National Centers For Environmental Prediction Global Forecast System Research Articles

A local ensemble transform Kalman filter data assimilation system for the NCEP global model

The accuracy and computational efficiency of a parallel computer implementation of the Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme on the model component of the 2004 version of the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP) is investigated.Numerical experiments are carried out at model resolution T62L28. All atmospheric observations that were operationally assimilated by NCEP in 2004, except for satellite radiances, are assimilated with the LETKF. The accuracy of the LETKF analyses is evaluated by comparing it to that of the Spectral Statistical Interpolation (SSI), which was the operational global data assimilation scheme of NCEP in 2004. For the selected set of observations, the LETKF analyses are more accurate than the SSI analyses in the Southern Hemisphere extratropics and are comparably accurate in the Northern Hemisphere extratropics and in the Tropics.The computationalwall-clock times achieved on a Beowulf cluster of 3.6 GHz Xeon processors make our implementation of the LETKF on the NCEP GFS a widely applicable analysis-forecast system, especially for research purposes. For instance, the generation of four daily analyses at the resolution of the NCAR-NCEP reanalysis (T62L28) for a full season (90 d), using 40 processors, takes less than 4 d of wall-clock time.

Evaluation of a Mesoscale Short-Range Ensemble Forecast System over the Northeast United States

Abstract A short-range ensemble forecast system was constructed over the northeast United States down to 12-km grid spacing using 18 members from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The ensemble consisted of 12 physics members with varying planetary boundary layer schemes and convective parameterizations as well as seven different initial conditions (ICs) [five National Centers for Environmental Prediction (NCEP) Eta-bred members at 2100 UTC and the 0000 UTC NCEP Global Forecast System (GFS) and Eta runs]. The full 18-member ensemble (ALL) was verified at the surface for the warm (May–September 2003) and cool (October 2003–March 2004) seasons. A randomly chosen subset of seven physics (PHS) members at each forecast hour was used to quantitatively compare with the seven IC members. During the warm season, the PHS ensemble predictions for surface temperature and wind speed had more skill than the IC ensemble and a control (shared PHS and IC member) run initialized 12 h later (CTL12). During the cool and warm seasons, a 14-day running-mean bias calibration applied to the ALL ensemble (ALLBC) added 10%–30% more skill for temperature, wind speed, and sea level pressure, with the ALLBC far outperforming the CTL12. For the 24-h precipitation, the PHS ensemble had comparable probabilistic skill to the IC ensemble during the warm season, while the IC subensemble was more skillful during the cool season. All ensemble members had large diurnal surface biases, with ensemble variance approximating ensemble uncertainty only for wind direction. Selection of ICs was also important, because during the cool season the NCEP-bred members introduced large errors into the IC ensemble for sea level pressure, while none of the subensembles (PHS, IC, or ALL) outperformed the GFS–MM5 for sea level pressure.

Verification of Probabilistic Quantitative Precipitation Forecasts over the Southwest United States during Winter 2002/03 by the RSM Ensemble System

Abstract The National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) is used to generate ensemble forecasts over the southwest United States during the 151 days of 1 November 2002 to 31 March 2003. RSM forecasts to 24 h on a 12-km grid are produced from 0000 and 1200 UTC initial conditions. Eleven ensemble members are run each forecast cycle from the NCEP Global Forecast System (GFS) ensemble analyses (one control and five pairs of bred modes) and forecast lateral boundary conditions. The model domain covers two NOAA River Forecast Centers: the California Nevada River Forecast Center (CNRFC) and the Colorado Basin River Forecast Center (CBRFC). Ensemble performance is evaluated for probabilistic forecasts of 24-h accumulated precipitation in terms of several accuracy and skill measures. Differences among several NCEP precipitation analyses are assessed along with their impact on model verification, with NCEP stage IV blended analyses selected to represent “truth.” Forecast quality and potential value are found to depend strongly on the verification dataset, geographic region, and precipitation threshold. In general, the RSM forecasts are skillful over the CNRFC region for thresholds between 1 and 50 mm but are unskillful over the CBRFC region. The model exhibits a wet bias for all thresholds that is larger over Nevada and the CBRFC region than over California. Mitigation of such biases over the Southwest will pose serious challenges to the modeling community in view of the uncertainties inherent in verifying analyses.