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Amateur Engel Privat 19 This will give you direct mathematical evidence of the best performing models, once you get a large enough sample size. The good news is that NCEP and Environment Canada provide this data to the public completely free. Gallery "Collections" is dedicated to the sexy Pornhup.Com girls, in this gallery collected the entire video. Some websites don't distinguish between those types. Follow weather. HRRR Dirty Hobby Anal maps or customized GIFs Save your favorite comparisons to your home screen Try the Comparator. Post-processing -As mentioned before, the output you see on a website is not the raw model output. Shown HERE is a photo taken in Bail Electronic Services showroom of Bail's staff of the period, including Fred Swart and Greg Whiter. Numerical weather prediction is extremely complex! I find it particularly interesting that everyone agrees that the off hour model runs are trash.

Many climate models are actually "Earth system" models that include grid points in the soil and under water, and include dynamics and physics parameterizations to prognosticate soil temperature, soil moisture, SST etc.
Post-processing -As mentioned before, the output you see on a website is not the raw model output. Rather, the output was post-processed from the native model levels to isobaric or iso-height surfaces.
There are different ways to interpolate vertically. This is especially important when viewing output from ensembles.
Also keep in mind that while you can find "CAPE" as a field to view in model output, you should determine if it's surface-based, mixed-layer, most-unstable, or some other level CAPE.
Some websites don't distinguish between those types. Also, did they use the virtual temperature correction? The GFS didn't until a few years ago.
Not sure about anything else. As MClarkson said, you can compile your own error statistics by obtaining a large sample size to determine any deficiencies or particular strengths of a model.
However, to really know, you'll need to know every aspect of the model to be sure. This is because you are crossing the line between pure quantitative statistics and into feature-based identification, which computers are much farther behind compared to how they do with pure quantitative statistics.
I've spent the last 5 years or so in grad school learning about many of these elements and I still know that I don't know s--t about models.
They're just so crazy complicated, and the complexity will only increase in the future. Last edited by a moderator: Jul 3, MClarkson said:. Compile error statistics.
Yes, some fields are pretty hard to get reliable real-world measurements, but for many fields you can compare model forecasts directly with results and integrate the errors over time.
This will give you direct mathematical evidence of the best performing models, once you get a large enough sample size.
You can write computer code to automatically statistically adjust for calculated biases, the modeller can add this manually after his or her own research to computer model forecasts, or you let the forecasters know about it and let the adjust it on their own.
All three work, but I prefer the first option for simple error stats and the second option for more complex regime switching decision trees.
I agree a large and meaningful study almost certainly has to be automated. If you can automatically download and decode grib2 files, you can do this on your own.
No, it is not particularly easy but not insanely hard either, at least not like coding those models in the first place , and you probably want a command-line linux platform.
After I was first exposed to linux-grib2 processes, it took another couple months before I had everything ready to go for comparing various model data to observations often metars for surface data.
The good news is that NCEP and Environment Canada provide this data to the public completely free. Shown above is GFS Electronic Imports' later 15 McKeon Road showroom.
The photograph was taken around and gives an idea of the large range of products the company sold by that time. Most of the products then marketed were for amateur radio use, with a move away from CB radio.
Scanners had also become prominent in the company's sales figures, as GFS Electronic Imports were exclusive Australian distributors for JIL Japan Industries Limited who manufactured the then popular models SX and SX programmable scanning receivers.
An SX scanner can be seen in the top left hand corner of this photo. Also seen at the lower left of the photo are a number of MFJ Enterprises' products, for whom GFS were Australian distributors.
The most popular of MFJ's products were their antenna tuning units, however they also manufactured such things as electronic morse code keyers, dummy loads and receiver converters.
MFJ Enterprises were located in Starkville, Mississippi, USA. Airband receivers were also a popular product around this period.
In the late 's to early 's, Victorian Country Fire Authority volunteer fire fighters had a need to monitor one or more of the seven VHF highband FM communication channels then in use by the CFA.
GFS Electronic Imports provided a solution to this requirement in the way of two cigarette packet sized crystal controlled receivers.
Known as 'Listening Sets' they were the models FS and PK The good news: We will show you the old version of our site so you can still use our portal and when you will have updated or upgraded your browser, you can easily switch to the new site by clicking the rocket icon up in the nav bar.
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