Browse
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
The development and challenges of the Tropical Rainfall Measuring Mission (TRMM) project are defined, and the role of Joanne Simpson as the project scientist is described as the project progressed from early development to a highly successful operational satellite system for rainfall measurement. Close interaction between the project scientist and the project staff of TRMM was one key to the success of the mission.
Abstract
The development and challenges of the Tropical Rainfall Measuring Mission (TRMM) project are defined, and the role of Joanne Simpson as the project scientist is described as the project progressed from early development to a highly successful operational satellite system for rainfall measurement. Close interaction between the project scientist and the project staff of TRMM was one key to the success of the mission.
Abstract
No Abstract available.
Abstract
No Abstract available.
Abstract
No Abstract available.
Abstract
No Abstract available.
Abstract
No Abstract available.
Abstract
No Abstract available.
Abstract
No Abstract available.
Abstract
No Abstract available.
