Preface Many physical processes that influence Earth's climate and weather occur on spatial (temporal) scales smaller (shorter) than typical grid sizes (time steps) of general circulation models, and thus must be parameterized. Computer models are essential tools for understanding atmospheric phenomena and for making accurate predictions of any changes in the Earth's climate, weather, and resources of renewable energy resulting from anthropogenic activities that generate greenhouse gases and particulates into the atmosphere. This book focuses on the atmospheric subgrid processes ─ collectively called fast physics ─by reviewing and synthesizing relevant physical understanding, parameterization developments, various measurement technologies, and model evaluation framework. The publication is divided into three parts, containing seventeen chapters (Chapters 2-17) to reflect and synthesize the multiple aspects involved. The first chapter briefly introduces the historical development of fast physics parameterizations and the involved complexities; the last chapter summarizes emerging challenges, new opportunities and future research directions. Part I deals with major subgrid processes, with eight chapters (Chapters 2-9) each covering different processes more or less in the conventional compartmentalized format with an emphasis on individual processes, including but, not limited to radiative transfer, aerosols, and aerosol direct & indirect effects, entrainment-mixing processes, their microphysical influences, convection & convective clouds, stratiform clouds such as stratus and stratocumulus clouds, planetary boundary layer processes, land surface and interactions with atmosphere, and gravity waves. On top of the conventional treatments, some promising ideas/approaches have recently emerged to unify the treatment of individual processes and thus allows for consideration of process interactions. Part II is devoted to such unifying efforts, with four chapters (Chapters 10-13) covering four different endeavors: the unifying parameterizations based on assumed probability density functions; the EDMF approach that combines the Eddy Diffusivity and Mass Flux approaches to unify turbulence and convection; application of machine learning techniques; and innovative top-down attempts that consider the involved totality by borrowing ideas from systems theory, statistical physics, and non-linear sciences. Part III (Chapters 14-17) is devoted to assessments, model evaluation, and model-measurement integration, with four chapters that focus on satellite and airborne remote sensing measurements, surface-based remote sensing measurements, in-situ and laboratory measurements, and model evaluation, and model-measurement integration, respectively.