Geospatial Visual Analytics applied to Short-term  Weather Forecasting

Storm Tracking Visual Analytics

Geospatial Visual Analytics applied to Probabilistic  Weather Forecasting

Social Media Visual Analytics

Visualization  Theory

Collaborators​:

Prof. Dr. Min Chen
Prof. Dr. Daniel Keim

Prof. Dr. Robert Laramee
Prof. Dr. Alfie Abdul-Raman

Prof. Dr. Benjamin Bach

Ms. Menna-tallah El-Assadi

Mr. Mathias Kraus

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We are working on the understanding of visualization guidelines based on the principles of Grounded Theory

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At the same time, we are running a community project, VisGuides.org, for the discussion, critique, and curation of visualization concepts and guidelines.

Visual Tools based on Citizen Data for Improving the Communication of Severe Weather Events
 

PI: Alexandra Diehl

Collaborators:

Prof. Dr. Renato Pajarola

Prof. Dr. Sara Fabrikant

Prof. Dr. Claudio Tessone 

Dr. Oliver Gruebner
Dr. Manuel Mariani

Dr. Ian Ruginski

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Geospatial visual analytics applied to weather forecasting presents several challenges. Forecasters need to analyze forecasts and make quick decisions on forecasts on a daily basis to avoid, prevent, and alert society about potential hazards.

During the last years, I have been working very closely with meteorology to develop efficient visual analytics tasks that can help them to make rapid decisions. Some of the efforts on short-term and probabilistic forecasting are already published, and others are ongoing projects, such as the case of Visual Analytics for Nowcasting.

 One of the next big scientific challenges that I want to tackle is how to process and make use of citizen data, which are heterogeneous, vague, and noisy, but yet a very valuable source of information. In particular, a big challenge that I want to tackle is the research and development of tools to help in the understanding and quantification of the uncertainty of citizen data, for the particular use in scientific models coming from meteorology.