Serving high-resolution sptatiotemporal climate data is hard, let’s go shopping

James Hiebert, Pacific Climate Impacts Consortium (University of Victoria)
Wednesday 14:00 - 14:25
Session 2, Track 7, Slot 3

The world is a big place and time is infinite. Scientists who study any aspect of the Earth’s climate are immediately faced with the exponentially growing amount of data that are required to represent properties of the climate in both time and space. The bulk of these data is a substantial barrier to extracting meaningful information from their contents. This barrier can be prohibitive to smaller-scale researchers and communities that want to study and understand the impact of the climate on their localities. Fortunately, a substantial amount of free and open source software (FOSS) exists upon which one can build a great geospatial data application.

The Pacific Climate Impacts Consortium (PCIC), a regional climate services provider in British Columbia, Canada, has been making a concerted effort to use geospatial FOSS in order to expand the availability, comprehensibility and transparency of big climate data sets from the Coupled Model Intercomparison Project (CMIP5) experiment. With a full stack of geospatial FOSS and open protocols we have built and deployed a web platform capable of visualizing and distributing high-resolution spatiotemporal raster climate data.

Our web application consists of:
+ back-end storage with raw NetCDF4/HDF5 files
+ a PostgreSQL/PostGIS database for indexed metadata
+ ncWMS for maps and visualization
+ the PyDAP OPeNDAP server for data requests
+ a web user interface to tie it all together

This presentation will provide a case study for enabling scientific collaboration using FOSS and open standards. We will describe our application architecture, present praise for and critique of the components we used, and provide a detailed discussion of the components that we had to improve or write ourselves. Finally, though our use case is specific to climate model output, we will provide some commentary as to how this use case relates to other applications of spatiotemporal data.