PyIRI: Whole‐Globe Approach to the International Reference Ionosphere Modeling Implemented in Python
Abstract The International Reference Ionosphere (IRI) model is widely used in the ionospheric community and considered the gold standard for empirical ionospheric models. The development of this model was initiated in the late 1960s using the FORTRAN language; for its programming approach, the model...
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| Format: | Article |
| Language: | English |
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Wiley
2024-04-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003739 |
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| author | Victoriya V. Forsythe Dieter Bilitza Angeline G. Burrell Kenneth F. Dymond Bruce A. Fritz Sarah E. McDonald |
| author_facet | Victoriya V. Forsythe Dieter Bilitza Angeline G. Burrell Kenneth F. Dymond Bruce A. Fritz Sarah E. McDonald |
| author_sort | Victoriya V. Forsythe |
| collection | DOAJ |
| description | Abstract The International Reference Ionosphere (IRI) model is widely used in the ionospheric community and considered the gold standard for empirical ionospheric models. The development of this model was initiated in the late 1960s using the FORTRAN language; for its programming approach, the model outputs were calculated separately for each given geographic location and time stamp. The Consultative Committee on International Radio (CCIR) and International Union of Radio Science (URSI) coefficients provide the skeleton of the IRI model, as they define the global distribution of the maximum useable ionospheric frequency foF2 and the propagation factor M(3,000)F2. At the U.S. Naval Research Laboratory, a novel Python tool was developed that enables global runs of the IRI model with significantly lower computational overhead. This was made possible through the Python rebuild of the core IRI component (which calculates ionospheric critical frequency using the CCIR or URSI coefficients), taking advantage of NumPy matrix multiplication instead of using cyclic addition. This paper explains in detail this new approach and introduces all components of the PyIRI package. |
| format | Article |
| id | doaj-art-10a4c1398438423698d1a332b9a98a1e |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-10a4c1398438423698d1a332b9a98a1e2025-01-14T16:27:27ZengWileySpace Weather1542-73902024-04-01224n/an/a10.1029/2023SW003739PyIRI: Whole‐Globe Approach to the International Reference Ionosphere Modeling Implemented in PythonVictoriya V. Forsythe0Dieter Bilitza1Angeline G. Burrell2Kenneth F. Dymond3Bruce A. Fritz4Sarah E. McDonald5U.S. Naval Research Laboratory Washington DC USADepartment of Physics and Astronomy George Mason University Fairfax VA USAU.S. Naval Research Laboratory Washington DC USAU.S. Naval Research Laboratory Washington DC USAU.S. Naval Research Laboratory Washington DC USAU.S. Naval Research Laboratory Washington DC USAAbstract The International Reference Ionosphere (IRI) model is widely used in the ionospheric community and considered the gold standard for empirical ionospheric models. The development of this model was initiated in the late 1960s using the FORTRAN language; for its programming approach, the model outputs were calculated separately for each given geographic location and time stamp. The Consultative Committee on International Radio (CCIR) and International Union of Radio Science (URSI) coefficients provide the skeleton of the IRI model, as they define the global distribution of the maximum useable ionospheric frequency foF2 and the propagation factor M(3,000)F2. At the U.S. Naval Research Laboratory, a novel Python tool was developed that enables global runs of the IRI model with significantly lower computational overhead. This was made possible through the Python rebuild of the core IRI component (which calculates ionospheric critical frequency using the CCIR or URSI coefficients), taking advantage of NumPy matrix multiplication instead of using cyclic addition. This paper explains in detail this new approach and introduces all components of the PyIRI package.https://doi.org/10.1029/2023SW003739International Reference IonosphereIRIPythonelectron densityionospheric climatologyionospheric model |
| spellingShingle | Victoriya V. Forsythe Dieter Bilitza Angeline G. Burrell Kenneth F. Dymond Bruce A. Fritz Sarah E. McDonald PyIRI: Whole‐Globe Approach to the International Reference Ionosphere Modeling Implemented in Python Space Weather International Reference Ionosphere IRI Python electron density ionospheric climatology ionospheric model |
| title | PyIRI: Whole‐Globe Approach to the International Reference Ionosphere Modeling Implemented in Python |
| title_full | PyIRI: Whole‐Globe Approach to the International Reference Ionosphere Modeling Implemented in Python |
| title_fullStr | PyIRI: Whole‐Globe Approach to the International Reference Ionosphere Modeling Implemented in Python |
| title_full_unstemmed | PyIRI: Whole‐Globe Approach to the International Reference Ionosphere Modeling Implemented in Python |
| title_short | PyIRI: Whole‐Globe Approach to the International Reference Ionosphere Modeling Implemented in Python |
| title_sort | pyiri whole globe approach to the international reference ionosphere modeling implemented in python |
| topic | International Reference Ionosphere IRI Python electron density ionospheric climatology ionospheric model |
| url | https://doi.org/10.1029/2023SW003739 |
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