PAVICS

The download page permits users to download subsets of the data found throughout the site. This includes over 40 different datasets that are hosted by ClimateData.ca that can then be downloaded in various shapes and forms. However, there are potentially limitless possible options for every dataset on this site and although some calculations can be done “on-the-fly”, most cannot. To address this, in 2021 ClimateData.ca introduced “Analysis” features where users can tailor various datasets to their needs. Examples of this include the ability to compute custom climate indices that are not availabe on the site. For example, the site features data for the annual number of days with a temperature above 30 and 32°C but if the user wanted to know about the number of days above 34°C, they could use the Analysis page. To initiate the process, the user needs to identify the data that they want via the Download page form, enter their email and hit submit. If the data is not already available, the calculations will then be run in the background and the user will be notified once the data is ready

This “Analyze” functionality uses the Finch web processing service and the Weaver execution management service under the hood. Finch and Weaver are part of the PAVICS and Birdhouse ecosystems.

For advanced users who wish to access full datasets and undertake their own analysis, all of the datasets available on ClimateData.ca are accessible via the PAVICS platform. PAVICS is a virtual laboratory facilitating the analysis of climate data. It provides access to several data collections ranging from observations, climate projections and reanalyses. It also provides a Python programming environment to analyze this data without the need to download it. This working environment is constantly updated with the most efficient libraries for climate data analysis, in addition to ensuring quality control on the provided data and associated metadata. PAVICS also offers a suite of tools to streamline the analysis of climate change’s impacts on hydrology. It relies on Raven, a hydrological modeling framework that lets hydrologists define custom hydrological models or emulate existing ones. These models are minimally driven by daily time series of temperature and precipitation, and return series of flow and state variables such as snow pack and soil water.