Analogue Ensemble Averaging Method for Bias Correction of 2-m Temperature of the Medium-Range Forecasts in China
Abstract
:1. Introduction
2. Forecast and Observation Data
3. Methods and Verification Scores
3.1. Analogue Ensemble Averaging Method
3.2. Verification Scores
4. Results
4.1. Comparisons of Different Forecast Lead Time Results between Analogue Ensemble Averaging Forecast and Numerical Weather Prediction Methods
4.2. Tests of Forecast Ability at the Stations
4.3. Forecast Case
5. Conclusions and Discussion
- (1)
- The analogue ensemble averaging method has a good correction effect on the long forecast time of 180–348 h and effectively reduces the systematic error of the model forecasts of the 2-m temperature, which is higher at night and lower during the day. The forecast deviation is reduced by approximately 0.5 °C, and the MAE and RMSE are reduced by approximately 10–20%. During the test period from 1 May to 28 June 2022, the RMSE reduction rate of 240 h forecast reached 91% (the proportion of samples with reduced RMSE to all samples). Comparing the correction effect of different forecast lead times, the analogue ensemble averaging forecast method still has a better correction effect in longer forecast lead times.
- (2)
- After comparisons based on the spatial prediction results from 2405 stations, it is shown that the application of the analogue ensemble averaging forecast method effectively reduces the RMSEs of forecasts in Southwest China, Northwest China, and North China. The improvement rate of different forecast times reaches 31.4%. This method has a more obvious effect on the correction of complex terrain areas.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Forecast Lead Time | Testing Period | Training Period | Selected Lead Time | Analog Ensemble Members |
---|---|---|---|---|
180 h | 20220501–0628 (59 d) | 20181225–20220423 (1216 d) | 168 h, 180 h, 192 h | 30 |
192 h | –20220422 (1215 d) | 180 h, 192 h, 204 h | ||
204 h | 1215 d | 192 h, 204 h, 216 h | ||
216 h | –20220421 (1214 d) | 204 h, 216 h, 228 h | ||
228 h | 1214 d | 216 h, 228 h, 240 h | ||
240 h | –20220420 (1213 d) | 228 h, 240 h, 252 h | ||
252 h | 1213 d | 240 h, 252 h, 264 h | ||
264 h | –20220419 (1212 d) | 252 h, 264 h, 276 h | ||
276 h | 1212 d | 264 h, 276 h, 288 h | ||
288 h | –20220418 (1211 d) | 276 h, 288 h, 300 h | ||
300 h | 1211 d | 288 h, 300 h, 312 h | ||
312 h | –20220417 (1210 d) | 300 h, 312 h, 324 h | ||
324 h | 1210 d | 312 h, 324 h, 336 h | ||
336 h | –20220416 (1209 d) | 324 h, 336 h, 348 h | ||
348 h | 1209 d | 336 h, 348 h, 360 h |
Forecast Lead Time | Decreasing Percent |
---|---|
192 h | 31.4% |
240 h | 29.6% |
288 h | 23.5% |
336 h | 24.4% |
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Hu, Y.; Wang, Q.; Shen, X. Analogue Ensemble Averaging Method for Bias Correction of 2-m Temperature of the Medium-Range Forecasts in China. Atmosphere 2022, 13, 2097. https://doi.org/10.3390/atmos13122097
Hu Y, Wang Q, Shen X. Analogue Ensemble Averaging Method for Bias Correction of 2-m Temperature of the Medium-Range Forecasts in China. Atmosphere. 2022; 13(12):2097. https://doi.org/10.3390/atmos13122097
Chicago/Turabian StyleHu, Yingying, Qiguang Wang, and Xueshun Shen. 2022. "Analogue Ensemble Averaging Method for Bias Correction of 2-m Temperature of the Medium-Range Forecasts in China" Atmosphere 13, no. 12: 2097. https://doi.org/10.3390/atmos13122097
APA StyleHu, Y., Wang, Q., & Shen, X. (2022). Analogue Ensemble Averaging Method for Bias Correction of 2-m Temperature of the Medium-Range Forecasts in China. Atmosphere, 13(12), 2097. https://doi.org/10.3390/atmos13122097