Irradiance and cloud optical properties from photovoltaic power data

The rapid increase in solar photovoltaic (PV) installations worldwide has resulted in the electricity grid becoming increasingly dependent on atmospheric conditions, thus requiring more accurate forecasts of incoming solar irradiance. In this context, measured data from PV systems are a valuable source of information about the optical properties of the atmosphere, in particular the cloud optical depth (COD). This work reports first results from an inversion algorithm developed to infer global, direct and diffuse irradiance as well as atmospheric optical properties from PV power measurements, with the goal of assimilating this information into numerical weather prediction (NWP) models. High resolution measurements from both PV systems and pyranometers were collected as part of the BMWi-funded MetPVNet project, in the Allg&#228;u region during autumn 2018 and summer 2019. These data were then used together with a PV model and both the DISORT and MYSTIC radiative transfer schemes within libRadtran (Emde et al., 2016; Mayer and Kylling, 2005)&#8288; to infer cloud optical depth as well as direct, diffuse and global irradiance under highly variable atmospheric conditions. Hourly averages of each of the retrieved quantities were compared with the corresponding predictions of the COSMO weather model as well as data from satellite retrievals, and periods with differing degrees of variability and different cloud types were analysed. The DISORT-based algorithm is able to accurately retrieve COD, direct and diffuse irradiance components as long as the cloud fraction is high enough, whereas under broken cloud conditions the presence of 3D effects can lead to large errors. In that case the global horizontal irradiance is derived from tilted irradiance measurements and/or PV data using a lookup table based on the MYSTIC 3D Monte Carlo radiative transfer solver (Mayer, 2009)&#8288;. This work will provide the basis for future investigations using a larger number of PV systems to evaluate the improvements to irradiance and power forecasts that could be achieved by the assimilation of inferred irradiance into an NWP model. References Emde, C., Buras-Schnell, R., Kylling, A., Mayer, B., Gasteiger, J., Hamann, U., Kylling, J., Richter, B., Pause, C., Dowling, T. and Bugliaro, L.: The libRadtran software package for radiative transfer calculations (version 2.0.1), Geosci. Model Dev., 9(5), 1647&#8211;1672, doi:10.5194/gmd-9-1647-2016, 2016. Mayer, B.: Radiative transfer in the cloudy atmosphere, EPJ Web Conf., 1, 75&#8211;99, doi:10.1140/epjconf/e2009-00912-1, 2009. Mayer, B. and Kylling, A.: Technical note: The libRadtran software package for radiative transfer calculations - description and examples of use, Atmos. Chem. Phys., 5(7), 1855&#8211;1877, doi:10.5194/acp-5-1855-2005, 2005.

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

10.5194/egusphere-egu21-7581 ◽

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

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.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 Allg&#228;u 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.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. &#160;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.References &#160; Buchmann, T., 2018: Potenzial von Photovoltaikanlagen zur Ableitung raum-zeitlich hoch aufgel&#246;ster Globalstrahlungsdaten. Heidelberg University, http://archiv.ub.uni-heidelberg.de/volltextserver/24687/. Emde, C., and Coauthors, 2016: The libRadtran software package for radiative transfer calculations (version 2.0.1). Geosci. Model Dev., 9, 1647&#8211;1672, doi:10.5194/gmd-9-1647-2016. https://www.geosci-model-dev.net/9/1647/2016/. 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. Sol. Energy, 164, 12&#8211;24, doi:10.1016/j.solener.2018.02.012. https://doi.org/10.1016/j.solener.2018.02.012.

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Ground Reflectance Retrieval on Horizontal and Inclined Terrains Using the Software Package REFLECT

Remote Sensing ◽

10.3390/rs10101638 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1638

Author(s):

Yacine Bouroubi ◽

Wided Batita ◽

François Cavayas ◽

Nicolas Tremblay

Keyword(s):

Optical Properties ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Software Package ◽

Near Infrared ◽

Solar Spectrum ◽

Sensor Calibration ◽

Topographic Effects ◽

Atmospheric Conditions ◽

Spectral Bands

This paper presents the software package REFLECT for the retrieval of ground reflectance from high and very-high resolution multispectral satellite images. The computation of atmospheric parameters is based on the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) routines. Aerosol optical properties are calculated using the OPAC (Optical Properties of Aerosols and Clouds) model, while aerosol optical depth is estimated using the dark target method. A new approach is proposed for adjacency effect correction. Topographic effects were also taken into account, and a new model was developed for forest canopies. Validation has shown that ground reflectance estimation with REFLECT is performed with an accuracy of approximately ±0.01 in reflectance units (for the visible, near-infrared, and mid-infrared spectral bands), even for surfaces with varying topography. The validation of the software was performed through many tests. These tests involve the correction of the effects that are associated with sensor calibration, irradiance, and viewing conditions, atmospheric conditions (aerosol optical depth AOD and water vapour), adjacency, and topographic conditions.

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

10.1117/12.2599586 ◽

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

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A Remote Sensing and Atmospheric Correction Method for Assessing Multispectral Radiative Transfer through Realistic Atmospheres and Clouds

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-18-0078.1 ◽

2019 ◽

Vol 36 (2) ◽

pp. 203-216 ◽

Cited By ~ 3

Author(s):

Jarred L. Burley ◽

Steven T. Fiorino ◽

Brannon J. Elmore ◽

Jaclyn E. Schmidt

Keyword(s):

Remote Sensing ◽

Radiative Transfer ◽

Performance Metrics ◽

Meteorological Data ◽

Weather Prediction ◽

Atmospheric Correction ◽

Atmospheric Conditions ◽

Diffuse Transmission ◽

Cloud Data ◽

Atmospheric Propagation

Abstract The ability to quickly and accurately model actual atmospheric conditions is essential to remote sensing analyses. Clouds present a particularly complex challenge, as they cover up to 70% of Earth’s surface, and their highly variable and diverse nature necessitates physics-based modeling. The Laser Environmental Effects Definition and Reference (LEEDR) is a verified and validated atmospheric propagation and radiative transfer code that creates physically realizable vertical and horizontal profiles of meteorological data. Coupled with numerical weather prediction (NWP) model output, LEEDR enables analysis, nowcasts, and forecasts for radiative effects expected for real-world scenarios. A recent development is the inclusion of the U.S. Air Force’s World-Wide Merged Cloud Analysis (WWMCA) cloud data in a new tool set that enables radiance calculations through clouds from UV to radio frequency (RF) wavelengths. This effort details the creation of near-real-time profiles of atmospheric and cloud conditions and the resulting radiative transfer analysis for virtually any wavelength(s) of interest. Calendar year 2015 data are analyzed to establish climatological limits for diffuse transmission in the 300–1300-nm band, and the impacts of various geometry, cloud microphysical, and atmospheric conditions are examined. The results show that 80% of diffuse band transmissions are estimated to fall between 0.248 and 0.889 under the assumptions of cloud homogeneity and maximum overlap and are sufficient for establishing diffuse transmission percentiles. The demonstrated capability provides an efficient way to extend optical wavelength cloud parameters across the spectrum for physics-based multiple-scattering effects modeling through cloudy and clear atmospheres, providing an improvement to atmospheric correction for remote sensing and cloud effects on system performance metrics.

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An Evaluation of Radiative Transfer Simulations of Cloudy Scenes from a Numerical Weather Prediction Model at Sub-Millimetre Frequencies Using Airborne Observations

Remote Sensing ◽

10.3390/rs12172758 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2758

Author(s):

Stuart Fox

Keyword(s):

Optical Properties ◽

Radiative Transfer ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Full Range ◽

Numerical Weather Prediction Model ◽

Atmospheric Measurements ◽

Numerical Weather ◽

Nwp Model ◽

Cloud Ice

The Ice Cloud Imager (ICI) will be launched on the next generation of EUMETSAT polar-orbiting weather satellites and make passive observations between 183 and 664 GHz which are sensitive to scattering from cloud ice. These observations have the potential to improve weather forecasts through direct assimilation using "all-sky" methods which have been successfully applied to microwave observations up to 200 GHz in current operational systems. This requires sufficiently accurate representations of cloud ice in both numerical weather prediction (NWP) and radiative transfer models. In this study, atmospheric fields from a high-resolution NWP model are used to drive radiative transfer simulations using the Atmospheric Radiative Transfer Simulator (ARTS) and a recently released database of cloud ice optical properties. The simulations are evaluated using measurements between 89 and 874 GHz from five case studies of ice and mixed-phase clouds observed by the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 research aircraft. The simulations are strongly sensitive to the assumed cloud ice optical properties, but by choosing an appropriate ice crystal model it is possible to simulate realistic brightness temperatures over the full range of sub-millimetre frequencies. This suggests that sub-millimetre observations have the potential to be assimilated into NWP models using the all-sky method.

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Interactive Aerosol Feedbacks on Photolysis Rates in the GEM-MACH Air Quality Model for an Athabasca Oil-Sands Modelling Study

10.5194/egusphere-egu2020-10470 ◽

2020 ◽

Author(s):

Mahtab Majdzadeh ◽

Craig Stroud ◽

Ayodeji Akingunola ◽

Paul Makar ◽

Christopher Sioris ◽

...

Keyword(s):

Optical Properties ◽

Air Quality ◽

Radiative Transfer ◽

Forest Fire ◽

Lookup Table ◽

Aerosol Optical Properties ◽

Extinction Efficiency ◽

Scattering Cross ◽

Photolysis Rates ◽

On Line

The radiative transfer module of an on-line chemical transport models requires input data from aerosol extinction efficiency, single scatter albedo and asymmetry factor, in order to predict the radiative state of the atmosphere. These aerosol optical properties (aerosol optical depth, AOD), may be integrated vertically for comparison to satellite observations. These optical effects may also influence the shorter wavelengths associated with atmospheric gas photolysis, influencing atmospheric reactivity. These processes may be harmonized in an on-line reaction transport model, such as Environment and Climate Change Canada&#8217;s GEM-MACH (GEM: Global Environmental Multi-scale &#8211; MACH: Modelling Air quality and Chemistry). Previous photolysis routine in the radiative transfer module, MESSY-JVAL (Modular Earth Sub-Model System), in GEM-MACH, made use of a climatology of aerosol optical properties, and the previous on-line version made use of a homogeneous mixture Mie code for meteorological radiative transfer calculations.We calculated a new lookup table for the extinction efficiency, absorption and scattering cross sections of each aerosol type. The new version of MESSY-JVAL uses GEM-MACH predicted aerosol size distributions, chemical composition and relative humidity in each vertical column at each time step as input, reads aerosol absorption and scattering cross section data from the new lookup table and calculates aerosol optical properties, that are then used to modify both photolysis and meteorological radiative transfer calculations.In order to evaluate these modifications to the model, we performed a series of simulations with GEM-MACH with wildfire emissions inputs from the Canadian Forest Fire Emissions Prediction System (CFFEPS) and compared the model AOD output with satellite and AERONET (Aerosol Robotic Network) measurement data. Comparison of the hourly AERONET and monthly-averaged satellite AOD demonstrates major improvements in the revised model AOD predictions. The impact of the updated photolysis rates and meteorological radiative transfer calculations on predictions of oxidant mixing ratios and rates of pollutant oxidation (nitrogen dioxide conversion to nitric acid) will be assessed both within and below the forest fire plume.

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Statistical Analyses of Satellite Cloud Object Data from CERES. Part III: Comparison with Cloud-Resolving Model Simulations of Tropical Convective Clouds

Journal of the Atmospheric Sciences ◽

10.1175/jas3871.1 ◽

2007 ◽

Vol 64 (3) ◽

pp. 762-785 ◽

Cited By ~ 15

Author(s):

Yali Luo ◽

Kuan-Man Xu ◽

Bruce A. Wielicki ◽

Takmeng Wong ◽

Zachary A. Eitzen

Keyword(s):

Physical Properties ◽

Optical Depth ◽

Weather Prediction ◽

Cloud Microphysics ◽

Retrieval Algorithm ◽

Medium Size ◽

Size Category ◽

Cloud Optical Depth ◽

Large Size ◽

Cloud Resolving Model

Abstract The present study evaluates the ability of a cloud-resolving model (CRM) to simulate the physical properties of tropical deep convective cloud objects identified from a Clouds and the Earth’s Radiant Energy System (CERES) data product. The emphasis of this study is the comparisons among the small-, medium-, and large-size categories of cloud objects observed during March 1998 and between the large-size categories of cloud objects observed during March 1998 (strong El Niño) and March 2000 (weak La Niña). Results from the CRM simulations are analyzed in a way that is consistent with the CERES retrieval algorithm and they are averaged to match the scale of the CERES satellite footprints. Cloud physical properties are analyzed in terms of their summary histograms for each category. It is found that there is a general agreement in the overall shapes of all cloud physical properties between the simulated and observed distributions. Each cloud physical property produced by the CRM also exhibits different degrees of disagreement with observations over different ranges of the property. The simulated cloud tops are generally too high and cloud-top temperatures are too low except for the large-size category of March 1998. The probability densities of the simulated top-of-the-atmosphere (TOA) albedos for all four categories are underestimated for high albedos, while those of cloud optical depth are overestimated at its lowest bin. These disagreements are mainly related to uncertainties in the cloud microphysics parameterization and inputs such as cloud ice effective size to the radiation calculation. Summary histograms of cloud optical depth and TOA albedo from the CRM simulations of the large-size category of cloud objects do not differ significantly between the March 1998 and 2000 periods, consistent with the CERES observations. However, the CRM is unable to reproduce the significant differences in the observed cloud-top height while it overestimates the differences in the observed outgoing longwave radiation and cloud-top temperature between the two periods. Comparisons between the CRM results and the observations for most parameters in March 1998 consistently show that both the simulations and observations have larger differences between the large- and small-size categories than between the large- and medium-size, or between the medium- and small-size categories. However, the simulated cloud properties do not change as much with size as observed. These disagreements are likely related to the spatial averaging of the forcing data and the mismatch in time and space between the numerical weather prediction model from which the forcing data are produced and the CERES observed cloud systems.

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

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-8-11285-2015 ◽

2015 ◽

Vol 8 (10) ◽

pp. 11285-11321 ◽

Cited By ~ 1

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 (&vartheta;s), and pixel zenith angle/view angle (&vartheta;z). The effects of these parameters are described and the functions for radiance, Iλ(τc, θ0, &vartheta;s, &vartheta;z) and the red-blue ratio, RBR(τc, θ0, &vartheta;s, &vartheta;z) 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(&vartheta;s, &vartheta;z), in addition to RBRmeas(&vartheta;s, &vartheta;z) 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.

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

10.1117/12.2599591 ◽

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

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The performance of Aeolus in heterogeneous atmospheric conditions using high-resolution radiosonde data

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-7-1393-2014 ◽

2014 ◽

Vol 7 (2) ◽

pp. 1393-1455

Author(s):

X. J. Sun ◽

R. W. Zhang ◽

G. J. Marseille ◽

A. Stoffelen ◽

D. Donovan ◽

...

Keyword(s):

Optical Properties ◽

High Resolution ◽

Relative Humidity ◽

Weather Prediction ◽

Error Variance ◽

Radiosonde Data ◽

Atmospheric Conditions ◽

Wind Profiles ◽

Height Assignment ◽

Aerosol Backscatter

Abstract. The ESA Aeolus mission aims to measure wind profiles from space. In preparation for launch we aim to assess the expected bias in retrieved winds from the Mie and Rayleigh channel signals induced by atmospheric heterogeneity. Observation biases are known to be detrimental when gone undetected in Numerical Weather Prediction (NWP). Aeolus processing equipment should therefore be prepared to detect heterogeneous atmospheric scenes and take measures, e.g., reject or reduce the weight of observations when used in NWP. Radiosondes provide the wind vector at about 10 m resolution. We present a method to simulate co-located cloud and aerosol optical properties from radiosonde observations. We show that cloud layers can be detected along the radiosonde path from radiosonde measured relative humidity and temperature. A parameterization for aerosol backscatter and extinction along the radiosonde path is presented based on a climatological aerosol backscatter profile and radiosonde relative humidity. The resulting high-resolution database of atmospheric wind and optical properties serves as input for Aeolus wind simulations. It is shown that Aeolus wind error variance grows quadratically with bin size and the wind-shear over the bin. Strong scattering aerosol or cloud layers may cause biases exceeding 1ms−1 for typical tropospheric conditions and 1 km Mie channel bin size, i.e., substantially larger than the mission bias requirement of 0.4 ms−1. Advanced level-2 processing of Aeolus winds including estimation of atmosphere optical properties is needed to detect regions with large heterogeneity, potentially yielding biased winds. Besides applicable for Aeolus the radiosonde database of co-located high-resolution wind and cloud information can be used for the validation of atmospheric motion wind vectors (AMV) or to correct their height assignment errors.

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