Abstract
Performing climate science within the context of climate change requires creative solutions to challenges such as data collection and storage management, optimizations for better memory and CPU usage, in addition to ensuring that analysis outputs are trustworthy. This talk will showcase xclim and finch, two pieces of software built for performing climate analyses on large datasets using Python, WPS, and the PANGEO software stack of technologies.
Prerequisites
Esposure to Web Services, Some knowledge of Environmental Research Software, Scientific Python libraries
Description
As we navigate the complexities of performing climate science, the need for reliable methodologies and efficient algorithms becomes increasingly apparent. This talk explores the joint capabilities of Xclim, a robust climate analysis engine, and Finch, an innovative web service for climate services, both built in Python.
Xclim, a sophisticated climate analysis library built on xarray and dask, addresses the challenges of climate change analysis. With features such as climate model ensemble selection, bias adjustment, numerous climate index calculations, and data health check-ups, Xclim is an essential tool for performing simple yet powerful climate analyses.
Finch extends Xclim's capabilities by offering climate analysis via Web Processing Services (WPS). Both Xclim and Finch serve as the software foundation of Environment and Climate Change Canada's ClimateData.ca platform. In this presentation, we will explore the integration of Xclim's engine and Finch's web services, demonstrating how they work together to transform remote and extensive climate datasets into actionable insights.
Join us as we provide an overview of the practical applications of Xclim and Finch for climate analysis. Gain insights into how this collaborative approach enhances accessibility and functionality in the field of climate science.
