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Extracting the tropospheric short-wave influences on subseasonal prediction of precipitation in the United States using CFSv2

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Abstract

The development of subseasonal precipitation forecasts on regional scales is becoming an active area of research. Climate forecast models have shown deficiencies in predicting the extreme precipitation anomalies at medium to long-range timescales. This study explores the subseasonal relationships between tropospheric short-waves and regional precipitation anomalies across the continental United States and evaluates capabilities of the NCEP Climate Forecast System Version 2 (CFSv2) in resolving these relationships. A regional precipitation proxy is derived from the prediction of the upper tropospheric short-waves based on multiple linear regressions. Across the six climate regions defined by NOAA, the 30-day reforecasts of this short-wave based precipitation proxy are compared to identify the combinations of month and zonal wavenumber that exhibit the highest prediction score. Forecast of this precipitation proxy over certain regions is found to outperform the direct precipitation output of CFSv2 out to 4 weeks, suggesting a subseasonal predictability in precipitation that can be harvested from persistent circulation features.

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Notes

  1. For example, forecasts initiated on 6, 11, 16, 21, 26, and 31 May were used as d-25, d-20, d-15, d-10, d-5, and d-0 lead times for June; 5, 10, 15, 20, 25, and 30 June (July) were used as d-25, d-20, d-15, d-10, d-5, and d-0 lead times for July (August). Lead time nomenclature is similar to that used in Zuo et al. (2013).

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Acknowledgments

The authors thank two anonymous reviewers whose thorough review and comments greatly improved this paper. Conversations with Oi-Yu (JoJo) Chung regarding statistical methodologies were much appreciated. This research was partially supported by DOE DE-FOA-0001531 and SERDP 17-RCSEED01-002.

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Correspondence to Martin Schroeder.

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Schroeder, M., Wang, SY.S., Gillies, R.R. et al. Extracting the tropospheric short-wave influences on subseasonal prediction of precipitation in the United States using CFSv2. Clim Dyn 48, 3967–3974 (2017). https://doi.org/10.1007/s00382-016-3314-1

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  • DOI: https://doi.org/10.1007/s00382-016-3314-1

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