Presentation is loading. Please wait.

Presentation is loading. Please wait.

1 Examining Seasonal Variation of Space-based Tropospheric NO 2 Columns 05.02.2009 Lok Lamsal.

Published byWillis Sherman Modified over 8 years ago

Similar presentations

Presentation on theme: "1 Examining Seasonal Variation of Space-based Tropospheric NO 2 Columns 05.02.2009 Lok Lamsal."— Presentation transcript:

1 1 Examining Seasonal Variation of Space-based Tropospheric NO 2 Columns 05.02.2009 Lok Lamsal

2 2 Table of Contents Overview of space-based tropospheric NO 2 observations Retrievals of NO 2 from OMI and SCIAMACHY Validation of satellite retrievals / Examine seasonal variation  Using surface measurements  Using bottom-up inventory Discuss various error sources

3 3 Instrument Satellite platform Launch year Equator crossing time Resolution Horizontal (km 2 ) Global coverage GOME-1ERS-2199510:30 LT320×403 days SCIAMACHYENVISAT200210:00 LT60×306 days OMIEOS Aura200413:30 LT13×24 ̶ 42×1621 day GOME-2METOP20069:30 LT80×401 day Satellite Instruments Measuring Tropospheric NO 2

4 4 Applications: Identifying Sources -Industrial areas, cities, power plants (anthropogenic) -Ship tracks -Biomass burning -Soil emissions

5 5 Applications: Inferring Strength of Emissions Fossil Fuel Tg N Yr -1 Biomass Burning Tg N Yr -1 Soil Tg N Yr -1 Surface NOx Emissions Tg N Yr -1 InstrumentYearAuthors 43 (total)GOME1997Leue et al., 2001 37.7GOME1996/97Martin et al., 2003 25.65.88.940.3GOME2000Gaegle’ et al., 2005 46.1SCIAMACHY2004/05Martin et al., 2006

6 6 Applications:Show Temporal Pattern of Emissions Beirle et al, ACP, 2003 Weekly cycle of NO 2 by GOME measurements

7 7 Applications: Estimating trend Richter et al, Nature, 2005

8 8 Review of Satellite Tropospheric NO 2 Retrievals STEP 1: DOAS fit  Total NO 2 slant column density STEP 2: Remove the stratospheric part  Tropospheric NO 2 slant column density Earth surface Trop NO 2 Strat NO 2 STEP 3: Convert slant column to vertical  Tropospheric NO 2 column AMF critically depends on: Surface reflectivity A-priori NO 2 profile shape Cloud and aerosol properties Viewing geometry

9 9 Differences Among Retrievals Algorithm componentStandard product (SP)DOMINO (DP)SCIAMACHY Slant columnDOAS fit (405-465nm)Same as SPDOAS fit (429-452nm) Stripe correction?YesNoNot applicable Stratospheric estimate Analysis of observation TM4 assimilationRef. sector method Trop NO 2 profileGEOS-Chem annualDaily TM4Daily GEOS-Chem Scattering weightsTOMRADDAKLIDORT Surface reflectivityGOME monthly climatologySame as SP Surface pressureClimatologicalECMWFGEOS-4 Cloud pressure/fractionO 2 -O 2 cloud algorithmSame as SPFRESCO

10 10 Differences Between OMI SP and DP North America Europe DP-SP (%)

11 11 Differences Between OMI and SCIAMACHY Retrievals (SP)(DP)

12 12 Differences among GOME Retrievals Bremen Dal/SAO BIRA/KNMI van Noije, ACP, 2006

13 13 Validation of Tropospheric NO 2 from Satellites NO x emissions have large variation in space & time. NO 2 field sampled by satellite can hardly match with correlative measurements. Suitable validation datasets are currently limited:  DOAS UV-visible spectrometers from NDACC => stratospheric columns  In-situ aircraft (excellent but expensive)  NO 2 lidar (expensive)  MAXDOAS (promising, under development)  In-situ surface measurements (indirect, model involved)  NO x emission inventory (indirect, model involved)

14 14 Surface Measurements for Validation (1) Selected 10 surface sites ∆  Photolytic converter (SEARCH) ×  Molybdenum converter (AQS/EPA) Molybdenum converter overestimates true NO 2 by up to 50%, interference from reactive nitrogen species 3 7 From rural areas Lamsal et al, JGR, 2008

15 15 Validation Approach with Surface Measurements O Ω 0.1 × 0.1 O Ω 2 × 2.5  Information on NO 2 profile shape from GEOS-Chem (2) Transform column field to surface concentration field  Spatial gradient from satellite observation In-situ surface NO 2, Satellite-derived surface NO 2

16 16 NO x Emissions for Validation (1) NO x sources and their seasonal variation (2) Emission data are available globally at resolution better than 1 o x1 o Anthropogenic ( power plant, industry, transportation ) Natural ( soil, lightning ) Biomass burning ( fire ) Anthropogenic emissions for 2000 http://www.aero.jussieu.fr/projet/ACCENT/POET.php

17 17 Validation Approach with Surface NO x Emissions Tropospheric NO 2 column + Bottom-up NO x emissions where k = 12 Top-down method: Martin et al., 2003; Wang et al., 2007; Boersma et al., 2008 61 TgNYr -1

18 18 Comparison of Satellite-derived NO 2 with Surface Measurements Satellite vs in-situ (photolytic)Satellite vs in-situ (molybdenum) DP SP SCIA 10:00 13:30

19 19 Comparison Between Top-down and Bottom-up Surface NO x Emissions Emission ratio over the stations

20 20 Comparison Between Top-down and Bottom-up Surface NO x Emissions over Polluted Areas Overall picture similar over NA, overestimation by DP, strong seasonality with SP, and excellent agreement with SCIAMACHY Differences remain similar over Europe and East Asia Satellite-derived emissions higher over Europe and East Asia, could indicate limitation of bottom-up inventory

21 21 What is Right and Where is the Difference Coming From? Satellite retrievals Surface measurements Photolytic ( True NO 2, negligible interference from HONO, NO 3 ) Molybdenum ( Corrected for interference, corrected molybdenum matches with photolytic ) Bottom-up inventory Anthropogenic emissions dominate, has little or no seasonal variation Region with good confidence, Updated emissions with monthly variation GEOS-Chem simulation GEOS-Chem NO y evaluated (with aircraft data), unlikely to be big error source Inversion approaches Not likely due to similar results from two approaches Whatever difference we observe must be coming from retrievals

22 22 Error Sources in Tropospheric NO 2 Retrieval Boersma et al., 2004 Errors in total slant column density (DOAS fit) Insufficient correction of stratospheric NO 2 Errors in air mass factor (AMF)  Cloud fraction ( 15 - 30%)  Surface reflectivity (15 - 35%)  NO 2 profile shapes ( up to 15%)  Aerosols ( up to 30%)

23 23 Contribution of Stratospheric Correction and AMF to the Difference Between DP and SP Difference between DP and SP tropospheric NO 2 columns caused by the difference in stratospheric correction Difference between DP and SP tropospheric NO 2 columns caused by the difference in AMF

24 24 StratosphereAMFTotal Contribution of Stratospheric Correction and AMF to the Difference Between DP and SP

25 25 Conclusions Tropospheric NO 2 columns from satellite differ substantially which can affect NO x source characterization. Use of surface measurements and bottom-up inventory provide similar results indicating large descrepancy in OMI retrievals. Seasonal variation of SCIAMACHY retrievals is consistent with surface measurements and bottom-up inventory Both stratospheric correction and AMF responsible for the observed differences between DP and SP

26 26 Comparison Between Top-down and Bottom-up Surface NO x Emissions RegionsTop-down (TgNYr -1 ) Bottom-up (TgNYr -1 ) SPDPSCIAMACHY North America 6.08.68.56.1 OECD Europe 3.15.04.84.1 East Asia 7.211.210.48.6

27 27 Comparison Between Top-down and Bottom-up Surface NO x Emissions 8.6 TgNYr -1 8.5 TgNYr -1 6.0 TgNYr -1 6.1 TgNYr -1

Download ppt "1 Examining Seasonal Variation of Space-based Tropospheric NO 2 Columns 05.02.2009 Lok Lamsal."

Similar presentations

Page 1 Tropospheric NO 2 workshop, KNMI, De Bilt NL, 10-12 Sept 2007M. Van Roozendael Tropospheric NO 2 from space: retrieval issues and perspectives for.

Page 1 Tropospheric NO 2 workshop, KNMI, De Bilt NL, Sept 2007M. Van Roozendael Tropospheric NO 2 from space: retrieval issues and perspectives for.
Inferring SO 2 and NO x Emissions from Satellite Remote Sensing Randall Martin with contributions from Akhila Padmanabhan, Gray O’Byrne, Sajeev Philip.

Inferring SO 2 and NO x Emissions from Satellite Remote Sensing Randall Martin with contributions from Akhila Padmanabhan, Gray O’Byrne, Sajeev Philip.
Aircraft GC 2006 ems GC Streets ems East Asian contrib Lightning contrib Aircraft GC 2006 ems GC Streets ems No Asian No Lightning Long-range transport.

Aircraft GC 2006 ems GC Streets ems East Asian contrib Lightning contrib Aircraft GC 2006 ems GC Streets ems No Asian No Lightning Long-range transport.
N emissions and the changing landscape of air quality Rob Pinder US EPA Office of Research and Development Atmospheric Modeling & Analysis Division.

N emissions and the changing landscape of air quality Rob Pinder US EPA Office of Research and Development Atmospheric Modeling & Analysis Division.
07.04.2009 Lok Lamsal, Randall Martin, Aaron van Donkelaar Folkert Boersma, Ruud Dirksen (KNMI) Edward Celarier, Eric Bucsela, James Gleason (NASA) E.S.

Lok Lamsal, Randall Martin, Aaron van Donkelaar Folkert Boersma, Ruud Dirksen (KNMI) Edward Celarier, Eric Bucsela, James Gleason (NASA) E.S.
Simulation of Absorbing Aerosol Index & Understanding the Relation of NO 2 Column Retrievals with Ground-based Monitors Randall Martin (Dalhousie, Harvard-Smithsonian)

Simulation of Absorbing Aerosol Index & Understanding the Relation of NO 2 Column Retrievals with Ground-based Monitors Randall Martin (Dalhousie, Harvard-Smithsonian)
Interpreting MLS Observations of the Variabilities of Tropical Upper Tropospheric O 3 and CO Chenxia Cai, Qinbin Li, Nathaniel Livesey and Jonathan Jiang.

Interpreting MLS Observations of the Variabilities of Tropical Upper Tropospheric O 3 and CO Chenxia Cai, Qinbin Li, Nathaniel Livesey and Jonathan Jiang.
GEOS-Chem meeting, 12 April 2007 Preliminary results for the year-to-year variation in satellite-derived NOx sources S. Koumoutsaris 1, I. Bey 1, N. Moore.

GEOS-Chem meeting, 12 April 2007 Preliminary results for the year-to-year variation in satellite-derived NOx sources S. Koumoutsaris 1, I. Bey 1, N. Moore.
U N I V E R S I T Y O F W A S H I N G T O N S C H O O L O F N U R S I N G Global partitioning of NO x emissions using satellite observations Lyatt Jaeglé.

U N I V E R S I T Y O F W A S H I N G T O N S C H O O L O F N U R S I N G Global partitioning of NO x emissions using satellite observations Lyatt Jaeglé.
Retrieval of SO 2 Vertical Columns from SCIAMACHY and OMI: Air Mass Factor Algorithm Development Chulkyu Lee, Aaron van Dokelaar, Gray O’Byrne: Dalhousie.

Retrieval of SO 2 Vertical Columns from SCIAMACHY and OMI: Air Mass Factor Algorithm Development Chulkyu Lee, Aaron van Dokelaar, Gray O’Byrne: Dalhousie.
Constraints on the Production of Nitric Oxide by Lightning as Inferred from Satellite Observations Randall Martin Dalhousie University With contributions.

Constraints on the Production of Nitric Oxide by Lightning as Inferred from Satellite Observations Randall Martin Dalhousie University With contributions.
1 Surface nitrogen dioxide concentrations inferred from Ozone Monitoring Instrument (OMI) 12.04.2007 3 rd GEOS-Chem USERS ` MEETING, Harvard University.

1 Surface nitrogen dioxide concentrations inferred from Ozone Monitoring Instrument (OMI) rd GEOS-Chem USERS ` MEETING, Harvard University.
Satellite Remote Sensing of Global Air Pollution

Satellite Remote Sensing of Global Air Pollution
AQUA AURA The Berkeley High Spatial Resolution(BEHR) OMI NO2 Retrieval: Recent Trends in NO2 Ronald C. Cohen University of California, Berkeley $$ NASA.

AQUA AURA The Berkeley High Spatial Resolution(BEHR) OMI NO2 Retrieval: Recent Trends in NO2 Ronald C. Cohen University of California, Berkeley $$ NASA.
Indirect Validation of Tropospheric Nitrogen Dioxide Retrieved from the OMI Satellite Instrument: Insight into the Seasonal Variation of Nitrogen Oxides.

Indirect Validation of Tropospheric Nitrogen Dioxide Retrieved from the OMI Satellite Instrument: Insight into the Seasonal Variation of Nitrogen Oxides.
ICDC7, Boulder, 26-30 September 2005 CH 4 TOTAL COLUMNS FROM SCIAMACHY – COMPARISON WITH ATMOSPHERIC MODELS P. Bergamaschi 1, C. Frankenberg 2, J.F. Meirink.

ICDC7, Boulder, September 2005 CH 4 TOTAL COLUMNS FROM SCIAMACHY – COMPARISON WITH ATMOSPHERIC MODELS P. Bergamaschi 1, C. Frankenberg 2, J.F. Meirink.
Lok Lamsal, Nickolay Krotkov, Randall Martin, Kenneth Pickering, Chris Loughner, James Crawford, Chris McLinden TEMPO Science Team Meeting Huntsville,

Lok Lamsal, Nickolay Krotkov, Randall Martin, Kenneth Pickering, Chris Loughner, James Crawford, Chris McLinden TEMPO Science Team Meeting Huntsville,
Randall Martin Space-based Constraints on Emission Inventories of Nitrogen Oxides Chris Sioris, Kelly Chance (Smithsonian Astrophysical Observatory) Lyatt.

Randall Martin Space-based Constraints on Emission Inventories of Nitrogen Oxides Chris Sioris, Kelly Chance (Smithsonian Astrophysical Observatory) Lyatt.
Using Satellite Data to Infer Surface Emissions and Boundary Layer Concentrations of NO x (and SO 2 ) Randall Martin With contributions from: Xiong Liu,

Using Satellite Data to Infer Surface Emissions and Boundary Layer Concentrations of NO x (and SO 2 ) Randall Martin With contributions from: Xiong Liu,
Trends and seasonal variability in tropospheric NO 2 Ronald van der A, David Peters, Henk Eskes, Folkert Boersma ESA-NRSCC DRAGON Cooperation Programme.

Trends and seasonal variability in tropospheric NO 2 Ronald van der A, David Peters, Henk Eskes, Folkert Boersma ESA-NRSCC DRAGON Cooperation Programme.

Similar presentations

Ads by Google