Four-Dimensional Model Assimilation of Data A Strategy for the Earth System Sciences (1991) / Chapter Skim
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7 Conclusions and Recommendations
7 Conclusions and Recommendations
Pages 61-69
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From page 61... ...
Temporal and spatial consistency enters in the description through a numerical assimilating model that possesses predictive, quality control, and validation capabilities, based on the governing equations for He given geophysical systems. Data assimilation produces data sets that, by virtue of their integrity and consistency, provide information and value that significantly exceed those of the incoming observations.
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From page 62... ...
3. Since the physical and dynamical consistency of model-assimilated data sets results in a level of information and added value that significantly exceeds that of the incoming observations, assimilation data sets have been and will be used even more extensively by the scientific community for diagnostic, predictive, and process studies, supplemented by original observations when needed.
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From page 63... ...
As such, they form an exceedingly valuable resource that has become the data set of choice for the great majority of atmospheric researchers. Model-assimilated data sets for the ocean, land surface, and other components of climate change and global change processes should prove to be equally valuable for research once they have been developed.
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From page 64... ...
This need to detect and monitor climate and global change as it occurs places unusual demands on both observing and data assimilation capabilities. Development and application of global and regionally assimilated data sets prepared by state-of-the-art prediction models for understanding the eanh-ocean-annosphere system and the physical processes that determine its evolution require a nationally focused program.
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From page 65... ...
Funding agencies should routinely provide sufficient funds and computer capacity for this purpose, including provision for timely communication of such new and experimental data sets to designated operational assimilation centers. Over the past two decades, operational data assimilation for global weather forecasting has been developed to the point where it is invaluable for quality control and validation of remotely sensed and experimental data.
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From page 66... ...
Existing global model-assimilated data sets for climate studies are compromised by changes in assimilating models, in methods of dealing with raw data, in the observing networks and systems, and in assimilation procedures. These factors introduce discontinuities and inconsistencies in long time series of model-assimilated geophysical data, originally produced for weather prediction purposes, that make them only marginally useful for climate and global change analysis and predictions.
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From page 67... ...
The mesoscale assimilation systems should also ensure effective nesting with larger-scale analysis systems. Use of model-based assimilation systems for the generation of mesoscale data sets is especially appropriate because many new mesoscale data sources can resolve smaller spatial and temporal scales Man current synopiic-scale operational models are designed to do.
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From page 68... ...
· Accessibility to archived model-assimilated data sets should be part of a preplanned, service-oriented archive system, ;ncluding online electronic links, low-cost publication media for use on individual workstations, routine inclusion of metadata and software for unpacking and manipulating data sets, and high-quality imaging capabilities. The assembly of a properly formatted and sequenced interdisciplinary assimilated data archive that will span approximately the last 40 years and be extended into the future is a major and crucial task for studies of climate and global change and will require a designated, funded management activity.
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From page 69... ...
These programs should be explicitly coordinated with related system development, such as EOSDIS, the High Performance Computer and Communications program, and national computer-linked networks. 69 A serious shortage of research scientists with the background, interest, and ability to develop advanced four-dimensional data assimilation methodology and use it in producing high-quality model-assimilated data sets exists at present.
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