Sensitivities of the NCEP Global Forecast System

An important issue in developing a forecast system is its sensitivity to additional observations for improving initial conditions, to the data assimilation (DA) method used, and to improvements in the forecast model. These sensitivities are investigated here for the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP). Four parallel sets of 7-day ensemble forecasts were generated for 100 forecast cases in mid-January to mid-March 2016. The sets differed in their 1) inclusion or exclusion of additional observations collected over the eastern Pacific during the El Nino Rapid Response (ENRR) field campaign, 2) use of a hybrid 4D-EnVar versus a pure EnKF DA method to prepare the initial conditions, and 3) inclusion or exclusion of stochastic parameterizations in the forecast model. The Control forecast set used the ENRR observations, hybrid DA, and stochastic parameterizations. Errors of the ensemble-mean forecasts in this Control set were compared with those in the other sets, with emphasis on the upper-tropospheric geopotential heights and vorticity, midtropospheric vertical velocity, column-integrated precipitable water, near-surface air temperature, and surface precipitation. In general, the forecast errors were found to be only slightly sensitive to the additional ENRR observations, more sensitive to the DA methods, and most sensitive to the inclusion of stochastic parameterizations in the model, which reduced errors globally in all the variables considered except geopotential heights in the tropical upper troposphere. The reduction in precipitation errors, determined with respect to two independent observational datasets, was particularly striking.