Abstract
El Niño-Southern Oscillation (ENSO) is characterized by large-scale fluctuations of sea surface temperature (SST) in the central and eastern tropical Pacific accompanied by changes in the atmospheric circulation. ENSO events are of two main types: El Niño and La Niña. Oceanic Niño index (ONI) determines the five consecutive three-month running mean of SST anomalies, in the Niño 3.4 region (5° S-5° N, 170° W-120° W). El Niño is a phenomenon in the equatorial Pacific Ocean characterized by a value of greater than 0.5 °C for ONI. La Niña is a phenomenon in the equatorial Pacific Ocean characterized by a value of less than -0.5 °C for ONI. The lingering of either of these two phenomena could induce severe droughts, whereas either of them following the other could cause massive floods. In both cases, deaths and substantial pecuniary loss are unavoidable, making their forecast of great significance. This study takes over the challenge of forecasting these two phenomena with one year lead time, which has proven difficult in the literature. This research's contribution is restructuring ENSO events' predictors, including SST, sea level pressure, surface wind speed, and wind stress in a spatially and temporally meaningful way and designing a convolutional neural network that takes advantage of this structure to forecast ENSO events of different types (i.e., Central Pacific and Eastern Pacific) in the next year. Not only a high precision in forecasts was achieved but also it was shown that the proposed model has the potential to achieve higher recalls if a larger number of samples from the positive class would become available.
Highlights
E L NIÑO–SOUTHERN Oscillation (ENSO) is the theory describing the tropical climate variability in terms of the sea surface temperature (SST) in the Pacific and the overlying atmosphere
convolutional neural network (CNN) with one convolutional layer produced the highest accuracies in forecasting Central Pacific (CP)-El Niño, Eastern Pacific (EP)-El Niño, and CP-La Niña
The seemingly low accuracy of the proposed model in forecasting ENSO events for the year should be taken into account with three facts: the training dataset is small with only 65 samples, SST was missed for 25% of samples, and forecasting ENSO events with one year lead time using machine learning has proven to be a challenging task in the literature
Summary
E L NIÑO–SOUTHERN Oscillation (ENSO) is the theory describing the tropical climate variability in terms of the sea surface temperature (SST) in the Pacific and the overlying atmosphere. ENSO is characterized by large-scale fluctuations of SST in the central and eastern tropical Pacific accompanied by changes in the atmospheric circulation. ENSO events are of two main types: El Niño and La Niña. An El Niño happens when SSTs in the central western Pacific are above average and the tropical easterly winds weaken. A La Niña happens when SST in the eastern tropical Pacific is above average and the tropical. Manuscript received November 18, 2020; revised January 27, 2021; accepted March 9, 2021. Date of publication March 11, 2021; date of current version April 2, 2021
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.