Articles | Volume 11, issue 10
https://doi.org/10.5194/amt-11-5813-2018
https://doi.org/10.5194/amt-11-5813-2018
Research article
 | 
23 Oct 2018
Research article |  | 23 Oct 2018

Screening for snow/snowmelt in SNPP VIIRS aerosol optical depth algorithm

Jingfeng Huang, Istvan Laszlo, Lorraine A. Remer, Hongqing Liu, Hai Zhang, Pubu Ciren, and Shobha Kondragunta

Related authors

Parameterizing spectral surface reflectance relationships for the Dark Target aerosol algorithm applied to a geostationary imager
Mijin Kim, Robert C. Levy, Lorraine A. Remer, Shana Mattoo, and Pawan Gupta
Atmos. Meas. Tech., 17, 1913–1939, https://doi.org/10.5194/amt-17-1913-2024,https://doi.org/10.5194/amt-17-1913-2024, 2024
Short summary
Opinion: Aerosol remote sensing over the next 20 years
Lorraine A. Remer, Robert C. Levy, and J. Vanderlei Martins
Atmos. Chem. Phys., 24, 2113–2127, https://doi.org/10.5194/acp-24-2113-2024,https://doi.org/10.5194/acp-24-2113-2024, 2024
Short summary
MAGARA: a Multi-Angle Geostationary Aerosol Retrieval Algorithm
James A. Limbacher, Ralph A. Kahn, Mariel D. Friberg, Jaehwa Lee, Tyler Summers, and Hai Zhang
Atmos. Meas. Tech., 17, 471–498, https://doi.org/10.5194/amt-17-471-2024,https://doi.org/10.5194/amt-17-471-2024, 2024
Short summary
Increasing Aerosol Optical Depth Spatial And Temporal Availability By Merging Datasets from Geostationary And Sun-Synchronous Satellites
Pawan Gupta, Robert C. Levy, Shana Mattoo, Lorraine Remer, Zhaohui Zhang, Virginia Sawyer, Jennifer Wei, Sally Zhao, Min Oo, V. Praju Kiliyanpilakkil, and Xiaohua Pan
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-259,https://doi.org/10.5194/amt-2023-259, 2024
Revised manuscript accepted for AMT
Short summary
Analysis of the GEFS-Aerosols annual budget to better understand aerosol predictions simulated in the model
Li Pan, Partha S. Bhattacharjee, Li Zhang, Raffaele Montuoro, Barry Baker, Jeff McQueen, Georg A. Grell, Stuart A. McKeen, Shobha Kondragunta, Xiaoyang Zhang, Gregory J. Frost, Fanglin Yang, and Ivanka Stajner
Geosci. Model Dev., 17, 431–447, https://doi.org/10.5194/gmd-17-431-2024,https://doi.org/10.5194/gmd-17-431-2024, 2024
Short summary

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Multi-angle aerosol optical depth retrieval method based on improved surface reflectance
Lijuan Chen, Ren Wang, Ying Fei, Peng Fang, Yong Zha, and Haishan Chen
Atmos. Meas. Tech., 17, 4411–4424, https://doi.org/10.5194/amt-17-4411-2024,https://doi.org/10.5194/amt-17-4411-2024, 2024
Short summary
Comparison of diurnal aerosol products retrieved from combinations of micro-pulse lidar and sun photometer observations over the KAUST observation site
Anton Lopatin, Oleg Dubovik, Georgiy Stenchikov, Ellsworth J. Welton, Illia Shevchenko, David Fuertes, Marcos Herreras-Giralda, Tatsiana Lapyonok, and Alexander Smirnov
Atmos. Meas. Tech., 17, 4445–4470, https://doi.org/10.5194/amt-17-4445-2024,https://doi.org/10.5194/amt-17-4445-2024, 2024
Short summary
First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia
Yeseul Cho, Jhoon Kim, Sujung Go, Mijin Kim, Seoyoung Lee, Minseok Kim, Heesung Chong, Won-Jin Lee, Dong-Won Lee, Omar Torres, and Sang Seo Park
Atmos. Meas. Tech., 17, 4369–4390, https://doi.org/10.5194/amt-17-4369-2024,https://doi.org/10.5194/amt-17-4369-2024, 2024
Short summary
Aerosol optical depth data fusion with Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT-2) instruments GEMS, AMI, and GOCI-II: statistical and deep neural network methods
Minseok Kim, Jhoon Kim, Hyunkwang Lim, Seoyoung Lee, Yeseul Cho, Yun-Gon Lee, Sujung Go, and Kyunghwa Lee
Atmos. Meas. Tech., 17, 4317–4335, https://doi.org/10.5194/amt-17-4317-2024,https://doi.org/10.5194/amt-17-4317-2024, 2024
Short summary
Stratospheric aerosol characteristics from SCIAMACHY limb observations: two-parameter retrieval
Christine Pohl, Felix Wrana, Alexei Rozanov, Terry Deshler, Elizaveta Malinina, Christian von Savigny, Landon A. Rieger, Adam E. Bourassa, and John P. Burrows
Atmos. Meas. Tech., 17, 4153–4181, https://doi.org/10.5194/amt-17-4153-2024,https://doi.org/10.5194/amt-17-4153-2024, 2024
Short summary

Cited articles

Aerosol ATBD: VIIRS Aerosol optical depth and Particle Size Parameter Algorithm Theoretical Basis Document (Revision B), available at: https://www.star.nesdis.noaa.gov/jpss/documents/ATBD/D0001-M01-S01-020_JPSS_ATBD_VIIRS-AOT-APSP_B.pdf, last access: 1 October 2018. 
Aerosol OAD: VIIRS Aerosol Products (AOD, APSP & SM) Intermediate Product (IP)/Environmental Data Records (EDR) Software – OAD (Revision F), available at: http://npp.gsfc.nasa.gov/sciencedocs/2015-09/474-00073_OAD-VIIRS-Aerosols-IP-EDR_H.pdf, last access: 1 October 2018. 
Al-Saadi, J., Szykman, J., Pierce, R. B., Kittaka, C., Neil, D., Chu, D. A.,Remer, L. A., Gumley, L., Prins, E., Weinstock, L., MacDonald, C., Wayland, R., Dimmick, F., and Fishman, J.: Improving national air quality forecasts with satellite aerosol observations, B. Am. Meteorol. Soc., 86, 1249–1261, https://doi.org/10.1175/BAMS-86-9-1249, 2005. 
Doherty, S. J., Warren, S. G., Grenfell, T. C., Clarke, A. D., and Brandt, R. E.: Light-absorbing impurities in Arctic snow, Atmos. Chem. Phys., 10, 11647–11680, https://doi.org/10.5194/acp-10-11647-2010, 2010. 
Gao, B. C.: NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sens. Environ., 58, 257–266, https://doi.org/10.1016/S0034-4257(96)00067-3, 1996. 
Download
Short summary
A new snow/snowmelt screening approach – combining a normalized difference snow index (NDSI)- and brightness temperature (BT)-based snow test, snow adjacency test and spatial filter – is proposed to significantly reduce the snow/snowmelt contamination in the NOAA’s operational Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol optical depth (AOD) product, particularly over Northern Hemisphere high-latitude regions during spring thaw.