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

被引:39
|
作者
Sayer, A. M. [1 ,2 ]
Hsu, N. C. [2 ]
Lee, J. [2 ,3 ]
Carletta, N. [2 ,4 ]
Chen, S. -H. [2 ,4 ]
Smirnov, A. [2 ,4 ]
机构
[1] Univ Space Res Assoc, Goddard Earth Sci Technol & Res, Greenbelt, MD 20771 USA
[2] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA
[3] Univ Maryland, Earth Syst Sci Interdisciplinary Ctr, College Pk, MD 20742 USA
[4] Sci Syst & Applicat Inc, Lanham, MD USA
关键词
OPTICAL DEPTH RETRIEVALS; LONG-TERM TREND; SATELLITE RETRIEVALS; CLOUD CONTAMINATION; REGIONAL EVALUATION; LAND SURFACES; THICKNESS; AERONET; OCEANS; VALIDATION;
D O I
10.1002/2017JD026934
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
The Deep Blue (DB) and Satellite Ocean Aerosol Retrieval (SOAR) algorithms have previously been applied to observations from sensors like the Moderate Resolution Imaging Spectroradiometers (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) to provide records of midvisible aerosol optical depth (AOD) and related quantities over land and ocean surfaces, respectively. Recently, DB and SOAR have also been applied to Advanced Very High Resolution Radiometer (AVHRR) observations from several platforms (NOAA11, NOAA14, and NOAA18), to demonstrate the potential for extending the DB and SOAR AOD records. This study provides an evaluation of the initial version (V001) of the resulting AVHRR-based AOD data set, including validation against Aerosol Robotic Network (AERONET) and ship-borne observations, and comparison against both other AVHRR AOD records and MODIS/SeaWiFS products at select long-term AERONET sites. Although it is difficult to distil error characteristics into a simple expression, the results suggest that one standard deviation confidence intervals on retrieved AOD of +/-(0.03 + 15%) over water and +/-(0.05 + 25%) over land represent the typical level of uncertainty, with a tendency toward negative biases in high-AOD conditions, caused by a combination of algorithmic assumptions and sensor calibration issues. Most of the available validation data are for NOAA18 AVHRR, although performance appears to be similar for the NOAA11 and NOAA14 sensors as well. Plain Language Summary Aerosols are small particles in the atmosphere like desert dust, volcanic ash, smoke, industrial haze, and sea spray. Understanding them is important for applications such as hazard avoidance, air quality and human health, and climate studies. Satellite instruments provide an important tool to study aerosol loadings over the world. This paper evaluates a new satellite-based data set of aerosol loading, from a set of instruments called the Advanced Very High Resolution Radiometers (AVHRRs), using ground-based observations and by comparing to other satellite data products.
引用
收藏
页码:9945 / 9967
页数:23
相关论文
共 25 条
  • [21] A robust and flexible satellite aerosol retrieval algorithm for multi-angle polarimetric measurements with physics-informed deep learning method
    Tao, Minghui
    Chen, Jinxi
    Xu, Xiaoguang
    Man, Wenjing
    Xu, Lina
    Wang, Lunche
    Wang, Yi
    Wang, Jun
    Fan, Meng
    Shahzad, Muhammad Imran
    Chen, Liangfu
    REMOTE SENSING OF ENVIRONMENT, 2023, 297
  • [22] Evaluation and Comparison of MODIS C6 and C6.1 Deep Blue Aerosol Products in Arid and Semi-Arid Areas of Northwestern China
    Yang, Leiku
    Tian, Xinyao
    Liu, Chao
    Ji, Weiqian
    Zheng, Yu
    Liu, Huan
    Lu, Xiaofeng
    Che, Huizheng
    REMOTE SENSING, 2022, 14 (08)
  • [23] Preliminary Investigation of a New AHI Aerosol Optical Depth (AOD) Retrieval Algorithm and Evaluation with Multiple Source AOD Measurements in China
    Yang, Fukun
    Wang, Yang
    Tao, Jinhua
    Wang, Zifeng
    Fan, Meng
    de Leeuw, Gerrit
    Chen, Liangfu
    REMOTE SENSING, 2018, 10 (05)
  • [24] Retrieval of aerosol optical depth from GF-6 wide field of view 16-m data based on deep blue algorithm
    Xu, Chang
    Zhang, Jinye
    Liu, Ruibei
    Lv, Hui
    Hu, Ziyue
    Wu, Xulong
    JOURNAL OF APPLIED REMOTE SENSING, 2022, 16 (04)
  • [25] Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table
    Huttunen, Jani
    Kokkola, Harri
    Mielonen, Tero
    Mononen, Mika Esa Juhani
    Lipponen, Antti
    Reunanen, Juha
    Lindfors, Anders Vilhelm
    Mikkonen, Santtu
    Lehtinen, Kari Erkki Juhani
    Kouremeti, Natalia
    Bais, Alkiviadis
    Niska, Harri
    Arola, Antti
    ATMOSPHERIC CHEMISTRY AND PHYSICS, 2016, 16 (13) : 8181 - 8191