Remote sensing in the UV-vis

Transcription

1 Remote sensing in the UV-vis Remote sensing by satellites The inversion problem The forward model DOAS technique Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 1

2 Passive remote sensing Sun > Earth > Satellite > Scientist???? Lamp > Object > Detector > Analysis > measure radiation > infer information on quantities that affect the radiation Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 2

3 Ultraviolet / visual / near-infrared Reflected sunlight Absorption from atmospheric entry to exit trace gases (O3, NO2, SO2, H2O, CH4, CO, CO2, N2, ) SCIAMACHY/ENVISAT Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 3

4 The inversion problem in the retrieval Inversion Forward Model Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 4

5 Retrieval y = F(x) y: vector, measured, x: vector, to be derived F: forward model Auxiliary information: Measurement error: S y Best guess for x: x 0 Default method Non-linear least squares - iteratively find minimum of cost function: CF = (y F(x))T Sy 1 (y F(x)) (Levenberg-Marquardt) Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 5

6 Well-posed problems total column retrieval Differential Optical Absorption Spectroscopy: fitting absorption structures Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 6

7 Ill-posed problems Profile retrieval > more information requested as available Least squares gives problems > noise amplification Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 7

8 Example Nadir ozone profile retrieval Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 8

9 Ozone profile from nadir nm ozone Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 9

10 Noise amplification Simple two layer model: λ 1 λ 2 Pick numbers: Solution: : 1.00 x x 2 = I ± I 1 : 0.99 x x 2 = I ± I 2 x 1,2 = 10; I 1,2 = 20; E( I) = 1 x 1 + x 2 = 20 ± 1 x 1 -x 2 = 0 ± 141 Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 10

11 Extra term in cost function Solution: regularisation Optimal Estimation (y F(x)) T S y 1 (y F(x)) + (x x a ) T S a 1 (x x a ) x a : a-priori, S a : a-priori error covariance Damps unrealistic solutions Based on Bayes theorem: P(x y) = P(x)P(y x)/p(y) P probability density function See e.g. Rodgers Inverse Methods for atmospheric sounding Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 11

12 Optimal Estimation Linear forward model ( linearize y = F(x) ) y = Kx Analytic solution for CF minimum: xˆ = x a + S a K T ( KS a K T + Moderately non-linear case: apply iteratively S y ) 1 ( y Kx a ) Information from a-priori Information from measurement Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 12

13 GOME Balloon Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 13

14 Forward Model Atmospheric Radiation Transfer (UV-VIS nadir) Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 14

15 The stage θ Plane parallel atmosphere Radiance I(z,θ,φ) Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 15

16 Radiation transfer: processes Absorption O 3 O 3 Scattering N 2 (or O 2, or cloud, or aerosol) N 2 Extinction = Absorption + Scattering Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 16

17 Radiation Transfer Equation Optical depth: dτ = -ext dz = -(abs + scat) dz TOA: τ = 0, Surface: τ = τ* di µ = ei + sj dz di µ = I ω J, d τ J P θ s = = = µ = (Source) (scatterin scattering cos θ = g, ω = d Ω function) angle, ' s e P ( Ω ', Ω = P(cos Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 17 ) I ( Ω θ ), s ),

18 Passive remote sensing in the solar spectral range The source of light is the sun: Solar spectrum: µm, consisting of the: Ultraviolet: UV < 400 nm Visible: 400 nm < VIS < 700 nm Near-Infrared: 700 nm < NIR < 3 µm. Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 18

19 Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 19

20 Earth reflectance spectrum (cloudfree Sahara scene measured by SCIAMACHY) Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 20

21 Approach for remote sensing of atmospheric composition Choose a quantitative signature = unique identification of the quantity of interest: To detect absorbers: use spectral features Trace gases have spectral absorption lines To detect scattering particles: use brightness + colour + angular features Clouds: brightness, whiteness, fractal shape, rainbow Aerosols: colour, polarization Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 21

22 Detection of trace gases Trace gases are most easy to detect, because the absorption lines of a molecule are its unique signature. From the absorption lines the amount of trace gas can be determined. the deeper an absorption line in the atmospheric spectrum, the more gas there is. The precise quantitative determination of the total amount of gas depends on: - Vertical distribution of the gas (not known). - Interference with clouds, aerosols. Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 22

23 Detection of scatterers: clouds and aerosols Clouds and aerosols give usually a brighter scene, because they scatter more light than the clear atmosphere. But they are difficult to quantify precisely, because they usually do not have unique scattering features. Sometimes their angular scattering pattern is unique: - Spherical droplets have rainbows, which are depending on particle size. Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 23

24 Interaction of solar radiation with the atmosphere sun satellite atmosphere O 3 clouds NO 2 surface aerosol s Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 24

25 Radiation-matter interaction processes Rayleigh scattering by air Absorption by trace gases Scattering and absorption by aerosol particles Scattering and absorption by cloud particles Reflection by the surface. Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 25

26 Analysis of satellite measurements Requirement: radiative transfer model of the atmosphere = a formula (or a computer code) for describing the transport of sunlight passing through the atmosphere, absorbed by trace gases, scattered by air molecules, clouds and aerosols, reflected by the surface, and finally arriving at the satellite. Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 26

27 Calculated reflectance spectrum in the UV-VIS Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 27

28 Ozone Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 28

29 Ozone absorption spectrum measured in the laboratory Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 29

30 Reflectance spectrum of the Netherlands (cloudfree) measured by GOME Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 30

31 Absorption line in spectrum of reflected light Spectrum of atmospheric radiation Spectrum of absorption cross-section per molecule λ λ Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 31

32 Differential Optical Absorption Spectroscopy = DOAS Fit the absorption cross-section spectrum σ(λ) to the logarithm of atmospheric reflectance spectrum R(λ), to find the vertical column density N of the trace gas. Assumption is: R(λ) = R 0 (λ) exp (-τ s (λ)) where: R (λ) : reflectance with the trace gas R 0 (λ) : reflectance without the trace gas τ s (λ) : slant optical thickness of trace gas Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 32

33 DOAS formula: R(λ) = R 0 (λ) exp (-τ s (λ)) ln R(λ) = ln R 0 (λ) τ s (λ) -ln R(λ) + ln R 0 (λ) = N s σ(λ) where: ln I 0 (λ): low-order polynomial in λ N s : slant column density of trace gas N = N s / M: vertical column density of trace gas M = air mass factor Geometric path approximation: M 1/cos θ + 1/cos θ 0 = 1/µ 0 + 1/µ Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 33

34 DOAS spectral fit of ozone R(λ) -ln R(λ)+ln R 0 (λ) N s σ(λ) difference (residue) Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 34

35 Air Mass Factor for ozone Approximation: N = N s / M = 1/µ 0 + 1/µ Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 35

36 Ozone measurements by SCIAMACHY Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 36

37 NO 2 Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 37

38 How to measure NO 2 from the reflectance spectrum? GOME, 25 July 1995,The Netherlands Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 38

39 DOAS spectral fit of NO 2 DOAS FIT -> Slant column of NO 2 Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 39

40 Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 40

41 Retrieval using model informatie and satellite measurements stratosphere troposphere N trop vertical = N total slant N strat slant M airmass trop Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 41

42 Tropospheric NO 2 1. DOAS slant column (GwinDOAS, developed at BIRA-IASB) 2. Assimilation strat. slant column (TM4-DAM, developed at KNMI) 3. Modelling tropospheric amf (DAK, developed at KNMI) Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 42

43 Summary UV-VIS spectrometry is the preferred method to detect trace gases like ozone and NO 2. A radiative transfer model (including scattering) is needed to interpret these spectra. There are suitable spectrometers in space: GOME, SCIAMACHY, OMI. These instruments show important geophysical phenomena: ozone hole, tropospheric pollution. Day 3 L4 - Retrieval of UV-Vis - Hennie Kelder 43