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As @Erik notes, the answer to your question is vacuously "yes". Digging a little deeper, we can break this into two questions: Given the vast amount of weather data available (http://opendata.stackexchange.com/questions/10154/sources-of-weather-data/10155), is it possible to find correlations (either linear or non-linear) between known data such as ...


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From what I see, for some reason the GSD has split the 't/td' column into two separate columns, and has multiplied the temperatures by 10. Why? Maybe to get rid of the decimal point... So when you see a value < -500 it's actually < -50 °C. Other changes: - Pressure and altitude have been switched. - Pressure has also been multiplied by 10, so it's not ...


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The GSD file is just a text (ASCII) file, which you can read with almost anything you want. Since you are using python I would recommend looking at Pandas and especially the pandas.read_csv functionality. I am using this URL as an example now: https://rucsoundings.noaa.gov/get_soundings.cgi?data_source=Op40&latest=latest&start_year=2019&...


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Given only this information and nothing else, there isn't really a definitively correct answer. In reality, it depends on the nature of the precipitation. 61% could mean that there is a 61 percent chance of a thunderstorm between 4 and 5 PM, but since thunderstorms are short-lived, it might only be raining for 10 out of 60 minutes. 61% could also mean that ...


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Alec Bennett calculates resistance to heat flux values in an urban canyon as part of his MSc in meteorology dissertation: Heat Fluxes from Street Canyons. The calculated values range from 76.6 to 84.2 and are based on measurements taken on May 18th and 19th 2004 from 08:00 to 17:00 (both days). See the two tables in the Appendix on page 68 for the full ...


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Your question is extremely broad and it would be comprehensive to answer in detail. How it is done varies from one application to another. But in general, all weather forecasts come from a numerical prediction which is based on an analysis. The analysis is created by combining a prior (the previous forecast) with observations. There are many ways this can be ...


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Given the proprietary nature of that information, I don't think that can really be answered. But my best guess is that it may be updated by a computer, and adjusted by a meteorologist to better align with observations. There is probably some algorithm that mediates between the two.


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