This list provides the meteorological conditions which can be integrated by default:
Apparent ("feels like") temperature
Human felt temperature, determined by air temperature, wind speed, and humidity.
Cloud coverage ratio
Ratio of sky occluded by clouds.
Ambient steam saturation temperature.
Ambient ratio of steam saturation.
Quantity of ozone
Quantity of ozone substance over an area unit in Dobson Unit.
Amount of precipitation per time unit.
Precipitation probability based on historical meteorological conditions.
Sea level air pressure
Air pressure, measured at the height of the weather station, reduced to sea level.
Ambient air temperature.
Defined by WMO, WTO, and ICNIRP commision.
Measurement of the transparency of ambient air.
Direction from which the wind is coming. 0° at true north, clockwise. Not defined for wind speed = 0.
Wind gust speed
Maximum gust speed.
Horizontal wind speed.
We store every meteorological condition as a separate datapoint on the aedifion.io platform to historicize its state. More on this in the subchapter Datapoint and observation convention.
Besides the current state of a meteorological condition some use-cases require prediction data. We offer hourly predictions up to 168 hours (7 days) in the future and update them every hour. On ordering you can flexibly choose which horizons you need. The predicted states are aligned to the top of the prediction’s timestamp.
We combine every prediction horizon with the meteorological state monitored to create unique datapoints. You can use the unique datapoints to address the historicized meteorological conditions and their predications on the platform. More on this in the subchapter Datapoint and observation convention.
Like any other datapoint on the aedifion.io platform the weather datapoints are identifiable by an alphanumeric identifier which is unique for each project. The timeseries data for particular weather datapoints is stored as observations with a tuple of value and timestamp.
The naming convention for weather datapoints is:
aedifion_weather-<name of meteorological condition>_<preiction horizon>
How we handle predictions: Every predication exists of a predicted value and the timestamp in the future the predication is made for. This timestamp is equal to the prediction horizon. We hold on to this prediction value and prediction horizon combination to make predictions accessible on aedifion.io. It’s easier to explain in an example:
Your use-case is not covered by the weather data services provided? Do not hesitate to contact us.