Aerosol measurements from Space. Gerrit de Leeuw FMI & Uni of Helsinki, Finland & TNO, Utrecht, Netherlands

Transcription

1 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

2 ACCENT AT-2 Outcomes The Remote Sensing 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 Satellite Composition Measurements A. Piters and B. Buchmann 8. Applications of Tropospheric Composition Observations from Satellites S. Beirle and P. Monks 10 Conclusions and Outlook (from outline) J. Burrows and U. Platt Appendix: Satellites and Instruments

3 Characteristics of optical instruments used in aerosol retrieval Sensor MERIS AATSR SeaWiFS MODIS PARASOL MISR AVHRR TOMS SEVIRI OMI GOME-2 Resolution x x24 80x40 at (fine (bands 1- (UV- nadir [km] resolution) 2) 2&VIS) x48 (reduced (bands 3- (UV-1) resolution) 7) 1.0 (bands 8-36) Swath width [km] Europe Africa S.America Multi-view No 2 No No Yes 9 No No No No No Polarization No No No No 3 No No No No No S and P in nm channel Platform Envisat Envisat Seastar/ Terra / Myriade Terra NOAA Nimbus-7 MSG AURA METOP Orbview-2 Aqua Series Earth Probe Launch March March August December December December October November January July October / May, 2002 Equator ascending ascending descending descending descen- descending descending ascending n/a ascen- descending, crossing 10:00 10:00 12:30 10:30 / ding 10:30 1:30-2:30 noon ding 09:30 time ascending 13:30/ ascending 13:42 13:30 ascending 13:30-13:30 14:30 Heritage - ATSR Polder-1 - AVHRR TOMS - TOMS GOME ATSR-2 Polder-2 series series Kokhanovsky, A., and G. de Leeuw, 2009: Satellite Aerosol Remote Sensing Over Land. Springer, 2009

4 Aerosols from Space SeaWiFS: Sahara Dust outbreak MODIS: California Forest Fires

5 ATSR-2: Pollution over NW Europe

6 Los Alamos Fire, New Mexico May 9, 2000 MISR 60 Forward MISR Nadir MISR 60 Aft

7 AOD Parasol: polarization Ångström 670/865 Fine mode AOD Coarse mode AOD Spherical Coarse mode AOD Non-spherical

8 CALIPSO data F-M Breon, BAW workshop

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

10 Aerosol products from satellites All these instruments provide aerosol products, for clear (cloud-free) atmosphere The primary parameter is Aerosol Optical Depth (AOD; often also called AOT): a measure for the amount of aerosol If more than one wavelength is available the Ångström parameter can be derived: measure for the shape of the size distribution Related / derived parameters: Aerosol type (composition) Single scattering albedo Fine / Coarse mode fraction Spherical / Non-spherical coarse mode Effective mode radius Absorbing aerosol AAI (PM2.5) Geostationary: high temporal res. CALIPSO: Vertical profiles Over bright surfaces and clouds Classification Most instruments provide 1 or 2 of these products! It usually works best for high AOD: can these products also be provided for low AOD? With what accuracy?

11 How do we retrieve aerosol information? (AATSR ADV as example) Satellite observation: Instrument characteristics Calibration Cloud and surface effects Radiative Transfer Model Optical properties aerosol Meteorology?

12 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

13 ATSR-2: INDOEX AOD Mixture Robles Gonzalez et al., 2006 Mixture of aerosols produced over land (industrial, fossil fuel and biomass burning, dust) and over sea Minimizing error function to determine optimum mixture Provides: AOD Angstrom coefficient Mixture Over the ocean the mixture gradually changes from continental to sea salt Validation with campaign data

14 AATSR China, MODIS Monitoring aerosols in China Anu-Maija Sundström 2 Gerrit de Leeuw 1 AERONET, Beijing Pekka Kolmonen 1, Larisa Sogacheva 2 and, Lyana Curier 1 1: Finnish Meteorological Institute 2: University of Helsinki Satellites provide the spatial variability AMFIC meeting,, , , Beijing Ground based measurements are more accurate, with better time resolution

15 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 , very polluted AOD

16 Comparison of sensors for a single scene Kokhanovsky et al., IJRS, 2009

17 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: 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.

18 Comparison of Sensors: Land ICARE Total AOT 670 nm Parasol MODIS MERIS Fine Mode AOT 550nm MODIS much better than MERIS for total AOT. Parasol and MODIS similar results for fine mode AOT. A few outliers lower the correlation (REF: ICARE)

19 AOD from different instruments Single scene: There are local differences between algorithms Average values for large spatial areas of almost all algorithms are close Algorithm could be tuned to provide optimized values for the conditions encountered Trends: Operational: For operational processing this is not possible In that case differences are much larger and correlations are low 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 ENVISAT: use in combination (future: Earthcare, Sentinel-3) Optimize AOD fields by using multiple results from different sensors, including AERONET (Kinne)

20 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 different results Information only on aerosol in optically active size range But: the good news is the good agreement between several instruments and algorithms

21 Needs of scientific community Vertical structure Microphysical properties Dedicated vs operational products Accurate Known data quality Good data accessibility L1, L2 MODIS is widely used in the scientific community Data products are easily accessible: NASA websites ICARE data center (sponsored by, e.g. CNES, 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)

22 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