Atmospheric Inverse Methods for Flux Optimization

Python package containing functions for the application of inverse methods to the optimization of surface fluxes to be consistent with atmospheric observations. My use-case is primarily continental-scale biological carbon dioxide flux optimization using atmospheric carbon dioxide mole fraction observations. A paper with more details is in preparation. Similar work is being done, using similar methods with a different approach, by the NOAA/GMD CarbonTracker-Lagrange Inversion code. This code is designed to be run from within Python, where theirs is designed as a series of scripts to be run from the command line. I feel the flexibility from the data structures I chose to use, specifically inheriting from classes based on scipy's LinearOperators allows greater flexibility in what this code can do. Other software packages in Python that tackle similar problems include Data Assimilation with Python: a Package for Experimental Research (DAPPER) and Python Observing System Simulation Experiments (PyOSSE), both of which have more focus on identical-twin OSSEs and Ensemble Kalman Filters. These packages do not use standard Python packaging frameworks to specify dependencies, and my reasons for prefering my package to the CT-Lagrange inversion code also apply here.