Comparative Study on Probabilistic Forecasts of Heavy Rainfall in Mountainous Areas of the Wujiang River Basin in China Based on TIGGE Data
Abstract
:1. Introduction
2. Multi-Model Ensemble Post-Processing Methods
2.1. The BMA Model
2.2. The LR Model
3. Data and Methods of Verification and Evaluation
3.1. The Study Area and Datasets
3.2. Verification and Evaluation Methods
4. Results
4.1. The Length of Training Period
4.2. Comparison, Verification, and Evaluation of Different Models
4.3. Case Study of Heavy Rainfall Forecasting
4.3.1. Comparison of Probability Forecasts
4.3.2. The Deterministic Forecast
4.4. Verifications over the Season
5. Conclusions and Discussion
- The BMA forecasting model was sensitive to the length of the training period and the forecast quality of the model around the forecast period. Additionally, for the forecast skill, a longer training period is not necessarily better. The training period of 2 years was the best for BMA, whereas the LR model required more statistical samples compared with the BMA method. The LR prediction effect was optimal when the length of the training period was 5 years.
- The multi-model ensemble prediction method was not always superior to RAW under any lead time or at any precipitation level. According to the BS, for precipitation events exceeding 10 mm with lead times of 1–7 days, the BMA forecasting technique outperformed the LR and the RAW with lead times of 2–7 days, while for heavy precipitation events exceeding 25 mm and 50 mm, the forecast skill of RAW was equivalent to that of BMA, whose improvement is little. This can be due to two reasons. First, the four-model multi-member ensemble average forecast used in this study was equivalent to the BMA ensemble of the four members. Compared with the multiple and comprehensive forecast results obtained by the four models with 131 members, the improvement was greatly limited. Second, the correcting effect of the BMA method itself has certain limitations. According to the verification results, for heavy and moderate rainfall events, forecasts of BMA and the LR were more reliable than those of the RAW, and the BMA model shows the best performance.
- The LR and the RAW could only provide the probability forecast at a certain threshold, while the BMA had the advantage of producing a highly concentrated full PDF curve and rendering the deterministic forecast. The PDF curve can control the uncertainty range of forecast results, and can also render quantitative forecasts through analyzing the percentile data. With respect to heavy precipitation forecasting, it is recommended to refer to forecasting results at the 75th–90th percentiles, which are more reasonable. Nevertheless, extreme weather events with low probability forecasts cannot be ignored because they might also occur. As for the light precipitation event, the BMA forecast results at the 50th percentile were closer to the observation.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Centers | Country | Model Spectral Resolution | Ensemble Members (Perturbed) | Spatial Resolution | Forecast Length (Days) |
---|---|---|---|---|---|
ECMWF | Europe | T399L62/T255L62 | 50 | 0.5° × 0.5° | 15 |
UKMO | United Kingdom | 11 | 0.5° × 0.5° | 7 + 6 h | |
CMA | China | T213L31 | 14 | 0.5° × 0.5° | 15 |
JMA | Japan | 26 | 0.5° × 0.5° | 11 | |
NCEP | America | T126L28 | 20 | 0.5° × 0.5° | 16 |
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Qi, H.; Zhi, X.; Peng, T.; Bai, Y.; Lin, C. Comparative Study on Probabilistic Forecasts of Heavy Rainfall in Mountainous Areas of the Wujiang River Basin in China Based on TIGGE Data. Atmosphere 2019, 10, 608. https://doi.org/10.3390/atmos10100608
Qi H, Zhi X, Peng T, Bai Y, Lin C. Comparative Study on Probabilistic Forecasts of Heavy Rainfall in Mountainous Areas of the Wujiang River Basin in China Based on TIGGE Data. Atmosphere. 2019; 10(10):608. https://doi.org/10.3390/atmos10100608
Chicago/Turabian StyleQi, Haixia, Xiefei Zhi, Tao Peng, Yongqing Bai, and Chunze Lin. 2019. "Comparative Study on Probabilistic Forecasts of Heavy Rainfall in Mountainous Areas of the Wujiang River Basin in China Based on TIGGE Data" Atmosphere 10, no. 10: 608. https://doi.org/10.3390/atmos10100608