| I've been working with weather models for 10 years and I often get asked "How accurate is X?" or "Which model is more accurate?" Many people think "accuracy" is a single number or a single thing - it is more complex than this and depends on your needs. This chapter on Numerical Weather Predictions [0] is great, especially the section on "Forecast Quality and Verification" (p777). The eye-opener for me was "Binary/Categorical Event". An example of a binary event is rain, one model could predict rain correctly but a second model might not predict the rain at all. This doesn't mean the second model was completely wrong, it still predicted the rain but it predicted the rain passing further to the south. [0] https://www.eoas.ubc.ca/books/Practical_Meteorology/mse3/Ch2... I've also noticed some model are better than other at predicting one phenomena while other models might be better in certain regions. For example, many people report that Canada's GDPS is better at higher latitudes whereas NOAA's GFS is better at equatorial regions. One final note, just because someone is solving an WRF model without verifying the results, doesn't mean it's wrong. Many numerical techniques and physical models within WRF have been validated analytical and experimental models. But it is also true that someone can naively setup a WRF model that gives bad results. I use a 900m WRF model that predicts the wind shadow around an island and we use it to find the best beach for a picnic - and it works. But this same model predicts the general pattern of rain but it doesn't get the start and stop time of rain correct. People get fixated on accuracy as a single thing and use it as a single basis for argument but to take a quote from the chapter [0] above "One of the least useful measures of quality is forecast accuracy" (ref. p777, Forecast Quality and Verification, third paragraph). |
The US Navy's COAMPS model is good for littoral regions.