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

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100 Chapter 5 circumvent time-consuming radiative transfer calculations other schemes are used that simplify clouds as an elevated Lambertian reflector with a given Lambertian cloud albedo (e.g. Joiner et al. [2004], Koelemeijer et al. [2001]). In this paper we choose to simulate the cloud as an elevated reflecting surface that is described by a bidirectional reflection function (BDRF) as a function of cloud optical thickness. We adopted the parameterization of Kokhanovsky et al. [2003] for this purpose. This BDRF represents the reflection by an idealized semi-infinite non-absorbing water cloud and was derived from the asymptotic theory of radiative transfer (see Kokhanovsky et al. [2003] for details). We ignore three-dimensional radiative transfer effects and thus the independent pixel approximation can be used (see e.g. Marshak et al. [1999]). The reflectivity of a cloudy scene is then simulated as r(p c ,f c ,τ c ,A s ) = f c r cld (p c ,τ c ) + [1−f c ]r clr (A s ). (5.1) Here, r cld and r clr represent the reflectivity for the clouded part and the clear-sky part of the observed scene, respectively. A fraction f c of the observed satellite scene is covered by a homogeneous cloud layer with a certain optical thickness τ c and a certain cloud top pressure p c . The remaining part of the scene is considered cloud-free with a Lambertian reflecting surface (surface albedo A s ) at the ground. Other parameters than p c , f c , τ c and A s , e.g. surface pressure, are assumed to be known a priori. To simulate the reflectivity in Eq. (5.1) we used the plane-parallel vector radiative transfer model that is described in Chapter 3. This model includes polarization and one order of rotational Raman scattering. For this study, we convoluted the simulated reflectivity spectrum with a Gaussian slitfunction with full width at half maximum of 0.2 nm in the NUV window, of 0.3 nm in the VIS window, and of 0.4 nm in the NIR window. Subsequently, the smoothed spectra were sampled at spectral sampling intervals of 0.2 nm. Furthermore we assumed a polarization insensitive instrument in the simulation. These choices represent a compromise between the different spectral characteristics of the GOME, GOME-2, SCIAMACHY and OMI instruments. The plane-parallel model atmosphere is based on the US standard atmosphere [NOAA, 1976] and is subdivided into 1-km thick layers for the lowest 10 km and 2-km layers for higher altitudes. The optical properties of air are calculated using data provided by Bates [1984], Peck and Fisher [1964] and Penney et al. [1974] (see Appendix 3.A for more details). The absorption cross sections of O 2 and H 2 O are taken from the HITRAN2004 database [Rothman et al., 2005]; those of O 3 are adopted from Burrows et al. [1999a]; the absorption cross sections of NO 2 are taken from Vandaele et al. [1998]; and those of O 2 -O 2 at 360 nm, 380 nm and 477 nm are taken from Greenblatt et al. [1990]. Absorption by other gases is assumed to be insignificant. The effect of inelastic Raman scattering on a reflectivity spectrum is commonly described by a filling-in or Ring spectrum, which is defined as R(λ) = I ram(λ) − I ray (λ) I ray (λ) . (5.2) Here, I ray (λ) represents a spectrum of reflected intensity that is simulated using the Rayleigh scattering approximation (all scattering is assumed to be elastic), and I ram (λ) denotes the intensity spectrum that takes inelastic Raman scattering into account. The deviation of the Ring spectrum from zero is
Retrieval of cloud properties from NUV, VIS and NIR 101 called the Ring effect. This effect is most prominent in the ultraviolet wavelength range where scattering by molecules is strong and where many Fraunhofer lines are present. The filling-in of the O 2 A band is of minor importance for GOME(-2) and nadir-viewing SCIAMACHY measurements [Sioris and Evans, 2000]. Therefore, we adopt the Rayleigh scattering approximation in the NIR window, which simplifies the radiative transfer model considerably. 5.3 Sensitivity of measurement to cloud properties The reflectivity spectrum can be dissected into three spectral components that are sensitive to cloud properties: (1) the spectral continuum, (2) the Ring spectrum, and (3) molecular absorption features. The relative importance of each spectral component is different for the NUV, VIS and NIR window. Fig. 5.1 shows a simulated reflectivity spectrum r for a clear-sky scene (A s =0.05) and a fully clouded scene (p c = 500 hPa, f c =1, τ c =40) normalized to its value at the shortest wavelength of each wavelength interval. For all three spectral windows the clear-sky reflectivity r clr decreases towards longer wavelengths. This property of the spectral continuum is a direct consequence of molecular scattering, which involves a scattering cross section that is proportional to λ −4 (e.g. [Bucholtz, 1995]). At shorter wavelengths more solar radiation is scattered into the satellite instrument’s line of sight than at longer wavelengths. For fully clouded scenes hardly any wavelength dependence of the reflectivity continuum spectrum is observed. Here the wavelength independent reflection by the bright cloud surface overwhelms the atmospheric scattering contribution. For the NUV window, and to a lesser degree for the VIS window, this results even in a small increase of the reflectivity towards longer wavelengths. The atmosphere is optically more dense at shorter wavelengths due to stronger molecular scattering. As a result less light that is reflected by the cloud emerges from the atmosphere at these wavelengths into the direction of the satellite instrument. In general, the wavelength dependence of the continuum is most pronounced in the NUV and VIS windows and therefore the reflectivity spectra at these wavelengths provide a clear sensitivity to cloud properties. The importance of the spectral continuum for the retrieval of cloud parameters is investigated in Sections 5.5 and 5.6. Clouds affect the Ring spectrum as well. Due to the Ring effect radiation is effectively scattered from the wings of the Fraunhofer lines to the line-center, which results in an enhanced reflectivity at the center of a Fraunhofer line and a reduced reflectivity at the wings. The strength of the Ring effect depends on the depth and shape of the Fraunhofer line and on the relative amount of measured photons which are (both elastically and inelastically) scattered by air molecules. In the cloudy case less light is scattered by air molecules than in a cloud-free atmosphere, which results in less pronounced Ring spectra in cloudy observations. The sensitivity of the Ring spectrum to clouds is highest for the spectral range that shows the strongest filling-in features, i.e. at the Ca II K and H line near 393.4 and 397.0 nm. Figure 5.2 shows that the filling-in of the Fraunhofer lines can reach 11% for a clear-sky scene in nadir viewing geometry and a solar zenith angle of 40 degrees, whereas for a low cloud at 800 hPa it is 8% and decreases to approximately 4% for a higher cloud at 500 hPa. In this study we investigate the relevance of inelastic Raman scattering for cloud parameter retrieval in the NUV spectral window only, because Ring spectra are much weaker in the VIS and NIR, due to the lack of
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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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40 Chapter 3 nm with a spectral res
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42 Chapter 3 contributions of lower
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44 Chapter 3 if we use the effectiv
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46 Chapter 3 This in turn results i
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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 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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