  The Analog Ensemble (AnEn) technique has been shown effective on several
weather problems. Unlike previous weather analogs that are sought within a
large spatial domain and an extended temporal window, AnEn strictly confines
space and time, and independently generates results at each grid point within a
short time window. AnEn can find similar forecasts that lead to accurate and
calibrated ensemble forecasts. The central core of the AnEn technique is a
similarity metric that sorts historical forecasts with respect to a new target
prediction. A commonly used metric is Euclidean distance. However, a
significant difficulty using this metric is the definition of the weights for
all the parameters. Generally, feature selection and extensive weight search
are needed.
  This paper proposes a novel definition of weather analogs through a Machine
Learning (ML) based similarity metric. The similarity metric uses neural
networks that are trained and instantiated to search for weather analogs. This
new metric allows incorporating all variables without requiring a prior feature
selection and weight optimization. Experiments are presented on the application
of this new metric to forecast wind speed and solar irradiance. Results show
that the ML metric generally outperforms the original metric. The ML metric has
a better capability to correct for larger errors and to take advantage of a
larger search repository. Spatial predictions using a learned metric also show
the ability to define effective latent features that are transferable to other
locations.
