Pipelines for downscaling CMIP6
Statistical downscaling and post-processing have been a part of both weather forecasting and climate modeling applications for decades. These statistical techniques are used to correct persistent biases in atmospheric model outputs. While many methods for statistical downscaling have been proposed over the years, few have been made open source. This fact has made it difficult to systematically explore the performance of different methods in the context of the broad range of applications their output is use for. Furthermore, reproducing previous downscaling efforts has proved impossible in most cases and has lead to man reinventions of the downscaling wheel.
The recent growth in popularity of machine learning, and more specifically deep learning, is leading to the development of new downscaling methods that apply machine learning techniques to downscale climate model output. These approaches are appealing in part because they have the potential to make higher-order connections between modeled and observed climate. Additionally, the deep learning community has developed a productive culture of benchmarking approaches, leading to demonstrated improvements in target ML problems. We hope to do the same in the downscaling space.
We plan to focus on three areas of development:
- Development of end-to-end pipelines for applying downscaling methods to CMIP6 data
- Development of methods for comparing downscaled data against other downscaled climate data products.
- Development of metrics to help inform the selection of different methods
- Implementation of new and existing downscaling methods in a common framework (this work is underway already)
Anticipated Data Needs
We plan to focus on downscaling the traditional target variables first (i.e. daily precipitation and temperature). However, if collaborators are interested in downscaling other variables, the tool set we hope to develop should support that. We have made data requests to have daily precipitation/temperature/wind/humidity/etc made available before the workshop.
Anticipated Software Tools
Xarray, Dask, Scikit-learn, xsd (https://github.com/jhamman/xsd)
Note: Xsd is a new tool we are developing to provide a common scikit-learn-like API on top of common downscaling approaches (e.g. BCSD, Analog-Regression, Quantile-Mapping). We are actively looking for collaborators on the development of this project. More information on the xsd roadmap is available here
Anyone interested in statistical downscaling, machine learning, or climate applications.