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Ronald C. Cohen UC Berkeley $ TEMPO, NASA ACMAP and GEOCAPE

Data Assimilation of TEMPO NO 2 : Winds, Emissions and PBL mixing. Ronald C. Cohen UC Berkeley $ TEMPO, NASA ACMAP and GEOCAPE. Resolution and hourly repeats makes data assimilation uniquely suited to TEMPO NO 2. Resolution in observations and a priori matters.

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Ronald C. Cohen UC Berkeley $ TEMPO, NASA ACMAP and GEOCAPE

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  1. Data Assimilation of TEMPO NO2: Winds, Emissions and PBL mixing Ronald C. CohenUC 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 http://behr.cchem.berkeley.edu/

  4. OMI

  5. TEMPO (actual is twice resolution shown)

  6. Model Resolution

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

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

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

  10. 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.

  11. Data Assimilation of NO2 column observations • Data Assimilation Engine: NCAR Data Assimilation Research Testbed • Ensemble Adjustment Kalman Filter • 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. 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

  12. Nested model

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

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

  15. Using TEMPO to constrain Winds

  16. L Valin et al., GRL 2013

  17. Truth (Denver) NO2 Column Winds

  18. Prior with biased winds NO2 Column Winds

  19. Truth – Prior (Denver) Sampled with TEMPO

  20. Posterior After Assimilation NO2 Column Winds

  21. Truth (Denver) NO2 Column Winds

  22. 40% reduction in wind speed errors

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

  24. PBL Mixing

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

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

  27. Winds, Emissions, OH and NO2 1 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 Data assimilation of TEMPO will be effective for constraining emissions—possibly on hourly/daily timescales 3 4 Data assimilation of TEMPO will also be able to constrain meterological fields e.g. winds, soil moisture

  28. Thank you!

  29. Thank you!

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