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Positive temperature bias in results after switching to WRF 4.4 using NoahMP


New member
Dear WRF community,

I posted this already as a Github issue in the WRF repository, but I was asked to also post it here.

After switching from WRF 4.3.3 to 4.4, the simulation output changed significantly: Large positive biases to T2 temperature from weather stations are found. The case is a real case in Germany and a comparison to 20 DWD weather stations was performed. I used NoahMP as LSM, so I assume it could be related to the latest changes, although I am not aware that any of these changes are specifically important to my setup. I have previously ran this case with many different WRF versions, different grids and different parameterizations and the results were always more or less consistent, until I switched to 4.4.

The code compiled fine in all cases using the same libraries and the same gfortran compiler.

The expected metrics of the average of 12 rural weather stations compared to measurements look like this in WRF 4.3.1 and 4.3.3:

T2 / K-0.0691.476
Q2 / g/kg-0.5301.132
WS10 / m/s0.4411.118

In WRF 4.4, the metrics show a large positive bias in T2:

T2 / K0.9402.004
Q2 / g/kg-0.6921.270
WS10 / m/s0.5741.189

A difference is also seen comparing the WRF output to sounding data from 3 stations. The visually inspected simulation output looks fine in both 4.3.1 and 4.4, but clearly different.

I first suspected the urban physics, because my first test with WRF 4.4 was using the BEP model and because there have been changes in the urban physics in the 4.3 bugfix releases and also in 4.4. But then I tested another case without urban model and had a similar T2 bias. Both cases used different PBL and surface layer models. Microphysics, cumulus, radiation and LSM were the same. I suspected NoahMP only because of the recent changes there and because I could not identify changes in the other physics options I was using.

Attached are my namelist.input files for the two cases I have run so far. I will do some more testing soon and I will also try to visually highlight the differences of the results between 4.3.3 and 4.4.


  • namelist.input.bep.txt
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  • namelist.input.slab.txt
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We have talked to our expert about this issue. It seems that there are no significant code changes directly related to surface temperature. Most code changes from v4.3 to v4.4 in NoahMP are bug fixes and improvements in snowpack processes and tile drainage/irrigation schemes. One potential factor would be that the canopy heat storage is added in the vegetation temperature calculations, but it it is not known yet if this is the cause for the warm bias. We will keep you updated for more information after the systematic comparison runs are done to compare the two versions of the code.
I have also now tested two forecasts, one in US and one in Europe, 4.2 vs 4.4, same namelists, both using Noah (not MP!), and saw 0.2c higher temperature on average and about 2% lower humidity. Interesting...
It's independent of the LSM - see left with RUC, right with Noah(not MP).

These charts are both showing (T2 WRF 4.2) - (T2 WRF 4.4)

Significant positive and negative biases, +2c degrees through the bay area and -2 degrees through the great lakes.


  • diff.png
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I tested the Noah LSM and the differences between WRF 4.3.3 and WRF 4.4 are much less and I don't observe a general warm bias in T2. However, the outputs are still not the same and especially soil temperatures show a small warm bias of 0.2 K ... 0.3 K (but in this case it brings the 4.4 results closer to measurements). I used ghg_input=0 for WRF 4.4 to have a more comparable output. The rsl output files are the same except for the timings.

Here is again the comparison of my outputs to some measurement stations using Noah LSM:

T2 / K-0.1551.795
Q2 / g/kg0.0001.029
WS10 / m/s-0.2601.269
TS005 / K-1.6293.426
TS020 / K-0.2012.450

T2 / K-0.1611.790
Q2 / g/kg-0.0021.064
WS10 / m/s-0.0881.248
TS005 / K-1.3193.194
TS020 / K0.0712.466

In my case, a significant warm bias is seen only for NoahMP and not for Noah.
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