Rotational Raman scattering in the Earth's atmosphere ... - SRON

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86 Chapter 4 Figure 4.3: (a) Simulated reflectivity spectrum R including Raman scattering with the use of two different solar spectra: by Kurucz [1995] (solid curve) and by Chance and Spurr [1997] (dotted curve). See Gueymard [2004] for more details on these solar spectra. (b) Difference between the two reflectivity spectra in percentages.
Accurate modeling of spectral fine-structure in Earth radiance spectra measured with GOME 87 points of the earthshine spectrum. Commonly this is achieved by means of an interpolation scheme, which introduces errors in the reflectivity spectrum. To demonstrate these interpolation errors we consider a linear interpolation scheme and study the introduced error on the reflectivity spectrum for the GOME sampling scenario and additionally two improved sampling scenarios. We start with a simulation of a GOME solar spectrum utilizing an instrument response function which is composed of a Gaussian slit function with a FWHM of 0.17 nm and a boxcar sample function with a detector pixel width ∆λ = 0.12 nm (see Eq. (4.7)). Here, the solar and earthshine sampling grids have been adopted from a typical GOME solar and earthshine measurement. We use the solar spectrum on a 1 cm −1 grid of Chance and Spurr [1997] for the simulation. First, a GOME solar spectrum is simulated on the solar sampling grid. Then this spectrum is interpolated on the Earth wavelength grid. This interpolated solar spectrum is used to determine a reflectivity spectrum. Second, we determine a reflectivity spectrum that includes a solar spectrum directly sampled on the earthshine grid. The difference between the two reflectivity spectra yields the interpolation error of GOME. Figure 4.2 shows that for the GOME sampling this error reaches values up to 3.4% (the root-meansquare is 0.9%) in the spectral range 390–400 nm. In a next step, we increase the sampling by factors of 2 and 3 without changing the spectral smoothing. Even in the case in which the solar spectrum is well sampled, an interpolation error of up to 1.6% (the rms is 0.5%) remains on the spectrum. Although the interpolation error can be slightly reduced by using other interpolation schemes, e.g. the maximum value is decreased to 1.5% (the rms is 0.4%) for a cubic spline interpolation, clearly an interpolation approach does not provide the optimal technique for resampling the solar spectrum. It does not make maximum use of the information present in the GOME measurements. An additional problem of the reflectivity concept is caused by rotational Raman scattering in the Earth’s atmosphere. In general the simulation of a reflectivity spectrum does not rely on a solar spectrum. However, because of the effect of inelastic Raman scattering in the Earth’s atmosphere the reflectivity spectrum is superimposed by a so-called Ring spectrum. This spectrum describes the effect of the filling in of solar Fraunhofer lines (and absorption features attributable to atmospheric trace gases) on the reflectivity spectrum. Because of that, the simulation of a GOME reflectivity spectrum depends on knowledge of the solar spectrum on a spectral grid that is finer than the spectral sampling of GOME. However, large uncertainties exist with respect to the solar spectrum [Gueymard, 2004, Gurlit et al., 2005, Weber, 1999], which introduces additional errors in the GOME measurement simulation. Figure 4.3 shows differences of up to 9% in the reflectivity spectrum using two different solar spectra as model input. Both the interpolation error and the errors in the simulation of the Ring effect, can be reduced significantly when a solar spectrum is retrieved on a fine spectral grid from GOME observations, which is subsequently used for the simulation of GOME Earth radiance measurements. This technique is described in Section 4.4.
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VRIJE UNIVERSITEIT Rotational Raman
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Contents 1 Introduction 1 1.1 Obser
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1 Introduction 1.1 Observing skylig
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Introduction 3 Extracting the wealt
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Introduction 5 and Spurr, 1997, Vou
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Introduction 7 energy: E rot (v,J)
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Introduction 9 Figure 1.4: Probabil
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Introduction 11 scattered radiance
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Introduction 13 approximation in wh
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Introduction 15 scattering have bee
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2 A doubling-adding method to inclu
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A doubling-adding method to include
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Page 41 and 42: A doubling-adding method to include
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Page 46 and 47: 40 Chapter 3 nm with a spectral res
Page 48 and 49: 42 Chapter 3 contributions of lower
Page 50 and 51: 44 Chapter 3 if we use the effectiv
Page 52 and 53: 46 Chapter 3 This in turn results i
Page 54 and 55: 48 Chapter 3 This may be shown in a
Page 56 and 57: 50 Chapter 3 The forward and adjoin
Page 58 and 59: 52 Chapter 3 Here, ∆ = diag [1, 1
Page 60 and 61: 54 Chapter 3 Figure 3.3: Relative R
Page 62 and 63: 56 Chapter 3 biases the simulation
Page 64 and 65: 58 Chapter 3 3.4.2 Approximation me
Page 66 and 67: 60 Chapter 3 where I ram,app and I
Page 68 and 69: 62 Chapter 3 3.5 Simulation of pola
Page 70 and 71: 64 Chapter 3 Figure 3.10: Relative
Page 72 and 73: 66 Chapter 3 dependence on the vert
Page 74 and 75: 68 Chapter 3 Figure 3.13: Same as F
Page 76 and 77: 70 Chapter 3 3.6 Summary A vector r
Page 78 and 79: 72 Chapter 3 where λ is the wavele
Page 80 and 81: 74 Chapter 3 where S l is the expan
Page 82 and 83: 76 Chapter 3 3.C Appendix: Evaluati
Page 85 and 86: 4 Accurate modeling of spectral fin
Page 87 and 88: Accurate modeling of spectral fine-
Page 91: Accurate modeling of spectral fine-
Page 103 and 104: 5 Retrieval of cloud properties fro
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Page 125 and 126: References Aben, I., F. Helderman,
Page 127 and 128: References 121 Ellery, A., D. Wynn-
Page 129 and 130: References 123 Lacis, A. A., J. Cho
Page 131 and 132: References 125 Schulz, F., K. Stamn
Page 133 and 134: Summary A spectrum of sunlight that
Page 135 and 136: Summary 129 Earth radiance spectrum
Page 137 and 138: Samenvatting Het door de aardatmosf
Page 139 and 140: Samenvatting 133 met een vector-mod
Page 141: List of publications Peer-reviewed
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