Weather data

In this section, we explain the basic concept of weather data integration.

Meteorological conditions

This list 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 commision.

Average visibility

visibility

km

Measurement of the transparency of ambient air.

Wind direction

windBearing

° [0,360]

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.

Prediction horizons

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.

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:

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 aedifion_weather-temperature_1h .

  • For datapoint aedifion_weather- dewpoint _1h: 2020-02-20 23:00:00

  • For datapoint aedifion_weather- dewpoint _3h: 2020-02-21 01:00:00

Your use-case is not covered by the weather data services provided? Do not hesitate to contact us.