4.2 Progress Toward Global Aerosol Analysis Capabilities at NCEP

Tuesday, 14 January 2020: 1:45 PM
254B (Boston Convention and Exhibition Center)
Cory R. Martin, RedLine Performance Solutions at NCEP EMC, College Park, MD; and D. T. Kleist, A. Collard, S. Lu, S. W. Wei, M. Pagowski, and I. Stajner

Handout (2.7 MB)

Relevant to not only air quality, but also subseasonal-to-seasonal forecasting, an accurate representation of aerosol composition in global numerical weather prediction (NWP) models is becoming increasingly important to operational centers. While global aerosol forecasts have been produced by the National Centers for Environmental Prediction (NCEP) since 2012, the initialization of each forecast cycle is not currently constrained by observations. This is in contrast to other NWP centers such as the European Centre for Medium-Range Weather Forecasts (ECMWF), the US Navy, and NASA, all of which utilize satellite-based observations to improve their aerosol forecasts. NCEP has recently implemented the latest version of the Global Forecast System (GFS) with the Finite-Volume Cubed Sphere (FV3) dynamical core. As part of a future implementation of the Global Ensemble Forecast System (GEFS), it is planned for one member to include the Goddard Chemistry Aerosol Radiation and Transport (GOCART) scheme to provide inline aerosol prediction. Here we present a 3D-Variational (3DVar) global aerosol analysis capability using the Gridpoint Statistical Interpolation (GSI) system in which satellite-based observations of Aerosol Optical Depth (AOD) are assimilated from both MODIS as well as VIIRS instruments to improve GFS-GOCART global aerosol forecasts. Multiple case studies are investigated, including one from June 2019 where satellite observations are able to detect a volcanic ash plume not present in the forecast model before assimilation, and that after the analysis cycle, the plume is now included in the subsequent forecast. Sensitivity to the results and forecast skill to the quality control of the AOD observations is explored. Current limitations of the prediction system are also described with near-future goals outlined.
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