Whattttt? This is bad behavior on your part as a redistributor. It is more polite to do a bulk download (NDFD) and iterate against that directly.
36 karma · joined October 14, 2022
Whattttt? This is bad behavior on your part as a redistributor. It is more polite to do a bulk download (NDFD) and iterate against that directly.
The fact that error correction seems to be struggling implies unaccounted for noise that is not heat. Who knows maybe gravitational waves heck your setup no matter what you do!
pywgrib https://www.cpc.ncep.noaa.gov/products/people/lxu/cookbook/a... containerized https://hub.docker.com/repository/docker/jmarks213/container...
I am not familiar with those books or their content but that definitely reads as if the intent has been substantially changed. A typo 100 years ago might have been a letter off in the type setter; the typos these days are rewrites!
This is unacceptable. Typo's are not just aggravating but as they accumulate they begin to veer towards mutating the authors original intent.
Two critiques: - Kindle would be a much better product if kindle.amazon.com took me to a dedicated UX that is not washed out by the e-commerce bloat that currently surrounds it. - You have to carefully purchase Kindle editions of books. There are definitely Kindle edition books for sale that are digitally scanned, imported, and compiled as a Kindle edition with no proof reading having occurred leaving you stuck with typo riddled messes.
Definitely. Training on the historical data creates compelling forecasts but it comes off as a magic box. Where are the missing physics for the high performance cluster?
You are a bit misleading here. The model is trained on historical data but each run off of new instrument readings will be generated a few times in an ensemble.
What do you mean?
It could very well be that it is less than optimal to compare lots intended for residential with lots you would develop a convenience store on.
That is one angle of view. Alternatively, you could be encouraging vacant lot (or equivalent structure) owners to sell if neighbors are improving properties while they are not.
Write a Kotlin multiplatform client side business logic module in tandem with your Kotlin backend. The multiplatform module compiles for both your Android and Apple environments and for extra flexibility you are able to quickly port code from the client business logic module to the backend (or vice versa).
I second this sentiment. It is sorta bizarre, there is not really a tech scene in the area except for a few niche roles in support of Pax River Naval Base (I'm also operating my SaaS down here https://weatherthetrip.com/download :peace_sign:). I remember visiting this store back in the 2000's and was surprised when I saw it still in business a few months back.
Surely the urge to cheat is unbearable with millions of dollars on the hook. (Alot of apps on the app stores also claim to have quarter million 5 star reviews. Uhhuh)
I published a road trip weather app that crunches forecasts for you if you're going for a drive and would like to avoid the worst of the weather. It's great for evaluating whether to start a trip during the evening or the next morning. Timestamps are built using Google directions so you have about as accurate a forecast as you can in 2024.
> I am almost never interested in "30% chance of shower at 08:00pm" type of forecast.
I understand this sentiment but that is sorta where medium term forecasting is right now.
Android or iOS https://weatherthetrip.com/download
A great source of data for this topic is the Fatality and Injury Reporting System Tool (FIRST) [a National Highway Traffic Safety Administration product]. Link: https://cdan.dot.gov/query
It has more data (covers all states) although VIN numbers do not appear to be in the dataset.
I think I'll win more share of the U.S. market wink nudge nudge
That would be something for sure