Executive Summary

The purpose of this report is (1) to define the need for a nationally focused program to generate routinely research-quality, model-assimilated geophysical data sets and (2) to outline a basic strategy to implement an integrated national geophysical archive system to ensure the availability of such data sets to serve a broad range of national needs.

The report concisely surveys the current applications and usefulness of research and operational model-assimilated atmospheric and oceanic data sets, assesses current activities for the development of model assimilation technology and new applications, and identifies the pressing national need in the 1990s—to manage and utilize effectively the overwhelming volume of earth system data already scheduled from greatly enhanced ground-based and satellite observing systems. A basic strategy for the earth system sciences is outlined, namely, to use the power and consistency of proven model assimilation technology to generate routinely assimilated geophysical data sets for both operational and long-term use and to maintain these data sets in an integrated national geophysical archive system designed to ensure the ready availability of these data sets to the scientific and public policy communities. By extending this technology backward in time to about the beginning of the development of model-based geophysical data in the 1950s through reanalyses with state-of-the-art model assimilation, the consistency and integrity of these data sets will provide information and value (e.g., for the study of climate and global change) that significantly exceed those of the incoming observations.



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Four-Dimensional Model Assimilation of Data: A Strategy for the Earth System Sciences Executive Summary The purpose of this report is (1) to define the need for a nationally focused program to generate routinely research-quality, model-assimilated geophysical data sets and (2) to outline a basic strategy to implement an integrated national geophysical archive system to ensure the availability of such data sets to serve a broad range of national needs. The report concisely surveys the current applications and usefulness of research and operational model-assimilated atmospheric and oceanic data sets, assesses current activities for the development of model assimilation technology and new applications, and identifies the pressing national need in the 1990s—to manage and utilize effectively the overwhelming volume of earth system data already scheduled from greatly enhanced ground-based and satellite observing systems. A basic strategy for the earth system sciences is outlined, namely, to use the power and consistency of proven model assimilation technology to generate routinely assimilated geophysical data sets for both operational and long-term use and to maintain these data sets in an integrated national geophysical archive system designed to ensure the ready availability of these data sets to the scientific and public policy communities. By extending this technology backward in time to about the beginning of the development of model-based geophysical data in the 1950s through reanalyses with state-of-the-art model assimilation, the consistency and integrity of these data sets will provide information and value (e.g., for the study of climate and global change) that significantly exceed those of the incoming observations.

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Four-Dimensional Model Assimilation of Data: A Strategy for the Earth System Sciences Geophysical data assimilation is a quantitative, objective method to infer the state of the earth-atmosphere-ocean system from heterogeneous, irregularly distributed, and temporally inconsistent observational data with differing accuracies. The current method for atmospheric and oceanic sciences uses numerical prediction models, which incorporate our best knowledge of the physics and dynamics of the continually evolving earth system, to integrate these observations into temporally, spatially, and internally consistent data sets that provide a better description over time of the system's state than that provided by the raw observations. These model-assimilated data sets have proven to be extremely valuable as initial conditions for numerical weather prediction and also for a great variety of diagnostic research studies that have increased our understanding of atmospheric and oceanic behavior and, more generally, of how the climate system works. The increased understanding has, in turn, led to the development of improved numerical models and data assimilation techniques. Currently, several major operational forecast centers produce model-assimilated data sets for the atmosphere as part of their numerical prediction activities. Use of these global-scale data sets as initial conditions for numerical weather prediction models has resulted in significant increases in forecast accuracy. New assimilation techniques, such as sequential estimation and variational data assimilation, along with the installation of more powerful computer systems, will lead to further increases in accuracy. Data assimilation with developing regional and mesoscale models, and future extension into stratospheric models, will lead to advances in our understanding of and predictive capability for atmospheric circulations on these scales as well. The state of ocean modeling and the production of model-assimilated data sets are less advanced than for the atmosphere, mainly because the amount of oceanic data collected routinely is much less than for the atmosphere and because there traditionally has been no requirement for real-time ocean forecasting. The relatively slow circulation of the oceans only partially compensates for the paucity of observations. Nevertheless, pilot studies on the feasibility of producing useful model-assimilated data sets have been carried out and look promising. Model-assimilated data sets are recognized as the best means of ensuring that the various oceanic observations are as dynamically consistent with one another as possible, just as in the atmosphere. Another area where model-assimilated data sets have the potential to combine large amounts of heterogeneous observational data into a coherent whole is in the analysis of the hydrological cycle. This cycle consists of three dominant subsystems: the oceanic source, the atmospheric conveyor, and the land surface catchment and runoff. Each of these presents formidable, but not insurmountable, challenges in observational data handling

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Four-Dimensional Model Assimilation of Data: A Strategy for the Earth System Sciences and integration into model representation of the hydrological cycle. More detailed and more extensive observational coverage of evaporation, precipitation, change in soil moisture, and other parameters, including ocean and land surface processes, that define the hydrological cycle is needed. An important goal of the U.S. Global Change Research Program is to develop coupled interactive models that link the atmosphere, oceans, land masses, and biosphere into a comprehensive whole. Model-assimilated data sets produced by such coupled model systems would provide an improved description of the entire global system and of all its interacting components. Atmospheric chemistry and oceanic biogeochemical modeling would also be included in this comprehensive modeling of global processes. Data assimilation techniques have been used successfully with limited chemical transport models for both the troposphere and the stratosphere. It should be emphasized that further progress toward development of comprehensive global models that produce model-assimilated data sets useful for global monitoring will require the implementation of new observing systems, many of which are already scheduled for the 1990s. Research problems and studies on a wide range of spatial and temporal scales will benefit from the availability of high-quality model-assimilated data sets. In the atmosphere these include global, regional, and mesoscale weather systems; planetary waves; low-frequency variability; atmosphere-ocean and atmosphere-land interactions; dynamics-chemistry-radiation interactions; and the whole hydrological cycle. Similar research studies in the oceans will also benefit as observational coverage is increased to provide global oceanic data sets. Pilot studies indicate that interactively coupled atmospheric and oceanic models lead to more realistic modeled behavior of both fluids. A major challenge of the coming decades is in tracking global and regional climatic trends. Model-assimilated data sets currently in existence were produced mainly for operational forecasting purposes and are not temporally and internally consistent over long time periods because of frequent changes in the early model development period in numerical models and assimilation techniques and different practices with regard to incorporation of off-schedule and research data. Therefore, a model data assimilation analysis of the entire useful climatic record over the past 40 years is essential for removing the impacts of any model biases on the assimilated data sets and producing a temporally and spatially consistent data set for the study of climatic trends. Although such an extended analysis of past data would be a relatively expensive undertaking, the need for tracking and predicting global change in terms of scientific and economic factors justifies the effort. Data assimilation includes a systematic, structured, and open-ended learning process. Learning occurs through iterative confrontations of different types

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Four-Dimensional Model Assimilation of Data: A Strategy for the Earth System Sciences of ''current'' observations from nature with each other and with the predicted model state, which serves as the "background' field. This confrontation provides a disciplined and rich opportunity for learning about the behavior of the geophysical systems, the quality of the observations, the interpretation of observational evidence, and the accuracy of the assimilating model. The background fields, for example, are sufficiently accurate to be useful for detecting observations with uncharacteristically large errors. However, instances have occurred in which the model rejected observations that truly represented a significant change, resulting in a subsequent incorrect forecast. Methods have been developed to catch these critical events, but it cannot be said that state-of-the-art models have entirely resolved this problem. Data assimilation has proven especially valuable in isolating the systematic error characteristics and biases of satellite-based remote sensors and also observational stations and should therefore have a major role in both assessing the quality of future satellite and ground-based data and assuring their use to best advantage. Furthermore, since the error characteristics of model-assimilated data sets can be estimated, the data sets themselves are also used routinely to validate the performance of prediction models. A survey of existing archives of model-assimilated data sets indicates that they are frequently incomplete, are saved in a variety of locations and in a variety of formats, and are not easily accessible in general to the research community. Nevertheless, they provide good starting points for development of an integrated national archival system that would have uniform formats and provide ease of access to users. Future changes in technology and cost will facilitate the process of compact archiving of vast data sets and will make their use by individual researchers much more feasible. New data storage technology, including publishable media such as compact data (CD) disks, will permit scientists to store significant amounts of data at their own computer workstations. New devices and computer software routines for imaging data fields will also facilitate use of model-assimilated data sets by researchers. Large increases in heterogeneous observational data from greatly enhanced ground-based and satellite observing systems in the future will require an expanded use of model assimilation of data and the production of model-assimilated data sets, both for operational and research purposes. For example, in the United States the ongoing National Weather Service (NWS) Modernization Program, involving implementation of new observing systems like the WSR-88D Doppler radar, profilers, advanced satellite systems, and the Automated Surface Observing System (ASOS), is expected to result in an overall 100-fold increase in the amount of observational data that must be integrated into a dynamically consistent data set for operational prediction and warning purposes. Similar challenges are raised in

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Four-Dimensional Model Assimilation of Data: A Strategy for the Earth System Sciences physical and biogeochemical oceanography and for the earth system by the increased amount of data expected in the future. Research on data assimilation systems capable of managing the new observing systems of the 1990s is presently under way, but this effort will need additional support to be operational in time for the new observing systems already scheduled. The operational centers and the larger research community must contribute jointly to the design of these systems to ensure that the needs of the broad community are met. Continuing advances in computer technology and modes of parallel computer operation will permit more sophisticated data assimilation techniques to be used at national forecast centers and laboratories, will allow oceanic and other geophysical model-assimilated data sets to be routinely generated, and will increase the feasibility of reanalyses of long data sets from the past as the state-of-the-art technology requires. Affordable facilities for acquiring, processing, and evaluating large data sets, along with appropriate software, are expected to become increasingly available for use by individual researchers. CONCLUSIONS The panel reached four major conclusions: Four-dimensional (space and time) data assimilation as a subdiscipline of geophysical sciences is fundamental for the synthesis of diverse, temporally inconsistent, and spatially incomplete observations into a coherent representation of an evolving geophysical system. Viewed as an integral element of the scientific process, four-dimensional model assimilation of geophysical data is a systematic, quantitative, objective, iterative means of inference and testing aimed at advancing understanding and prediction of nonlinear dynamical geophysical systems where interactions occur continually among relevant physical, chemical, and biogeochemical processes. 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. Atmospheric model data assimilation is a proven strategy in an advanced state of development. Although still in an early stage of development for the earth sciences as a whole, model data assimilation is strategically situated to address the pressing national need to observe and understand global change as it occurs in the coming decades. The immediate need is to

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Four-Dimensional Model Assimilation of Data: A Strategy for the Earth System Sciences develop and provide a nationally focused capability to synthesize, test, and utilize for understanding and prediction the information from the greatly enhanced new ground-based and satellite observing systems already scheduled for the next two decades. RECOMMENDATIONS The panel recommends the following strategy for a nationally focused program for four-dimensional model assimilation of data for the earth system: The strategy for the nationally focused program will: Develop and expand applications of model-based data assimilation efforts to interdisciplinary areas that are necessary for integrated earth ocean-atmosphere-biogeochemical models. Implement an integrated, multicenter, nationally focused archive system for model-assimilated and-tested data sets and provide for ready access to these sets by the scientific community over the long term. Provide for continuing scientific exchange and collaboration among the various groups and individuals engaged in geophysical data assimilation, particularly scientists involved with the Earth Observing System Data and Information System; the national meteorological, oceanographic, and climate centers; and the High Performance Computer and Communications Initiative. Provide for the generation of routine research-quality, model-assimilated and-tested geophysical data sets to serve a broad range of national endeavors, including climate and global change research and predictions. Establish a working group to develop an implementation plan for this nationally focused program. The strategy and implementation plan should include the following specific actions: To validate and maintain quality control of new types of remotely sensed and experimental in situ geophysical data, including research data from field programs, the application of operational data assimilation models is essential. 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. A coordinated national program should be implemented and funded to develop consistent, long-term assimilated data sets (extended back to about

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Four-Dimensional Model Assimilation of Data: A Strategy for the Earth System Sciences 1950) for study of climate and global change. This effort will reanalyze, with a state-of-the-art global-and regional-scale data assimilation model, all atmospheric and oceanic data available since about 1950 in order to produce the best possible, validated, temporally and spatially consistent data sets for the study of climate and global change. Model biases should be identified and eliminated as far as possible at this time. This reanalysis should be repeated as advances in the state of the art require. Data assimilation models for the mesoscale should be developed in concert with regional prediction models. The systems should integrate data from the enhanced observational capabilities of the coming decade that will be provided through the NWS Modernization Program, advanced satellite systems, and the Earth Observing System (EOS). These models should be capable of integrating specially observed data with the conventional data stream and also provide for realistic responses to various types of system forcing for the time and space scales emphasized. The mesoscale assimilation systems should also ensure effective nesting with larger-scale analysis systems. In order to provide ready access by the scientific community, a multicenter geophysical data archive system, electronically linked for maximum effectiveness for assimilated data set management, transfer, and usage, should be created. The data management activity for the multicenter archiving system will ensure routine compilation and availability of observed, model-assimilated, and model-predicted data sets in a structural format jointly adopted and coordinated with the Earth Observing System Data and Information System (EOSDIS). There is a vital need to archive together with the data sets the modeling codes, information on the input data and how they were processed, and so forth for the scientist to be able to evaluate and understand the archived data sets. Accessibility to archived model-assimilated data sets should be part of a preplanned, service-oriented archive system, including on-line 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. Interdisciplinary and discipline graduate education and research opportunities in four-dimensional geophysical data assimilation should be created. To provide essential expertise to cope with the 100-fold increases in observational data from greatly enhanced observing systems in the 1990s and beyond, the development of graduate education, including advanced degree and research opportunities in the fundamental subdiscipline of four-dimensional data assimilation, is needed. Immediate support should be

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Four-Dimensional Model Assimilation of Data: A Strategy for the Earth System Sciences provided for graduate courses and research fellowships in university departments of atmospheric, oceanic, global change, and related earth system sciences. These programs should be explicitly coordinated with related system development, such as EOSDIS, the High Performance Computer and Communications Initiative, and national computer-linked networks.