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Despite strong limitations, discrete random multiplicative cascades are often used to address scale issues that are ubiquitous in geosciences. A blunt extension based on the parsimonious framework of universal multifractals has recently been suggested. It preserves its simplicity and intuitiveness while overcoming its non-stationarity features. It relies on smoothing through a geometrical moving average the increments at each cascade step. Here, a space-time extension is suggested. Theoretically expected multifractal behaviour is retrieved on numerical simulations for typical rainfall parameters. A new algorithm to generate the missing half of multifractal fields in one, two or three dimensions is developed and tested on rainfall fields and numerical simulations. It consists in stochastically generating half of the increments and deterministically iteratively reconstructing the others to retrieve the available data and ensure a smooth transition with the unknown portion while preserving the multifractal behaviour. Potential applications to nowcasting of hydro-meteorological extremes are discussed. [ABSTRACT FROM AUTHOR]

This study focuses on assessing different quantile mapping (QM) bias correction approaches based on empirical and parametric methods to bias-correct the Indian Monsoon Data Assimilation and Analysis (IMDAA) precipitation data and subsequently calibrate the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model operational forecasts. The two coastal cities Chennai and Mumbai, India, are chosen here to support the Integrated Flood Warning System (IFLOWS), initiated by the Ministry of Earth Sciences, Government of India, which provides early warning and decision support during flooding. The empirical QM methods are relatively better in correcting the quantiles with calibrated precipitation close to the observed cumulative distribution at these coastal cities. However, in extreme rainfall cases, the skill of calibrated precipitation through parametric methods seems promising and suitable for flood forecasting applications. Hence, this study demonstrates QM methods' performance and their potential in downscaling precipitation that has significant implications for urban flood models. [ABSTRACT FROM AUTHOR]