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HIGH RESOLUTION RAPID REFRESH COUPLED WITH SMOKE (HRRR-
SMOKE): REAL-TIME AIR QUALITY MODELING SYSTEM AND ITS APPLICATION TO CASE STUDIES
Ravan Ahmadov1,2 ([email protected])
Acknowledgement: G. Grell2, E. James1,2, C. Alexander2, S.Benjamin2, B.Jamison1,2, T.Alcott2, J. Stewart1,2, S. Freitas3, G. Pereira3,4, I. Csiszar5,
M. Tsidulko8, B. Pierce6, S. McKeen1,2, S. Peckham7, L. Deanes8, A. Edman10, M. Goldberg11, B. Sjoberg11 JPSS proving ground and risk
reduction program
1 Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, Boulder, CO, USA
2 Earth System Research Laboratory, NOAA, Boulder, CO, USA
3 NASA Goddard Space Flight Center & USRA/GESTAR, Greenbelt, MD, USA
4 Federal University of São João del-Rei, MG, Brazil
5 Center for Satellite Applications and Research, NOAA/NESDIS, College Park, MD, USA,
6 Advanced Satellite Products Branch, Center for Satellite Applications and Research, NOAA/NESDIS, Madison, WI, USA
7 Cold Regions Research and Engineering Laboratory, US Army Corps of Engineers, Hanover, NH, USA
8 I.M. Systems Group, Inc, Rockville, MD, USA
9 National Weather Service, NOAA, USA
10 now at Penn State, University Park, PA, USA
11 NOAA's Joint Polar Satellite System Program Office
4th Biannual Western Modeling WorkshopSeptember 8, 2017
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Introduction
High-Resolution Rapid Refresh (HRRR) is a numerical weather prediction system running operationally at the National
Weather Service and in real time at NOAA Earth System Research Laboratory/ Global Systems Division (NOAA/ESRL/GSD).
The model is run at 3km resolution over the CONUS domain with an hourly update cycle.
At present mostly offline air quality models with relatively coarser resolution are used for smoke forecast. Some of these
models do not simulate plume rise. A very few air quality models use the satellite Fire Radiative Power (FRP) data to
estimate wildfire emissions and plume rise.
The goal of this project “Towards the Inclusion of VIIRS Fire Products into the HRRR Real-Time Forecasts” funded by the
JPSS PGRR program is to include the VIIRS products like FRP data into a coupled air quality model (HRRR-Smoke), in
order to improve the numerical prediction of fire emissions and smoke dispersion in forecast models used at NOAA, and also
to improve weather forecasting.
The HRRR-Smoke model configuration is based on the HRRR model with added smoke tracer emitted as fine particulate
matter by biomass burning emissions (including simulation of plume rise by the model).
The primary advantages of the HRRR-Smoke modeling system:
High spatial resolution to allow simulation of mesoscale flows
and smoke dispersion over complex terrain.
Full coupling between meteorology and smoke: feedback of
smoke on predicted radiation, cloudiness, and precipitation
(using double moment microphysics).
Biomass burning emissions and inline plume rise
parameterization based on the satellite FRP data.
A rapidly updating data assimilation cycle for meteorology;
HRRR-Smoke uses meteorological input data prepared by the
GSI data assimilation system and boundary conditions from
another weather forecast model: Rapid Refresh (RAP).
The forecast lead time is 36 hours. Four times a day (00, 06,
12 and 18UTC) a new forecast starts.
HRRR-Smoke model
Operational weather forecast models at NWS:
RAP (white), 13km resolution
HRRR model CONUS domain (green), 3km resolution
(https://rapidrefresh.noaa.gov/)
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Mapping the VIIRS FRP data to the HRRR-Smoke CONUS grid
The clustering procedure performs a
combination of all detected fires from VIIRS
according to the model spatial resolution
and grid configuration.
Averaged VIIRS FRP data mapped over 3x3km
HRRR CONUS grid pixels for July 19, 2017
Real-time VIIRS FRP data
from the NESDIS ftp server
Workflow of the real-time HRRR-Smoke modeling system
Processing the FRP data on
NOAA HPC in real-time
(mapping, calculating fire
size over the HRRR domain)
Calculating fire emissions,
and other parameters
using the prep_chem tool
HRRR-Smoke real-time runs
on NOAA HPC, 36hr forecasts
Initial and boundary conditions for
meteorology from the experimental
RAP and HRRR weather forecast
models run on NOAA HPC
Post processing of the
HRRR-Smoke output using
Unified Post Processor
Plotting of the FRP and
smoke fields and posting
on the GSD web-site
The experimental HRRR-Smoke
started in June, 2016.
The system is run 4 times a day
at 00, 06, 12 and 18 UTC. It takes
~4 hours to complete entire
cycle. Forecast plots are posted
as simulations progress.
Two NOAA HPC systems (Jet and
Theia) are used.
Multiple real-time datasets
(meteorological observations, GFS
model output, satellite…) are used.
The forecast lead time is 36 hours.
Providing output files in
GRIB2 format to various users
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The real-time HRRR-Smoke web-site for public access (rapidrefresh.noaa.gov/hrrr/HRRRsmoke/)
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This plot shows simulated fire emitted fine particulate matter (PM2.5 or fire smoke) concentrations and wind at the first model level (~8m above ground).
The following plot shows forecast of near-surface fire smoke for July 19, 8pm EDT over the CONUS and its subdomain. This forecast is based on the
model simulation of 24 hours from the model initialization time, which is 8pm EDT, July 18, 2017.
Smoke forecast for July 19, 2017 (rapidrefresh.noaa.gov/hrrr/HRRRsmoke/)
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Paugam et al., ACP, 2016
Fire plume rise dynamics
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Smoldering and flaming emissions in HRRR-Smoke
M.Bela et al. (WRF-Chemtutorial)
To calculate plume rise we need to know heat flux.
The traditional approach in WRF-Chem to calculate plume rise:
Use constant fire released heat flux numbers for a given land use class,
e.g. Tropical Forest: min and max heat flux = 30, 80 kW/m2
New approach used in HRRR-Smoke:
Heat flux ~ FRP/ burnt_area
FRP measured by satellites
Burnt_area is determined by using fire size
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Smoke concentration along WE x-sectionForecast for June 27, 4pm EDT
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1) VISUALIZATION OF FIRE RADIATIVE POWER (FRP)
FROM VIIRS SATELLITE DATA (AUGUST 17-31, 2015)
Active fires from the Suomi NPP VIIRS: Product status
and first evaluation results, Csiszar et al., JGR, 2013/
www.guardian.co.uk
AIR QUALITY MONITORING SITES ACROSS THE WESTERN US
www.epa.gov/aqs
• Mean Bias = -48.4 ug/m3
• RMSE = 289.8 ug/m3
• r = .49
• Mean Bias = -16.8 ug/m3
• RMSE = 45.8 ug/m3
Modeled vertically integrated aerosol concentrations VIIRS AOD
HRRR-Smoke simulated vertically integrated aerosol
concentrations and aerosol optical depth from VIIRS for August 27, 2015
VIIRS data are also very useful for independent model verification!
The model does NOT assimilate the VIIRS AOD data!
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VIIRS smoke mask and HRRR-Smoke forecast for vertically integrated smoke, July 28 2016
VIIRS AOD with smoke mask HRRR-Smoke
VIIRS AOD data could be used in model evaluations
21GOES-R image, 00:30 UTC, July 20
Qualitative comparisons of the GOES-R images with the HRRR-Smoke forecasts
Vertically integrated smoke forecast
01UTC, July 20 The Detliwer fire
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Recent megafire in Central Valley, CA (http://www.latimes.com)
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Simulating smoke feedback (direct and indirect) in HRRR-Smoke
using double moment Thompson microphysics scheme
Adapted from J.Fast (WRF-Chem tutorial)
Including smoke feedback on meteorology reduces forecasted temperature bias.
Mean temperature bias
Sensitivity study using HRRR-Smoke for August 12-20, 2016
Temperature bias verification of 18 hour model forecast using radiosounding measurements in the US
HRRR-Smoke weather prediction with and w/o smoke feedback on meteorology
Difference in shortwave radiation between HRRR-Smoke simulations without smoke feedback and feedback included, 8pm EDT, August 20, 2016
No feedback
With feedback
Difference
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Recent updates to the HRRR-Smoke modeling system
Starting August, 2017:HRRR-Smoke is ingesting
FRP data from the
MODIS Terra and Aqua satellites
in addition to VIIRS
HRRR-Smoke preprocessor is
using FEER (Fire Energetics and
Emissions Research) emission
coefficients [Ichoku and Ellison,
2014] to estimate the biomass
burning emissions
Smoke feedback on radiation
and microphysics is included in
the real-time HRRR-Smoke
26GOES-R
https://rapidrefresh.noaa.gov/hrrr/HRRRsmoke/
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Concluding remarks and future plans
The real-time HRRR-Smoke modeling system provides an online smoke forecasting tool, which is used by incident
meteorologists, air quality agencies, researchers and the public.
The model can help us to improve weather forecasting by accounting for smoke impact on meteorology.
The real-time VIIRS provides valuable datasets (AOD) for modeling fire emissions and model verification.
Adding visibility product to the forecast output;
Transitioning our smoke parameterization to NOAA’s future NGGPS global modeling system;
Using HRRR-Smoke (including NGGPS-Smoke in future) for upcoming intensive field campaigns in the US (FIREX, FIRE-
Chem, WE-CAN) and further verification and refinement of the model using future aircraft measurements (e.g.
parameterization of injection heights and emission factors);
Use high frequency GOES-R FRP data for rapid update of fire detection and emissions; Synergy with the GOES-R initiated
fire and smoke related activities;
More rigorous evaluations are necessary. How best we can validate the smoke models?
THANK YOU FOR YOUR ATTENTION!
QUESTIONS OR COMMENTS ?
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