Dexterity of WRF and GFS models in contrast with vertical radiosonde profiles
The study focused on evaluating the dexterity of numerical prediction models using radiosonde data, which consists of collecting information from the atmospheric profile, which is fundamental for the initialization of the models. For this analysis, two models, GFS and WRF, were compared with radioso...
| Autores: | , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | Brasil |
| Institución: | Universidade Federal de Santa Maria (UFSM) |
| Repositorio: | Revista Ciência e Natura (Online) |
| Idioma: | portugués |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/87743 |
| Acceso en línea: | https://periodicos.ufsm.br/cienciaenatura/article/view/87743 |
| Access Level: | acceso abierto |
| Palabra clave: | Radiossonda WRF Modelo Radiosonde Model |
| Sumario: | The study focused on evaluating the dexterity of numerical prediction models using radiosonde data, which consists of collecting information from the atmospheric profile, which is fundamental for the initialization of the models. For this analysis, two models, GFS and WRF, were compared with radiosonde observations. The observations used are from the radiosondes launched from the International Airport of Santarém/PA - Maestro Wilson Fonseca. Overall, both models underestimated air temperature measurements at low levels but aligned better at high altitudes. As for the zonal wind, the GFS overestimated at some levels, while the WRF had smaller discrepancies. The models face challenges in predicting air temperature, suggesting limitations in region-specific physics and boundary conditions. Both models had similar performance in the zonal wind forecast. The study highlights the importance of considering such discrepancies in weather forecasting and how these tools can be adjusted to improve their forecasts. |
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