Weather forecaster skills for your resume and career

I mentioned communication skills above, so I won't discuss that again. Another important soft skill is critical thinking because you must apply your knowledge to unique weather patterns that you have never seen before. Forecasters also typically work as a part of a team, so good teamwork and collaboration skills are essential. The weather never closes or takes days off, so many forecasters have to work rotating shifts, including nights, weekends, and holidays. Also, forecasting duties can sometimes be hectic or fast-paced, especially during high-impact weather events, so being able to work in such environments and manage your own stress is helpful.

Experience with emergency management can also be useful for forecasters who issue weather warnings. A background in social science and psychology, in addition to meteorology, can be important to examine how best to phrase or disseminate forecasts and especially warnings such that most people heed the warnings and take potentially life-saving action.

Knowledge of meteorology and the workings of the weather, usually attained through a college degree in meteorology or atmospheric science, is by far the most important. For example, every computer forecast model tells a story. It is up to the forecaster to interpret those stories, decide which one or blend of them is likely correct if they disagree, and translate that story into words the public or customers can understand. Skills in data analysis, quantitative reasoning, and computer programming can also be useful because forecasters often develop their own products, perform research, and sometimes run or maintain their own computer forecast models.

For some private-sector forecasting jobs, which span a broad spectrum of positions including private weather forecasting firms, transportation and shipping companies, utilities, renewable energy generation, media, and the insurance and reinsurance industries, backgrounds in business, markets, trading, renewable energy, risk analysis, and media communication can be very helpful, depending on the position.

Communication is one area. The ability to communicate scientific information to non-scientific audiences is increasingly important. Also coding, artificial intelligence, machine learning, and generally 'data science' skills are increasingly relevant.

Here are two technical skills that apply particularly to the private and government-research sectors: Proficiency in the use of Python and/or R to mine weather and climate datasets for relationships between weather/climate variables. Ability to configure and run weather and climate prediction models.