  Data-driven weather prediction (DDWP) models are increasingly becoming
popular for weather forecasting. However, while operational weather forecasts
predict a wide variety of weather variables, DDWPs currently forecast a
specific set of key prognostic variables. Non-prognostic ("diagnostic")
variables are sometimes modeled separately as dependent variables of the
prognostic variables (c.f. FourCastNet), or by including the diagnostic
variable as a target in the DDWP. However, the cost of training and deploying
bespoke models for each diagnostic variable can increase dramatically with more
diagnostic variables, and limit the operational use of such models. Likewise,
retraining an entire DDWP each time a new diagnostic variable is added is also
cost-prohibitive. We present an two-stage approach that allows new diagnostic
variables to be added to an end-to-end DDWP model without the expensive
retraining. In the first stage, we train an autoencoder that learns to embed
prognostic variables into a latent space. In the second stage, the autoencoder
is frozen and "downstream" models are trained to predict diagnostic variables
using only the latent representations of prognostic variables as input. Our
experiments indicate that models trained using the two-stage approach offer
accuracy comparable to training bespoke models, while leading to significant
reduction in resource utilization during training and inference. This approach
allows for new "downstream" models to be developed as needed, without affecting
existing models and thus reducing the friction in operationalizing new models.
