RESUMEN
Tropical cyclone Seroja was one of the first tropical cyclones to significantly impact Indonesian land, and the strongest one in such close proximity to Timor Island. In April 2021 Seroja brought historic flooding to near-equatorial regions of Indonesia and East Timor, as well as impacting Western Australia. Here we show that the unusual near-equatorial cyclogenesis in close proximity to a land mass was due to "perfect storm" conditions associated with multiple wave interactions. Specifically, this was associated with enhanced equatorial convection on the leading edge of a Madden-Julian Oscillation (MJO) event. Within the MJO, the interaction between a convectively coupled equatorial Rossby wave and two convectively coupled Kelvin waves span up the initial vortex and accelerated cyclogenesis. On average, such favorable atmospheric conditions can occur once per year. These results indicate the potential for increased predictability of tropical cyclones over the Maritime Continent.
RESUMEN
This study aims to analyze the effect of the differences in intensity and track of tropical cyclones upon significant wave heights and direction of ocean waves in the southeast Indian Ocean. We used the tropical cyclone data from Japan Aerospace Exploration Agency (JAXA) starting from December 1997 to November 2017. The significant wave height and wave direction data are reanalysis data from Copernicus Marine Environment Monitoring Service (CMEMS), and the mean sea level pressure, surface wind speed, and wind direction data are reanalysis data from European Center for Medium-Range Weather Forecasts (ECMWF) from December 1997 to November 2017. The results show that the significant wave height increases with the increasing intensity of tropical cyclones. Meanwhile, the direction of the waves is influenced by the presence of tropical cyclones when tropical cyclones enter the categories of 3, 4, and 5. Tropical cyclones that move far from land tend to have higher significant wave height and wider affected areas compared to tropical cyclones that move near the mainland following the coastline.
RESUMEN
Determinant factors which contribute to the prediction should take into account multivariate analysis for capturing coarse-to-fine contextual information. From the preliminary descriptive analysis, it shows that environmental factor such as UV (ultraviolet) is one of the essential factors that should be considered to observe the COVID-19 epidemic drivers. Moreover, there are education, government, morphological, health, economic, and behavioral factors contributing to the growth of COVID-19. Besides descriptive analysis, in this research, multivariate analysis is considered to provide comprehensive explanations about factors contributing to pandemic dynamics. To achieve rich explanations, visual attribution of explainable Convolution-LSTM is utilized to see high contributing factors responsible for the growth of daily COVID-19 cases. Our model consists of 1 D CNN in the first layer to capture local relationships among variables followed by LSTM layers to capture local dependencies over time. It produces the lowest prediction errors compared to the other existing models. This permits us to employ gradient-based visual attribution for generating saliency maps for each time dimension and variable. These are then used for explaining which variables throughout which period of the interval is contributing for a given time-series prediction, likewise as explaining that during that time intervals were the joint contribution of most vital variables for that prediction. The explanations are useful for stakeholders to make decisions during and post pandemics. The explainable Convolution-LSTMcode is available here: https://github.com/cbasemaster/time-series-attribution.
