Abstract
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.
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Acknowledgements
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.
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Zhu, Y., Li, W., Zhou, X., Hou, D. (2019). Stochastic Representation of NCEP GEFS to Improve Sub-seasonal Forecast. In: Randall, D., Srinivasan, J., Nanjundiah, R., Mukhopadhyay, . (eds) Current Trends in the Representation of Physical Processes in Weather and Climate Models. Springer Atmospheric Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-13-3396-5_15
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