Aerosol measurements from Space

Gerrit de Leeuw FMI & Uni of Helsinki, Finland & TNO, Utrecht, Netherlands

ACCENT AT-2 Follow-up meeting Mainz, 22 June 2009 ACCENT AT-2 Outcomes

The of Tropospheric Composition from Space Editors: J. Burrows, P. Borrell & U. Platt Preface 1 Tropospheric Remote sensing from space (early draft) J. Burrows and U. Platt 2: Solar backscattered Radiation: UV, visible and near IR – trace gases A. Richter and T. Wagner 3: Thermal Infrared: Absorption and emission – trace gases and parameters C. Clerbaux, J. Drummond, J. –M. Flaud. J. Orphal 4. Microwave: Absorption and Emission – trace gases and parameters K. Künzi 5. Solar backscattered radiation: scattering – Clouds A. Kokhanovsky 6. Retrieval of aerosol properties G. de Leeuw, S. Kinne, J.F. Leon, J. Pelon, D. Rosenfeld, M. Schaap, P. Veefkind, B. Veihelmann, D. Winker, W. von Hoyningen Huene 7. Quality Assurance and Validation of Composition Measurements A. Piters and B. Buchmann 8. Applications of Tropospheric Composition Observations from S. Beirle and P. Monks 10 Conclusions and Outlook (from outline) J. Burrows and U. Platt Appendix: Satellites and Instruments Characteristics of optical instruments used in aerosol retrieval Se n s or M E R I S AAT SR Se a W i F S M O D I S P A R A S O L M I S R AVHR R T O M S SE V I R I OM I GOM E - 2 Re so l u ti o n 0. 3 1. 0 1 . 1 0. 2 5 6 x 7 0. 2 5 1. 1 39 1 - 3 13x 24 80x 40 at (fi n e ( b an ds 1- (UV- na d i r [ k m] r e s o l u tio n) 2) 2& VI S) 1. 2 0. 5 0 13x 48 (re d u c e d ( b an ds 3- (UV-1 ) r e s o l u tio n) 7) 1. 0 ( b an ds 8- 36) Sw a t h 115 0 500 2 8 0 1 233 0 240 0 380 270 0 280 0 Eu r o pe 260 0 192 0 w i dth Afri c a [k m ] S. A m e - r i ca M u lt i- v i e w No 2 No No Ye s 9 No No No No No Po l a r i z a ti o n No No No No 3 No No No No No S a n d P i n 312- 79 0 nm ch an ne l Pl a tfo r m Env i sat E n v i sat S e astar / Te rr a / My r i ade NO AA Ni mb u s -7 MS G A U R A MET O P Orb v i e w-2 Aq u a Seri es Ea rt h Prob e La un ch Mar c h Mar c h Au g u s t De c e mb er De c e mb er De c e mb er Oc t o b e r Nov e mb e r J a nua r y Ju ly Oc t o b e r 200 2 200 2 19 9 7 199 9 / 200 4 199 9 197 8 19 78 200 4 200 4 200 6 May , 200 2 Equ a to r as ce nding as ce nding d e s c e nding de s c e nding de s c e n - de s c e nding de s c e nding as ce nding n/a asc e n- de s c e nding , cro ssing 10: 00 10: 0 0 1 2 : 30 10: 3 0 / ding 10: 30 1: 30- 2: 30 no o n ding 09: 30 ti m e as ce nding 13: 30 / as c e nding 13: 42 13: 3 0 as c e nding 13: 30- 13: 30 14: 30 H erit a g e - AT SR-1 - - Pold e r-1 - AVHR R TO M S - T O M S GOM E AT S R -2 Pold e r-2 se ri e s se ri e s

Kokhanovsky, A., and G. de Leeuw, 2009: Satellite Aerosol Remote Sensing Over Land. Springer, 2009 Aerosols from Space

SeaWiFS: Sahara Dust outbreak MODIS: California Forest Fires ATSR-2: Pollution over NW Europe Los Alamos Fire, New Mexico May 9, 2000

MISR 60˚ Forward

MISR Nadir

MISR 60˚ Aft Parasol: polarization

AOD

Ångström 670/865

Fine mode AOD Coarse mode AOD Spherical

Coarse mode AOD Non-spherical CALIPSO data

F-M Breon, BAW workshop Instrument characteristics used in aerosol retrieval • Spectral information (most • Multiple angles (AATSR, instruments): POLDER, MISR): • One wavelength provides a single fit, i.e. AOD • Elimination of surface effects • Multiple wavelengths provide the • Information on scattering phase spectral shape of the AOD function (Ångström param.) • UV wavelengths: dark over land • Information on plume structure • IR wavelengths for cloud flagging • Requires assumption on surface effects • Aerosol retrieval is an underdetermined problem: aerosol • Polarisation (POLDER): distributions have more degrees of • Irregular particles freedom than independent pieces of information provided by a satellite •Surface instrument!

Most instruments were designed for other purposes than aerosol retrieval! Exceptions: POLDER, MODIS, MISR, () Aerosol products from satellites • All these instruments provide Related / derived parameters: aerosol products, for clear • Aerosol type (composition) (cloud-free) atmosphere • Single scattering albedo • The primary parameter is Aerosol • Fine / Coarse mode fraction • Spherical / Non-spherical coarse Optical Depth (AOD; often also mode called AOT): a measure for the • Effective mode radius amount of aerosol • Absorbing aerosol AAI • If more than one wavelength is • (PM2.5) available the Ångström parameter • Geostationary: high temporal res. can be derived: • CALIPSO: measure for the shape of the size • Vertical profiles distribution • Over bright surfaces and clouds • Classification

¾Most instruments provide 1 or 2 of these products! ¾It usually works best for high AOD: can these products also be provided for low AOD? ¾With what accuracy? How do we retrieve aerosol information? (AATSR ADV as example)

Satellite observation: Radiative Transfer Model •Instrument characteristics •Optical properties aerosol •Calibration •? •Cloud and surface effects Crucial steps in aerosol retrieval • Cloud screening: any residual cloud in a scene results in high AOD • Surface contributions: • Eliminate: multiple view • Reduce: UV wavelengths over land, NIR over ocean • Measure and model (single view, e.g. MODIS) • Polarisation • Radiative transfer model • Compare modeled reflectance at top of atmosphere with measurement • ’Best fit’ provides desired aerosol parameters ATSR-2: INDOEX • Mixture of aerosols • AOD produced over land (industrial, fossil fuel and biomass burning, dust) and over sea • Minimizing error function to determine optimum mixture • Provides: •AOD • Mixture • Angstrom coefficient •Mixture • Over the ocean the mixture gradually changes from continental to sea salt • Validation with campaign Robles Gonzalez et al., 2006 data AATSR China, 3.4.2005 MODIS

MonitoringMonitoring aerosolsaerosols inin ChinaChina

Anu-Maija Sundström2 Gerrit de Leeuw1 AERONET, Beijing Pekka Kolmonen1, Larisa Sogacheva2 and, Lyana Curier1

1: Finnish Meteorological Institute 2: University of Helsinki Satellites provide the spatial variability AMFIC meeting, 16-17.10.2008, Beijing Ground based measurements are more accurate, with better time resolution Satellite vs ground based • Satellites need ground based measurements: • Validation • Evaluation • Satellites may fill the gaps when properly used • Satellite data assimilation to constrain model results Satellite and ground based data are complementary Satellite products (not all of these from the same satellite): AOD(λ), Ångström coefficient, indication of chemical composition, effective radius, coarse/fine fraction; PM2.5 through correlation with AOD: varies with site Accuracy of AOD: 0.03 over water, 0.05 over land (from evaluation vs AERONET Note: clean air AOD ∼0.05-0.1, very polluted AOD ∼0.5-1.0 Comparison of sensors for a single scene

Kokhanovsky et al., IJRS, 2009 ICARE

Comparison of Sensors: Ocean

Parasol MODIS MERIS Seviri Calipso Comparison of Sensors: Ocean

Parasol and MODIS yield similar results, although with a small bias for the former. SEVIRI is more dispersed, with some bias, but provide a much larger statistics. MERIS and CALIPSO results are poor.

Clearly, the monitoring of AOD over the ocean shall be done with either Parasol, MODIS or Seviri. Seviri coverage not global. (REF: ICARE) ICARE Comparison of Sensors: Land

Parasol MODIS MERIS Total AOT 670 nm Total AOT 670

MODIS much better than MERIS for total AOT. Parasol and MODIS similar results for fine mode AOT. A few outliers lower the correlation

Fine Mode AOT 550nm Fine Mode AOT (REF: ICARE) AOD from different instruments Single scene: Operational: • There are local differences • For operational processing this between algorithms is not possible • Average values for large spatial • In that case differences are areas of almost all algorithms much larger and correlations are are close low • Algorithm could be tuned to provide optimized values for the conditions encountered Trends: •Use multiple sensors and synergistic approaches to improve the retrieval results (SYNAER: SCIAMCHY/AATSR; AATSR/MERIS; OMI/MODIS; …) •OMI, POLDER, CALIPSO, MODIS on A-train; AATSR, MERIS, SCIAMACHY on : use in combination (future: Earthcare, Sentinel-3) •Optimize AOD fields by using multiple results from different sensors, including AERONET (Kinne) Satellite: retrieval of aerosol properties

• Satellites provide a snapshot for a large area, with the same instrument, the same method and the same algorithm However: • The satellite data is less accurate than in situ data • Different instruments provide different results • Different methods provide different results • Different algorithms provide But: the good news is the good different results agreement between several • Information only on aerosol in instruments and algorithms optically active size range Needs of scientific community

• Vertical structure • MODIS is widely used in the • Microphysical properties scientific community • Dedicated vs operational • Data products are easily products accessible: • Accurate • NASA websites • Known data quality • ICARE data center (sponsored by, e.g. CNES, • Good data accessibility L1, L2 EU, etc.) • ICARE has MERIS data, but these cannot be distributed! • ESA DUE and DUP projects, but data (L1) gathering takes a large effort (even though improvements) Conclusions • Satellite retrieval methods need further development: • to reach maturity • be useful for scientific and operational applications • The use of satellites has substantially increased, e.g., IPCC: TAR (2001) > AR4 (2007) • Use of multiple sensors and possible synergy in retrieval algorithm, e.g.: •A-Train • ENVISAT •Earthcare • Sentinel 3 • Use models to estimate the a priori used in the retrieval (currently climatology is used) • Combine different results, from different sources (satellites, ground-based, models) to obtain the best possible product