Data Assimilation of TEMPO NO2: Winds, Emissions and PBL mixing

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Presentation transcript:

Data Assimilation of TEMPO NO2: 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 NO2

Resolution in observations and a priori matters http://behr.cchem.berkeley.edu/

OMI

TEMPO (actual is twice resolution shown)

Model Resolution

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

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

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

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.

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

Nested model

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

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

Using TEMPO to constrain Winds

L Valin et al., GRL 2013

Truth (Denver) NO2 Column Winds

Prior with biased winds NO2 Column Winds

Truth – Prior (Denver) Sampled with TEMPO

Posterior After Assimilation NO2 Column Winds

Truth (Denver) NO2 Column Winds

40% reduction in wind speed errors

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.

PBL Mixing

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

Vertical Profile of NO2 Truth Black Prior Blue Posterior Green

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

Thank you!

Thank you!