Advancing the U.S. global chemical weather forecasting capabilities with next-generation,UFS-based fully coupled prediction systems
- 1CIRES, University of Colorado Boulder, Boulder, CO
- 2NOAA/NWS/NCEP/EMC
- 3NOAA/OAR/GSL
- 4NASA/GSFC/GMAO
- 5GMU
- 6NOAA/OAR/ARL
- 7IMSG
- 8NOAA/OAR/CSL
- 9NOAA/NESDIS/STAR
- 10UCAR/UCP/JCSDA
Recent NOAA collaborative efforts supported through the Unified Forecast System Research-to-Operations (UFS-R2O) Project have led to the development of advanced coupled systems to improve aerosol predictions on a global scale. These systems, integrated within the UFS framework, include online-coupled prognostic model components for atmosphere, ocean, sea ice, and waves, and rely upon state-of-the-science interoperable atmospheric physics schemes accessible via the Common Community Physics Package (CCPP) framework. Interoperability has been a key design element for those systems from the start, and thus it has also driven the incorporation of predictive aerosol processes. The approach to aerosol development within the UFS focuses on two primary outcomes: to build the next-generation upgrade to the currently operational Global Ensemble Forecast System (GEFS), and to create a research-oriented platform that allows developing and assessing the latest physical and chemical processes updates. In collaboration with NASA/GMAO, a novel aerosol component (UFS-Aerosols) was developed to succeed GEFS-Aerosols. This UFS component implements NASA’s 2nd generation GOCART model and brings the MAPL infrastructure layer into the UFS framework, enabling tighter collaborations with NASA and ensuring model interoperability across U.S. modeling centers thanks to its NUOPCcompliant interface. It also includes an updated FENGSHA dust scheme along with refinements to surface emissions. Furthermore, a CCPP-compliant implementation of aerosol processes based on GEFS-Aerosols was developed within the UFS framework to support and advance atmospheric chemistry research. This presentation will provide an overview of the architecture of each system as well as results from preliminary evaluations.
How to cite: Montuoro, R., Clune, T. L., Silva, A. M. D., Baker, B., Zhang, L., Pan, L., Bhattacharjee, P. S., Wang, S., He, J., Heinzeller, D., Ahmadov, R., Chawla, A., Stajner, I., Frost, G. J., Grell, G. A., McQueen, J., and Kondragunta, S.: Advancing the U.S. global chemical weather forecasting capabilities with next-generation,UFS-based fully coupled prediction systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13536, https://doi.org/10.5194/egusphere-egu22-13536, 2022.