There is a need to improve the prediction of tropical cyclone (TC) rainfall as climate change has led to increased TC rainfall rates. Enhanced reliability in predicting TC tracks has paved the way for statistical methodologies to utilize them in estimating current TC rainfall, achieved by identifying similar past TC tracks and obtaining their corresponding rainfall data. The widely used Fuzzy C Means (FCM) clustering algorithm, though popular, has limitations stemming from its clustering-centric design, hindering its ability to pinpoint the most appropriate similar TCs. Our study introduces the Sinkhorn Distance as a novel measure of TC similarity in rainfall prediction. Our findings indicate that the incorporation of Sinkhorn Distance significantly enhances the accuracy of TC rainfall predictions across WNP. When compared to the conventional approach using FCM, our Sinkhorn Distance-based methodology consistently yields better results, as demonstrated by metrics like RMSE and correlation coefficients. Collectively, the inclusion of Sinkhorn Distance stands as a valuable addition to our toolkit for discerning similar TC tracks, thus elevating the precision of TC rainfall predictions. With ongoing advancements in statistical and AI techniques, we anticipate even more refined approaches to further enhance our predictive capabilities. This study represents a leap forward in meeting the critical need for more accurate TC rainfall forecasts in the WNP Region.

Understanding spatial and temporal variations in terrestrial waters is key to assessing the global hydrological cycle. The future Surface Water and Ocean Topography (SWOT) satellite mission will observe the elevation and slope of surface waters at <100 m resolution. Methods for incorporating SWOT measurements into river hydrodynamic models have been developed to generate spatially and temporally continuous discharge estimates. However, most of SWOT data assimilation studies have been performed on a local scale. We developed a novel framework for estimating river discharge on a global scale by incorporating SWOT observations into the CaMa-Flood hydrodynamic model. The local ensemble transform Kalman filter with adaptive local patches was used to assimilate SWOT observations. We tested the framework using multi-model runoff forcing and/or inaccurate model parameters represented by corrupted Manning’s coefficient. Assimilation of virtual SWOT observations considerably improved river discharge estimates for continental-scale rivers at high latitudes (>50°) and also downstream river reaches at low latitudes. High assimilation efficiency in downstream river reaches was due to both local state correction and the propagation of corrected hydrodynamic states from upstream river reaches. Accurate global river discharge estimates were obtained (Kling–Gupta efficiency [KGE] > 0.90) in river reaches with > 270 accumulated overpasses per SWOT cycle when no model error was assumed. Introducing model errors decreased this accuracy (KGE ≈ 0.85). Therefore, improved hydrodynamic models are essential for maximizing SWOT information. These synthetic experiments showed where discharge estimates can be improved using SWOT observations. Further advances are needed for data assimilation on global-scale.