Baseline Models

Six different baseline models were created and trained:

1. Convolutional neural network (CNN)

2. Encoder-decoder (ED)

3. Heteroskedastic regression (HSR)

4. Multi-layer perceptron (MLP)

5. Randomized prior network (RPN)

6. Conditional variational autoencoder (cVAE)

There are Jupyter Notebooks that describe how to load and train the simple CNN and MLP models. The environments and code used to train each model, as well as the pre-trained models, are found in the baseline_models/ folder on GitHub.

The dataset used for the baseline models corresponds to the Low-Resolution Real Geography dataset. The subset of variables used to train our models is shown below:

Input	Target	Variable	Description	Units	Dimensions
X		T	Air temperature	K	(lev, ncol)
X		q	Specific humidity	kg/kg	(lev, ncol)
X		PS	Surface pressure	Pa	(ncol)
X		SOLIN	Solar insolation	W/m²	(ncol)
X		LHFLX	Surface latent heat flux	W/m²	(ncol)
X		SHFLX	Surface sensible heat flux	W/m²	(ncol)
	X	dT/dt	Heating tendency	K/s	(lev, ncol)
	X	dq/dt	Moistening tendency	kg/kg/s	(lev, ncol)
	X	NETSW	Net surface shortwave flux	W/m²	(ncol)
	X	FLWDS	Downward surface longwave flux	W/m²	(ncol)
	X	PRECSC	Snow rate	m/s	(ncol)
	X	PRECC	Rain rate	m/s	(ncol)
	X	SOLS	Visible direct solar flux	W/m²	(ncol)
	X	SOLL	Near-IR direct solar flux	W/m²	(ncol)
	X	SOLSD	Visible diffuse solar flux	W/m²	(ncol)
	X	SOLLD	Near-IR diffuse solar flux	W/m²	(ncol)