Institute of Environmental Physics and Remote Sensing IUP/IFE-UB Physics/Electrical Engineering Department 1 ACCENT AT-2 3 nd Annual Meeting, June 6, 2005, Oberpfaffenhofen, Germany Institute of Environmental Physics * University of Bremen **University of Heidelberg The Impact of Clouds on UV/visible measurements of trace gases from space Andreas Richter* and Thomas Wagner**

2 Outline This is a very basic talk, summarizing the simplistic thoughts of a retriever of GOME and SCIAMACHY tropospheric trace gas data who views clouds as a necessary evil in life and retrieval. Introduction Radiative transfer in a cloudy atmosphere Effects of clouds on UV/vis measurements Methods to measure cloud properties in the UV/vis Methods to deal with clouds in UV/vis measurements of trace gases Outlook

3 What does a Satellite see from Space? clouds surface reflection scattering on aerosols is visible as haze scattering on molecules is only apparent in the limb parts of the picture and through the bluish shade of the image atmospheric absorption can not be seen directly In the UV/visible spectral region, the satellite view of the world is dominated by

4 What Happens in the Atmosphere without Clouds? Absorption Scattering Absorption on the ground Scattering / Reflection on the ground Aerosol / Molecules Atmosphere

5 What Changes if Clouds are Present? Absorption Scattering Absorption on the ground Scattering / Reflection on the ground Scattering from a cloud Transmission through a cloud Scattering / reflection oh a cloud Scattering within a cloud Aerosol / Molecules Cloud Atmosphere

6 Rayleigh scattering ~ 4 TOA spectral albedo measured by GOME Observations: over the Ocean

7 TOA spectral albedo measured by GOME O 3, H 2 O & O 2 absorption Observations: over the Ocean

8 TOA spectral albedo measured by GOME Inelastic Raman scattering (Ring effect) Observations: over the Ocean

9 Colour of the surface TOA spectral albedo measured by GOME Observations: over Vegetation

10 Colour & brightness of the surface TOA spectral albedo measured by GOME Observations: over the Desert

11 Brightness & Whiteness TOA spectral albedo measured by GOME Observations: over Clouds

12 Albedo Effect in the UV/vis, surface reflectivity is low with the exception of ice or snow in the presence of clouds, the instrument receives a much larger signal cloud reflectivity depends on cloud droplet size and optical depth, but only weakly even a small cloud can dominate the signal over dark surfaces wavelength dependence changes completely (white)

13 Atmospheric Scattering in the UV/visible Rayleigh: elastic on molecules proportional to -4 polarized Mie: elastic on aerosols / droplets proportional to unpolarized Raman: proportional to Rayleigh inelastic unpolarized

14 Polarisation Effect over dark surfaces, signal has large Rayleigh scattering contribution Rayleigh scattering is polarising depending on scattering angle (strongest effect at twilight) many instruments (e.g. GOME) have different sensitivity for different polarisations cloud contributions are mainly from Mie scattering and therefore not polarised >> r < r ~ r red: s-polarisation, blue: p-polarisation

15 Shadow Effects broken clouds result in shadows on the ground shadows are also relevant between clouds shadow position is displaced relative to cloud effect is larger at low sun and large scanning angle displacement is small relative to typical pixel sizes of UV/vis instruments this might change as ground pixel sizes decrease

16 Light Path Effects within clouds, light paths can get very large scattering between clouds can also become important (Ping Pong effect) observed from the ground, any photon coming from a cloud has experienced a very long path within the cloud observed from space, most photons coming from a cloud have penetrated the cloud only weakly still some sensitivity enhancement to processes in the upper part of the cloud exists

17 How do Clouds Impact the Instrument? Clouds also can have “technical” effects on satellite measurements: broken cloud fields may illuminate the instrument slit inhomogeneously and distort the instrument function or wavelength alignment clouds change the spectral distribution of the incoming light, generally by enhancing vis/NIR radiation relative to UV radiation. This can result in larger straylight contributions in the UV broken cloud fields change intensity rapidly and as read-out of linear detectors is often sequential, changes in intensity can distort the spectral shape of the measurements

18 How do Clouds Impact the Measurements? More important is the direct impact of clouds on the retrieval: Changes in the spectral composition of the signal Changes in the sensitivity to different altitudes in the atmosphere Complete loss of sensitivity in some instances How important the impact is generally depends on vertical position in the atmosphere (the lower the worse) the wavelength region (UV is more affected than visible) the surface albedo (dark surfaces are more difficult)

19 Shielding Effect the part of an absorber profile situated below a cloud is basically “hidden” from view for the satellite only through thin clouds over reflecting surfaces, sensitivity towards the lower part of the profile is still relevant the shielding effect is larger than expected from the geometrical size of the cloud because of its brightness albedo = 0.25 albedo = 0.75 Rayleigh scattering 50% cloud cover but only 25% surface contribution!

20 Albedo Effect the part of an absorber above a cloud is better visible from space as the ratio of photons that go through it increases through the albedo effect the lower the cloud, the larger the effect in the UV this is more important than in the visible as Rayleigh scattering is proportional to -4 albedo = 0.25 Rayleigh scattering some photons are scattered before reaching the absorber most photons are absorbed on the ground

21 Albedo Effect the part of an absorber above a cloud is better visible from space as the ratio of photons that go though it increases through the albedo effect the lower the cloud, the larger the effect in the UV this is more important than in the visible as Rayleigh scattering is proportional to -4 albedo = 0.25 albedo = 0.75 Rayleigh scattering many photons are scattered below the absorber

22 Light Path Enhancement most photons are scattered in the top layer of the cloud within this layer, they experience very long light paths this results in enhanced sensitivity to absorbers in the upper part of a cloud sensitivity decreases towards the lower parts of the cloud thin clouds or aerosol layers can enhance sensitivity in the altitude where they are located effect is much smaller than for ground-based observations

23 Sampling Effect clouds are often associated to transport events photochemical smog conditions are linked to clear sky situations high and low pressure systems, low fog and inversions are all linked to clouds clouds are unevenly distributed with season and location satellite measurements are biased towards certain situations, and no amount of averaging will help

24 Raman (Ring) Effect Raman Scattering is inelastic i.e. the wavelength of the outgoing photon differs from that of the incoming Raman scattering intensity is proportional to Rayleigh scattering source of the Ring effect this implies that Raman scattered photons “loose differential absorption memory” (molecular filling in) in the presence of clouds, this effect is greatly reduced Raman scattering is non polarising isotropic proportional to -4 responsible for about 4% of all Rayleigh scattered light O2O2 N2N2

25 Raman (Ring) Effect Raman Scattering is inelastic i.e. the wavelength of the outgoing photon differs from that of the incoming Raman scattering intensity is proportional to Rayleigh scattering source of the Ring effect this implies that Raman scattered photons “loose differential absorption memory” (molecular filling in) in the presence of clouds, this effect is greatly reduced albedo = 0.25 Rayleigh scattering Raman scattering – loss of absorption signal

26 How can cloud properties be measured in the UV/vis? The main properties one would like to know for retrieval: cloud fraction cloud top height cloud top reflectance cloud bottom Quantities that can be used to retrieve cloud properties: everything that was discussed in previous section i.e.: brightness, colour, Raman intensity, absorption Requirement: cloud properties should be measured at time and sampling of atmospheric measurement => use same instrument

27 Example: Hurricane Fran O 4, O 2, Ring and polarisation all change over clouds such as Hurricane Fran ( ) => this can be used to retrieve cloud properties! Fran NOAA GOES-8 Satellite, 16:02 UTC

28 Intensity Methods Basic idea: clouds are bright, surface is dark after correction for Rayleigh scattering and surface reflectance, intensity can be assumed to be proportional to cloud cover fast and simple method GOME + SCIAMACHY provide higher spatial resolution broad band detectors (PMD) Example Algorithms: PCRA, RCFA, HIRCU, SPCA... Problems: snow / ice interference no cloud top height or other additional information for low cloud fractions, accurate surface reflectance is critical => how to determine? assumption has to be made on cloud albedo e.g. all clouds have same reflectance

29 Colour Methods Basic idea: clouds are white, surface is coloured after correction for surface reflectance and Rayleigh scattering, distance from “white point” can be assumed to be proportional to cloud cover fast and simple method GOME + SCIAMACHY provide higher spatial resolution broad band detectors (PMD) Example Algorithms: OCRA Problems: snow / ice interference no cloud top height or other additional information for low cloud fractions, accurate surface reflectance is critical => how to determine?

30 Absorption Methods Basic idea: for some species with differential absorption in the UV/vis (O 2, O 4 ), the vertical distribution in the atmosphere is known and relatively stable the expected signal for different cloud fractions and cloud top heights can be modelled comparison with measurements provides estimate for cloud fraction and cloud top height Example Algorithms: ICFA, FRESCO, SACCURA, GOMECAT, OMI Problems: surface reflectance is also needed, same problems as other methods: –snow / ice interference –for low cloud fractions, accurate surface reflectance is critical => how to determine? assumption has to be made on cloud optical depth slower

31 Combination Methods Basic idea: use intensity or colour for cloud fraction use absorption of O2 or O4 for cloud top height and possibly cloud albedo use IR channel if available for distinction between clouds and ice/snow Example Algorithms: SACCURA, GOMECAT, ROCCIN

32 Raman Scattering Method Basic idea: most Raman scattering takes place in the lower troposphere in the presence of clouds, the relative Raman contribution is decreased Raman scattering can be determined from Ring effect and compared to model Example Algorithms: OMI Problems: surface reflectance is also needed, same problems as other methods: –snow / ice interference –for low cloud fractions, accurate surface reflectance is critical => how to determine? assumption has to be made on cloud albedo e.g. all clouds have same reflectance Ring signature is sometimes difficult to distinguish from instrumental effects

33 How can Clouds be Accounted for ? a large part of UV/vis measurements from space are cloud contaminated the impact of clouds on the retrieved numbers is large the effect must be compensated for meaningful statistical averages and comparison with models use of external information (met data,...) is complex and not always possible approaches based on measurements themselves are preferred Real world clouds in one pixel are 3-dimenional broken at different altitudes of different optical depth Model clouds are usually 1 or 2-dimensional covering the full pixel have one cloud top altitude and one (often fixed) optical depth

34 Cloud Screening Idea: use only measurements without cloud contamination don’t apply any corrections Problem: there are only very few cloud free measurements! Pragmatic approach: relax criterion to 5%, 10% or 20% cloud cover => absorber below cloud top height will be underestimated => absorber above cloud top height will be overestimated Refinement: Correct empirically or by using –cloud top height –the vertical profile of the absorber Or: go to geostationary and reduce pixel size

35 Model based Correction Approach: separate pixel into cloud free and cloudy part using the measured cloud fraction compute clear sky airmass factor based on model profile compute fully cloud covered airmass factor based on model profile use intensity weighted average for the column retrieval Problem: the tropospheric airmass factor for the cloudy case depends not only on the assumed profile shape (that’s also true for the clear sky situation), but also strongly on the absolute amount of the absorber assumed below cloud top height. This is the so called “ghost column”. this is a serious problem for interpretation of the results!

36 Summary clouds have dramatic impact on space borne UV/vis measurements, in particular for tropospheric species effects include instrumental issues, sensitivity changes and sampling information on cloud properties can be retrieved from UV/vis measurements in a number of different ways correction schemes have been developed but they all introduce significant uncertainty in cloud contaminated measurements the basic fact will remain that most clouds are not transparent for UV/vis photons the aim should be to get as many cloud free measurements as possible (small pixels, several measurements per day) => geostationary

37 Do we understand the impact of clouds? Clouds are bright Clouds shield troposphere  VCD of partly clouded backscan is expected to be lower than mean of forward scans © S. Beirle, IUP Heidelberg this is not observed: Correlation of mean NO 2 TVCD (10 15 molec/cm 2 ) of forescans and the respective backscans (SCIAMACHY)

38 Is the Effective Cloud Cover a good Concept? different cloud situations may have the same TOA reflectance they all get assigned to the same cloud fraction the same correction will be applied in the retrieval But: the airmass factor for a tropospheric absorber may be quite different! © P. Wang, KNMI de Bilt

39 Can cloud phase be measured? © O. Jourdan, IUP Bremen Brightness T from AATSR Phase index SCIAMACHY ice water supercooled water mixed phase if cloud cover = 1 phase index can be used to distinguish between liquid and ice phase clouds results are consistent with AATSR brightness temperatures supercooled and mixed phase clouds also part of the scene, only with synergy with other sensors (e.g. AATSR)

40 Current Developments - Outlook aerosols and thin clouds have similar effects on UV/vis measurements and concepts used in the aerosol community should be considered for use in UV/vis trace gas measurements as well the effect of absorption within clouds has so far been neglected for satellite measurements but will have to be assessed further cloud properties (particle phase, cloud optical thickness, cloud base height, cloud particle size,...) can in principle be retrieved and might help for trace gas retrieval cloud / ice discrimination is feasible if IR measurements are included instrument synergies (e.g. SCIAMACHY and MERIS) could provide much improved cloud products for trace gas retrievals