Aashish Chaudhary will be presenting a poster on ClimatePipes: Analysis and Visualization of Climate and Geospatial datasets for Climate Change Adaptation and Mitigation
Abstract Text: The impact of climate change will resonate through a broad range of fields including public health, infrastructure, water resources, and many others. Long-term coordinated planning, funding, and action are required for climate change adaptation and mitigation. In recent years, multitudes of climate data have been collected as part of the U.S. Global Change Research Program and the Coupled Model Intercomparison Project (CMIP). Unfortunately, widespread use of climate data (simulated and observed) in non-climate science communities is impeded by factors such as large data size, lack of adequate metadata, poor documentation, and lack of sufficient computational and visualization resources. We present ClimatePipes to address many of these challenges by creating an open source platform that provides stateoftheart, user-friendly data access, analysis, and visualization for climate and other relevant geospatial datasets, making the climate data available to non-researchers, decision-makers, and other stakeholders. The overarching goals of ClimatePipes are:
1) Enable users to explore real-world questions related to climate change.
2) Provide tools for data access, analysis, and visualization.
3) Facilitate collaboration by enabling users to share datasets, workflows, and visualization.
ClimatePipes uses an open source web-based application platform for its widespread support on mainstream operating systems, easeofuse, and inherent collaboration support. We developed open source tools for interactive visualizations and analyses for climate and geospatial datasets that use HTML5 and CSS3. The backend of the ClimatePipes is built using Python, Visualization Toolkit (VTK, http://vtk.org), Climate Data Analysis Tools (CDAT, http://uvcdat.llnl.gov), OpenClimateGIS (https://earthsystemcog.org/projects/openclimategis) and other climate and geospatial data processing tools such as GDAL and PROJ4. This work has been supported by the Department of Energy (DOE) under award number DE-SC0006493.