Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach

This paper describes a neural network algorithm for the estimation of liquid water cloud optical properties from the Polarization and Directionality of Earth's Reflectances-3 (POLDER-3) instrument aboard the Polarization &amp; Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The algorithm has been trained on synthetic multi-angle, multi-wavelength measurements of reflectance and polarization and has been applied to the processing of 1 year of POLDER-3 data. Comparisons of the retrieved cloud properties with Moderate Resolution Imaging Spectroradiometer (MODIS) products show that the neural network algorithm has a low bias of around 2 in cloud optical thickness (COT) and between 1 and 2&thinsp;µm in the cloud effective radius. Comparisons with existing POLDER-3 datasets suggest that the proposed scheme may have enhanced capabilities for cloud effective radius retrieval, at least over land. An additional feature of the presented algorithm is that it provides COT and effective radius retrievals at the native POLDER-3 Level 1B pixel level.</p

Retrieval of Non-Spherical Dust Aerosol Properties from Satellite Observations

An accurate and generalized global retrieval algorithm from satellite observations is a prerequisite to understand the radiative effect of atmospheric aerosols on the climate system. Current operational aerosol retrieval algorithms are limited by the inversion schemes and suffering from the non-uniqueness problem. In order to solve these issues, a new algorithm is developed for the retrieval of non-spherical dust aerosol over land using multi-angular radiance and polarized measurements of the POLDER (POLarization and Directionality of the Earth’s Reflectances) and wide spectral high-resolution measurements of the MODIS (MODerate resolution Imaging Spectro-radiometer). As the first step to account for the non-sphericity of irregularly shaped dust aerosols in the light scattering problem, the spheroidal model is introduced. To solve the basic electromagnetic wave scattering problem by a single spheroid, we developed an algorithm, by transforming the transcendental infinite-continued-fraction-formeigen equation into a symmetric tri-diagonal linear system, for the calculation of the spheroidal angle function, radial functions of the first and second kind, as well as the corresponding first order derivatives. A database is developed subsequently to calculate the bulk scattering properties of dust aerosols for each channel of the satellite instruments. For the purpose of simulation of satellite observations, a code is developed to solve the VRTE (Vector Radiative Transfer Equation) for the coupled atmosphere-surface system using the adding-doubling technique. An alternative fast algorithm, where all the solid angle integrals are converted to summations on an icosahedral grid, is also proposed to speed-up the code. To make the model applicable to various land and ocean surfaces, a surface BRDF (Bidirectional Reflectance Distribution Function) library is embedded into the code. Considering the complimentary features of the MODIS and the POLDER, the collocated measurements of these two satellites are used in the retrieval process. To reduce the time spent on the simulation of dust aerosol scattering properties, a single-scattering property database of tri-axial ellipsoid is incorporated. In addition, atmospheric molecule correction is considered using the LBLRTM (Line-By-Line Ra- diative Transfer Model). The Levenberg-Marquardt method was employed to retrieve all the interested dust aerosol parameters and surface parameters simultaneously. As an example, dust aerosol properties retrieved over the Sahara Desert are presented

Retrieval of Liquid Water Cloud Properties from POLDER-3 Measurements Using a Neural Network Ensemble Approach

This paper describes a neural network algorithm for the estimation of liquid water cloud optical properties from the Polarization and Directionality of Earth's Reflectances-3 (POLDER-3) instrument aboard the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The algorithm has been trained on synthetic multi-angle, multi-wavelength measurements of reflectance and polarization and has been applied to the processing of 1 year of POLDER-3 data. Comparisons of the retrieved cloud properties with Moderate Resolution Imaging Spectroradiometer (MODIS) products show that the neural network algorithm has a low bias of around 2 in cloud optical thickness (COT) and between 1 and 2m in the cloud effective radius. Comparisons with existing POLDER-3 datasets suggest that the proposed scheme may have enhanced capabilities for cloud effective radius retrieval, at least over land. An additional feature of the presented algorithm is that it provides COT and effective radius retrievals at the native POLDER-3 Level 1B pixel level

Surface reflectance characteristics of Langjökull, Iceland

ATM, ETM+ and MODIS radiance data collected for the surface of Langjokull during August 2007. Radiance data from the three different instruments was then converted to surface broadband albedo using a number of different methods. The differences between the methods used to derive the surface albedo were then compared, followed by an analysis of the impact of spatial resolution and sample size on the differences between the derived surface albedo. Analysis of the different datasets combined with transect sample analyses of the derived surface albedos indicated that the most precise methods of deriving albedo from the ATM and ETM+ instruments was through the use of FLAASH for the ATM and dark pixel atmospheric correction or 6S atmospheric correction for the ETM+. These first analyses also indicated that the MODIS data was broadly in agreement with these two datasets. However, analysis of the differences between the different spatial resolution datasets across the three different spatial extents revealed a number of differences between the datasets. At the smallest scale the lower resolution datasets were shown to contain a bias towards representing certain areas in individual pixels. Consequently, the correlation between the datasets was reduced across greater sample sizes. Moreover, this bias was shown to prevent the ETM+ and MODIS imagery from averaging highly variable surfaces. The result of these measurement characteristics was for the low resolution instruments to overestimate the albedo value of snow in the accumulation area, greatly underestimate the albedo in the firn zone and struggle to capture the spatial variability in the ablation zone. The implications of these characteristics for lower resolution data have consequently been outlined, with the extent of the differences between both the low resolution datasets and the ATM dataset, indicating that where possible ETM+ imagery should be used to monitor surface albedo characteristics as it was the most closely correlated with the ATM data

Satellite Observations of Desert Dust-induced Himalayan Snow Darkening

The optically thick aerosol layer along the southern edge of the Himalaya has been subject of several recent investigations relating to its radiative impacts on the South Asian summer monsoon and regional climate forcing. Prior to the onset of summer monsoon, mineral dust from southwest Asian deserts is transported over the Himalayan foothills on an annual basis. Episodic dust plumes are also advected over the Himalaya, visible as dust-laden snow surface in satellite imagery, particularly in western Himalaya. We examined spectral surface reflectance retrieved from spaceborne MODIS observations that show characteristic reduction in the visible wavelengths (0.47 nm) over western Himalaya, associated with dust-induced solar absorption. Case studies as well as seasonal variations of reflectance indicate a significant gradient across the visible (0.47 nm) to near-infrared (0.86 nm) spectrum (VIS-NIR), during premonsoon period. Enhanced absorption at shorter visible wavelengths and the resulting VIS-NIR gradient is consistent with model calculations of snow reflectance with dust impurity. While the role of black carbon in snow cannot be ruled out, our satellite-based analysis suggests the observed spectral reflectance gradient dominated by dust-induced solar absorption during premonsoon season. From an observational viewpoint, this study underscores the importance of mineral dust deposition toward darkening of the western Himalayan snow cover, with potential implications to accelerated seasonal snowmelt and regional snow albedo feedbacks

Retrieval of aerosol optical depth over the Arctic cryosphere during spring and summer using satellite observations

The climate in the Arctic has warmed much more quickly in the last 2 to 3 decades than at the mid-latitudes, i.e., during the Arctic amplification (AA) period. Radiative forcing in the Arctic is influenced both directly and indirectly by aerosols. However, their observation from ground or airborne instruments is challenging, and thus measurements are sparse. In this study, total aerosol optical depth (AOD) is determined from top-of-atmosphere reflectance measurements by the Advanced Along-Track Scanning Radiometer (AATSR) on board ENVISAT over snow and ice in the Arctic using a retrieval called AEROSNOW for the period 2003 to 2011. AEROSNOW incorporates an existing aerosol retrieval algorithm with a cloud-masking algorithm, alongside a novel quality-flagging methodology specifically designed for implementation in the high Arctic region (≥ 72∘ N). We use the dual-viewing capability of the AATSR instrument to accurately determine the contribution of aerosol to the reflection at the top of the atmosphere for observations over the bright surfaces of the cryosphere in the Arctic. The AOD is retrieved assuming that the surface reflectance observed by the satellite can be well parameterized by a bidirectional snow reflectance distribution function (BRDF). The spatial distribution of AOD shows that high values in spring (March, April, May) and lower values in summer (June, July, August) are observed. The AEROSNOW AOD values are consistent with those from collocated Aerosol Robotic Network (AERONET) measurements, with no systematic bias found as a function of time. The AEROSNOW AOD in the high Arctic was validated by comparison with ground-based measurements at the PEARL, OPAL, Hornsund, and Thule stations. The AEROSNOW AOD value is less than 0.15 on average, and the linear regression of AEROSNOW and AERONET total AOD yields a slope of 0.98, a Pearson correlation coefficient of R=0.86, and a root mean square error (RMSE) of =0.01 for the monthly scale in both spring and summer. The AEROSNOW observation of increased AOD values over the high Arctic cryosphere during spring confirms clearly that Arctic haze events were well captured by this dataset. In addition, the AEROSNOW AOD results provide a novel and unique total AOD data product for the springtime and summertime from 2003 to 2011. These AOD values, retrieved from spaceborne observation, provide a unique insight into the high Arctic cryospheric region at high spatial resolution and temporal coverage.</p

Modeling Atmosphere-Ocean Radiative Transfer: A PACE Mission Perspective

The research frontiers of radiative transfer (RT) in coupled atmosphere-ocean systems are explored to enable new science and specifically to support the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) satellite mission. Given (i) the multitude of atmospheric and oceanic constituents at any given moment that each exhibits a large variety of physical and chemical properties and (ii) the diversity of light-matter interactions (scattering, absorption, and emission), tackling all outstanding RT aspects related to interpreting and/or simulating light reflected by atmosphere-ocean systems becomes impossible. Instead, we focus on both theoretical and experimental studies of RT topics important to the science threshold and goal questions of the PACE mission and the measurement capabilities of its instruments. We differentiate between (a) forward (FWD) RT studies that focus mainly on sensitivity to influencing variables and/or simulating data sets, and (b) inverse (INV) RT studies that also involve the retrieval of atmosphere and ocean parameters. Our topics cover (1) the ocean (i.e., water body): absorption and elastic/inelastic scattering by pure water (FWD RT) and models for scattering and absorption by particulates (FWD RT and INV RT); (2) the air-water interface: variations in ocean surface refractive index (INV RT) and in whitecap reflectance (INV RT); (3) the atmosphere: polarimetric and/or hyperspectral remote sensing of aerosols (INV RT) and of gases (FWD RT); and (4) atmosphere-ocean systems: benchmark comparisons, impact of the Earth's sphericity and adjacency effects on space-borne observations, and scattering in the ultraviolet regime (FWD RT). We provide for each topic a summary of past relevant (heritage) work, followed by a discussion (for unresolved questions) and RT updates

Modeling Atmosphere-Ocean Radiative Transfer: A PACE Mission Perspective

The research frontiers of radiative transfer (RT) in coupled atmosphere-ocean systems are explored to enable new science and specifically to support the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) satellite mission. Given (i) the multitude of atmospheric and oceanic constituents at any given moment that each exhibits a large variety of physical and chemical properties and (ii) the diversity of light-matter interactions (scattering, absorption, and emission), tackling all outstanding RT aspects related to interpreting and/or simulating light reflected by atmosphere-ocean systems becomes impossible. Instead, we focus on both theoretical and experimental studies of RT topics important to the science threshold and goal questions of the PACE mission and the measurement capabilities of its instruments. We differentiate between (a) forward (FWD) RT studies that focus mainly on sensitivity to influencing variables and/or simulating data sets, and (b) inverse (INV) RT studies that also involve the retrieval of atmosphere and ocean parameters. Our topics cover (1) the ocean (i.e., water body): absorption and elastic/inelastic scattering by pure water (FWD RT) and models for scattering and absorption by particulates (FWD RT and INV RT); (2) the air-water interface: variations in ocean surface refractive index (INV RT) and in whitecap reflectance (INV RT); (3) the atmosphere: polarimetric and/or hyperspectral remote sensing of aerosols (INV RT) and of gases (FWD RT); and (4) atmosphere-ocean systems: benchmark comparisons, impact of the Earth’s sphericity and adjacency effects on space-borne observations, and scattering in the ultraviolet regime (FWD RT). We provide for each topic a summary of past relevant (heritage) work, followed by a discussion (for unresolved questions) and RT updates

Modeling atmosphere-ocean radiative transfer: A PACE mission perspective

The research frontiers of radiative transfer (RT) in coupled atmosphere-ocean systems are explored to enable new science and specifically to support the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) satellite mission. Given (i) the multitude of atmospheric and oceanic constituents at any given moment that each exhibits a large variety of physical and chemical properties and (ii) the diversity of light-matter interactions (scattering, absorption, and emission), tackling all outstanding RT aspects related to interpreting and/or simulating light reflected by atmosphere-ocean systems becomes impossible. Instead, we focus on both theoretical and experimental studies of RT topics important to the science threshold and goal questions of the PACE mission and the measurement capabilities of its instruments. We differentiate between (a) forward (FWD) RT studies that focus mainly on sensitivity to influencing variables and/or simulating data sets, and (b) inverse (INV) RT studies that also involve the retrieval of atmosphere and ocean parameters. Our topics cover (1) the ocean (i.e., water body): absorption and elastic/inelastic scattering by pure water (FWD RT) and models for scattering and absorption by particulates (FWD RT and INV RT); (2) the air-water interface: variations in ocean surface refractive index (INV RT) and in whitecap reflectance (INV RT); (3) the atmosphere: polarimetric and/or hyperspectral remote sensing of aerosols (INV RT) and of gases (FWD RT); and (4) atmosphere-ocean systems: benchmark comparisons, impact of the Earth’s sphericity and adjacency effects on space-borne observations, and scattering in the ultraviolet regime (FWD RT). We provide for each topic a summary of past relevant (heritage) work, followed by a discussion (for unresolved questions) and RT updates

Analytical Modeling and Performance Assessment of Micropulse Photon-counting Lidar System

The melting of polar ice sheets and evidence of global warming continue to remain prominent research interests among scientists. To better understand global volumetric change of ice sheets, NASA intends to launch Ice, Cloud and land Elevation Satellite-2 (ICESat-2) in 2017. ICESat-2 employs a high frequency photon-counting laser altimeter, which will provide significantly greater spatial sampling. However, the combined effects of sub-beam complex surfaces, as well as system effects on returning photon distribution have not been systematically studied. To better understand the effects of various system attributes and to help improve the theory behind lidar sensing of complex surfaces, an analytical model using a first principles 3-D Monte Carlo approach is developed to predict system performance. Based on the latest ICESat-2 design, this analytical model simulates photons which propagate from the laser transmitter to the scene, and reflected to the detector model. A radiometric model is also applied in the synthetic scene. Such an approach allows the study of surface elevation retrieval accuracy for landscapes, as well as surface reflectivities. It was found that ICESat-2 will have a higher precision on a smoother surface, and a surface with smaller diffuse albedo will on average result in smaller bias. Furthermore, an adaptive density-based algorithm is developed to detect the surface returns without any geometrical knowledge. This proposed approach is implemented using the aforementioned simulated data set, as well as airborne laser altimeter measurement. Qualitative and quantitative results are presented to show that smaller laser footprint, smoother surface, and lower noise rate will improve accuracy of ground height estimation. Meanwhile, reasonable detection accuracy can also be achieved in estimating both ground and canopy returns for data generated using Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. This proposed approach was found to be generally applicable for surface and canopy finding from photon-counting laser altimeter data