Calibrated multi-model ensemble seasonal prediction of Bangladesh summer monsoon rainfall

Bangladesh summer monsoon rainfall (BSMR), typically from June through September (JJAS), represents the main source of water for multiple sectors. However, its high spatial and interannual variability makes the seasonal prediction of BSMR crucial for building resilience to natural disasters and for...

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Main Authors: Acharya, Nachiketa, Montes, Carlo, Hassan, S.M.Q., Sultana, Razia, Rashid, Md. Bazlur, Mannan, Md. Abdul, Krupnik, Timothy J.
Format: Journal Article
Language:Inglés
Published: Wiley 2023
Subjects:
Online Access:https://hdl.handle.net/10568/132226
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author Acharya, Nachiketa
Montes, Carlo
Hassan, S.M.Q.
Sultana, Razia
Rashid, Md. Bazlur
Mannan, Md. Abdul
Krupnik, Timothy J.
author_browse Acharya, Nachiketa
Hassan, S.M.Q.
Krupnik, Timothy J.
Mannan, Md. Abdul
Montes, Carlo
Rashid, Md. Bazlur
Sultana, Razia
author_facet Acharya, Nachiketa
Montes, Carlo
Hassan, S.M.Q.
Sultana, Razia
Rashid, Md. Bazlur
Mannan, Md. Abdul
Krupnik, Timothy J.
author_sort Acharya, Nachiketa
collection Repository of Agricultural Research Outputs (CGSpace)
description Bangladesh summer monsoon rainfall (BSMR), typically from June through September (JJAS), represents the main source of water for multiple sectors. However, its high spatial and interannual variability makes the seasonal prediction of BSMR crucial for building resilience to natural disasters and for food security in a climate-risk-prone country. This study describes the development and implementation of an objective system for the seasonal forecasting of BSMR, recently adopted by the Bangladesh Meteorological Department (BMD). The approach is based on the use of a calibrated multi-model ensemble (CMME) of seven state-of-the-art general circulation models (GCMs) from the North American Multi-Model Ensemble project. The lead-1 (initial conditions of May for forecasting JJAS total rainfall) hindcasts (spanning 1982–2010) and forecasts (spanning 2011–2018) of seasonal total rainfall for the JJAS season from these seven GCMs were used. A canonical correlation analysis (CCA) regression is used to calibrate the raw GCMs outputs against observations, which are then combined with equal weight to generate final CMME predictions. Results show, compared to individual calibrated GCMs and uncalibrated MME, that the CCA-based calibration generates significant improvements over individual raw GCM in terms of the magnitude of systematic errors, Spearman's correlation coefficients, and generalised discrimination scores over most of Bangladesh areas, especially in the northern part of the country. Since October 2019, the BMD has been issuing real-time seasonal rainfall forecasts using this new forecast system.
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spelling CGSpace1322262025-11-06T13:08:19Z Calibrated multi-model ensemble seasonal prediction of Bangladesh summer monsoon rainfall Acharya, Nachiketa Montes, Carlo Hassan, S.M.Q. Sultana, Razia Rashid, Md. Bazlur Mannan, Md. Abdul Krupnik, Timothy J. climate services forecasting monsoons modelling rainfall Bangladesh summer monsoon rainfall (BSMR), typically from June through September (JJAS), represents the main source of water for multiple sectors. However, its high spatial and interannual variability makes the seasonal prediction of BSMR crucial for building resilience to natural disasters and for food security in a climate-risk-prone country. This study describes the development and implementation of an objective system for the seasonal forecasting of BSMR, recently adopted by the Bangladesh Meteorological Department (BMD). The approach is based on the use of a calibrated multi-model ensemble (CMME) of seven state-of-the-art general circulation models (GCMs) from the North American Multi-Model Ensemble project. The lead-1 (initial conditions of May for forecasting JJAS total rainfall) hindcasts (spanning 1982–2010) and forecasts (spanning 2011–2018) of seasonal total rainfall for the JJAS season from these seven GCMs were used. A canonical correlation analysis (CCA) regression is used to calibrate the raw GCMs outputs against observations, which are then combined with equal weight to generate final CMME predictions. Results show, compared to individual calibrated GCMs and uncalibrated MME, that the CCA-based calibration generates significant improvements over individual raw GCM in terms of the magnitude of systematic errors, Spearman's correlation coefficients, and generalised discrimination scores over most of Bangladesh areas, especially in the northern part of the country. Since October 2019, the BMD has been issuing real-time seasonal rainfall forecasts using this new forecast system. 2023-12-15 2023-10-12T19:00:28Z 2023-10-12T19:00:28Z Journal Article https://hdl.handle.net/10568/132226 en Open Access application/pdf Wiley Acharya, N., Montes, C., Hassan, S. M. Q., Sultana, R., Rashid, Md. B., Mannan, Md. A., & Krupnik, T. J. (2023). Calibrated multi‐model ensemble seasonal prediction of Bangladesh summer monsoon rainfall. International Journal of Climatology, joc.8246. https://doi.org/10.1002/joc.8246
spellingShingle climate services
forecasting
monsoons
modelling
rainfall
Acharya, Nachiketa
Montes, Carlo
Hassan, S.M.Q.
Sultana, Razia
Rashid, Md. Bazlur
Mannan, Md. Abdul
Krupnik, Timothy J.
Calibrated multi-model ensemble seasonal prediction of Bangladesh summer monsoon rainfall
title Calibrated multi-model ensemble seasonal prediction of Bangladesh summer monsoon rainfall
title_full Calibrated multi-model ensemble seasonal prediction of Bangladesh summer monsoon rainfall
title_fullStr Calibrated multi-model ensemble seasonal prediction of Bangladesh summer monsoon rainfall
title_full_unstemmed Calibrated multi-model ensemble seasonal prediction of Bangladesh summer monsoon rainfall
title_short Calibrated multi-model ensemble seasonal prediction of Bangladesh summer monsoon rainfall
title_sort calibrated multi model ensemble seasonal prediction of bangladesh summer monsoon rainfall
topic climate services
forecasting
monsoons
modelling
rainfall
url https://hdl.handle.net/10568/132226
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