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Reanalysis: Reanalysis involves revisiting existing data or findings using different methods, perspectives, or updated criteria. It aims to verify, reinterpret, or refine previous conclusions, offering new insights or confirming the robustness of prior results in scientific studies or data analysis. See also Method, Analysis.
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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.

 
Author Concept Summary/Quotes Sources

Climatology on Reanalysis - Dictionary of Arguments

Edwards I 58
Reanalysis/Climatology/Edwards: Analyzed weather data aren’t of much use to climatologists because forecasters frequently revise their analysis models (as often as every six months in some cases). Each change in the analysis model renders the data it produces incommensurable with those produced by the previous model. Reanalysis eliminates this problem by using a single “frozen” model to analyze historical observational data over some long period (40–50 years or even more). Because analysis models are built to combine readings from all available observing systems, reanalysis also overcomes the otherwise thorny problem of comparing instruments such as radiosondes and satellite radiometers. The result is a physically self-consistent global data set for the entire reanalysis period. Potentially, this synthetic data set would be more accurate than any individual observing system.(1)
(…) some scientists hope that reanalysis will eventually generate definitive data sets, useable for climate trend analysis, that will be better than raw observational records. For the moment, however, they are stuck with infrastructural inversion - that is, with probing every detail of every record, linking changes in the data record to social and technical changes in the infrastructure that created it, and revising past data to bring them into line with present standards and systems. >Infrastructure/Edwards
, >Climate data/Edwards.
Edwards I 358
In reanalysis, investigators reprocess decades of original sensor data using a single “frozen” weather analysis and forecasting system. The result is a single complete, uniformly gridded, physically consistent global data set. Reanalysis offered a comprehensive solution to data friction such as that created by heterogeneous data sources, including satellite radiances not easily converted into traditional gridded forms. With reanalysis, many hoped, it would be possible to produce a dynamic data image of the planetary atmosphere over 50 years or more - essentially a moving picture that might reveal more precisely how, where, and how much Earth’s climate had changed. Global reanalysis might produce the most accurate, most complete data sets ever assembled. Yet the majority of gridpoint values in these data sets would be generated by the analysis model, not taken directly from observations. Whether or not it eventually leads to better understanding of climate change—a matter about which, at this writing, scientists still disagree - reanalysis represents a kind of ultimate moment in making data global. >Models/Climatology, >Climate data/Edwards, >Parameterization/metereology, >Homogenization/climatology.
Edwards I 447
From the earliest national and global networks through the 1980s, every empirical study of global climate derived from the separate stream of “climate data.” Climatological stations calculated their own averages, maxima, minima, and other figures. Central collectors later inverted the climate data infrastructure, scanning for both isolated and systematic errors and working out ways to adjust for them, seeking to “homogenize” the record. All of these efforts presumed (…) that only traditional “climate data” could form the basis of that record. But as numerical weather prediction skill advanced and computer power grew, a new idea emerged: What about a do-over? What if you could rebuild climate statistics “from scratch,” from daily weather data? And what if you could do this not simply by recalculating individual station averages, but by feeding every available scrap of weather data into a state-of-the-art 4-D assimilation system, as if taking a moving data image with a single camera? The roots of reanalysis lay in the Global Weather Experiment’s parallel data streams.
Edwards I 449
Four-dimensional data assimilation: Trenberth argued that the name “four-dimensional data assimilation” misstated the nature of operational analysis, which was actually “three and a half dimensional.” In other words, operational analyses looked backward in time, integrating data from the recent past (up to the observational cutoff), but they did not look forward in time, correcting the analysis with data arriving in the first few hours after the cutoff. But data assimilation systems purpose-built for reanalysis
I 450
could potentially offer this capability, leading (in principle) to more accurate, more smoothly varying analyses.(2)
I 456
Reanalysis provoked enormous excitement. By the early 2000s, other institutions, including the Japan Meteorological Agency, had launched major reanalysis projects, and numerous smaller, experimental projects had been started.(3) Investigators at NOAA’s (National Oceanic and Athmospheric Administration) Earth System Research Laboratory used surface pressure data from the pre-radiosonde era to extend reanalysis back to 1908, complementing existing studies to create a full century of reanalysis data, and they have begun to consider reaching even further back, into the late nineteenth century.(4) By 2007,
Edwards I 457
publications concerned with reanalysis for climate studies were appearing at a rate of 250 per year.(5)
Parameterization: All the assimilation models used in reanalysis to date exhibit biases of various kinds, due mainly to imperfect physical parameterizations. >Parameterization/metereology, >Model bias/climatology.
Edwards I 459
Reanalysis: How well has reanalysis worked? Reanalyses and traditional climate data agree well—though not perfectly—for variables constrained directly by observations, such as temperature. But derived variables generated mainly by the model still show considerable differences.(6) For example, reanalysis models do not yet correctly balance precipitation and evaporation over land and oceans, whose total quantity should be conserved.(7) This affects their calculations of rainfall distribution, a climate variable that is extremely important to human populations and to natural ecosystems.
Edwards I 461
Reanalysis offers something that traditional climate data will never achieve: physically consistent data across all climate variables. Traditional climate data are “single variable”: you get a set of averages for temperature, another one for pressure, a third for precipitation, a fourth for sunshine, and so on. Each type of observation is independent of the others, but in the real atmosphere these quantities (and many others) are interdependent. Reanalysis models simulate that interdependence, permitting a large degree of cross-correction, and they generate all variables for every gridpoint. This allows scientists to study structural features of the atmosphere and the circulation not directly measured by instruments. >Human Fingerprint/climatology.

1. T. R. Karl et al., eds., Temperature Trends in the Lower Atmosphere: Steps for Understanding and Reconciling Differences (US Climate Change Science Program, 2006), 35.
2. Trenberth, K.E. Atmospheric circulation climate changes. Climatic Change 31, 427–453 (1995). https://doi.org/10.1007/BF01095156
3. K. Onogi et al., “JRA-25: Japanese 25-Year Re-Analysis Project—Progress and Status,” Quarterly Journal of the Royal Meteorological Society 131, no. 613 (2005). 22. G. P. Compo et al., “Feasibility of a 100-Year Reanalysis Using Only Surface Pressure Data,” Bulletin of the American Meteorological Society 87, no. 2 (2006): 175–; J. S. Whitaker et al., “Reanalysis without Radiosondes using Ensemble Data Assimilation,” Monthly Weather Review 132, no. 5 (2004): 1190–.
4. R. M. Dole et al., Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change (US Climate Change Science Program, 2008), 10.
5.. L. R. Lait, “Systematic Differences Between Radiosonde Measurements,” Geophysical Research Letters 29, no. 10 (2002): 1382.
6. R. B. Rood, “Reanalysis,” in Data Assimilation for the Earth System, ed. R. Swinbank et al. (Kluwer, 2003).
7. L. Bengtsson et al., “The Need for a Dynamical Climate Reanalysis,” Bulletin of the American Meteorological Society 88, no. 4 (2007): 495–.

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


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