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Four-dimensional model assimilation of data
Physics of the Earth and Planetary Interiors, 72(1992)127—133 Elsevier Science Publishers B.V., Amsterdam
127
Book Reviews
Four-Dimensional Model Assimilation of Data. National Research Council, National Academy Press, Washington, D.C., 1991, 78 PP., £16.50, IBSN 0-309-04536-3.
I was initially surprised at being asked to review a book on four-dimensional data assimilation for Physics of the Earth and Planetary Interiors, as the development of this field is largely owing to the needs of operational numerical weather prediction. But the analysis of incomplete observations, using prior knowledge, has a long tradition in all geophysical sciences, and the extension to the use of prior mathematical models of dynamical evolving systems is applicable to many. The models can be used to combine observational data distributed heterogeneously in space and time by predicting the dynamical changes occurring in the systems, and blending past information with new information in a systematic way. The report was prepared, for the US National Research Council, by the Panel on Model-Assimilated Data Sets for Atmospheric and Oceanic Research. The panel’s name explains the report’s thesis: four-dimensional assimilation (using numerical weather prediction models) has been used for some years for atmospheric research, and the technique is ready for application to earth science as a whole. Enormous volumes of data will be coming in the next decade from the Earth Observing System. Their assimilation into predictive models representing the atmosphere, oceans, land, and biosphere, will provide data sets which are more nearly complete and consistent than could be achieved by analysing data from a single observation time. The data assimilation process, by continually confronting theoretical knowledge (as embodied in numerical models) with observations, provides rich opportunities for learning about both. Chapters 3 and 4 discuss applications, and
future needs. Four-dimensional assimilation has been used for 15 years or more in global atmospheric models, and techniques are being developed for mesoscale models. Its routine use in physical oceanography is less advanced because of the lack of data, but it is an active area of research. There is growing recognition of the importance of viewing the atmosphere, oceans, land masses, and biosphere as interacting components of an integrated earth system. Data assimilation into coupled models will both aid, and benefit from, large scale field experiments, and related research into the hydrological cycle. Assimilated data sets are required for the study of atmospheric variability at many scales, ocean— atmosphere systems such as El Niño, and interactions of dynamics, chemistry and radiation in the middle atmosphere. Chapters 5 and 6 discuss the practical problems. Discrepancies between the model assimilated picture, and new observations, can provide the basis for quality control and validation of observations, analyses, and models. Archival, and distribution, of the basic data, and the model assimilated data sets is a challenge which can be met by modern technology. So can the provision of computer power to run the complex mathematical models. The report contains neither equations, nor detailed descriptions of assimilation techniques. There is but one figure. The writing is polished, and the advocacy of four-dimensional model assimilation of data well argued. But I doubt that a general reader would read it right through for pleasure. It is aimed at those responsible for the direction of research funding.
ANDREW C. LORENC (Bracknell, UK) _____________________________________________
127
Book Reviews
Four-Dimensional Model Assimilation of Data. National Research Council, National Academy Press, Washington, D.C., 1991, 78 PP., £16.50, IBSN 0-309-04536-3.
I was initially surprised at being asked to review a book on four-dimensional data assimilation for Physics of the Earth and Planetary Interiors, as the development of this field is largely owing to the needs of operational numerical weather prediction. But the analysis of incomplete observations, using prior knowledge, has a long tradition in all geophysical sciences, and the extension to the use of prior mathematical models of dynamical evolving systems is applicable to many. The models can be used to combine observational data distributed heterogeneously in space and time by predicting the dynamical changes occurring in the systems, and blending past information with new information in a systematic way. The report was prepared, for the US National Research Council, by the Panel on Model-Assimilated Data Sets for Atmospheric and Oceanic Research. The panel’s name explains the report’s thesis: four-dimensional assimilation (using numerical weather prediction models) has been used for some years for atmospheric research, and the technique is ready for application to earth science as a whole. Enormous volumes of data will be coming in the next decade from the Earth Observing System. Their assimilation into predictive models representing the atmosphere, oceans, land, and biosphere, will provide data sets which are more nearly complete and consistent than could be achieved by analysing data from a single observation time. The data assimilation process, by continually confronting theoretical knowledge (as embodied in numerical models) with observations, provides rich opportunities for learning about both. Chapters 3 and 4 discuss applications, and
future needs. Four-dimensional assimilation has been used for 15 years or more in global atmospheric models, and techniques are being developed for mesoscale models. Its routine use in physical oceanography is less advanced because of the lack of data, but it is an active area of research. There is growing recognition of the importance of viewing the atmosphere, oceans, land masses, and biosphere as interacting components of an integrated earth system. Data assimilation into coupled models will both aid, and benefit from, large scale field experiments, and related research into the hydrological cycle. Assimilated data sets are required for the study of atmospheric variability at many scales, ocean— atmosphere systems such as El Niño, and interactions of dynamics, chemistry and radiation in the middle atmosphere. Chapters 5 and 6 discuss the practical problems. Discrepancies between the model assimilated picture, and new observations, can provide the basis for quality control and validation of observations, analyses, and models. Archival, and distribution, of the basic data, and the model assimilated data sets is a challenge which can be met by modern technology. So can the provision of computer power to run the complex mathematical models. The report contains neither equations, nor detailed descriptions of assimilation techniques. There is but one figure. The writing is polished, and the advocacy of four-dimensional model assimilation of data well argued. But I doubt that a general reader would read it right through for pleasure. It is aimed at those responsible for the direction of research funding.
ANDREW C. LORENC (Bracknell, UK) _____________________________________________