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Paul N. Edwards on Weather Forecasting - Dictionary of Arguments

Edwards I 362
Weather Forecasting/Edwards: From the dawn of synoptic forecasting, weather forecasting comprised three principal steps: (1) collect the available data, (2) interpret the data to create a picture of the weather situation, and (3) predict how that picture will change during the forecast period. The second step, originally known as “diagnosis,” transformed raw data from a relatively few points into a coherent, self-consistent picture of atmospheric structure and motion.(1) As in a medical diagnosis, forecasters combined theory and experiential knowledge to reach a shared understanding of reality from incomplete and potentially ambiguous indications (symptoms).
Analysis: For early NWP (Numerical Weather Prediction) , diagnosis or “analysis” proved the most difficult aspect of forecasting. Ultimately, it was also the most rewarding. In the long run, analysis would also connect forecasting with climatology in new, unexpected, and important ways. >Reanalysis/Climatology
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Edwards I 364
Interpretation: Before numerical weather prediction, analysis was an interpretive process that involved a shifting combination of mathematics, graphical techniques, and pattern recognition. Human interpretation played a crucial role in data collection; (…).(2)
Edwards I 369
Objective analysis: The JNWPU’s (Northwestern Polytechnical University) first, experimental analysis program defined a 1000×1000 km square around each gridpoint. Next, it searched for all available observed data within that square. If it found no data, the program skipped that gridpoint and moved to the next one. If it did find data, the program fitted a quadratic surface to all the data points within the search square. It then interpolated a value on that surface for the gridpoint. (…)This technique worked well for areas densely covered by observations, but performed poorly in large data-void regions.(3) >Models/Climatology, >Climate data/Edwards.
Edwards I 391
Models/weather forecasting: Traditionally, scientists and philosophers alike understood mathematical models as expressions of theory - as constructs that relate dependent and independent variables to one another according to physical laws. On this view, you make a model to test a theory (or one expression of a theory). You take some measurements, fill them in as values for initial conditions in the model, then solve the equations, iterating into the future. from the point of view of operational forecasting, the main goal of analysis is not to explain weather but to reproduce it. You are generating a global data image, simulating and observing at the same time, checking and adjusting your simulation and your observations against each other. As the philosopher Eric Winsberg has argued, simulation modeling of this sort doesn’t test theory; it applies theory. This mode - application, not justification, of theory - is “unfamiliar to most philosophy of science.”(4)


1. V. Bjerknes, Dynamic Meteorology and Hydrography, Part II. Kinematics (Gibson Bros., Carnegie Institute, 1911); R. Daley, Atmospheric Data Analysis (Cambridge University Press, 1991).
2. See 14. P. Bergthorsson and B. R. Döös, “Numerical Weather Map Analysis,” Tellus 7, no. 3 (1955), 329.
3. As one of the method’s designers observed, “straightforward interpolation between observations hundreds or thousands of miles apart is not going to give a usable value.” G. P. Cressman, “Dynamic Weather Prediction,” in Meteorological Challenges: A History, ed. D. P. McIntyre (Information Canada, 1972), 188.
4. E. Winsberg, “Sanctioning Models: The Epistemology of Simulation,” Science in Context 12, no. 2 (1999), 275.

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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.

Edwards I
Paul N. Edwards
A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming Cambridge 2013


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