Machine Learning Uncovers Aerosol Size Information From Chemistry and Meteorology to Quantify Potential Cloud‐Forming Particles
暂无分享,去创建一个
J. Peischl | J. Jimenez | B. Anderson | A. Nenes | F. Yu | R. Moore | L. Ziemba | S. Yum | P. DeMott | I. Pollack | P. Campuzano‐Jost | T. Park | Taehyoung Lee | A. Beyersdorf | L. G. Huey | L. Huey | B. Palm | F. Flocke | C. Fredrickson | E. Levin | I. Bourgeois | C. Thompson | Michelle J. Kim | B. Nault | Q. Peng | Minsu Park | B. Anderson | A. Nair | Ezra Levin | P. C. Jost | Lu Xu | Brett D. Palm | Richard H. Moore | Jose L. Jimenez | Fangqun Yu | C. Thompson
[1] J. Jimenez,et al. The importance of size ranges in aerosol instrument intercomparisons: a case study for the Atmospheric Tomography Mission , 2021, Atmospheric Measurement Techniques.
[2] Andrew R. Whitehill,et al. Investigation of factors controlling PM2.5 variability across the South Korean Peninsula during KORUS-AQ , 2020, Elementa.
[3] M. DeMaria,et al. Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning , 2020, Geophysical Research Letters.
[4] J. Dramsch,et al. 70 years of machine learning in geoscience in review , 2020, Advances in Geophysics.
[5] F. Yu,et al. Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements , 2020, Atmospheric Chemistry and Physics.
[6] E. Ray,et al. A large source of cloud condensation nuclei from new particle formation in the tropics , 2019, Nature.
[7] J. Peischl,et al. ATom: L2 In Situ Measurements from NOAA Nitrogen Oxides and Ozone (NOyO3) Instrument , 2019 .
[8] J. Crounse,et al. ATom: L2 In Situ Data from Caltech Chemical Ionization Mass Spectrometer (CIT-CIMS) , 2019 .
[9] P. Campuzano‐Jost,et al. ATom: L2 Measurements from CU High-Resolution Aerosol Mass Spectrometer (HR-AMS) , 2019 .
[10] F. Yu,et al. Spatioseasonal Variations of Atmospheric Ammonia Concentrations Over the United States: Comprehensive Model‐Observation Comparison , 2019, Journal of Geophysical Research: Atmospheres.
[11] K. Froyd,et al. ATom: L2 In Situ Measurements of Aerosol Microphysical Properties (AMP) , 2019 .
[12] J. Jimenez,et al. Aerosol size distributions during the Atmospheric Tomography Mission (ATom): methods, uncertainties, and data products , 2019, Atmospheric Measurement Techniques.
[13] A. Segers,et al. Machine learning for observation bias correction with application to dust storm data assimilation , 2019, Atmospheric Chemistry and Physics.
[14] Joachim Denzler,et al. Deep learning and process understanding for data-driven Earth system science , 2019, Nature.
[15] J. D. de Gouw,et al. Secondary organic aerosol production from local emissions dominates the organic aerosol budget over Seoul, South Korea, during KORUS-AQ , 2018, Atmospheric Chemistry and Physics.
[16] David C. Carslaw,et al. Random forest meteorological normalisation models for Swiss PM 10 trend analysis , 2018 .
[17] J. Jimenez,et al. Evaluation of the new capture vapourizer for aerosol mass spectrometers (AMS) through laboratory studies of inorganic species , 2016 .
[18] K. Jucks,et al. Planning, implementation, and scientific goals of the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) field mission , 2016 .
[19] J. Peischl,et al. The Deep Convective Clouds and Chemistry (DC3) Field Campaign , 2015 .
[20] Andreas Ziegler,et al. ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R , 2015, 1508.04409.
[21] A. Sorooshian,et al. On the competition among aerosol number, size and composition in predicting CCN variability: a multi-annual field study in an urbanized desert. , 2015, Atmospheric chemistry and physics.
[22] Douglas M. Bates,et al. Fast and Elegant Numerical Linear Algebra Using the RcppEigen Package , 2013 .
[23] N. Speybroeck. Classification and regression trees , 2012, International Journal of Public Health.
[24] Glenn E. Shaw,et al. The Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) mission: design, execution, and first results , 2010 .
[25] F. Yu,et al. Simulation of particle size distribution with a global aerosol model: contribution of nucleation to aerosol and CCN number concentrations , 2009 .
[26] A. Nenes,et al. Scanning Flow CCN Analysis—A Method for Fast Measurements of CCN Spectra , 2009 .
[27] Dirk Richter,et al. Organic aerosol formation in urban and industrial plumes near Houston and Dallas, Texas , 2009 .
[28] C. Twohy,et al. Droplet nuclei in non-precipitating clouds: composition and size matter , 2008 .
[29] J. Hudson. Variability of the relationship between particle size and cloud‐nucleating ability , 2007 .
[30] Katrin Fuhrer,et al. Field-deployable, high-resolution, time-of-flight aerosol mass spectrometer. , 2006, Analytical chemistry.
[31] M. Andreae,et al. Size Matters More Than Chemistry for Cloud-Nucleating Ability of Aerosol Particles , 2006, Science.
[32] S. Menon,et al. The radiative influence of aerosol effects on liquid-phase cumulus and stratiform clouds based on sensitivity studies with two climate models , 2006 .
[33] A. Nenes,et al. A Continuous-Flow Streamwise Thermal-Gradient CCN Chamber for Atmospheric Measurements , 2005 .
[34] George Tselioudis,et al. GCM Simulations of the Aerosol Indirect Effect: Sensitivity to Cloud Parameterization and Aerosol Burden , 2002 .
[35] V. Ramanathan,et al. Aerosols, Climate, and the Hydrological Cycle , 2001, Science.
[36] Ronald,et al. Drizzle Suppression in Ship Tracks. , 2000 .
[37] V. Ramanathan,et al. Reduction of tropical cloudiness by soot , 2000, Science.
[38] Rosenfeld,et al. Suppression of rain and snow by urban and industrial air pollution , 2000, Science.
[39] J. Hansen,et al. Radiative forcing and climate response , 1997 .
[40] Olivier Boucher,et al. The sulfate‐CCN‐cloud albedo effect , 1995 .
[41] R. Pincus,et al. Effect of precipitation on the albedo susceptibility of clouds in the marine boundary layer , 1994, Nature.
[42] D. W. Johnson,et al. The Measurement and Parameterization of Effective Radius of Droplets in Warm Stratocumulus Clouds , 1994 .
[43] B. Albrecht. Aerosols, Cloud Microphysics, and Fractional Cloudiness , 1989, Science.
[44] K. Liou,et al. The role of cloud microphysical processes in climate: an assessment from a one-dimensional perspective , 1989 .
[45] T. L. Wolfe,et al. An assessment of the impact of pollution on global cloud albedo , 1984 .
[46] S. P. Lloyd,et al. Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.
[47] J. A. Hartigan,et al. A k-means clustering algorithm , 1979 .
[48] S. Twomey. The Influence of Pollution on the Shortwave Albedo of Clouds , 1977 .
[49] S. Twomey. Pollution and the Planetary Albedo , 1974 .
[50] J. W. Fitzgerald. Dependence of the Supersaturation Spectrum of CCN on Aerosol Size Distribution and Composition , 1973 .
[51] C. Junge,et al. Relationship of Cloud Nuclei Spectra to Aerosol Size Distribution and Composition , 1971 .
[52] S. Twomey,et al. The nuclei of natural cloud formation part II: The supersaturation in natural clouds and the variation of cloud droplet concentration , 1959 .
[53] J. Uin,et al. Southern Great Plains (SGP) Aerosol Observing System (AOS) Instrument Handbook , 2021 .
[54] Stefanie Seiler,et al. Finding Groups In Data , 2016 .
[55] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[56] Corinne Le Quéré,et al. Climate Change 2013: The Physical Science Basis , 2013 .
[57] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[58] L. Breiman. Random Forests , 2001, Machine Learning.