Rain-on-me is an idea for an App that I have had for a while and have been trying to get people interested in it at Hackathons I attend. At the Techcrunch hackathon last weekend my pitch convinced Rob Finean, who I worked with at the Climate change hack, and we ended up winning in the Intel Mashery category (we used the wunderground API to get our realtime data).
The Rain-on-me idea is to use realtime rain data to predict how much rain will occur at my current location over the next hour or so (we divided the hour up into five minute intervals). This country, and others, has weather enthusiasts who operate their own weather stations and the data from these stations has been aggregated by the Weather Underground and made available on the Internet. Real-time data from local weather stations upwind of me could be used to predict what rain I am going to experience in the near future.
Anybody who has looked at weather station data, amateur or otherwise, knows that measured wind direction/speed can be surprisingly variable and that sometimes sensor stop reporting. But this is a hack, so lets be optimistic; station reporting intervals seem to be around 30 minutes, with some reporting every 15 mins and others once an hour, which is theory is good enough for our needs.
Being techies we were working on a design that showed quantity of rain and probability of occurring (this was early on and I had grand plans for modeling data from multiple stations). Rob had a circular plot design and Manoj (team member on previous hacks who has been bitten by the Raspberry pi bug) suggested designing it to run on a smart watch; my only contribution to the design was the use of five minute intervals.
The simplicity of the data presentation allows viewers to rapidly obtain a general idea of the rain situation in their location over the next hour (the hour is measured from the location of the minute hand; the shades of blue denote some combination of quantity of rain and probability of occurring).
This is the first App I’ve seen that actually makes sense on a smart watch. In fact if the watches communicated rain status at their current location then general accuracy over the next hour could become remarkably good.
Rainfall is only one of the things in my life that I would like predicted for the next hour. I want British rail to send me the predicted arrival time of the train I am on my way to catch (I may not need to rush so much if it is a few minutes late), when is the best time, in the next hour, to turn up at my barber for a hair cut (I want minimum waiting time after I arrive), average number of bikes for hire at my local docking station (should I leave now or is it safe to stay where I am a bit longer), etc.
Predicting events in the next hour of people’s lives is the future of Apps!
The existing rain-on-me implementation is very primitive; it uses the one weather station having the shortest perpendicular distance from the line going through the current location coming from the current wind direction (actually the App uses an hour of Saturday’s data since it was not raining on the Sunday lunchtime when we presented). There is plenty of room for improving the prediction reliability.
Other UK weather data sources include the UK Metoffice which supplies rainfall radar and rainfall predictions at hourly intervals for the next five days (presumably driven from the fancy whole Earth weather modeling they do); they also have an API for accessing hourly data from the 150 sites they operate.
The Weather Underground API is not particularly usable for this kind of problem. The call to obtain a list of stations close to a given latitude/longitude gives the distance (in miles and kilometers, isn’t there a formula to convert one to the other) of those station from what looks like the closest large town, so a separate call is needed for each station id to get their actual location!!! Rather late in the day I found out that the UK Metoffice has hidden away (or at least not obviously linked to) the Weather Observations Website which appears to be making available data from amateur weather stations.