Stochastic Representation of NCEP GEFS to Improve Sub-seasonal Forecast
The National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) has been in daily operation to provide probabilistic guidance for public since December 1992. Since July 2017, the GEFS was extended from 16 days to 35 days forecast to support NCEP Climate Prediction Center (CPC)’s sub-seasonal forecast. The latest GEFS version was upgraded in three areas to improve sub-seasonal forecast: (1) introducing a new set of stochastic physical perturbations to improve model uncertainty representation for the tropics; (2) a 2-tiered SST approach to consider ocean impact; and (3) a new scale-aware convection scheme to improve model physics for tropical convection and MJO forecasts. The new set of stochastic physical perturbations include stochastic kinetic energy backscatter to make up subscale energy lost during model integration; stochastic physics perturbation tendency with five different spatial and temporal scales to perturb physical tendency; and stochastic perturbed humidity on the model lower level. After upgraded to new set of stochastic physical perturbations, the MJO forecast skill has been improved from 12.5 days of a 25-month period to nearly 22 days by combining all three modifications include stochastic physics. In the extratropics, the 500-hPa geopotential height; surface temperature and precipitation are improved for sub-seasonal timescale as well. However, the raw forecast skills of surface temperature and precipitation are extremely low, and the results imply that calibration may be important and necessary for surface temperature and precipitation forecast for the sub-seasonal timescale due to the large systematic model errors.
KeywordsNCEP GEFS Stochastic representation Sub-seasonal forecast
The authors would like to thank all of the helps from EMC ensemble team members, and Dr. Bing Fu helped to provide Figs. 2 and 3; Mr. Eric Sinsky provided Figs. 4 and 6 in particular. This study is partially supported through NWS’s Office of Science and Technology Integration (OSTI) and NOAA’s Climate Program Office (CPO)’s Modeling, Analysis, Predictions, and Projections (MAPP) program.
- Hou, D., Z. Toth, Y. Zhu, and W. Yang. 2008. Evaluation of the impact of the stochastic perturbation schemes on global ensemble forecast. In Proceedings of the 19th conference on probability and statistics, New Orleans, LA, American Meteor Society. https://ams.confex.com/ams/88Annual/webprogram/Paper134165.html.
- Li, W., Y. Zhu, X. Zhou, D. Hou, E. Sinsky, C. Melhauser, M. Pena, H. Guan, and R. Wobus. 2018. Evaluating the MJO prediction skill from different configurations of NCEP GEFS extended forecast. Climate Dynamics, https://doi.org/10.1007/s00382-018-4423-9.
- Lorenz, E. 1969. The predictability of a flow which possesses many scales of motion. Tellus 21: 289–307. https://doi.org/10.1111/j.2153-3490.1969.tb00444.x.CrossRefGoogle Scholar
- Palmer, T.N., R. Buizza, F. Doblas-Reyes, T. Jung, M. Leutbecher, G. Shutts, M. Steinheimer, and A. Weisheimer. 2009. Stochastic parametrization and model uncertainty. Technical Report ECMWF RD Tech. Memo. 598, 42 pp. http://www.ecmwf.int/publications/.
- Shin, D.W., and T.N. Krishnamurti. 2003. Short- to medium-range superensemble precipitation forecasts using satellite products: 1. Deterministic forecasting. Journal of Geophysical Research 108 (D8): 8383. https://doi.org/10.1029/2001jd001511.
- Shutts, G., and T.N. Palmer. 2004. The use of high-resolution numerical simulations of tropical circulation to calibrate stochastic physics schemes. In Proceedings of the ECMWF/CLIVAR simulation and prediction of intra-seasonal variability with emphasis on the MJO, Reading, United Kingdom, European Centre for Medium-Range Weather Forecasts, 83–102.Google Scholar
- Troccoli, A. 2010. Seasonal climate forecasting. Meteorological Applications 17: 251–268. https://doi.org/10.1002/met.184.
- Zhou, X., Y. Zhu, D. Hou, and D. Kleist. 2016. Comparison of the ensemble transform and the ensemble Kalman filter in the NCEP global ensemble forecast system. Weather and Forecasting 31: 2058–2074.Google Scholar
- Zhu, Y., X. Zhou, W. Li, D. Hou, C. Melhauser, E. Sinsky, M. Pena, B. Fu, H. Guan, W. Kolczynski, R. Wobus, and V. Tallapragada. 2018. An assessment of subseasonal forecast skill using an extended global ensemble forecast system (GEFS). Journal of Geophysical Research 6732–6745. https://doi.org/10.1029/2018JD028506.Google Scholar