Editorial Type:
Article
Article Type:
Research Article

Evaluation of the NCEP Global Forecast System at the ARM SGP Site

Fanglin Yang Environmental Modeling Center, National Centers for Environmental Prediction, Camp Springs, Maryland

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Hua-Lu Pan Environmental Modeling Center, National Centers for Environmental Prediction, Camp Springs, Maryland

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Steven K. Krueger Department of Meteorology, University of Utah, Salt Lake City, Utah

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Shrinivas Moorthi Environmental Modeling Center, National Centers for Environmental Prediction, Camp Springs, Maryland

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Stephen J. Lord Environmental Modeling Center, National Centers for Environmental Prediction, Camp Springs, Maryland

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Print Publication:
01 Dec 2006
DOI:
https://doi.org/10.1175/MWR3264.1
Page(s):
3668–3690
Restricted access

Abstract

This study evaluates the performance of the National Centers for Environmental Prediction Global Forecast System (GFS) against observations made by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program at the southern Great Plains site for the years 2001–04. The spatial and temporal scales of the observations are examined to search for an optimum approach for comparing grid-mean model forecasts with single-point observations. A single-column model (SCM) based upon the GFS was also used to aid in understanding certain forecast errors. The investigation is focused on the surface energy fluxes and clouds. Results show that the overall performance of the GFS model has been improving, although certain forecast errors remain. The model overestimated the daily maximum latent heat flux by 76 W m−2 and the daily maximum surface downward solar flux by 44 W m−2, and underestimated the daily maximum sensible heat flux by 44 W m−2. The model’s surface energy balance was reached by a cancellation of errors. For clouds, the GFS was able to capture the observed evolutions of cloud systems during major synoptic events. However, on average, the model largely underestimated cloud fraction in the lower and midtroposphere, especially for daytime nonprecipitating low clouds because shallow convection in the GFS does not produce clouds. Analyses of surface radiative fluxes revealed that the diurnal cycle of the model’s surface downward longwave flux (SDLW) was not in phase with that of the ARM-observed SDLW. SCM experiments showed that this error was caused by an inaccurate scaling factor, which was a function of ground skin temperature and was used to adjust the SDLW at each model time step to that computed by the model’s longwave radiative transfer routine once every 3 h. A method has been proposed to correct this error in the operational forecast model. It was also noticed that the SDLW biases changed from mostly negative in 2003 to slightly positive in 2004. This change was traced back to errors in the near-surface air temperature. In addition, the SDLW simulated with the newly implemented Rapid Radiative Transfer Model longwave routine in the GFS is usually 5–10 W m−2 larger than that simulated with the previous routine. The forecasts of surface downward shortwave flux (SDSW) were relatively accurate under clear-sky conditions. The errors in SDSW were primarily caused by inaccurate forecasts of cloud properties. Results from this study can be used as guidance for the further development of the GFS.

Corresponding author address: Fanglin Yang, EMC/NCEP, 5200 Auth Rd., Camp Springs, MD 20724. Email: fanglin.yang@noaa.gov

Abstract

This study evaluates the performance of the National Centers for Environmental Prediction Global Forecast System (GFS) against observations made by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program at the southern Great Plains site for the years 2001–04. The spatial and temporal scales of the observations are examined to search for an optimum approach for comparing grid-mean model forecasts with single-point observations. A single-column model (SCM) based upon the GFS was also used to aid in understanding certain forecast errors. The investigation is focused on the surface energy fluxes and clouds. Results show that the overall performance of the GFS model has been improving, although certain forecast errors remain. The model overestimated the daily maximum latent heat flux by 76 W m−2 and the daily maximum surface downward solar flux by 44 W m−2, and underestimated the daily maximum sensible heat flux by 44 W m−2. The model’s surface energy balance was reached by a cancellation of errors. For clouds, the GFS was able to capture the observed evolutions of cloud systems during major synoptic events. However, on average, the model largely underestimated cloud fraction in the lower and midtroposphere, especially for daytime nonprecipitating low clouds because shallow convection in the GFS does not produce clouds. Analyses of surface radiative fluxes revealed that the diurnal cycle of the model’s surface downward longwave flux (SDLW) was not in phase with that of the ARM-observed SDLW. SCM experiments showed that this error was caused by an inaccurate scaling factor, which was a function of ground skin temperature and was used to adjust the SDLW at each model time step to that computed by the model’s longwave radiative transfer routine once every 3 h. A method has been proposed to correct this error in the operational forecast model. It was also noticed that the SDLW biases changed from mostly negative in 2003 to slightly positive in 2004. This change was traced back to errors in the near-surface air temperature. In addition, the SDLW simulated with the newly implemented Rapid Radiative Transfer Model longwave routine in the GFS is usually 5–10 W m−2 larger than that simulated with the previous routine. The forecasts of surface downward shortwave flux (SDSW) were relatively accurate under clear-sky conditions. The errors in SDSW were primarily caused by inaccurate forecasts of cloud properties. Results from this study can be used as guidance for the further development of the GFS.

Corresponding author address: Fanglin Yang, EMC/NCEP, 5200 Auth Rd., Camp Springs, MD 20724. Email: fanglin.yang@noaa.gov

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