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Mark P. Baldwin, Thomas Birner, Guy Brasseur, John Burrows, Neal Butchart, Rolando Garcia, Marvin Geller, Lesley Gray, Kevin Hamilton, Nili Harnik, Michaela I. Hegglin, Ulrike Langematz, Alan Robock, Kaoru Sato, and Adam A. Scaife

1. Introduction The history of stratospheric and mesospheric discoveries over the past ~100 years is a fascinating story of perplexing observations, followed by experimentation, theory, and iterative modeling of the unexplained phenomena to identify their physical and chemical origins. Advances in our understanding have been made possible by 1) improved and more detailed observations of both dynamical and chemical quantities (including in situ, ground-based remote sensing, and remote sensing

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Stanley G. Benjamin, John M. Brown, Gilbert Brunet, Peter Lynch, Kazuo Saito, and Thomas W. Schlatter

, accelerated research in these related areas, often following major forecasting failures. Forecasting, in general, and numerical prediction models have aided tremendously in understanding the atmosphere–Earth system (e.g., Randall et al. 2019 ). Repeated application of the scientific method—asking questions, constructing new hypotheses, and then conducting experiments and collecting observations to test them—is behind the development of weather forecasting and all of the science topics of this monograph

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I. Gultepe, A. J. Heymsfield, P. R. Field, and D. Axisa

apply for radar observations because of assumed PSD and m – D relationships. Integrated methods are now being used for precipitation research, for example, using a 94-GHz W-band radar, lidar, and CloudSat radar, as well as an NWP model approach based on bulk and bin microphysical algorithms. Iguchi et al. (2012) simulated convective clouds that formed over the northwest Pacific of Japan during 14–28 May 2001 ( Fig. 6-7 ). Bin-based microphysical simulations based on Japan’s Japan

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Ronald B. Smith

weather stations (e.g., Mt. Washington, New Hampshire, and Sonnblick, Austria). Now, as we begin the twenty-first century, many powerful observational tools are in use: instrumented aircraft, satellite passive visible and infrared imagery, active lidar and radar remote sensing, and even water isotope analysis. Accordingly, our knowledge has increased manyfold. In the last 60 years, many small and a few large coordinated field projects have provided a valuable observational database for mountain

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Margaret A. LeMone, Wayne M. Angevine, Christopher S. Bretherton, Fei Chen, Jimy Dudhia, Evgeni Fedorovich, Kristina B. Katsaros, Donald H. Lenschow, Larry Mahrt, Edward G. Patton, Jielun Sun, Michael Tjernström, and Jeffrey Weil

corresponding gradients. These relationships have been derived from observations of turbulent flow in the laboratory and in the atmosphere, complemented with considerations of dimensionality and symmetry, plausible physical arguments, scale analysis, and similarity theories. Semiempirical theories also played an especially important role in seeking practical solutions to the so-called turbulence closure problem as applied to the ABL parameterization schemes discussed in section 10 . This problem finds its

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Raymond McCord and Jimmy Voyles

high, too erratic, or failed other expectations. A higher level of QC could be applied by comparing observations from different sources or by comparing observations to a model simulation where the model was driven by a different observed dataset. The quality measurement experiments (QMEs) were very popular in the ARM Program in the mid-1990s, with many of them developed to help evaluate radiometric instrument data quality ( Mlawer and Turner 2016 , chapter 14; Peppler et al. 2016 , chapter 12

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Christa D. Peters-Lidard, Faisal Hossain, L. Ruby Leung, Nate McDowell, Matthew Rodell, Francisco J. Tapiador, F. Joe Turk, and Andrew Wood

-based observations of snow depth can be obtained through intensive depth probe sampling ( Elder et al. 1991 ), photographs ( Tappeiner et al. 2001 ; König and Sturm 1998 ), and snow pits (e.g., Cline et al. 2004 ) along with ground-based and airborne radar ( Machguth et al. 2006 ) and lidar ( Deems et al. 2013 ). Ground-based measurement of SWE is done through snow pillows with pressure transducers ( Beaumont 1965 ). Routine airborne SWE monitoring is conducted over CONUS using gamma ray sensing ( Carroll and

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Zamin A. Kanji, Luis A. Ladino, Heike Wex, Yvonne Boose, Monika Burkert-Kohn, Daniel J. Cziczo, and Martina Krämer

discussed below are depicted in Fig. 1-1 . Deposition ice nucleation is the only heterogeneous IN mechanism where liquid water is presumed to be absent. Ice nucleates from supersaturated vapor with respect to ice (RH i > 100%) on an INP directly. Recently, Marcolli (2014) suggested that observations interpreted as showing deposition IN could also be pore condensation and freezing (PCF; see Fig. 1-1 ) occurring by condensation of water in cavities found on the surfaces of INPs because of the

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Sonia M. Kreidenweis, Markus Petters, and Ulrike Lohmann

emissions on large-scale cloud fields could be identified. Christensen and Stephens (2011) offered an explanation for this from 2.5 years of CALIOP space-borne lidar data of ship tracks off the west coast of North America (20°–60°N and 150°–110°W). While these observations confirmed the dominant role of microphysical effects from ship tracks increasing cloud liquid water and cloud optical depth in the open-cell regime, as found, for example, by Durkee et al. (2000b) , they found the opposite effect

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Greg M. McFarquhar, Darrel Baumgardner, Aaron Bansemer, Steven J. Abel, Jonathan Crosier, Jeff French, Phil Rosenberg, Alexei Korolev, Alfons Schwarzoenboeck, Delphine Leroy, Junshik Um, Wei Wu, Andrew J. Heymsfield, Cynthia Twohy, Andrew Detwiler, Paul Field, Andrea Neumann, Richard Cotton, Duncan Axisa, and Jiayin Dong

of several A-Train ice cloud retrieval products with in situ measurements collected during the SPARTICUS campaign . J. Appl. Meteor. Climatol. , 52 , 1014 – 1030 , doi: 10.1175/JAMC-D-12-054.1 . 10.1175/JAMC-D-12-054.1 Donovan , D. P. , and A. C. A. P. van Lammeren , 2001 : Cloud effective particle size and water content profile retrievals using combined lidar and radar observations: 1. Theory and examples . J. Geophys. Res. , 106 , 27 425 – 27 448 , doi: 10.1029/2001JD900243 . 10

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