A hybrid analog-ensemble, convolutional-neural-network method for post-processing precipitation forecasts
Sha, Y., II, D. J. G., West, G., Stull, R.. (2022). A hybrid analog-ensemble, convolutional-neural-network method for post-processing precipitation forecasts. Monthly Weather Review, doi:10.1175/MWR-D-21-0154.1
| Title | A hybrid analog-ensemble, convolutional-neural-network method for post-processing precipitation forecasts |
|---|---|
| Author(s) | Yingkai Sha, David John Gagne II, Gregory West, Roland Stull |
| Abstract | An ensemble precipitation forecast postprocessing method is proposed by hybridizing the analog ensemble (AnEn), minimum divergence Schaake shuffle (MDSS), and convolutional neural network (CNN) methods. This AnEn-CNN hybrid takes the ensemble mean of Global Ensemble Forecast System (GEFS) 3-hourly precipitation forecasts as input and produces bias-corrected, probabilistically calibrated, and physically realistic gridded precipitation forecast sequences out to 7 days. The AnEn-CNN hybrid postprocessing is trained on the European Centre for MediumRange Weather Forecasts Reanalysis version 5 (ERA5), and verified against station observations across British Columbia (BC), Canada, from 2017 to 2019. The AnEn-CNN hybrid produces more skillful forecasts than a quantile-mapped GEFS baseline and other conventional AnEn methods, with a roughly 10% increase in continuous ranked probability skill score. Further, it outperforms other AnEn methods by 0%-60% in terms of Brier skill score (BSS) for heavy precipitation periods across disparate hydrological regions. Longer forecast lead times exhibit larger performance gains. Verification against 7-day accumulated precipitation totals for heavy precipitation periods also demonstrates that precipitation sequences are realistically reconstructed. Case studies further show that the AnEn-CNN hybrid scheme produces more realistic spatial precipitation patterns and precipitation intensity spectra. This work pioneers the combination of conventional statistical postprocessing and neural networks, and is one of only a few studies pertaining to precipitation ensemble postprocessing in BC. |
| Publication Title | Monthly Weather Review |
| Publication Date | Jun 1, 2022 |
| Publisher's Version of Record | https://dx.doi.org/10.1175/MWR-D-21-0154.1 |
| OpenSky Citable URL | https://n2t.net/ark:/85065/d7z60sw2 |
| OpenSky Listing | View on OpenSky |
| CISL Affiliations | TDD, MILES |