Correction of MODIS aerosol retrieval for 3D radiative effects in broken cloud fields

2013 ◽  
Author(s):  
Guoyong Wen ◽  
Alexander Marshak ◽  
Lorraine Remer ◽  
Robert Levy ◽  
Norman Loeb ◽  
...  
Keyword(s):  
Radiative Effects ◽  
Aerosol Retrieval

The 3D radiative effects of clouds in aerosol retrieval: Can we remove them?

2009 ◽  
Author(s):  
Evgueni Kassianov ◽  
Mikhail Ovchinnikov ◽  
Larry K. Berg ◽  
Sally A. McFarlane ◽  
Connor Flynn ◽  
...  
Keyword(s):  
Radiative Effects ◽  
Aerosol Retrieval

Solar radiative effects of a Saharan dust plume observed during SAMUM assuming spheroidal model particles

Tellus B ◽  
2009 ◽  
Vol 61 (1) ◽  
Author(s):  
Sebastian Otto ◽  
Eike Bierwirth ◽  
Bernadett Weinzierl ◽  
Konrad Kandler ◽  
Michael Esselborn ◽  
...  
Keyword(s):  
Saharan Dust ◽  
Radiative Effects ◽  
Spheroidal Model

Radiative Effects on Boundary-Layer Flow of a Nanofluid on a Continuously Moving or Fixed Permeable Surface

2012 ◽  
Vol 5 (3) ◽  
pp. 176-183 ◽  
Author(s):  
Ali J. Chamkha ◽  
Mohamed Modather ◽  
Saber M.M. EL-Kabeir ◽  
Ahmed M. Rashad
Keyword(s):  
Boundary Layer ◽  
Boundary Layer Flow ◽  
Permeable Surface ◽  
Radiative Effects ◽  
Layer Flow

Radiative effects and chemical compositions of fine particles modulating urban heat island in Nanjing, China

2021 ◽  
Vol 247 ◽  
pp. 118201
Author(s):  
Hao Wu ◽  
Tijian Wang ◽  
Qin'geng Wang ◽  
Yang Cao ◽  
Yawei Qu ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Fine Particles ◽  
Chemical Compositions ◽  
Heat Island ◽  
Radiative Effects ◽  
Urban Heat

scholarly journals Research on Aerosol Retrieval Algorithm of Multi-parameter Optimization Model

2021 ◽  
Vol 658 (1) ◽  
pp. 012039
Author(s):  
Jiabao Yue ◽  
Donghai Xie ◽  
Jie Yu ◽  
Lin Zhu ◽  
Zhengyang He
Keyword(s):  
Parameter Optimization ◽  
Optimization Model ◽  
Retrieval Algorithm ◽  
Aerosol Retrieval

scholarly journals 250-m Aerosol Retrieval from FY-3 Satellite in Guangzhou

2021 ◽  
Vol 13 (5) ◽  
pp. 920
Author(s):  
Zhongting Wang ◽  
Ruru Deng ◽  
Pengfei Ma ◽  
Yuhuan Zhang ◽  
Yeheng Liang ◽  
...  
Keyword(s):  
Mixing Model ◽  
Blue Band ◽  
Aerosol Retrieval ◽  
Aerosol Distribution ◽  
Medium Resolution ◽  
Fine Spatial Resolution ◽  
Yearly Average ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Aerosol distribution with fine spatial resolution is crucial for atmospheric environmental management. This paper proposes an improved algorithm of aerosol retrieval from 250-m Medium Resolution Spectral Image (MERSI) data of Chinese FY-3 satellites. A mixing model of soil and vegetation was used to calculate the parameters of the algorithm from moderate-resolution imaging spectroradiometer (MODIS) reflectance products in 500-m resolution. The mixing model was used to determine surface reflectance in blue band, and the 250-m aerosol optical depth (AOD) was retrieved through removing surface contributions from MERSI data over Guangzhou. The algorithm was used to monitor two pollution episodes in Guangzhou in 2015, and the results displayed an AOD spatial distribution with 250-m resolution. Compared with the yearly average of MODIS aerosol products in 2015, the 250-m resolution AOD derived from the MERSI data exhibited great potential for identifying air pollution sources. Daily AODs derived from MERSI data were compared with ground results from CE318 measurements. The results revealed a correlation coefficient between the AODs from MERSI and those from the ground measurements of approximately 0.85, and approximately 68% results were within expected error range of ±(0.05 + 15%τ).

scholarly journals Evaluation of NASA Deep Blue/SOAR aerosol retrieval algorithms applied to AVHRR measurements

2017 ◽  
Vol 122 (18) ◽  
pp. 9945-9967 ◽  
Author(s):  
A. M. Sayer ◽  
N. C. Hsu ◽  
J. Lee ◽  
N. Carletta ◽  
S.-H. Chen ◽  
...  
Keyword(s):  
Deep Blue ◽  
Aerosol Retrieval ◽  
Retrieval Algorithms

scholarly journals Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask

2013 ◽  
Vol 6 (1) ◽  
pp. 1815-1858 ◽  
Author(s):  
S. P. Burton ◽  
R. A. Ferrare ◽  
M. A. Vaughan ◽  
A. H. Omar ◽  
R. R. Rogers ◽  
...  
Keyword(s):  
High Spectral Resolution ◽  
Dependent Observations ◽  
Aerosol Retrieval ◽  
Direct Measurements ◽  
Aerosol Classification ◽  
Lidar Ratio ◽  
Langley Research Center ◽  
High Spectral Resolution Lidar ◽  
Aerosol Types ◽  
Aerosol Type

Abstract. Aerosol classification products from the NASA Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL-1) on the NASA B200 aircraft are compared with coincident V3.01 aerosol classification products from the CALIOP instrument on the CALIPSO satellite. For CALIOP, aerosol classification is a key input to the aerosol retrieval, and must be inferred using aerosol loading-dependent observations and location information. In contrast, HSRL-1 makes direct measurements of aerosol intensive properties, including the lidar ratio, that provide information on aerosol type. In this study, comparisons are made for 109 underflights of the CALIOP orbit track. We find that 62% of the CALIOP marine layers and 54% of the polluted continental layers agree with HSRL-1 classification results. In addition, 80% of the CALIOP desert dust layers are classified as either dust or dusty mix by HSRL-1. However, agreement is less for CALIOP smoke (13%) and polluted dust (35%) layers. Specific case studies are examined, giving insight into the performance of the CALIOP aerosol type algorithm. In particular, we find that the CALIOP polluted dust type is overused due to an attenuation-related depolarization bias. Furthermore, the polluted dust type frequently includes mixtures of dust plus marine aerosol. Finally, we find that CALIOP's identification of internal boundaries between different aerosol types in contact with each other frequently do not reflect the actual transitions between aerosol types accurately. Based on these findings, we give recommendations which may help to improve the CALIOP aerosol type algorithms.

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