My colleagues run the Advanced Research Weather Research & Forecasting (WRF) model in predictive mode, and then test 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. An Eulerian grid model is then be used to represent air pollutant dispersion.

There have historically been discussions of problems with the model in places where there is complex terrain, and whether a finer-scale horizontal grid spacing would solve the issues (e.g. unable to capture local effects in the wind field or PBL). However, now there are models that are configured with the lowest recommended grid-spacing (1/3 km) and many more complex local weather patterns are being accurately modeled. 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 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?

  • $\begingroup$ 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? $\endgroup$ – Fred Dec 7 '15 at 1:40
  • $\begingroup$ @farrenthorpe - the point Fred is making is not about the grid resolution. It can be as high as is currently possible. He I believe is asking why is it not a neat number such as 0.25 or 0.5 km ? $\endgroup$ – gansub Dec 7 '15 at 2:06
  • $\begingroup$ @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 $\endgroup$ – farrenthorpe Dec 7 '15 at 2:54
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    $\begingroup$ @farrenthorpe - Presuming you are operating in a LES domain - the answer to your question is in this reference - journals.ametsoc.org/doi/pdf/10.1175/JAS3435.1. Primarily EDM is an eddy diffusivity approach and does not simulate properly short range dispersion $\endgroup$ – gansub Dec 7 '15 at 3:17
  • $\begingroup$ @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. $\endgroup$ – farrenthorpe Dec 7 '15 at 15:35

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