Journal ArticleDOI
MODIS Collection 6 aerosol products: Comparison between Aqua's e-Deep Blue, Dark Target, and "merged" data sets, and usage recommendations
Andrew M. Sayer,Andrew M. Sayer,L. A. Munchak,N. C. Hsu,Robert C. Levy,Corey Bettenhausen,Myeong-Jae Jeong +6 more
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TLDR
In this paper, the authors compare the performance of the Enhanced Deep Blue (DB) and Dark Target (DT) algorithms over land, and a DT over-water algorithm over desert/urban areas.Abstract:
The Moderate Resolution Imaging Spectroradiometer (MODIS) Atmospheres data product suite includes three algorithms applied to retrieve midvisible aerosol optical depth (AOD): the Enhanced Deep Blue (DB) and Dark Target (DT) algorithms over land, and a DT over-water algorithm. All three have been refined in the recent “Collection 6” (C6) MODIS reprocessing. In particular, DB has been expanded to cover vegetated land surfaces as well as brighter desert/urban areas. Additionally, a new “merged” data set which draws from all three algorithms is included in the C6 products. This study is intended to act as a point of reference for new and experienced MODIS data users with which to understand the global and regional characteristics of the C6 DB, DT, and merged data sets, based on MODIS Aqua data. This includes validation against Aerosol Robotic Network (AERONET) observations at 111 sites, focused toward regional and categorical (surface/aerosol type) analysis. Neither algorithm consistently outperforms the other, although in many cases the retrieved AOD and the level of its agreement with AERONET are very similar. In many regions the DB, DT, and merged data sets are all suitable for quantitative applications, bearing in mind that they cannot be considered independent, while in other cases one algorithm does consistently outperform the other. Usage recommendations and caveats are thus somewhat complicated and regionally dependent.read more
Citations
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Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors
Aaron van Donkelaar,Randall V. Martin,Randall V. Martin,Michael Brauer,N. Christina Hsu,Ralph A. Kahn,Robert C. Levy,Alexei Lyapustin,Andrew M. Sayer,David M. Winker +9 more
TL;DR: This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM2.5 data sources can yield valuable improvements to PM 2.5 characterization on a global scale.
Journal ArticleDOI
The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations
David N. Walters,Anthony J. Baran,Anthony J. Baran,Ian A. Boutle,M. E. Brooks,Paul Earnshaw,John M. Edwards,Kalli Furtado,Peter Hill,Adrian Lock,James Manners,Cyril J. Morcrette,Jane Mulcahy,Claudio Sanchez,Chris Smith,Rachel Stratton,Warren Tennant,Lorenzo Tomassini,Kwinten Van Weverberg,Simon Vosper,Martin Willett,J. Browse,Andrew C. Bushell,Kenneth S. Carslaw,Mohit Dalvi,Richard Essery,Nicola Gedney,Steven C. Hardiman,Ben Johnson,Colin E. Johnson,Andrew Jones,Colin Jones,Graham Mann,Sean Milton,Heather Rumbold,Alistair Sellar,Masashi Ujiie,Michael Whitall,Keith D. Williams,M. Zerroukat +39 more
TL;DR: The Global Atmosphere 3.0 (GA3.0) as mentioned in this paper is a configuration of the Met Office Unified Model (MetUM) developed for use across climate research and weather prediction activities.
Journal ArticleDOI
Drylands face potential threat under 2[thinsp][deg]C global warming target
TL;DR: This study shows drylands have warmed, and will continue to warm, more than the humid lands that are primarily responsible for emissions, and this target is acceptable only for humid lands, whereas drylands will bear greater warming risks.
Journal ArticleDOI
Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998-2018).
Melanie S. Hammer,Melanie S. Hammer,Aaron van Donkelaar,Aaron van Donkelaar,Chi Li,Chi Li,Alexei Lyapustin,Alexei Lyapustin,Andrew M. Sayer,Andrew M. Sayer,N. Christina Hsu,Robert C. Levy,Michael J. Garay,Olga V. Kalashnikova,Ralph A. Kahn,Michael Brauer,Michael Brauer,Joshua S. Apte,Daven K. Henze,Li Zhang,Li Zhang,Qiang Zhang,Bonne Ford,Jeffrey R. Pierce,Randall V. Martin,Randall V. Martin,Randall V. Martin +26 more
TL;DR: Global estimates of annual PM2.5 concentrations and trends for 1998-2018 are developed using advances in satellite observations, chemical transport modeling, and ground-based monitoring, identifying significant trends for eastern North America, Europe, and globally.
Journal ArticleDOI
A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information.
Gongbo Chen,Shanshan Li,Luke D. Knibbs,Nicholas A. S. Hamm,Wei Cao,Tiantian Li,Jianping Guo,Hongyan Ren,Michael J. Abramson,Yuming Guo +9 more
TL;DR: Taking advantage of a novel application of modeling framework and the most recent ground-level PM2.5 observations, the machine learning method showed higher predictive ability than previous studies.
References
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Journal ArticleDOI
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TL;DR: In this paper, the authors used a revised version of the Carnegie-Ames-Stanford-Approach (CASA) biogeochemical model and improved satellite-derived estimates of area burned, fire activity, and plant productivity to calculate fire emissions for the 1997-2009 period on a 0.5° spatial resolution with a monthly time step.
Journal ArticleDOI
Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols
Thomas F. Eck,Brent N. Holben,Jeffrey S. Reid,Oleg Dubovik,Alexander Smirnov,N. T. O'Neill,Ilya Slutsker,Stefan Kinne +7 more
TL;DR: In this paper, the spectral variation of α is typically not considered in the analysis and comparison of values from different techniques, and the spectral measurements of τ a from 340 to 1020 nm obtained from ground-based Aerosol Robotic Network radiometers located in various locations where either biomass burning, urban, or desert dust aerosols are prevalent.
Journal ArticleDOI
The Collection 6 MODIS aerosol products over land and ocean
Robert C. Levy,Shana Mattoo,L. A. Munchak,Lorraine A. Remer,Andrew M. Sayer,Andrew M. Sayer,Falguni Patadia,Falguni Patadia,N. C. Hsu +8 more
TL;DR: The Collection 6 (C6) algorithm as mentioned in this paper was proposed to retrieve aerosol optical depth (AOD) and aerosol size parameters from MODIS-observed spectral reflectance.