3A.4 High-Resolution Real-Time Forecasting of Smoke and Visibility for the CONUS and Alaska: The HRRR-Smoke System

Monday, 13 January 2020: 2:45 PM
157C (Boston Convention and Exhibition Center)
E. P. James, Cooperative Institute for Research in Environmental Sciences, Boulder, CO; and R. Ahmadov, G. Grell, C. Alexander, S. Benjamin, S. McKeen, M. M. Bela, K. Y. Wong, G. Pereira, S. R. Freitas, I. A. Csiszar, M. Tsidulko, S. Kondragunta, and C. Xu

The High-Resolution Rapid Refresh with Smoke (HRRR-Smoke) model is based on the HRRR numerical weather prediction modeling system. The model simulates primary aerosols (smoke) from wildland fires in real time on 3km resolution grid over the entire CONUS domain. The current version of the HRRR-Smoke modeling system estimates biomass burning (BB) emissions and simulates fire plume rise in an inline mode by using the fire radiative power data from the VIIRS (onboard S-NPP and NOAA-20) and MODIS (Terra and Aqua) satellite instruments using as input data. The model includes the direct feedback of smoke on radiation and visibility diagnostics. HRRR-smoke is planned for operational implementation within the meteorological HRRR in 2020.

In this presentation, we describe retrospective simulations with the HRRR-Smoke system for the period of July - August 2018, during which thick smoke from wildfires impacted large portions of the western CONUS. We evaluate the impact of the direct radiative feedback of the smoke upon the meteorological forecasts during this period, using comparisons with observations from radiosondes and surface METAR weather stations. It is found that the inclusion of the direct radiative effect of the smoke allows significantly improved 2m temperature forecasts in regions of thick smoke. We also demonstrate that explicitly including the effect of smoke upon visibility permits much improved surface visibility forecasts over the western CONUS. These favorable results provided the justification for including the direct radiative feedback effect of the smoke within the upcoming operational RAPv5 and HRRRv4.

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