Relative Humidity Bias Accumulates After First Day — Is FDDA Needed?

[ZHOU Wan]

Hi all,
I'm running WRF for a multi-day summer case (August 2022) over a subtropical urban area. The temperature simulation is fairly accurate, but I observed a consistent underestimation of relative humidity after the first day of simulation. On the first day, RH matches observations reasonably well, but from the second day onward, the daily maximum RH becomes increasingly underestimated, especially during the daytime and at night.



I’ve tested several physical scheme combinations to address this issue:

1. Changed land surface model from Noah (sf_surface_physics = 2) to Noah-MP (sf_surface_physics = 4)
2. Switched PBL scheme from bl_pbl_physics = 7 to MYNN (bl_pbl_physics = 5)
3. Updated radiation schemes to RRTMGto better match the new PBL:
   • ra_lw_physics = 4 (RRTMG longwave)
   • ra_sw_physics = 4 (RRTMG shortwave)

Despite these changes, the RH simulation still shows a similar bias pattern.

My question is:
1、Could this issue be caused by the lack of updated lateral boundary conditions?
2、Would using FDDA (Four-Dimensional Data Assimilation) or nudging with daily reanalysis (e.g., ERA5) help maintain water vapor consistency and reduce RH bias?

Any suggestions or related experiences would be greatly appreciated!

Thanks in advance！

PS: namelist.input

&physics
physics_suite = 'CONUS'
mp_physics = 5, 5, 5,
cu_physics = 1, 0, 0,
ra_lw_physics = 4, 4, 4,
ra_sw_physics = 4, 4, 4,
bl_pbl_physics = 5, 5, 5,
sf_sfclay_physics = 1, 1, 1,
sf_surface_physics = 4, 4, 4,
radt = 10, 10, 10,
bldt = 10, 10, 10,
cudt = 5, 0, 0,
icloud = 1,
num_land_cat = 61,
num_soil_layers = 4,
sf_urban_physics = 1, 1, 1,
sf_surface_mosaic = 1,
mosaic_cat = 3,
slucm_distributed_drag = .false.,
use_wudapt_lcz = 1,

 

[ZHOU Wan]

I'd like to add one more observation and question based on the analysis of specific humidity (Q2).

In our case (August 2022, subtropical urban area), we noticed that the underestimation of relative humidity (RH) is closely related to a systematic decrease in specific humidity (Q2) starting from the second day of simulation. This is clearly illustrated in the attached plots below for two stations (BB and BN), where WRF Q2 drops significantly after Day 1, while ERA5 and observations remain much higher.
This suggests that the RH bias is likely driven by a dry bias in Q2, not just temperature-related errors.

So we’re wondering: How is Q2 determined in WRF? And how can we improve its simulation? Appreciate any suggestions!​

 

[ZHOU Wan]

I’ve noticed that the first model level (z[0]) in our WRF simulations is consistently located several tens of meters above the actual terrain height (HGT) at our observation stations.
Since both T2 and Q2 are diagnostic variables in WRF, derived using Monin-Obukhov similarity theory and extrapolated from the lowest model level, I’m wondering:

Could this vertical offset between the terrain height and the first model level be a significant source of error in the diagnosed T2 and Q2, particularly under conditions with strong vertical gradients near the surface (e.g., during stable nights or sharp moisture transitions)?