Avaliação da precipitação na produtividade agrícola da cana-de-açúcar: estudo de caso usina coruripe para as safras de 2000/2005

This study evaluates the influence of rainfall, southern oscillation index (SOI), multivariate index of El Niño Southern Oscillation (ENSO), sea surface temperature anomaly of the sea surface (SSTA) in the Atlantic dipole index and normalized difference vegetation (NDVI/derived from satellites METEOSAT 9 and 10) on agricultural productivity of cane sugar (ton/ha) on the southern coast of the State of Alagoas for the period 2000-2005. Analyses were performed using rainfall Coruripe Plant, located in the municipality of Coruripe/AL. The results show that the annual variability of the agricultural productivity of cane sugar mill Coruripe is modulated largely by rainfall. Additionally, characterized and interannual variability annual agricultural productivity of cane sugar related to climate variability. The results also suggest that the NDVI data from the satellites METEOSAT 9 and 10 used in this work, proved to be, useful observations to characterize the agricultural productivity of cane sugar mill Coruripe.

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A Vector Autoregressive ENSO Prediction Model

Journal of Climate ◽

10.1175/jcli-d-15-0306.1 ◽

2015 ◽

Vol 28 (21) ◽

pp. 8511-8520 ◽

Cited By ~ 15

Author(s):

David Chapman ◽

Mark A. Cane ◽

Naomi Henderson ◽

Dong Eun Lee ◽

Chen Chen

Keyword(s):

Surface Temperature ◽

State Vector ◽

Inverse Method ◽

Temperature Anomaly ◽

Southern Oscillation ◽

Sea Surface ◽

Var Model ◽

Extended State ◽

Vector Autoregressive ◽

Sea Surface Temperature Anomaly

Abstract The authors investigate a sea surface temperature anomaly (SSTA)-only vector autoregressive (VAR) model for prediction of El Niño–Southern Oscillation (ENSO). VAR generalizes the linear inverse method (LIM) framework to incorporate an extended state vector including many months of recent prior SSTA in addition to the present state. An SSTA-only VAR model implicitly captures subsurface forcing observable in the LIM residual as red noise. Optimal skill is achieved using a state vector of order 14–17 months in an exhaustive 120-yr cross-validated hindcast assessment. It is found that VAR outperforms LIM, increasing forecast skill by 3 months, in a 30-yr retrospective forecast experiment.

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Java Sea Surface Temperature Variability during ENSO 1997 – 1998 and 2014 – 2015

Omni-Akuatika ◽

10.20884/1.oa.2018.14.1.429 ◽

2018 ◽

Vol 14 (1) ◽

Author(s):

Hilda Heryati ◽

Widodo Setiyo Pranowo ◽

Noir Primadona Purba ◽

Achmad Rizal ◽

Lintang Permata Yuliadi

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

El Niño ◽

Temperature Anomaly ◽

El Nino ◽

Southern Oscillation ◽

Sea Surface ◽

Strong Linkage ◽

Sea Surface Temperature Anomaly ◽

Java Sea

Sea Surface Temperature (SST) is one of the important parameter to describe seawater characteristic. There is a strong linkage between SST and El Nino Southern Oscillation (ENSO). The purpose of this research is to investigate SST of Java Sea during in period 1997—1998 and 2014– 2015. We use datasets from Hycom archieves, INDESO, and SOI. The result shows El Nino is started in March 1997 until April 1998 (peak in March 1998), then La Nina is started in June to December 1998 (peak in July 1998). Maximum Sea Surface Temperature Anomaly (SSTA) is occurred in August – September 1998 (0.8 °C – 0.9 °C). During 2014–2015, a propagation of El Nino is founded. El Nino is started in August until November 2014 (-7.6 < SOI < -11.4, peak in August), and is followed in May to October 2015 (-12 < SOI < -20.2, peak in October). During 2014–2015, a maximum Sea Surface Temperature Anomaly (SSTA) is founded in May 2014 (0.5 °C).

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