1. Data Assimilation of TEMPO NO2: Winds, Emissions and PBL mixing
Ronald C. Cohen UC Berkeley
$ TEMPO, NASA ACMAP and GEOCAPE

2. Resolution and hourly repeats makes data assimilation uniquely suited to TEMPO NO2

3. Resolution in observations and a priori matters

5. OMI

6. TEMPO (actual is twice resolution
shown)

7. Model Resolution

8. OH 2006-2013 at 12km WRF-CHEM 30% increase downtown
16% decrease downwind
20% decrease in OH uniformly across the domain

9. Constant emissions. Model resolution 1 km and 12 km. ~33% difference.
L Valin et al., GRL 2013

10. Emissions and Winds from Data Assimilation
Xueling Liu
Also:
A. Mizzi, J. Anderson, NCAR
I. Fung, UC Berkeley

11. Ensemble Data Assimilation
Combining a physics/chemistry model with observations in a way that optimizes both.
Bauer, Thorpe & Brunet, The quiet revolution of numerical weather prediction Nature 2015.

12. Data Assimilation of NO2 column observations
Forecast model: WRF-Chem CTM
d01 (coarse domain) dx=12km, Grids=220*140 : Met Initialization and lateral boundary conditions from NARR.
d02 (fine domain) dx=3km, Grids=281*221: Both met and chemistry initialized and forced by d01.
Data Assimilation Engine: NCAR
Data Assimilation Research Testbed
Ensemble Adjustment Kalman Filter
Updated chem variables: NOx Emission, NO, NO2, HNO3, and O3
one-way nested domain
Anthr: NEI 2011
Bio: MEGAN
Met input: NARR
PBL scheme:YSU
Chem mechanism: RADM2
Averaging Kernel Profiles at 2230 locations in Denver region
Denver
Observation Simulator for TEMPO
Frequency: daytime hourly; resolution: 2×4.5km2
Generation of scene-dependent AKs
Assuming cloud-free scenes, calculate AK based on the parameters--terrain pressure, albedo, solar zenith angle, view zenith angle and relative azimuth angle.
Capture the spatial and temporal variation of AKs.
Model level
6 am
12 pm AK AK

13. Nested model

14. Emissions (mol/km2-hr)
X. Liu, et al., Assimilation of satellite NO2 observations at high spatial resolution, ACP, 2017.

15. Accurate emissions requires small errors (~1m/s) in winds

16. Using TEMPO to constrain Winds

17. L Valin et al., GRL 2013

18. Truth (Denver)
NO2 Column
Winds

19. Prior with biased winds
NO2 Column
Winds

20. Truth – Prior (Denver)
Sampled with TEMPO

21. Posterior After Assimilation
NO2 Column
Winds

22. Truth (Denver)
NO2 Column
Winds

23. 40% reduction in wind speed errors

24. To be fair, assimilation was NO2 only, not all existing information
To be fair, assimilation was NO2 only, not all existing information. Improvement is smaller on top of existing wind observations near Denver.

25. PBL Mixing

26. Using NO2 to improve model Boundary Layer Dynamics (Soil Moisture)
Truth Prior Posterior
NO2
column
Soil
Moisture

27. Vertical Profile of NO2
Truth Black Prior Blue Posterior Green

28. 1 2 3 4 Winds, Emissions, OH and NO2
Winds affect the NO2 lifetime in interesting and important ways 2
Retrievals that account for daily wind variations are needed for quantitative accuracy in our inferences of emissions and lifetime 3
Data assimilation of TEMPO will be effective for constraining emissions—possibly on hourly/daily timescales 4
Data assimilation of TEMPO will also be able to constrain meterological fields e.g. winds, soil moisture

29. Thank you!

30. Thank you!