NASA's Modern Era Retrospective-Analysis for Research and Applications: Integrating Earth Observations. Bosilovich, M. G. 1(1-4):82367+.
NASA's Modern Era Retrospective-Analysis for Research and Applications: Integrating Earth Observations [link]Paper  abstract   bibtex   
[Excerpt] To gain the benefit of the many types of meteorological observations for weather forecasts, the data are analyzed (or assimilated) to provide initial conditions for numerical weather forecast models. The resulting analyses are the merger of model and many types of observed data. Weather forecasts greatly benefitted from the assimilation of satellite remotely sensed observations. After years of these analyses had been produced, researchers hoped they would yield insight on many aspects of the Earth's climate. For example, a consistent picture of the global circulation was produced (Trenberth and Olson, 1988), though not without uncertainties. Changes and improvements to the operational analyses and forecast models introduced spurious climate shifts in the time series of such analyses, so that the resulting interannual variability was not well represented. Bengtsson and Shukla (1988) proposed a reanalysis, or retrospective-analysis, of the observations, using a fixed analysis/forecast system to provide more consistent time series of the analyzed data products. Since then, retrospective-analyses have been produced at NASA (Schubert et al. 1993), the National Centers for Environmental Prediction (NCEP, Kalnay et al. 1996; Kanamitsu et al. 2002), the European Centre for Medium-range Weather Forecasts (ECMWF, Uppala et al. 2005) and the Japanese Meteorological Agency (JMA, Onogi et al, 2007). These have all been used extensively in climate and weather research. There are advantages and disadvantages to reanalyses for climate study. The main advantages are that global Earth observations (disparate distributions in space and time) are assimilated leading to uniformly gridded and globally available data. Reanalyses also combine many different types of observations into a single analysis. The model diagnostics include data that would not otherwise be observed, providing insight into the Earth system. On the other hand, the influence of the imperfect global models affects the resulting reanalyses, any improvements in modeling and data quality control all lead to differences in the climate produced by the aforementioned reanalyses. Hence, as models, data assimilation, observational quality control and computing power improves, so shall the climate information in reanalyses (Bengtsson et al. 2007).
@article{bosilovichNASAModernEra2008,
  title = {{{NASA}}'s Modern Era Retrospective-Analysis for Research and Applications: Integrating {{Earth}} Observations},
  author = {Bosilovich, Michael G.},
  date = {2008},
  journaltitle = {IEEE Earthzine},
  volume = {1},
  pages = {82367+},
  url = {http://mfkp.org/INRMM/article/13384698},
  abstract = {[Excerpt] To gain the benefit of the many types of meteorological observations for weather forecasts, the data are analyzed (or assimilated) to provide initial conditions for numerical weather forecast models. The resulting analyses are the merger of model and many types of observed data. Weather forecasts greatly benefitted from the assimilation of satellite remotely sensed observations. After years of these analyses had been produced, researchers hoped they would yield insight on many aspects of the Earth's climate. For example, a consistent picture of the global circulation was produced (Trenberth and Olson, 1988), though not without uncertainties. Changes and improvements to the operational analyses and forecast models introduced spurious climate shifts in the time series of such analyses, so that the resulting interannual variability was not well represented. Bengtsson and Shukla (1988) proposed a reanalysis, or retrospective-analysis, of the observations, using a fixed analysis/forecast system to provide more consistent time series of the analyzed data products. Since then, retrospective-analyses have been produced at NASA (Schubert et al. 1993), the National Centers for Environmental Prediction (NCEP, Kalnay et al. 1996; Kanamitsu et al. 2002), the European Centre for Medium-range Weather Forecasts (ECMWF, Uppala et al. 2005) and the Japanese Meteorological Agency (JMA, Onogi et al, 2007). These have all been used extensively in climate and weather research.

There are advantages and disadvantages to reanalyses for climate study. The main advantages are that global Earth observations (disparate distributions in space and time) are assimilated leading to uniformly gridded and globally available data. Reanalyses also combine many different types of observations into a single analysis. The model diagnostics include data that would not otherwise be observed, providing insight into the Earth system. On the other hand, the influence of the imperfect global models affects the resulting reanalyses, any improvements in modeling and data quality control all lead to differences in the climate produced by the aforementioned reanalyses. Hence, as models, data assimilation, observational quality control and computing power improves, so shall the climate information in reanalyses (Bengtsson et al. 2007).},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13384698,climate,data,earth-observation,earth-system,meteorology,modelling,remote-sensing},
  number = {1-4}
}
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