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by amluto
738 days ago
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> We thin out and throw away a ton of surface observations already during the data assimilation process to initialize our forecast models anyways - data from aloft is far more valuable and impactful from a forecast impact perspective. I regularly notice that the NWS forecasts, even in the very short term, get the surface conditions rather wrong. (This is by comparison to a an inadvertent but, I think, quite accurate surface temperature and humidity measurement that I have.) I fully believe that the measurements aloft do a great job of predicting the conditions aloft, but I wonder whether the results would be further improved by even a fairly simple model to map the forecast results back to detailed surface conditions. After all, many of consumers of weather forecasts, e.g. people caring about personal comfort, climate control energy predictions and pre-heating/pre-cooling of buildings, etc. care about surface conditions more than they care about conditions aloft. |
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Observations for the surface don't have this effect for two reasons: (1) they can be dominated by local influences (like local topography) that poorly constrain the background atmospheric state, and (2) the majority of numerical weather models do not directly model the planetary boundary layer (the layer of the atmosphere closest to the ground), and instead parameterize processes that occur here. What this means, practically, is that the information content of surface observations is low (1), and even when it isn't, there isn't a mechanism to effectively propagate this information outside of a single grid cell or even column in the actual forecast model (2).
That's why observations are typically used to bias-correct forecast models - it's a form of localization or downscaling.