scholarly journals Capability of multi-viewing-angle photo-polarimetric measurements for the simultaneous retrieval of aerosol and cloud properties

2010 ◽  
Vol 3 (2) ◽  
pp. 1229-1262 ◽  
Author(s):  
O. P. Hasekamp
Keyword(s):  
Satellite Remote Sensing ◽  
Essential Role ◽  
High Accuracy ◽  
Aerosol Layer ◽  
Viewing Angle ◽  
Clear Sky ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Land Surfaces ◽  
Sky Conditions

Abstract. An important new challenge in the field of multi-angle photopolarimetric satellite remote sensing is the retrieval of aerosol properties under cloudy conditions. In this paper the possibility has been explored to perform a simultaneous retrieval of aerosol and cloud properties for partly cloudy scenes and for fully cloudy scenes where the aerosol layer is located above the cloud, using multi-angle photo-polarimetric measurements. Also, for clear sky conditions a review is given of the capabilities of multi-angle photo-polarimetric measurements in comparison with other measurement types. It is shown that already for clear sky conditions polarization measurements are highly important for the retrieval of aerosol optical and microphysical properties over land surfaces with unknown reflection properties. Furthermore, it is shown that multi-angle photo-polarimetric measurements have the capability to distinguish between aerosols and clouds, and thus facilitate a simultaneous retrieval of aerosol and cloud properties. High accuracy (0.002–0.004) of the polarimetric measurements plays an essential role here.

scholarly journals Capability of multi-viewing-angle photo-polarimetric measurements for the simultaneous retrieval of aerosol and cloud properties

2010 ◽  
Vol 3 (4) ◽  
pp. 839-851 ◽  
Author(s):  
O. P. Hasekamp
Keyword(s):  
Satellite Remote Sensing ◽  
Essential Role ◽  
High Accuracy ◽  
Aerosol Layer ◽  
Viewing Angle ◽  
Clear Sky ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Land Surfaces ◽  
Sky Conditions

Abstract. An important new challenge in the field of multi-angle photo-polarimetric satellite remote sensing is the retrieval of aerosol properties under cloudy conditions. In this paper the possibility has been explored to perform a simultaneous retrieval of aerosol and cloud properties for partly cloudy scenes and for fully cloudy scenes where the aerosol layer is located above the cloud, using multi-angle photo-polarimetric measurements. Also, for clear sky conditions a review is given of the capabilities of multi-angle photo-polarimetric measurements in comparison with other measurement types. It is shown that already for clear sky conditions polarization measurements are highly important for the retrieval of aerosol optical and microphysical properties over land surfaces with unknown reflection properties. Furthermore, it is shown that multi-angle photo-polarimetric measurements have the capability to distinguish between aerosols and clouds, and thus facilitate a simultaneous retrieval of aerosol and cloud properties. High accuracy (0.002–0.004) of the polarimetric measurements plays an essential role here.

scholarly journals Multispectral information from TANSO-FTS instrument – Part 2: Application to aerosol effect on greenhouse gas retrievals

2013 ◽  
Vol 6 (11) ◽  
pp. 3313-3323 ◽  
Author(s):  
H. Herbin ◽  
L. C. Labonnote ◽  
P. Dubuisson
Keyword(s):  
Greenhouse Gas ◽  
Near Infrared ◽  
Computation Time ◽  
Channel Selection ◽  
Atmospheric Parameter ◽  
Aerosol Layer ◽  
Clear Sky ◽  
Aerosol Parameters ◽  
Similar Accuracy ◽  
Sky Conditions

Abstract. This article is the second in a series of studies investigating the benefits of multispectral measurements to improve the atmospheric parameter retrievals. In the first paper, we presented an information content (IC) analysis from the thermal infrared (TIR) and shortwave infrared (SWIR) bands of Thermal And Near infrared Sensor for carbon Observations–Fourier Transform Spectrometer (TANSO-FTS) instrument dedicated to greenhouse gas retrieval in clear sky conditions. This second paper presents the potential of the spectral synergy from TIR to visible for aerosol characterization, and their impact on the retrieved CO2 and CH4 column concentrations. The IC is then used to determine the most informative spectral channels for the simultaneous retrieval of greenhouse gas total columns and aerosol parameters. The results show that a channel selection spanning the four bands can improve the computation time and retrieval accuracy. Therefore, the spectral synergy allows obtaining up to almost seven different aerosol parameters, which is comparable to the most informative dedicated instruments. Moreover, a channel selection from the TIR to visible bands allows retrieving CO2 and CH4 total columns simultaneously in the presence of one aerosol layer with a similar accuracy to using all channels together to retrieve each gas separately in clear sky conditions.

scholarly journals Monitoring of snow surface near-infrared bidirectional reflectance factors with added light-absorbing particles

2019 ◽  
Vol 13 (6) ◽  
pp. 1753-1766 ◽  
Author(s):  
Adam Schneider ◽  
Mark Flanner ◽  
Roger De Roo ◽  
Alden Adolph
Keyword(s):  
Near Infrared ◽  
Bidirectional Reflectance ◽  
Nondestructive Technique ◽  
Snow Albedo ◽  
Considerable Uncertainty ◽  
Clear Sky ◽  
Surface Areas ◽  
Microphysical Properties ◽  
Sky Conditions

Abstract. Broadband snow albedo can range from 0.3 to 0.9 depending on microphysical properties and light-absorbing particle (LAP) concentrations. Beyond the widely observed direct and visibly apparent effect of darkening snow, it is still unclear how LAPs influence snow albedo feedbacks. To investigate LAPs' indirect effect on snow albedo feedbacks, we developed and calibrated the Near-Infrared Emitting and Reflectance-Monitoring Dome (NERD) and monitored bidirectional reflectance factors (BRFs) hourly after depositing dust and black carbon (BC) particles onto experimental snow surfaces. After comparing snow infrared BRFs to snow specific surface areas (SSAs), we found that both measured and modeled snow infrared BRFs are correlated with snow SSA. These results, however, demonstrate a considerable uncertainty of ±10 m2 kg−1 in the determination of snow SSA from our BRF measurements. The nondestructive technique for snow SSA retrieval that we present here can be further developed for science applications that require rapid in situ snow SSA measurements. After adding large amounts of dust and BC to snow, we found more rapid decreasing of snow BRFs and SSAs in snow with added LAPs compared to natural (clean) snow but only during clear-sky conditions. These results suggest that deposition of LAPs onto snow can accelerate snow metamorphism via a net positive snow grain-size feedback.

scholarly journals Improving Daytime Planetary Boundary Layer Height Determination from CALIOP: Validation Based on Ground-Based Lidar Station

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Zhao Liu ◽  
Augustin Mortier ◽  
Zhengqiang Li ◽  
Weizhen Hou ◽  
Philippe Goloub ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Planetary Boundary Layer ◽  
Absolute Difference ◽  
Aerosol Layer ◽  
Planetary Boundary Layer Height ◽  
Layer Height ◽  
Boundary Layer Height ◽  
Clear Sky ◽  
Ground Based Lidar ◽  
Sky Conditions

An integrated algorithm by combining the advantages of the wavelet covariance method and the improved maximum variance method was developed to determine the planetary boundary layer height (PBLH) from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements, and an aerosol fraction threshold was applied to the integrated algorithm considering the applicability of the two methods. We compared the CALIOP retrieval with the measurements of PBLH derived from nine years of ground-based Lidar synchronous observations located in Lille, north of France. The results indicate that a good correlation (R≥0.79) exists between the PBLHs derived from CALIOP and ground-based Lidar under clear sky conditions. The mean absolute differences of PBLHs are, respectively, of 206 m and 106 m before and after the removal of the aloft aerosol layer. The results under cloudy sky conditions show a lower agreement (R=0.48) in regard of the comparisons performed under clear sky conditions. Besides, the spatial correlation of PBLHs decreases with the increasing spatial distance between CALIOP footprint and Lille observation platform. Based on the above analysis, the PBLHs can be effectively derived by the integrated algorithm under clear sky conditions, while larger mean absolute difference (i.e., 527 m) exists under cloudy sky conditions.

scholarly journals Improved retrieval of direct and diffuse downwelling surface shortwave flux in cloudless atmosphere using dynamic estimates of aerosol content and type: application to the LSA-SAF project

2014 ◽  
Vol 14 (6) ◽  
pp. 8333-8392 ◽  
Author(s):  
X. Ceamanos ◽  
D. Carrer ◽  
J.-L. Roujean
Keyword(s):  
Land Surface ◽  
Radiative Forcing ◽  
High Sensitivity ◽  
Incident Radiation ◽  
Aerosol Layer ◽  
Production Chain ◽  
Weather Forecasts ◽  
Clear Sky ◽  
Atmospheric Inputs ◽  
Sky Conditions

Abstract. Downwelling surface shortwave flux (DSSF) is a key parameter to address many climate, meteorological, and solar energy issues. Under clear sky conditions, DSSF is particularly sensitive to the variability both in time and space of the aerosol load and chemical composition. Hitherto, this dependence has not been properly addressed by the Satellite Application Facility on Land Surface Analysis (LSA-SAF), which operationally disseminates instantaneous DSSF products over the continents since 2005 considering unchanging aerosol conditions. In the present study, an efficient method is proposed for DSSF retrieval that will overcome the limitations of the current LSA-SAF product. This method referred to as SIRAMix (Surface Incident Radiation estimation using Aerosol Mixtures) is based on an accurate physical parameterization that is coupled with a radiative transfer-based look up table of aerosol properties. SIRAMix considers an aerosol layer constituted of several major aerosol species that are conveniently mixed to match real aerosol conditions. This feature of SIRAMix allows it to provide not only accurate estimates of global DSSF but also the direct and diffuse DSSF components, which are crucial radiative terms in many climatological applications. The implementation of SIRAMix is tested in the present article using atmospheric inputs from the European Center for Medium-Range Weather Forecasts (ECMWF). DSSF estimates provided by SIRAMix are compared against instantaneous DSSF measurements taken at several ground stations belonging to several radiation measurement networks. Results show an average root mean square error (RMSE) of 23.6 W m−2, 59.1 W m−2, and 44.9 W m−2 for global, direct, and diffuse DSSF, respectively. These scores decrease the average RMSE obtained for the current LSA-SAF product by 18.6%, which only provides global DSSF for the time being, and, to a lesser extent, for the state of the art in matter of DSSF retrieval (RMSE decrease of 10.9%, 6.5%, and 19.1% for global, direct, and diffuse DSSF with regard to the McClear algorithm). In addition to the retrieval of DSSF, SIRAMix is able to quantify the radiative forcing at the surface due to a given atmospheric component (e.g., gases or aerosols). The main limitation of the proposed approach is its high sensitivity to the quality of the ECMWF aerosol inputs, which is proved to be sufficiently accurate for reanalyses but not for forecasted data. This outcome will be taken into account in the forthcoming implementation of SIRAMix in the operational production chain of the LSA-SAF project.

scholarly journals Model Selection in Atmospheric Remote Sensing with an Application to Aerosol Retrieval from DSCOVR/EPIC, Part 1: Theory

2020 ◽  
Vol 12 (22) ◽  
pp. 3724
Author(s):  
Sruthy Sasi ◽  
Vijay Natraj ◽  
Víctor Molina García ◽  
Dmitry S. Efremenko ◽  
Diego Loyola ◽  
...  
Keyword(s):  
Model Selection ◽  
Optical Thickness ◽  
Marginal Likelihood ◽  
Likelihood Estimation ◽  
Retrieval Algorithm ◽  
Aerosol Layer ◽  
Aerosol Model ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Atmospheric Remote Sensing

The retrieval of aerosol and cloud properties such as their optical thickness and/or layer/top height requires the selection of a model that describes their microphysical properties. We demonstrate that, if there is not enough information for an appropriate microphysical model selection, the solution’s accuracy can be improved if the model uncertainty is taken into account and appropriately quantified. For this purpose, we design a retrieval algorithm accounting for the uncertainty in model selection. The algorithm is based on (i) the computation of each model solution using the iteratively regularized Gauss–Newton method, (ii) the linearization of the forward model around the solution, and (iii) the maximum marginal likelihood estimation and the generalized cross-validation to estimate the optimal model. The algorithm is applied to the retrieval of aerosol optical thickness and aerosol layer height from synthetic measurements corresponding to the Earth Polychromatic Imaging Camera (EPIC) instrument onboard the Deep Space Climate Observatory (DSCOVR) satellite. Our numerical simulations show that the heuristic approach based on the thesolution minimizing the residual, which is frequently used in literature, is completely unrealistic when both the aerosol model and surface albedo are unknown.

scholarly journals Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm

2019 ◽  
Author(s):  
Daniel J. Miller ◽  
Michal Segal-Rozenhaimer ◽  
Kirk Knobelspiesse ◽  
Jens Redemann ◽  
Brian Cairns ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Aerosol Layer ◽  
Large Dataset ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Spectral Bands ◽  
Neural Network Algorithm ◽  
Cloud Optical Thickness ◽  
Neural Network Input

Abstract. In this study we developed a neural network (NN) that can be used to relate a large dataset of multi-angular and multi-spectral polarimetric remote sensing observations to retrievals of cloud microphysical properties. This effort builds upon our previous work, which explored the sensitivity of neural network input, architecture, and other design requirements for this type of remote sensing problem. In particular this work introduces a framework for appropriately weighting total and polarized reflectances, which have vastly different magnitudes and measurement uncertainties. The NN is trained using an artificial training set and applied to Research Scanning Polarimeter (RSP) data obtained during the ORACLES field campaign (Observations of Aerosols above Clouds and their Interactions). The polarimetric RSP observations are unique in that they observe the same cloud from a very large number of angles within a variety of spectral bands resulting in a large dataset that can be explored rapidly with a NN approach. The usefulness applying a NN to a dataset such as this one stems from the possibility of rapidly obtaining a retrieval that could be subsequently applied as a first-guess for slower but more rigorous physical-based retrieval algorithms. This approach could be particularly advantageous for more complicated atmospheric retrievals – such as when an aerosol layer lies above clouds like in ORACLES. For the ORACLES 2016 dataset comparisons of the NN and standard parametric polarimetric (PP) cloud retrieval give reasonable results for droplet effective radius (re : R = 0.756, RMSE = 1.74 μm) and cloud optical thickness (τ : R = 0.950, RMSE = 1.82). This level of statistical agreement is shown to be similar to comparisons between the two most well-established cloud retrievals, namely the the polarimetric cloud retrieval and the bispectral total reflectance cloud retrieval. The NN retrievals from the ORACLES 2017 dataset result in retrievals of re (R = 0.54, RMSE = 4.77 μm) and τ (R = 0.785, RMSE = 5.61) that behave much more poorly. In particular we found that our NN retrieval approach does not perform well for thin (τ  <3), inhomogeneous, or broken clouds. We also found that correction for above-cloud atmospheric absorption improved the NN retrievals moderately – but retrievals without this correction still behaved similarly to existing cloud retrievals with a slight systematic offset.

scholarly journals Empirically derived parameterizations of the direct aerosol radiative effect based on ORACLES aircraft observations

2021 ◽  
Vol 14 (1) ◽  
pp. 567-593
Author(s):  
Sabrina P. Cochrane ◽  
K. Sebastian Schmidt ◽  
Hong Chen ◽  
Peter Pilewskie ◽  
Scott Kittelman ◽  
...  
Keyword(s):  
Asymmetry Parameter ◽  
Single Scattering ◽  
Radiative Effect ◽  
Study Region ◽  
Clear Sky ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Aerosol Property ◽  
532 Nm ◽  
Aerosol Radiative

Abstract. In this paper, we use observations from the NASA ORACLES (ObseRvations of CLouds above Aerosols and their intEractionS) aircraft campaign to develop a framework by way of two parameterizations that establishes regionally representative relationships between aerosol-cloud properties and their radiative effects. These relationships rely on new spectral aerosol property retrievals of the single scattering albedo (SSA) and asymmetry parameter (ASY). The retrievals capture the natural variability of the study region as sampled, and both were found to be fairly narrowly constrained (SSA: 0.83 ± 0.03 in the mid-visible, 532 nm; ASY: 0.54 ± 0.06 at 532 nm). The spectral retrievals are well suited for calculating the direct aerosol radiative effect (DARE) since SSA and ASY are tied directly to the irradiance measured in the presence of aerosols – one of the inputs to the spectral DARE. The framework allows for entire campaigns to be generalized into a set of parameterizations. For a range of solar zenith angles, it links the broadband DARE to the mid-visible aerosol optical depth (AOD) and the albedo (α) of the underlying scene (either clouds or clear sky) by way of the first parameterization: P(AOD, α). For ORACLES, the majority of the case-to-case variability of the broadband DARE is attributable to the dependence on the two driving parameters of P(AOD, α). A second, extended, parameterization PX(AOD, α, SSA) explains even more of the case-to-case variability by introducing the mid-visible SSA as a third parameter. These parameterizations establish a direct link from two or three mid-visible (narrowband) parameters to the broadband DARE, implicitly accounting for the underlying spectral dependencies of its drivers. They circumvent some of the assumptions when calculating DARE from satellite products or in a modeling context. For example, the DARE dependence on aerosol microphysical properties is not explicit in P or PX because the asymmetry parameter varies too little from case to case to translate into appreciable DARE variability. While these particular DARE parameterizations only represent the ORACLES data, they raise the prospect of generalizing the framework to other regions.

scholarly journals Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm

2020 ◽  
Vol 13 (6) ◽  
pp. 3447-3470
Author(s):  
Daniel J. Miller ◽  
Michal Segal-Rozenhaimer ◽  
Kirk Knobelspiesse ◽  
Jens Redemann ◽  
Brian Cairns ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Aerosol Layer ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Spectral Bands ◽  
Neural Network Algorithm ◽  
Cloud Optical Thickness ◽  
Design Requirements ◽  
Neural Network Input

Abstract. In this study we developed a neural network (NN) that can be used to retrieve cloud microphysical properties from multiangular and multispectral polarimetric remote sensing observations. This effort builds upon our previous work, which explored the sensitivity of neural network input, architecture, and other design requirements for this type of remote sensing problem. In particular this work introduces a framework for appropriately weighting total and polarized reflectances, which have vastly different magnitudes and measurement uncertainties. The NN is trained using an artificial training set and applied to research scanning polarimeter (RSP) data obtained during the ORACLES field campaign (ObseRvations of Aerosols above CLouds and their intEractionS). The polarimetric RSP observations are unique in that they observe the same cloud from a very large number of angles within a variety of spectral bands, resulting in a large dataset that can be explored rapidly with a NN approach. The usefulness of applying a NN to a dataset such as this one stems from the possibility of rapidly obtaining a retrieval that could be subsequently applied as a first guess for slower but more rigorous physical-based retrieval algorithms. This approach could be particularly advantageous for more complicated atmospheric retrievals – such as when an aerosol layer lies above clouds like in ORACLES. For RSP observations obtained during ORACLES 2016, comparisons between the NN and standard parametric polarimetric (PP) cloud retrieval give reasonable results for droplet effective radius (re: R=0.756, RMSE=1.74 µm) and cloud optical thickness (τ: R=0.950, RMSE=1.82). This level of statistical agreement is shown to be similar to comparisons between the two most well-established cloud retrievals, namely, the polarimetric and the bispectral total reflectance cloud retrievals. The NN retrievals from the ORACLES 2017 dataset result in retrievals of re (R=0.54, RMSE=4.77 µm) and τ (R=0.785, RMSE=5.61) that behave much more poorly. In particular we found that our NN retrieval approach does not perform well for thin (τ<3), inhomogeneous, or broken clouds. We also found that correction for above-cloud atmospheric absorption improved the NN retrievals moderately – but retrievals without this correction still behaved similarly to existing cloud retrievals with a slight systematic offset.

Quantification of parallax errors in sky simulator domes for clear sky conditions

2002 ◽  
Vol 34 (4) ◽  
pp. 313-327 ◽  
Author(s):  
J Mardaljevic
Keyword(s):  
High Accuracy ◽  
Scale Model ◽  
Dome A ◽  
Clear Sky ◽  
Design Goal ◽  
Simulation Techniques ◽  
Practical Operation ◽  
Bounded Volume ◽  
Sky Conditions ◽  
Computer Simulation Techniques

Scale model illuminance measurements in sky simulator domes are inherently subject to parallax errors. The magnitude of these errors under a number of Commission Internationale de l’É clairage (CIE) clear sky configurations is quantified using computer simulation techniques. In practical operation of a sky simulator dome, a second parallax error in the normalization measurements for horizontal illuminance is likely to compound the parallax error in the other illuminance measurements. This additional parallax error is accounted for in the simulations. The concept of a parallax-bounded volume is introduced. This is the volume of the dome which, on the basis of parallax alone, must contain a scale model if it is not to be subject to errors in the measurement of illuminance beyond a given tolerance. The findings indicate that, on the basis of a credible design goal for the sky simulator dome, high accuracy illuminance predictions (610%) are practically unattainable.

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