Awesome
Earth System Emulator (ESEm)
A tool for emulating geophysical datasets including (but not limited to!) Earth System Models.
Why ESEm?
While excellent tools exist for regression and emulation, and similarly for efficient calibration, there isn't a single package that makes it easy for Earth scientists to emulate and calibrate their models. ESEm provides a simple interface to do so, with a thin wrapper around familiar emulation engines and efficient sampling tools.
ESEm can use Iris Cubes or xarray DataArrays to retain useful geophysical information about the data being emulated and also streamlines the typical task of co-locating models and observations for comparison using e.g. CIS.
These tasks aren't just restricted to emulating and calibrating models though and can be used in any situation where regression of Earth system data is needed.
Documentation
Detailed instructions and example notebooks can be found in our official documentation at https://esem.readthedocs.io/en/latest/
Installation
ESEm can be easily installed using pip, including tensorflow (with GPU support):
$ pip install esem
Optionally also install GPFlow, keras or scikit-learn e.g.,:
$ pip install esem[gpflow]
For more detailed instructions, including using conda to install alongside iris or xarray see https://esem.readthedocs.io/en/latest/installation.html
Citation
If you use ESEm in your research please be sure to cite our paper:
@Article{gmd-2021-267,
AUTHOR = {Watson-Parris, D. and Williams, A. and Deaconu, L. and Stier, P.},
TITLE = {Model calibration using ESEm v1.0.0 -- an open, scalable Earth System Emulator},
JOURNAL = {Geoscientific Model Development Discussions},
VOLUME = {2021},
YEAR = {2021},
PAGES = {1--24},
URL = {https://gmd.copernicus.org/preprints/gmd-2021-267/},
DOI = {10.5194/gmd-2021-267}
}
Contributing
Contributions to ESEm of any size are very welcome, please see our Contributing page for more details.
Get in touch
- Ask general installation and usage questions ("How do I?") in our Discussions tab.
- Report bugs and suggest features in the Issues tab
License
Copyright 2019-2021 Duncan Watson-Parris
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.