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    conduit

    @conduit

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    Latest posts made by conduit

    • RE: Why is Wind speed in hurricanes so wrong?

      @Ottavs First of all, it's in knots, not mph. Second, sustained wind measurements are much more complicated:
      https://en.wikipedia.org/wiki/Maximum_sustained_wind

      Basically, it's a 1 minute measurement. That's it. Windy is showing you the average wind speed for an hour. They could show you only the max wind speeds everywhere for the hour, but it would likely be very misleading.

      posted in Frequently Asked Questions
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      conduit
    • RE: Why is Wind speed in hurricanes so wrong?

      @Ottavs It's only measuring how much is dropping for that hour, it's not adding up all the rain over time to a single area. 1.2 inches of rain in an hour is a lot.

      posted in Frequently Asked Questions
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      conduit
    • RE: Why is Wind speed in hurricanes so wrong?

      @Ottavs Right. It's showing averages. You could have 3 measurements for a city, for example, 50mph, 50mph, and a gust of 150mph. The average is 83mph.

      posted in Frequently Asked Questions
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      conduit
    • RE: Why is Wind speed in hurricanes so wrong?

      @Ottavs Because it is showing averages, not peak measurements.

      posted in Frequently Asked Questions
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      conduit
    • RE: Why is Wind speed in hurricanes so wrong?

      @Ricky_Lightning See my post, this is not a "data withholding" problem. It's math. There are only so many ways to aggregate a bunch of measurements into one while preserving the character of the data.

      posted in Frequently Asked Questions
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      conduit
    • RE: Why is Wind speed in hurricanes so wrong?

      @Siff The explanation makes perfect sense from a data science perspective. The grid is aggregating data...it is a window function. The simplest example is an average (rectangular window function). Take the average of 10, 10, 20, & 80. The average is 30. Clearly, 80 was included, but the output (30). When the OP says that it "smoothes" away extremal data, they literally mean smooth:
      https://en.wikipedia.org/wiki/Smoothing

      That said, there are better methods that can preserve extremal measurements. These are called "High Resolution Schemes" or "Total Variation Diminishing" methods:
      https://en.wikipedia.org/wiki/High-resolution_scheme
      https://en.wikipedia.org/wiki/Total_variation_denoising

      These let you recover data like this:
      0_1504971007927_Screen Shot 2017-09-09 at 11.28.54 AM.png

      The challenge with using these for weather is scale and computing power.

      Stay safe out there!

      posted in Frequently Asked Questions
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      conduit