> The reason we encode velocity data as an image is so we can pass it off to the GPU on the iPhone and iPad. Both the storm prediction and the smooth animations are calculated on the device itself, rather than the server, and all the magic happens directly on the GPU.
Sadly, until we have a breakthrough in battery technology, all of those will be parallel processors programmed to be asleep as often as they can.
I don't know anything about it, but I can think of numerous ways to mitigate the issues you mention. E.g.:
* Does it actually matter that the absolute value of temperature sensors is inaccurate, or is it enough that they have good relative accuracy (do they?)
* Do you really need to tell if someone is indoors or outdoors given a large enough sample of users in a given area? That also seems like something you could detect with heuristics (work hours, jumps up/down in temperature).
There's a lot of ways to tease useful signals from noisy data. Is it really the case that smart phone temperature sensors in the aggregate are useless as an input into weather forecasting models?
I can't answer much to your second question but I imagine it's easier in colder environments, especially if the phone is not in a pocket. In addition, here's a publication that claims to be able to get useful signals from noisy data, so I don't know if I would say the measurements are useless [2].
[1] Fig. 10 in https://www.sensirion.com/fileadmin/user_upload/customers/se...
[2] http://onlinelibrary.wiley.com/doi/10.1002/grl.50786/full
Fixed weather stations have corrective mappings associated with them. For example the station at the airport might be assumed to be 1 degree warmer than the generalised temperature for your city. It might be known to collect 5% more rain when the wind is northerly, and 8% less for southerly. These mappings are very important, because they let you shut-down a weather station, and build a new one in a different location, and compare the data from the two.
if the weather stations aren't in consistent locations, with discoverable local behaviors, then converting the raw data into generalised data about the area is going to be very difficult.
What I do know is that whilst teasing signals out of noisy data is achievable when the data are unbiased, the possibility of biases makes it significantly harder, especially when you don't know what the biases might be. This can be a significant problem with all sorts of data that you might think were fairly good, including both in-situ and satellite measurements.
i'd been browsing through some WMO reports and other associated NWP lit recently and i'd seen stuff on GPS-RO, but nothing on anyone assimilating "smart" devices.
There are researchers who use this data - we have collected about 4 billion atmospheric pressure measurements that we have distributed for academic and government research. The primary researchers are Cliff Mass and his lab at the University of Washington. There are also groups in Canada and the US that are using the data. IBM is now also collecting and using smartphone pressure data through their mobile apps. [2]
Generally speaking, the current trend is to take the live data stream, run it through a quality-control algorithm and then use kalman filters in the WRF data assimilation package.
There are some papers published, but it is still early. I will find some links to papers if you'd like to read them. [3]
[2] http://www.nytimes.com/2015/10/29/technology/ibm-to-acquire-...
[3] Utility of Dense Pressure Observations for Improving Mesoscale Analyses and Forecasts: http://www.atmos.washington.edu/~hakim/papers/madaus_hakim_m...
"It also learns every time you actively report to the community on sky conditions and hazards, translating this information into weather predictions."
does sunshine actively run a numerical weather prediction model like WRF?
it'd be great to assimilate all these extra sensors into the NWP centre's models, but i imagine things like cal/val and WMO agreements to share data might make things difficult for commercial companies?
And yes it would be great to have all these sensor readings available to NOAA, Environment Canada, ECMWF, and everywhere. I have made lots of progress in getting them to talk about it, but it's a long road before any government starts using this data in its own NWP models.
https://www.congress.gov/bill/114th-congress/house-bill/1561...
there's some stuff being written this year as well for the side that i work (space). with all the upcoming sensor gaps, they're looking at alternative ways to cover.
Weather forecasting accuracy is still constrained by both the available observations and the ability to run the forecast models. This new supercomputer won't be nearly enough to process all the new data that will arrive in the next few years, so the pendulum will swing back to the bottleneck being computing power soon enough. I think the most exciting things will happen over the next 5-10 years, as we end up with another 2-3 order of magnitude increase in the available observations to the models and we'll build new supercomputers to process them. I bet we can get incredible improvements, things like reliable 2-week weather forecasts.