Analysis on the occurrence of Tropical Cyclone in the South Pacific Region using recurrent Neural Network with LSTM

Weather prediction over the years has been a challenge for the meteorological centers in the South Pacific region. This paper presents Recurrent Neural Network (RNN) Architecture with Long Short Term Memory (LSTM) times-series weather data for prediction. From the gathered dataset, the Sea Surface Temperature (SST) is studied since it is known to be the foundation of the cyclone formation. This paper focuses on two scenarios. The first part is predicting upcoming SST using dataset from January 2013 to December 2017. The second part is taking out data of two different cyclones and predicting the SST for the next 14 days. Once the SST prediction is made, the predicted SST is compared with SST in the dataset for those 14 days. The main aim of this paper is to predict the SST using RNN and LSTM to anticipate the occurrence of tropical cyclones. The paper will focus on the reason for this study, a discussion of the model used, how the cyclones are formed, regarding the current threshold, the analysis of the dataset and lastly, the results from the experiment carried out

Intensity of tropical cyclones during pre and post monsoon seasons in relation to accumulated tropical heat potential over Bay of Bengal

The aim of the present study is to understand the impact of oceanic heat potential in relation to the intensity of tropical cyclones (TC) in the Bay of Bengal during the pre-monsoon (April–May) and post-monsoon (October–November) cyclones for the period 2006–2010. To accomplish this, the two-layer gravity model (TLGM) is employed to estimate daily tropical cyclone heat potential (TCHP) utilizing satellite altimeter data, satellite sea surface temperature (SST), and a high-resolution comprehensive ocean atlas developed for Indian Ocean, subsequently validated with in situ ARGO profiles. Accumulated TCHP (ATCHP) is estimated from genesis to the maximum intensity of cyclone in terms of minimum central pressure along their track of all the cyclones for the study period using TLGM generated TCHP and six-hourly National Centre for Environmental Prediction Climate Forecast System Reanalysis data. Similarly, accumulated sea surface heat content (ASSHC) is estimated using satellite SST. In this study, the relationship between ATCHP and ASSHC with the central pressure (CP) which is a function of TC intensity is developed. Results reveal a distinct relationship between ATCHP and CP during both the seasons. Interestingly, it is seen that requirement of higher ATCHP during pre-monsoon cyclones is required to attain higher intensity compared to post-monsoon cyclones. It is mainly attributed to the presence of thick barrier layer (BL) resulting in higher enthalpy fluxes during post-monsoon period, where as such BL is non-existent during pre-monsoon period