Fusion of gauge-based, reanalysis, and satellite precipitation products using Bayesian model averaging approach : Determination of the influence of different input sources
(2023) In Journal of Hydrology 618.- Abstract
Selection of the number and which of multisource precipitation datasets is crucially important for precipitation fusion. Considering the effects of different inputs, this study proposes a new framework based on the Bayesian model averaging (BMA) algorithm to integrate precipitation information from gauge-based analysis CPC, reanalysis-derived dataset ERA5, and satellite-retrieval products IMERG-E and GSMaP-RT. The BMA weights were optimized for the period 2001–2010 using daily measurements and then applied to the period 2011–2015 for model validation. Seven BMA-merged precipitation products (i.e., MCE, MCI, MCG, MCEI, MCEG, MCIG, and MCEIG) were thoroughly evaluated across mainland China and then compared against the state-of-the-art... (More)
Selection of the number and which of multisource precipitation datasets is crucially important for precipitation fusion. Considering the effects of different inputs, this study proposes a new framework based on the Bayesian model averaging (BMA) algorithm to integrate precipitation information from gauge-based analysis CPC, reanalysis-derived dataset ERA5, and satellite-retrieval products IMERG-E and GSMaP-RT. The BMA weights were optimized for the period 2001–2010 using daily measurements and then applied to the period 2011–2015 for model validation. Seven BMA-merged precipitation products (i.e., MCE, MCI, MCG, MCEI, MCEG, MCIG, and MCEIG) were thoroughly evaluated across mainland China and then compared against the state-of-the-art ensemble-based product, MSWEP. The results indicate that the BMA predictions performed substantially better than the reanalysis and satellite precipitation datasets in both daily statistics and seasonal analyses. MCE, MCI, and MCEG demonstrated better performances relative to CPC in terms of individual metrics. Moreover, MCI, MCG, and MCEI generally outperformed MSWEP over the entire study area, particularly in local regions, such as southwestern China and the eastern Tibetan Plateau. During Typhoon Rammasun in 2014, MCG and MCEG provided greater detail for heavy rainfall events than the four ensemble members and the MSWEP product. Thus, the performance of the BMA predictions exhibited evident differences because of various input sources. CPC was the major internal influencing factor with the highest weight score. Meanwhile, the increased-input CPC dataset into the BMA-based schemes exerted a significant influence on the precipitation estimates, which markedly facilitated the performance improvement of the fusion model, and its improved degree (greater than 14 %) was obtained using a ‘changed-initial’ comparison method. Our results demonstrate that the developed modifiable BMA framework is useful for analyzing the impacts of ensemble members on BMA predictions and suggests that it is considerate in the use of different input sources for generating ensemble-based precipitation products.
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- author
- Wei, Linyong ; Jiang, Shanhu ; Dong, Jianzhi ; Ren, Liliang ; Liu, Yi ; Zhang, Linqi ; Wang, Menghao and Duan, Zheng LU
- organization
- publishing date
- 2023-03
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Bayesian model averaging, Ensemble-based precipitation product, Mainland China, Performance improvement, Precipitation, Precipitation fusion
- in
- Journal of Hydrology
- volume
- 618
- article number
- 129234
- publisher
- Elsevier
- external identifiers
-
- scopus:85147857832
- ISSN
- 0022-1694
- DOI
- 10.1016/j.jhydrol.2023.129234
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2023 Elsevier B.V.
- id
- aa59638d-7e11-4b7f-8c61-e4736d7b26de
- date added to LUP
- 2023-06-30 15:41:17
- date last changed
- 2024-05-29 11:15:21
@article{aa59638d-7e11-4b7f-8c61-e4736d7b26de,
abstract = {{<p>Selection of the number and which of multisource precipitation datasets is crucially important for precipitation fusion. Considering the effects of different inputs, this study proposes a new framework based on the Bayesian model averaging (BMA) algorithm to integrate precipitation information from gauge-based analysis CPC, reanalysis-derived dataset ERA5, and satellite-retrieval products IMERG-E and GSMaP-RT. The BMA weights were optimized for the period 2001–2010 using daily measurements and then applied to the period 2011–2015 for model validation. Seven BMA-merged precipitation products (i.e., MCE, MCI, MCG, MCEI, MCEG, MCIG, and MCEIG) were thoroughly evaluated across mainland China and then compared against the state-of-the-art ensemble-based product, MSWEP. The results indicate that the BMA predictions performed substantially better than the reanalysis and satellite precipitation datasets in both daily statistics and seasonal analyses. MCE, MCI, and MCEG demonstrated better performances relative to CPC in terms of individual metrics. Moreover, MCI, MCG, and MCEI generally outperformed MSWEP over the entire study area, particularly in local regions, such as southwestern China and the eastern Tibetan Plateau. During Typhoon Rammasun in 2014, MCG and MCEG provided greater detail for heavy rainfall events than the four ensemble members and the MSWEP product. Thus, the performance of the BMA predictions exhibited evident differences because of various input sources. CPC was the major internal influencing factor with the highest weight score. Meanwhile, the increased-input CPC dataset into the BMA-based schemes exerted a significant influence on the precipitation estimates, which markedly facilitated the performance improvement of the fusion model, and its improved degree (greater than 14 %) was obtained using a ‘changed-initial’ comparison method. Our results demonstrate that the developed modifiable BMA framework is useful for analyzing the impacts of ensemble members on BMA predictions and suggests that it is considerate in the use of different input sources for generating ensemble-based precipitation products.</p>}},
author = {{Wei, Linyong and Jiang, Shanhu and Dong, Jianzhi and Ren, Liliang and Liu, Yi and Zhang, Linqi and Wang, Menghao and Duan, Zheng}},
issn = {{0022-1694}},
keywords = {{Bayesian model averaging; Ensemble-based precipitation product; Mainland China; Performance improvement; Precipitation; Precipitation fusion}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Journal of Hydrology}},
title = {{Fusion of gauge-based, reanalysis, and satellite precipitation products using Bayesian model averaging approach : Determination of the influence of different input sources}},
url = {{http://dx.doi.org/10.1016/j.jhydrol.2023.129234}},
doi = {{10.1016/j.jhydrol.2023.129234}},
volume = {{618}},
year = {{2023}},
}