OMI Validation Needs Mark Kroon – KNMI (on behalf of the OMI validation team) Aura Science Team Meeting Aura Validation Working Group Pasadena, CA, USA 01 October 2007

OMI Validation Priorities Nitrogen Dioxide (NO2) Air quality, emission estimates, sparse correlative data Ozone (O3) Air quality, human health hazard, ozone (hole) recovery, remaining retrieval challenges (tot-O3C, trop-O3C) Aerosols Air quality, retrieval challenges, physics of aerosols Sulphur Dioxide (SO2) Air quality, emission estimates, aviation warning Clouds Influence to (tropospheric) trace gas retrievals “Minor” trace gases (BrO, OClO, HCHO, CHO-CHO) Shortage of correlative data in general

GOME tropospheric NO2 intercomparison Why such differences? Who is right? Van Noije et al., ACP, 2006

The 3 steps to tropospheric NO2 VCDs STEP 1: DOAS  NO2 SCD STEP 2: Remove the stratospheric part  tropospheric NO2 (TSCD) Strat. NO2 STEP 3: Convert TSCD into tropospheric VCDNO2 NO2 Surface

Examples of solutions currently in use Property Current treatment in AMF calculation Groups Surface albedo - GOME/TOMS data base All groups Cloud fraction and cloud top height - Screening based on cloud fraction - Explicit correction using IPA and accounting for ghost column - Bremen, Heid - KNMI, NASA, SAO NO2 profiles - Scenarios - Monthly mean profiles (MOZART) - Daily profiles (GEOS-CHEM) - Daily profiles (TM4) - Heid, NASA - Bremen - SAO - KNMI Aerosols - Neglected - Scenarios (Lowtran) - Implicitly corrected by cloud treatment - Complex aerosol model - Heid - KNMI, NASA

Nitrogen Dioxide (1) Retrieval Challenges Most retrievals calculate same Slant Column Density Air Mass Factor calculation differs by research group Different versions of column NO2 and trop. NO2 (level 1B publ.) Need for validating retrieval input and satellite output data NO2 profiles in polluted regions, NO2 diurnal cycle Cloud fraction and cloud height (related issue) Total / tropospheric NO2 columns in polluted regions Campaigns versus Networks DANDELIONS-1 and 2 have proven relevance of observations Need for network in polluted regions providing continuity

Ground-based NO2 measuring instruments Chemiluminescent NOx analyzer Molybdenum converter Photolytic DOAS, LIF, TILDAS, LIDAR (research grade instruments) Commonly used instrument Specific to NO Indirect measurement of NO2 Significant interference from other reactive nitrogen compounds Specific to NO2 Some interference from HONO Not widely available Lok Lamsal - Relationship between ground-level NO2 concentrations and OMI tropospheric NO2 columns

Interference in molybdenum converter analyzer Compounds Conversion efficiency Experiments NO2, ethylnitrate (C2H5NO3 ) ~ 100% Winer et al., 1974 PAN (Peroxyacytyl nitrate) 92% HNO3, PAN, n-propyl nitrate, n-butyl nitrate ≥98% Grosjean and Harrison, 1985 Ammonia, gas phase olefins, particulate nitrate No significant interference Dunlea et al., 2007 Difficult issue: Loss of HNO3 on stainless steel of inlet Difficult to quantify the conversion efficiency Lok Lamsal - Relationship between ground-level NO2 concentrations and OMI tropospheric NO2 columns

Nitrogen Dioxide (2) Ground Truth Molybdenum systems measure more than NO2 Most NO2 specific systems are “research grade” NO2 lidar systems are expensive “A Brewer/Dobson”-like network for NO2 Reference network of observations providing continuity SAOZ/DOAS network at sunrise and sunset insufficient (model) Pandora, direct sun, (mini)MAX-DOAS, in-situ, Double Brewer

Total Ozone Column OMI retrievals at high SZA remain challenging (e.g. OMI-TOMS) Same holds for ground based observations (e.g. single Brewer) SAUNA-I and SAUNA-II may provide answers If not sufficient a SAUNA –III is needed Cloud height influence identified (climatology vs O2-O2) Need for analysis TC-4 data (lidar, CAFS) If not sufficient need for more campaign data

Tropospheric Ozone Column Strong interest from Air Quality perspective Air quality constituent, respiratory illnesses Obtained from OMI-MLS and other techniques (e.g. Schoeberl) ~10% of total column, 1%*300DU=3DU=10% of trop Campaigns versus Networks Aircraft in-situ/remote sensing in polluted regions (tethered) Balloons and Ozone lidars in polluted regions Ground truth with (mini) MAX-DOAS

Aerosols Retrieval Issues Retrievals use auxiliary data (surface albedo, aerosol microphysical properties, wind speed, etc.) Retrievals themselves are accurate (c2), outcome does not correlate well with Aeronet or Sat-Sat (MODIS, PARASOL) Validation of auxiliary data Aerosol microphysical properties, global distributions of aerosols (e.g. type), layer altitudes, transport Airborne campaigns flying PALMS-like systems (e.g. type)

Results of OMAERO-MODIS Comparisons 1-21 June 2006 Oceans worldwide No sunglint ALL collocations (regardless of OMI/MODIS coverage and MODIS QA) Only pixels completely covered by sufficiently cloud-free and quality- assured MODIS pixels Good agreement with quality-assured MODIS AOT Images taken from 90_OMAERO_Release_Notes.

AERONET Comparison Examples – 2005 MD Science Center OMAERO tends to overestimate over land

Sulphur Dioxide (SO2) Retrieval issues Depends on height of layer, profile and aerosols In situ SO2 observations from aircraft near volcanoes for plume characterization areas of high SO2 pollution (China, East Europe) Importance of aircraft profiling of SO2 in PBL simultaneous measurements of aerosol type (dust vs sulfate or soot) and SO2 profiles. Ground based column SO2 measurements double Brewer instruments (not single Brewers) (MAX)-DOAS type systems need for advanced Brewer SO2 algorithm

Aerosol Aircraft spirals SO2 First validation during EAST-AIRE regional experiment over NE China in April 2005. SO2 observations from instrumented aircraft flights are compared with OMI SO2 maps. Aerosol Aircraft spirals Here we present data from a case study conducted over Shenyang in NE China as part of EAST-AIRE in April 2005. SO2 observations from instrumented aircraft flights are compared with OMI SO2 maps. Date: Mon, 4 Dec 2006 16:02:51 -0500 (EST) From: Can Li <canli@atmos.umd.edu> Dr. Krotkov, The angstrom coeff. changed with height, from about 1.4 at about 200 m to close to unit at about 4000 m.  I can also derive angtrom from the integrated aerosol scattering extinction.  The integrated angstrom would be about 1.35 for both spirals. If I use this angstrom coeff. to extropolate AOT at 310 nm, the results would be 1.91 for the 1sh spiral, and 2.03 for the 2nd spiral, without considering aerosol absorption.  Assuming a SSA of 0.9 at 310 nm (could be even lower), the AOT would be 2.12 and 2.25 for two spirals, respectively. If we extropolate scattering at 310 nm with angstrom at different heights, the integrated scattering extinction would be lower, 1.74 for the 1st spiral and 1.86 for the 2nd spiral.  Taking aerosol absorption into account, AOT at 310 nm would be 1.93 for the 1st spiral, and 2.07 for the 2nd spiral. Can Li All,Attached please find a tiff plot showing scattering profiles over Shenyang on April 5th, 2005.Assuming well-mixed aerosols from sfc to a few hundred meters altitude (the lowest atltitude of the two spirals were about 200 m), also assuming very low aerosol loadings above the top of the spirals (which is necessarily true for our case), the integrated aerosol scattering extiction for two sprials would be 1.00 for the first spiral (panel a in plot) and 1.07 for the 2nd spiral (panel b).If we take the surface-retrieved SSA of ~0.9 [Xia et al., 2006] over that area, then the total aerosol extinction (AOT) would be 1.11 for the 1st spiral and 1.19 for the 2nd spiral.All AOT and scattering extinction are at 500 nm, interpolated using angstrom exponent calculated from scattering measurements at 3 wavelengths.please let me know if you have any questions.Can Li SO2

Clouds Clouds and Retrievals Cloud height (UV-VIS-IR) and Cloud fraction (model dep.) Both influence trace gas retrievals, particularly tropospheric column estimates but also total ozone column (e.g. OMI-TOMS) Validation Sat-Sat is upcoming (e.g. MODIS, Parasol, CloudSat, Calipso) TC-4 data will help to validate / evaluate OMI data Ground radar/lidar for PBL and cloud height

Please Consult Presentation Thomas Kurosu Collection 3 Retrievals: Outlook Standard Data Products: HCHO, BrO, HCHO Check with validation sources Fine-tune fitting window Optimize smoothing Take a closer look at Spatial Zoom Data Science Data Products: CHO-CHO, IO Glyoxal: migration to Collection 3 pending Iodine Monoxide: not seen yet, but we keep looking Time Frame Standard data products: Delivery of new version 1.1.0 in time for switch-over to L1b Collection 3 to forward (Summer 2007) Intend complete reprocessing with v1.1.0 Science data products: Migration will proceed in parallel with update of standard products Please Consult Presentation Thomas Kurosu From : Thomas Kurosu, “Other Trace Gases (a.k.a. HCHO, BrO, and OClO) Status & Outlook”, OSTM Baltimore 2007