The Weather On-Demand Framework
This paper describes the Weather On-Demand (WOD) forecasting framework which is a software stack used to run operational and on-demand weather forecasts. The WOD framework is a distributed system for the following: (1) running the Weather Research and Forecast (WRF) model for data assimilation and f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/1/91 |
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| author | Ólafur Rögnvaldsson Karolina Stanislawska João A. Hackerott |
| author_facet | Ólafur Rögnvaldsson Karolina Stanislawska João A. Hackerott |
| author_sort | Ólafur Rögnvaldsson |
| collection | DOAJ |
| description | This paper describes the Weather On-Demand (WOD) forecasting framework which is a software stack used to run operational and on-demand weather forecasts. The WOD framework is a distributed system for the following: (1) running the Weather Research and Forecast (WRF) model for data assimilation and forecasts by triggering either scheduled or on-demand jobs; (2) gathering upstream weather forecasts and observations from a wide variety of sources; (3) reducing output data file sizes for permanent storage; (4) making results available through Application Programming Interfaces (APIs); (5) making data files available to custom post-processors. Much effort is put into starting processing as soon as the required data become available, and in parallel where possible. In addition to being able to create short- to medium-range weather forecasts for any location on the globe, users are granted access to a plethora of both global and regional weather forecasts and observations, as well as seasonal outlooks from the National Oceanic and Atmospheric Administration (NOAA) in the USA through WOD integrated-APIs. All this information can be integrated with third-party software solutions via WOD APIs. The software is maintained in the Git distributed version control system and can be installed on suitable hardware, bringing the full flexibility and power of the WRF modelling system to the user in a matter of hours. |
| format | Article |
| id | doaj-art-5cc43f2a26b44e14b6002c3d4d88ba12 |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-5cc43f2a26b44e14b6002c3d4d88ba122025-01-24T13:21:59ZengMDPI AGAtmosphere2073-44332025-01-011619110.3390/atmos16010091The Weather On-Demand FrameworkÓlafur Rögnvaldsson0Karolina Stanislawska1João A. Hackerott2Belgingur Ltd., IS-104 Reykjavík, IcelandBelgingur Ltd., IS-104 Reykjavík, IcelandTempo OK Ltda., Sao Paulo 05510-020, BrazilThis paper describes the Weather On-Demand (WOD) forecasting framework which is a software stack used to run operational and on-demand weather forecasts. The WOD framework is a distributed system for the following: (1) running the Weather Research and Forecast (WRF) model for data assimilation and forecasts by triggering either scheduled or on-demand jobs; (2) gathering upstream weather forecasts and observations from a wide variety of sources; (3) reducing output data file sizes for permanent storage; (4) making results available through Application Programming Interfaces (APIs); (5) making data files available to custom post-processors. Much effort is put into starting processing as soon as the required data become available, and in parallel where possible. In addition to being able to create short- to medium-range weather forecasts for any location on the globe, users are granted access to a plethora of both global and regional weather forecasts and observations, as well as seasonal outlooks from the National Oceanic and Atmospheric Administration (NOAA) in the USA through WOD integrated-APIs. All this information can be integrated with third-party software solutions via WOD APIs. The software is maintained in the Git distributed version control system and can be installed on suitable hardware, bringing the full flexibility and power of the WRF modelling system to the user in a matter of hours.https://www.mdpi.com/2073-4433/16/1/91APIdata assimilationNWP frameworkWRF |
| spellingShingle | Ólafur Rögnvaldsson Karolina Stanislawska João A. Hackerott The Weather On-Demand Framework Atmosphere API data assimilation NWP framework WRF |
| title | The Weather On-Demand Framework |
| title_full | The Weather On-Demand Framework |
| title_fullStr | The Weather On-Demand Framework |
| title_full_unstemmed | The Weather On-Demand Framework |
| title_short | The Weather On-Demand Framework |
| title_sort | weather on demand framework |
| topic | API data assimilation NWP framework WRF |
| url | https://www.mdpi.com/2073-4433/16/1/91 |
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