ENSO, consisted of several thresholds, namely weak, moderate, and strong. The correlational value and RMSE only represented the performance of SPI and RDI on different time scales. The drought severity index would decline along with an increase in the time scales. The strong El Nino phase could be significant to the seasonal and annual drought. In other words, ENSO was impactful on the precipitation and dynamics of drought. Drought periods were due to the moderate and strong El Nino phase, while the weak phase led to a normal condition. For this reason, ENSO could be functioned as an indicator to predict drought.

Penelitian ini bertujuan untuk memprediksi nilai indeks ENSO yaitu Sea Surface Temperature (Nino 1.2, Nino 3, Nino 3.4 dan Nino 4), Southern Oscillation Index (SOI) dan Multivariate ENSO Index versi 2 (MEI.v2) yang diambil dari tahun 1979-2018. Prediksi dilakukan dengan menggunakan metode JST-backpropagation dengan memvariasikan learning rate dan momentum. Semua indeks menghasilkan nilai akurasi prediksi ENSO yang tinggi, namun indeks Nino 4 merupakan indeks yang memiliki akurasi tertinggi karena nilai Mean Square Error (MSE) pelatihan dan pengujiannya yang relatif lebih kecil dibandingkan dengan indeks lainnya. Indeks Nino 4 memiliki MSE pelatihan 0,0072739 yang berhenti pada epoch ke-69 dan MSE pengujian 0,0085917 dengan akurasi prediksi 99,9989%. Hasil ini diperoleh dari arsitektur JST-backpropagation 12-10-1 dengan nilai learning rate 0,10 dan momentum 0,40. Prediksi ENSO berdasarkan indeks Nino 4 untuk tahun 2021 menunjukkan keadaan iklim dunia dalam kondisi normal. This study aims to predict ENSO index using Sea Surface Temperature (Nino 1.2, Nino 3, Nino 3.4 and Nino 4 indexes), Southern Oscillation Index (SOI), and Multivariate ENSO Index version 2 (MEI.v2) during 1979 - 2018. The prediction was carried out using the ANN-backpropagation method by varying the learning rate and momentum. All indices produce high ENSO prediction accuracy values, but the Nino 4 index is the best one because the Mean Square Error (MSE) for training and testing steps are relatively smaller than other indexes. The Nino 4 index has a training MSE of 0.0072739 which stops at the 69th epoch and a testing MSE of 0.0085917 with a predictive accuracy of 99.9989%. These results were obtained from the back-propagation architecture ANN 12-10-1 with a learning rate of 0.10 and a momentum of 0.40. The prediction of ENSO in 2021 based on the Nino 4 index shows that the world climate condition is under normal conditions.

Telah dilakukan analisis El-Nino dan pengaruhnya terhadap curah hujan di Manado Sulut dengan menggunakan data curah hujan bulanan dan Southern Oscillation Index SOI selama thn 1999-2009. Data curah hujan diperoleh dari BMKG Kayuwatu Manado Sulut dan data SOI diunduh dari website Biro Meteorologi Australia BoM. Analisis dilakukan dengan analisis regresi linear sederhana. Diperoleh hasil bahwa curah hujan kota Manado secara umum dari thn 1999-2008 dipengaruhi oleh SOI namun pada thn 2009 ketika terjadi El-nino, curah hujan bulanan Manado tidak dipengaruhi oleh efek El-nino. Hal ini karena pada saat yang sama suhu muka laut perairan Indonesia juga menghangat. EL-NINO AND ITS EFFECT ON RAINFALL IN MANADONORTH SULAWESIABSTRACTAnalysis of El-Nino and its effect on rainfall in Manado, North Sulawesi, using monthly rainfall data and the Southern Oscillation Index SOI during the years 1999-2009 has been carried out. Rainfall data obtained from BMKG Kayuwatu Manado and SOI data downloaded from the website of Australian Bureau of Meteorology (BoM). The analysis was performed with simple linear regression analysis. The results obtained indicate that rainfall in Manado , in general, was influenced by SOI from the years 1999-2008, but when there is an El-Nino in 2009, monthly rainfall in Manado is not affected by the El-Nino effect. This is due to, at the same time, sea surface temperature in Indonesian territory are also warm.

El Nino Southern-Oscillation (ENSO) refers to the cycle of coherent and sometimes very strong variations in the sea surface temperature (SST), convective rainfall, surface pressure and atmospheric circulation across the equatorial Pacific Ocean. The research investigated the relationship between ENSO and rainfall across the Sudano-Sahelian region of northern Nigeria. Rainfall data for seven (7) locations were sourced from the Nigerian Meteorological Agency (NIMET) covering the period from 1950 to 2019 and El-Nino Southern Oscillation (ENSO) data which comprised of Southern Oscillation Index (SOI) covering the same period. The Southern Oscillation Index (SOI) data was obtained from National Oceanographic Atmospheric Administration (NOAA) Climate Prediction Centre. A bivariate correlation analysis between rainfall and SOI were computed for all the seven locations to determine the relationship between ENSO and Sudano-Sahelian rainfall. The result shows a significant relationship for Potiskum, Maiduguri and Katsina with correlation coefficient (r) values of 0.25, 0.26 and 0.27 respectively while Kano Gusau Nguru and Sokoto show no significant relationship. It is therefore concluded that there is no strong relationship between rainfall and ENSO in Sudano-Sahelian part of northern Nigeria. It is recommended that further research could be carried out to investigate the influence of ENSO on other climate parameters such as temperature, humidity and evapotranspiration in the study area.

The climate in Indonesia usually runs yearly; there are times when a decrease in rainfall results in drought, and at other times, the rainfall increases resulting in flooding. One of the causes of changes in precipitation in Indonesia, including in most parts of the world, is ENSO (El Nino-Southern Oscillation), often called El Nino. This study aimed to determine the relationship between ENSO index data (SST Nino 3.4 anomaly) and monthly rainfall data in Buleleng Regency. This study uses secondary data, namely monthly rainfall data at 16 rain posts in Buleleng Regency and ENSO Index data from BMKG Region III Denpasar. Data were collected through observation, document recording and analyzed using statistical correlation methods. Furthermore, the results are processed spatially, namely by the Isohyet method. The research results show that the impact of El Nino on rainfall in Buleleng varies spatially and depends on the intensity of El Nino. In June-July-August (JJA/dry season) and September-October-November (SON/transition season), the impact of El Nino on rainfall variability in Buleleng Regency is more significant than other months, strong El Nino causes a decrease in rainfall in the majority of the Buleleng region with the characteristic of Below Normal rain (30% decrease in precipitation from the average), El Nino of weak - moderate intensity causes a reduction in rainfall in a small part of the Buleleng area with the dominant rain characteristic of Below Normal.Keywords: El Nino, Precipitation Variability, and Correlation Index.

Paddy and maize are two important food crops in Indonesia and mainly produced in Java Island. This research aimed to know the impact of El Nino and La Nina on paddy and maize farmer’s supply in Java. Cross sectional data from four provinces in Java was combined with time series data during 1987-2006. Paddy supply was estimated using log model, while maize supply used autoregressive model; each was estimated using two types of regression function. First, it included dummy variable of El Nino and La Nina to know their influence into paddy and maize supply. Second, Southern Oscillation Index was used to analyze the supply changing when El Nino or La Nina occur. The result showed that El Nino and La Nina did not influence paddy supply, while La Nina influenced maize supply in Java. Maize supply increased when La Nina occurred.

We used long-term observations of grassland aboveground net plant production (ANPP, 19392016), growing seasonal advanced very-high-resolution radiometer remote sensing normalized difference vegetation index (NDVI) data (1982-2016), and simulations of actual evapotranspiration (1912-2016) to evaluate the impact of Pacific Decadal Oscillation (PDO) and El Nino-Southern Oscillation (ENSO) sea surface temperature (SST) anomalies on a semiarid grassland in northeastern Colorado. Because ANPP was well correlated (R-2 = 0.58) to cumulative April to July actual evapotranspiration (iAET) and cumulative growing season NDVI (iNDVI) was well correlated to iAET and ANPP (R-2 = 0.62 [quadratic model] and 0.59, respectively), we were able to quantify interactions between the long-duration (15-30 yr) PDO temperature cycles and annual-duration ENSO SST phases on ANPP. We found that during cold-phase PDOs, mean ANPP and iNDVI were lower, and the frequency of low ANPP years (drought years) was much higher, compared to warm-phase PDO years. In addition, ANPP, iNDVI, and iAET were highly variable during the cold-phase PDOs. When NINO-3 (ENSO index) values were negative, there was a higher frequency of droughts and lower frequency of wet years regardless of the PDO phase. PDO and NINO-3 anomalies reinforced each other resulting in a high frequency of above-normal iAET (52%) and low frequency of drought (20%) when both PDO and NINO-3 values were positive and the opposite pattern when both PDO and NINO-3 values were negative (24% frequency of above normal and 48% frequency of drought). Precipitation variability and subsequent ANPP dynamics in this grassland were dampened when PDO and NINO-3 SSTs had opposing signs. Thus, primary signatures of these SSTs in this semiarid grassland are (1) increased interannual variability in ANPP during cold-phase PDOs, (2) drought with low ANPP occurring in almost half of those years with negative values of PDO and NINO-3, and (3) high precipitation and ANPP common in years with positive PDO and NINO-3 values.

Drought is a global and recurrent natural phenomenon, the inevitable consequence of meteorological variability. This natural hazard brings about devastating effects because water is one of the most fundamental commodities for human survival, and a lack of water can result in varying consequences, from mere inconvenience to life threatening instances. Drought cannot be prevented but its effects can be mitigated through the design of appropriate water resource infrastructure and management strategies. The goal of this thesis is to model the spatial and temporal characteristics of drought occurrence in Australia, the driest continent. In doing so predictions can be made, levels of risk can be evaluated and conditional estimates of drought can be based on climatic state variables. For insight into the nature of drought in Australia, multivariate models of drought characteristics are developed. Preliminary analysis demonstrates high correlations between several drought characteristics, these are the drought severity, intensity and duration. This thesis applies the copula concept, which is a versatile means of modelling their dependence structure. Copulas are multivariate uniform distributions, which allow the joint behaviour of variables to be modelled independently from their marginal distributions. This research extends the application of copulas by investigating the effect of climate variability on copula models and subsequent drought characteristics. Two different copula families are fitted to the drought characteristics to demonstrate the importance of tail dependence when modelling extreme climatic events. An important application of these models is the calculation of return periods of extreme drought events exceeding certain thresholds, taking account of variability in climatic indices. A second objective is to forecast drought at various spatial resolutions. The most straightforward method are regression and ARMA models that incorporate global climatic indicators. The effect of climatic variation on Australia's precipitation is examined by investigating the association between climatic indices and the multivariate distribution of drought at numerous sites across Australia. Two classification strategies for forecasting rainfall are compared using significance testing based on multiple comparison techniques. Further to this, rainfall forecasting relationships are explored using global sea-surface temperature anomalies. The versatility of copula models is demonstrated through short-term rainfall predictions for neighbouring rainfall districts, using separate copulas conditioned on antecedent climate conditions. This technique is shown to improve rainfall predictions in neighbouring districts and improve estimates of drought probability.
Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Sciences, 2010