Comments on "Remote sensing of aerosol properties from multi-wavelength and multi-pixel information over the ocean"

2018 ◽  
Author(s):  
Anonymous
2020 ◽  
Vol 12 (20) ◽  
pp. 3350
Author(s):  
Shashwat Shukla ◽  
Valentyn Tolpekin ◽  
Shashi Kumar ◽  
Alfred Stein

The Moon has a large potential for space exploration and mining valuable resources. In particular, 3He provides rich sources of non-radioactive fusion fuel to fulfill cislunar and Earth’s energy demands, if found economically feasible. The present study focuses on developing advanced techniques to prospect 3He resources on the Moon from multi-sensor remote sensing perspectives. It characterizes optical changes in regolith materials due to space weathering as a new retention parameter and introduces a novel machine learning inversion model for retrieving the physical properties of the regolith. Our analysis suggests that the reddening of the soil predominantly governs the retention, along with attenuated mafic band depths. Moreover, semi-variograms show that the spatial variability of 3He is aligned with the episodic weathering events at different timescales. We also observed that pyroclastic regoliths with high dielectric constant and increased surface scattering mechanisms exhibited a 3He abundant region. For ejecta cover, the retention was weakly associated with the dielectric contrast and a circular polarization ratio (CPR), mainly because of the 3He-deficient nature of the regolith. Furthermore, cross-variograms revealed inherent cyclicity attributed to the sequential process of weathering effects. Our study provides new insights into the physical nature and near-surface alterations of lunar regoliths that influence the spatial distribution and retention of solar wind implanted 3He.


2009 ◽  
Author(s):  
R. A. Perez-Herrera ◽  
S. Diaz ◽  
M. Fernández-Vallejo ◽  
M. López-Amo ◽  
M. A. Quintela ◽  
...  

2008 ◽  
Author(s):  
Shalei Song ◽  
Pingxiang Li ◽  
Wei Gong ◽  
Liangpei Zhang ◽  
Tao Chen

2017 ◽  
Vol 196 ◽  
pp. 238-252 ◽  
Author(s):  
R. Román ◽  
B. Torres ◽  
D. Fuertes ◽  
V.E. Cachorro ◽  
O. Dubovik ◽  
...  

Author(s):  
Gong Wei ◽  
Song Shalei ◽  
Zhu Bo ◽  
Shi Shuo ◽  
Li Faquan ◽  
...  

2004 ◽  
Vol 22 (10) ◽  
pp. 3347-3351 ◽  
Author(s):  
P. S. Pillai ◽  
K. Krishna Moorthy

Abstract. Simultaneous data on Aerosol Optical Depth (AOD) and size segregated, near-surface, aerosol mass concentration was obtained from a Multi wavelength Solar Radiometer (MWR) and Quartz Crystal Microbalance Impactor (QCM), respectively. These were used to examine the association between near-surface aerosol properties and columnar AOD. The spectral AODs were approximated to the Ångström relation τp=βλ-α, and the wavelength exponent α and turbidity coefficient β have been obtained. In general, α was found to be well associated with the relative abundance of accumulation mode aerosols (estimated from the simultaneous QCM data) while β followed the variations of the coarse mode aerosol mass concentration; the association being closer during periods of continental airmass.


2016 ◽  
Vol 9 (7) ◽  
pp. 2377-2389 ◽  
Author(s):  
Galina Wind ◽  
Arlindo M. da Silva ◽  
Peter M. Norris ◽  
Steven Platnick ◽  
Shana Mattoo ◽  
...  

Abstract. The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a “simulated radiance” product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land–ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers.This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled.In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model subgrid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms.Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (M{O/Y}D04). The M{O/Y}D04 product is of course normally produced from M{O/Y}D021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a M{O/Y}D021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source.We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.


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