Lok Lamsal, Nickolay Krotkov, Randall Martin, Kenneth Pickering, Chris Loughner, James Crawford, Chris McLinden TEMPO Science Team Meeting Huntsville,

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Lok Lamsal, Nickolay Krotkov, Randall Martin, Kenneth Pickering, Chris Loughner, James Crawford, Chris McLinden TEMPO Science Team Meeting Huntsville, Alabama May 2015 Development of TEMPO NO 2 Algorithm to Infer Vertical Columns from Total Slant Columns 1)Stratosphere-troposphere separation 2)Sensitivity to NO 2 profiles

Attention Needed for Removal of Stratospheric NO 2 Fraction of total NO 2 column in the troposphere can be small Urban/Industrial areas: 30-80% Rural/background areas: 10-30% Need unbiased method to remove stratospheric NO 2 Figure from Chris McLinden Fraction OMI (2009) annual mean GMI model (July average) 6 AM 12 PM 6 PM

Candidate Stratosphere-troposphere Separation Algorithms 1)Reference sector method (zonal invariance and data from Pacific) 2)Image processing /wave analysis 3) Goddard method for OMI (OMNO2)  Observation based, spatial filtering, filling, and interpolation 4) KNMI method for OMI (DOMINO)  Data assimilation x Stratospheric NO 2 July 21, 2006

NASA GMI model Adaptation of OMI algorithms to TEMPO may require improvements when there is a large gradient in NO 2 field 3) Observation based 4) Assimilation Candidate Stratosphere-troposphere Separation Algorithms OMI heritage algorithm for TEMPO Rapid decline around sunrise, slow increase during day, rapid increase around sunset

Two CMAQ simulations: Model set up Horizontal resolution4 km x 4 km Vertical levels45 (surface-100 hPa) Chemical mechanismCB05 AerosolsAE5 Dry depositionM3DRY Vertical diffusionACM2 Boundary conditionRAQMS; 12 km x 12 km Biogenic emissionsCalculated within CMAQ with BEIS Biomass burning emissionsFINNv1 Lightning emissionsCalculated within CMAQ Anthropogenic emissionsNEI-2005 projected to 2012 Simulation 1Simulation 2 PBL schemeACM2 ( Assymetric Convective Model v2 ) YSU ( Yonsei Univ.) High Resolution CMAQ Simulations to Study Retrieval Sensitivity to Diurnal Changes in NO 2 Profiles

Evaluation of Modeled NO 2 Profiles: Methods ► Location: Padonia, Maryland ► Observation period: 3-4 spirals for 14 days in July 2011 (Hours covered 6 AM – 5 PM, local time) ► NO 2 observations:  Aircraft (P3B) measurements (200 m - ~4 km) NCAR data  Surface measurements by photolytic converter instrument  Spatial resolution comparable between model (4x4km) and spiral (radius ~4km) ► Observed PBL heights: Estimation based on temperature, water vapor, O 3 mixing ratios, and RH (Donald Lenschow) ► Collocation and sampling:  Model and surface measurements sampled for the days and time of aircraft spirals  Spiral data sampled to model vertical grids

Diurnal Changes in NO 2 Vertical Distribution Models capture overall diurnal variation, but some differences related to emissions, PBL height, vertical mixing are evident. Padonia, MD (July)

3 PM Surface reflectivities: 0.1 to 0.15 at 0.01 steps Solar zenith angles: 10° to 85° at 5° steps Aerosol optical depths: 0.1 to 0.9 at 0.1 steps 6 AM Improved Model Simulation Reduces Retrieval Errors

Model Need to Well Represent PBL Mixing to Minimize Errors from NO 2 Profiles ► PBL scheme alone can cause different AMF errors ► Greater performance for certain hours for both ACM2 and YSU ► Diurnal pattern in AMF errors for ACM2 ► We need model that represents PBL mixing and emissions to minimize errors in retrievals