Radiation Parameterization Programs for Use in General Circulation Models.

Liou, Kuo‐Nan; Ou, S. C.; Kinne, Stefan; Koenig, George G.

doi:10.21236/ada148015

Cited by 22 publications

(20 citation statements)

References 14 publications

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“…Even when we used the optimum soil heat capacity for each individual night, the average MAE was 1.50 K. Finally, there is error due to the model's parameterization of emissivities. Liou et al (1984) found the model error near the ground to be on the order of 0.2°C per day, which would mean less than 0.1°C over the present 10-h simulations.…”

Section: Clear-cut Simulationscontrasting

confidence: 50%

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Biomass, thermal inertia, and radiative freeze occurrence in leafless forests

Potter¹,

Zasada²

1999

Can. J. For. Res.

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Using field measurements of air temperature, wind, and relative humidity from a clear-cut site and two wooded sites in northern Wisconsin, we used a radiative transfer model to simulate temperatures on seven calm, clear nights similar to those on which freezes typically occur. Each night was simulated twice for the wooded sites. One simulation ignored the presence of vegetation, the other approximated the vegetation's heat storage capacity and its influence on air temperatures. The simulations including biomass heat storage showed smaller mean absolute temperature errors and decreased magnitude of systematic model error when compared with the simulations ignoring vegetation. The results suggest that the thermal inertia of forest biomass may play a significant role in controlling forest temperatures on calm, clear nights and, hence, in controlling freeze occurrence.Résumé : À l'aide de mesures de la température de l'air, du vent et de l'humidité relative prises sur le terrain dans un site coupé à blanc et deux sites boisés dans le nord du Wisconsin, nous avons utilisé un modèle de transfert radiatif pour simuler la température lors de sept nuits claires et calmes semblables à celles où se produisent typiquement les gels. Chaque nuit a été simulée deux fois pour les sites boisés. Une simulation faisait abstraction de la présence de végétation et l'autre tenait compte de la capacité d'emmagasinage de la chaleur par la végétation et de son influence sur la température de l'air. Les simulations qui comportaient le stockage de chaleur par la biomasse montraient de plus faibles erreurs moyennes absolues de température et diminuaient l'importance de l'erreur systématique du modèle comparativement aux simulations qui ignoraient la végétation. Les résultats suggèrent que l'inertie thermale de la biomasse forestière pourrait jouer un rôle important dans le contrôle de la température de la forêt par nuits claires et calmes et, par conséquent, dans le contrôle des épisodes de gel.[Traduit par la Rédaction] Potter and Zasada 221

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Section: Clear-cut Simulationscontrasting

confidence: 50%

“…We used the IRTM of Liou et al (1984). It calculates absorption by the H 2 O rotation band at 15 mm, H 2 O continuum at 10 mm, H 2 O vibration-rotation at 6.3 mm, and CO 2 at 15 mm, and it adjusts for overlap between the H 2 O rotation and CO 2 bands.…”

Section: Modelmentioning

confidence: 99%

Biomass, thermal inertia, and radiative freeze occurrence in leafless forests

Potter¹,

Zasada²

1999

Can. J. For. Res.

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“…The best track data of TC (including tracks, central SLP, and maximum sustained wind) is obtained from the JTWC (https://www.metoc.navy.mil/jtwc/jtwc.html?best-tracks). The detailed configurations are shown in Table 1, including domains, simulation time, and physical parameterizations (Chou & Suarez, 1994; Hong et al., 2004; Hong & Lim, 2006; Hong et al., 2006; Iacono et al., 2008; Janjić, 1994; Kain, 2004; Tiedtke, 1989; C. Zhang et al., 2011). The second and third domains of TC move automatically with the WRF resolved vortex center.…”

Section: Models and Methodsmentioning

confidence: 99%

“…The WRF model version 3.9 is adopted to simulate the three synoptic cases. (Chou & Suarez, 1994;Hong et al, 2004;Hong & Lim, 2006;Iacono et al, 2008;Janjić, 1994;Kain, 2004;Tiedtke, 1989;C. Zhang et al, 2011).…”

Section: Model Configurationmentioning

confidence: 99%

The Generalized Application of a New Surface Pressure Tendency Equation in Synoptic Weather Systems

Zhang

2023

JGR Atmospheres

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Surface pressure is one of the basic meteorological variables in atmospheric science. Differing from other surface physical quantities such as temperature and humidity, surface pressure is a comprehensive product of the deep structure of the upper air column, and its change reflects the adjustment and evolution of the vertical arrangement of the atmosphere (Mass & Madaus, 2014). For some synoptic-scale systems, such as tropical cyclones (TCs), explosive cyclones (ECs), and anticyclones (ACs), the evolution of central surface pressure marks the development of systems, while for meso-scale systems such as supercells and squall lines, the surface pressure tendency provides coherent mesoscale structural information, with cold pools closely relating to the growth of convection for instance (Wheatley & Stensrud, 2010). Uncovering the mechanisms leading to surface pressure change can offer in-depth insights into a diverse set of weather systems. According to the definition of air pressure, the surface pressure depends on the mass of the air column above the unit area assuming hydrostatic balance, so mass divergence is the direct cause of the change of surface pressure implied by thermodynamic processes such as density advection and temperature advection (Lynch, 2003). The surface pressure tendency equation (SPTE), as

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“…It contains a planetary boundary layer (PBL) parameterization (Mahrt et al, 1984), which includes two soil layers and makes use of geographic databases of surface roughness, soil type, albedo, and other surface fields. A gravity wave drag parameterization (Vernekar et al, 1991) is included, as is a radiative transfer package developed by Liou et al (1984), Ou et al (1988), andSchattel (1992). The dry adiabatic adjustment and the large scale precipitation parameterization remain essentially unchanged from the GWC GSM, but moist convection is parameterized with the European Centre for Medium-Range Weather Forecasts (ECMWF) mass flux scheme (Tiedtke, 1989).…”

Section: The Apgsmmentioning

confidence: 99%