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.

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 Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

2017 ◽  
Vol 10 (9) ◽  
pp. 3215-3230 ◽  
Author(s):  
André Ehrlich ◽  
Eike Bierwirth ◽  
Larysa Istomina ◽  
Manfred Wendisch
Keyword(s):  
Remote Sensing ◽  
Grain Size ◽  
Sea Ice ◽  
Optical Thickness ◽  
Liquid Water ◽  
Retrieval Algorithm ◽  
Continuous Transition ◽  
Cloud Properties ◽  
Cloud Optical Thickness ◽  
The Impact

Abstract. The passive solar remote sensing of cloud properties over highly reflecting ground is challenging, mostly due to the low contrast between the cloud reflectivity and that of the underlying surfaces (sea ice and snow). Uncertainties in the retrieved cloud optical thickness τ and cloud droplet effective radius reff, C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the generally unknown effective snow grain size reff, S. Therefore, in a first step the effects of the assumed snow grain size are systematically quantified for the conventional bispectral retrieval technique of τ and reff, C for liquid water clouds. In general, the impact of uncertainties of reff, S is largest for small snow grain sizes. While the uncertainties of retrieved τ are independent of the cloud optical thickness and solar zenith angle, the bias of retrieved reff, C increases for optically thin clouds and high Sun. The largest deviations between the retrieved and true original values are found with 83 % for τ and 62 % for reff, C. In the second part of the paper a retrieval method is presented that simultaneously derives all three parameters (τ, reff, C, reff, S) and therefore accounts for changes in the snow grain size. Ratios of spectral cloud reflectivity measurements at the three wavelengths λ1 = 1040 nm (sensitive to reff, S), λ2 = 1650 nm (sensitive to τ), and λ3 = 2100 nm (sensitive to reff, C) are combined in a trispectral retrieval algorithm. In a feasibility study, spectral cloud reflectivity measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART) during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012) were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct snow-covered sea ice edge are analysed. The retrieved values of τ, reff, C, and reff, S show a continuous transition of cloud properties across snow-covered sea ice and open water and are consistent with estimates based on satellite data. It is shown that the uncertainties of the trispectral retrieval increase for high values of τ, and low reff, S but nevertheless allow the effective snow grain size in cloud-covered areas to be estimated.

scholarly journals Comparison of CALIPSO-Like, LaRC, and MODIS Retrievals of Ice-Cloud Properties over SIRTA in France and Florida during CRYSTAL-FACE

2007 ◽  
Vol 46 (3) ◽  
pp. 249-272 ◽  
Author(s):  
M. Chiriaco ◽  
H. Chepfer ◽  
P. Minnis ◽  
M. Haeffelin ◽  
S. Platnick ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiant Energy ◽  
Energy System ◽  
Effective Radius ◽  
Cloud Detection ◽  
Regional Study ◽  
Crystal Face ◽  
Ice Clouds ◽  
Microphysical Properties ◽  
Cloud Properties

Abstract This study compares cirrus-cloud properties and, in particular, particle effective radius retrieved by a Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)-like method with two similar methods using Moderate-Resolution Imaging Spectroradiometer (MODIS), MODIS Airborne Simulator (MAS), and Geostationary Operational Environmental Satellite imagery. The CALIPSO-like method uses lidar measurements coupled with the split-window technique that uses the infrared spectral information contained at the 8.65-, 11.15-, and 12.05-μm bands to infer the microphysical properties of cirrus clouds. The two other methods, using passive remote sensing at visible and infrared wavelengths, are the operational MODIS cloud products (using 20 spectral bands from visible to infrared, referred to by its archival product identifier MOD06 for MODIS Terra) and MODIS retrievals performed by the Clouds and the Earth’s Radiant Energy System (CERES) team at Langley Research Center (LaRC) in support of CERES algorithms (using 0.65-, 3.75-, 10.8-, and 12.05-μm bands); the two algorithms will be referred to as the MOD06 and LaRC methods, respectively. The three techniques are compared at two different latitudes. The midlatitude ice-clouds study uses 16 days of observations at the Palaiseau ground-based site in France [Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA)], including a ground-based 532-nm lidar and the MODIS overpasses on the Terra platform. The tropical ice-clouds study uses 14 different flight legs of observations collected in Florida during the intensive field experiment known as the Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida Area Cirrus Experiment (CRYSTAL-FACE), including the airborne cloud-physics lidar and the MAS. The comparison of the three methods gives consistent results for the particle effective radius and the optical thickness but discrepancies in cloud detection and altitudes. The study confirms the value of an active remote sensing method (CALIPSO like) for the study of subvisible ice clouds, in both the midlatitudes and Tropics. Nevertheless, this method is not reliable in optically very thick tropical ice clouds, because of their particular microphysical properties.

scholarly journals Lidar Studies on The Optical Characteristics of High Altitude Cirrus Clouds at A Low Latitiude Station, Gadanki (13.5°N , 79.2°E ) India

2014 ◽  
Vol XL-8 ◽  
pp. 253-256
Author(s):  
G. S. Jayeshlal ◽  
M. Satyanarayana ◽  
G. S. Motty ◽  
R. K. Dhaman ◽  
V. Krishnakumar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Altitude ◽  
Radiation Effects ◽  
Global Climate ◽  
Short Wave ◽  
Cirrus Clouds ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Radio Detection ◽  
Lidar Measurements

Light Detection and Ranging (LIDAR) which is analogous to Radio Detection And Ranging (RADAR), has become an important and unique technology for atmospheric research and applications. The technology is widely used for the remote sensing of the Earth’s atmosphere, oceans, vegetation and the characteristics of Earth’s topography. Remote sensing of atmosphere, for its structure, composition and dynamics, is one of the proven applications of the lidar systems. More importantly the lidar technique is applied for the study of clouds, aerosols and minor constituents in the atmosphere. It provides the information on the above with good spatial and temporal resolutions and accuracy. The high altitude cirrus clouds which play an important role in the Earth’s radiative budget and global climate can be studied by using the LIDAR system. These clouds absorb long-wave outgoing radiation from Earth’s surface while reflecting part of the incoming short-wave solar radiation. Lidar measurements are useful in deriving the altitude, top height, bottom height and the optical properties of cirrus clouds, which are essential in understanding the cloud-radiation effects. The optical depth, the effective lidar ratio and the depolarization of the clouds are also derived by inverting the lidar signals from the cirrus clouds. In this paper we present the results on the lidar derived optical and microphysical properties of the cirrus clouds at a tropical station Gadanki (13.5&deg;N, 79.2&deg;E) India during two year period from 2009 to210. The seasonal variations of the cloud properties during the observation period are presented and discussed with reference to earlier period.

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 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 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 Remote sensing of aerosols over snow using infrared AATSR observations

2011 ◽  
Vol 4 (1) ◽  
pp. 33-71 ◽  
Author(s):  
L. G. Istomina ◽  
W. von Hoyningen-Huene ◽  
A. A. Kokhanovsky ◽  
E. Schultz ◽  
J. P. Burrows
Keyword(s):  
Remote Sensing ◽  
Ground Level ◽  
High Arctic ◽  
Visible Range ◽  
Small Scale ◽  
Aerosol Layer ◽  
Easterly Wind ◽  
Cloud Properties ◽  
Ir Reflectance ◽  
Along Track

Abstract. Infrared (IR) retrievals of aerosol optical thickness (AOT) are challenging because of the low reflectance of aerosol layer at longer wavelengths. In this paper we present a closer analysis of this problem, performed with radiative transfer (RT) simulations for coarse and accumulation mode of four main aerosol components. It shows the strong angular dependence of aerosol IR reflectance at low solar elevations resulting from significant asymmetry of aerosol phase function at these wavelengths. This results in detectable values of aerosol IR reflectance at certain non-nadir observation angles providing the advantage of multiangle remote sensing instruments for a retrieval of AOT at longer wavelengths. Such retrievals can be of importance e.g. in case of a very strong effect of the surface on the top of atmosphere (TOA) reflectance in the visible range of spectrum. In current work, a new method to retrieve AOT over snow has been developed using the measurements of Advanced Along Track Scanning Radiometer (AATSR) on board the ENVISAT satellite. The algorithm uses AATSR channel at 3.7 μm and utilizes its dual-viewing observation technique implying the forward view with an observation zenith angle around 55 degrees and the nadir view. It includes cloud/snow discrimination, extraction of the atmospheric reflectance out of measured brightness temperature (BT) at 3.7 μm, interpolation of look-up tables (LUTs) for a given aerosol reflectance. The algorithm uses LUTs, separately simulated with RT forward calculations. The resulting AOT at 500 nm is estimated from the value at 3.7 μm using a fixed Angström parameter. The presented method has been validated against ground-based Aerosol Robotic Network (AERONET) data for 4 high Arctic stations and shows good agreement. A case study has been performed at W-Greenland on 5 July 2008. The day before was characterized by a noticeable dust event. The retrieved AOT maps of the region show a clear increase of AOT in the Kangerlussuaq area. The area of increased AOT was detected on 5 July on the ice sheet east of Kangelussuaq opposite to the observed north easterly wind at ground level. This position can be explained by a small scale atmospheric circulation transporting the mobilised mineral dust upslope, after its intrusion into the upper branch of the circulation. The performed study of atmospheric reflectance at 3.7 μm also shows possibilities of detection and retrievals of cloud properties over snow surface.

scholarly journals Remote sensing of aerosols over snow using infrared AATSR observations

2011 ◽  
Vol 4 (6) ◽  
pp. 1133-1145 ◽  
Author(s):  
L. G. Istomina ◽  
W. von Hoyningen-Huene ◽  
A. A. Kokhanovsky ◽  
E. Schultz ◽  
J. P. Burrows
Keyword(s):  
Remote Sensing ◽  
Ground Level ◽  
High Arctic ◽  
Visible Spectral Range ◽  
Small Scale ◽  
Aerosol Layer ◽  
Easterly Wind ◽  
Accumulation Mode ◽  
Cloud Properties ◽  
Ir Reflectance

Abstract. Infrared (IR) retrievals of aerosol optical thickness (AOT) are challenging because of the low reflectance of aerosol layer at longer wavelengths. In this paper we present a closer analysis of this problem, performed with radiative transfer (RT) simulations for coarse and accumulation mode of four main aerosol components. It shows the strong angular dependence of aerosol IR reflectance at low solar elevations resulting from the significant asymmetry of aerosol phase function at these wavelengths. This results in detectable values of aerosol IR reflectance at certain non-nadir observation angles providing the advantage of multiangle remote sensing instruments for a retrieval of AOT at longer wavelengths. Such retrievals can be of importance e.g. in case of a very strong effect of the surface on the top of atmosphere (TOA) reflectance in the visible spectral range. In the current work, a new method to retrieve AOT of the coarse and accumulation mode particles over snow has been developed using the measurements of Advanced Along Track Scanning Radiometer (AATSR) on board the ENVISAT satellite. The algorithm uses AATSR channel at 3.7 μm and utilizes its dual-viewing observation technique, implying the forward view with an observation zenith angle of around 55 degrees and the nadir view. It includes cloud/snow discrimination, extraction of the atmospheric reflectance out of measured brightness temperature (BT) at 3.7 μm, and interpolation of look-up tables (LUTs) for a given aerosol reflectance. The algorithm uses LUTs, separately simulated with RT forward calculations. The resulting AOT at 500 nm is estimated from the value at 3.7 μm using a fixed Angström parameter. The presented method has been validated against ground-based Aerosol Robotic Network (AERONET) data for 4 high Arctic stations and shows good agreement. A case study has been performed at W-Greenland on 5 July 2008. The day before was characterized by a noticeable dust event. The retrieved AOT maps of the region show a clear increase of AOT in the Kangerlussuaq area. The area of increased AOT was detected on 5 July on the ice sheet east of Kangelussuaq, opposite to the observed north easterly wind at ground level. This position can be explained by a small scale atmospheric circulation transporting the mobilized mineral dust upslope, after its intrusion into the upper branch of the circulation. The performed study of atmospheric reflectance at 3.7 μm also shows possibilities for the detection and retrievals of cloud properties over snow surfaces.

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