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

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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Irradiance and cloud optical properties from photovoltaic power data

10.5194/dach2022-298 ◽

2021 ◽

Author(s):

James Barry ◽

Anna Herman-Czezuch ◽

Nicola Kimiaie ◽

Stefanie Meilinger ◽

Christopher Schirrmeister ◽

...

Keyword(s):

Optical Properties ◽

Radiative Transfer ◽

Optical Depth ◽

Software Package ◽

Weather Prediction ◽

Lookup Table ◽

Atmospheric Conditions ◽

Cloud Optical Depth ◽

Pv Systems ◽

Diffuse Irradiance

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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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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Variations in optical properties of aerosols on monsoon seasonal change and estimation of aerosol optical depth using ground-based meteorological and air quality data

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-14-19747-2014 ◽

2014 ◽

Vol 14 (13) ◽

pp. 19747-19789

Author(s):

F. Tan ◽

H. S. Lim ◽

K. Abdullah ◽

T. L. Yoon ◽

B. Holben

Keyword(s):

Optical Properties ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Statistical Tests ◽

Air Pollutant ◽

Quality Data ◽

Supplementary Information ◽

Northeast Monsoon ◽

Data Set ◽

Proposed Model

Abstract. In this study, the optical properties of aerosols in Penang, Malaysia were analyzed for four monsoonal seasons (northeast monsoon, pre-monsoon, southwest monsoon, and post-monsoon) based on data from the AErosol RObotic NETwork (AERONET) from February 2012 to November 2013. The aerosol distribution patterns in Penang for each monsoonal period were quantitatively identified according to the scattering plots of the aerosol optical depth (AOD) against the Angstrom exponent. A modified algorithm based on the prototype model of Tan et al. (2014a) was proposed to predict the AOD data. Ground-based measurements (i.e., visibility and air pollutant index) were used in the model as predictor data to retrieve the missing AOD data from AERONET because of frequent cloud formation in the equatorial region. The model coefficients were determined through multiple regression analysis using selected data set from in situ data. The predicted AOD of the model was generated based on the coefficients and compared against the measured data through standard statistical tests. The predicted AOD in the proposed model yielded a coefficient of determination R2 of 0.68. The corresponding percent mean relative error was less than 0.33% compared with the real data. The results revealed that the proposed model efficiently predicted the AOD data. Validation tests were performed on the model against selected LIDAR data and yielded good correspondence. The predicted AOD can beneficially monitor short- and long-term AOD and provide supplementary information in atmospheric corrections.

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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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Investigation of the adiabatic assumption for estimating cloud micro- and macrophysical properties from satellite and ground observations

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-933-2016 ◽

2016 ◽

Vol 16 (2) ◽

pp. 933-952 ◽

Cited By ~ 24

Author(s):

D. Merk ◽

H. Deneke ◽

B. Pospichal ◽

P. Seifert

Keyword(s):

Optical Depth ◽

Microwave Radiometer ◽

Cloud Droplet ◽

Satellite Observations ◽

Data Set ◽

Cloud Optical Depth ◽

Cloud Radar ◽

Aerosol Cloud ◽

Droplet Concentration ◽

Aerosol Cloud Interactions

Abstract. Cloud properties from both ground-based as well as from geostationary passive satellite observations have been used previously for diagnosing aerosol–cloud interactions. In this investigation, a 2-year data set together with four selected case studies are analyzed with the aim of evaluating the consistency and limitations of current ground-based and satellite-retrieved cloud property data sets. The typically applied adiabatic cloud profile is modified using a sub-adiabatic factor to account for entrainment within the cloud. Based on the adiabatic factor obtained from the combination of ground-based cloud radar, ceilometer and microwave radiometer, we demonstrate that neither the assumption of a completely adiabatic cloud nor the assumption of a constant sub-adiabatic factor is fulfilled (mean adiabatic factor 0.63 ± 0.22). As cloud adiabaticity is required to estimate the cloud droplet number concentration but is not available from passive satellite observations, an independent method to estimate the adiabatic factor, and thus the influence of mixing, would be highly desirable for global-scale analyses. Considering the radiative effect of a cloud described by the sub-adiabatic model, we focus on cloud optical depth and its sensitivities. Ground-based estimates are here compared vs. cloud optical depth retrieved from the Meteosat SEVIRI satellite instrument resulting in a bias of −4 and a root mean square difference of 16. While a synergistic approach based on the combination of ceilometer, cloud radar and microwave radiometer enables an estimate of the cloud droplet concentration, it is highly sensitive to radar calibration and to assumptions about the moments of the droplet size distribution. Similarly, satellite-based estimates of cloud droplet concentration are uncertain. We conclude that neither the ground-based nor satellite-based cloud retrievals applied here allow a robust estimate of cloud droplet concentration, which complicates its use for the study of aerosol–cloud interactions.

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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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Analysis of Two Dimensionality Reduction Techniques for Fast Simulation of the Spectral Radiances in the Hartley-Huggins Band

Atmosphere ◽

10.3390/atmos10030142 ◽

2019 ◽

Vol 10 (3) ◽

pp. 142 ◽

Cited By ~ 9

Author(s):

Ana del Águila ◽

Dmitry Efremenko ◽

Víctor Molina García ◽

Jian Xu

Keyword(s):

Optical Properties ◽

Radiative Transfer ◽

Dimensionality Reduction ◽

Empirical Orthogonal Functions ◽

Gas Absorption ◽

Correction Function ◽

Data Set ◽

Acceleration Techniques ◽

Computational Performance ◽

Specific Subset

The new generation of atmospheric composition sensors such as TROPOMI is capable of providing spectra of high spatial and spectral resolution. To process this vast amount of spectral information, fast radiative transfer models (RTMs) are required. In this regard, we analyzed the efficiency of two acceleration techniques based on the principal component analysis (PCA) for simulating the Hartley-Huggins band spectra. In the first one, the PCA is used to map the data set of optical properties of the atmosphere to a lower-dimensional subspace, in which the correction function for an approximate but fast RTM is derived. The second technique is based on the dimensionality reduction of the data set of spectral radiances. Once the empirical orthogonal functions are found, the whole spectrum can be reconstructed by performing radiative transfer computations only for a specific subset of spectral points. We considered a clear-sky atmosphere where the optical properties are defined by Rayleigh scattering and trace gas absorption. Clouds can be integrated into the model as Lambertian reflectors. High computational performance is achieved by combining both techniques without losing accuracy. We found that for the Hartley-Huggins band, the combined use of these techniques yields an accuracy better than 0.05% while the speedup factor is about 20. This innovative combination of both PCA-based techniques can be applied in future works as an efficient approach for simulating the spectral radiances in other spectral regions.

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The CAMS reanalysis of atmospheric composition

10.5194/acp-2018-1078 ◽

2018 ◽

Author(s):

Antje Inness ◽

Melanie Ades ◽

Anna Agusti-Panareda ◽

Jérôme Barré ◽

Anna Benedictow ◽

...

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Chemical Species ◽

Reanalysis Data ◽

Horizontal Resolution ◽

Atmospheric Composition ◽

Data Set ◽

Forecasting System ◽

One Year ◽

Total Column

Abstract. The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis is the latest global reanalysis data set of atmospheric composition produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), consisting of 3-dimensional time-consistent atmospheric composition fields, including aerosols and chemical species. The dataset currently covers the period 2003–2016 and will be extended in the future by adding one year each year. A reanalysis for greenhouse gases is being produced separately. The CAMS reanalysis builds on the experience gained during the production of the earlier Monitoring Atmospheric Composition and Climate (MACC) reanalysis and CAMS interim reanalysis. Satellite retrievals of total column CO, tropospheric column NO2, aerosol optical depth and total column, partial column and profile ozone retrievals were assimilated for the CAMS reanalysis with ECMWF’s Integrated Forecasting System. The new reanalysis has an increased horizontal resolution of about 80 km and provides more chemical species at a better temporal resolution (3-hourly analysis fields, 3-hourly forecast fields and hourly surface forecast fields) than the previously produced CAMS interim reanalysis. The CAMS reanalysis has smaller biases compared to independent ozone, carbon monoxide, nitrogen dioxide and aerosol optical depth observations than the previous two reanalyses and is much improved and more consistent in time, especially compared to the MACC reanalysis. The CAMS reanalysis is a dataset that can be used to compute climatologies, study trends, evaluate models, benchmark other reanalyses or serve as boundary conditions for regional models for past periods.

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

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-10-14557-2010 ◽

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.

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