Satellite observations of aerosols and related gases and applications for aerosol deposition

Transcription

1 Satellite observations of aerosols and related gases and applications for aerosol deposition Mian Chin 1, HongbinYu 1, P.K. Bhartia 1, JassimAl-Saadi 2, ShobhaKongragunta 3, Alexei Lyapustin 1, Omar Torres 1, and Alexander Marshak 1 1 NASA Goddard Space Flight Center, USA 2 NASA Langley Research Center, USA 3 NOAA NESDIS, USA 1

2 Introduction Satellite observations of trace gases and aerosols started in the late 197s have been obtaining global and regional views of their spatial and temporal distributions, providing a wide range of applications However, estimating atmospheric deposition from satellite data is quite challenging and only possible under specific conditions Today I will 1. briefly overview some of the past, current, and near future satellite observations for trace gases and aerosols 2. present a recent study on using satellite data for dust transport and deposition as an example of feasibility 2

3 PART 1: SATELLITE OBSERVATIONS OF ATMOSPHERIC COMPOSITION 3

4 Past & Present polar-orbiting UV/VIS/IR instruments for tropospheric gases TOMS Series Trop O 3 column only GOME & SCIAMACHY Series Terra/MOPITT Trace Gases: NO 2, SO 2, HCHO. + GHG Aqua/AIRS Aura/OMI, TES, MLS CO CO, NO x, SO 2, CH 4, NH 3, O 3, NO 2, SO 2, HCHO, CO,. MetOp/IASI CO, NO x, SO 2, CH 4, NH 3, Suomi-NPP/OMPS, CrIS O 3, CO,... Satellite measurements of trace gases are usually sensitive to the species in the free troposphere, with much reduced sensitivity in the BL (except NO 2 ), imposing difficulties for estimating deposition 4

5 Past & Present polar-orbiting UV/VIS/IR instruments for aerosols TOMS Series AVHRR Series UV, Column AOD, AAOD, UVAI Column AOD over water only No aerosol speciation can be directly measured. Retrieving aerosol species has to rely on observable optical and/or microphysical properties with assumptions. SeaWiFS Terra & Aqua/MODIS Terra/MISR Aura/OMI PARASOL/POLDER AOD CALIPSO, CATS AOD, fine/coarse modes AOD, microphysics info AOD, AAOD, UVAI AOD, AAOD, microphys. Aerosol vertical profiles AOD, size info Suomi-NPP/VIIRS 5

6 Multiple polar-orbiting satellite observations of aerosol optical depth (AOD) AVHRR-CDR AVHRR-GACP TOMS SeaWiFS MISR MODIS [Figure from Chin et al., ACP 214] 6

7 Aerosol observations from geostationary weather satellites GOES-16 (formerly known GOES-R): US weather satellite operated by NOAA, launched late 216 Primary instrument: Advanced Baseline Imager (ABI), multichannel, multispectral imager from UV to IR Viewing region: North/South America and surrounding ocean every 5 min (CONUS) or 15 min (full disk) Himawari 8: Japanese weather satelliteoperated by the Japan Meteorological Agency, launched late 214 Primary instrument: Advanced Himawari Imager (AHI, same instrument as ABI) Viewing region: Asian-Pacific region every 1 min Both retrieve AOD 7

8 Deep Space Climate Observatory (DISCOVR) Orbit: Sun-Earth L1 Lagrangianpoint, 1,5, km (93, miles) from Earth, viewing the entire sun-lit earth NASA instrument: Earth Polychromatic Imaging Camera (EPIC), UV to NIR wavelength Example: EPIC data on 216 day 87 13h12m. Left: TOA RGB. Middle: surface reflectance. Right: aerosol optical depth (AOD) retrieved by Alexei Lyapustin. 8

9 Atmospheric Composition Virtual Constellation (AC-VC) The Committee on Earth Observation Satellites (CEOS) Atmospheric Composition Virtual Constellation (AC-VC) is one of seven virtual constellations that assemble a set of space and ground segment capabilities operating together in a coordinated manner to produce a virtual system that overlaps in coverage in order to meet a combined and common set of Earth observation requirements. The goal of the AC-VC is to collect and deliver data to develop and improve predictive capabilities for changes in the ozone layer, air quality, and climate forcing associated with changes in atmospheric composition. Within the GEOSS framework, AC-VC supports multiple societal benefit areas, including health, disasters, energy, climate, and ecosystem 9

10 Global pollution monitoring constellation: Tropospheric chemistry missions funded for launch TEMPO (hourly) Sentinel-4 (hourly) GEMS (hourly) Sentinel-5P (once per day) Courtesy Jhoon Kim, Andreas Richter licy-relevant science and environmental services enabled by common observations Improved emissions, at common confidence levels, over industrialized Northern Hemisphere Improved air quality forecasts and assimilation systems Improved assessment, e.g., observations to support United Nations Convention on Long Range Transboundary Air Pollution

11 Tropospheric chemistry mission parameters (as of 9/216) Europe Sentinel 4 USA TEMPO Korea GEMS Europe Sentinel 5 Precursor TROPOMI it Geostationary Geostationary Geostationary Low-Earth ain Europe and surrounding North America Asia-Pacific Global isit 1 hour 1 hour 1 hour 1 day us Detailed Design, Phase C Instrument delivery 217 Instrument delivery 217 Instrument complete nch load ucts tial pling inal uct lution s 221 (Flight Acceptance Review first instrument) UV-Vis-NIR 35-5, nm O 3, trop. O 3, NO 2, SO 2, HCHO, AAI, AOD, height-resolved aerosol pending host selection UV-Vis 29-49, nm O 3, trop. O 3, -2km O 3, NO 2, HCHO, SO 2, CHOCHO, AOD, AAI 8 km x 8 km at 45N 2.22 km N/S x 5.15 km 8.9 km N/S x 11.7 km Two instruments in sequence on MTG-S; use TIR sounder on MTG-S (expected sensitivity to O3 and CO). Synergy with imager on MTG-I w.r.t. aerosol and clouds km N/S x 5.15 km GEO-CAPE precursor or initial component of GEO-CAPE. Synergy with GOES-R/S ABI w.r.t. aerosol and clouds UV-Vis 3-5 nm O 3, NO 2, SO 2, HCHO, AOD UV-Vis-NIR-SWIR 27-5, , nm O 3, trop. O 3, NO 2, SO 2, HCH AAI, AOD, height-resolved aerosol, CO, CH km N/S x 8 km 7 km x 3.5 km nadir 7 km N/S x 8 km (gas), 3.5 km N/S x 8 km (aerosol) Synergy with AMI and GOCI-2 instruments w.r.t. aerosol and clouds. 7 km x 7 km nadir In formation with S-NPP for synergy w.r.t. clouds and O 3.

12 PART 2: USING SATELLITE OBSERVATIONS FOR ESTIMATING ATMOSPHERIC DEPOSITION DUST AS AN EXAMPLE [Research led by Hongbin Yu, NASA GSFC] 12

13 Using satellite data to estimate dust transport and deposition Dust is the one aerosol specie that can be retrieved/derived from satellite aerosol measurements with relatively high confidence, because of its unique optical and microphysical properties Absorbing at shorter wavelength Mostly large particles (coarsemode) Non-spherical shape Well-known source regions and distinguishable transport routes 13

14 A-Train (+other) provides several capabilities of observing global dust & pollution from space Dust, generally large and non-spherical particles, can be separated from other types based on A-Train measurements Sensor Technique Observables MODIS Multiple wavelength AOD & particle size AIRS, IASI thermal IR Coarse mode AOD & height info MISR POLDER multi-angle, multiple wavelengths Multi-angle, multiple wavelengths, polarization AOD & particle shape AOD & particle shape CALIOP polarization lidar Vert. profiles & particle shape 14

15 Step-by-step estimation of dust transport and deposition 1. Aerosol extinction/backscatter profile from CALIOP Dust particles are large in size and nonspherical in shape, so large depolarization ratio. Pure dust : δ.3 (or.2) Pure non-dust : δ.7 (or.2) Dust fraction in mixture is accounted for 2. Dust extinction profile Extinction (m -1 ) = Mass Concentration (g/m 3 ) * MEE (=.37 m 2 /g) 3. Profile of Dust Mass Concentration MERRA Reanalysis wind No leak from top zonal F = 4. Dust Mass Flux m(z)u(z) dz 5. Mass Balance Dust deposition Yu et al., Remote Sens. Environ., 215 & Yu et al., Geophys. Res. Lett., 215

16 Using CALIOP data to derive dust mass fluxes across the North Atlantic Blue: cloud-free Red: above-cloud Cloud top 43 Tg 13 Tg 175 Tg Integrated dust flux Average dust profile 1 S-3 N 1. Obtain aerosol extinction profiles from CALIOP (cloudfree and above cloud) 2. Estimate dust aerosol extinction profiles based on the depolarization ratio 3. Convert dust extinction profile to dust mass concentration profile 4. Calculate dust transport mass fluxes based on mass concentration and wind speed/direction Yu et al., Remote Sens. Environ.,

17 Correlation of dust flux with in-situ observations Dust flux is calculated along the 55 km meridional line just upwind of the interested regions Dust flux in the lowest 1km layer is responsible for dust detected at surface ug/m 3 is the lowest seasonal mean PM1-18 (ug m -3 ) CALIOP Dust Flux (<1km) (Tg) Puerto Rico PM1 Conc. CALIOP Dust Flux (< 1 km) R= Cayenne PM1 Conc. CALIOP Dust Flux (< 1 km) CALIOP Dust Flux (< 1 km) (Tg) Dust Concentration (ug m -3 ) CALIOP Dust Flux (< 1 km) (Tg) R= Barbados Dust Conc. CALIOP Dust Flux (< 1 km) R= ug/m 3 is the lowest seasonal mean

18 Budget of dust flux and deposition in Amazonia units: Tg + combined: Total influx from east and north: 34 Tg yr -1 Fraction from meridional contribution: 9 Tgyr -1 (26%) Dust deposition in Amazon: 28 Tgyr -1 Comparison with other estimates and with model-calculated deposition: units: Tg 7-year average ± std dev.

19 Estimating dust deposition to the North Atlantic, the Caribbean basin, and Amazon Strong seasonal & interannual variability Region dependent Meridionaltransport not negligible Most important in. and are more importa Dominated in and. Negligible in other seasons. 19

20 Dust Flux: CALIOP vs. MERRA2 Modern Era Reanalysis for Research and Applications (MERRA) version 2: Total AOD assimilated with MODIS, MISR, and AERONET data. Comparison of CALIOP-estimated and MERRA2/AOD-corrected dust mass flux: -3N and vertically integrated Dust Mass Flux, 8-year ave. & range CALIOP dust flux is much higher (up to a factor of 4) than MERRA2 simulation at places far from Sahara (e.g., Caribbean Basin).

21 Dust Deposition: CALIOP vs. MERRA2 Two estimates of MERRA2 dust deposition: CAL based on parameters of dry & wet removals DIV mass balance (simillar to CALIOP estimate CALIOP MERRA2-DIV MERRA2-CAL CALIOP MERRA2-DIV MERRA2-CAL CAR NAT Dust Deposi on (Tg) SO JJ M D Caribbean Basin (27-214) AMZ 16 2 North Atlan c Ocean (27-214) CALIOP MERRA2-DIV MERRA2-CAL Dust Deposi on (Tg) Amazon Basin (27-214)

22 Remarks for discussion Many satellite observations in the past, present, and near future measuring atmospheric composition, especially aerosols, providing global and regional views and spatial/temporal variations Challenges of using the satellite data for atmospheric deposition: For aerosols, the vertical information of species of interest (e.g., sulfate, nitrate, BC ) have to be retrieved from total aerosol optical measurements, which can be very difficult. Currently it is only feasible for dust For gas species, in general the low sensitivity near surface makes the estimates very difficult Combining satellite observations with models might be the best way to estimate the atmospheric deposition, however the estimate remains unconstrained, because many processes are not observable 22