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- Author or Editor: Margaret LeMone x
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Abstract
Based on personal experience and input from colleagues, the natural history of a field program is discussed, from conception through data analysis and synthesis of results. For convenience, the life cycle of a field program is divided into three phases: the prefield phase, the field phase, and the aftermath. As described here, the prefield phase involves conceiving the idea, developing the scientific objectives, naming the program, obtaining support, and arranging the logistics. The field phase discussion highlights the decision making process, balancing input from data and numerical models, and human interactions. The data are merged, analyzed, and synthesized into knowledge mainly after the field effort.
Three major conclusions are drawn. First, it is the people most of all who make a field program successful, and cooperation and collegial consensus building are vital during all phases; good health and a sense of humor both help make this possible. Second, although numerical models are now playing a central role in all phases of a field program, not paying adequate attention to the observations can lead to problems. And finally, it cannot be overemphasized that both funding agencies and participants must recognize that it takes several years to fully exploit the datasets collected, with the corollary that high-quality datasets should be available long term.
Abstract
Based on personal experience and input from colleagues, the natural history of a field program is discussed, from conception through data analysis and synthesis of results. For convenience, the life cycle of a field program is divided into three phases: the prefield phase, the field phase, and the aftermath. As described here, the prefield phase involves conceiving the idea, developing the scientific objectives, naming the program, obtaining support, and arranging the logistics. The field phase discussion highlights the decision making process, balancing input from data and numerical models, and human interactions. The data are merged, analyzed, and synthesized into knowledge mainly after the field effort.
Three major conclusions are drawn. First, it is the people most of all who make a field program successful, and cooperation and collegial consensus building are vital during all phases; good health and a sense of humor both help make this possible. Second, although numerical models are now playing a central role in all phases of a field program, not paying adequate attention to the observations can lead to problems. And finally, it cannot be overemphasized that both funding agencies and participants must recognize that it takes several years to fully exploit the datasets collected, with the corollary that high-quality datasets should be available long term.
Abstract
Over the last 100 years, boundary layer meteorology grew from the subject of mostly near-surface observations to a field encompassing diverse atmospheric boundary layers (ABLs) around the world. From the start, researchers drew from an ever-expanding set of disciplines—thermodynamics, soil and plant studies, fluid dynamics and turbulence, cloud microphysics, and aerosol studies. Research expanded upward to include the entire ABL in response to the need to know how particles and trace gases dispersed, and later how to represent the ABL in numerical models of weather and climate (starting in the 1970s–80s); taking advantage of the opportunities afforded by the development of large-eddy simulations (1970s), direct numerical simulations (1990s), and a host of instruments to sample the boundary layer in situ and remotely from the surface, the air, and space. Near-surface flux-profile relationships were developed rapidly between the 1940s and 1970s, when rapid progress shifted to the fair-weather convective boundary layer (CBL), though tropical CBL studies date back to the 1940s. In the 1980s, ABL research began to include the interaction of the ABL with the surface and clouds, the first ABL parameterization schemes emerged; and land surface and ocean surface model development blossomed. Research in subsequent decades has focused on more complex ABLs, often identified by shortcomings or uncertainties in weather and climate models, including the stable boundary layer, the Arctic boundary layer, cloudy boundary layers, and ABLs over heterogeneous surfaces (including cities). The paper closes with a brief summary, some lessons learned, and a look to the future.
Abstract
Over the last 100 years, boundary layer meteorology grew from the subject of mostly near-surface observations to a field encompassing diverse atmospheric boundary layers (ABLs) around the world. From the start, researchers drew from an ever-expanding set of disciplines—thermodynamics, soil and plant studies, fluid dynamics and turbulence, cloud microphysics, and aerosol studies. Research expanded upward to include the entire ABL in response to the need to know how particles and trace gases dispersed, and later how to represent the ABL in numerical models of weather and climate (starting in the 1970s–80s); taking advantage of the opportunities afforded by the development of large-eddy simulations (1970s), direct numerical simulations (1990s), and a host of instruments to sample the boundary layer in situ and remotely from the surface, the air, and space. Near-surface flux-profile relationships were developed rapidly between the 1940s and 1970s, when rapid progress shifted to the fair-weather convective boundary layer (CBL), though tropical CBL studies date back to the 1940s. In the 1980s, ABL research began to include the interaction of the ABL with the surface and clouds, the first ABL parameterization schemes emerged; and land surface and ocean surface model development blossomed. Research in subsequent decades has focused on more complex ABLs, often identified by shortcomings or uncertainties in weather and climate models, including the stable boundary layer, the Arctic boundary layer, cloudy boundary layers, and ABLs over heterogeneous surfaces (including cities). The paper closes with a brief summary, some lessons learned, and a look to the future.
