In this section, we explain the basic concept of weather data integration.
The following table provides the meteorological conditions which can be integrated by default:
|Meteorological condition||Name||Unit / Value set||Info|
|Apparent ("feels like") temperature||apparentTemperature||°C||Human-felt temperature, determined by air temperature, wind speed, and humidity.|
|Cloud coverage ratio||cloudCover||[0, 1]||Ratio of sky occluded by clouds.|
|Dew point||dewpoint||°C||Ambient steam saturation temperature.|
|Relative humidity||humidity||[0, 1]||Ambient ratio of steam saturation.|
|Quantity of ozone||ozone||DU||Quantity of ozone substance over an area unit in Dobson Unit.|
|Precipitation intensity||precipIntensity||mm/h||Amount of precipitation per time unit.|
|Precipitation probability||precipProbability||[0, 1]||Precipitation probability based on historical meteorological conditions.|
|Sea level air pressure||pressure||hPA||Air pressure, measured at the height of the weather station, reduced to sea level.|
|Temperature||temperature||°C||Ambient air temperature.|
|UV index||uvIndex||-||Defined by WMO, WTO, and ICNIRP commission.|
|Average visibility||visibility||km||Measurement of the transparency of ambient air.|
|Wind direction||windBearing||° |
|Direction from which the wind is coming. 0° at true north, clockwise. Not defined for wind speed = 0.|
|Wind gust speed||windGust||m/s||Maximum gust speed.|
|Wind speed||windSpeed||m/s||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 predictions on the platform. More on this in the subchapter Datapoint and observation convention.
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>_<prediction horizon>
Every prediction exists of a predicted value and the timestamp in the future the prediction 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:
Imagine the following setup:
- The project aedifion office wants to use temperature and dewpoint conditions.
- This data is needed for a prediction horizon of 1h and 3h in the future.
- The current date time is 2020-02-20 22:00:00.
Now, how would the datapoint string-id for temperature with 1h prediction be, and which timestamps will be used for the most recent predictions of the dewpoint datapoints at the current date time?
- The datapoint string-id for temperature with 1h prediction horizon will be
- For datapoint
aedifion_weather-dewpoint_1h: 2020-02-20 23:00:00
- For datapoint
aedifion_weather-dewpoint_3h: 2020-02-21 01:00:00