Development Roadmap¶
Author: Joe Hamman Date:September 15, 2019
Background and scope¶
Scikit-downscale is a toolkit for statistical downscaling using Xarray. It is meant to support the development of new and existing downscaling methods in a common framework. It implements a fit/predict API that accepts Xarray objects, similiar to Python’s Scikit-Learn, for building a range of downscaling models. For example, implementing a BCSD workflow may look something like this:
from skdownscale.pointwise_models import PointWiseDownscaler
from skdownscale.models.bcsd import BCSDTemperature, bcsd_disaggregator
# da_temp_train: xarray.DataArray (monthly)
# da_temp_obs: xarray.DataArray (monthly)
# da_temp_obs_daily: xarray.DataArray (daily)
# da_temp_predict: xarray.DataArray (monthly)
# create a model
bcsd_model = PointWiseDownscaler(BCSDTemperature(), dim='time')
# train the model
bcsd_model.train(da_temp_train, da_temp_obs)
# predict with the model (downscaled_temp: xr.DataArray)
downscaled_temp = bcsd_model.predict(da_temp_predict)
# disaggregate the downscaled data (final: xr.DataArray)
final = bcsd_disaggregator(downscaled_temp, da_temp_obs_daily)
We are currently envisioning the project having three componenets (described in the components section below). While we haven’t started work on the deep learning models component, this is certainly a central motivation to this package and I am looking forward to starting on this work soon.
Principles¶
Open - aim to take the sausage making out downscaling; open-source methods, comparable, extensible
Scalable - plug into existing frameworks (e.g. dask/pangeo) to scale up, allow for use a single points to scale down
Portable - unopionated when it comes to compute platform
Tested - Rigourously tested, both on the computational and scientific implementation
Components¶
pointwise_models: a collection of linear models that are intended to be applied point-by-point. These may be sklearn Pipelines or custom sklearn-like models (e.g. BCSDTemperature).
global_models: (not implemented) concept space for deep learning-based models.
metrics: (not implemented) concept space for a benchmarking suite
Models¶
Scikit-downscale should provide a collection of a common set of downscaling models and the building blocks to construct new models. As a starter, I intend to implement the following models:
Pointwise models¶
BCSD_[Temperature, Precipitation]: Wood et al 2002
ARRM: Stoner et al 2012
Delta Method
Hybrid Delta Method
?
Other methods, like LOCA, MACA, BCCA, etc, should also be possible.
Global models¶
This category of methods is really what is motivating the development of this package. We’ve seen some early work from TJ Vandal in this area but there is more work to be done. For now, I’ll just jot down what a possible API might look like:
from skdownscale.global_models import GlobalDownscaler
from skdownscale.global_models.deepsd import DeepSD
# ...
# create a model
model = GlobalDownscaler(DeepSD())
# train the model
model.train(da_temp_train, da_temp_obs)
# predict with the model (downscaled_temp: xr.DataArray)
downscaled_temp = model.predict(da_temp_predict)
Dependencies¶
Core: Xarray, Pandas, Dask, Scikit-learn, Numpy, Scipy
Optional: Statsmodels, Keras, PyTorch, Tensorflow, etc.