Talk abstract: A Beginners Guide to Weather and Climate Data

Weather is part of our everyday lives. Who doesn’t check the rain radar before heading out, or the weather forecast when planning a weekend away? But where does this data come from, what is it made of? The answer is a mix of measurements, models and statistics. This session looks at the observations, predictions and forecast models, and weather data as a variable to consider in machine learning models. Learn how it is done and ways you can use weather and climate data from several examples.

Bio: Margriet is a Developer Advocate for the IBM Watson Data Platform. She has a passion for creating clear plots and models from diverse data sets using tools like Cloudant NoSQL databases, data warehouses, Spark, and Python notebooks. Previously she worked as a climate scientist where she explored large observational datasets of carbon uptake by forests and the output of global-scale climate models. She has written several scientific articles and regularly presents at international conferences.