Abstract: 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, and what is it made of? The answer is a mix of measurements, models and statistics, meaning that the use of weather and climate data can get complex very quickly.
This session provides a brief overview of the science behind weather and climate forecasts and provides you with the tools to get started with weather data - even if you aren't a meteorologist. Learn how to connect weather data to other data sources, how to visualize weather and climate data in an interactive weather dashboard embedded in a Python notebook, and other ways you can use weather data for yourself, from examples using weather APIs, maps, PixieDust and Machine Learning.
Bio: Margriet is a Data Scientist and Developer Advocate for the IBM Watson Data Platform. She has a background in Climate Science where she explored large observational datasets of carbon uptake by forests and the output of global scale weather and climate models. Now she uses this knowledge to create clear visualisations and models from diverse data sets using cloud databases, data warehouses, Spark, and Python notebooks.
Developer Advocate at IBM