## 2 Used NWP System and Data Setup with Data Latency  
### Global Forecast System (GFS)  
GFS is a global numerical weather prediction system developed by NCEP. It comprises the forecast model and the data assimilation system to provide the initial condition. The forecast model is based on the primitive dynamical equations, which is a spectral model on a hybrid sigma/pressure coordinate. Recently, NCEP replaced the dynamic core embedded in the GFS forecast model with the Finite Volume cubed-sphere (FV3). Operationally, each GFS forecast goes out 16 days in advance. For the first 240 forecast hours (10 days), the model runs at a resolution of T1534 (~13 km). The remaining 6 days are run at a reduced resolution of T574 (~27 km). It has 64 vertical levels. The GFS runs a hybrid four-dimensional ensemble variational (4 DEnVar) data assimilation system, the Gridpoint Statistical Interpolation (GSI), to generate its initial conditions. The flow-dependent error covariance is estimated from the 80 GFS ensemble members running at a reduced horizontal resolution from the previous cycle, using the ensemble Kalman filter (EnKF) scheme. The weights for the static and flow-dependent background error covariance are 0.125 and 0.875, respectively [15; 16; 17; 18; 19; 20].  
Observation types assimilated operationally in GFS consist of conventional observations (Table 1) and satellite measurements (Table 2) including satellite IR and MW radiances, satellite-based Atmospheric Motion Vectors (AMVs), and the Global Positioning System Radio Occultation (GPSRO) bending angles. Tables 1 and 2 provide a detailed list of the observations assimilated. Observations are assimilated within a 6-h assimilation window centered at the analysis time (0000, 0600, 1200, and 18000 UTC). The DBNet system supplements the hyperspectral IR and MW sounder measurements that are being operationally used in the GFS data assimilation system in addition to routine data delivery through GTS.  
The GFS runs two analysis cycles, an early run and a late run, four times a day at the six-hour interval (0000, 0600, 1200, and 1800 UTC) operationally. The late run, which is called the global data assimilation system (GDAS) run, is triggered 5 h 50 min after the analysis time. This run is least affected by data latency because of its long data cut-off time. This allows all observations to be assimilated. The GDAS run does not provide the long-range forecasts for forecasters. Instead, it provides the short-range forecasts up to 9 h that are used as the first guess for the GDAS and GFS run in the next analysis cycle. The early run (called the GFS run) is the main run that produces the long-range forecasts delivered to the forecasters. This early run has the data cut-off time of 2 h 45 min for the timely delivery of the model outputs, meaning that the GFS run is directly affected by data latency. In other words, considering that two analysis cycles have the same 6-h assimilation window from -3 h to +3 h centered at the analysis time, the initial condition from the GFS run is made in the absence of the observation data on the corresponding time slots within the 6-h assimilation window because the data are not delivered.