scholarly journals Analisis Kondisi Atmosfer Saat Kejadian Hujan Lebat dan Angin Kencang di Probolinggo Berdasarkan Citra Satelit dan Citra Radar

2021 ◽  
Vol 5 (2) ◽  
pp. 142-156
Author(s):  
Nur Habib Muzaki ◽  
Keyword(s):  
Temperature Anomaly ◽  
Remote Sensing Data ◽  
Convective Cloud ◽  
Heavy Rain ◽  
Sea Surface ◽  
Air Masses ◽  
Sea Surface Temperature Anomaly ◽  
Strong Winds ◽  
Rain Events ◽  
Spatial Maps

The phenomenon of extreme weather, heavy rain and strong winds hit four sub-districts in Probolinggo Regency, East Java on January 3, 2020 at 17.00 WIB. Based on data from the East Java Regional Disaster Management Agency (BPBD), the incidence of heavy rain and strong winds resulted in damage to as many as 204 houses. This study uses remote sensing data in the form of C-Band Radar and Himawari-8 Satellite and Copernicus ECMWF renalysis data. The data is processed into spatial maps and graphs which are then analyzed descriptively. The results of data analysis show that the reflectivity value reaches 43 dBZ and the wind speed reaches 13.57 m / s with a rainfall of 15.83 mm / hour at 10.00 WIB. Based on the analysis of the Himawari-8 Satellite, the peak temperature of the clouds reached -73.1 oC and the atmospheric lability data showed that the atmosphere was unstable, which could indicate the possibility of heavy rain and strong winds. The value of vortices in the 1000 mb - 500 mb layer is negative and the humidity value ranges from 85% - 90% and a positive sea surface temperature anomaly value and the presence of windshields result in convergence of air masses which can support convective cloud growth as the cause of heavy rain events and strong winds in Probolinggo Regency, East Java

Time Series Analysis of CMIP5 Model and Observed Sea Surface Temperature Anomaly Along Indian Coastal Zones

2019 ◽  
Vol 86 (sp1) ◽  
pp. 239
Author(s):  
Dhanya Joseph ◽  
Vazhamattom Benjamin Liya ◽  
Girindran Rojith ◽  
Pariyappanal Ulahannan Zacharia ◽  
George Grinson
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Anomaly ◽  
Sea Surface ◽  
Coastal Zones ◽  
Cmip5 Model ◽  
Series Analysis ◽  
Sea Surface Temperature Anomaly

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.

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