Economics Dictionary of ArgumentsHome
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| Weather data: Weather data comprises information on atmospheric conditions like temperature, humidity, wind speed, precipitation, and atmospheric pressure recorded at specific times and locations. See also Weather forecasting._____________Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments. | |||
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Meteorology on Weather Data - Dictionary of Arguments
Edwards I 394 Weather data/metereology/Edwards: Meanwhile, global data sets are produced by simulations, which are constrained but not determined by instrumental observations. In earlier work I described this relationship as “model-data symbiosis,” a mutually beneficial but also mutually dependent relationship.(1) This idea aligns with recent work by philosophers of science on “models as mediators”—a semi-autonomous “third force” in science, functioning in the spaces between the real world, instrumentation, and theory.(2) As Margaret Morrison and Mary Morgan argue, Scientific models have certain features which enable us to treat them as a technology. They provide us with a tool for investigation, giving the user the potential to learn about the world or about theories or both. Because of their characteristics of autonomy and representational power, and their ability to effect a relation between scientific theories and the world, they can act as a powerful agent in the learning process. That is to say, models are both a means to and a source of knowledge.(3) Edwards I 395 The concept of model-data symbiosis also supports the claims of the philosophers Stephen Norton and Frederick Suppe, who argue that “to be properly interpreted and deployed, data must be modeled.” Defining scientific methods essentially as ways of controlling for the possibility of artifactual results, Norton and Suppe argue that model-data symbiosis pervades all sciences—even the laboratory sciences, in which data modeling allows investigators to remove or correct for artifactual elements. “Even raw data,” they argue, “involve modeling built into the instrumentation.” One example is a thermoelectric probe, which derives ambient temperature from the current generated by two dissimilar metals joined inside the probe. Relating these currents to temperature requires parameters for each metal’s magnetic permeability. The probe’s temperature measurements must be understood as outputs of a physically instantiated mathematical model.(4) Edwards: If Norton and Suppe are right, seeking purity in either models (as theories) or data (as unmediated points of contact with the world) is not only misguided but impossible. Instead, the question is how well scientists succeed in controlling for the presence of artifactual elements in both theory and observation – Edwards I 396 and this is exactly how the iterative cycle of improving data assimilation systems (and the observing network) proceeds. Thus, in global climate science (and perhaps in every model-based science), neither pure data nor pure models exist. Not only are data “theory-laden”; models are “data-laden.” Today: Modern analysis models blend data and theory to render a smooth, consistent, comprehensive and homogeneous grid of numbers (…) a data image, rather than a data set. >Models/metereology. Edwards I 397 Models: Using models to make data global legitimized the possibility of alternative data images. The logic goes as follows: You will never get perfect knowledge of initial conditions. No practical observing mesh will ever be fine enough to do full justice to the atmosphere’s huge range of scales of energy and motion, from the molecular to the global. Furthermore, there will always be errors in the instruments, errors in the transmission, and errors in the analysis model. On top of that, the chaotic nature of weather physics means that tiny variations in initial conditions (here, read “analyzed global data”) often produce highly divergent outcomes. Therefore, using a single analyzed data set as input to a single deterministic forecast model will always entail a substantial margin of error, especially for periods longer than one or two days. Solution: In the early 1990s, forecasters began to turn this apparent defect in their method into an advantage. In a technique known as “ensemble forecasting,” for every forecast period they now generate an “ensemble” Edwards I 398 of slightly different data sets - different global data images, versions of the atmosphere—which collectively reflect the probable range of error. Typically the ensemble contains twelve or more such data sets. Forecasters then run the forecast model on each of these data sets, producing a corresponding ensemble of forecasts.(6) Edwards: Characterized statistically, the differences among these forecasts represent a forecast of the forecast error. >Climate data/climatology. 1. Edwards, “Global Climate Science, Uncertainty and Politics.” 2. Morgan and Morrison, Models as Mediators. 3. M. Morrison and M. S. Morgan, “Models as Mediating Instruments,” in Models as Mediators: Perspectives on Natural and Social Sciences, ed. M. S. Morgan and M. Morrison (Cambridge University Press, 1999). 4. Norton and Suppe, “Why Atmospheric Modeling Is Good Science,” 70, 72, 5. Lorenz, “Deterministic Nonperiodic Flow”; E. N. Lorenz, “A Study of the Predictability of a 28-Variable Atmospheric Model (28-Variable Atmosphere Model Constructed by Expanding Equations of Two-Level Geostrophic Model in Truncated Double-Fourier Series),” Tellus 17 (1965): 321–; E. S. Epstein, “Stochastic Dynamic Prediction,” Tellus 21, no. 6 (1969): 739–; C. E. Leith, “Theoretical Skill of Monte Carlo Forecasts,” Monthly Weather Review 102, no. 6 (1974): 409–; R. N. Hoffman and E. Kalnay, “Lagged Average Forecasting, an Alternative to Monte Carlo Forecasting,” Tellus, Series A—Dynamic Meteorology and Oceanography 35 (1983): 100–. 6. Z. Toth and E. Kalnay, “Ensemble Forecasting At NMC: The Generation of Perturbations,” Bulletin of the American Meteorological Society 74, no. 12 (1993): 2317–; M. S. Tracton and E. Kalnay, “Operational Ensemble Prediction at the National Meteorological Center: Practical Aspects,” Weather and Forecasting 8, no. 3 (1993): 379–._____________Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. Translations: Dictionary of Arguments The note [Concept/Author], [Author1]Vs[Author2] or [Author]Vs[term] resp. "problem:"/"solution:", "old:"/"new:" and "thesis:" is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition. |
Meteorology Edwards I Paul N. Edwards A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming Cambridge 2013 |
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> Counter arguments in relation to Weather Data
