
Show HN: Weather data created by phones for machine learning classifier training - jacobsheehy
https://play.google.com/store/apps/details?id=com.allclearweather.android&show
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jacobsheehy
I'm building 'virtual weather stations' out of disparate, unused data sources.

This effort is to create new data sources that describe our atmosphere. I
don't know if any of it will be useful but I cannot find much else out there
that is working towards this kind of effort. So here we are.

The main experiments I'm working on are 1) sky photos, 2) sensor data like
barometers in phones, and 3) user-submitted current weather.

1) Sky photos. I think that we can use a machine learning classifier that is
built from user-submitted labelled photos of the sky. We should be able to
them auto-tag weather data in the photos and get (numerically?) useful
historical weather data from all outdoor sky photos. Additionally live photos
could used to gain real-time information as well. Smartphone cameras are
getting really good and can detect significant detail.

2) Most phones have a barometer now, other sensors as well like hygrometers
etc. This data needs major QC and protection for user privacy and for
preparation for integration and assimilation into models like WRF. This is a
ways off but research has been done already here and there can be significant
progress with good quality data. Here is what data from a single phone in
Colorado looks like spanning a day or so:
[https://imgur.com/a/45lTZ6j](https://imgur.com/a/45lTZ6j). The sensor code
for Android is open source to encourage other apps and make the process of
collecting atmosphere data from smartphones much easier. [2]

3) Current conditions reporting. I found that MADIS stations don't update
frequently enough to keep track of rapidly changing weather events. Users can
submit current conditions and an algorithm picks which to show based on
recency, trust, and other metrics. So far it can create a more accurate
current conditions experience for the user.

I have taken significant effort to show the weather data from NOAA in a clear
and readable way. The large graphics and animations have significant logic
code behind them to attempt to most appropriately display the conditions
described by MADIS stations. For example, even when most APIs will return a
thunderstorm icon for a 30% chance (I find that misleading) All Clear takes an
approach of showing a faded flashing bolt with a clear indication that it is
'chance'.

Other efforts are made to improve weather data communication, such as
automatically converting text forecasts to units set by the user. It's not
simple! But it is a useful thing for users. (See this forecast API
result:[https://api.weather.gov/gridpoints/OKX/33,37/forecast/](https://api.weather.gov/gridpoints/OKX/33,37/forecast/)
for example). It has text forecast only in US units and I don't see any
options to change it otherwise.

Thanks!

[2]
[https://github.com/JacobSheehy/AllClearSensorLibrary](https://github.com/JacobSheehy/AllClearSensorLibrary)

