Analysis of atmospheric temperature data by 4D spatial–temporal statistical model
Abstract The meteorological data such as temperature of the upper atmosphere is ssential for accurate weather forecasting. The Universal Rawinsonde Observation Program (RAOB) establishes an extensive radiosonde network worldwide to observe atmospheric meteorological data from the surface to the low...
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/79add7a6286a48c4a7f04fab8b545d09 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Abstract The meteorological data such as temperature of the upper atmosphere is ssential for accurate weather forecasting. The Universal Rawinsonde Observation Program (RAOB) establishes an extensive radiosonde network worldwide to observe atmospheric meteorological data from the surface to the low stratosphere. The RAOB data data has very high accuracy but can offer a very limited spatial coverage. Meanwhile, ERA-Interim reanalysis data is widely available but with low-quality. We propose a 4D spatiotemporal statistical model which can make effective inferences from ERA-Interim reanalysis data to RAOB data. Finally, we can obtain a huge amount of RAOB data with high-quality and can offer a very wide spatial coverage. In empirical research, we collected data from 200 launch sites around the world in January 2015. The 4D spatiotemporal statistical model successfully analyzed the observation gaps at different pressure levels. |
|---|