Sponsor
The work of E.B., S.M., and E.K. on this paper was funded through the Monsoon Mission II (Grant IITMMMIIUNIVMARYLANDUSA2018INT1) provided by the Ministry of Earth Science, Government of India. Additionally, the work of S.M. and E.K. on this paper was funded by the NASA grant IRET-QRS-22-0001. S.M. and E.K. acknowledge the support from Eugenia and Michael Brin. S.M. also acknowledges the NSF RTG grant DMS- 2136228.
Published In
EGU General Assembly Conference Abstracts
Document Type
Article
Publication Date
2-2024
Subjects
Ocean Models (Physical sciences)
Abstract
Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northwardpropagating mode that determines the active and break phases of the monsoon and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the portion of the rainfall corresponding to MISO rather than the full rainfall signal. Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics and are then weighted according to their distance from the data-drivenMISOforecast in this subspace. We thereby achieve improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10- to 30-d lead times, an interval that is generally considered to be a predictability gap. The temporal correlation of rainfall forecasts is improved by up to 0.28 in this time range. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point toward a future of combining dynamical and data-driven forecasts for Earth system prediction.
Rights
Copyright © 2024 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
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DOI
10.1073/pnas.2312573121
Persistent Identifier
https://archives.pdx.edu/ds/psu/41758
Citation Details
Bach, E., Krishnamurthy, V., Shukla, J., Mote, S., Surjalal Sharma, A., Kalnay, E., & Ghil, M. (2023, May). Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes. In EGU General Assembly Conference Abstracts (pp. EGU-10243).