Imperial College - 19 Feb. 2008 DESERT DUST SATELLITE RETRIEVAL INTERCOMPARISON Elisa Carboni 1, G.Thomas 1, A.Sayer 1, C.Poulsen 2, D.Grainger 1, R.Siddans.

Slides:

Advertisements

Similar presentations

02/04/2009 University of Manchester ADIENT meeting 5 – Gareth Thomas, Maria Frontoso 1 WP 1.3: Satellite data in support of ADIENT aircraft flights Gareth.

Advertisements

Claire McConnell A new Saharan dust source activation frequency map derived from MSG-SEVIRI IR-channels.

Collection 6 Aerosol Products Becoming Available

Upgrades to the MODIS near-IR Water Vapor Algorithm and Cirrus Reflectance Algorithm For Collection 6 Bo-Cai Gao & Rong-Rong Li Remote Sensing Division,

What’s new in MODIS Collection 6 Aerosol Deep Blue Products? N. Christina Hsu, Rick Hansell, MJ Jeong, Jingfeng Huang, and Jeremy Warner Photo taken from.

Gerrit de Leeuw 1,2,3, Larisa Sogacheva1, Pekka Kolmonen 1, Anu-Maija Sundström 2, Edith Rodriguez 1 1 FMI, Climate Change Unit, Helsinki, Finland 2 Univ.

Gregory Leptoukh, David Lary, Suhung Shen, Christopher Lynnes What’s in a day?

The Zambezi River Valley and surrounding areas, July, 2011.

Aerosol radiative effects from satellites Gareth Thomas Nicky Chalmers, Caroline Poulsen, Ellie Highwood, Don Grainger Gareth Thomas - NCEO/CEOI-ST Joint.

Quantifying aerosol direct radiative effect with MISR observations Yang Chen, Qinbin Li, Ralph Kahn Jet Propulsion Laboratory California Institute of Technology,

OMI NO 2 observations of boreal forest fires Nicolas Bousserez, Randall V. Martin, Lok Lamsal, and the ARCTAS team Dalhousie University Halifax, Nova Scotia.

© Imperial College LondonPage 1 Quantifying the direct radiative effect of Saharan dust aerosol over north-west Africa and the tropical Atlantic Richard.

Aerosol-cci: WP2220: Cloud mask comparison Gerrit de Leeuw.

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),

Aerosol Climate Change Initiative Stratospheric activities around the Aerosol_CCI project C. Bingen, C. Robert, A. Bourrassa & Aerosol_CCI Team F. Vanhellemont,

Visualization, Exploration, and Model Comparison of NASA Air Quality Remote Sensing data via Giovanni Ana I. Prados, Gregory Leptoukh, Arun Gopalan, and.

Atmospheric Correction Algorithms for Remote Sensing of Open and Coastal Waters Zia Ahmad Ocean Biology Processing Group (OBPG) NASA- Goddard Space Flight.

Direct LW radiative effect of Saharan dust aerosols Vincent Gimbert, H.E. Brindley, J.E. Harries Imperial College London GIST 24, 16 Dec 2005, Imperial.

MERIS US Workshop, Silver Springs, 14 th July 2008 MERIS US Workshop Vicarious Calibration Methods and Results Steven Delwart.

Intercomparison of satellite retrieved aerosol optical depth over ocean Gunnar Myhre 1,2 Frode Stordal 1,2 Mona Johnsrud 1 Alexander Ignatov 3 Michael.

Evaluation aerosol CCI satellite retrievals MACC assimilations Reading 2012.

VALIDATION OF SUOMI NPP/VIIRS OPERATIONAL AEROSOL PRODUCTS THROUGH MULTI-SENSOR INTERCOMPARISONS Huang, J. I. Laszlo, S. Kondragunta,

Giovanni for AQ Gregory Leptoukh NASA Goddard Space Flight Center Goddard Earth Sciences Data and Information Services Center (GES DISC)

AMFIC: Aerosol retrieval Gerrit de Leeuw, FMI/UHEL/TNO Pekka Kolmonen, FMI Anu-Maija Sundström, UHEL Larisa Sogacheva, UHEL Juha-Pekka Luntama, FMI Sini.

Latest results on the comparison between OMI and ground-based data at two European sites (Rome and Villeneuve d’Ascq) Virginie Buchard, Colette Brogniez,

Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie.

Three Dimensional Air Quality System (3D-AQS) Air Quality Data Summit U.S. EPA, Research Triangle Park, NC February 12, 2008 Jill Engel-Cox Battelle Memorial.

GOES and GOES-R ABI Aerosol Optical Depth (AOD) Validation Shobha Kondragunta and Istvan Laszlo (NOAA/NESDIS/STAR), Chuanyu Xu (IMSG), Pubu Ciren (Riverside.

The first decade OMI Near UV aerosol observations: An A-train algorithm Assessment of AOD and SSA The long-term OMAERUV record Omar Torres, Changwoo Ahn,

Variational Assimilation of MODIS AOD using GSI and WRF/Chem Zhiquan Liu NCAR/NESL/MMM Quanhua (Mark) Liu (JCSDA), Hui-Chuan Lin (NCAR),

Aristeidis K. Georgoulias Konstantinos Kourtidis Konstantinos Konstantinidis AMFIC Web Data Base AMFIC Annual Meeting - Beijing October 2008 Democritus.

Aerosol Optical Depth during the Northern CA Fires of 2008 In situ aerosol light scattering and absorption measurements in Reno Nevada, 2008, indicated.

T2 T1 (biomass burning aerosol) (dust) Findings (1) OMI and AATS AOD retrievals have been analyzed for four spatially and temporally near-coincident events.

Evaluation aerosol CCI retrievals Reading the participants / the task AATSR F v142 AATSR O v202/v2q2 AATSR S v040/v031 MERIS A v21 MERIS B v11 MERIS.

Monitoring aerosols in China with AATSR Anu-Maija Sundström 2 Gerrit de Leeuw 1 Pekka Kolmonen 1, and Larisa Sogacheva 1 AMFIC , Barcelona 1:

The Second TEMPO Science Team Meeting Physical Basis of the Near-UV Aerosol Algorithm Omar Torres NASA Goddard Space Flight Center Atmospheric Chemistry.

QA filtering of individual pixels to enable a more accurate validation of aerosol products Maksym Petrenko Presented at MODIS Collection 7 and beyond Retreat.

UV Aerosol Product Status and Outlook Omar Torres and Changwoo Ahn OMI Science Team Meeting Outline -Status -Product Assessment OMI-MODIS Comparison OMI-Aeronet.

Fog- and cloud-induced aerosol modification observed by the Aerosol Robotic Network (AERONET) Thomas F. Eck (Code 618 NASA GSFC) and Brent N. Holben (Code.

Slide 1 NATO UNCLASSIFIEDMeeting title – Location - Date Satellite Inter-calibration of MODIS and VIIRS sensors Preliminary results A. Alvarez, G. Pennucci,

Aerosol_cci ECV. Aerosol_cci > Thomas Holzer-Popp > ESA Living Planet Symposium, Bergen, 1 July 2010 slide 2 Major improvements over precursor AOD datasets.

Intercomparison of OMI NO 2 and HCHO air mass factor calculations: recommendations and best practices A. Lorente, S. Döerner, A. Hilboll, H. Yu and K.

Validation of OMI NO 2 data using ground-based spectrometric NO 2 measurements at Zvenigorod, Russia A.N. Gruzdev and A.S. Elokhov A.M. Obukhov Institute.

1 N. Christina Hsu, Deputy NPP Project Scientist Recent Update on MODIS C6 Deep Blue Aerosol Products and Beyond N. Christina Hsu, Corey Bettenhausen,

Synergy of MODIS Deep Blue and Operational Aerosol Products with MISR and SeaWiFS N. Christina Hsu and S.-C. Tsay, M. D. King, M.-J. Jeong NASA Goddard.

Characterization of Aerosol Data Quality from MODIS for Coastal Regions Jacob Anderson Mentor: Gregory Leptoukh.

Estimating PM 2.5 from MODIS and MISR AOD Aaron van Donkelaar and Randall Martin March 2009.

DEPARTMENT OF GEOMATIC ENGINEERING Validation of MODIS Cloud products through an intercomparison with MISR, GOES and ground-based radar/lidar Jan-Peter.

NGAC verification NGAC verification is comparing NGAC forecast (current AOT only) with observations from ground-based and satellite measurements and with.

Daily observation of dust aerosols infrared optical depth and altitude from IASI and AIRS and comparison with other satellite instruments Christoforos.

An Observationally-Constrained Global Dust Aerosol Optical Depth (AOD) DAVID A. RIDLEY 1, COLETTE L. HEALD 1, JASPER F. KOK 2, CHUN ZHAO 3 1. CIVIL AND.

CM-SAF Board Meeting, Helsinki, September 2006 CM-SAF TOA radiation status report D. Caprion Royal Meteorological Institute of Belgium.

GEWEX Aerosol Assessment Panel members Sundar Christopher, Rich Ferrare, Paul Ginoux, Stefan Kinne, Jeff Reid, Paul Stackhouse Program Lead : Hal Maring,

Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.

Intercomparison of satellite retrieved aerosol optical depth over ocean G. Myhre, F. Stordal, M. Johnsrud, A. Ignatov, M.I. Mishchenko, I.V. Geogdzhayev,

Analysis of satellite imagery to map burned areas in Sub-Saharan Africa CARBOAFRICA conference “Africa and Carbon Cycle: the CarboAfrica project” Accra.

New Aerosol Models for Ocean Color Retrievals Zia Ahmad NASA-Ocean Biology Processing Group (OBPG) MODIS Meeting May 18-20, 2011.

Pushing the limits of dark-target aerosol remote sensing from MODIS Robert C. Levy (SSAI and 613.2) Contributors: S. Mattoo (SSAI), L. Remer (NASA), R.

Horizontal variability of aerosol optical properties observed during the ARCTAS airborne experiment Yohei Shinozuka 1*, Jens Redemann 1, Phil Russell 2,

Braak et al., OMI Science Team Meeting 6 June 2007 OMI Multiwavelength Aerosol Product Validation Status Remco Braak, Ben Veihelmann, Pepijn Veefkind.

Assessment of Upper atmospheric plume models using Calipso Satellites and Environmental Assessment and Forecasting Chowdhury Nazmi, Yonghua Wu, Barry Gross,

MPI-Meteorology Hamburg, Germany Evaluation of year 2004 monthly GlobAER aerosol products Stefan Kinne.

Fourth TEMPO Science Team Meeting

DOAS workshop 2015, Brussels, July 2015

SEVIRI Solar Channel Calibration system

Statistical Methods for Model Evaluation – Moving Beyond the Comparison of Matched Observations and Output for Model Grid Cells Kristen M. Foley1, Jenise.

Hiren Jethva1,2, Omar Torres2, Yasuko Yoshida3 1USRA/GESTAR

Estimating PM2.5 using MODIS and GOES aerosol optical depth retrievals in IDEA Hai Zhang , Raymond M. Hoff1 , Shobha Kondragunta2.

G. Myhre, F. Stordal, M. Johnsrud, A. Ignatov, M. I. Mishchenko, I. V

Presentation transcript:

Imperial College - 19 Feb DESERT DUST SATELLITE RETRIEVAL INTERCOMPARISON Elisa Carboni 1, G.Thomas 1, A.Sayer 1, C.Poulsen 2, D.Grainger 1, R.Siddans 2, C.Ahn 3, D.Antoine 4, S.Bevan 5, R.Braak 6, H.Brindley 7, S.DeSouza-Machado 8, J.Deuze 9, D.Diner 10, F.Ducos 9, W.Grey 5, C.Hsu 11, O.V.Kalashnikova 10, R.Kahn 10, C.Salustro 11, D.Tanre‘ 9, O.Torres 11, B.Veihelmann 6 (1) University of Oxford, Oxford, UK. (2) Rutherford Appleton Laboratory, Didcot, UK. (3) Science Systems and Applications, Maryland, USA (4) Laboratoire d'Océanographie de Villefranche (LOV), FRANCE (5) Swansea University, UK (6) KMNI, NL (7) Imperial College, UK (8) University of Maryland Baltimore County, USA (9) LOA, UST de lille, FRANCE (10) JPL, Pasadina, USA (11) GSFC NASA, USA

OUTLINE: Introduction Scope Dataset included Aeronet comparison Results of individual datasets Dataset vs dataset land ocean Means of all dataset Conclusion Desert Dust satellite Retrieval Intercomparison (DRI)

Desert dust retrieval intercomparison Main purpose/tasks: Look at desert plume from satellite over bright surface Identify the differences in the different desert dust aerosol retrievals with the aim of helping understand the retrieval problem, not to find the 'best' one and identify a winner Help the algorithm developer to identify strengths and the weaknesses Further algorithm and aerosol characterisation improvements

Desert dust retrieval intercomparison - Datasets SEVIRI: ORAC (E.Carboni, C.Poulsen, G.Thomas, D.Grainger, R.Siddans, A.Sayer) Globaerosol (C.Poulsen, G.Thomas, D.Grainger, R.Siddans, E.Carboni, A.Sayer) Imperial VIS (H.Brindley) Imperial IR (H.Brindley) AATSR: ORAC (A.Sayer, G. Thomas, E.Carboni, D.Grainger, C.Poulsen, R.Siddans) Globaerosol (G.Thomas, C.Poulsen, D.Grainger, R.Siddans, E.Carboni, A.Sayer) Swansea (W.Grey, S.Bevan) AIRS: JCET (S.DeSouza-Machado) OMI: NASA-GSFC (O.Torres, C.Ahn) KNMI (B.Veihelmann, R.Braak) MISR: JPL (D.Diner, R.Kahn, O.V.Kalashnikova) MERIS: LOV (D.Antoine) SEAWIFS: LOV (D.Antoine) MODIS: NASA-GSFC (C. Hsu, C.Salustro) POLDER: Ocean (D.Tanre', J.Deuze, F.Ducos) Land (D.Tanre', J.Deuze, F.Ducos)

Desert dust retrieval intercomparison region of comparison: lat: 0:45(N) deg lon: -50(W):50(E) deg Period: March 2006 Strategy:first retrieval run algorithm as they are comparison and discussion second retrieval run modified algorithm second comparison and identification of the problems AOD 550nm a) Daily image (average in regular common grid 0.5 lat. lon. box)‏ b) Average in a radius of 50Km from Aeronet sites to compare with average in a 30min on Aeronet data Data provided: All satellite dataset vs. all All satellite dataset vs. AERONET TECHNICAL DISCUSSION

Desert dust retrieval intercomparison - Datasets SEVIRI: ORAC Globaerosol Imperial VIS Imperial IR AATSR: ORAC Globaerosol Swansea AIRS: JCET OMI: NASA-GSFC KNMI MISR: JPL MERIS: LOV SEAWIFS: LOV MODIS: NASA-GSFC POLDER: Ocean Land Time UTC: 12:12 10: 13: 16: 12:12 Orbit local time 10:30 x Ocean, x Land x Aeronet x Rerieval over:

SEVIRI ORAC vs AERONET

SATELLITE vs AERONET AATSR ORACAATSR SWAAATSR GLOBMERIS LOV MISROMI KNMIMODIS OMI NASA SEAWIF LOV POLDER OCEAN SEVIRI IMPERIAL IRSEVIRI ORAC

Satellite AOD of dataset (8 March 2006) – different coverage MERIS LOV AATSR ORACAATSR SWAAIRSAATSR GLOB MISR OMI NASA OMI KNMIMODIS AATSRGLOB SEAWIF LOV POLDER OCEAN POLDER LAND SEVIRI ORACSEVIRI GLOB SEVIRI IMPERIAL IR SEVIRI IMPERIAL VIS

Satellite datasets monthly means MERIS LOV AATSR ORACAATSR SWAAIRSAATSR GLOB MISR OMI NASA OMI KNMIMODIS AATSRGLOB SEAWIF LOV POLDER OCEAN POLDER LAND SEVIRI ORACSEVIRI GLOB SEVIRI IMPERIAL IR SEVIRI IMPERIAL VIS

COMPARISON satellite vs satellite INSERT SCATTER-DENSITY PLOTS!!! WHICH ONE??? in the following slide I used (like exemple) AATSR ORAC (first in alphabetic order), which one I can use?

satellite vs satellite – AATSRGLOB - LAND here one example for AATSRORAC (first in alphabetic order), which one I can use?

satellite vs satellite - AATSRGLOB - LAND

satellite vs satellite – AATSRGLOB - OCEAN

satellite vs satellite - AATSRGLOB - OCEAN

Correlation coefficient (CC) LAND OCEAN we like

Root mean square differences (RMSD) LAND OCEAN we like

Monthly average of all dataset – March 2006 INSERT AOD MOVIE!!!

Monthly average of all dataset – March 2006 AOD Nday STDSTD/AOD

DRI - Conclusion - All dataset show a reasonably good agreement with Aeronet - the discrepancy increase significantly when compare satellite vs satellite dataset. Possibly due to the fact that Aeronet itself make a good datacut (=> comparison satellite-Aeronet are done only in good conditions). - the satellite dataset itself could be affect by cloud contaminations and other errors...?? - The monthly mean of the satellite dataset differ, mainly due to different satellite coverage (overpass, swap...) and cut of data. - Cut of data is one of the more affecting point. e.g. observing the monthly means over ocean (where all the retrieval are more confident, and the general comparisons with aeronet are better) between same satellite there are still discrepancy, possible due to aerosol model and retrieval algorithm but also due to datacut. - some dataset make a restrictive data cut and cut mainly the higher part of the plume. - A way to follow AOD for march 2006 is the average of all dataset and it present incredibly good continuity also in the passage ocean-land and between area with different number of datasets. Anyway STD between dataset are sometime comparable with the value of average AOD itself, and is higher in correspondence with the desert dust plume. STD/AOD could be >1 especially over land bright surface... (unlukly aeronet station are mainly outside this region)