1. Potential use of TEMPO AOD & NO2 retrievals to support wild fire plume & O3 & PM2.5 forecast in National Air Quality Forecasting Capability (NAQFC)
Pius Lee, Youhua Tang, Tianfeng Chai,
Li Pan, Hyuncheol Kim, Daniel Tong and Barry Baker
NOAA Air Resources Laboratory
airquality.weather.gov
TEMPO Sc. Team Meeting May 31 – June , Harvard

2. Use satellite data to improve NAQFC
Data assimilation: Adjust model initial conditions and/or emissions.
Verification: Track model performance over certain regions and/or certain periods in contrary with traditional surface monitors that has limited spatial and temporal coverages.
TEMPO Sc. Team Meeting May 31 – June , Harvard

3. NAQFC use near-real-time satellite data
NAQFC is providing daily forecast for O3 and PM2.5 using CMAQ driven by NAM meteorology;
Besides anthropogenic and biogenic emissions, wildfire, wind-blown dust emissions are important;
Pollutant inflows from outside the CONUS, such as influences from long-range transported Sahara dust and Canadian forest wildfires should be quantified.
TEMPO Sc. Team Meeting May 31 – June , Harvard

4. Smoke forecasts with HYSPLIT:
Example of data assimilation to constrain emission
Smoke forecasts with HYSPLIT:
NOAA NESDIS HMS Smoke and fire detection
Smoke column from the HYSPLIT model (blue) and NESDIS Hazardous Mapping System (orange)
Incorporates imagery from NOAA and NASA satellites (GOES-West, GOES-East, Terra/Aqua MODIS, AVHRR on NOAA-15/-17/-18)
Provides fire locations, starting time and duration
NOAA 16 (GOES-R) FRP 89.5W
USFS’s BlueSky: Estimate PM2.5 emissions and plume rise
Emission is aggregated and assumptions made for the forecasting period
TEMPO Sc. Team Meeting May 31 – June , Harvard

5. Derived from NESDIS GASP
Comparison in dust concentration in the PBL at 18Z on Aug
HYSPLIT
Derived from NESDIS GASP

6. Top-down estimation of wildfire smoke emission based on HYSPLIT model and NESDIS GOES Aerosol/Smoke products (GASP) to improve smoke forecasts
An independent HYSPLIT simulation starting at each HMS fire location with given starting time and duration is run with unit source, at several possible release height to generate a Transfer Coefficient Matrix (TCM).
Source terms are solved by minimizing a cost function built to mostly measure the differences between model predictions and observations, following a general data assimilation approach.
Hourly gridded GASP
Source: Chai et al. AE 2015
TEMPO Sc. Team Meeting May 31 – June , Harvard

7. NAQFC PM2.5 forecasts with CMAQ
BlueSky to derive wildfire emission similar to HYSPLIT added to CMAQ PM2.5 forecasts in the operational NAQFC modeling system since Feb 2016
NAQFC without fire source NAQFC with fire source
August 21, hr PM2.5 Max

8. State-level Changing Factors
Con’d example of data assimilation to constrain emission
NO2 Emission Data Assimilation (EDA): Use fused satellite (OMI) and ground (AQS) data to trend emission changes
Fusing AQS & OMI
State-level Changing Factors
Comparison of OMI and AQS (x100) Samples
Source: Tong et al., AE 2015;
Tong et al., GRL 2016
(2005 to 2012) 8

9. MDA8 O3 (ppb) regional statistics for August 2015
Day-1 performance
obs
Bias
Normalized mean bias%
RMSE
Coeff corr, r
Index of agreement
CON
NAQFC
40.0
6.8
17.0
11.5
0.70
0.60
With EDA
3.1
7.8
9.8
0.64 PC
45.2
0.12
0.27
10.0
0.85
0.72
-1.1
-2.4
9.9 RM
48.0
2.1
4.9
8.7
-1.8
-3.6
8.4 NE
40.2
9.7
31.4
12.5
0.80
0.55
3.9
15.5
8.2
0.65 UM
36.0
9.0
25.0
11.4
0.86
0.58
4.5
12.33
8.8
0.82 SE
33.2
10.1
30.3
0.54
6.1
18.1
9.5
0.81 LM
34.0
11.6
33.5
14.4
0.75
0.47
26.5
13.5
0.48
TEMPO Sc. Team Meeting May 31 – June , Harvard

10. 24 hour average PM2.5 concentrations
regional statistics for August 2015
[µg/m3]
Sample size
Obs.
Mean
Bias
RMSE
Corr.
coeff.
CONUS
13100
NAQFC
10.0
6.78
-3.22
10.12
0.34
With EDA
9.08
-0.92
8.40
0.66 PC
3000
14.0
5.67
-8.33
14.98
0.57
12.22
-1.78
12.14
0.63 RM
1235
12.9
5.56
-7.46
19.46
0.61
12.91
0.01
15.77
0.70 NE
1850
7.8
7.91
0.11
3.78
0.56
7.74
-0.06
3.87
0.52 UM
2400
7.7
8.08
0.38
4.02
0.54
8.05
0.35
3.98
0.53 SE
2050
9.2
6.85
-2.35
5.35
0.29
6.72
-2.48
4.61 LM
1550
10.2
6.70
-3.30
5.79
0.25
7.70
-2.30
5.28
0.31
CONUS-wide statistics are improved.
Largest improvements are for wildfire-impacted western US regions

11. MODIS NAQFC derived via CRTM valid on July 1 2011
Example of data assimilation for improving initial conditions
MODIS AOD (collection 5.1) compared to a prior CMAQ AOD calculated with CRTM (Community Radiative Transfer Model)
MODIS NAQFC derived via CRTM
valid on July
TEMPO Sc. Team Meeting May 31 – June , Harvard

12. Example of data assimilation for improving initial conditions
Surface ASO4J (accumulation-mode sulfate) increment after GSI-AOD assimilation

13. Source: Tang et al., JAWMA 2014
Example of data assimilation for improving initial conditions
Surface PM2.5 base case (left) and the run with GSI assimilation (right) including both AOD and surface PM2.5 assimilations
Source: Tang et al., JAWMA 2014
TEMPO Sc. Team Meeting May 31 – June , Harvard

14. e.g. of Verification: Nested 4km CMAQ run in SENEX Field Campaign June 14 2013
TEMPO Sc. Team Meeting May 31 – June , Harvard

15. Similar comparison, but for July 17 2013
TEMPO Sc. Team Meeting May 31 – June , Harvard

16. Both GOCART and RAQMS have D.A.
CALIPSO satellite detected a Sahara dust intrusion episode in Aug, 2016
Both GOCART and RAQMS have D.A.
TEMPO Sc. Team Meeting May 31 – June , Harvard

17. Summary: Satellite data improves NAQFC
Data assimilation:
Constrain emission strengths and locations: both intermittent and continuous sources;
Adjust initialization fields;
Adjust lateral boundary conditions by considering global models (e.g. RAQMS from Pierce for intruding plumes constrained by satellite data.
Verification (potentially input for data fusion):
Identify discrepancies and pin-point potential weaknesses at fine temporal and spatial resolutions;
Explore finer resolution PM forecast to be fused with satellite-data derived surface level PM concentration.
TEMPO Sc. Team Meeting May 31 – June , Harvard