pyCRTM: A python interface for the community radiative transfer model
•A new python interface for the community radiative transfer model (pyCRTM) is presented.•Applications of pyCRTM leveraging other python tools such as jupyter notebooks.•Applications of pyCRTM to academic and research applications in data Assimilation, calibration and validation are presented. The C...
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Veröffentlicht in: | Journal of quantitative spectroscopy & radiative transfer 2022-09, Vol.288, p.108263, Article 108263 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | •A new python interface for the community radiative transfer model (pyCRTM) is presented.•Applications of pyCRTM leveraging other python tools such as jupyter notebooks.•Applications of pyCRTM to academic and research applications in data Assimilation, calibration and validation are presented.
The Community Radiative Transfer Model (CRTM) is a powerful and versatile scalar radiative transfer model for satellite data assimilation and remote sensing applications. It is implemented as an object-oriented Fortran library, enabling flexible code development and optimal runtime performance on clusters. The downsides of the Fortran interface are a steep learning curve for students and the reduced productivity of users that is typical for static compiled languages, in contrast to dynamic interpreted languages like Python. pyCRTM is a new software framework that directly interfaces the CRTM Fortran data structures and procedures in Python, leveraging both the simplicity and ease of use of Python syntax as well as the flexibility arising from the vast contemporary Python ecosystem. The goal of pyCRTM is to lower the barrier of entry for university students to learn and use the CRTM and to boost the productivity of researchers seeking to create new methods in radiative transfer and data assimilation, or seeking to apply the CRTM to study atmospheric phenomena without having to go through the pre-existing complexity of the CRTM Fortran interface. |
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ISSN: | 0022-4073 1879-1352 |
DOI: | 10.1016/j.jqsrt.2022.108263 |