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.

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

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 Marine liquid cloud geometric thickness retrieved from OCO-2's oxygen A-band spectrometer

2019 ◽  
Vol 12 (3) ◽  
pp. 1717-1737 ◽  
Author(s):  
Mark Richardson ◽  
Jussi Leinonen ◽  
Heather Q. Cronk ◽  
James McDuffie ◽  
Matthew D. Lebsock ◽  
...  
Keyword(s):  
Optical Depth ◽  
Single Layer ◽  
Cloud Droplet ◽  
Satellite Observation ◽  
Cloud Optical Depth ◽  
First Case ◽  
Phase Classification ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Top Pressure ◽  
Geometric Thickness

Abstract. This paper introduces the OCO2CLD-LIDAR-AUX product, which uses the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar and the Orbiting Carbon Observatory-2 (OCO-2) hyperspectral A-band spectrometer. CALIPSO provides a prior cloud top pressure (Ptop) for an OCO-2-based retrieval of cloud optical depth, Ptop and cloud geometric thickness expressed in hPa. Measurements are of single-layer liquid clouds over oceans from September 2014 to December 2016 when collocated data are available. Retrieval performance is best for solar zenith angles <45∘ and when the cloud phase classification, which also uses OCO-2's weak CO2 band, is more confident. The highest quality optical depth retrievals agree with those from the Moderate Resolution Imaging Spectroradiometer (MODIS) with discrepancies smaller than the MODIS-reported uncertainty. Retrieved thicknesses are consistent with a substantially subadiabatic structure over marine stratocumulus regions, in which extinction is weighted towards the cloud top. Cloud top pressure in these clouds shows a 4 hPa bias compared with CALIPSO which we attribute mainly to the assumed vertical structure of cloud extinction after showing little sensitivity to the presence of CALIPSO-identified aerosol layers or assumed cloud droplet effective radius. This is the first case of success in obtaining internal cloud structure from hyperspectral A-band measurements and exploits otherwise unused OCO-2 data. This retrieval approach should provide additional constraints on satellite-based estimates of cloud droplet number concentration from visible imagery, which rely on parameterization of the cloud thickness.

Hyperspectral Remote Sensing and Radiative Transfer Simulation as a Tool for Monitoring Coral Reef Health

2002 ◽  
Vol 36 (1) ◽  
pp. 4-13 ◽  
Author(s):  
Hiroya Yamano ◽  
Masayuki Tamura ◽  
Yoshimitsu Kunii ◽  
Michio Hidaka
Keyword(s):  
Remote Sensing ◽  
Coral Reef ◽  
Radiative Transfer ◽  
Feasibility Studies ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Dead Coral ◽  
Hyperspectral Sensors ◽  
Reef Health ◽  
Radiative Transfer Modeling

Recent advances in the remote sensing of coral reefs include hyperspectral remote sensing and radiative transfer modeling. Hyperspectral data can be regarded as continuous and the derivative spectroscopy is effective for extracting coral reef components, including sand, macroalgae, and healthy, bleached, recently dead, and old dead coral. Radiative transfer models are effective for feasibility studies of satellite or airborne remote sensing. Using these techniques, we simulate and analyze the apparent reflectance of coral reef benthic features associated with bleaching events, obtained by hyperspectral sensors on various platforms (ROV, boat, airplane, and satellite), and suggest that the coral reef health on reef flats can be discriminated precisely. Remote sensing using hyperspectral sensors should significantly contribute to mapping and monitoring coral reef health.

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

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

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

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