Retrieval of Cloud Optical Depth: Synergies between Whole Sky Imagers and Radiative Transfer Modeling.

2021 ◽  
Author(s):  
Caterina Peris-Ferrús ◽  
José Luís Gómez-Amo ◽  
Francesco Scarlatti ◽  
Roberto Román ◽  
Claudia Emde ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Optical Depth ◽  
Cloud Optical Depth ◽  
Radiative Transfer Modeling

scholarly journals Coupling sky images with three-dimensional radiative transfer models: a new method to estimate cloud optical depth

2015 ◽  
Vol 8 (10) ◽  
pp. 11285-11321 ◽  
Author(s):  
F. A. Mejia ◽  
B. Kurtz ◽  
K. Murray ◽  
L. M. Hinkelman ◽  
M. Sengupta ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Optical Depth ◽  
Zenith Angle ◽  
Microwave Radiometer ◽  
Three Dimensional ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Cloud Detection ◽  
Cloud Optical Depth ◽  
Direct Normal Irradiance

Abstract. A method for retrieving cloud optical depth (τc) using a ground-based sky imager (USI) is presented. The Radiance Red-Blue Ratio (RRBR) method is motivated from the analysis of simulated images of various τc produced by a 3-D Radiative Transfer Model (3DRTM). From these images the basic parameters affecting the radiance and RBR of a pixel are identified as the solar zenith angle (θ0), τc, solar pixel angle/scattering angle (ϑs), and pixel zenith angle/view angle (ϑz). The effects of these parameters are described and the functions for radiance, Iλ(τc, θ0, ϑs, ϑz) and the red-blue ratio, RBR(τc, θ0, ϑs, ϑz) are retrieved from the 3DRTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τc, where RBR increases with τc up to about τc = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured Iλmeas(ϑs, ϑz), in addition to RBRmeas(ϑs, ϑz) to obtain a unique solution for τc. The RRBR method is applied to images taken by a USI at the Oklahoma Atmospheric Radiation Measurement program (ARM) site over the course of 220 days and validated against measurements from a microwave radiometer (MWR); output from the Min method for overcast skies, and τc retrieved by Beer's law from direct normal irradiance (DNI) measurements. A τc RMSE of 5.6 between the Min method and the USI are observed. The MWR and USI have an RMSE of 2.3 which is well within the uncertainty of the MWR. An RMSE of 0.95 between the USI and DNI retrieved τc is observed. The procedure developed here provides a foundation to test and develop other cloud detection algorithms.

scholarly journals Irradiance and atmospheric optical properties from photovoltaic power data: model improvements and first results

2021 ◽  
Author(s):  
James Barry ◽  
Dirk Böttcher ◽  
Johannes Grabenstein ◽  
Klaus Pfeilsticker ◽  
Anna Herman-Czezuch ◽  
...  
Keyword(s):  
Optical Properties ◽  
Radiative Transfer ◽  
Optical Depth ◽  
Reanalysis Data ◽  
Forward Model ◽  
Atmospheric Conditions ◽  
Data Set ◽  
Cloud Optical Depth ◽  
First Results ◽  
Weather Models

<p>Photovoltaic (PV) power data are a valuable but as yet under-utilised resource that could be used to characterise global irradiance with unprecedented spatio-temporal resolution. The resulting knowledge of atmospheric conditions can then be fed back into weather models and will ultimately serve to improve forecasts of PV power itself. This provides a data-driven alternative to statistical methods that use post-processing to overcome inconsistencies between ground-based irradiance measurements and the corresponding predictions of regional weather models (see for instance Frank et al., 2018). This work reports first results from an algorithm developed to infer global horizontal irradiance as well as atmospheric optical properties such as aerosol or cloud optical depth from PV power measurements.</p><p>Building on previous work (Buchmann, 2018), an improved forward model of PV power as a function of atmospheric conditions was developed. As part of the BMWi-funded project MetPVNet, PV power data from twenty systems in the Allgäu region were made available, and the corresponding irradiance, temperature and wind speed were measured during two measurement campaigns in autumn 2018 and summer 2019. System calibration was performed using all available clear sky days; the corresponding irradiance was simulated using libRadtran (Emde et al., 2016). Particular attention was paid to describing the dynamic variations in PV module temperature in order to correctly take into account the heat capacity of the solar panels.</p><p>PV power data from the calibrated systems were then used together with both the DISORT and MYSTIC radiative transfer codes (Emde et al., 2016) to infer aerosol optical depth, cloud optical depth and irradiance under all sky conditions.  The results were compared to predictions from the COSMO weather model, and the accuracy of the inverted quantities was compared using both a simple and more complex forward model. The potential of the method to extract irradiance data over a larger area as well as the increase in information from combining neighbouring PV systems will be explored in future work.</p><p><strong>References</strong><br>  <br>Buchmann, T., 2018: Potenzial von Photovoltaikanlagen zur Ableitung raum-zeitlich hoch aufgelöster Globalstrahlungsdaten. Heidelberg University, http://archiv.ub.uni-heidelberg.de/volltextserver/24687/.<br>Emde, C., and Coauthors, 2016: The libRadtran software package for radiative transfer calculations (version 2.0.1). <em>Geosci. Model Dev.</em>, 9, 1647–1672, doi:10.5194/gmd-9-1647-2016. https://www.geosci-model-dev.net/9/1647/2016/.<br>Frank, C. W., S. Wahl, J. D. Keller, B. Pospichal, A. Hense, and S. Crewell, 2018: Bias correction of a novel European reanalysis data set for solar energy applications.<em> Sol. Energy</em>, 164, 12–24, doi:10.1016/j.solener.2018.02.012. https://doi.org/10.1016/j.solener.2018.02.012.</p>

Retrieval of cloud optical depth through radiative transfer and remote sensing: from 1D to 3D approach.

2021 ◽  
Author(s):  
Caterina Peris-Ferrús ◽  
José-Luis Gómez-Amo ◽  
Pedro Catalán-Valdelomar ◽  
Francesco Scarlatti ◽  
Claudia Emde ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Optical Depth ◽  
Cloud Optical Depth

scholarly journals Spectral invariant behavior of zenith radiance around cloud edges simulated by radiative transfer

2010 ◽  
Vol 10 (6) ◽  
pp. 14557-14581
Author(s):  
J. C. Chiu ◽  
A. Marshak ◽  
Y. Knyazikhin ◽  
W. J. Wiscombe
Keyword(s):  
Radiative Transfer ◽  
Optical Depth ◽  
Cloud Optical Depth ◽  
Cloud Properties ◽  
Atmospheric Radiation Measurement ◽  
Visible Wavelengths ◽  
Thermodynamic Phase ◽  
The Relationship ◽  
Invariant Relationship ◽  
Spectral Invariant

Abstract. A previous paper discovered a surprising spectral-invariant relationship in shortwave spectrometer observations taken by the Atmospheric Radiation Measurement (ARM) program. Here, using radiative transfer simulations, we study the sensitivity of this relationship to the properties of aerosols and clouds, to the underlying surface type, and to the finite field-of-view (FOV) of the spectrometer. Overall, the relationship is mostly sensitive to cloud properties and has little sensitivity to the other factors. At visible wavelengths, the relationship primarily depends on cloud optical depth regardless of cloud thermodynamic phase and drop size. At water-absorbing wavelengths, the slope of the spectral-invariant relationship depends primarily on cloud optical depth; the intercept, by contrast, depends primarily on cloud absorption properties, suggesting a new retrieval method for cloud drop effective radius. These results suggest that the spectral-invariant relationship can be used to infer cloud properties even with insufficient or no knowledge about spectral surface albedo and aerosol properties.

scholarly journals Spectrally-invariant behavior of zenith radiance around cloud edges simulated by radiative transfer

2010 ◽  
Vol 10 (22) ◽  
pp. 11295-11303 ◽  
Author(s):  
J. C. Chiu ◽  
A. Marshak ◽  
Y. Knyazikhin ◽  
W. J. Wiscombe
Keyword(s):  
Radiative Transfer ◽  
Optical Depth ◽  
Retrieval Method ◽  
Cloud Optical Depth ◽  
Cloud Properties ◽  
Atmospheric Radiation Measurement ◽  
Visible Wavelengths ◽  
Thermodynamic Phase ◽  
The Relationship ◽  
Invariant Relationship

Abstract. In a previous paper, we discovered a surprising spectrally-invariant relationship in shortwave spectrometer observations taken by the Atmospheric Radiation Measurement (ARM) program. The relationship suggests that the shortwave spectrum near cloud edges can be determined by a linear combination of zenith radiance spectra of the cloudy and clear regions. Here, using radiative transfer simulations, we study the sensitivity of this relationship to the properties of aerosols and clouds, to the underlying surface type, and to the finite field-of-view (FOV) of the spectrometer. Overall, the relationship is mostly sensitive to cloud properties and has little sensitivity to other factors. At visible wavelengths, the relationship primarily depends on cloud optical depth regardless of cloud phase function, thermodynamic phase and drop size. At water-absorbing wavelengths, the slope of the relationship depends primarily on cloud optical depth; the intercept, by contrast, depends primarily on cloud absorbing and scattering properties, suggesting a new retrieval method for cloud drop effective radius. These results suggest that the spectrally-invariant relationship can be used to infer cloud properties near cloud edges even with insufficient or no knowledge about spectral surface albedo and aerosol properties.

scholarly journals Experimental and modeled UV erythemal irradiance under overcast conditions: the role of cloud optical depth

2012 ◽  
Vol 12 (23) ◽  
pp. 11723-11732 ◽  
Author(s):  
M. Antón ◽  
L. Alados-Arboledas ◽  
J. L. Guerrero-Rascado ◽  
M. J. Costa ◽  
J. C Chiu ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Optical Depth ◽  
Relative Difference ◽  
Visible Range ◽  
Exponential Dependence ◽  
Cloud Optical Depth ◽  
Clear Sky ◽  
Modification Factor ◽  
High Bias ◽  
Sky Conditions

Abstract. This paper evaluates the relationship between the cloud modification factor (CMF) in the ultraviolet erythemal range and the cloud optical depth (COD) retrieved from the Aerosol Robotic Network (AERONET) "cloud mode" algorithm under overcast cloudy conditions (confirmed with sky images) at Granada, Spain, mainly for non-precipitating, overcast and relatively homogenous water clouds. Empirical CMF showed a clear exponential dependence on experimental COD values, decreasing approximately from 0.7 for COD = 10 to 0.25 for COD = 50. In addition, these COD measurements were used as input in the LibRadtran radiative transfer code allowing the simulation of CMF values for the selected overcast cases. The modeled CMF exhibited a dependence on COD similar to the empirical CMF, but modeled values present a strong underestimation with respect to the empirical factors (mean bias of 22%). To explain this high bias, an exhaustive comparison between modeled and experimental UV erythemal irradiance (UVER) data was performed. The comparison revealed that the radiative transfer simulations were 8% higher than the observations for clear-sky conditions. The rest of the bias (~14%) may be attributed to the substantial underestimation of modeled UVER with respect to experimental UVER under overcast conditions, although the correlation between both dataset was high (R2 ~ 0.93). A sensitive test showed that the main reason responsible for that underestimation is the experimental AERONET COD used as input in the simulations, which has been retrieved from zenith radiances in the visible range. In this sense, effective COD in the erythemal interval were derived from an iteration procedure based on searching the best match between modeled and experimental UVER values for each selected overcast case. These effective COD values were smaller than AERONET COD data in about 80% of the overcast cases with a mean relative difference of 22%.

scholarly journals 2.5 Dimensional Radiative Transfer Modeling of Proto-Planetary Nebula Dust Shells with 2-DUST

2003 ◽  
Vol 209 ◽  
pp. 141-142
Author(s):  
Toshiya Ueta ◽  
Margaret Meixner
Keyword(s):  
Radiative Transfer ◽  
Structure Formation ◽  
Optical Depth ◽  
Inclination Angle ◽  
Planetary Nebula ◽  
Planetary Nebulae ◽  
Asymptotic Giant Branch ◽  
Dust Shells ◽  
Radiative Transfer Modeling

Our observing campaign of proto-planetary nebula (PPN) dust shells has shown that the structure formation in planetary nebulae (PNs) begins as early as the late asymptotic giant branch (AGB) phase due to intrinsically axisymmetric superwind. We describe our latest numerical efforts with a multi-dimensional dust radiative transfer code, 2-DUST, in order to explain the apparent dichotomy of the PPN morphology at the mid-IR and optical, which originates most likely from the optical depth of the shell combined with the effect of inclination angle.

scholarly journals Changes in Surface Broadband Shortwave Radiation Budget during the 2017 Eclipse

2020 ◽  
Author(s):  
Guoyong Wen ◽  
Alexander Marshak ◽  
Si-Chee Tsay ◽  
Jay Herman ◽  
Ukkyo Jeong ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Radiative Transfer ◽  
Optical Depth ◽  
Solar Eclipse ◽  
Surface Flux ◽  
Radiation Budget ◽  
Visible Radiation ◽  
Cloud Optical Depth ◽  
Global Average ◽  
The Impact

Abstract. While solar eclipses are known to greatly diminish the visible radiation reaching the surface of the Earth, less is known about the magnitude of the impact. We explore both the observed and modelled level of change in surface radiation during the eclipse of 2017. We deployed a pyranometer and Pandora spectrometer instrument to Casper, Wyoming and Columbia, Missouri to measure surface broadband shortwave (SW) flux and atmospheric properties during the 21 August 2017 solar eclipse event. We performed detailed radiative transfer simulations to understand the role of clouds in spectral and broadband solar radiation transfer in the Earth’s atmosphere for the normal (non-eclipse) spectrum and red-shift solar spectra for eclipse conditions. The theoretical calculations showed that the non-eclipse-to-eclipse surface flux ratio depends strongly on the obscuration of solar disk and slightly on cloud optical depth. These findings allowed us to estimate what the surface broadband SW flux would be for non-eclipse conditions from observations during the eclipse and further to quantify the impact of the eclipse on the surface broadband SW radiation budget. We found that the eclipse caused local reductions of time-averaged surface flux of about 379 W m−2 (50 %) and 329 W m−2 (46 %) during the ∼3 hours course of the eclipse at the Casper and Columbia sites, respectively. We estimated that the Moon’s shadow caused a reduction of approximately 7–8 % in global average surface broadband SW radiation. The eclipse has a smaller impact on surface flux reduction for cloudy conditions than a clear atmosphere; the impact decreases with the increase of cloud optical depth. However, the relative time-averaged reduction of local surface SW flux during a solar eclipse is approximately 45 % and it is not sensitive to cloud optical depth. The reduction of global average SW flux relative to climatology is proportional to the non-eclipse and eclipse flux difference in the penumbra area and depends on cloud optical depth in the Moon’s shadow and geolocation due to the change of solar zenith angle. We also discuss the influence of cloud inhomogeneity on the observed SW flux. Our results not only quantify the reduction of the surface solar radiation budget but also advance the understanding of broadband SW radiative transfer under solar eclipse conditions.

scholarly journals Physical Parameterization of Hyperspectral Reflectance in the Oxygen A-Band for Single-Layer Water Clouds

2020 ◽  
Vol 12 (14) ◽  
pp. 2252
Author(s):  
Jie Yang ◽  
Siwei Li ◽  
Feiyue Mao ◽  
Qilong Min ◽  
Wei Gong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Optical Depth ◽  
Relative Error ◽  
Single Layer ◽  
Hyperspectral Remote Sensing ◽  
Transfer Program ◽  
Hyperspectral Reflectance ◽  
Cloud Optical Depth ◽  
Physical Parameterization

Previous studies have shown that it is feasible to retrieve multiple cloud properties simultaneously based on the space-borne hyperspectral observation in the oxygen A-band, such as cloud optical depth, cloud-top height, and cloud geometrical thickness. However, hyperspectral remote sensing is time-consuming if based on the precise radiative transfer solution that counts multiple scatterings of light. To speed up the radiation transfer solution in cloud scenarios for nadir space-borne observations, we developed a physical parameterization of hyperspectral reflectance in the oxygen A-band for single-layer water clouds. The parameterization takes into account the influences of cloud droplet forward-scattering and nonlinear oxygen absorption on hyperspectral reflectance, which are improvements over the previous studies. The performance of the parameterization is estimated through comparison with DISORT (Discrete Ordinates Radiative Transfer Program Multi-Layered Plane-Parallel Medium) on the cases with solar zenith angle θ, the cloud optical depth τc, and the single-scattering albedo ω in the range of 0 ≤ θ ≤ 75, 5 ≤ τc ≤ 50, 0.5 ≤ ω ≤ 1. The relative error of the cloud reflectance is within 5% for most cases, even for clouds with optical depths around five or at strong absorption wavelengths. We integrate the parameterization with a slit function and a simplified atmosphere to evaluate its performance in simulating the observed cloud reflection at the top of the atmosphere by OCO-2 (Orbiting Carbon Observatory-2). To better visualize the possible errors from the new parameterization, gas molecular scattering, aerosol scattering, and reflection from the underlying surface are ignored. The relative error of the out-of-band radiance is less than 4% and the relative error of the intra-band radiance ratio is less than 4%. The radiance ratio is the ratio of simulated observations with and without in-cloud absorption and is used to assess the accuracy of the parameterization in quantifying the in-cloud absorption. The parameterization is a preparation for rapid hyperspectral remote sensing in the oxygen A-band. It would help to improve retrieval efficiency and provide cloud geometric thickness products.

scholarly journals Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

2016 ◽  
Vol 9 (8) ◽  
pp. 4151-4165 ◽  
Author(s):  
Felipe A. Mejia ◽  
Ben Kurtz ◽  
Keenan Murray ◽  
Laura M. Hinkelman ◽  
Manajit Sengupta ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Optical Depth ◽  
Zenith Angle ◽  
Microwave Radiometer ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Cloud Detection ◽  
Cloud Optical Depth ◽  
Angle Scattering ◽  
Atmospheric Radiation Measurement

Abstract. A method for retrieving cloud optical depth (τc) using a UCSD developed ground-based sky imager (USI) is presented. The radiance red–blue ratio (RRBR) method is motivated from the analysis of simulated images of various τc produced by a radiative transfer model (RTM). From these images the basic parameters affecting the radiance and red–blue ratio (RBR) of a pixel are identified as the solar zenith angle (θ0), τc, solar pixel angle/scattering angle (ϑs), and pixel zenith angle/view angle (ϑz). The effects of these parameters are described and the functions for radiance, Iλτc, θ0, ϑs, ϑz, and RBRτc, θ0, ϑs, ϑz are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τc, where RBR increases with τc up to about τc = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured Iλmeasϑs, ϑz, in addition to RBRmeasϑs, ϑz, to obtain a unique solution for τc. The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement (ARM) program site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min et al. (2003) method for overcast skies. τc values ranged from 0 to 80 with values over 80, being capped and registered as 80. A τc RMSE of 2.5 between the Min et al. (2003) method and the USI are observed. The MWR and USI  have an RMSE of 2.2, which is well within the uncertainty of the MWR. The procedure developed here provides a foundation to test and develop other cloud detection algorithms.

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