Table 2.
The assimilation parameters which have the most impact on the assimilation and the values used for this reanalysis. They are set in fortran namelists within the input.nml file.
| Parameter | Value | Description |
|---|---|---|
| filter_kind | 1 | Ensemble Adjustment Kalman Filter (EAKF)13 |
| ens_size | 80 | Number of ensemble members |
| num_groups | 1 | Number of groups into which members are divided |
| inf_flavor | 5 | Enhanced, spatially and temporally varying, adaptive, prior, covariance inflation14. |
| inf_lower_bound | 0.0 | Inflation mean lower bound |
| inf_upper_bound | 100.0 | Inflation mean upper bound (is never reached) |
| inf_sd_lower_bound | 0.6 | Inflation standard deviation lower bound |
| inf_sd_max_change | 1.05 | Inflation standard deviation maximum change in an assimilation cycle |
| inf_damping | 0.9 | Inflation damping (acts opposite of inflation to enhance temporal adaptiveness)15 |
| horiz_dist_only | .false. | Include the vertical distance in calculations of the distance between observations and state variables |
| cutoff | 0.15 | Half width (in radians) of the spatial localization function16 |
| vert_normalization_scale_height | 1.5 | Scaling factor relating vertical distance to horizontal in localization calculations. 1.5 scale heights = 1 radian in the horizontal |
| sampling_error_correction | .true. | Apply a correction to account for the limited ensemble size17 |
| input_qc_threshold | 3.0 | Reject observations having a quality control value larger than this |
| outlier_threshold | 3.0 | Reject observations which are more than this number of total spread standard deviations different from the ensemble mean |