Climate and chemical transport models all need input files including meteorology data, emission inventory, surface data (e.g. land use type) to initialize the simulation field.

Take the case of WRF for regional meteorology data. I always use the 1 degree x 1 degree NCEP FNL analysis data in grib2 format as the model-driven data supporting the boundary and initial information every 6 hours.

Now, I find that the ECMWF interim reanalysis data with spatial 0.25 degree x 0.25 degree can also be used as the WRF input.

So, I'm wondering does finer input data induce better model performance? How much can be improved if the answer is true?

I have thought out some pros of finer input file (in both spatial and temporal scale):

pros: (1) finer input field means the physical process of smaller scale(e.g turbulent transport of water) can be depicted better in some extent; (2) finer temporal variation of chemical species can emulate the real emission process better(e.g, emission related to traffic in a day).

Are there any cons that come with the performance improvement except the computational cost?

  • $\begingroup$ I suggest you edit and change the term performance to accuracy or predictability. Performance also means speed and this will definitely not improve. $\endgroup$
    – Jan Doggen
    Sep 9, 2016 at 14:51
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    $\begingroup$ The pros and cons listed in the question on not related to higher spatial resolution of the input data but to higher model grid resolution. If you run WRF on 0.25 degree or finer resolution then it would be reasonable to use ECMWF instead of NCEP data. However, for some consistency reasons (e.g. use same model for global and regional runs) it might be reasonable to use the boundary conditions that are more consistent with your model and not higher resolved. Another question would be whether ERA-Interim actually performs better in the region of your interest compared to the NCEP analysis. $\endgroup$ Sep 9, 2016 at 17:02
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    $\begingroup$ With respect to reducing the grid cell size of your model grid: You should have a look, which processes on sub-grid scale are parameterized - i.e. clouds - and which of these parameterizations should be deactivated below specific grid resolution. Otherwise it could happen that these processes - i.e. cloud processes - are twice included (explicitly calculated and parameterized). $\endgroup$ Sep 9, 2016 at 17:07
  • $\begingroup$ @daniel.neumann. Thanks for your wonderful answer!. I found there are many choice to drive the model from WRF free data. ds083.3 and ds084.1 etc are all finer than my usually choice(1 degree ds083.2). If I use WRF for futher chemical transport modeling focus on the area with relatively small scale(< 10 Km), the finer (0.25 degree)dataset are more suitable for the simluation? $\endgroup$ Sep 10, 2016 at 9:34

3 Answers 3


My experience with the WRF is in tropical cyclone simulation, but here are some things to consider:

  • Smaller scale information might already be resolved in your higher resolution initial conditions, improving the spin-up time of effects you are trying to model
  • I believe the ECMWF resolution has been statistically downscaled from a 0.75 degree dataset, I might be wrong on that one though. That would mean that your 0.25 degree is just some interpolated data
  • Some models perform better than others in specific regions
  • Higher resolution is not better if your parameterization is wrong
  • You can find caveats of specific datasets here: PSD Gridded Climate Datasets.

Yes, of course finer input data can lead to better model accuracy, but only if the model's conceptualization is close to the mark. Of the many problems of climatic model misinterpretation, I would emphasize the following:

a) The implied precision of most climatic modelling output is absurd, so take a long, hard and skeptical look at the data in relation to realistic error bounds.

b) Down-scaled models tend to give an impression of higher accuracy. For the most part, this is illusory.

c) Halve the grid dimension and you require four times as much input data. In reality there usually isn't that much good quality data available, and even if there is (in principle), there usually isn't the manpower capability to process it.

d) Reduce the grid size, and you have to pay much closer attention to the conceptualization. For example, physiographic impacts upon rainfall, land-water ratio, and microclimatic factors become more important.

In my experience of working on AOGCM outputs in East Africa, I found that smaller grids only highlights local climatic discrepancies between model and evidence-based instrumental data. Even with smaller grids, the models totally fail to capture meso-climatic impacts of large lakes, or the steep climatic gradients over a scale of about 100 km. So the message is clear: global climatic models are great for general climatic trends, dubious for rainfall trends, and downright misleading for most small scale climate trend analysis. My worry is that one of leading categories of GCM users, water resources analysts, don't yet seem to have a clear grasp of GCM limitations. There are dangers in treating climatic computer models as a 'magical black box'. There are inherent dangers in gridding the available data realistically (a major source of error!). And whenever you choose a finer grid size, always pay very close attention to differences between hind-cast outputs and the actual spread of climatic data on the ground.

  • $\begingroup$ Gordon, you said "I found that smaller grids only highlights local climatic discrepancies between model and evidence-based instrumental data." In scientific numerical analysis the convergence of model with finer grid to instrumental observations is a necessary criteria for validity of a model. Would it be true to conclude from your answer that AOGCM models are fundamentally wrong? This is a very good input. $\endgroup$ Sep 12, 2016 at 22:01
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    $\begingroup$ The AOGCMs are just that - global. There's always a danger with sweeping generalizations, but as a rule of thumb, I would say it is all a matter of scale. AOGCMs are great for the big picture, but they just are not designed for small scale work. So they are not fundamentally wrong - just used inappropriately. $\endgroup$ Sep 14, 2016 at 4:16

To begin, climatology does not have real 25km x 25km data, since they have only about 6000 meteostations, while the 25x25 grid on dry land amounts to 240,000 stations. Artificial make-up of this kind of data must use variety of assumptions that might be not necessary true or coherent. Feeding a model with goofy data would accomplish nothing.

Second, finer input data do nothing if the model does not resolve dynamic patterns of the same scale. Unless you see a model that correctly reproduces main details of global atmospheric circulation (I mean just crude topology of vortices-hurricanes and their average frequency), fine input is meaningless. More, a good correct model should start with any realistic and non-detailed data, and develop a stable realistic trajectory with realistic means. Climate and transport data should not need any "input", the model must generate the data on its own.

  • $\begingroup$ Ali Chen's point is taken, but the problem is that model generated climate data is so often taken as 'real' data without due consideration of the differential between hindcast data and on-the-ground instrumental data. $\endgroup$ Sep 13, 2016 at 1:01

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