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

Slides:



Advertisements
Similar presentations
Global transport and radiative forcing of biomass burning aerosols Yang Chen, Qinbin Li, Ralph Kahn Jet Propulsion Laboratory California Institute of Technology,
Advertisements

Assessing Health Effects of Particulate Matter Using MODIS Aerosol Data Zhiyong Hu
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.
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.
A Dictionary of Aerosol Remote Sensing Terms Richard Kleidman SSAI/NASA Goddard Lorraine Remer UMBC / JCET Short.
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,
Extracting Atmospheric and Surface Information from AVIRIS Spectra Vijay Natraj, Daniel Feldman, Xun Jiang, Jack Margolis and Yuk Yung California Institute.
A 21 F A 21 F Parameterization of Aerosol and Cirrus Cloud Effects on Reflected Sunlight Spectra Measured From Space: Application of the.
Surface Skin Temperatures Observed from IR and Microwave Satellite Measurements Catherine Prigent, CNRS, LERMA, Observatoire de Paris, France Filipe Aires,
Remote Sensing of Pollution in China Dan Yu December 10 th 2009.
GOES-R AEROSOL PRODUCTS AND AND APPLICATIONS APPLICATIONS Ana I. Prados, S. Kondragunta, P. Ciren R. Hoff, K. McCann.
Development of the SEVIRI Aerosol Retrieval Algorithm (SARA) Aerosol retrievals using MSG-SEVIRI images Y.S. Bennouna and G. de Leeuw.
ESTEC July 2000 Estimation of Aerosol Properties from CHRIS-PROBA Data Jeff Settle Environmental Systems Science Centre University of Reading.
Evaluating Remote Sensing Data Or How to Avoid Making Great Discoveries by Misinterpreting Data Richard Kleidman ARSET-AQ Applied Remote Sensing Education.
Reflected Solar Radiative Kernels And Applications Zhonghai Jin Constantine Loukachine Bruce Wielicki Xu Liu SSAI, Inc. / NASA Langley research Center.
Visible Satellite Imagery Spring 2015 ARSET - AQ Applied Remote Sensing Education and Training – Air Quality A project of NASA Applied Sciences Week –
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.
Quick Review of Remote Sensing Basic Theory Paolo Antonelli CIMSS University of Wisconsin-Madison Benevento, June 2007.
University of Wisconsin - Madison (UW) University of Hawaii (UH) Texas A& M (TAMU) University of Colorado at Boulder (CU) University of Alabama in Huntsville.
Influence of ice supersaturation, temperature and dynamics on cirrus occurrence near the tropopause N. Lamquin (1), C.J. Stubenrauch (1), P.-H. Wang (2)
University of Wisconsin - Madison (UW) University of Hawaii (UH) Texas A& M (TAMU) University of Colorado at Boulder (CU) University of Alabama in Huntsville.
EARLINET and Satellites: Partners for Aerosol Observations Matthias Wiegner Universität München Meteorologisches Institut (Satellites: spaceborne passive.
Chapter 4: How Satellite Data Complement Ground-Based Monitor Data 3:15 – 3:45.
Orbit Characteristics and View Angle Effects on the Global Cloud Field
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
EUMETSAT METEOROLOGICAL SATELLITE CONFERENCE 15/09/2013 – 20/09/2013, VIENNA EUMETSAT METEOROLOGICAL SATELLITE CONFERENCE 15/09/2013 – 20/09/2013, VIENNA.
Upper haze on the night side of Venus from VIRTIS-M / Venus Express limb observations D. Gorinov (1,2), N. Ignatiev (1,2), L. Zasova (1,2), G. Piccioni.
GOES and GOES-R ABI Aerosol Optical Depth (AOD) Validation Shobha Kondragunta and Istvan Laszlo (NOAA/NESDIS/STAR), Chuanyu Xu (IMSG), Pubu Ciren (Riverside.
RETRIEVING BRDF OF DESERT USING TIME SERIES OF MODIS IMAGERY Haixia Huang, Bo Zhong, Qinhuo Liu, and Lin Sun Presented by Bo Zhong Institute.
Estimation of Cloud and Precipitation From Warm Clouds in Support of the ABI: A Pre-launch Study with A-Train Zhanqing Li, R. Chen, R. Kuligowski, R. Ferraro,
Direct LW radiative forcing of Saharan dust aerosols Vincent Gimbert, H.E. Brindley, J.E. Harries Imperial College London GIST 25, 24 Oct 2006, UK Met.
DEVELOPING HIGH RESOLUTION AOD IMAGING COMPATIBLE WITH WEATHER FORECAST MODEL OUTPUTS FOR PM2.5 ESTIMATION Daniel Vidal, Lina Cordero, Dr. Barry Gross.
Monitoring aerosols in China with AATSR Anu-Maija Sundström 2 Gerrit de Leeuw 1 Pekka Kolmonen 1, and Larisa Sogacheva 1 AMFIC , Barcelona 1:
Optical properties Satellite observation ? T,H 2 O… From dust microphysical properties to dust hyperspectral infrared remote sensing Clémence Pierangelo.
The Second TEMPO Science Team Meeting Physical Basis of the Near-UV Aerosol Algorithm Omar Torres NASA Goddard Space Flight Center Atmospheric Chemistry.
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote SEnsing Training A project of NASA Applied Sciences Pawan Gupta Satellite.
Numerical simulations of optical properties of nonspherical dust aerosols using the T-matrix method Hyung-Jin Choi School.
COMPARATIVE TEMPERATURE RETRIEVALS BASED ON VIRTIS/VEX AND PMV/VENERA-15 RADIATION MEASUREMENTS OVER THE NORTHERN HEMISPHERE OF VENUS R. Haus (1), G. Arnold.
Timothy Logan University of North Dakota Department of Atmospheric Science.
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.
SATELLITE REMOTE SENSING OF TERRESTRIAL CLOUDS Alexander A. Kokhanovsky Institute of Remote Sensing, Bremen University P. O. Box Bremen, Germany.
Radiative transfer in the thermal infrared and the surface source term
The Orbiting Carbon Observatory (OCO) Mission: Retrieval Characterisation and Error Analysis H. Bösch 1, B. Connor 2, B. Sen 1, G. C. Toon 1 1 Jet Propulsion.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.
23-27 November 2004CONAE Workshop, Cordoba, Argentina1 Infrared methods for deriving volcanogenic sulphur dioxide Dr Fred Prata 1 Adjunct Professor Michigan.
AEROCOM AODs are systematically smaller than MODIS, with slightly larger/smaller differences in winter/summer. Aerosol optical properties are difficult.
Data acquisition From satellites with the MODIS instrument.
Developing a Dust Retrieval Algorithm Jeff Massey aka “El Jeffe”
UCLA Vector Radiative Transfer Models for Application to Satellite Data Assimilation K. N. Liou, S. C. Ou, Y. Takano and Q. Yue Department of Atmospheric.
A Brief Overview of CO Satellite Products Originally Presented at NASA Remote Sensing Training California Air Resources Board December , 2011 ARSET.
Retrieval of desert dust aerosols vertical profiles from IASI measurements in the TIR atmospheric window Sophie Vandenbussche, Svetlana Kochenova, Ann-Carine.
A comparison of AMSR-E and AATSR SST time-series A preliminary investigation into the effects of using cloud-cleared SST data as opposed to all-sky SST.
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.
AGU 2008 Highlight Le Kuai Lunch seminar 12/30/2008.
1. Титульный..
Fourth TEMPO Science Team Meeting
Extinction measurements
G. Mevi1,2, G. Muscari1, P. P. Bertagnolio1, I. Fiorucci1
Dust detection methods applied to MODIS and VIIRS
Vicarious calibration by liquid cloud target
GEO-CAPE to TEMPO GEO-CAPE mission defined in 2007 Earth Science Decadal Survey Provide high temporal & spatial resolution observations from geostationary.
G. Mevi1,2, G. Muscari1, P. P. Bertagnolio1, I. Fiorucci1
Need for TEMPO-ABI Synergy
ABI Visible/Near-IR Bands
NPOESS Airborne Sounder Testbed (NAST)
GOES -12 Imager April 4, 2002 GOES-12 Imager - pre-launch info - radiances - products Timothy J. Schmit et al.
Presentation transcript:

Daily observation of dust aerosols infrared optical depth and altitude from IASI and AIRS and comparison with other satellite instruments Christoforos Tsamalis, Alain Chédin and Sophie Peyridieu Laboratoire de Météorologie Dynamique CNRS/IPSL, Ecole Polytechnique

EGU 2011 Christoforos Tsamalis 2 Why we are interested in dust aerosols? Dust aerosols affect the earth’s radiative budget with participation in both the direct and indirect effects, in both spectra solar and terrestrial, influence the hydrological cycle by acting as cloud condensation and ice nuclei, modify the oxidizing capacity of the atmosphere and thus the concentration of some tropospheric trace gases, participate in the fertilization with iron of the ocean and degrade the restitution of atmospheric and surface parameters from satellite instruments. MODIS 6/3/2004

EGU 2011 Christoforos Tsamalis 3 Remote sensing of aerosols in the IR  Remote sensing in the visible domain has been widely used to obtain better characterization of aerosols and their effect on solar radiation.  On the opposite, remote sensing of aerosols in the infrared (IR) domain still remains marginal. Advantages of infrared remote sensing: retrieval of aerosols mean altitude and coarse mode effective radius in addition to optical depth at 10 µm, possibility of observations during night and day, over land and sea. Drawback: the influence of atmospheric state.

EGU 2011 Christoforos Tsamalis 4 Characteristics of AIRS and IASI AIRSIASI LaunchMay 2002October 2006 SatelliteAquaMetOp-A Local time (night/day)1:30/13:3021:30/9:30 InstrumentGrating spectrometer Fourier transform spectrometer Spectral range cm -1 / µm (non continuous) cm -1 / µm Spectral resolution0.5-2 cm cm -1 (apodized) Spatial resolution at nadir 13.5 km12 km Channels Full swath width2300 km2200 km IASI will provide observations for more than 15 years on board of MetOp-A, B, C.

EGU 2011 Christoforos Tsamalis 5 Aerosols inversion method in the IR II. Application of cloud mask. III. Retrieving atmospheric situation by using the LUTs for Atmosphere. IV. Retrieving aerosols properties (optical depth, mean altitude and coarse mode effective radius) by using the LUTs for Aerosols. Mineral transported model Aerosol software package Hess et al., 1998 DISORT Spectroscopy database Jacquinet-Husson et al., 2008 Thermodynamic data set Chédin et al., 1985 Chevallier et al., 1998 OPAC Radiative transfer model Scott and Chédin, 1981 Scattering algorithm Stamnes et al., 1988 Look Up Tables Atmosphere Look Up Tables Aerosols I. Construction of Brightness Temperature Look Up Tables

EGU 2011 Christoforos Tsamalis 6 Aerosols products from IR 8 years of AIRS observations and more than 3 years of IASI observations with space-time resolution of 1 degree – 1 month. Peyridieu et al., 2010 MODIS AOD IASI AODAIRS AOD AIRS ALTITUDE

EGU 2011 Christoforos Tsamalis 7 Towards a better resolution : 1 day-1 spot Aerosols present strong variability with average residence time of roughly one week in the lower troposphere, so the best possible resolution (spatial and temporal) is necessary for their optimal observation and use (study of their implication in the atmospheric processes). Use of daily data for assimilation in numerical models and improvement of their performances. AIRS or IASI provide aerosol altitude with good spatial coverage on a daily basis when CALIPSO multiannual data must be used. Tsamalis et al., EGU, 2011

EGU 2011 Christoforos Tsamalis 8 Daily optical depth from IASI Good spatial correlation between IASI and MODIS

EGU 2011 Christoforos Tsamalis 9 AIRS and IASI daily optical depth comparison with MODIS Preliminary results (still too noisy) depict good temporal correlation with coefficients: AIRS – MODIS: R 2 =0.51 IASI – MODIS: R 2 =0.56 While there is better agreement with IASI.

EGU 2011 Christoforos Tsamalis 10 Achieving spatial resolution of 1 spot After  Slight modification of the inversion method permits the optical depth retrieval with 1 spot spatial resolution.  Comparison between 1 degree resolution and spot resolution for IASI during July 2007 demonstrates good correlation with coefficient R 2 =0.93.

EGU 2011 Christoforos Tsamalis 11 AIRS altitude comparison with CALIPSO First results with daily resolution demonstrate relatively successful retrieval, although AIRS tends to underestimate the dust altitude, while they still need improvement. Similar study with IASI under development but more complicated.

EGU 2011 Christoforos Tsamalis 12 Conclusions and perspectives o Dust aerosol properties with the best possible resolution (1 day-1 spot) retrieved from AIRS and IASI for July 2007 (the most active month of the dust season above the tropical Atlantic Ocean) compared with - MODIS for the optical depth - CALIPSO for the altitude  indicate that, most of the time, the infrared daily products compare well with these two instruments daily observations.  Further developments of the algorithm (more dust aerosol models, zenith angle up to 40° instead of 30°).  Improvement and quantification of errors for daily products.  Retrieving aerosol properties during daytime.  Retrieving aerosol properties above continents and particularly above deserts (difficult to achieve at solar wavelengths) by using the IASI surface properties (emissivity and surface temperature from Capelle et al., in preparation). Emissivity at 8.54 µm – May 2010 Surface temperature – May 2010

EGU 2011 Christoforos Tsamalis 13 Thank you! This work has been supported in part by the European Community under the contract FP7/ (MACC project). We thank the ICARE Data and Services Center for providing access to the CALIPSO data ( MODIS data were obtained through NASAs Giovanni, an online visualization and analysis tool, developed and maintained by the NASA GES DISC.