Relative Humidity and Land-use

Topics related to current and future physics in the WRF as well as any problems you may have.

Relative Humidity and Land-use

Postby orestis25 » Wed Jul 30, 2008 7:13 am

I have finally calculated RH at 2m height using T2,Q2 and PSFC.
I find that WRF overestimates RH in comparison to real measurements (20-30% more) and I'm trying to figure out the reason.

There are probably 100 good reasons why this should be happening but , right now, I cannot think of anyone-he he he.

Seriously speaking, I looked at the values given for landuse that the model uses. The stations are only 2 or 6 in the USGS categories (isn't this the one used from WRF?) which means dryland and crops and moisture availability at 30% for summer.

The stations are on the other hand in an urban landscape (including an airport) and I highly doubt that alfalfa surroundings are used. Urban landscapes means 10% moisture availability.

Do you know of any way of incorporating this differences in the results of WRF, I cannot afford to re-run WRF.
I know that moisture availabilty affects both T2 (through latent heat) and mixing ratio (more vapor). I'm not trying to "fix" the results, but in my opinion there is a systematic error and some corrections should be applied to be a bit closer to reality.

Leaving the effects on T2 out (assuming them to be secondary), which formula connects Q2 with Land-use?

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Re: Relative Humidity and Land-use

Postby jimmyc » Wed Jul 30, 2008 11:20 am

Sounds like you need to explore the LSM, surface layer and PBL scheme code. I doubt there is much you can do numerically without rerunning WRF. Perhaps you seek a more statistical correction.
The views expressed in this message do not necessarily reflect those of NOAA or the National Weather Service or the University of Oklahoma.
James Correia, Jr
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Re: Relative Humidity and Land-use

Postby PBLer » Wed Jul 30, 2008 12:27 pm

Surface temperature and moisture errors, in my experience, are most strongly linked to the LSM, but of course, can be linked to other model components as well. The land surface categories are obvious targets, but not trivial to fix. I think the climatological soil moisture availability is only used with the thermal diffusion LSM (sf_surface_physics=1). However, if you use the RUC LSM, these values are overwritten by the initial condition fields (i.e. in the GFS data) and then evolve throughout the forecast. I would suggest trying a more complete LSM and see if your biases/errors are reduced. Personally, I would avoid using the default climatological values if possible.

Also, for future reporting of model biases, it would be nice to have more information on whether the biases are day/night and which particular model physics are employed. Model biases of all magnitudes and signs are reported everywhere, but that information can be confusing without some detail of the model set up.

good luck...
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