| Autor: |
Min Oo, Angela Benedetti, Jeff McQueen, Edward J. Hyer, Robert E. Holz, Juli I. Rubin, Peng Xian, Steven D. Miller, Brent N. Holben, Jianglong Zhang, Willem Marais, Thomas F. Eck, Amanda Gumber, Peter Calarco, Taichu Tanaka, Jun Wang, Jeffrey S. Reid |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IGARSS |
| DOI: |
10.1109/igarss47720.2021.9555160 |
| Popis: |
Various forms of global compositional forecasting are now commonplace across the world's operational centers. Biomass burning smoke is often forecast just like other aspects of our weather to support numerous applications such as air quality, transportation, and climate. Recent developments in the field have been bolstered by a new generation of advanced satellite sensors and algorithms on an international constellation of geostationary and polar orbiting satellites. The academic community frequently solicits operational developers for input on development needs and what should be operationalized. Yet, the volume of new data sources is currently outpacing Moore's Law and the forecasting community's ability to process and utilize new data sources data. Targeted to the academic community and using the 2020 western biomass-burning season as an example, this presentation will provide a brief review of how developers view next generation products for use in coupled observational and data assimilation systems that may be required to meet challenges posed by global extreme smoke event forecasting. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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