Comparison of surface radiative flux parameterizations

Niemelä, Sami; Räisänen, Petri; Savijärvi, Hannu

doi:10.1016/s0169-8095(01)00085-0

Cited by 100 publications

(104 citation statements)

References 8 publications

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“…RH error ranges spanned errors in observations (Déry and Stieglitz, 2002) and empirical methods (e.g., Bohn et al, 2013;Feld et al, 2013). Q si error ranges spanned errors in empirical methods (Bohn et al, 2013), radiative transfer models (Jing and Cess, 1998), satellite-derived products (Jepsen et al, 2012), and NWP models (Niemelä et al, 2001b). Q li error ranges spanned errors in empirical methods (Bohn et al, 2013;Flerchinger et al, 2009;Herrero and Polo, 2012) and NWP models (Niemelä et al, 2001a).…”

Section: Error Magnitudesmentioning

confidence: 99%

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

Raleigh

Lundquist

Clark

2015

Hydrol. Earth Syst. Sci.

156

128

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Abstract. Physically based models provide insights into key hydrologic processes but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snowaffected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology and which error characteristics are most important. Here we employ global sensitivity analysis to explore how (1) different error types (i.e., bias, random errors), (2) different error probability distributions, and (3) different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use the Sobol' global sensitivity analysis, which is typically used for model parameters but adapted here for testing model sensitivity to coexisting errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 840 000 Monte Carlo simulations across four sites and five different scenarios. Model outputs were (1) consistently more sensitive to forcing biases than random errors, (2) generally less sensitive to forcing error distributions, and (3) critically sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes found in areas with drifting snow, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a more dominant impact when precipitation uncertainty was due solely to gauge undercatch. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling.

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Section: Error Magnitudesmentioning

confidence: 99%

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

Raleigh

Lundquist

Clark

2015

Hydrol. Earth Syst. Sci.

156

128

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“…Feister and Grewe, 1995), O 3 column = 300 DU, NO 2 column = 0.3 DU, no clouds, AOT (550 nm) = 0.235 (scaled to different wavelengths using an Angstrom exponent of 1.0), single scattering albedo ω 0 = 0.99. Since our measurements of G include wavelengths of up to 3000 nm and the TUV model code stops at 1000 nm, we used a parameterization by Paltridge and Platt (1976) to estimate potential clear-sky G (Niemela et al, 2001). The predicted clear-sky j (NO 2 )↓ is plotted versus estimated clear-sky G for the two selected sites in Fig.…”

Section: Water Vapourmentioning

confidence: 99%

“…More complex and accurate formulas were derived in the literature (Niemela et al, 2001) but an assessment of these formulas is beyond the scope of this study. A near linear relationship between j (NO 2 ) and G under clearsky conditions below about 400 W m −2 was also reproduced qualitatively using TUV simulations at wavelengths below 1000 nm.…”

Section: Water Vapourmentioning

confidence: 99%

Relationship between the NO<sub>2</sub> photolysis frequency and the solar global irradiance

Trebs

Bohn²,

Ammann

et al. 2009

Atmos. Meas. Tech.

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Abstract. Representative values of the atmospheric NO 2 photolysis frequency j (NO 2 ) are required for the adequate calculation and interpretation of NO and NO 2 concentrations and exchange fluxes near the surface. Direct measurements of j (NO 2 ) at ground level are often not available in field studies. In most cases, modeling approaches involving complex radiative transfer calculations are used to estimate j (NO 2 ) and other photolysis frequencies for air chemistry studies. However, important input parameters for accurate modeling are often missing, most importantly with regard to the radiative effects of clouds. On the other hand, solar global irradiance ("global radiation", G) is nowadays measured as a standard parameter in most field experiments and in many meteorological observation networks around the world. Previous studies mainly reported linear relationships between j (NO 2 ) and G. We have measured j (NO 2 ) using spectro-or filter radiometers and G using pyranometers side-by-side at several field sites. Our results cover a solar zenith angle range of 0-90 • , and are based on nine field campaigns in temperate, subtropical and tropical environments during the period 1994-2008. We show that a second-order polynomial function (intercept = 0): and B 2 = (−4.84 ± 0.31) × 10 −9 W −2 m 4 s −1 can be used to estimate ground-level j (NO 2 ) directly from G, independent of solar zenith angle under all atmospheric conditions. The absolute j (NO 2 ) residual of the empirical function is ±6 × 10 −4 s −1 (2σ ). The relationship is valid for sites below 800 m a.s.l. and with low surface albedo (α < 0.2). It is not valid in high mountains, above snow or ice and sandy or dry soil surfaces.

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“…The albedo of the soil surface, A, is the ratio between reflected and incident solar radiation; it is between 0.15 and 0.25 for common dry soils [28], thus the average value of 0.20 is employed here. M is the atmospheric transmissivity, which for a desert climate is approximately given by formulas such as [29]:…”

Section: Mathematical Formulationmentioning

confidence: 99%

Experiments of thermographic landmine detection with reduced size and compressed time

Muscio

Corticelli

2004

Infrared Physics & Technology

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Comparison of surface radiative flux parameterizations

Cited by 100 publications

References 8 publications

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

Relationship between the NO<sub>2</sub> photolysis frequency and the solar global irradiance

Experiments of thermographic landmine detection with reduced size and compressed time

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Comparison of surface radiative flux parameterizations

Cited by 100 publications

References 8 publications

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

Relationship between the NO&lt;sub&gt;2&lt;/sub&gt; photolysis frequency and the solar global irradiance

Experiments of thermographic landmine detection with reduced size and compressed time

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Relationship between the NO<sub>2</sub> photolysis frequency and the solar global irradiance