Satellite Remote Sensing I UV/vis observations of the troposphere

1. Satellite Remote Sensing I UV/vis observations of the troposphere Lecture at the PANDA summer school 2015 Bremen, August 25, 2015 Andreas Richter Institute of Environmental Physics University of Bremen Bremen, Germany ( richter@iup.physik.uni-bremen.de )

2. Overview • What is Remote Sensing? • How can trace gases be detected using UV/vis remote sensing from satellite ? • What are typical light paths for UV/vis satellite observations? • What are the sensitivities of the measurements? • What needs to be considered when comparing satellite data to other observations / to models? • Some examples of UV/vis instruments & applications

3. Who am I? www.doas-bremen.de • Leading DOAS group at Institute of Environmental Physics, University of Bremen, head: Prof. John Burrows. • Working on all aspects of UV/vis remote sensing • Instruments (ground, airborne, satellite) • Radiative transfer • Retrieval algorithms • Data interpretation • Some atmospheric topics I‘m interested in • NOx emissions, distributionsand chemistry • Emission changes / trends • Halogen chemistry inpolar regions

4. Basic Principles of Remote Sensing • “Remote sensing is the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation“ (Lillesand and Kiefer 1987) • “The art of dividing up the world into little multi-coloured squares and then playing computer games with them to release unbelievable potential that's always just out of reach.” (Jon Huntington, Commonwealth Scientific and Industrial Research Organisation Exploration, Geoscience, Australia)

5. Validation Changed Radiation Radiation Sensor Object Measurement A priori information Data Analysis Final Result Forward Model Schematic of Remote Sensing Observation

6. The Electromagnetic Spectrum • nearly all energy on Earth is supplied by the sun through radiation • wavelengths from many meters (radio waves) to nm (X-ray) • short wavelength = high energy • radiation interacts with atmosphere and surface • absorption (heating, shielding) • excitation (energy input, chemical reactions) • re-emission (energy balance)

7. Wavelength Ranges in Remote Sensing UV: some absorptions + profile information aerosols vis: surface information (vegetation) some absorptions aerosol information IR: temperature information cloud information water / ice distinction many absorptions / emissions + profile information MW: no problems with clouds ice / water contrast surfaces some emissions + profile information

8. Wavelength Ranges in Remote Sensing UV: some absorptions + profile information aerosols vis: surface information (vegetation) some absorptions aerosol information IR: temperature information cloud information water / ice distinction many absorptions / emissions + profile information MW: no problems with clouds ice / water contrast surfaces some emissions + profile information

9. How are trace gases detected in the UV/vis? Method used is Absorption Spectroscopy Radiation coming from the sun is attenuated as it passes through the atmosphere The wavelength dependence of the absorption is molecule specific The magnitude of the absorption depends on the amount of absorber along the light path This is described by Lambert Beer‘s law: ρ = density of absorber λ = wavelength σ = absorption cross-section of absorber

10. Scattering from a cloud Emission from a cloud Transmission through a cloud Cloud Scattering within a cloud Scattering / reflection on a cloud Transmission through a cloud Emission from the ground Scattering / Reflection on the ground Absorption on the ground Radiative Transfer in the Atmospherepassive sensor, UV / visible / IR Atmosphere Absorption Scattering Emission Aerosol / Molecules

11. Scattering from a cloud Transmission through a cloud Cloud Scattering within a cloud Scattering / reflection on a cloud Transmission through a cloud Scattering / Reflection on the ground Absorption on the ground Radiative Transfer in the Atmospherepassive sensor, UV / visible Atmosphere Absorption Scattering Aerosol / Molecules

12. Radiativetransfer in the atmosphere The change of radiationalong the light path: Solution iscomplex, in particular in sphericalgeometry Complicationsarepolarisation, inealasicscattering, angular dependentsurfacereflectivity, complexphasefunctions of aerosolsand cloudparticles Even ifonlyonescatteringeventisconsidered, manypossible light pathsneed tobeincluded

13. Retrieval Approach Simulation Forward Model A priori OK? Measurement Parameters • In general, a retrieval always consists of • Solutions are for example using the Optimal Estimation method • Often, several pieces of information can be extracted such as concentrations in two layers • A widely used method which is fast and simple is to separate the retrieval of • the amount of absorber found in the measurement and • The length of the light path • This is often done using the Differential Optical Absorption Spectroscopy (DOAS)

14. DOAS equation I The intensity measured at the instrument is the extraterrestrial intensity weakened by absorption, Rayleigh scattering and Mie scattering along the light path: integral over light path scattering efficiency unattenuated intensity absorption by all trace gases j extinction by Mie scattering extinction by Rayleigh scattering exponential from Lambert Beer’s law

15. DOAS equation II If the absorption cross-sections do not vary along the light path, we can simplify the equation by introducing the slant column SC. Slant Column=absorberconcentrationintegratedalong light path

16. DOAS equation III As Rayleigh and Mie scattering efficiency vary smoothly with wavelength, they can be approximated by low order polynomials. Also, the absorption cross-sections can be separated into a high (“differential”) and a low frequency part, the later of which can also be included in the polynomial: differential cross-section slant column polynomial

17. DOAS equation IV Finally, the logarithm is taken and the scattering efficiency included in the polynomial. The result is a linear equation between the optical depth, a polynomial and the slant columns of the absorbers. by solving it at many wavelengths (least squares approximation), the slant columns of several absorbers can be determined simultaneously. DOAS retrieves absorber amount along light path – but what is the light path?

18. Satellite Viewing Geometries • Direct Sun / Moon • Lot‘s of light • Simple geometry • Vertical resolution • No tropospheric observations • Low spatial resolution • Few measurements • Limb scattered light • Vertical resolution • Many measurements • Complex light path • No tropospheric observations • Low spatial resolution • Nadir scattered light • High spatial resolution • Sensitivity to the troposphere • Little vertical resolution • Complex light path

19. Typical light paths: UV altitude sensitivity • Dark surface • Strong Rayleigh scattering • Most photons are scattered above absorption layer => Low sensitivity to BL signals!

20. Typical light paths: visible altitude sensitivity • Brighter surface • Significant Rayleigh Scattering • Many photons are scattered above absorption layer => Reduced sensitivity to BL signals!

21. Bright surface (snow, ice): UV and visible altitude sensitivity • Surface reflection dominates • Multiple scattering in surface layer => Enhanced sensitivity to BL signals!

22. Typical light paths: NIR altitude sensitivity • Bright surface (except for oceans) • Negligible Scattering => Very good sensitivity to BL signals!

23. Typical light paths: visible with cloud altitude sensitivity

24. Typical light paths: visible with cloud altitude sensitivity • Even if cloud covers only part of the scene, most photons are reflected there • Sensitivity below cloud is reduced • Sensitivity above cloud is enhanced => Cloud fraction and cloud height are important!

25. Tropospheric sensitivity of UV/vis observations Wavelength • Sensitivity decreases towards UV Vertical profile of absorber • Sensitivity decreases to surface unless it is very bright Clouds • Sensitivity below clouds is close to 0 • Thin clouds (or aerosols) can increase / decrease sensitivity Surface albedo • Sensitivity increases with surface reflectance Solar zenith angle • Lower sun increases sensitivity geometrically, at least in mid and upper troposphere • Lower sun decreases sensitivity to the surface layers

26. Satellite Orbits (Near) Polar Orbit: • orbits cross close to the pole • global measurements are possible • low earth orbit LEO (several 100 km) • ascending and descending branch • special case: sun-synchronous orbit: • overpass over given latitude always at the same local time, providing similar illumination • for sun-synchronous orbits: day and night branches Geostationary Orbit: • satellite has fixed position relative to the Earth • parallel measurements in a limited area from low to middle latitudes • 36 000 km flight altitude, equatorial orbit http://www2.jpl.nasa.gov/basics/bsf5-1.htm http://www.ccrs.nrcan.gc.ca/ccrs/learn/tutorials/fundam/chapter2/chapter2_2_e.html

27. Considerations for use of UV/vis satellite data Spatial resolution Satellite data is coarse in comparison to in-situ observations but finer than global models => averaging is needed Spatial sampling Due to orbital pattern, scan and cloud gaps, only part of the globe is covered by observations every day=> other data must be sampled accordingly Time of overpass Sun sun-sychronous orbits have overpasses at a constant local time => other data needs to be interpolated to that time Vertical resolution Satellite data have coarse or no vertical resolution=> other data must be integrated to become comparable Impact of a priori The vertical sensitivity of the retrievals (averaging kernel) needs to be considered Summary: We should always compare apples to apples, not oranges!

28. Effect of Spatial Resolution: Example NO2 OMI: 13:30 LT • For species with short atmospheric life time, horizontal variability is large • Spatial resolution of sensor is relevant for interpretation • Spatial resolution also influences cloud fraction • Time of overpass may also play a role!

29. Examples

30. TOMS Instrument: UV discrete (6) wavelengths grating spectrometer Operational: October 1978 - 2004 Spatial resolution: 50 x 50 km2 Global coverage: 1.5 days Species: O3, SO2

31. TOMS: Ozone columns October 1996 October 1997 October 1998 Ziemke, J. R et al., (2001), “Cloud slicing”: A new technique to derive upper tropospheric ozone from satellite measurements, J. Geophys. Res., 106(D9), 9853–9867 Large scale tropospheric ozone patterns retrieved using the cloud slicing method During El Nino year, clear ozone maximum over Indonesia Origins: photochemical smog from biomass burning and change in circulation pattern

32. GOME / GOME-2 GOME-2 A on MetOpA since 1.2007 80 x 40 km2 1.5 days • 40 x 40 km2 • 3 days GOME-2 B on MetOpB since 1.2013 80 x 40 km2 1.5 days Instrument: 4 channelUV/vis grating spectrometer Operational on ERS-2 7.1995 – 6.2003 - 2011 Spatial resolution 320 x 40 km2 Global coverage: 3 days Species: O3, NO2, HCHO, CHOCHO, BrO, IO, SO2, H2O

33. GOME annual changes in tropospheric NO2 1996 - 2002 Satellite NO2 Trends • 7 years of GOME satellite data • DOAS retrieval + CTM-stratospheric correction • seasonal and local AMF based on 1997 MOART-2 run • cloud screening • NO2 reductions in Europe and parts of the US • strong increase over China • consistent with significant NOx emission changes A. Richter et al., Increase in troposphericnitrogendioxideover China observedfromspace, Nature, 4372005

34. NO2 Trends: Comparison with bottom up estimates EDGAR v4.2 GOME and SCIAMACHY Hilboll, A., Richter, A., and Burrows, J. P.: Long-term changes of tropospheric NO2 over megacities derived from multiple satellite instruments, Atmos. Chem. Phys. 13, 4145-4169, doi:10.5194/acp-13-4145-2013, 2013 Overall pattern in emission data base is correct Increase in China is underestimated Increase in India and Middle East is overestimated Decrease in Europe / US is underestimated

35. Satellite NO2 Trends above Central Eastern China • Until 2011, there was continuous increase in NO2 • After two years of stagnation, 2014 saw a large decrease • economic slow down? • Improved technology? • Switch in fuels used? • Other factors?

36. SCIAMACHY • Vis channels 3-4 • NIR channels 5-6 • SWIR channels 7-8 • scanner modules • telescope • pre-disperser • UV channels 1-2 www.sciamachy.de Instrument: 8 channel UV/vis/NIR grating spectrometernadir, limb + occultation measurements Operational on ENVISAT 8.2003 – 4.2012 Spatial resolution (30) 60 x 30 km2 Global coverage: 6 days Species: O3, NO2, HCHO, CHOCHO, BrO, IO, SO2, H2O, CH4, CO2, CO

37. SCIAMACHY: CO2 in the Northern Hemisphere SCIAMACHY and GOSAT: CO2 Buchwitz et al., ACP, 2007; Schneising et al., ACP, 2008 Buchwitz et al., personal communication, 2014 • Detection of annual cycle • Detection of year-to-year increase • Detection of spatial variability • Not yet accurate enough for Kyoto monitoring on country level

38. OMI www.knmi.nl/omi/ Instrument: UV/vis imaging grating spectrometer (push-broom) Operational on Aura since October 2004 Spatial resolution: up to 13 x 24 km2 Global coverage: 1 day Species: O3, NO2, HCHO, CHOCHO, BrO, SO2

39. OMI: SO2 columns 9.2004 – 6.2005 Carn, S. A., et al., t (2007), Sulfur dioxide emissions from Peruvian copper smelters detected by the Ozone Monitoring Instrument, Geophys. Res. Lett., 34, L09801, doi:10.1029/2006GL029020. SO2 signals from volcanoes in Ecuador and Columbia Clear signature of Peruvian copper smelters Very large sources of local pollution Effect of (temporary) shut down and (permanent) implementation of emission reductions (H2SO4 production) can be monitored

40. Summary and Conclusions Satellite observations of tropospheric composition in the UV/vis and NIR provide consistent global datasets for many species including major air pollutants such as O3, CO, NO2, and HCHO The measurements are averaged horizontally and vertically which makes them difficult to compare to point measurements Remote sensing in an indirect method that necessitates use of a priori information in the data retrieval which has an impact on the results Visible and NIR measurements provide good sensitivity to the boundary layer In spite of the relative large uncertainties involved in satellite remote sensing , they provide a unique source of information on the composition of the troposphere

41. What is the future of UV/vis satellite measurements of tropospheric trace gases? • Satellite measurements will be improved by • Better spatial resolution (Sentinel 5P, Sentinel 5, CARBONSAT) • Better temporal resolution (geostationary observations Sentinel 4) • Better coverage of species and vertical resolution (extension of the wavelengths covered (from UV to IR) • High vertical resolution (active systems) • The usefulness of satellite data will be improved by better integration with other measurements • Satellite data will be strongly integrated in atmospheric models

42. Active measurements: CALIOP aerosol http://www-calipso.larc.nasa.gov/

43. Thank you for your attention and questions please! www.doas-bremen.de http://www.animationlibrary.com/animation/25494/Alarm_jumps/