<p>The <a href="https://a.atmos.washington.edu/wrfrt/" rel="nofollow noreferrer">University of Washington</a> runs the Advanced Research <a href="http://www.wrf-model.org/index.php" rel="nofollow noreferrer">Weather Research &amp; Forecasting (WRF)</a> model in predictive mode, and then tests the skill of the model anywhere from 12 - 84 hours out from the initialization time.  The accuracy of the wind and the temperature profile are considered to be the most important indicators of the model's skill.  The <a href="https://www.cmascenter.org/cmaq/" rel="nofollow noreferrer">CMAQ</a> Eulerian grid model is then be used to represent air pollutant dispersion, using the WRF data and grids.</p>
<p>I've been told that the models don't do well with grid-spacings any finer than 1/3 km. In fact, some people have said that the 4/3 km spacing can perform just as well, and that we've reached a point where Eulerian grid models don't gain much more by going to finer grids.</p>
<p>I know that for modeling pollutant dispersion at fine horizontal scales, <a href="https://en.wikipedia.org/wiki/Lagrangian_and_Eulerian_specification_of_the_flow_field" rel="nofollow noreferrer">Lagrangian methods have historically been used instead of Eulerian grid-cell</a> methodology. However, with recent developments in computing, gridded modeling domains have been able to use much finer grid-spacings.  What is the finest grid spacing that Eulerian dispersion grid-cell models should realistically use? Why?</p>

CC BY-SA 3.0
2015-12-07T01:40:10.710Z
This is not a criticism. I find a scale of 1/3 km intriguing because 1/3 doesn't produce a neat number in digital computing. A scale of 0.25 km or 0.5 km would result in neat numbers for calculations. Why would a scale of 1/3 km (0.333 km) be used in such modelling?
Fred
CC BY-SA 3.0
2015-12-07T02:54:51.947Z
@Fred sorry I missed your point.  In our case, the reason 1/3 is used is (I believe) is due to grid nesting and maximum computational capability for forecast due date.  That is, the nested domains break up into 9 pieces rather than 16, which is a big computational difference, but the gain is nearly the same.  And they just go to two decimal places... 1.33
f.thorpe
CC BY-SA 3.0
2015-12-07T15:35:29.017Z
@gansub FYI grid resolution is really not an appropriate term for modeling as it is more specific to remote sensing.  A model has grid-spacing which varies by nest/domain.
f.thorpe
CC BY-SA 3.0
2015-12-07T20:32:51.773Z
Please forgive me but I am having some trouble understanding your question. Are you talking about truncation errors associated with discretization in the Eulerian framework which give rise to numerical diffusion and dispersion?
Isopycnal Oscillation
CC BY-SA 3.0
2016-02-21T01:21:34.920Z
At what point, the resolution is not only a question of computer: do you have the terrain data at this precision ? (not only z, but also the surfaces properties, including plants and soil, for instance). Do you also resolve cloud precisely enough to account for accurate lighting of that terrain, and thus predict evaporation accurately ? On lakes and oceans, how fine is your coupling ?
Fabrice NEYRET
CC BY-SA 3.0
2016-02-21T01:23:16.360Z
Ok course the third dimension is a different story: only the bottom is involved in precisions issues concerning the boundary real condition. Above, you do need as much resolution as you can to treat correctly the thin inversion layer, for a start.
Fabrice NEYRET
<meteorology><atmosphere><models><air-quality><grid-spacing>
What is the smallest grid spacing that Eulerian air quality models should use?
f.thorpe
# What is the smallest grid spacing that Eulerian air quality models should use?

(Asked by: username_0 on 2015-12-06)

The [University of Washington](https://a.atmos.washington.edu/wrfrt/) runs the Advanced Research [Weather Research & Forecasting (WRF)](http://www.wrf-model.org/index.php) model in predictive mode, and then tests the skill of the model anywhere from 12 - 84 hours out from the initialization time. The accuracy of the wind and the temperature profile are considered to be the most important indicators of the model's skill. The [CMAQ](https://www.cmascenter.org/cmaq/) Eulerian grid model is then be used to represent air pollutant dispersion, using the WRF data and grids.

I've been told that the models don't do well with grid-spacings any finer than 1/3 km. In fact, some people have said that the 4/3 km spacing can perform just as well, and that we've reached a point where Eulerian grid models don't gain much more by going to finer grids.

I know that for modeling pollutant dispersion at fine horizontal scales, [Lagrangian methods have historically been used instead of Eulerian grid-cell](https://en.wikipedia.org/wiki/Lagrangian_and_Eulerian_specification_of_the_flow_field) methodology. However, with recent developments in computing, gridded modeling domains have been able to use much finer grid-spacings. What is the finest grid spacing that Eulerian dispersion grid-cell models should realistically use? Why?

**Tags:** meteorology, atmosphere, models, air-quality, grid-spacing

> **Comments:**
> **username_1**: This is not a criticism. I find a scale of 1/3 km intriguing because 1/3 doesn't produce a neat number in digital computing. A scale of 0.25 km or 0.5 km would result in neat numbers for calculations. Why would a scale of 1/3 km (0.333 km) be used in such modelling?  
> **username_0**: @username_1 sorry I missed your point.  In our case, the reason 1/3 is used is (I believe) is due to grid nesting and maximum computational capability for forecast due date.  That is, the nested domains break up into 9 pieces rather than 16, which is a big computational difference, but the gain is nearly the same.  And they just go to two decimal places... 1.33  
> **username_0**: @gansub FYI grid resolution is really not an appropriate term for modeling as it is more specific to remote sensing.  A model has grid-spacing which varies by nest/domain.  
> **username_2**: Please forgive me but I am having some trouble understanding your question. Are you talking about truncation errors associated with discretization in the Eulerian framework which give rise to numerical diffusion and dispersion?  
> **username_3**: At what point, the resolution is not only a question of computer: do you have the terrain data at this precision ? (not only z, but also the surfaces properties, including plants and soil, for instance). Do you also resolve cloud precisely enough to account for accurate lighting of that terrain, and thus predict evaporation accurately ? On lakes and oceans, how fine is your coupling ?  
> **username_3**: Ok course the third dimension is a different story: only the bottom is involved in precisions issues concerning the boundary real condition. Above, you do need as much resolution as you can to treat correctly the thin inversion layer, for a start.  

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## 0 answers

