3 ms·

I am proud of forecast.io for sticking with a 'normal' color palette here, rather than the non-standard purples they have chosen for radar; as a meteorologist

by graupel 13y ago

I am proud of forecast.io for sticking with a 'normal' color palette here, rather than the non-standard purples they have chosen for radar; as a meteorologist the purple radar hurts my brain, but this map is completely cool. Also there are definitely some quality control issues with the RTMA data, so you can bump into the garbage-in-garbage-out issue with it, but overall, this is a very nice start to something that could ultimately be quite useful.

thegrossman 13y ago
Thank MapBox. If it were up to me, I'd do something funky like a blackbody-esque color palette and then get yelled at because it'd be hard to read. :-)
thegrossman 13y ago
Also: Validation is tricky, since we can't just compare the output to ground station observations, as we incorporate ground station data into the model. Eventually I want to generate alternative versions that randomly exclude specific stations so we can use them for comparison.
- nkurz 13y ago
  I think RTMA already includes ground station measurements, so analyzing performance using a leave-N-out strategy wouldn't be a good verification: http://nomads.ncep.noaa.gov/txt_descriptions/RTMA_doc.shtml http://nomads.ncep.noaa.gov/txt_descriptions/RTMA_doc.shtml http://eamcweb4.usfs.msu.edu/mm5-case/RAWS/RTMA%20papers/pondeca_2012_RTMA.pdf http://eamcweb4.usfs.msu.edu/mm5-case/RAWS/RTMA%20papers/pon... Instead, I think you'd need to find temperature measurements that are completely independent and use them for verification. Along this line, I'm not sure how refitting the data to ground stations would produce a better match anywhere except at those ground stations (overfitting). Or are you using ground stations that are truly independent?
  - thegrossman 13y ago
    When we compare it to RTMA, we leave out RTMA from the list of data sources. Likewise, eventually I'd like to do the same with a subset of the ground stations we use. (The problem with finding completely independent measurements is that we'd want to use them as an input!)
deleted 13y ago
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by graupel 13y ago

thegrossman 13y ago

Thank MapBox. If it were up to me, I'd do something funky like a blackbody-esque color palette and then get yelled at because it'd be hard to read. :-)

thegrossman 13y ago

Also: Validation is tricky, since we can't just compare the output to ground station observations, as we incorporate ground station data into the model. Eventually I want to generate alternative versions that randomly exclude specific stations so we can use them for comparison.

nkurz 13y ago
I think RTMA already includes ground station measurements, so analyzing performance using a leave-N-out strategy wouldn't be a good verification: http://nomads.ncep.noaa.gov/txt_descriptions/RTMA_doc.shtml http://nomads.ncep.noaa.gov/txt_descriptions/RTMA_doc.shtml http://eamcweb4.usfs.msu.edu/mm5-case/RAWS/RTMA%20papers/pondeca_2012_RTMA.pdf http://eamcweb4.usfs.msu.edu/mm5-case/RAWS/RTMA%20papers/pon... Instead, I think you'd need to find temperature measurements that are completely independent and use them for verification. Along this line, I'm not sure how refitting the data to ground stations would produce a better match anywhere except at those ground stations (overfitting). Or are you using ground stations that are truly independent?
- thegrossman 13y ago
  When we compare it to RTMA, we leave out RTMA from the list of data sources. Likewise, eventually I'd like to do the same with a subset of the ground stations we use. (The problem with finding completely independent measurements is that we'd want to use them as an input!)

deleted 13y ago

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