Atmospheric Measurements from Space

Atmospheric Measurements from Space MPI Mainz Germany Thomas Wagner Satellite Group MPI Mainz Part 1: Basics Break Part 2: Applications

Part 1: Basics of satellite remote sensing Why atmospheric satellite observations? Remote sensing which parts of the EM spectrum? Orbits and viewing geometries Spatial and spectral resolution Information content

comparison to model results:...test of our understanding of the system Earth Chemistry (OH + NO2 => HO2 + NO) Physics (e.g. cloud formation) Meteorology (e.g. winds) Emissions (e.g. fossil fuel burning) Numerical simulations 4-dimensional fields (e.g. trace gases)

comparison to model results:...test of our understanding of the system Earth Chemistry (OH + NO2 => HO2 + NO) + very precise + time information - only one location Physics (e.g. cloud formation) + total atmospheric column Meteorology (e.g. winds) + vertical and/or Numerical simulations + time information 4-dimensional fields - Only one location - only sporadic (e.g. trace gases) measurements - typ. average for extended volumina - limited coverage =? horizontal information + very precise Emissions (e.g. fossil fuel burning) +global observation +integrative measurement +(limited) time information - Relatively large uncertainty...is it the truth? Measurements - typ. Average for extended volumina in-situ ground Remote sensing ground balloon / aircraft Satellite kkk

First image of the Earth from space Tiros 1 1960 First complete image Tiros IX, Februar 1965

What do we actually see? Apollo 17 1972

What do we actually see? The atmosphere? Apollo 11 1969

Remote sensing typically means observation of EM radiation Two radiation sources: UV vis NIR Thermal IR MW -Sometimes also stars are used. -first active instruments in vis and MW spectral range

Important: How transparent is the atmosphere?

Measurements of radiometers: Clouds are bright 29 Apr 2006 at 1330 UTC AVHRR, chan. 1 AVHRR, chan. 3 0.58-0.68µm 1.58-1.64µm Visible Near IR Dundee Satellite Receiving Station (http://www.sat.dundee.ac.uk) AVHRR, chan. 4 10.3-11.3µm Thermal IR

Measurements of radiometers: aerosols are coloured Dust Storm over the Red Sea MODIS color image using 0.47, 0.55 &0.66 for blue green and red

CALIPSO (LIDAR) observations 31.05.2008 http://www-calipso.larc.nasa.gov/ clouds aerosols 532nm total attenuatted backscatter

molecular absorption processes provide fingerprints in spectra UV/Vis and NIR: Electronic and vibrational transitions (Absorption) Thermal IR: Vibrational transitions (Emission & Absorption) Microwave: Rotational transitions (Emission & Absorption)

Clerbaux et al., 2003 IMG spectrum (in transmittance units) in the 600 2500 cm 1 spectral range recorded over South Pacific ( 75.24, 28.82) on 4 April 1997, 04:00:42GMT (top). Radiative transfer simulations for absorption contributions due to strong (middle) and weak (bottom) absorbers are also provided.

Set of Atmospheric Absorbers Identified in GOME Spectra at the Satellite Group at the Institut für Umweltphysik DOAS satellite algorithms for satellite spectra GOME & SCIA spectral properties O 3 UV OClO HCHO O 4 H 2 O O 2 Spectral albedo 0.6 0.4 0.2 0.0 Satellite group: http://satellite.iup.uni-heidelberg.de 300 400 500 600 700 800 Wavelength [nm] SO 2 NO 2 BrO O 3 vis SCIAMACHY: 240 2300nm => CO, CH 4, CO 2

Example of a GOME BrO analysis 60 % Meßspektrum 7 % Quotientenspektrum Using the Differential Optical Absorption Spectroscopy (DOAS) enables the detection of very weak absorption structures 7 % 0.3 % 1.2 % Ring BrO O3 <0.01 % OClO Several trace gas absorption cross sections are simultaneously fitted to the measured spectrum 2.2 % 0.1 % O4 NO2 0.2 % Restsruktur 345 350 355 360 Wellenlänge [nm]

UV / vis / NIR: ideal case: only absorption has to be considered

UV / vis / NIR: typical case: also scattering on molecules and aerosols is important Radiative transfer simulations are needed

IR / MW: ideal case 1: surface is much warmer than atmosphere: only absorption has to be considered

IR / MW: ideal case 2: surface is much colder than atmosphere: only emission has to be considered

IR / MW: typical case: T surface and T atmosphere are similar: emission and absorption have to be considered Clouds and aerosols further complicate the measurement Radiative transfer simulations are needed Typically the thermal contrast between the surface and the air directly above is small

satellite Example of radiative transfer modelling for satellite nadir geometry, 370 nm, no clouds Look from the side Rayleigh-scattering ground reflection TRACY-II Tim Deutschmann, IUP Heidelberg Look from above

satellite Example of radiative transfer modelling for satellite nadir geometry, 370 nm, with cloud (OD 40) from the surface to 8 km Look from the side Rayleigh-scattering ground reflection particle scattering TRACY-II Tim Deutschmann, IUP Heidelberg Look from above

Albedo: 5% e.g. Ocean Height dependence of the sensitivity OD: 4 OD: 50

Short summary: Which parts of the EM spectrum can be used? - atmospheric windows have to be considered - light sources: sun, earth (stars, moon, laser) - emission and absorption features of molecules can be analysed in observed spectra - only in few cases the retrieval is straight forward - typically radiative transfer modelling has to be used - to observe the boundary layer the UV / vis / NIR spectral range has to be used

Orbits and viewing geometries Typical low polar orbits are used (~800km) - the whole earth can be observed - often sun-synchronous - typical time for global coverage: 1-6 days Geostationary orbits (~36000km) -only part of the globe is covered -typically coarser spatial resolution -diurnal cycle is captured

Different Viewing Geometries => Tropospheric composition only from nadir looking instruments => In particular the boundary layer can be observed ( this is the layer where we live and most of the sources are!)

Spectral Irradiance [W/m2/nm] 2.5 2.0 1.5 1.0 0.5 0.0 GOME solar spectrum MERIS spectral channels 300 400 500 600 700 800 Wavelength [nm] Spectral sampling Compromise between spectral and spatial resolution Satellite footprints GOME-1 SCIAMACHY OMI MODIS image, Cyprus, 30.01.2001 http://earthobservatory.nasa.gov/

Flight direction Neighbouring orbits GOME H 2 O observations 1 single observation H2O slant column density [1e23 molec/cm²]

GOME H 2 O measurements 1 single observation H2O slant column density [1e23 molec/cm²]

GOME H 2 O measurements 1 satellite orbit H2O slant column density [1e23 molec/cm²]

GOME H 2 O measurements 1 day H2O slant column density [1e23 molec/cm²]

GOME H 2 O measurements 3 days H2O slant column density [1e23 molec/cm²]

GOME H 2 O measurements 1 month (August 1998) H2O slant column density [1e23 molec/cm²]

GOME H 2 O measurements 1 month (December 1998) H2O slant column density [1e23 molec/cm²]

Short summary: Orbits and viewing geometries - typically low polar orbits (~800km) - different viewing geometries - tropospheric information only from nadir view - coarse spatial resolution for spectral measurements - low sampling frequency

Information content Typically no or limited information along the line of sight (good profile information from limb / occultation) Often integrated quantities are retrieved (e.g. the atmospheric vertical / slant column density). Higher collision frequency of at lower altitudes dz In IR and MW profile information from pressure broadening

Information content Measurement sensitivity varies along line of sight (in general lower towards the surface, especially in the presence of clouds) For the comparison with other data sets (e.g. model results) the measurement sensitivity has to be considered (e.g. using averaging kernels) Measurement ( x, y, z) TOA = z= 0 AK measurement ( x, y, z, z' ) Model( x, y, z') dz' e.g. concentration or total or partial column density Can be extended to 3 dimension e.g. concentration or partial column density

Averaging Kernel Averaging Kernel for tropospheric column density (vertically integrated trace gas concentration) Model profile = VCD meas?

Averaging Kernel for tropospheric column density Averaging Kernel (vertically integrated trace gas concentration) Clear sky Cloudy sky Model profile

General problem: Only disagreement is significant! Both model profiles yield the same VCD AK

Averaging kernel for nadir looking IR observations (several layers can be resolved) CO from MOPITT http://eosweb.larc.nasa.gov

Averaging kernel for limb observations http://www.ifac.cnr.it/retrieval/documents/ak_report.pdf HNO 3 from MIPAS Thermal IR Narrow kernels are good

Comparison to ozone sonde O 3 from SCIAMACHY (UV) Segers et al., ACP 2005

Short summary: Information content - general problem: satellite sensitivity often shows strong spatial dependencies (e.g. height dependence) - but from the measurement only limited (or even no) information on the vertical distribution can be obtained

Short summary: Information content => the direct quantitative interpretation of the derived satellite data products is difficult (especially for nadir observations) => In addition to the retrieved measured values (e.g. total column densities) also information on the measurement sensitivity has to be provided (averaging kernels) => When compared to other data sets, only disagreement is significant

http://joseba.mpch-mainz.mpg.de/bild_no2.htm Mean tropospheric NO 2 column density (10 15 molec/cm 2 ) from measurements of the SCIAMACHY instrument onboard the ESA satellite ENVISAT, for the years 2003-2006. Steffen Beirle, MPI Mainz, Germany.

http://joseba.mpch-mainz.mpg.de/bild_no2.htm Enjoy the clean air! Mean tropospheric NO 2 column density (10 15 molec/cm 2 ) from measurements of the SCIAMACHY instrument onboard the ESA satellite ENVISAT, for the years 2003-2006. Steffen Beirle, MPI Mainz, Germany.