Confronting Models with Data

APRIL 2003 AMERICAN METEOROLOGICAL SOCIETY | T he use of data to evaluate models is fundamental to science. Ideally, evaluations can be controlled and optimized in the laboratory; in most cases, however, atmospheric scientists have to perform model–data intercomparisons by taking advantage of the uncontrolled opportunities that nature provides. A model-evaluation project is complicated in at least two distinct ways. The technical complexities are obvious and daunting: Data must be collected and analyzed, models must be developed and run, and the two sets of numbers must be brought into meaningful juxtaposition. This is hard enough. An additional and equally complex task, however, is to foster communication and fruitful interactions among the diverse scientific communities whose cooperation and combined expertise are needed in order to carry out the technical work. The Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study (GCSS) is a case in point. GCSS was organized in the early 1990s by K. Browning and colleagues (Browning et al. 1993, 1994). The challenges that arise as GCSS brings observations and models together are a microcosm of challenges that face all of atmospheric science. Over a period of years, GCSS has devised what we call the “GCSS process” a mode of operation that appears to optimize its scientific productivity. The GCSS process was devised partly through trial and error and partly through introspection. The primary purpose of this article is to outline the key elements of the GCSS proCONFRONTING MODELS WITH DATA