| Summary: | This python code supports the draft manuscript "Simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation" which describes a way to infer the daily maps of the sea ice concentration and empirical properties of the sea ice (relating to its snow cover and its physical properties, such as air inclusions) along with the creation of a new empirical model for the sea ice surface emissivity. This is done using knowledge of the atmosphere state, skin temperature and ocean water emissivity from the European Centre for Medium-range Weather Forecasts (ECMWF) weather forecasting model and the observed radiances at microwave frequencies from the Advanced Microwave Scanning Radiometer 2 (AMSR2). The inverse modelling and state estimation is achieved by combining empirical machine learning elements in a Bayesian-inspired network along with a number of physical components. The work also introduces the idea of an "empirical state", in this case describing the aspects of the sea ice physical state which affect the observations, and which is defined by the inputs to the new empirical model component (in machine learning terms, it is defined by the latent input state of a neural network). The training is done using the Keras and Tensorflow software. See the draft manuscript for full details. The GitHub repository for this code is at https://github.com/ecmwf-projects/empirical-state-learning-seaice-emissivity-model The code used in the draft manuscript is archived at https://doi.org/10.5281/zenodo.10013542 The data used in the draft manuscript is archived at https://doi.org/10.5281/zenodo.10033377 Licensing Copyright 2023 European Centre for Medium-Range Weather Forecasts (ECMWF) Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the ...
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