Crowdsourcing and IoT at Météo-France
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1 Crowdsourcing and IoT at Météo-France Gwénaëlle Le Bloa Emilie Mallet Météo-France/Direction des Systèmes d Observation/MSO/PPC Eumetnet workshop, February
2 Crowdsourcing : why? Context fewer resources devoted to human observation Needs in present weather observations Metropolitan France Overseas France and beyond... forecasting climatology control source : Wikipedia Opportunities active community of meteorology enthusiasts emergence of citizen science Crowdsourcing and IoT at Météo-France 02
3 Crowdsourcing : what has been done and what is planned? β-tests : Operational : V1 V2 V3 Android IOS evolutions jan analysis of opportunities apr. jul. oct apr. jul. oct adaptation of the MF app jan. entry of observations visualization visualization interface internal to MF α-testing internal to Meteo-France volunteering β-testing external volunteering testers mountain? sea? jan evolutions new version of MF app development of a website enhancement of controls operational database feed information audience quality of the collected observations evolutions according to user feedback Crowdsourcing and IoT at Météo-France 03
4 Crowdsourcing : Android app Crowdsourcing and IoT at Météo-France 04
5 Crowdsourcing : IOS app Crowdsourcing and IoT at Météo-France 05
6 Crowdsourcing : internal visualization interface Crowdsourcing and IoT at Météo-France 06
7 Crowdourcing : what can be entered? Weather phenomena and impacts 2 profiles General Advanced possible to combine symbols complements next version ongoing thinking on the perimeter black ice avalanche nebulosity rain fog, icing fog intensity (weak, moderate, strong) frost dust thunderstorm intensity (weak, moderate, strong), start & end hail intensity (weak, moderate, strong), start & end, size (classes) snow fall intensity (weak, moderate, strong), start & end snow cover height (classes) strong wind intensity (according to damages) comments whirlwind (land, sea) land slip high waves flood sea flood Crowdsourcing and IoT at Météo-France 07
8 Crowdsourcing : user feedback (app) Internal to Meteo-France excellent feedback already used by forecasters for subjective validation of products External excellent feedback...but claim for Max : in one day more symbols, parameters, complements more controls more options Number of observations per day Crowdsourcing and IoT at Météo-France 08
9 IoT : 1- First steps with Vehicles data Research conducted within a partnership with the Continental company Questions : Are these data, which ones, are interesting for meteorology? How can we integrate them in our meteorological products to improve them? Work : Crowdsourcing and IoT at Météo-France 09
10 IoT : 1- First steps with Vehicles data DATA : Date and position Altitude Speed External temperature Atmospheric pressure ESP : Electronic Stability Program ABS : Anti-lock Breaking System Wiper status - "off" "inter" "slow" "med" "fast" Lighting status ["off","pos","low","high","ffog","rfog"] Defrost status - ["off","frnt","rear"] Crowdsourcing and IoT at Météo-France 010
11 IoT : 1- First steps with Vehicles data Plotting the data... Evaluating the quality of the mesure of temperature OK but the resolution is the degree Rear fog lights on (Back) Crowdsourcing and IoT at Météo-France 011
12 IoT : 2- First steps with Netatmo weather data Density map of the 41,984 Netatmo weather stations over metropolitan France (left) in comparison with 1,135 Météo-France automatic weather stations (right) What For? To conduct in the PRECIP team of the Centre National de Recherches Météorologiques (CNRM) to assess the crowdsourced weather stations contribution in the study of deep convection at the fine scale Crowdsourcing and IoT at Météo-France 012
13 Météo-France (head of project on crowdsourcing) (Netamo)
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