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  Long-lead statistical forecasts of the Indian Summer Monsoon rainfall based on causal precursors

Di Capua, G., Kretschmer, M., Runge, J., Alessandri, A., Donner, R. V., van den Hurk, B., Vellore, R., Krishnan, R., Coumou, D. (2019): Long-lead statistical forecasts of the Indian Summer Monsoon rainfall based on causal precursors. - Weather and Forecasting, 34, 5, 1377-1394.
https://doi.org/10.1175/WAF-D-19-0002.1

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 Creators:
Di Capua, Giorgia1, Author              
Kretschmer, Marlene1, Author              
Runge, J.2, Author
Alessandri, A.2, Author
Donner, Reik V.1, Author              
van den Hurk, B.2, Author
Vellore, R.2, Author
Krishnan, R.2, Author
Coumou, Dim1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: Skillful forecasts of the Indian summer monsoon rainfall (ISMR) at long lead times (4–5 months in advance) pose great challenges due to strong internal variability of the monsoon system and nonstationarity of climatic drivers. Here, we use an advanced causal discovery algorithm coupled with a response-guided detection step to detect low-frequency, remote processes that provide sources of predictability for the ISMR. The algorithm identifies causal precursors without any a priori assumptions, apart from the selected variables and lead times. Using these causal precursors, a statistical hindcast model is formulated to predict seasonal ISMR that yields valuable skill with correlation coefficient (CC) ~0.8 at a 4-month lead time. The causal precursors identified are generally in agreement with statistical predictors conventionally used by the India Meteorological Department (IMD); however, our methodology provides precursors that are automatically updated, providing emerging new patterns. Analyzing ENSO-positive and ENSO-negative years separately helps to identify the different mechanisms at play during different years and may help to understand the strong nonstationarity of ISMR precursors over time. We construct operational forecasts for both shorter (2-month) and longer (4-month) lead times and show significant skill over the 1981–2004 period (CC ~0.4) for both lead times, comparable with that of IMD predictions (CC ~0.3). Our method is objective and automatized and can be trained for specific regions and time scales that are of interest to stakeholders, providing the potential to improve seasonal ISMR forecasts.

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 Dates: 2019
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1175/WAF-D-19-0002.1
PIKDOMAIN: RD1 - Earth System Analysis
PIKDOMAIN: RD4 - Complexity Science
eDoc: 8563
Organisational keyword: RD1 - Earth System Analysis
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Atmosphere
Research topic keyword: Monsoon
Model / method: Machine Learning
Regional keyword: Asia
Working Group: Earth System Modes of Operation
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Weather and Forecasting
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 34 (5) Sequence Number: - Start / End Page: 1377 - 1394 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/Weather-Forecasting