Galileo's Near Infrared Mapping Spectrometer (NIMS) Science Predictions for Io
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Balancing Predictive and Reactive Science Planning for Mars 2020 Perseverance
The design of the science planning process for a space science mission needs to find a balance between operational and resource constraints and scientific decision-making. Science planning has previously been characterized as either predictive or reactive. Predictive science planning is needed when constraints drive science activities to be planned far in advance. For example, a combination of long one-way light time plus high-stakes science decisions drove the Cassini-Huygens mission to Saturn to have an extremely predictive planning process. On the other extreme, reactive science planning is needed when constraints drive science activities to be planned based on the results of the previous plan. For example, the Mars Exploration Rover mission interacted with the surface of Mars, and so the planning team needed to know the state of the rover at the end of each planning cycle before starting the next cycle. Operational and resource constraints that require management on intermediate timescales has led to the development of a science planning process between these two extremes. For example, the Mars Science Laboratory is a technically complex rover and has a parallel predictive process that allows the operations team to manage engineering constraints several days in advance while maintaining the reactive tactical planning process similar to that of MER. The Mars 2020 Perseverance rover is a technically complex rover in the MSL style, but has an added layer of science complexity: it is tasked with collecting a returnable cache of scientifically valuable samples of Mars within prime mission. Thus, the science planning process also needs to accommodate high-stakes longer-term science decisions in the style of Cassini. In order to balance the push-pull of these constraints, we have developed a science campaign-focused operational paradigm for Mars 2020 Perseverance that allows for both predictive planning to accommodate technological complexity and high-stakes science decisions as well as reactive planning to accommodate the realities of interacting with the martian surface. This paradigm influenced the design of operational processes and operational tools.
Spanovich, Nicole↗
NASA's Earth Science Research and Environmental Predictions
NASA Earth Science program began in the 1960s with cloud imaging satellites used for weather observations. A fleet of satellites are now in orbit to investigate the Earth Science System to uncover the connections between land, Oceans and the atmosphere. Satellite systems using an array of active and passive remote sensors are used to search for answers on how is the Earth changing and what are the consequences for life on Earth? The answer to these questions can be used for applications to serve societal needs and contribute to decision support systems for weather, hazard, and air quality predictions and mitigation of adverse effects. Partnerships with operational agencies using NASA's observational capabilities are now being explored. The system of the future will require new technology, data assimilation systems which includes data and models that will be used for forecasts that respond to user needs.
Hilsenrath, E.↗
Cassini orbit determination performance during the first eight orbits of the Saturn satellite tour
From June 2004 through July 2005, the Cassini/Huygens spacecraft has executed nine successful close-targeted encounters by three major satellites of the Saturnian system. Current results show that orbit determination has met design requirements for targeting encounters, Hugens descent, and predicting science instrument pointing for targetd satellite encounters. This paper compares actual target dispersion against, the predicte tour covariance analyses.
navigation↗
Celebrating 10 Years of the Sub-Seasonal to Seasonal Prediction Project and Looking to the Future
The conference clearly demonstrated the increasing interest and growth of the scientific community working on the development and application of sub-seasonal to seasonal prediction since the start of the World Weather Research Programme (WWRP)/World Climate Research Programme (WCRP) sub-seasonal to seasonal (S2S) prediction project in 2013. The conference, which was held at the University of Reading (United Kingdom), was organized into three main themes as briefly summarized below, with eleven invited talks, 74 oral contributed talks, and 101 posters. The conference also included a two-hour breakout session, wherein eight groups discussed the current state and prospect for S2S prediction, and an early career researcher event. A summary of these discussions and recommendations is presented below. The conference web page (https://research.reading.ac.uk/s2s-summit2023/) is archived at the University of Reading. Introductory comments by representatives of the World Meteorological Organization (WMO) WWRP and WCRP emphasized the importance of the weather–climate linkage, targeted by S2S forecasts (from 2 weeks to a season ahead), addressing the challenges of creating “end-to-end” forecasts that encompass the entire climate-services chain from the prediction science and forecast, to the development and issuing of forecast products tailored to informing user-decisions. They also emphasized the efficacy of multi-model ensemble efforts and databases to foster collaborations internationally and between operational centres and academia. Although the WWRP/WCRP S2S project comes to an end in 2023, S2S prediction will remain an important focus for WWRP and WCRP. In WWRP, a new project called SAGE (Sub-seasonal to seasonal predictions for Agriculture and Environment) will start in 2024. Another important legacy of the S2S project will be the maintenance of the S2S database (Vitart et al. 2017) and the establishment of a WMO Lead Center for sub-seasonal prediction multi-model ensemble (LC-SSPMME) which will provide real-time multi-model S2S climate information. In two keynote presentations, Prof. Brian Hoskins (University of Reading) and Dr. Gilbert Brunet (Australian Bureau of Meteorology) discussed the potential of S2S predictability and the ongoing journey for understanding and improving these predictions. This conference was a sequel to the International Conference on Sub-seasonal to Seasonal Prediction (Robertson et al., 2014) which took place in College Park (Maryland, USA) in February 2014 to celebrate the start of the WWRP/WCRP S2S project, and to WCRP and WWRP conferences in Boulder, USA, in 2018 (Merryfield et al., 2020). A significant development compared to the previous S2S conferences was the large number of presentations on research to operation (R2O) and S2S applications and on the use of artificial intelligence and machine learning (AI/ML) methods for S2S prediction. Some of these methods provide empirical S2S forecasts which are competitive with state-of-the-art dynamical models. Other presentations demonstrated that AI/ML can provide alternative calibration of dynamical model outputs to traditional methods. Several talks and posters highlighted the increasing use of AI/ML, including deep learning, in S2S forecast post-processing and using AI to identify higher flow-dependent skill. Finally, some presentations demonstrated the value of AI/ML methods for a better understanding of S2S sources of predictability and attribution of extreme events.
S. J. Woolnough↗
The Magnetospheric Constellation Mission (MC-DRACO)
The Magnetospheric Constellation Mission Dynamic Response and Coupling Observatory (MC-DRACO), the logical outgrowth of a sequence of STP missions, will explore plasma transport and energy conversion processes over a broad range of spatial sizes. Designed to be a meso-/macroscope for the magnetotail, it will resolve persistent controversies and yield a new understanding on which to build a predictive science of next generation magnetospheric meteorology.
Moore, Thomas E.↗
Stellar Ablation of Planetary Atmospheres
We review observations and theories of the solar ablation of planetary atmospheres, focusing on the terrestrial case where a large magnetosphere holds off the solar wind, so that there is little direct atmospheric impact, but also couples the solar wind electromagnetically to the auroral zones. We consider the photothermal escape flows known as the polar wind or refilling flows, the enhanced mass flux escape flows that result from localized solar wind energy dissipation in the auroral zones, and the resultant enhanced neutral atom escape flows. We term these latter two escape flows the "auroral wind." We review observations and theories of the heating and acceleration of auroral winds, including energy inputs from precipitating particles, electromagnetic energy flux at magnetohydrodynamic and plasma wave frequencies, and acceleration by parallel electric fields and by convection pickup processes also known as "centrifugal acceleration." We consider also the global circulation of ionospheric plasmas within the magnetosphere, their participation in magnetospheric disturbances as absorbers of momentum and energy, and their ultimate loss from the magnetosphere into the downstream solar wind, loading reconnection processes that occur at high altitudes near the magnetospheric boundaries. We consider the role of planetary magnetization and the accumulating evidence of stellar ablation of extrasolar planetary atmospheres. Finally, we suggest and discuss future needs for both the theory and observation of the planetary ionospheres and their role in solar wind interactions, to achieve the generality required for a predictive science of the coupling of stellar and planetary atmospheres over the full range of possible conditions.
Moore, Thomas E.↗
Current Sheets in the Corona and the Complexity of Slow Wind
The origin of the slow solar wind has long been one of the most important problems in solar/heliospheric physics. Two observational constraints make this problem especially challenging. First, the slow wind has the composition of the closed-field corona, unlike the fast wind that originates on open field lines. Second, the slow wind has substantial angular extent, of order 30 degrees, which is much larger than the widths observed for streamer stalks or the widths expected theoretically for a dynamic heliospheric current sheet. We propose that the slow wind originates from an intricate network of narrow (possibly singular) open-field corridors that emanate from the polar coronal hole regions. Using topological arguments, we show that these corridors must be ubiquitous in the solar corona. The total solar eclipse in August 2008, near the lowest point of cycle 23 affords an ideal opportunity to test this theory by using the ultra-high resolution Predictive Science's (PSI) eclipse model for the corona and wind. Analysis of the PSI eclipse model demonstrates that the extent and scales of the open-field corridors can account for both the angular width of the slow wind and its closed-field composition. We discuss the implications of our slow wind theory for the structure of the corona and heliosphere at solar minimum and describe further observational and theoretical tests.
Antiochos, Spiro↗
Cassini Orbit Determination Results: January 2006 - End of Prime Mission
After the forty-fifth flyby of Titan, the Cassini spacecraft has successfully completed the planned four-year prime mission tour of the Saturnian system. This paper reports on the orbit determination performance of the Cassini spacecraft over two years spanning 2006 - 2008. In this time span, Cassini's orbit progressed through the magnetotail and pi-transfer phases of the mission. Thirty-four accurate close encounters of Titan, one close flyby of Iapetus and one 50 km flyby of Enceladus were performed during this period. The Iapetus and Enceladus flybys were especially challenging and so the orbit determination supporting these encounters will be discussed in more detail. This paper will show that in most cases orbit determination has exceeded the navigation requirements for targeting flybys and predicting science instrument pointing during these encounters.
Lapetus↗
Synthesis of 3-D Coronal-Solar Wind Energetic Particle Acceleration Modules
Acute space radiation hazards pose one of the most serious risks to future human and robotic exploration. Large solar energetic particle (SEP) events are dangerous to astronauts and equipment. The ability to predict when and where large SEPs will occur is necessary in order to mitigate their hazards. The Coronal-Solar Wind Energetic Particle Acceleration (C-SWEPA) modeling effort in the NASANSF Space Weather Modeling Collaborative [Schunk, 2014] combines two successful Living With a Star (LWS) (http:lws.gsfc.nasa.gov) strategic capabilities: the Earth-Moon-Mars Radiation Environment Modules (EMMREM)[Schwadron et al., 2010] that describe energetic particles and their effects, with the Next Generation Model forthe Corona and Solar Wind developed by the Predictive Science, Inc. (PSI) group. The goal of the C-WEPA effort is to develop a coupled model that describes the conditions of the corona, solar wind, coronal mass ejections (CMEs) and associated shocks, particle acceleration, and propagation via physics-based modules. Assessing the threat of SEPs is a difficult problem. The largest SEPs typically arise in conjunction with X classflares and very fast (1000 kms) CMEs. These events are usually associated with complex sunspot groups(also known as active regions) that harbor strong, stressed magnetic fields. Highly energetic protonsgenerated in these events travel near the speed of light and can arrive at Earth minutes after the eruptiveevent. The generation of these particles is, in turn, believed to be primarily associated with the shock waveformed very low in the corona by the passage of the CME (injection of particles fromthe flare sitemay also playa role). Whether these particles actually reach Earth (or any other point) depends on their transport in theinterplanetary magnetic field and their magnetic connection to the shock.
exploration↗
Seagrass Health Modeling and Prediction with NASA Science Data
Previous research has demonstrated that MODIS data products can be used as inputs into the seagrass productivity model developed by Fong and Harwell (1994). To further explore this use to predict seagrass productivity, Moderate Resolution Imaging Spectroradiometer (MODIS) custom data products, including Sea Surface Temperature, Light Attenuation, and Chlorophyll-a have been created for use as model parameter inputs. Coastal researchers can use these MODIS data products and model results in conjunction with historical and daily assessment of seagrass conditions to assess variables that affect the productivity of the seagrass beds. Current monitoring practices involve manual data collection (typically on a quarterly basis) and the data is often insufficient for evaluating the dynamic events that influence seagrass beds. As part of a NASA-funded research grant, the University of Mississippi, is working with researchers at NASA and Radiance Technologies to develop methods to deliver MODIS derived model output for the northern Gulf of Mexico (GOM) to coastal and environmental managers. The result of the project will be a data portal that provides access to MODIS data products and model results from the past 5 years, that includes an automated process to incorporate new data as it becomes available. All model parameters and final output will be available through the use National Oceanic and Atmospheric Administration?s (NOAA) Environmental Research Divisions Data Access Program (ERDDAP) tools as well as viewable using Thematic Realtime Environmental Distributed Data Services (THREDDS) and the Integrated Data Viewer (IDV). These tools provide the ability to create raster-based time sequences of model output and parameters as well as create graphs of model parameters versus time. This tool will provide researchers and coastal managers the ability to analyze the model inputs so that the factors influencing a change in seagrass productivity can be determined over time.
Robinson, Harold D.↗
GEWEX America Prediction Project (GAPP) Science and Implementation Plan
The purpose of this Science and Implementation Plan is to describe GAPP science objectives and the activities required to meet these objectives, both specifically for the near-term and more generally for the longer-term. The GEWEX Americas Prediction Project (GAPP) is part of the Global Energy and Water Cycle Experiment (GEWEX) initiative that is aimed at observing, understanding and modeling the hydrological cycle and energy fluxes at various time and spatial scales. The mission of GAPP is to demonstrate skill in predicting changes in water resources over intraseasonal-to-interannual time scales, as an integral part of the climate system.
TIME SCALES↗
Realistic Covariance Prediction for the Earth Science Constellation
Routine satellite operations for the Earth Science Constellation (ESC) include collision risk assessment between members of the constellation and other orbiting space objects. One component of the risk assessment process is computing the collision probability between two space objects. The collision probability is computed using Monte Carlo techniques as well as by numerically integrating relative state probability density functions. Each algorithm takes as inputs state vector and state vector uncertainty information for both objects. The state vector uncertainty information is expressed in terms of a covariance matrix. The collision probability computation is only as good as the inputs. Therefore, to obtain a collision calculation that is a useful decision-making metric, realistic covariance matrices must be used as inputs to the calculation. This paper describes the process used by the NASA/Goddard Space Flight Center's Earth Science Mission Operations Project to generate realistic covariance predictions for three of the Earth Science Constellation satellites: Aqua, Aura and Terra.
Duncan, Matthew↗
Improving GES Disc Data Search and Discovery Through AI Metadata Augmentation
NASA’s Goddard Earth Science (GES) Data and Information Services Center (DISC) is one of twelve data centers in NASA's Science Mission Directorate (SMD), providing vital earth science data to a diverse user base. To enhance the discoverability of this data, GES DISC employs a keyword search system, which leverages scientific keywords embedded in dataset metadata. However, the evolving nature of scientific applications of our data necessitates regular review and augmentation of these keywords. To address this, we developed a service to automatically predict missing science keywords in the metadata. This service constructs a knowledge graph from the latest GES DISC metadata within NASA’s Common Metadata Repository (CMR). Using an open-source library, we trained a machine learning model to predict absent science keywords in the metadata. Our preliminary results indicate that the model has high levels of accuracy at predicting science keywords in the dataset metadata when exposed to data not included in its training. These predicted keywords were then evaluated by GES DISC data curation scientists and compared against other AI tools for metadata augmentation. We aim to enhance the overall usability and accessibility of NASA’s earth science data by implementing this tool in our data curation processes.
Kendall Gilbert↗
Realistic Covariance Prediction For the Earth Science Constellations
Routine satellite operations for the Earth Science Constellations (ESC) include collision risk assessment between members of the constellations and other orbiting space objects. One component of the risk assessment process is computing the collision probability between two space objects. The collision probability is computed via Monte Carlo techniques as well as numerically integrating relative probability density functions. Each algorithm takes as inputs state vector and state vector uncertainty information for both objects. The state vector uncertainty information is expressed in terms of a covariance matrix. The collision probability computation is only as good as the inputs. Therefore, to obtain a collision calculation that is a useful decision-making metric, realistic covariance matrices must be used as inputs to the calculation. This paper describes the process used by NASA Goddard's Earth Science Mission Operations Project to generate realistic covariance predictions for three of the ESC satellites: Aqua, Aura, and Terra
Duncan, Matthew↗
Dawn Orbit Determination Team: Trajectory and Gravity Prediction Performance During Vesta Science Phases
The Dawn spacecraft was launched on September 27th, 2007. Its mission is to consecutively rendezvous with and observe the two largest bodies in the asteroid belt, Vesta and Ceres. It has already completed over a year's worth of direct observations of Vesta (spanning from early 2011 through late 2012) and is currently on a cruise trajectory to Ceres, where it will begin scientific observations in mid-2015. Achieving this data collection required careful planning and execution from all spacecraft teams. Dawn's Orbit Determination (OD) team was tasked with accurately predicting the trajectory of the Dawn spacecraft during the Vesta science phases, and also determining the parameters of Vesta to support future science orbit design. The future orbits included the upcoming science phase orbits as well as the transfer orbits between science phases. In all, five science phases were executed at Vesta, and this paper will describe some of the OD team contributions to the planning and execution of those phases.
gravity gradient torque↗
Nonlinear dynamics and predictability in the atmospheric sciences
Systematic applications of nonlinear dynamics to studies of the atmosphere and climate are reviewed for the period 1987-1990. Problems discussed include paleoclimatic applications, low-frequency atmospheric variability, and interannual variability of the ocean-atmosphere system. Emphasis is placed on applications of the successive bifurcation approach and the ergodic theory of dynamical systems to understanding and prediction of intraseasonal, interannual, and Quaternary climate changes.
Ghil, M.↗
Afterbody Heating Predictions for a Mars Science Laboratory Entry Vehicle
The Mars Science Laboratory mission intends to deliver a large rover to the Martian surface within 10 km of its target site. One candidate entry vehicle aeroshell consists of a 3.75-m diameter, 70-deg sphere-cone forebody and a biconic afterbody similar to that of Viking. This paper presents computational fluid dynamics predictions of laminar afterbody heating rates for this configuration and a 2010 arrival at Mars. Computational solutions at flight conditions used an 8-species Mars gas model in chemical and thermal non-equilibrium. A grid resolution study examined the effects of mesh spacing on afterbody heating rates and resulted in grids used for heating predictions on a reference entry trajectory. Afterbody heating rate reaches its maximum value near 0.6 W/sq cm on the first windward afterbody cone at the time of peak freestream dynamic pressure. Predicted afterbody heating rates generally are below 3% of the forebody laminar nose cap heating rate throughout the design trajectory. The heating rates integrated over time provide total heat load during entry, which drives thermal protection material thickness.
Edquist, Karl T.↗