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

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.

scholarly journals Irradiance and cloud optical properties from photovoltaic power data

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

<p class="western" align="justify">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.</p> <p class="western" align="justify">High resolution measurements from both PV systems and pyranometers were collected as part of the BMWi-funded MetPVNet project, in the Allgäu 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)⁠ 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)⁠. 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.</p> <p class="western"><strong>References</strong></p> <p class="western">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), 1647–1672, doi:10.5194/gmd-9-1647-2016, 2016.</p> <p class="western">Mayer, B.: Radiative transfer in the cloudy atmosphere, EPJ Web Conf., 1, 75–99, doi:10.1140/epjconf/e2009-00912-1, 2009.</p> <p class="western">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), 1855–1877, doi:10.5194/acp-5-1855-2005, 2005.</p>

scholarly journals Sea Ice Albedo from MISR and MODIS: Production, Validation, and Trend Analysis

2018 ◽  
Vol 11 (1) ◽  
pp. 9 ◽  
Author(s):  
Said Kharbouche ◽  
Jan-Peter Muller
Keyword(s):  
Sea Ice ◽  
Near Infrared ◽  
Time Window ◽  
Solar Spectrum ◽  
High Accuracy ◽  
Dynamic Surface ◽  
Spectral Bands ◽  
Langley Research Center ◽  
Terra Satellite ◽  
Cloud Mask

The Multi-angle Imaging SpectroRadiometer (MISR) sensor onboard the Terra satellite provides high accuracy albedo products. MISR deploys nine cameras each at different view angles, which allow a near-simultaneous angular sampling of the surface anisotropy. This is particularly important to measure the near-instantaneous albedo of dynamic surface features such as clouds or sea ice. However, MISR’s cloud mask over snow or sea ice is not yet sufficiently robust because MISR’s spectral bands are only located in the visible and the near infrared. To overcome this obstacle, we performed data fusion using a specially processed MISR sea ice albedo product (that was generated at Langley Research Center using Rayleigh correction) combining this with a cloud mask of a sea ice mask product, MOD29, which is derived from the MODerate Resolution Imaging Spectroradiometer (MODIS), which is also, like MISR, onboard the Terra satellite. The accuracy of the MOD29 cloud mask has been assessed as >90% due to the fact that MODIS has a much larger number of spectral bands and covers a much wider range of the solar spectrum. Four daily sea ice products have been created, each with a different averaging time window (24 h, 7 days, 15 days, 31 days). For each time window, the number of samples, mean and standard deviation of MISR cloud-free sea ice albedo is calculated. These products are publicly available on a predefined polar stereographic grid at three spatial resolutions (1 km, 5 km, 25 km). The time span of the generated sea ice albedo covers the months between March and September of each year from 2000 to 2016 inclusive. In addition to data production, an evaluation of the accuracy of sea ice albedo was performed through a comparison with a dataset generated from a tower based albedometer from NOAA/ESRL/GMD/GRAD. This comparison confirms the high accuracy and stability of MISR’s sea ice albedo since its launch in February 2000. We also performed an evaluation of the day-of-year trend of sea ice albedo between 2000 and 2016, which confirm the reduction of sea ice shortwave albedo with an order of 0.4–1%, depending on the day of year and the length of observed time window.

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

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.

scholarly journals Using the OMI Aerosol Index and Absorption Aerosol Optical Depth to evaluate the NASA MERRA Aerosol Reanalysis

2014 ◽  
Vol 14 (23) ◽  
pp. 32177-32231 ◽  
Author(s):  
V. Buchard ◽  
A. M. da Silva ◽  
P. R. Colarco ◽  
A. Darmenov ◽  
C. A. Randles ◽  
...  
Keyword(s):  
Optical Properties ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Biomass Burning ◽  
Saharan Dust ◽  
Aerosol Layer ◽  
Anthropogenic Aerosols ◽  
Aerosol Absorption ◽  
Modis Aod ◽  
Aerosol Index

Abstract. A radiative transfer interface has been developed to simulate the UV Aerosol Index (AI) from the NASA Goddard Earth Observing System version 5 (GEOS-5) aerosol assimilated fields. The purpose of this work is to use the AI and Aerosol Absorption Optical Depth (AAOD) derived from the Ozone Monitoring Instrument (OMI) measurements as independent validation for the Modern Era Retrospective analysis for Research and Applications Aerosol Reanalysis (MERRAero). MERRAero is based on a version of the GEOS-5 model that is radiatively coupled to the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) aerosol module and includes assimilation of Aerosol Optical Depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Since AI is dependent on aerosol concentration, optical properties and altitude of the aerosol layer, we make use of complementary observations to fully diagnose the model, including AOD from the Multi-angle Imaging SpectroRadiometer (MISR), aerosol retrievals from the Aerosol Robotic Network (AERONET) and attenuated backscatter coefficients from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission to ascertain potential misplacement of plume height by the model. By sampling dust, biomass burning and pollution events in 2007 we have compared model produced AI and AAOD with the corresponding OMI products, identifying regions where the model representation of absorbing aerosols was deficient. As a result of this study over the Saharan dust region, we have obtained a new set of dust aerosol optical properties that retains consistency with the MODIS AOD data that were assimilated, while resulting in better agreement with aerosol absorption measurements from OMI. The analysis conducted over the South African and South American biomass burning regions indicates that revising the spectrally-dependent aerosol absorption properties in the near-UV region improves the modeled-observed AI comparisons. Finally, during a period where the Asian region was mainly dominated by anthropogenic aerosols, we have performed a qualitative analysis in which the specification of anthropogenic emissions in GEOS-5 is adjusted to provide insight into discrepancies observed in AI comparisons.

scholarly journals Simultaneous retrieval of aerosol and cloud properties during the MILAGRO field campaign

2011 ◽  
Vol 11 (13) ◽  
pp. 6245-6263 ◽  
Author(s):  
K. Knobelspiesse ◽  
B. Cairns ◽  
J. Redemann ◽  
R. W. Bergstrom ◽  
A. Stohl
Keyword(s):  
Optical Properties ◽  
Size Distribution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Droplet Size ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Cloud Droplet Size Distribution ◽  
Distribution Parameters ◽  
Aerosol Properties

Abstract. Estimation of Direct Climate Forcing (DCF) due to aerosols in cloudy areas has historically been a difficult task, mainly because of a lack of appropriate measurements. Recently, passive remote sensing instruments have been developed that have the potential to retrieve both cloud and aerosol properties using polarimetric, multiple view angle, and multi spectral observations, and therefore determine DCF from aerosols above clouds. One such instrument is the Research Scanning Polarimeter (RSP), an airborne prototype of a sensor on the NASA Glory satellite, which unfortunately failed to reach orbit during its launch in March of 2011. In the spring of 2006, the RSP was deployed on an aircraft based in Veracruz, Mexico, as part of the Megacity Initiative: Local and Global Research Observations (MILAGRO) field campaign. On 13 March, the RSP over flew an aerosol layer lofted above a low altitude marine stratocumulus cloud close to shore in the Gulf of Mexico. We investigate the feasibility of retrieving aerosol properties over clouds using these data. Our approach is to first determine cloud droplet size distribution using the angular location of the cloud bow and other features in the polarized reflectance. The selected cloud was then used in a multiple scattering radiative transfer model optimization to determine the aerosol optical properties and fine tune the cloud size distribution. In this scene, we were able to retrieve aerosol optical depth, the fine mode aerosol size distribution parameters and the cloud droplet size distribution parameters to a degree of accuracy required for climate modeling. This required assumptions about the aerosol vertical distribution and the optical properties of the coarse aerosol size mode. A sensitivity study was also performed to place this study in the context of future systematic scanning polarimeter observations, which found that the aerosol complex refractive index can also be observed accurately if the aerosol optical depth is larger than roughly 0.8 at a wavelength of (0.555 μm).

scholarly journals How the Optical Properties of Leaves Modify the Absorption and Scattering of Energy and Enhance Leaf Functionality

2020 ◽  
pp. 349-384 ◽  
Author(s):  
Susan L. Ustin ◽  
Stéphane Jacquemoud
Keyword(s):  
Optical Properties ◽  
Infrared Spectrum ◽  
Related Species ◽  
Spectral Properties ◽  
Near Infrared ◽  
Solar Spectrum ◽  
Cellular Organization ◽  
Seed Plant ◽  
Or Groups ◽  
Shortwave Infrared

AbstractLeaves absorb, scatter, and transmit sunlight at all wavelengths across the visible, near-infrared, and shortwave-infrared spectrum. The optical properties of a leaf are determined by its biochemical and biophysical characteristics, including its 3-D cellular organization. The absorption and scattering properties of leaves together create the shape of their reflectance spectra. Terrestrial seed plant species share similar physiological and metabolic processes for fluxes of gases (CO2, O2, H2O), nutrients, and energy, while differences are primarily consequences of how these properties are distributed and their physical structures. Related species generally share biochemical and biophysical traits, and their optical properties are also similar, providing a mechanism for identification. However, it is often the minor differences in spectral properties throughout the wavelengths of the solar spectrum that define a species or groups of related species. This chapter provides a review and summary of the most common interactions between leaf properties and light and the physical processes that regulate the outcomes of these interactions.

scholarly journals Simultaneous retrieval of aerosol and cloud properties during the MILAGRO field campaign

2011 ◽  
Vol 11 (2) ◽  
pp. 6363-6413 ◽  
Author(s):  
K. Knobelspiesse ◽  
B. Cairns ◽  
J. Redemann ◽  
R. W. Bergstrom ◽  
A. Stohl
Keyword(s):  
Optical Properties ◽  
Size Distribution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Droplet Size ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Field Campaign ◽  
Cloud Droplet Size Distribution ◽  
Aerosol Properties

Abstract. Estimation of Direct Climate Forcing (DCF) due to aerosols in cloudy areas has historically been a difficult task, mainly because of a lack of appropriate measurements. The Aerosol Polarimetry Sensor (APS), on the upcoming NASA Glory mission, has the potential to retrieve both cloud and aerosol properties because of its polarimetric, multiple view angle, and multi spectral observations. The APS airborne prototype is the Research Scanning Polarimeter (RSP), which has similar characteristics and can be used to demonstrate APS capabilities. In the spring of 2006, the RSP was deployed on an aircraft based in Veracruz, Mexico, as part of the Megacity Initiative: Local and Global Research Observations (MILAGRO) field campaign. On March 13th, the RSP over flew an aerosol layer lofted above a low altitude marine stratocumulus cloud close to shore in the Gulf of Mexico. We investigate the feasibility of retrieving aerosol properties over clouds using these data. Our approach is to first determine cloud droplet size distribution using the angular location of the cloud bow and other features in the polarized reflectance. The selected cloud was then used in a multiple scattering radiative transfer model optimization to determine the aerosol optical properties and fine tune the cloud size distribution. In this scene, we were able to retrieve aerosol optical depth, the fine mode aerosol size distribution and the cloud droplet size distribution to a degree of accuracy required for climate modeling. This required assumptions about the aerosol vertical distribution and the optical properties of the coarse aerosol size mode. A sensitivity study was also performed to place this case study in the context of the potential for future systematic APS observations of this kind, which found that the aerosol complex refractive index can also be observed accurately if the aerosol optical depth is larger than roughly 0.8 at a wavelength of 0.555 μm.

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>

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