Subseasonal Forecast of Surface Air Temperature Using Superensemble Approaches: Experiments over Northeast Asia for 2018

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  • 1 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD) / Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Nanjing University of Information Science & Technology, Nanjing, China
  • 2 School of Atmospheric Sciences / Plateau Atmosphere and Environment Key Laboratory of Sichuan Province / Joint Laboratory of Climate and Environment Change, Chengdu University of Information Technology, Chengdu, China
  • 3 WeatherOnline Institute of Meteorological Applications, Wuxi, China
  • 4 Fujian Key Laboratory of Severe Weather, Fuzhou, China
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Abstract

Bridging the gap between weather forecasting and climate prediction, subseasonal to seasonal (S2S) forecasts are of great importance yet currently of relatively poor quality. Using the S2S Prediction Project database, the study evaluate products derived from four operational centers of CMA, KMA, NCEP and UKMO, and superensemble experiments including the straightforward ensemble mean (EMN), bias-removed ensemble mean (BREM), error-based superensemble (ESUP) and Kalman filter superensemble (KF), in forecasts of the surface air temperature with lead times of 6*–30 days over Northeast Asia in 2018. Validations after the pre-processing of a 5-day running mean suggest that the KMA model shows the highest skill for either the control run or the ensemble mean. The non-equal weighted ESUP is slightly superior to BREM, whereas they both show larger biases than EMN after the lead time of 22 days. The KF forecast constantly outperforms the others, decreasing mean absolute errors by 0.2–0.5°C relative to EMN. Forecast experiments of the 2018 Northeast Asia heat wave reveals that the superensembles remarkably improve the raw forecasts featuring biases >4°C. The prominent advancement of KF is further confirmed, showing the regionally averaged bias of ≤2°C and the hit rate of 2°C reaching up to 60% at the lead time of 22 days. The superensemble techniques, particularly the KF method dynamically adjusting the weights in accordance with the latest information available, are capable of improving forecasts of spatiotemporal patterns of surface air temperature on the subseasonal timescale, which could extend the skillful prediction lead time of extreme events such as heat waves to about 3 weeks.

Corresponding author: Xiefei Zhi, email: zhi@nuist.edu.cn

Abstract

Bridging the gap between weather forecasting and climate prediction, subseasonal to seasonal (S2S) forecasts are of great importance yet currently of relatively poor quality. Using the S2S Prediction Project database, the study evaluate products derived from four operational centers of CMA, KMA, NCEP and UKMO, and superensemble experiments including the straightforward ensemble mean (EMN), bias-removed ensemble mean (BREM), error-based superensemble (ESUP) and Kalman filter superensemble (KF), in forecasts of the surface air temperature with lead times of 6*–30 days over Northeast Asia in 2018. Validations after the pre-processing of a 5-day running mean suggest that the KMA model shows the highest skill for either the control run or the ensemble mean. The non-equal weighted ESUP is slightly superior to BREM, whereas they both show larger biases than EMN after the lead time of 22 days. The KF forecast constantly outperforms the others, decreasing mean absolute errors by 0.2–0.5°C relative to EMN. Forecast experiments of the 2018 Northeast Asia heat wave reveals that the superensembles remarkably improve the raw forecasts featuring biases >4°C. The prominent advancement of KF is further confirmed, showing the regionally averaged bias of ≤2°C and the hit rate of 2°C reaching up to 60% at the lead time of 22 days. The superensemble techniques, particularly the KF method dynamically adjusting the weights in accordance with the latest information available, are capable of improving forecasts of spatiotemporal patterns of surface air temperature on the subseasonal timescale, which could extend the skillful prediction lead time of extreme events such as heat waves to about 3 weeks.

Corresponding author: Xiefei Zhi, email: zhi@nuist.edu.cn
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