The Effects of Land Surface Process Perturbations in a Global Ensemble Forecast System

• Published in: Advances in atmospheric sciences Vol. 33; no. 10; pp. 1199 - 1208
• Main Authors: Deng, Guo, Zhu, Yuejian, Gong, Jiandong, Chen, Dehui, Wobus, Richard, Zhang, Zhe
• Format: Journal Article
• Language: English
• Published: Heidelberg Science Press 01.10.2016; Springer Nature B.V; National Meteorological Center, China Meteorological Administration, Beijing 100081, China%Environmental Modeling Center, National Centers for Environmental Prediction,5830 University Research Court, College Park, MD 20740, USA%Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
• Subjects: Atmospheric Sciences; Earth and Environmental Science; Earth Sciences; Geophysics/Geodesy; Ice cover; Latent heat; Meteorology; Sea ice; Soil moisture; Soil sciences; Soil temperature; Troposphere; Weather forecasting; 不确定性; 土壤水分; 土壤温度; 大气扰动; 数值天气预报; 调查预测; 陆面过程; 集合预报系统; ensemble transform with rescaling; GEFS; land surface processes; perturbation
• ISSN: 0256-1530; 1861-9533
• DOI: 10.1007/s00376-016-6036-8

Atmospheric variability is driven not only by internal dynamics, but also by external forcing, such as soil states, SST, snow, sea-ice cover, and so on. To investigate the forecast uncertainties and effects of land surface processes on numerical weather prediction, we added modules to perturb soil moisture and soil temperature into NCEP＇s Global Ensemble Forecast System （GEFS）, and compared the results of a set of experiments involving different configurations of land surface and atmospheric perturbation. It was found that uncertainties in different soil layers varied due to the multiple timescales of interactions between land surface and atmospheric processes. Perturbations of the soil moisture and soil temperature at the land surface changed sensible and latent heat flux obviously, as compared to the less or indirect land surface perturbation experiment from the day-to-day forecasts. Soil state perturbations led to greater variation in surface heat fluxes that transferred to the upper troposphere, thus reflecting interactions and the response to atmospheric external forcing. Various verification scores were calculated in this study. The results indicated that taking the uncertainties of land surface processes into account in GEFS could contribute a slight improvement in forecast skill in terms of resolution and reliability, a noticeable reduction in forecast error, as well as an increase in ensemble spread in an under-dispersive system. This paper provides a preliminary evaluation of the effects of land surface processes on predictability. Further research using more complex and suitable methods is needed to fully explore our understanding in this area.