The radar shows hours of snow yet
(it’s moving West to East)
and I’m going to have to head to bed. So I won’t be taking any more webcam screencaps lol.
I think the new formula seems to show it’s pretty good - though interestingly right now, the old formula is showing a higher percentage than the new one (because I’m above zero, and have high humidity) - so maybe we can do some sort of hybrid formula?
Andy
For Snow Probability, bottom graph, orange is your old algorithm, blue is the new one. I thought this graph summarized how well they actually track together, at least at higher probabilities. It never snowed at my house for this, but it did snow 20 miles away where I was at the time.
I included the Lightning Probability for show, I’m working on refining it (Orange). Blue on this graph is Fog Probability which is coming from the integration.
Apparent temperature is also a work in progress, it compares air temperature, wind chill (feels like), and a heat index temperature (slightly different from WBGT Wet Bulb Globe Temperature).
Question…
Does anyone have WeeWx setup to intake data from MQTT?
Sorry, late to the conversation. There are several formula in this book:
Predicting Storms
Also I have found the zambretti formula to be fairly accurate for the UK.
I have used the JS script provided here
I’d be very interested in an HA entity that returned the current prediction from this algorithm.
Hi,
did you ever finish the component?
B.
I modified that code (Zambretti) in python. I don’t take into account pressure changes as originally designed but it works fairly well.
If you could share some of those formulas in the book, I would be willing to try some out. I did incorporate a snow prediction formula based off a extensive study of personal weather stations that someone brought to my attention.
Hi,
Sorry for the delay in replying!
The book is available here
Regarding your component, can you point me at a guide to writing them? Do they need to be in Python. or would JS do?
Thanks!
B.
JavaScript will do; I tested all of mine in JS before moving them to Python. I specifically ran everything in Node-Red.
Thanks!
B.
What exactly do you want? I can give you what I have already.
High, sorry for the delay in replying.
Well, by component, I was assuming that it was something integrated into home assistant. Is that the case? If so, I’d certainly like to see what you’ve got. I thought that there might be some guide on how to do it that you followed, and if so, where is it? I’m especially interested in using them in Node Red.
Ta!
B.
I pull mine from UDP via WeatherFlow2MQTT into Home Assistant; the WF2MQTT container does all the work (it is available as an add-on also). But if you want to do it in Node-Red give me a little bit of time and I can pull/update what I have been using for testing.
Hi,
it’s ok, I clearly misunderstood what you were doing. Now I do, so thanks for the explanation!
Thanks!
B.
Wanted to also link to this thread which has a similar goal: Homeassistant 12h local weather forecast. ~94% accurate*
Ideally a generic custom_component would take in all of the sensors and just create a standard weather entity able to be used with any normal weather card, but doesn’t seem to be the case yet.
There is some exciting new development with this. Google Research recently released NeuralGCM, which is an AI-based forecasting library for Python. It comes with a pre-trained model which is great for tinkerers.
The model seems to create temperature, humidity, and water-in-cloud measurements, so I’m not sure it would have anything completely ready to replace a consumer forecasting system, but it is a new option I will be toying around with.
