Wetter.com: Pollen forecast for more than 35 different types – Data Sample

Sample data of our daily pollen forecast for more than 35 different types on zip code level


With this dataset you can use pollen forecast indices of over 35 types of pollen to help predict how these pollen may affect your customers health, your business decisions and offerings. You can enrich and improve your website, your analysis, your Data Science project, or trigger any event based on high accurate weather forecast. Forecast data includes pollen data for the next 5 days on daily level for a given ZIP code. In comparison to the full data set, this data sample provides information for one ZIP code.

The data can be found here:

The dataset has the following fields:

– date_forecast: Date on which the forecast was created
– country: country of the zip_code.
– zip_code: Zip code for which the index was calculated for a pollen type
– date: date for which the index was calculated
– pollen_index: Index for the pollen severity on a given date. The index ranges from 0 to 3.

The definition of the pollen index is:
0: no impact for this pollen type
1: low impact
2: medium impact
3: high impact

We offer the index for the following pollen types:

– Abies
– Acer
– Aesculus
– Ambrosia
– Artemisia
– Asteraceae
– Betula
– Carpinus
– Castanea
– Corylus
– Cruciferae
– Cyperaceae
– Fraxinus
– Fungus
– Galium
– Humulus
– Larix
– Pinaceae
– Pinus
– Plantago
– Populus
– Quercus ilex
– Rumex
– Salix
– Secale
– Chenopodium
– Tilia
– Urtica
– Alnus
– Erica
– Fagus
– Impatiens
– Juglans
– Poaceae
– Quercus
– Sambucus
– Picea
– Ulmus
– Varia
– Platanus
– Taxus

About the Provider:

For the past 20 years we have been collecting and analyzing weather data with our own meteorological unit, to provide the most accurate forecast possible for any given location in the world. Within our B2B Data Solutions Unit METEONOMIQS we not only offer historical weather data, but also help companies to use this data efficiently.

Visit the provider’s website for more information

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