Tropical Cyclone (TC) systems affect global ocean heat transport due to mixing of the upper ocean and impact climate dynamics. A higher Sea Surface Temperature (SST), other influencing factors remaining supportive, fuels TC genesis and intensification. The atmospheric thermodynamic profile, especially the sea-air temperature contrast (SAT), also contributes due to heat transfer and affects TC's maximum surface wind speed (V max ) explained by enthalpy exchange processes. Studies have shown that SST can approximately be used as a proxy for SAT. As a part of an ongoing effort in this work, we simplistically explored the connection between SST and V max from a climatological perspective. Subsequently, estimated V max is applied to compute Power Dissipation Index (an upper limit on TC's destructive potential). The model is developed using long-term observational SST reconstructions employed on three independent SST datasets and validated against an established model. This simple approach excluded physical parameters, such as mixing ratio and atmospheric profile, however, renders it generally suitable to compute potential intensity associated with TCs spatially and weakly temporally and performs well for stronger storms. A futuristic prediction by the HadCM3 climate model under doubled CO 2 indicates stronger storm surface wind speeds and rising SST, especially in the Northern Hemisphere.

Abstract. The operational medium-range weather forecasting based on numerical weather prediction (NWP) models are complemented by the forecast products based on ensemble prediction systems (EPSs). This change has been recognised as an essentially useful tool for medium-range forecasting and is now finding its place in forecasting the extreme events. Here we investigate extreme events (heatwaves) using a high-resolution NWP model and its ensemble models in union with the classical statistical scores to serve verification purposes. With the advent of climate-change-related studies in the recent past, the rising number of extreme events and their plausible socio-economic effects have encouraged the need for forecasting and verification of extremes. Applying the traditional verification scores and associated methods to both the deterministic and the ensemble forecast, we attempted to examine the performance of the ensemblebased approach in comparison to the traditional deterministic method. The results indicate an appreciable competence of the ensemble forecast at detecting extreme events compared to the deterministic forecast. Locations of the events are also better captured by the ensemble forecast. Further, it is found that the EPS smoothes down the unexpectedly increasing signals, thereby reducing the false alarms and thus proving to be more reliable than the deterministic forecast.

Thunderstorms are one of the most damaging natural hazards demanding in-depth understanding and prediction. These convective systems form in an unstable environment which is quantitatively expressed in terms of instability indices. These indices are studied over six locations across the Indian landmass in an attempt to predict thunderstorm activity on any given day. A combination of multiple regression, logistic regression, and range analysis provides new insight into the prediction of these storms. A supervised machine learning-based logistic regression model is developed in this study for thunderstorm prediction over Patna and can be further extended for operational forecasting of Thunderstorms over the region. Critical thresholds for the instability indices are determined over the considered locations providing valuable insight into the domain of Thunderstorm prediction

Ensemble prediction systems help in quantifying the inherent uncertainties in numerical weather prediction models. Verification of forecasts is essential, in order to monitor and improve forecast quality. Also, verification can be used to compare the capabilities of different numerical weather prediction models in predicting the weather. This study deals with a comparison of probabilistic rainfall forecasts obtained from the National Centre for Medium Range Weather Forecasting (NCMRWF) Global Ensemble Forecast System (NGEFS) and the UK Met Office Global and Regional Ensemble Prediction System (MOGREPS) for four monsoon seasons, June-September 2012-2015. Verification is done based on the Brier score, the Brier skill score, the reliability diagram, the relative operating characteristic (ROC) curve and the area under the ROC curve (A ROC ). The NMSG (India Meteorological Department NCMRWF merged satellite and gauge) observation dataset is used for verification. The Brier score values for verification of the MOGREPS are lower by approximately 6-14% across all rainfall thresholds and lead times, indicating that these forecasts match better with observations than the NGEFS. This is further reiterated by Brier skill score values of MOGREPS forecasts which are approximately 13-47% higher than the NGEFS. Furthermore, the reliability diagram shows that forecast probabilities are closer to observed frequencies for the MOGREPS than the NGEFS. The A ROC for the MOGREPS is also higher than for the NGEFS, hence indicating a better skill of the MOGREPS. From this study, it can be concluded that the MOGREPS showed a better capability in predicting rainfall during the southwest monsoons of 2012-2015 over the Indian region compared to the NGEFS.