Neural Network applications for NWP models 

Vladimir Krasnopolsky
  21Feb, Noon, in 2155

Neural network (NN) is a versatile and generic artificial intelligence (statistical learning) nonlinear tool.  In EMC data/model rich environment, we could take full advantage of such a flexible tool if applied it more courageously. Many NN applications have been and are being developed at EMC. Brief review of these NN applications for: (1) model initialization, (2) model physics, and (3) post-processing model outputs is presented.  Three of developed NN applications are discussed in more details: NN observation operator to assimilate surface parameters (SSH anomaly); fast long and short wave NN radiation for CGS and GFS; NN nonlinear multi-model ensemble for calculating precipitation rate over ConUS. NN methodologies developed, working on these applications, are generic and can be used and are used currently to solve a variety of problems in post-processing of model outputs, data assimilation, etc.