Investigating the sub-seasonal predictability of atmospheric rivers using NOAA's GEFS reforecasts
Malasala, M. N. R., Wu, K., WU, X., Tallapragada, V.. (2024). Investigating the sub-seasonal predictability of atmospheric rivers using NOAA's GEFS reforecasts.
| Title | Investigating the sub-seasonal predictability of atmospheric rivers using NOAA's GEFS reforecasts |
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| Genre | Conference Material |
| Author(s) | Murali Nageswara Rao Malasala, K. Wu, X. WU, V. Tallapragada |
| Abstract | This study investigates the predictive capabilities of the Global Ensemble Forecast System version 12 (GEFSv12) for Atmospheric Rivers (ARs) and associated precipitation on extended range and sub-seasonal time scales. Operational since September 2020, GEFSv12 offers a 20-year reforecast dataset and generates ensemble forecasts for up to 35 days. However, raw outputs are often underutilized due to uncertainties, highlighting the need for effective statistical post-processing. To enhance prediction accuracy, this research employs a deep learning post-processing method on GEFSv12 outputs of Integrated Vapor Transport (IVT) and precipitation. The study evaluates the model's performance in predicting various AR intensity categories, as defined by Ralph et al. (2019), over the U.S. West Coast during Weeks 3 to 4 and monthly intervals. Results demonstrate that the deep learning approach significantly improves the detection of AR events and associated precipitation compared to raw GEFSv12 forecasts, thereby enhancing overall prediction skill. |
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| Publication Date | Dec 13, 2024 |
| Publisher's Version of Record | |
| OpenSky Citable URL | https://n2t.net/ark:/85065/d71j9g3h |
| OpenSky Listing | View on OpenSky |
| CPAESS Affiliations | UCP, SPS |