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

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80 Chapter 4 period. GOME measures the radiation backscattered by the Earth in the ultraviolet and visible part of the electromagnetic spectrum, i.e. 240–790 nm, with a spectral resolution of approximately 0.17– 0.35 nm and a spectral sampling distance of 0.11–0.22 nm [van Geffen and van Oss, 2003]. It scanned the Earth across the satellite flight track with a spatial sampling of approximately 320 × 40 km 2 at the ground. Each day, a solar spectrum is measured as well. Many atmospheric trace gases have absorption bands in the GOME spectral range. These bands are associated with electronic transitions in combination with the vibrational splitting of the energy levels of the molecules of these trace gases. The detailed spectral fine-structure in the absorption features can be used to retrieve atmospheric trace gas abundances. For example, the vertically integrated amount of ozone can be determined from the spectral features in the Huggins absorption bands [Spurr et al., 2005] (325–335 nm). The vertical distribution of ozone can be retrieved in the range 290– 320 nm, where an accurate simulation of the fine spectral structures of the measurements is needed to obtain all information on the vertical ozone distribution present in the measurement [Hasekamp and Landgraf , 2001]. Information is also present on the total amount of nitrogen dioxide [Beirle et al., 2003, Boersma et al., 2005] (405–465 nm), sulfur dioxide [Eisinger and Burrows, 1998] (315– 330 nm), and bromine oxide [Chance, 1998] (340–360 nm). Furthermore, the depth of the Ca II Fraunhofer lines at 393 nm and 397 nm in the earthshine spectrum provide information on cloud-top pressure and oceanic chlorophyll content [Joiner et al., 2004] attributable to the filling in of these lines by inelastic Raman scattering. This effect is also known as the Ring effect (e.g. Vountas et al. [1998], de Beek et al. [2001], Chance and Spurr [1997], Sioris and Evans [1999], Joiner et al. [2004], Stam et al. [2002] and Chapters 2 and 3). To achieve the retrieval requirements, the GOME instrument was designed in such a way that the fine structures in earthshine spectra can be measured with high accuracy. Because the wavelength calibration of GOME is better than 0.05 nm [Fletcher and Lodge, 1996, van Geffen and van Oss, 2003], the measurements of the fine structures in the spectrum are expected to be mainly limited by the measurement noise. Therefore a proper interpretation of the GOME measurements is possible using models only with high accuracy on the spectrally fine structures. A common approach for the interpretation of GOME measurements is to determine a reflectivity spectrum, which is the ratio of a GOME Earth radiance and a solar irradiance spectrum. The advantage of the reflectivity spectrum is that it reduces many calibration errors, because many of them affect the solar and earthshine measurements in the same way. However, the reflectivity approach suffers from two main difficulties. Firstly, a shift exists between the solar and the earthshine spectrum, which makes it necessary to resample the solar measurement on the spectral sampling points of the earthshine measurement before the reflectivity spectrum can be determined. Spectral interpolation schemes are commonly used for this purpose, which introduce interpolation errors in the reflectivity spectrum. Second, the simulations of the Ring effect in reflectivity spectra require a solar spectrum on a spectral grid that is finer than that of the measurement. Large uncertainties exist with respect to this solar spectrum (e.g. Gueymard [2004], Gurlit et al. [2005], Weber [1999]), which result in biases in the modeled spectra. To achieve better modeling of reflectivity spectra, Chance and Spurr [1997] presented an improved version of the high-resolution solar spectrum of Kurucz [1995]. This spectrum was used to
Accurate modeling of spectral fine-structure in Earth radiance spectra measured with GOME 81 improve the Ring effect correction spectra that are fitted along in many trace gas retrievals, with additional fit parameters such as an amplitude and a spectral shift. Moreover, Chance et al. [2005] used the same solar spectrum to estimate an interpolation error for typical GOME reflectivity spectra. This interpolation error correction is fitted to the measured spectra in a similar way as the Ring effect correction is fitted. In this paper we present what we believe to be a new approach that does not suffer from interpolation errors and does not rely on a priori knowledge of the solar spectrum. The fitting of correction spectra is no longer required in our approach. To achieve this, we first retrieve a solar irradiance spectrum on a fine spectral grid from the GOME solar irradiance measurements. The retrieved solar spectrum allows an accurate treatment of the Ring effect in the case of low frequency calibration errors in the spectra. Furthermore, the solar spectrum retrieved on a fine spectral grid can be sampled on any desired (coarser) wavelength grid. As a result, the simulation suffers only from the undersampling error of the GOME solar spectrum, and any additional errors due to the interpolation can be avoided. The undersampling error can be minimized further when the solar spectrum is retrieved from a GOME solar irradiance spectrum and additionally a GOME Earth radiance spectrum. We demonstrate our approach for the range 390–400 nm, where two prominent Fraunhofer lines, i.e., the Ca II K and H lines, are present. The measurement simulation for this wavelength range is not complicated by atmospheric absorption features. The paper is organized as follows: In Section 4.2, we discuss the simulations of GOME earthshine and solar measurements. In Section 4.3, the GOME reflectivity spectrum and the related interpolation error are considered. In Section 4.4, we introduce a retrieval approach for a solar spectrum from GOME solar irradiance measurements. We demonstrate that such a spectrum improves the simulation of GOME Earth radiance spectra. Deviations can be attributed mainly to the undersampling error of GOME. In Section 4.5, a combination of a solar irradiance spectrum and an Earth radiance spectrum is used to further improve the retrieval of a solar spectrum. In turn, this approach reduces the undersampling error in the Earth radiance measurement simulations. We present our conclusion in Section 4.6. 4.2 GOME measurements The GOME instrument measures Earth radiance spectra in a sun-synchronized orbit in a nadir viewing mode. Once per day (every fourteenth orbit) GOME measures a solar irradiance spectrum. The solar measurement is observed with the same spectral resolution but is slightly shifted in wavelength with respect to the earthshine measurements, as was mentioned in Section 4.1. This spectral shift can be partially explained by a Doppler shift [Slijkhuis et al., 1999]. Because of the orientation of the satellite’s velocity with respect to the nadir viewing geometry of GOME there is almost no Doppler shift for the Earth radiance measurements. On the contrary, the direct solar measurement is carried out when the ERS-2 satellite crosses the terminator in the North Polar region coming from the night side [Weber, 1999]. Because the ERS-2 satellite is in a sun-synchronized orbit, GOME always moves with the same speed toward the sun, i.e. 6.9 km s −1 , during the solar measurement. This
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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 35 and 36: 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: 4 Accurate modeling of spectral fin
Page 89 and 90: Accurate modeling of spectral fine-
Page 103 and 104: 5 Retrieval of cloud properties fro
Page 105 and 106: Retrieval of cloud properties from
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
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Samenvatting Het door de aardatmosf
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Samenvatting 133 met een vector-mod
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