Improving the Stable Surface Layer in the NCEP Global Forecast System

This study examines the performance of the NCEP Global Forecast System (GFS) surface layer parameterization scheme for strongly stable conditions over land in which turbulence is weak or even disappears because of high near-surface atmospheric stability. Cases of both deep snowpack and snow-free conditions are investigated. The results show that decoupling and excessive near-surface cooling may appear in the late afternoon and nighttime, manifesting as a severe cold bias of the 2-m surface air temperature that persists for several hours or more. Concurrently, because of negligible downward heat transport from the atmosphere to the land, a warm temperature bias develops at the first model level. The authors test changes to the stable surface layer scheme that include introduction of a stability parameter constraint that prevents the land-atmosphere system from fully decoupling and modification to the roughness-length formulation. GFS sensitivity runs with these two changes demonstrate the ability of the proposed surface layer changes to reduce the excessive near-surface cooling in forecasts of 2-m surface air temperature. The proposed changes prevent both the collapse of turbulence in the stable surface layer over land and the possibility of numerical instability resulting from thermal decoupling between the atmosphere and the surface. The authors also execute and evaluate dailyGFS 7-day test forecasts with the proposed changes spanning a one-month period in winter. The assessment reveals that the systematic deficiencies and substantial errors in GFS near-surface 2-m air temperature forecasts are considerably reduced, along with a notable reduction of temperature errors throughout the lower atmosphere and improvement of forecast skill scores for light and medium precipitation amounts.