Stable Emulation of an Entire Suite of Model Physics in a State-of-the-Art GCM using a Neural Network
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'There has been a lot of recent interest in developing hybrid models that couple deterministic numerical models to statistical model components derived using machine learning techniques. One approach that we follow in this pilot study is to replace an existing computationally expensive deterministic model component with its fast machine-learning-based emulator, leading to the model speed-up and/or improvement. We developed a shallow neural network-based emulator of a complete suite of atmospheric physics parameterizations in NCEP Global Forecast System (GFS) general circulation model (GCM). The suite emulated by a single NN includes radiative transfer, cloud macro- and micro-physics, shallow and deep convection, boundary layer processes, gravity wave drag, land model, etc. NCEP GFS with the neural network replacing the original suite of ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["emulation","general circulation model","machine learning","model physics","neural network"]} <|eot_id|>
 Here is the Description of a dataset. Description:'There has been a lot of recent interest in developing hybrid models that couple deterministic numerical models to statistical model components derived using machine learning techniques. One approach that we follow in this pilot study is to replace an existing computationally expensive deterministic model component with its fast machine-learning-based emulator, leading to the model speed-up and/or improvement. We developed a shallow neural network-based emulator of a complete suite of atmospheric physics parameterizations in NCEP Global Forecast System (GFS) general circulation model (GCM). The suite emulated by a single NN includes radiative transfer, cloud macro- and micro-physics, shallow and deep convection, boundary layer processes, gravity wave drag, land model, etc. NCEP GFS with the neural network replacing the original suite of ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
There has been a lot of recent interest in developing hybrid models that couple deterministic numerical models to statistical model components derived using machine learning techniques. One approach that we follow in this pilot study is to replace an existing computationally expensive deterministic model component with its fast machine-learning-based emulator, leading to the model speed-up and/or improvement. We developed a shallow neural network-based emulator of a complete suite of atmospheric physics parameterizations in NCEP Global Forecast System (GFS) general circulation model (GCM). The suite emulated by a single NN includes radiative transfer, cloud macro- and micro-physics, shallow and deep convection, boundary layer processes, gravity wave drag, land model, etc. NCEP GFS with the neural network replacing the original suite of 
emulation; general circulation model; machine learning; model physics; neural network