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

Author(s):  
Guoyong Wen ◽  
Alexander Marshak ◽  
Lorraine Remer ◽  
Robert Levy ◽  
Norman Loeb ◽  
...  
2009 ◽  
Author(s):  
Evgueni Kassianov ◽  
Mikhail Ovchinnikov ◽  
Larry K. Berg ◽  
Sally A. McFarlane ◽  
Connor Flynn ◽  
...  

Tellus B ◽  
2009 ◽  
Vol 61 (1) ◽  
Author(s):  
Sebastian Otto ◽  
Eike Bierwirth ◽  
Bernadett Weinzierl ◽  
Konrad Kandler ◽  
Michael Esselborn ◽  
...  

2012 ◽  
Vol 5 (3) ◽  
pp. 176-183 ◽  
Author(s):  
Ali J. Chamkha ◽  
Mohamed Modather ◽  
Saber M.M. EL-Kabeir ◽  
Ahmed M. Rashad

2021 ◽  
Vol 247 ◽  
pp. 118201
Author(s):  
Hao Wu ◽  
Tijian Wang ◽  
Qin'geng Wang ◽  
Yang Cao ◽  
Yawei Qu ◽  
...  

2021 ◽  
Vol 658 (1) ◽  
pp. 012039
Author(s):  
Jiabao Yue ◽  
Donghai Xie ◽  
Jie Yu ◽  
Lin Zhu ◽  
Zhengyang He

2021 ◽  
Vol 13 (5) ◽  
pp. 920
Author(s):  
Zhongting Wang ◽  
Ruru Deng ◽  
Pengfei Ma ◽  
Yuhuan Zhang ◽  
Yeheng Liang ◽  
...  

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).


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

2013 ◽  
Vol 6 (1) ◽  
pp. 1815-1858 ◽  
Author(s):  
S. P. Burton ◽  
R. A. Ferrare ◽  
M. A. Vaughan ◽  
A. H. Omar ◽  
R. R. Rogers ◽  
...  

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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