Quantile mapping bias correction methods to IMDAA reanalysis for calibrating NCMRWF unified model operational forecasts

• Published in: Hydrological sciences journal Vol. 67; no. 6; pp. 870 - 885
• Main Authors: Niranjan Kumar, Kondapalli, Thota, Mohana Satyanarayana, Ashrit, Raghavendra, Mitra, Ashis K., Rajeevan, Madhavan Nair
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 26.04.2022; Taylor & Francis Ltd
• Subjects: Atmospheric precipitations; Bias; bias correction; Calibration; cumulative distribution; Data assimilation; Data collection; Earth sciences; Empirical analysis; Extreme weather; Flood forecasting; Flood models; Flood warning systems; Flooding; Floods; Hydrologic data; India; Mapping; Mathematical models; monsoon season; Parametric methods; Precipitation; Precipitation data; quantile mapping; Quantiles; Rain; Rainfall; River discharge; urban cities; Warning systems; Weather forecasting; India
• ISSN: 0262-6667; 2150-3435; 2150-3435
• DOI: 10.1080/02626667.2022.2049272

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.