REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN Retrieval of aerosol optical properties for clear-sky.

Slides:



Advertisements
Similar presentations
GOME-2 polarisation data and products L.G. Tilstra (1,2), I. Aben (1), P. Stammes (2) (1) SRON; (2) KNMI GSAG #42, EUMETSAT,
Advertisements

Atmospheric Correction Algorithm for the GOCI Jae Hyun Ahn* Joo-Hyung Ryu* Young Jae Park* Yu-Hwan Ahn* Im Sang Oh** Korea Ocean Research & Development.
GOME-2 FM3 (Metop-A) Instrument Review, EUMETSAT, Darmstadt, June 2012 Slide: 1 Rűdiger Lang, Rose Munro, Antoine Lacan, Richard Dyer, Marcel Dobber, Christian.
Summary of GEO-CAPE Aerosol Working Group Progress Members: Mian Chin, Pubu Ciren, Hiren Jethva, Joanna Joiner, Shobha Kondragunta, Alexei Lyapustin, Shana.
 nm)  nm) PurposeSpatial Resolution (km) Ozone, SO 2, UV8 3251Ozone8 3403Aerosols, UV, and Volcanic Ash8 3883Aerosols, Clouds, UV and Volcanic.
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.
MCST – MODLAND Eric F. Vermote –MODLAND representative.
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.
Constraining aerosol sources using MODIS backscattered radiances Easan Drury - G2
Menghua Wang NOAA/NESDIS/ORA E/RA3, Room 102, 5200 Auth Rd.
Quantifying aerosol direct radiative effect with MISR observations Yang Chen, Qinbin Li, Ralph Kahn Jet Propulsion Laboratory California Institute of Technology,
Page 1 1 of 19, OCO STM 2006 OCO Science Team Meeting March 22, 2006 Vijay Natraj (Caltech), Hartmut Bösch (JPL), Yuk Yung (Caltech) A Two Orders of Scattering.
Page 1 1 of 100, L2 Peer Review, 3/24/2006 Level 2 Algorithm Peer Review Polarization Vijay Natraj.
Page 1 1 of 21, 28th Review of Atmospheric Transmission Models, 6/14/2006 A Two Orders of Scattering Approach to Account for Polarization in Near Infrared.
ESTEC July 2000 Estimation of Aerosol Properties from CHRIS-PROBA Data Jeff Settle Environmental Systems Science Centre University of Reading.
Satellite Imagery ARSET Applied Remote SEnsing Training A project of NASA Applied Sciences Introduction to Remote Sensing and Air Quality Applications.
Visible Satellite Imagery Spring 2015 ARSET - AQ Applied Remote Sensing Education and Training – Air Quality A project of NASA Applied Sciences Week –
GOME-2 Polarisation Study — Final Presentation L.G. Tilstra (1,2), I. Aben (1), P. Stammes (2) (1) SRON; (2) KNMI EUMETSAT, Darmstadt,
Rutherford Appleton Laboratory Remote Sensing Group Ozone Profile Retrieval from MetOp R. Siddans, G. Miles, B. Latter A. Waterfall, B. Kerridge Acknowledgements:
WP 3: Absorbing Aerosol Index (AAI) WP 10: Level-1 validation L.G. Tilstra 1, I. Aben 2, and P. Stammes 1 1 Royal Netherlands Meteorological Institute.
EARLINET and Satellites: Partners for Aerosol Observations Matthias Wiegner Universität München Meteorologisches Institut (Satellites: spaceborne passive.
Menghua Wang, NOAA/NESDIS/ORA Atmospheric Correction using the MODIS SWIR Bands (1240 and 2130 nm) Menghua Wang (PI, NASA NNG05HL35I) NOAA/NESDIS/ORA Camp.
High Resolution MODIS Ocean Color Fred Patt 1, Bryan Franz 1, Gerhard Meister 2, P. Jeremy Werdell 3 NASA Ocean Biology Processing Group 1 Science Applications.
EUMETSAT METEOROLOGICAL SATELLITE CONFERENCE 15/09/2013 – 20/09/2013, VIENNA EUMETSAT METEOROLOGICAL SATELLITE CONFERENCE 15/09/2013 – 20/09/2013, VIENNA.
AMFIC: Aerosol retrieval Gerrit de Leeuw, FMI/UHEL/TNO Pekka Kolmonen, FMI Anu-Maija Sundström, UHEL Larisa Sogacheva, UHEL Juha-Pekka Luntama, FMI Sini.
Régis Borde Polar Winds EUMETRAIN Polar satellite week 2012 Régis Borde
S5P Ozone Profile (including Troposphere) verification: RAL Algorithm R.Siddans, G.Miles, B.Latter S5P Verification Workshop, MPIC, Mainz th May.
1 GOES-R AWG Product Validation Tool Development Aviation Application Team – Volcanic Ash Mike Pavolonis (STAR)
WP 8: Impact on Satellite Retrievals University of l’Aquila (DFUA [12]): Vincenzo Rizi Ecole Polytechnique (EPFL [13]): Bertrand Calpini Observatory of.
In Situ and Remote Sensing Characterization of Spectral Absorption by Black Carbon and other Aerosols J. Vanderlei Martins, Paulo Artaxo, Yoram Kaufman,
1 of 26 Characterization of Atmospheric Aerosols using Integrated Multi-Sensor Earth Observations Presented by Ratish Menon (Roll Number ) PhD.
Page 1 ENVISAT Validation Review – Frascati – 9-13 December st Envisat Validation Workshop MERIS Conclusions and recommendations.
WP 8: Impact on Satellite Retrievals University of l’Aquila (DFUA [12]) Ecole Polytechnique (EPFL [13]) Observatory of Neuchatel (ON [14]) Partners (according.
GE0-CAPE Workshop University of North Carolina-Chapel Hill August 2008 Aerosols: What is measurable and by what remote sensing technique? Omar Torres.
WP 8: Impact on Satellite Retrievals University of l’Aquila (DFUA [12]): Vincenzo Rizi Ecole Polytechnique (EPFL [13]): Bertrand Calpini Observatory of.
Monitoring aerosols in China with AATSR Anu-Maija Sundström 2 Gerrit de Leeuw 1 Pekka Kolmonen 1, and Larisa Sogacheva 1 AMFIC , Barcelona 1:
POLAR MULTI-SENSOR AEROSOL PROPERTIES OVER LAND Michael Grzegorski, Rosemary Munro, Gabriele Poli, Andriy Holdak and Ruediger Lang.
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.
Menghua Wang, NOAA/NESDIS/ORA Refinement of MODIS Atmospheric Correction Algorithm Menghua Wang (PI, NASA NNG05HL35I) NOAA/NESDIS/ORA Camp Springs, MD.
Satellite Imagery ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences NASA ARSET- AQ – EPA Training September 29,
Cloud Mask: Results, Frequency, Bit Mapping, and Validation UW Cloud Mask Working Group.
As components of the GOES-R ABI Air Quality products, a multi-channel algorithm similar to MODIS/VIIRS for NOAA’s next generation geostationary satellite.
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.
RMIB involvement in the Geostationary Earth Radiation Budget (GERB) and Climate Monitoring SAF projects Nicolas Clerbaux Remote sensing from Space Division.
SCIAMACHY TOA Reflectance Correction Effects on Aerosol Optical Depth Retrieval W. Di Nicolantonio, A. Cacciari, S. Scarpanti, G. Ballista, E. Morisi,
Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on October 28, 2011 to provide various atmospheric and land related environmental.
Aerosol Radiative Forcing from combined MODIS and CERES measurements
CM-SAF Board Meeting, Helsinki, September 2006 CM-SAF TOA radiation status report D. Caprion Royal Meteorological Institute of Belgium.
Generation of TOA Radiative Fluxes from the GERB Instrument Data. Part II: First Results Nicolas Clerbaux and GERB team Royal Meteorological Institute.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
Polarization analysis in MODIS Gerhard Meister, Ewa Kwiatkowska, Bryan Franz, Chuck McClain Ocean Biology Processing Group 18 June 2008 Polarization Technology.
US-Europe Data Exchange Meeting May Page 1 EUMETSAT Status Report Simon Elliott EUMETSAT Operations Department
Preliminary Analysis of Relative MODIS Terra-Aqua Calibration Over Solar Village and Railroad Valley Sites Using ASRVN A. Lyapustin, Y. Wang, X. Xiong,
Cloud Detection: Optical Depth Thresholds and FOV Considerations Steven A. Ackerman, Richard A. Frey, Edwin Eloranta, and Robert Holz Cloud Detection Issues.
Methane Retrievals in the Thermal Infrared from IASI AGU Fall Meeting, 14 th -18 th December, San Francisco, USA. Diane.
Bias analysis and correction for MetOp/AVHRR IR channel using AVHRR-IASI inter-comparison Tiejun Chang and Xiangqian Wu GSICS Joint Research and data Working.
Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: FP, 25 April 2014, ESTEC.
GSICS meeting 5-8 March 2012, Beijing, China Slide: 1 Discussion Using GOME and SCIAMACHY as re- calibration reference for Visible and Near-Infrared channels.
1GRWG/GDWG Annual Meeting Tsukuba 29 Feb – 4 Mar 2016 Rűdiger Lang, Rose Munro, Gabriele Poli, Antoine Lacan, Christian Retscher, Michael Grzegorski, Alexander.
Fourth TEMPO Science Team Meeting
Carbon monoxide from shortwave infrared measurements of TROPOMI: Algorithm, Product and Plans Jochen Landgraf, Ilse Aben, Otto Hasekamp, Tobias Borsdorff,
SEVIRI Solar Channel Calibration system
Spectral Band Adjustment Factor (SBAF) Tool
Absolute calibration of sky radiances, colour indices and O4 DSCDs obtained from MAX-DOAS measurements T. Wagner1, S. Beirle1, S. Dörner1, M. Penning de.
Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: FP, 25 April 2014, ESTEC Height-resolved aerosol R.Siddans.
GSICS UV sub-group: GOME-2 hyper-spectral radiances for calibration of imager data Rűdiger Lang, Rose Munro, Gabriele Poli, Antoine Lacan, Christian Retscher,
GOME-2 hyper-spectral reflectance measurements: Inter-comparison with AVHRR and Seviri Rűdiger Lang, Rose Munro, Antoine Lacan, Christian Retscher, Gabriele.
Dorothee Coppens.
Cloud trends from GOME, SCIAMACHY and OMI
Presentation transcript:

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN Retrieval of aerosol optical properties for clear-sky and partially cloudy scenes from METOP sensors M. Grzegorski, G. Poli, A. Holdak, R. Lang and R. Munro

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN Overview The Metop/GOME-2 polarisation measurement devices (PMDs) PMAp: Polar Multi-sensor Aerosol product developed at EUMETSAT – A multi-sensor Metop product  AOD over ocean & cloud products: operational in Q Examples and Verifications Work in progress & future plans First results: AOD over land (PMAp second generation)

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN METOP instrument level-1 data used by PMAp Instru ment Spatial resolution Spectral rangecomments GOMEMain science channel 80 x 40 km240nm -800nm, res nm AAI, low spatial resolution Polarization Monitoring Device 10 x 40 km Metop-B 5 x 40 km Metop-A 311nm-803nm, 15 bands AOD, aerosol type, AAI AVHRR-1.08 x 1.08 km580nm-12500nm, 5 bands Clouds, scene heterogeneity, desert dust IASI-12km (circular)3700–15500nm, resolution 0.5 cm -1 Coarse mode aerosols (desert dust, volcanic ash) Auxiliary data ECMWF wind speed (forecasting) Temporal interpolation necessary -Required for retrievals over ocean surface albedo, Surface elevation --Required for land surface retrievals Target spatial resolution

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN The GOME-2 Polarization Monitoring Devices Band-S No.pix1pixw.wav1wav Radiances & stokes fraction better spatial resolution stokes fraction s = Q/I

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN The Polarization Monitoring Devices q-fraction Q-Stokes fraction V-LIDORT GOME-2

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN PMAp: Very accurate co-location of AVHRR and IASI to the GOME-2 PMD pixel footprints Slide: 6 AVHRR collocation: an AVHRR pixel is collocated to a GOME-2 PMD pixel if it is inside the GOME-2 pixel IASI collocation: a IASI pixel may span up to 6 GOME-2 PMD pixels. Which GOME-2 pixel should it be collocated to? Spatial aliasing due to read-out timing of detectors is taken into account! 2011

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN PMAp: AOD retrieval algorithm I Three step retrieval: Step1: Pre-classification by AVHRR. Detection of clouds, cloud fraction Strong dust/ash events Pre-classification of possible aerosol types (depending on BTD – T4-T5 tests plus VIS/NIR wavelength dependency) no dust / fine mode, dust, ash, no classification Step2: Retrieval of a subset or all 28 AODs plus chlorophyll correction based on max. 28 aerosol models from LUT provided by O. Hasekamp (O3MSAF) using three alternative retrievals based on cloud, wind-speed/surface and geometry conditions Step3: Selection of the best fit out of the max. 28 AODs using least-square minimization to all wavelength (between 400 and 800 nm)

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN PMAp: AOD retrieval algorithm Geometry dependent test with intercomparison of: calculated surface signal calculated wind speed dependence calculated aerosol signa l Criterion: impact on TOA not too large w.r.t expected clear-sky conditions Cloud filter: AVHRR/VIS AVHRR/IR Use of reflectance or Stokes fractions Use of reflectance Use of corrected reflectance

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN Best case: Retrieval clear sky & dark surface Step 2: A set of AODs (for the pre-selected models) and chlorophyll corrections is estimated using three channels: UV [380 nm], VIS/green [520 nm], red edge [800 nm; main AOD band] using least-square minimization. AOD retrieved from 800 nm. Step 3: Selection of a aerosol type / chlorophyll / AOD set using least-square minimization of measured and modelled reflectance in all PMD channels. Stokes fractions are used in addition if applicable. (East part of the swath and close to sun-glint) aerosol model number PMD reflectance SZA=40, cos(RAZI)=-1, VZA=45, AOD= 4 SZA=40, nadir, aerosol model 3 PMD reflectance AOD LUT by O. Hasekamp (SRON/O3MSAF)

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN Alternate retrieval Combining reflectances & stokes fractions under conditions with large surface contribution Guess an AOD using one channel (reflectance or stokes fraction) using different aerosol models and a priori surface Check reliability: Stokes fraction PMD reflectance AOD LUT by O. Hasekamp (SRON/O3MSAF)

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN Partially cloudy conditions: Cloud radiance correction by AVHRR AVHRR cloud tests: Albedo test T4 test Uniformity test T4T5 test Retrieval for partly cloudy pixels: Limited to PMD 13/15 Corrected for cloud reflectance ChannelCentral wave- length[μm] Wavelength range [μm] A B Geometric cloud fraction: Use regular retrieval on corrected reflectances

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN PMAp results: Aerosol Optical Depth (23/05/2011)

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN PMAp results: AOD clear sky cases (23/05/2011)

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN Volcanic ash flag: Identifying pixels misclassified as cloud Orange: Strong ash test positive, cloud tests ignored Blue: cloud fraction < 0.3, AOD retrieved White: no retrieval or cloud fraction > 0.3 and negative ash test Aerosol optical depth Volcanic ash flag: Brightness temperature difference T4-T5 (10 μm – 12 μm) Thresholds in VIS and NIR (e.g. AVHRR CH3A/CH2)

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN Initial Verification of PMAp Comparison to MODIS Overpass difference: 1.0h MODIS / Terra and METOP Main retrieval R = 0.89 AOD (MODIS) AOD (PMAp) Alternate retrieval R = 0.83 AOD (MODIS) AOD (PMAp) Cloudy pixels R = 0.71 AOD (MODIS)

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN PMAp Cloud products PMAp: cloud fraction PMAp: cloud optical depth FRESCO ( Effective cloud fraction

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN PMAp tandem operations: AOD Metop A & Metop B (30/08/2013)

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN PMAp AOD Metop (Mid December 2013 to end January 2014)

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN PMAp AOD Metop vs AERONET (Mid January 2014 to February 2014)

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN PMAp AOD Metop vs AERONET (Mid January 2014 to February 2014)

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN The PMAp product operational implementation Product features Product features: Near real time 3 minutes granules, maximum 3 hours after sensing time Available via EUMETCast in EPS native and netcdf4. Full orbit offline data. Available from the EUMETSAT archive AOD, COD, volcanic ash flag Planned start of pre-operational dissemination: March 2014

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN The PMAp product test-data Product test and trail dissemination phase PMAp runs in EUMETSATs core ground segment #1 in operational mode since 16 th of January 2014 Test data is available to interested users on an offline basis (best effort) on an operational NRT EUMETCast test-dissemination basis (6 th February 2014) Want to be a test user? early access to data early access to al relevant documentation or

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN The PMAp product Product phase – schedule summary 16 th January 2014:Implementation on Core ground segment #1 6 th February 2014:Start of EUMETCast NRT test dissemination stream 17 th February 2014:Product Validation Board meeting – pre-operational product quality status 4 th March 2014:Start of trail-dissemination to all EUMETCast users 25 th March 2014: Operations Readiness Review (ORR) 26 th March 2014:Start of pre-operational status dissemination Q1 – Q3/2014:Extensive product validation and quality update campaign 20 th August 2014 (TBC):Product Validation Board meeting – operational product quality status

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN Future algorithm developments Extension of the AOD retrieval to pixels over land (preliminary land retrieval available on prototype level) AOD interpolation for different aerosol types at 460 nm AOD type selection using different aerosol indices between nm Corrections for partly cloudy pixels combining GOME and AVHRR around 630 nm A dedicated volcanic ash retrieval is currently being developed using the same framework: Temperature differences & NDVI (AVHRR) Shape of the IASI spectra (e.g. concept of Lieven Clarisse) GOME-2 UV ratio Online calibration of AVHRR channels 1, 4 and 5 using GOME-2 and IASI (development for PMAp and in the frame of GSICS)

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN PMAp AOD over land First result (30/08/2013) from Metop-A and B

REMOTE SENSING OF ATMOSPHERIC AEROSOL, CLOUDS, AND AEROSOL-CLOUD INTERACTION 16/12-19/12/2013, BREMEN Conclusions A new aerosol product over ocean from METOP instruments (PMAp) will be provided to users (operational in Q1/2014) The aerosol product is developed using a multi-instrument approach combining GOME, AVHRR and IASI AOD will be retrieved for clear-sky and partly cloudy scenes Cloud fraction, cloud optical depth and limited information on aerosol type like volcanic ash is provided in addition Verifications of the algorithms show promising results The second generation will provide AOD over land and an improved multi-sensor retrieval of volcanic ash