Frequency-intensity-distribution bias correction and trend analysis of high-resolution CMIP6 precipitation data over a tropical river basin

Jose, Dinu Maria; Dwarakish, G. S.

doi:10.1007/s00704-022-04078-5

Cited by 4 publications

(1 citation statement)

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“…The ensemble bias correction approach is studied to preserve internal variability (Vaittinada Ayar et al, 2021). Jose and Dwarakish (2022) suggested a new methodology of combining different bias correction statistical schemes targeting specific characteristics of rainfall like frequency and intensity correction. Maraun (2013) explains the drawbacks such as the lack of plausible stand and the downscaling of the climate models developed by the statistical bias correction approach.…”

Section: Introductionmentioning

confidence: 99%

Bias correction of CMIP6 simulations of precipitation over Indian monsoon core region using deep learning algorithms

Kesavavarthini

Anandan

Srinivas

et al. 2023

Intl Journal of Climatology

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General Circulation Models or Global Climate Models (GCMs) output consists of inevitable bias due to insufficient knowledge about parameterization schemes and other mathematical computations that involve thermodynamical and physical laws while designing climate models. Indian summer monsoon (southwest monsoon) accounts for 75%-90% of the annual rainfall over most climatic zones of India during the months, June, July, August, and September, which has a direct impact on the agricultural economy of India. The aim of this study is to bias correct the Coupled Model Intercomparison Project Phase -6 (CMIP6) GCMs' precipitation data for the historical period from 1985 to 2014 and two Shared Socioeconomic Pathways (SSP) SSP1-2.6 and SSP5-8.5, from the period 2015 to 2100, with reference to the India Meteorological Department (IMD) observed rainfall gridded dataset. The datasets used are for the rain-bearing Indian southwest monsoon season from the months, June to September. Monsoon Core Region is selected to carry out the bias correction using a couple of deep learning algorithms, namely one-dimensional Convolutional Neural Network (CNN1D) and Long Short-Term Memory Encoder-Decoder (LSTM-ED) Neural Network. The performance of both algorithms is evaluated with metrics. The LSTM-ED algorithm yielded better results with least error output. The bias-corrected data obtained using the LSTM-ED algorithm is then compared with IMD observed rainfall data for the climatic events such as ENSO (El Niño and La Niña) and Positive and Negative IOD (Indian Ocean Dipole).

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Section: Introductionmentioning

confidence: 99%