Predicting Fire Aerosols and their Impact on Subseasonal to Seasonal Weather Forecasts in NOAA’s Global Aerosol Forecast Systems
- 1University of Colorado Boulder , CIRES, Boulder, United States of America (kate.zhang@noaa.gov)
- 2Global Systems Laboratory, NOAA, Boulder, CO, US
- 3SAIC/Lynker at NCEP/NWS/EMC/NOAA, College Park, MD, US
- 4George Mason University, Fairfax, VA, US
- 5Air Resources Laboratory, NOAA, College Park, MD, US
- 6Environmental Modeling Center, NCEP/NWS/NOAA, College Park, MD, US
- 7Chemical Sciences Laboratory, NOAA, Boulder, CO, US
- *A full list of authors appears at the end of the abstract
Recognizing the uncertainties associated with fire emission, a crucial factor influencing the fire aerosol prediction, we have initiated studies to improve fire emission for subseasonal to seasonal (S2S) forecasts. Two global aerosol/chemistry forecast models are currently under development and have been fully coupled with the Unified Forecast System (UFS), encompassing ocean, sea ice, wave and land surface components for S2S forecasts at NOAA. One is UFS-Aerosols: the second-generation UFS coupled aerosol system, which embeds NASA’s 2nd-generation GOCART model in a National Unified Operational Prediction Capability (NUOPC) infrastructure, has been collaboratively developed by NOAA and NASA since 2021. It is planned to be implemented into the Global Ensemble Forecast System (GEFS) v13.0 for ensemble prototype 5 (EP5) experiments early this year. The other one is UFS-Chem: an innovative community model of chemistry online coupled with UFS, developed collaboratively between NOAA Oceanic and Atmospheric Research (OAR) laboratories and NCAR. The aerosol component implemented into UFS-Chem is based on the current operational GEFS-Aerosols v12.3 and utilizes the Common Community Physics Package (CCPP) infrastructure with updates to wet deposition, dust and fire emission, etc. Both these two global aerosols forecast models include the direct and semi-direct radiative feedback from online aerosols prediction. Various global fire emission data, as well as their ensemble product, are employed to quantify the uncertainties associated with fire aerosol prediction. The capabilities of UFS-Aerosols and UFS-Chem in medium-range and S2S predictions of fire aerosol are assessed and compared using observations from reanalysis data, ground-based measurements, and satellite data. Additionally, preliminary blending and machine learning methods have been developed to predict fire emission and improve the S2S prediction.
Li Zhang1,2, Georg A. Grell2, Partha S. Bhattacharjee3, Shan Sun2, Anders Jensen2, Jordan Schnell1,2, Haiqin Li1,2, Yunyao Li4, Barry Baker5, Judy Henderson2, Ravan Ahmadov2, Ligia Bernardet2, Daniel Tong4, Ziheng Sun4, Li Pan3, Bing Fu6, Raffaele Montuoro6, Jian He1,7, Rebecca Schwantes7, Siyuan Wang1,7, Gregory J. Frost7, Brian McDonald7, Fanglin Yang6, Ivanka Stajner6 1CIRES, University of Colorado, Boulder, CO, US; 2Global Systems Laboratory, NOAA, Boulder, CO, US; 3SAIC/Lynker at NCEP/NWS/EMC/NOAA, College Park, MD, US; 4George Mason University, Fairfax, VA, US; 5Air Resources Laboratory, NOAA, College Park, MD, US; 6Environmental Modeling Center, NCEP/NWS/NOAA, College Park, MD, US; 7Chemical Sciences Laboratory, NOAA, Boulder, CO, US;
How to cite: Zhang, L., Grell, G., Bhattacharjee, P., Sun, S., Jensen, A., Schnell, J., Li, H., Li, Y., Baker, B., Henderson, J., Ahmadov, R., Bernardet, L., Tong, D., Sun, Z., Pan, L., Fu, B., Montuoro, R., He, J., Schwantes, R., and Wang, S. and the NOAA team: Predicting Fire Aerosols and their Impact on Subseasonal to Seasonal Weather Forecasts in NOAA’s Global Aerosol Forecast Systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6827, https://doi.org/10.5194/egusphere-egu24-6827, 2024.
