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

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110 Chapter 5 well. With the obtained results it is hard to identify one spectral window that has the best capability to retrieve all three cloud parameters. Cloud top pressure can be retrieved best using the NIR window but this spectral range has a lower retrieval sensitivity for f c and τ c . The latter two parameters can be retrieved from the NUV window with a high retrieval sensitivity. The VIS window shows weakest performance, especially for the high cloud scenario for the retrieved p c , and for optically thick clouds for the retrieved τ c . A common approach in satellite remote sensing is to use only spectral structures relative to broadband spectral features of the measurement. For example, the differential optical absorption spectroscopy (DOAS) method uses this technique, which is employed in many trace gas retrieval applications in the ultraviolet, visible and near-infrared wavelength range (e.g. [Platt, 1994]). One important advantage of this technique is its low sensitivity to broadband calibration errors of the measurement. For the retrieval of cloud properties we therefore consider two related approaches in this study, where we first fit a scaling to the reflectivity measurement with a power-law function g(λ) = g 0 (λ/λ 0 ) α (5.13) Here, g 0 represents the scaling ratio at a reference wavelength λ 0 of the spectral interval and the exponent α governs the wavelength dependence of the scaling factor. Thus the state vector that is to be retrieved is x = [p c ,f c ,τ c ,A s ,g 0 ,α] T . In the retrieval, the two parameters g 0 and α are given a strong weight (by setting Γ i = 10 8 in Eq. (5.6)) to force that the scaling is fitted in any case. Here, the function g(λ) describes nearly all broadband spectral features of the reflectivity continuum in the three spectral windows. In a second approach we fit a wavelength independent scaling factor (corresponding to setting α = 0 in Eq. (5.13)) instead of using function g(λ) and thus we use only the spectral features relative to a constant background for the purpose of cloud parameter retrieval. The comparison of the retrieval sensitivity of both approaches with those where all spectral information of the reflectivity spectrum is used allows us to investigate the importance of the spectral continuum for the retrieval of cloud properties versus the importance of the differential Ring and absorption structures alone. Fig. 5.6 shows C pc , C fc , and C τc as function of cloud fraction for both scaling factor scenarios (g =g(λ) and g =g 0 ), where we consider a cloud scenario of a high optically thick cloud. In general the NUV and VIS windows provide little information on cloud parameters when the continuum is fitted with the wavelength dependent scaling factor. Even in the NUV window with the prominent Ring features at the Ca II Fraunhofer lines the sensitivities are low for all cloud fractions: C pc ≤ 0.45, C fc ≤ 0.25 and C τc ≈ 0. As is shown in Fig. 5.6 (g = g(λ)) a small but significant amount of information on cloud top pressure and cloud fraction can be retrieved from the NUV Ring structures alone, but most of the cloud information of the NUV window comes from the spectral continuum. When in addition to the relative Ring structures the spectral signature of the continuum is used (g=g 0 scenario), the NUV window provides sufficient information on cloud top pressure and cloud fraction (C pc ,C fc ≤ 0.9 for f c ≥ 0.2), whereas for the VIS window only the cloud fraction can be retrieved with a reasonable sensitivity (C fc ≤0.9 for f c >0.2). In contrast the NIR window shows a maximum sensitivity C pc ≈ 1 for both types of scaling factor. Here the information on cloud top pressure is
Retrieval of cloud properties from NUV, VIS and NIR 111 Figure 5.5: Retrieval sensitivities (C) to cloud top pressure (p c ), cloud fraction (f c ), cloud optical thickness (τ c ) and surface albedo (A s ) in the NUV, VIS and NIR spectral windows as a function of cloud fraction for a fixed noise floor η = 0.1%. Results are shown for the same cloud scenarios as in Fig. 5.3. A surface albedo of 0.05 was used in all windows.
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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
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50 Chapter 3 The forward and adjoin
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52 Chapter 3 Here, ∆ = diag [1, 1
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54 Chapter 3 Figure 3.3: Relative R
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56 Chapter 3 biases the simulation
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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-
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Page 105 and 106: Retrieval of cloud properties from
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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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