A comparison of three kinds of multimodel ensemble forecast techniques based on the TIGGE data
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Based on the ensemble mean outputs of the ensemble forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts), JMA (Japan Meteorological Agency), NCEP (National Centers for Environmental Prediction), and UKMO (United Kingdom Met Office) in THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE) datasets, for the Northern Hemisphere (10°–87.5°N, 0°–360°) from 1 June 2007 to 31 August 2007, this study carried out multimodel ensemble forecasts of surface temperature and 500-hPa geopotential height, temperature and winds up to 168 h by using the bias-removed ensemble mean (BREM), the multiple linear regression based superensemble (LRSUP), and the neural network based superensemble (NNSUP) techniques for the forecast period from 8 to 31 August 2007.
The forecast skills are verified by using the root-mean-square errors (RMSEs). Comparative analysis of forecast results by using the BREM, LRSUP, and NNSUP shows that the multimodel ensemble forecasts have higher skills than the best single model for the forecast lead time of 24–168 h. A roughly 16% improvement in RMSE of the 500-hPa geopotential height is possible for the superensemble techniques (LRSUP and NNSUP) over the best single model for the 24–120-h forecasts, while it is only 8% for BREM. The NNSUP is more skillful than the LRSUP and BREM for the 24–120-h forecasts. But for 144–168-h forecasts, BREM, LRSUP, and NNSUP forecast errors are approximately equal. In addition, it appears that the BREM forecasting without the UKMO model is more skillful than that including the UKMO model, while the LRSUP forecasting in both cases performs approximately the same.
A running training period is used for BREM and LRSUP ensemble forecast techniques. It is found that BREM and LRSUP, at each grid point, have different optimal lengths of the training period. In general, the optimal training period for BREM is less than 30 days in most areas, while for LRSUP it is about 45 days.
Key wordsmultimodel superensemble bias-removed ensemble mean multiple linear regression neural network running training period TIGGE
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- —, —, Z. Zhang, et al., 2000a: Multimodel ensemble forecasts for weather and seasonal climate. J. Climate, 13, 4197–4216.Google Scholar
- —, S. Basu, J. Sanjay, et al., 2007b: Evaluation of several different planetary boundary layer schemes within a single model, a unified model and a multimodel superensemble. Tellus A, 60, 42–61.Google Scholar
- Shi Xiangjun and Zhi Xiefei, 2007: Statistical characteristics of blockings in Eurasia from 1950 to 2004. Journal of Nanjing Institute of Meteorology, 30(3), 338–344. (in Chinese)Google Scholar
- Zhi Xiefei and Shi Xiangjun, 2006: Interannual variation of blockings in Eurasia and its relation to the flood disaster in the Yangtze River valley during boreal summer. Proceedings of the 10th WMO International Symposium on Meteorological Education and Training, 21–26 September 2006, Nanjing, China.Google Scholar
- Zhi Xiefei, Lin Chunze, Bai Yongqing, et al., 2009a: Superensemble forecasts of the surface temperature in Northern Hemisphere middle latitudes. Scientia Meteorologica Sinica, 29(5), 569–574. (in Chinese)Google Scholar
- —, —, —, et al., 2009b: Multimodel superensemble forecasts of surface temperature using TIGGE datasets. Preprints of the Third THORPEX International Science Symposium, 14–18 September 2009, Monterey, USA.Google Scholar
- —, Wu Qing, Bai Yongqing, et al., 2010: The multimodel superensemble prediction of the surface temperature using the IPCC AR4 scenario runs. Scientia Meteorologica Sinica, 30(5), 708–714. (in Chinese)Google Scholar