EMS Annual Meeting Abstracts
Vol. 18, EMS2021-502, 2021
https://doi.org/10.5194/ems2021-502
EMS Annual Meeting 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Pesonalising weather forecasts using AI techniques

Dimitrios Stamoulis and Panos Giannopoulos
Dimitrios Stamoulis and Panos Giannopoulos
  • Dept. of Digital Industry Technologies, National and Kapodistrian University of Athens, Greece

Communicating the scientific data of the weather forecasts to the general public has always been a challenge. Using computer graphics’ visual representations to convey the message to television viewers and through weather apps and websites has certainly helped a lot to popularize the weather forecast consumption by the general public. However, these representations are not information rich since they are abstraction; moreover they are not always very actionable on the receiver side to help one decide how s/he will “live” the forecast weather conditions. Therefore, there is a need to personalize the forecast based on past user experience and personal needs. The forecast has to become more human- and needs-oriented and more focused to the particular requirements of each individual person. The challenge is to move from providing the abstraction of atmospheric information to a real sense of how the weather will "feel" to the individual.

We therefore propose a new co-creation process in which the audience is called on to provide a daily feedback on how they lived the weather conditions personally, so that, “my personal forecast” can be produced making the forecast more actionable on the user side. Preliminary, but more personalized, such attempts include the “feels like” temperature forecasts. To arrive at the “my personal forecast”, AI-based recommender systems need to be applied, using fuzzy logic as the appropriate method for the user to express how s/he actually lived personally lived weather conditions every day. Over time this information can then be used to transform science-based descriptions of weather conditions into a sense of how the weather will be experienced at a personal level.

How to cite: Stamoulis, D. and Giannopoulos, P.: Pesonalising weather forecasts using AI techniques, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-502, https://doi.org/10.5194/ems2021-502, 2021.

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