Using Unsupervised and Supervised Learning and Digital Twin for Deep
  Convective Ice Storm Classification
Jason Swope Steve Chien Emily Dunkel Xavier Bosch-Lluis Qing Yue William Deal
Smart Ice Cloud Sensing (SMICES) is a small-sat concept in which a primary
radar intelligently targets ice storms based on information collected by a
lookahead radiometer. Critical to the intelligent targeting is accurate
identification of storm/cloud types from eight bands of radiance collected by
the radiometer. The cloud types of interest are: clear sky, thin cirrus,
cirrus, rainy anvil, and convection core.
  We describe multi-step use of Machine Learning and Digital Twin of the
Earth's atmosphere to derive such a classifier. First, a digital twin of
Earth's atmosphere called a Weather Research Forecast (WRF) is used generate
simulated lookahead radiometer data as well as deeper "science" hidden
variables. The datasets simulate a tropical region over the Caribbean and a
non-tropical region over the Atlantic coast of the United States. A K-means
clustering over the scientific hidden variables was utilized by human experts
to generate an automatic labelling of the data - mapping each physical data
point to cloud types by scientists informed by mean/centroids of hidden
variables of the clusters. Next, classifiers were trained with the inputs of
the simulated radiometer data and its corresponding label. The classifiers of a
random decision forest (RDF), support vector machine (SVM), Gaussian na\"ive
bayes, feed forward artificial neural network (ANN), and a convolutional neural
network (CNN) were trained. Over the tropical dataset, the best performing
classifier was able to identify non-storm and storm clouds with over 80%
accuracy in each class for a held-out test set. Over the non-tropical dataset,
the best performing classifier was able to classify non-storm clouds with over
90% accuracy and storm clouds with over 40% accuracy. Additionally both sets of
classifiers were shown to be resilient to instrument noise.