Presentation is loading. Please wait.

Presentation is loading. Please wait.

Direct aerosol radiative effects based on combined A-Train observations Jens Redemann, Y. Shinozuka, J. Livingston, M. Vaughan, P. Russell, M.Kacenelenbogen,

Published byAngelina Hodge Modified over 8 years ago

Similar presentations

Presentation on theme: "Direct aerosol radiative effects based on combined A-Train observations Jens Redemann, Y. Shinozuka, J. Livingston, M. Vaughan, P. Russell, M.Kacenelenbogen,"— Presentation transcript:

1 Direct aerosol radiative effects based on combined A-Train observations Jens Redemann, Y. Shinozuka, J. Livingston, M. Vaughan, P. Russell, M.Kacenelenbogen, O. Torres, L. Remer BAERI – NASA Langley - NASA Ames – SRI – NASA Goddard http://geo.arc.nasa.gov/AATS-website/ email: Jens.Redemann-1@nasa.govJens.Redemann-1@nasa.gov

2 Outline   Goal: To devise a new, methodology to derive direct aerosol radiative effects -  F aerosol (z) based on CALIOP, OMI and MODIS   Motivation, data sets and role of field observations   Methodology for combining CALIOP, OMI and MODIS data   Checking consistency of input data   Proof of concept for 4-month data set – Jan., Apr., Jul., Oct. 2007   Impact of input data sets   Comparisons to AERONET and CERES flux products   Conclusions

3 MODIS OMI CALIOP Goal: To use A-Train aerosol obs to constrain aerosol radiative properties to calculate  F aerosol (z) Myhre, Science, July 10, 2009: 1) 1)Observation-based methods too large 2) 2)Models show great divergence in regional and vertical distribution of DARF. 3) 3)“remaining uncertainty (in DARF) is probably related to the vertical profiles of the aerosols and their location in relation to clouds”.

4 Target:  F aerosol (z) +  F aerosol (z) Constraints/Input: - MODIS AOD (7/2 ) +  AOD - OMI AAOD (388 nm) +  AAOD - CALIPSO ext (532, 1064 nm) +  ext - CALIPSO back (532, 1064 nm) +  back Goal: To use A-Train aerosol obs to constrain aerosol radiative properties to calculate  F aerosol (z) Retrieval: ext (, z) +  ext ssa (, z) +  ssa g (, z) +  g MODIS aerosol models: 7 fine and 3 coarse mode distribution models define size and refractive indices of bi-modal log-normal size distribution → 100 combinations Free parameters: N fine, N coarse Issues to consider - Differences in data quality land/ocean - - Impact of model assumptions - - Spatial variability - - Aerosols above & near clouds Rtx code

5 Methodology: Role of field observations Use suborbital observations to: 1) 1) Guide choices in aerosol models 2) 2)Test retrievals of aerosol radiative properties 3) 3)Test calculated radiative fluxes 4) 4)Study spatial variability = uncertainty involved in extrapolating to data- sparse regions (e.g., above clouds) See poster A43A-0127 Kacenelenbogen et al. Re 4): Shinozuka and Redemann, ACP, 2011

6 Solution space: expansion from over-ocean MODIS models

7 ARCTAS data are corrected after Virkkula [2010]. Role of suborbital observations: 1) Test realism of aerosol models

8 7 fine + 3 coarse modes SSA and EAELidar Ratio and EAE

9 : retrieved parameters : observables : uncertainties in obs. : weighting factors Current choices in retrieval method: 1) 1)Metric / error / cost function 2) 2)4 Observables x i = AOD 550nm (±0.03±5%) AOD 1240 nm (±0.03±5%) - MODIS AAOD 388 nm ±(0.05+30%) - OMI  532 ±(0.1Mm -1 sr -1 +30%), - CALIOP 3) 3)Minimize X and select the top 3% of solutions that meet for all i

10 Example of successful retrieval from actual collocated MODIS, OMI, CALIOP (V3) data: Oct. 23, 2007

11 Consistency issues: AOD comparisons (CALIOP V3)   Eight months of data: January, April, July and October 2007 and 2009   Use CALIOP 5/40km-avg. (V3/V2) aerosol extinction profiles, and 5km aerosol and cloud layer products   Find all instantaneously collocated, MODIS MYD04_L2 (10x10km) aerosol retrievals traversed by 5km/40km CALIPSO track   Judicious use of data quality flags   Break down geographically → zonal mean AOD   See Redemann et al., ACPD for details   See Kacenelenbogen et al., ACP 2011 for potential explanations for CALIOP-MODIS differences

12 Latitudinal distribution of AOD differences between MODIS and CALIOP V3 MODIS-CALIOP AOD Latitude Redemann et al., ACPD Main findings, ocean: 1. 1.bias differences of 0.03 - 0.04 (with CALIOP<MODIS for all months), 2. 2.RMS  of 0.09 – 0.12 3. 3.r 2 is ~ 0.4-0.5 …all after judicious use of quality flags Shinozuka and Redemann, ACP, 2011

13 OMAERUV (Torres group)OMAERO (KNMI group) AOD 380nm ssa 380nm Consistency issues: Choice of OMI data

14 OMAERO data collocated with MODIS and CALIOP is a reasonable representation of global OMAERO data OMAERUV data collocated with MODIS and CALIOP is a poor representation of global OMAERUV

15 OMAERO data collocated with MODIS and CALIOP is a reasonable representation of global OMAERO data OMAERUV data collocated with MODIS and CALIOP is a poor representation of global OMAERUV

16 Target:  F aerosol (z) +  F aerosol (z) Constraints/Input: - MODIS AOD (7/2 ) +  AOD - OMI AAOD (388 nm) +  AAOD - CALIPSO ext (532, 1064 nm) +  ext - CALIPSO back (532, 1064 nm) +  back Retrieval of aerosol radiative properties from A-Train observations Retrieval: ext (, z) +  ext ssa (, z) +  ssa g (, z) +  g MODIS aerosol models: 7 fine and 3 coarse mode distributions define standard deviation and refractive indices of bi-modal log-normal size distribution → 100 combinations Free parameters: N fine, N coarse Rtx code

17 Target:  F aerosol (z) +  F aerosol (z) Constraints/Input: - MODIS AOD (7/2 ) +  AOD - OMI AAOD (388 nm) +  AAOD - CALIPSO ext (532, 1064 nm) +  ext - CALIPSO back (532, 1064 nm) +  back Retrieval of aerosol radiative properties from A-Train observations Retrieval: ext (, z) +  ext ssa (, z) +  ssa g (, z) +  g MODIS aerosol models: 7 fine and 3 coarse mode distributions define standard deviation and refractive indices of bi-modal log-normal size distribution → 100 combinations Free parameters: N fine, N coarse Rtx code Comparison: CERES F clear Airborne F clear Comparison: AERONET AOD, ssa, g Airborne test bed data

18 AERONET Inversion collocation with MODIS-OMI(OMAERO)-CALIPSO AERONET V2.0 (circles): ~68,200 collocated MODIS- OMI-CALIPSO obs in 4 months of data 93-96% of those have valid MOC retrieval 576 of those have collocated AERONET AOD measurements (±100km, ±1h) Only 45 of those have collocated AERONET SSA (V2/L2) retrieval

19 The top 3% X for all 4 months. Xavg=0.1640 Xstd= 0.0968 Xavg+Xstd=0.2608

20 AOD and SSA retrievals from MODIS – OMI - CALIPSO AOD ~540nm SSA ~540nm

21 24h avg direct radiative forcing from MODIS – OMI - CALIPSO Surface TOA

22 Conclusions A.MODIS-CALIOP AOD comparisons: bias differences of 0.03 - 0.04 (CAL<MOD), RMS differences of 0.09 – 0.12 after judicious use of quality flags. B.A methodology for the retrieval of aerosol radiative properties from MODIS AOD, OMI AAOD and CALIOP  532 has been devised. Proof of concept study complete for January+April+July+October 2007: –Results sensitive to choice of OMI data (OMAERUV vs. OMAERO) –OMAERUV possibly more accurate, but data collocated with MODIS+CALIOP not representative of global OMAERUV data set –Tests with collocated AERONET observations sparse –Tests with CERES irradiance measurements difficult to interpret C.Next questions to answer: 1)What is the trade-off between uncertainty and spatial sampling in input satellite data sets for calculating aerosol-induced changes in TOA or surface fluxes, i.e., how much uncertainty is involved in spatial extrapolation to data-sparse regions? 2)How to test the “consistency” between various satellite input data sets? 3)How do uncertainties in satellite data propagate to retrievals and flux estimates? D.Next steps to take: 1)Continue to investigate spatial variability in suborbital data to extend the MOC retrievals to aerosol above cloud (AAC) based on reduced data set 2)Extend study to use more MODIS channels and more OMI retrievals 3)Constrain OMI AAOD retrievals with CALIOP height input 4)Compare vertical distribution of direct forcing to models

Download ppt "Direct aerosol radiative effects based on combined A-Train observations Jens Redemann, Y. Shinozuka, J. Livingston, M. Vaughan, P. Russell, M.Kacenelenbogen,"

Similar presentations

Robin Hogan, Richard Allan, Nicky Chalmers, Thorwald Stein, Julien Delanoë University of Reading How accurate are the radiative properties of ice clouds.

Robin Hogan, Richard Allan, Nicky Chalmers, Thorwald Stein, Julien Delanoë University of Reading How accurate are the radiative properties of ice clouds.
Satellite Observations of Enhanced Pre- Monsoon Aerosol Loading and Tropospheric Warming over the Gangetic-Himalayan Region Ritesh Gautam 1, N. Christina.

Satellite Observations of Enhanced Pre- Monsoon Aerosol Loading and Tropospheric Warming over the Gangetic-Himalayan Region Ritesh Gautam 1, N. Christina.
GEOS-5 Simulations of Aerosol Index and Aerosol Absorption Optical Depth with Comparison to OMI retrievals. V. Buchard, A. da Silva, P. Colarco, R. Spurr.

GEOS-5 Simulations of Aerosol Index and Aerosol Absorption Optical Depth with Comparison to OMI retrievals. V. Buchard, A. da Silva, P. Colarco, R. Spurr.
1 An initial CALIPSO cloud climatology ISCCP Anniversary, 23-25 July 2008, New York Dave Winker NASA LaRC.

1 An initial CALIPSO cloud climatology ISCCP Anniversary, July 2008, New York Dave Winker NASA LaRC.
Aerosol radiative effects from satellites Gareth Thomas Nicky Chalmers, Caroline Poulsen, Ellie Highwood, Don Grainger Gareth Thomas - NCEO/CEOI-ST Joint.

Aerosol radiative effects from satellites Gareth Thomas Nicky Chalmers, Caroline Poulsen, Ellie Highwood, Don Grainger Gareth Thomas - NCEO/CEOI-ST Joint.
Global Climatology of Fine Particulate Matter Concentrations Estimated from Remote-Sensed Aerosol Optical Depth Aaron van Donkelaar 1, Randall Martin 1,2,

Global Climatology of Fine Particulate Matter Concentrations Estimated from Remote-Sensed Aerosol Optical Depth Aaron van Donkelaar 1, Randall Martin 1,2,
Constraining aerosol sources using MODIS backscattered radiances Easan Drury - G2

Constraining aerosol sources using MODIS backscattered radiances Easan Drury - G2
Quantifying aerosol direct radiative effect with MISR observations Yang Chen, Qinbin Li, Ralph Kahn Jet Propulsion Laboratory California Institute of Technology,

Quantifying aerosol direct radiative effect with MISR observations Yang Chen, Qinbin Li, Ralph Kahn Jet Propulsion Laboratory California Institute of Technology,
Retrieval of thermal infrared cooling rates from EOS instruments Daniel Feldman Thursday IR meeting January 13, 2005.

Retrieval of thermal infrared cooling rates from EOS instruments Daniel Feldman Thursday IR meeting January 13, 2005.
Reflected Solar Radiative Kernels And Applications Zhonghai Jin Constantine Loukachine Bruce Wielicki Xu Liu SSAI, Inc. / NASA Langley research Center.

Reflected Solar Radiative Kernels And Applications Zhonghai Jin Constantine Loukachine Bruce Wielicki Xu Liu SSAI, Inc. / NASA Langley research Center.
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.

1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
Direct Radiative Effect of aerosols over clouds and clear skies determined using CALIPSO and the A-Train Robert Wood with Duli Chand, Tad Anderson, Bob.

Direct Radiative Effect of aerosols over clouds and clear skies determined using CALIPSO and the A-Train Robert Wood with Duli Chand, Tad Anderson, Bob.
Space Borne and Ground Based Lidar NASA ARSET- EPA Training ARSET - AQ Applied Remote Sensing Education and Training – Air Quality A project of NASA Applied.

Space Borne and Ground Based Lidar NASA ARSET- EPA Training ARSET - AQ Applied Remote Sensing Education and Training – Air Quality A project of NASA Applied.
Direct aerosol radiative forcing based on combined A-Train observations – challenges in deriving all-sky estimates Jens Redemann, Y. Shinozuka, M.Kacenelenbogen,

Direct aerosol radiative forcing based on combined A-Train observations – challenges in deriving all-sky estimates Jens Redemann, Y. Shinozuka, M.Kacenelenbogen,
Aircraft spiral on July 20, 2011 at 14 UTC Validation of GOES-R ABI Surface PM2.5 Concentrations using AIRNOW and Aircraft Data Shobha Kondragunta (NOAA),

Aircraft spiral on July 20, 2011 at 14 UTC Validation of GOES-R ABI Surface PM2.5 Concentrations using AIRNOW and Aircraft Data Shobha Kondragunta (NOAA),
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.

1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
Surface Atmospheric Radiation Budget (SARB) working group update Seiji Kato 1, Fred Rose 2, David Rutan 2, Alexander Radkevich 2, Tom Caldwell 2, David.

Surface Atmospheric Radiation Budget (SARB) working group update Seiji Kato 1, Fred Rose 2, David Rutan 2, Alexander Radkevich 2, Tom Caldwell 2, David.
Application of Satellite Data to Particulate, Smoke and Dust Monitoring Spring 2015 ARSET - AQ Applied Remote Sensing Education and Training – Air Quality.

Application of Satellite Data to Particulate, Smoke and Dust Monitoring Spring 2015 ARSET - AQ Applied Remote Sensing Education and Training – Air Quality.
A Progress Report on Combining MODIS and CALIPSO Aerosol Data for Direct Radiative Effect Studies Jens Redemann, Qin Zhang, Philip Russell, John Livingston,

A Progress Report on Combining MODIS and CALIPSO Aerosol Data for Direct Radiative Effect Studies Jens Redemann, Qin Zhang, Philip Russell, John Livingston,
Trace gas and AOD retrievals from a newly deployed hyper-spectral airborne sun/sky photometer (4STAR) M. Segal-Rosenheimer, C.J. Flynn, J. Redemann, B.

Trace gas and AOD retrievals from a newly deployed hyper-spectral airborne sun/sky photometer (4STAR) M. Segal-Rosenheimer, C.J. Flynn, J. Redemann, B.

Similar presentations

Ads by Google