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Forecasting Indian Summer Monsoon Rainfall (ISMR) is a formidable task due to its intricate variability. This study harnesses the power of machine learning (ML) to decipher the chaotic trajectory within ISMR, drawing inspiration from ML's success in predicting analogous systems. By utilizing ERA-interim data, the method dissects ISMR's chaotic nature through correlation dimension-based techniques. Employing the Lorenz-96 model on daily rainfall data, trained with an Echo State Network (ESN), the technique discerns patterns within a span of 1 model time slightly trailing its performance in other systems. This discrepancy could stem from the intricacies of observational data and the training process involving 500 initial conditions. Notably, this method achieves accuracy in slightly over 50% of cases. Despite its current limitations, this approach exhibits promise in shedding light on the chaotic behaviour enforced in ISMR. As a result, it contributes to the advancement of monsoon forecasting techniques. Plain language summary: Predicting Indian Summer Monsoon Rainfall is a challenging task because it is highly variable. This study uses machine learning to better recognize the complex chaotic patterns in ISMR, using a type of data called ERA-interim. Apply a mathematical model called the Lorenz-96 model to daily rainfall data and train it using a neural network called an Echo State Network. This method can identify patterns in ISMR with up to about 1 model time unit, which is slightly less accurate compared to its performance in predicting other systems. This difference may be due to the complexities of the data used and the training process, which involves 500 initial conditions. Importantly, this approach is successful in predicting ISMR accurately in slightly over 50% of cases. While it has some limitations, this method shows promise in helping us recognize the chaotic behaviour of ISMR and may be used in improving monsoon forecasting techniques in future. [ABSTRACT FROM AUTHOR]

The aim of the present study is to investigate the response of ionospheric total electron content (TEC), Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) scintillations during 17 March 2015 St. Patrick's Day geomagnetic storm over Visakhapatnam, which is popularly known as Waltair (WALT) in the literature. GPS TEC observations obtained from five IGS stations (SGOC, IISC, HYDE, LCK4 and LHAZ) and WALT during the storm have been compared. The TEC derived from GPS, GLONASS constellations and CODE global ionosphere TEC map (GIM) over WALT has also been compared. Positive storm effect during the main phase of the storm and negative storm effect during the recovery phase of the storm were observed over the said stations. The variation of northern equatorial ionisation anomaly TEC (CODE GIM TEC maps) in response to the St. Patrick's Day storm over four Indian longitudes ( 75 ∘ E , 80 ∘ E , 85 ∘ E and 90 ∘ E ) has also been presented. Strong amplitude and phase scintillations were observed in the L-band signals of GPS and GLONASS constellations over WALT. Twelve satellite (Pseudo Random Noise) PRNs of GPS L1 and nine PRNs of each GLONASS L1 and L2-band signals were affected by strong amplitude and phase scintillation. The peak amplitude scintillation index (S4) obtained from the effected PRNs of GPS L1 signal and GLONASS L1-band signals over WALT range from 0.36 to 0.74 and 0.36 to 0.76, respectively. Strong fluctuations in rate of TEC index are noted over the said stations. This enhanced scintillation activity is mainly due to the main phase of the storm falls in the evening sector over the Indian region. [ABSTRACT FROM AUTHOR]