A robust alternative for correcting systematic biases in multi-variable climate model simulations. (May 2021)

• Record Type: Journal Article
• Authors: Mehrotra, R.; Sharma, A.
• Is Part Of: Environmental modelling & software. Volume 139(2021)
• Publication Date: 2021-05
• Subjects: Bias correction -- R package -- Persistence -- Multivariate -- Distribution based quantile matching -- Nested bias correction -- Model complexity -- Self-evolving robust bias correction; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105019
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The existing bias correction (BC) methods used in impact studies are routinely based on a fixed model structure and often ignore the nature and magnitude of biases, and their variations into the future. As a calibrated model is applied to bias correct the future time series, there is no feedback mechanism to assess the impact of model complexity on the model performance in the future. In this paper we propose a flexible modelling strategy to create a robust bias correction procedure, in the form of an open-source toolkit in the R statistical computing environment. The approach allows the user to apply a multi-dimensional bias correction model that is self-evolving and grows in complexity on the basis of the requirement of the raw data. The theoretical background and the capabilities of the software along with a sample application and results discussions are demonstrated in this paper. Highlights: A flexible modelling strategy to create a robust bias correction procedure. Available as an open-source toolkit in the R statistical computing environment. The multi-dimensional bias correction model is self-evolving. The model grows in complexity on the basis of the requirement of the raw data.