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Many current and future NASA and non-NASA missions are focusing on multidisciplinary science. The current paradigm for data identification and effective use by the NASA science community is based on the CODMAC model proposed in 1986.
The successful Mars atmospheric entry by the Mars Science Laboratory (MSL-Curiosity) combined with the success of the Earth atmospheric entry by the Stardust capsule have established PICA as a major Thermal Protection Systems (TPS) material. We expect that this class of materials will be on the short list selected by NASA for any atmospheric entry missions and that it will be the lead of that list of materials in any planning, feasibility studies or flight readiness studies. In addition to NASAs successes, the Dragon capsule, the successful commercial space vehicle built by SpaceX, uses PICA-X, while the European Space Agency is considering ASTERM for its exploration missions that involve atmospheric entries, both of these materials are of the same family as PICA. In the talk, a high-fidelity model will be detailed and discussed. The model tracks the chemical composition of the gases produced during pyrolysis. As in the conventional models, it uses equilibrium chemistry to determine the recession rate at high temperatures but switches to in-volume finite-rate ablation for lower temperatures. It also tracks the time evolution of the porosity of the material. Progress in implementing this high-fidelity model in a code will be presented. In addition, a set of basic experimental data being supported for model validation will be summarized. The validation process for the model development will be discussed. Preliminary results will be presented for a case where detailed pyrolysis product chemistry is computed. Finally, a wish list for a set of validation experiments will be outlined and discussed.
The Community Coordinated Modeling Center (CCMC) hosts a set of state-of-the-art space science models ranging from the solar atmosphere to the Earth's upper atmosphere. CCMC provides a web-based Run-on-Request system, by which the interested scientist can request simulations for a broad range of space science problems. To allow the models to be driven by data relevant to particular events CCMC developed a tool that automatically downloads data from data archives and transform them to required formats. CCMC also provides a tailored web-based visualization interface for the model output, as well as the capability to download the simulation output in portable format. CCMC offers a variety of visualization and output analysis tools to aid scientists in interpretation of simulation results. During eight years since the Run-on-request system became available the CCMC archived the results of almost 3000 runs that are covering significant space weather events and time intervals of interest identified by the community. The simulation results archived at CCMC also include a library of general purpose runs with modeled conditions that are used for education and research. Archiving results of simulations performed in support of several Modeling Challenges helps to evaluate the progress in space weather modeling over time. We will highlight the scientific benefits of CCMC space science model archive and discuss plans for further development of advanced methods to interact with simulation results.
Since the mid 1980s, much of the marine science research effort in the United Kingdom has been focused into large scale collaborative projects involving public sector laboratories and university departments, termed Community Research Projects. Two of these, the Biogeochemical Ocean Flux Study (BOFS) and the North Sea Project incorporated large scale data collection to underpin multidisciplinary modeling efforts. The challenge of providing project data sets to support the science was met by a small team within the British Oceanographic Data Centre (BODC) operating as a topical data center. The role of the data center was to both work up the data from the ship's sensors and to combine these data with sample measurements into online databases. The working up of the data was achieved by a unique symbiosis between data center staff and project scientists. The project management, programming and data processing skills of the data center were combined with the oceanographic experience of the project communities to develop a system which has produced quality controlled, calibrated data sets from 49 research cruises in 3.5 years of operation. The data center resources required to achieve this were modest and far outweighed by the time liberated in the scientific community by the removal of the data processing burden. Two online project databases have been assembled containing a very high proportion of the data collected. As these are under the control of BODC their long term availability as part of the UK national data archive is assured. The success of the topical data center model for UK Community Research Project data management has been founded upon the strong working relationships forged between the data center and project scientists. These can only be established by frequent personal contact and hence the relatively small size of the UK has been a critical factor. However, projects covering a larger, even international scale could be successfully supported by a network of topical data centers managing online databases which are interconnected by object oriented distributed data management systems over wide area networks.
The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides science support for several data sets relevant to agriculture and food security, including the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (LDAS), or FLDAS data set. The GES DISC is one of twelve NASA Earth Observing System (EOS) data centers that process, archive, document, and distribute data from Earth science missions and related projects. The GES DISC hosts a wide range of remote sensing and model data, and provides reliable and robust data access and other services to users worldwide. Beyond data archive and access, the GES DISC offers many services to visualize and analyze the data. This presentation provides a summary of the hydrological data available at the GES DISC, along with an overview of related data services. Specifically, the FLDAS data set has been adapted to work with domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing country settings. The FLDAS global monthly data have a 0.1 x 0.1 degree spatial resolution covering the period from January 1982 to present. Global FLDAS monthly anomaly and monthly climatology data are also available at the GES DISC to evaluate how current conditions compare to averages over the FLDAS 35-year period. Several case studies using the FLDAS soil moisture, evapotranspiration, rainfall, runoff, and surface temperature data will be presented.
The Deep Atmosphere Venus Investigation of Noble gases, Chemistry, and Imaging(DAVINCI) mission is currently scheduled to launch in June 2029 to explore Venus via two flybys and a probe descent scheduled for June 2031. The goals of this mission is to study the origin, evolution, and current state of Venus, especially the deep Venusian atmosphere using an atmospheric probe named Zephyr. The Zephyr will take key measurements in the deep atmosphere of Venus and be the first probe to take high-resolution aerial photographs of a mountainous tesserae surface as it descends over the Alpha Regio highlands region, which has the oldest surfaces of Venus. The Zephyr’s descent trajectory, which determines the flight over the Alpha Regio, is crucial to meet the science objectives of the DAVINCI mission. To ensure the success of the DAVINCI mission, the Entry, Descent, and Landing (EDL) modeling has undergone several developments to track its trajectory, its uncertainties, and calculate metrics on the science data that can be transmitted. This paper will cover these developments and the current status of the EDL modeling.
Observing and better understanding clouds and aerosols are priorities in the NASA Earth Science Decadal Survey, US National Climate Assessment, and Intergovernmental Panel on Climate Change Report. The Global Learning & Observations to Benefit the Environment (GLOBE) Program is NASA's largest and longest running citizen science program in the Earth Sciences that collects cloud observations. Since January 2017, NASA GLOBE students and citizen scientists have submitted over 800,000 cloud observations worldwide using both paper-based and smartphone app platforms. In this presentation, we compare satellite, model, and volunteer-reported total cloud cover. The comparison reveals a systematic misreporting of obscured skies (sky not visible due to smoke, dust, haze, etc.) versus overcast skies (100% total cloud cover) by GLOBE participants. We discuss implications for improving procedures for volunteer reporting under overcast and obscured sky conditions, and relevance for reporting extreme air pollution events in areas with little or no formal institutional monitoring networks.
AGU Earth and Space Science Informatics (ESSI) is at the forefront of data management, analysis, large scale experimentation, and infrastructure development pertaining to Earth and Space Science interests. The key topics of interest within ESSI are also evolving and diversifying over time. We aim to observe and quantify the various topics covered in ESSI, analyze their trends over time, and identify the contributors’ affiliations to gain an understanding of the landscape of ESSI and the direction of the research and management. The data for this work are abstracts submitted to AGU’s ESSI Fall meeting; They serve as a proxy for key research and development areas within ESSI. We use an unsupervised topic modeling technique called Latent Dirichlet Allocation to observe the underlying topics covered in ESSI and their trends over time. With this presentation, we showcase our results from the analysis and insights gained.
This presentation introduces the Data Assimilation and Reanalysis principle, then details the NASA Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2). The MERRA-2 is atmospheric reanalysis data spanning 1980 to the present. It has been produced by the NASA Global Modeling and Assimilation Office (GMAO) and is distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). In this presentation, I will introduce the MERRA-2 datasets associated with aerosol and air quality studies and use case studies to demonstrate the data tools developed at GES DISC to analyze and visualize MERRA-2 data, such as Giovanni and Jupyter Python notebook.
Climate and environmental change are expected to fundamentally alter the nation's hydrological cycle and water availability. Satellites provide global or near-global coverage using instruments, allowing for consistent, well-calibrated, and equivalent-quality data of the Earth system. A major goal for NASA climate and environmental change research is to create multi-instrument data sets to span the multi-decadal time scales of climate change and to combine these data with those from modeling and surface-based observing systems to improve process understanding and predictions. NASA and Earth science data and analyses will ultimately enable more accurate climate prediction, and characterization of uncertainties. NASA's Applied Sciences Program works with other groups, including other federal agencies, to transition demonstrated observational capabilities to operational capabilities. A summary of some of NASA tools for improved water resources management will be presented.
Assimilation of satellite ocean color data is a relatively new phenomenon in ocean sciences. However, with routine observations from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), launched in late 1997, and now with new data from the Moderate Resolution Imaging Spectroradometer (MODIS) Aqua, there is increasing interest in ocean color data assimilation. Here SeaWiFS chlorophyll data were assimilated with an established thre-dimentional global ocean model. The assimilation improved estimates of hlorophyll and primary production relative to a free-run (no assimilation) model. This represents the first attempt at ocean color data assimilation using NASA satellites in a global model. The results suggest the potential of assimilation of satellite ocean chlorophyll data for improving models.
NASA's Earth Science Program is dedicated to advancing Earth remote sensing and pioneering the scientific use of satellite measurements to improve human understanding of our home planet. Through the MEaSUREs Program, NASA is continuing its commitment to expand understanding of the Earth system using consistent data records. Emphasis is on linking together multiple data sources to form coherent time-series, and facilitating the use of extensive data in the development of comprehensive Earth system models. A primary focus of the MEaSUREs Program is the creation of Earth System Data Records (ESDRs). An ESDR is defined as a unified and coherent set of observations of a given parameter of the Earth system, which is optimized to meet specific requirements for addressing science questions. These records are critical for understanding Earth System processes; for the assessment of variability, long-term trends, and change in the Earth System; and for providing input and validation means to modeling efforts. Seven MEaSUREs projects will be archived and distributed through services at the Goddard Earth Sciences Data and Information Services Center (GES DISC).
The Chandra X-ray Observatory (CXO) was launched on July 23, 1999 and reached its final orbit on August 7, 1999. The CXO is in a highly elliptical orbit, approximately 140,000 km x 10,000 km, and has a period of roughly 63.5 hours (approx. 2.6 days). It transits the Earth's Van Allen belts once per orbit during which no science observations can be performed due to the high radiation environment. The Chandra X-ray Observatory Center (CXC) currently uses the National Space Science Data Center's "near Earth" AP-8/AE-8 radiation belt model to predict the start and end times of passage through the radiation belts. However, our scheduling software only uses a simple dipole model of the Earth's magnetic field. The resulting B, L magnet coordinates, do not always give sufficiently accurate predictions of the start and end times of transit of the Van Allen belts. We show this by comparing to the data from Chandra's on-board radiation monitor, the EPHIN (Electron, Proton, Helium Instrument particle detector) instrument. We present evidence that demonstrates this mis- of the radiation belts as well as data that also demonstrate the significant variability of one radiation belt transit to the next as experienced by the CXO. We present an explanation for why the dipole implementation of the AP-8/AE-8 gives inaccurate results. We are also investigating use of the Magnetospheric Specification and Forecast Model (MSM) - a model that also accounts for radiation belt variability and geometry.
HDF5 (Hierarchical Data Format 5) is open-source, high-performance software that consists of an abstract data model, library, and fileformat used for storing and managing extremely large and/or complex data collections. NASA Earth Observing System (EOS) Data and Information Systems use HDF5 as an archival format to store remote sensing data from EOS satellites. HDF5 is also used to store other types of Geoscience and Strophysical data, e.g., seismic data and data from Low-Frequency Array (LOFAR) radio telescopes. Data stored in HDF5 has reached tens of petabytes and is growing at an accelerated rate.With the growing amout of HDF5 Earth Science data to analyze and process, scientists need to adopt big data technologies including new storage paradigms such as cloud and object storage. To run models and perform data analysis they also need to utilizied efficient and diverse ways to access data, from high-performance computing's (HPC) Message Passing Interface (MPI) I/O and deep memory hierarchies (DMH) to non-HPC frameworks such as Apache Hadoop, Spark, and Drill. The HDF Group continually works to enable usage of big data technologies in HDF software.
NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.
Accurate intensity calibration of a linear Fourier-transform spectrometer typically requires the unknown science target and the two calibration targets to be acquired under identical conditions. We present a simple model suitable for vector calibration that enables accurate calibration via adjustments of measured spectral amplitudes and phases when these three targets are recorded at different detector or optics temperatures. Our model makes calibration more accurate both by minimizing biases due to changing instrument temperatures that are always present at some level and by decreasing estimate variance through incorporating larger averages of science and calibration interferogram scans.
NASA’s Earth Science Technology Office (ESTO) develops early-stage technologies to enhance scientific understanding. ESTO’s technologies can be reliably and confidently applied to a broad range of science measurements and missions, as well as facilitate practical applications to benefit society at large. As the lead technology office within the Earth Science Division of the NASA Science Mission Directorate, ESTO is focused on the technological challenges inherent to space-based investigations of our planet's dynamic, interrelated systems. Through flexible, science-driven technology strategies and a competitive selection process, ESTO-funded technologies have supported numerous Earth science missions as well as commercial applications. From next generation sensors and instruments to communication and information systems, ESTO technologies enable many NASA missions and data services. The Advanced Information Systems Technology (AIST) Program is one focal area of ESTO. AIST innovates information system technologies that enable the development of new observing systems as well as agile science investigations through data analytics and artificial intelligence tools and algorithms. AIST utilizes an end-to-end development approach with the goal of infusing mature technologies into future missions, measurements, and analysis. One thrust of AIST is data exploitation and analysis using an analytic center framework to make observational data and model output more accessible and usable to scientists conducting specific investigations as well as to extract higher-level science content and information from the data. The framework accelerates scientific discovery by harmonizing the data, tools, and computational resources. NASA’s upcoming Surface, Biology and Geology (SBG) mission is one such mission whose data architecture will leverage many of the technologies developed through the AIST program. This talk will highlight current and future program development goals.
The National Council on Radiation Protection and Measurement (NCRP) and the National Academy of Science (NAS) established that the uncertainty in the data and models associated with the high-altitude radiation environment could and should be reduced. In response, the National Aeronautics and Space Administration (NASA) and the U.S. Department of Energy Environmental Measurements Laboratory (EML) created the Atmospheric Ionizing Radiation (AIR) Project under the auspices of the High Speed Research (HSR) Program Office at the Langley Research Center. NASA's HSR Program was developed to address the potential of a second-generation supersonic transport. A critical element focussed on the environmental issues, including the threat to crew and passengers posed by atmospheric radiation. Various international investigators were solicited to contribute instruments to fly on an ER-2 aircraft at altitudes similar to those proposed for the High Speed Civil Transport (HSCT). A list of participating investigators, their institutions, and instruments with quantities measured is presented. The flight series took place at solar minimum (radiation maximum) with northern, southern, and east/west flights. The investigators analyzed their data and presented preliminary results at the AIR Workshop in March, 1998. A review of these results are included.