ALBERT

Description: This report describes the analysis component of the Goddard Earth Observing System, Data Assimilation System, Version 1 (GEOS-1 DAS). The general features of the data assimilation system are outlined, followed by a thorough description of the statistical interpolation algorithm, including specification of error covariances and quality control of observations. We conclude with a discussion of the current status of development of the GEOS data assimilation system. The main components of GEOS-1 DAS are an atmospheric general circulation model and an Optimal Interpolation algorithm. The system is cycled using the Incremental Analysis Update (IAU) technique in which analysis increments are introduced as time independent forcing terms in a forecast model integration. The system is capable of producing dynamically balanced states without the explicit use of initialization, as well as a time-continuous representation of non- observables such as precipitation and radiational fluxes. This version of the data assimilation system was used in the five-year reanalysis project completed in April 1994 by Goddard's Data Assimilation Office (DAO) Data from this reanalysis are available from the Goddard Distributed Active Center (DAAC), which is part of NASA's Earth Observing System Data and Information System (EOSDIS). For information on how to obtain these data sets, contact the Goddard DAAC at (301) 286-3209, EMAIL daac@gsfc.nasa.gov.

Description: The Data Assimilation Office (DAO) at Goddard Space Flight Center has produced a multiyear global assimilated data set with version 1 of the Goddard Earth Observing System Data Assimilation System (GEOS-1 DAS). One of the main goals of this project, in addition to benchmarking the GEOS-1 system, was to produce a research quality data set suitable for the study of short-term climate variability. The output, which is global and gridded, includes all prognostic fields and a large number of diagnostic quantities such as precipitation, latent heating, and surface fluxes. Output is provided four times daily with selected quantities available eight times per day. Information about the observations input to the GEOS-1 DAS is provided in terms of maps of spatial coverage, bar graphs of data counts, and tables of all time periods with significant data gaps. The purpose of this document is to serve as a users' guide to NASA's first multiyear assimilated data set and to provide an early look at the quality of the output. Documentation is provided on all the data archives, including sample read programs and methods of data access. Extensive comparisons are made with the corresponding operational European Center for Medium-Range Weather Forecasts analyses, as well as various in situ and satellite observations. This document is also intended to alert users of the data about potential limitations of assimilated data, in general, and the GEOS-1 data, in particular. Results are presented for the period March 1985-February 1990.

Description: A Big-Data environment is one that is capable of orchestrating quick-turnaround analyses involving large volumes of data for numerous simultaneous users. Based on our experiences with a prototype Big-Data analysis environment, we anticipate some important changes in research behaviors and processes while conducting scientific data-analysis research in the near future as such Big-Data environments become the mainstream. The first anticipated change will be the reduced effort and difficulty in most parts of the data management process. A Big-Data analysis environment is likely to house most of the data required for a particular research discipline along with appropriate analysis capabilities. This will reduce the need for researchers to download local copies of data. In turn, this also reduces the need for compute and storage procurement by individual researchers or groups, as well as associated maintenance and management afterwards. It is almost certain that Big-Data environments will require a different "programming language" to fully exploit the latent potential. In addition, the process of extending the environment to provide new analysis capabilities will likely be more involved than, say, compiling a piece of new or revised code.We thus anticipate that researchers will require support from dedicated organizations associated with the environment that are composed of professional software engineers and data scientists. A major benefit will likely be that such extensions are of higherquality and broader applicability than ad hoc changes by physical scientists. Another anticipated significant change is improved collaboration among the researchers using the same environment. Since the environment is homogeneous within itself, many barriers to collaboration are minimized or eliminated. For example, data and analysis algorithms can be seamlessly shared, reused and re-purposed. In conclusion, we will be able to achieve a new level of scientific productivity in the Big-Data analysis environments.

Description: The role of the Science Mission Directorate (SMD) is to enable NASA to achieve its science goals in the context of the Nation's science agenda. SMD's strategic decisions regarding future missions and scientific pursuits are guided by Agency goals, input from the science community including the recommendations set forth in the National Research Council (NRC) decadal surveys and a commitment to preserve a balanced program across the major science disciplines. Toward this end, each of the four SMD science divisions -- Heliophysics, Earth Science, Planetary Science, and Astrophysics -- develops fundamental science questions upon which to base future research and mission programs. Often the breakthrough science required to answer these questions requires significant technological innovation, e.g., instruments or platforms with capabilities beyond the current state of the art. SMD's targeted technology investments fill technology gaps, enabling NASA to build the challenging and complex missions that accomplish groundbreaking science.

Description: The role of the Science Mission Directorate (SMD) is to enable NASA to achieve its science goals in the context of the nation's science agenda. SMD's strategic decisions regarding future missions and scientific pursuits are guided by agency goals, input from the science community including the recommendations set forth in the National Research Council (NRC) decadal surveys and a commitment to preserve a balanced program across the major science disciplines. Toward this end, each of the four SMD science divisions -- Heliophysics, Earth Science, Planetary Science, and Astrophysics -- develops fundamental science questions upon which to base future research and mission programs.

Description: The role of the Science Mission Directorate (SMD) is to enable NASA to achieve its science goals in the context of the nation's science agenda. SMD's strategic decisions regarding future missions and scientific pursuits are guided by agency goals, input from the science community including the recommendations set forth in the National Research Council (NRC) decadal surveys and a commitment to preserve a balanced program across the major science disciplines. Toward this end, each of the four SMD science divisions -- Heliophysics, Earth Science, Planetary Science, and Astrophysics -- develops fundamental science questions upon which to base future research and mission programs.

Description: A mesoscale model simulation of a 'surrogate' atmosphere designed for the evaluation of adiabatic initialization and moisture enhancement techniques is briefly described. The simulation provides mesoscale precipitation rates and associated diabatic heating fields which are known exactly and are internally consistent within the context of the model. The advantage of this approach is that a numerical model can easily serve as a source of mesoscale data in the usual absence of internally consistent mesoscale observed data sets.

Description: The role of the Science Mission Directorate (SMD) is to enable NASA to achieve its science goals in the context of the Nation's science agenda. SMD's strategic decisions regarding future missions and scientific pursuits are guided by Agency goals, input from the science community-including the recommendations set forth in the National Research Council (NRC) decadal surveys-and a commitment to preserve a balanced program across the major science disciplines. Toward this end, each of the four SMD science divisions-Heliophysics, Earth Science, Planetary Science, and Astrophysics-develops fundamental science questions upon which to base future research and mission programs. Often the breakthrough science required to answer these questions requires significant technological innovation-e.g., instruments or platforms with capabilities beyond the current state of the art. SMD's targeted technology investments fill technology gaps, enabling NASA to build the challenging and complex missions that accomplish groundbreaking science.

Description: Summary of the Workshop on Autonomy for Future SMD Missions that was held on July 21, 2017 at the 2017 NASA Exploration Science Forum (Moffett Field, CA).

Description: Large-scale numerical simulation has been one of the most important approaches for understanding global geodynamical processes. In this approach, peta-scale floating point operations (pflops) are often required to carry out a single physically-meaningful numerical experiment. For example, to model convective flow in the Earth's core and generation of the geomagnetic field (geodynamo), simulation for one magnetic free-decay time (approximately 15000 years) with a modest resolution of 150 in three spatial dimensions would require approximately 0.2 pflops. If such a numerical model is used to predict geomagnetic secular variation over decades and longer, with e.g. an ensemble Kalman filter assimilation approach, approximately 30 (and perhaps more) independent simulations of similar scales would be needed for one data assimilation analysis. Obviously, such a simulation would require an enormous computing resource that exceeds the capacity of a single facility currently available at our disposal. One solution is to utilize a very fast network (e.g. 10Gb optical networks) and available middleware (e.g. Globus Toolkit) to allocate available but often heterogeneous resources for such large-scale computing efforts. At NASA GSFC, we are experimenting with such an approach by networking several clusters for geomagnetic data assimilation research. We shall present our initial testing results in the meeting.