The overabundance of data which now characterizes the work environment of meteorologists, data scientists and risk managers requires new methods of data treatment[1]meteosensibilite.com/gestion-du-risque-meteo/big-data-correlation-causalite-3098#more-3098.
Thus, Machine Learning entered into weather modelisation. Because they have chosen to work with non-linear models rather than linear ones, weather specialists are presented as quick to adopt new techniques, more suited to the realities they are trying to capture. This even made some say that weathermen had much to teach to economists[2]www.forbes.com/sites/stevekeen/2015/04/16/you-do-need-a-weatherman/, who fail to abandon their old econometric ‘recipes’.
DOI: 10.15200/winn.143073.30255 provided by The Winnower, a DIY scholarly publishing platform
References
| 1. | ↑ | meteosensibilite.com/gestion-du-risque-meteo/big-data-correlation-causalite-3098#more-3098 |
| 2. | ↑ | www.forbes.com/sites/stevekeen/2015/04/16/you-do-need-a-weatherman/ |