Estimation of daytime downward longwave radiation under clear and cloudy skies conditions over a sub-humid region

Carmona, Facundo; Rivas, Raúl Eduardo; Caselles, Vicente

doi:10.1007/s00704-013-0891-3

Cited by 71 publications

(68 citation statements)

References 32 publications

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“…2. Parameterizations [Brutsaert, 1975;Carmona et al, 2014;Prata, 1996] estimate LWDN using screen-level (approximately 2 m above the surface) air temperature and/or moisture measurements. Parameterizations are usually site specific, and the coefficients used in parameterizations may vary significantly by site [Brunt, 1932;Iziomon et al, 2003;Monteith and Szeicz, 1961;Swinbank, 1963].…”

Section: Introductionmentioning

confidence: 99%

An efficient hybrid method for estimating clear‐sky surface downward longwave radiation from MODIS data

Cheng

Liang

Wang³

et al. 2017

JGR Atmospheres

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This paper proposes an efficient hybrid method for estimating 1 km instantaneous clear‐sky surface downward longwave radiation (LWDN) from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared observations and the MODIS near‐infrared column water vapor (CWV) data product. The LWDN was formulated as a nonlinear function of surface upwelling longwave radiation estimated from the MODIS top‐of‐atmosphere (TOA) radiance of channels 29, 31, and 32, as well as CWV and the MODIS TOA radiance of channel 29. Ground measurements collected at 62 globally distributed sites from six networks were used to develop and validate the proposed hybrid method. The validation results showed that the bias and root‐mean‐square error (RMSE) were 0.0597 W/m2 and 21.008 W/m2. These results demonstrate that the performance of our method is superior to that of other studies reported in the literature. The drawback of our method is that LWDN is overestimated over high‐elevation areas with extremely low CWV (<0.5 g/cm2) and underestimated over regions with tropical climates that have extremely high CWV. A power function relating LWDN to CWV was derived and used as a complementary method to address these circumstances. The overestimation was overcome, and the bias and RMSE decreased from 9.407 W/m2 and 23.919 W/m2 to −0.924 W/m2 and 19.895 W/m2. The underestimation was also alleviated.

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Section: Introductionmentioning

confidence: 99%

An efficient hybrid method for estimating clear‐sky surface downward longwave radiation from MODIS data

Cheng

Liang

Wang³

et al. 2017

JGR Atmospheres

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“…Although it is not shown, it is also true that the differences between Tb KLAPS and T sfc correlate well with e and the correlation further improves when e is divided by T sfc . Consequently, similar to the previous studies (Brutsaert, 1975;Idso, 1981;Marty and Philipona, 2000;Zhnag et al, 2007;Long and Turner, 2008;Carmona et al, 2014), an empirical formula which relates the downwelling infrared radiation to the surface weather data can be derived. Depending on the spectral range and variable of interest, such as the radiance measured in the narrow window region vs. the irradiance representing the integrated flux in the whole infrared region, a slightly different formula (such as those given by Idso, 1981, Liu et al, 2013) is required.…”

Section: Predicted Clear-sky Tb (Tb P Clr )mentioning

confidence: 54%

A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

et al. 2015

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Abstract. For better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two-step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear-sky Tb obtained by an empirical formula as a function of surface air temperature and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of clear-sky conditions. It is designated as cloudfree data only when both the spectral and temporal tests confirm cloud-free data. Overall, most of the thick and uniform clouds are successfully detected by the spectral test, while the broken and fast-varying clouds are detected by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for six months, from January to June 2013. The overall proportion of correctness is about 88.3 % and the probability of detection is 90.8 %, which are comparable with or better than those of previous similar approaches. Two thirds of discrepancies occur when the new algorithm detects clouds while the ceilometer does not, resulting in different values of the probability of detection with different cloud-base altitude, 93.8, 90.3, and 82.8 % for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers.

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“…Within the published literature, a slew of sky temperature models and emissivity correlations that have been proposed to estimate the effective sky temperature and most of these models pertain to clear‐sky conditions. To compare the effectiveness of these correlations in estimating the sky temperature and thus the performance of a solar cooker, these correlations are broadly grouped into four functional families as follows: Type 1: Expressions based on P v (M1‐M10), Type 2: Expressions based on T dp (M11‐M15), Type 3: Expressions based on T a (and

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) (M16‐M19), Type 4: Expressions based on P v and T a (M20‐M26).…”

Section: Analysis Of Sky Temperaturementioning

confidence: 99%

An investigation into sky temperature estimation, its variation, and significance in heat transfer calculations of solar cookers

Karn

Chintala

Kumar

2019

Heat Trans. Asian Res.

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The determination of sky temperature assumes great importance in engineering applications such as radiative cooling of buildings. Many studies that involve a radiative exchange with the sky employ different reported models of sky temperature interchangeably.However, until now, hardly any systematic study has been done to quantify the errors/variations that might be encountered in calculating this radiative exchange employing these different correlations. In the current paper, first, a thorough analysis has been presented on the sky temperature correlations and a possible range of variation in sky temperature based on the estimation of sky emissivity is computed. Both diurnal-nocturnal variation in sky temperature and seasonal disparities in sky temperature have been reported. Next, the case of a box-type solar cooker has been taken up for Heat Transfer-Asian Res. 2019;48:1830-1856. wileyonlinelibrary.com/journal/htj 1830 | Nomenclature: ε, sky temperature emissivity; T sky , sky temperature; T a , ambient temperature; F 1 , first-figure-of-merit for solar cookers; ƞ o , optical efficiency; U L , overall coefficient of heat loss; T p , plate temperature; θ p , nondimensional plate temperature; U t , top heat transfer factor; U b , bottom heat transfer factor; U s , side heat transfer factor; Q″ t , rate of heat loss per unit area from absorber plate to inner glass cover; h cpg1 , convective heat transfer coefficient from plate to first glass cover; h cg1g2 , convective heat transfer coefficient between two glass covers; h w , convective heat transfer coefficient on the top of glass cover; ε g , emissivity of glass; T g1 , outward temperature of first glass cover; P v , vapor pressure of water; T dp , dew-point temperature; RH, relative humidity (in percentage); ∅, relative humidity (in fraction); H s , solar insolation; RMSE, root-mean-square error; MAPE, mean-absolute-percentage error; η ob , optical efficiency for the beam component of irradiance; η od , optical efficiency for the diffuse component of irradiance; Nu, Nusselt number; Ra, Raleigh number; h rpg1 , radiative heat transfer coefficient from plate to first glass cover; h rg1g2 , radiative heat transfer coefficient between two glass covers; h rg2s , radiative heat transfer coefficient from glass cover to surrounding; ε p , emissivity of plate; T g2 , outward temperature of second glass cover. investigation with respect to the possible influence of the sky temperature estimation in predicting its performance parameter, first-figure-of-merit on a daily, seasonal, and climatic basis. Our observations show an enormous difference in sky temperature depending upon the expressions of emissivity from which it is derived. The variability of sky temperature has a nominal influence on the prediction of first-figure-ofmerit, although a marked discrepancy is observed across the seasons at the same location.

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Estimation of daytime downward longwave radiation under clear and cloudy skies conditions over a sub-humid region

Cited by 71 publications

References 32 publications

An efficient hybrid method for estimating clear‐sky surface downward longwave radiation from MODIS data

An efficient hybrid method for estimating clear‐sky surface downward longwave radiation from MODIS data

A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

An investigation into sky temperature estimation, its variation, and significance in heat transfer calculations of solar cookers

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