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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Assimilation of SMAP Observations Over Land Improves the Simulation and Prediction of Tropical Cyclone Idai

This work is focused on the role of soil moisture in the prediction of tropical cyclones (TCs) approaching land and after landfall. Soil moisture conditions can impact the circulation and structure of an existing tropical cyclone (TC) when part or all of the circulation is over land. For example, dry land surface conditions may lead to faster dissipation of a TC over land (often associated with changes in precipitation structure), whereas very wet conditions may help sustain or in rare cases re-intensify a TC. Moreover, the presence of strong soil moisture gradients may affect the symmetry and development of the TC circulation leading to changes in its over-land track. While the link between soil moisture conditions and TC evolution in proximity to land is relatively well understood in theory, applications of these findings in the context of numerical weather prediction (NWP) have been limited. Here we present a case study that explores the potential of improving TC predictions through an improved soil moisture initialization in an NWP framework. Specifically, we examine the impact of assimilating observations from the NASA Soil Moisture Active Passive (SMAP) mission into the NASA Goddard Earth Observing System (GEOS) global weather model on the prediction of South-West Indian Ocean TC Idai (2019). SMAP provides accurate L-band (1.4 GHz) brightness temperatures (Tb) observations that are sensitive to soil moisture globally and at high revisit times of 2-3 days. It has previously been shown that the assimilation of SMAP Tb observations significantly improves modeled land surface states. Thus, it is expected that SMAP can be used to constrain land surface initial conditions and potentially benefit TC forecasts. Here we present two sets of retrospective forecasts of TC Idai that are compared in an Observing System Experiment framework at ¼ degree resolution: (i) forecasts initialized from an analysis that is comparable to the GEOS operational analysis (without SMAP Tb assimilation) and (ii) forecasts initialized from an analysis that additionally assimilates SMAP brightness temperature observations over land using a weakly-coupled land analysis. We find that the assimilation of SMAP meaningfully improves the representation of TC Idai’s structure as well as the prediction of its intensity and track. The analyzed TC size, as measured by the wind speed radius, is improved by up to 18% in the analysis with SMAP assimilation relative to the control run. The forecast intensity error, measured against the observed intensity, is reduced by up to 23%. At the 1/4-degree resolution used here, GEOS unavoidably under-estimates TC intensity and over-estimates TC size. The SMAP assimilation therefore corrects the model in the right direction, leading to a storm that is more energetic and more compact. Furthermore, we find that the along-track forecast error is reduced by up to 34%, indicating a more accurate propagation speed, which is consistent with the fact that TC speed over land is strongly affected by surface processes. The impact of SMAP assimilation on the forecast cross-track error is neutral. Across the TC forecast skill metrics used here, the improvements from SMAP DA are largest at lead times of 36 to 72 hours, suggesting that the predictability of forecasts at shorter lead times may be dominated by short-term convective processes, while the land and its longer memory gains in importance as a source of predictability on a 2-3 day timescale. We further investigated the underlying mechanisms leading to the skill improvements from SMAP data assimilation by isolating the land areas that directly influence TC Idai using a back trajectory analysis. We find that the assimilation of SMAP leads to wetter soil moisture conditions that cause an increased latent heat flux, which ultimately results in TC analyzed representation that has higher column-integrated total moisture content and total energy compared to the analysis in the control run without SMAP assimilation. Overall, the results highlight that the assimilation of SMAP observations into a global numerical weather prediction model can lead to pronounced improvements of TC predictions. This is a crucial step towards a better mitigation of the socio-economic impact of landfalling TCs and thus safeguarding human lives. Finally, our study presents an event-based approach that assesses the impact of land data assimilation for a particular weather event rather than by globally averaging differences in skill. We argue that global skill assessments – while necessary – can mute the impact of land data assimilation, because the land’s influence on the atmosphere is constrained to certain locations and certain times. Instead, the event-based approach better highlights the true potential of land data assimilation in the context of NWP, especially for extreme events when accurate predictions are critical.

Jana Kolassa↗
NASA NTRSDate not supplied

Assimilation of SMAP Observations Over Land Improves the Simulation and Prediction of Tropical Cyclone Idai

This work is focused on the role of soil moisture in the prediction of tropical cyclones (TCs) approaching land and after landfall. Soil moisture conditions can impact the circulation and structure of an existing tropical cyclone (TC) when part or all of the circulation is over land. For example, dry land surface conditions may lead to faster dissipation of a TC over land (often associated with changes in precipitation structure), whereas very wet conditions may help sustain or in rare cases re-intensify a TC. Moreover, the presence of strong soil moisture gradients may affect the symmetry and development of the TC circulation leading to changes in its over-land track. While the link between soil moisture conditions and TC evolution in proximity to land is relatively well understood in theory, applications of these findings in the context of numerical weather prediction (NWP) have been limited. Here we present a case study that explores the potential of improving TC predictions through an improved soil moisture initialization in an NWP framework. Specifically, we examine the impact of assimilating observations from the NASA Soil Moisture Active Passive (SMAP) mission into the NASA Goddard Earth Observing System (GEOS) global weather model on the prediction of South-West Indian Ocean TC Idai (2019). SMAP provides accurate L-band (1.4 GHz) brightness temperatures (Tb) observations that are sensitive to soil moisture globally and at high revisit times of 2-3 days. It has previously been shown that the assimilation of SMAP Tb observations significantly improves modeled land surface states. Thus, it is expected that SMAP can be used to constrain land surface initial conditions and potentially benefit TC forecasts. Here we present two sets of retrospective forecasts of TC Idai that are compared in an Observing System Experiment framework at ¼ degree resolution: (i) forecasts initialized from an analysis that is comparable to the GEOS operational analysis (without SMAP Tb assimilation) and (ii) forecasts initialized from an analysis that additionally assimilates SMAP brightness temperature observations over land using a weakly-coupled land analysis. We find that the assimilation of SMAP meaningfully improves the representation of TC Idai’s structure as well as the prediction of its intensity and track. The analyzed TC size, as measured by the wind speed radius, is improved by up to 18% in the analysis with SMAP assimilation relative to the control run. The forecast intensity error, measured against the observed intensity, is reduced by up to 23%. At the 1/4-degree resolution used here, GEOS unavoidably under-estimates TC intensity and over-estimates TC size. The SMAP assimilation therefore corrects the model in the right direction, leading to a storm that is more energetic and more compact. Furthermore, we find that the along-track forecast error is reduced by up to 34%, indicating a more accurate propagation speed, which is consistent with the fact that TC speed over land is strongly affected by surface processes. The impact of SMAP assimilation on the forecast cross-track error is neutral. Across the TC forecast skill metrics used here, the improvements from SMAP DA are largest at lead times of 36 to 72 hours, suggesting that the predictability of forecasts at shorter lead times may be dominated by short-term convective processes, while the land and its longer memory gains in importance as a source of predictability on a 2-3 day timescale. We further investigated the underlying mechanisms leading to the skill improvements from SMAP data assimilation by isolating the land areas that directly influence TC Idai using a back trajectory analysis. We find that the assimilation of SMAP leads to wetter soil moisture conditions that cause an increased latent heat flux, which ultimately results in TC analyzed representation that has higher column-integrated total moisture content and total energy compared to the analysis in the control run without SMAP assimilation. Overall, the results highlight that the assimilation of SMAP observations into a global numerical weather prediction model can lead to pronounced improvements of TC predictions. This is a crucial step towards a better mitigation of the socio-economic impact of landfalling TCs and thus safeguarding human lives. Finally, our study presents an event-based approach that assesses the impact of land data assimilation for a particular weather event rather than by globally averaging differences in skill. We argue that global skill assessments – while necessary – can mute the impact of land data assimilation, because the land’s influence on the atmosphere is constrained to certain locations and certain times. Instead, the event-based approach better highlights the true potential of land data assimilation in the context of NWP, especially for extreme events when accurate predictions are critical.

Jana Kolassa↗
NASA NTRS1998

Permanent GPS Geodetic Array in Southern California

The southern California Permanent GPS Geodetic Array (PGGA) was established in the spring of 1990 to evaluate continuous Global Positioning System (GPS) measurements as a new too] for monitoring crustal deformation. Southern California is an ideal location because of the relatively high rate of tectonic deformation, the high probability of intense seismicity, the long history of conventional and space geodetic measurements, and the availability of a well developed infrastructure to support continuous operations. Within several months of the start of regular operations, the PGGA recorded far-field coseismic displacements induced by the June 28, 1992 (M(sub w)=7.3), Landers earthquake, the largest magnitude earthquake in California in the past 40 years and the first one to be recorded by a continuous GPS array. Only nineteen months later, on 17 January 1994, the PGGA recorded coseismic displacements for the strongest earthquake to strike the Los Angeles basin in two decades, the (M(sub e)=6.7) Northridge earthquake. At the time of the Landers earthquake, only seven continuous GPS sites were operating in southern California; by the beginning of 1994, three more sites had been added to the array. However, only a pair of sites were situated in the Los Angeles basin. The destruction caused by the Northridge earthquake spurred a fourfold increase in the number of continuous GPS sites in southern California within 2 years of this event. The PGGA is now the regional component of the Southern California Integrated GPS Network (SCIGN), a major ongoing densification of continuous GPS sites, with a concentration in the Los Angeles metropolitan region. Continuous GPS provides temporally dense measurements of surface displacements induced by crustal deformation processes including interseismic, coseismic, postseismic, and aseismic deformation and the potential for detecting anomalous events such as preseismic deformation and interseismic strain variations. Although strain meters yield much higher short-term resolution to a period of about 1 year, a single continuous GPS site is significantly less expensive than a single strain meter and probably has better long-term stability beyond a 1-year period. Compared to less frequent field measurements, continuous GPS provides the means to better characterize the errors in GPS position measurements and thereby obtain more realistic estimates of derived parameters such as site velocities.

Green, Cecil H.↗
NASA NTRSDate not supplied

Virtual Fit Assessment: Validation using Historical Spacesuit Fit Data

Virtual fit tests using 3D body scans have provided a cost-effective means to predictively assess spacesuit fit for the current and future astronaut population. However, fit is a complex issue influenced by physical interferences, subjective preference, and many other factors. Fit can substantially differ between the type of hardware, environmental conditions, and tasks being performed. Namely, fit changes across different contexts, such as 3D printed mockup evaluations, pressurized one-g suited test events, neutral buoyancy and other training events, and flight extravehicular activities (EVA). These challenges make it difficult to validate virtual fit frameworks to physical suit sizing. This study proposes a new method of validation using the wealth of historically archived suit fit data. Physically Assessed Fit (PAF) data was assessed from astronauts and test volunteers who wore the legacy Extravehicular Mobility Unit (EMU). The data was collected during the past decades at NASA and considered to be most reliable and dependable. Each participant was 3D scanned for body shape and spacesuit-critical dimensions were measured per NASA guidelines. The participants’ size preference for hard upper torso (HUT) assembly was retrieved from their pressurized suit fit check records. Fit assessments were also updated after one-g EVA training, neutral buoyancy training, and EVA flights. Virtually Assessed Fit (VAF) was done by overlaying the CAD model of a HUT with the 3D body scans of the PAF participants. Each body scan was iteratively adjusted for the position inside the HUT to minimize the suit-to-body contact interference while satisfying a set of prescribed requirements. Then the residual contact area, depth, and volume were quantified as VAF metrics. This process was repeated for the different HUT sizes, including medium, large, and extra-large. Statistical modeling is currently in progress and will be presented at the conference. A statistical classifier will be developed to predict a fit probability for different HUT sizes as a function of the corresponding person’s VAF metrics. The HUT size with the highest fit probability will constitute the most likely size selection for the person, and the prediction will be compared against the corresponding PAF data. The participant data will be randomly pre-grouped into either a model development or validation subset. While the model development subset will be used to build the probability model, the validation subset will be used to assess the model accuracy. Also, the locations and magnitudes of suit-body-contacts will be estimated from VAF. This information can identify the critical suit geometry and body shape features that influence suit fit. The variations found in PAF size selections by subjects with similar VAF metrics will allow for investigations into subjective preferences, for example, tight versus loose fit. Overall, this study is expected to provide a structured validation of a virtual suit fit framework, which has not been possible in the past. The outcome can also provide useful insights and potential limitations for interpreting virtual fit tests for future spacesuit designs and population accommodation.

Han Kim↗
DOE OSTI2020

National Spherical Torus eXperiment - Upgrade NSTX-U: Structural Analysis of Passive Plates Support Brackets

The objective of this analysis is to estimate and assess the stresses in the passive plates caused by a limiting plasma disruption. The objective of this calculation is also to address observed damage to mounting hardware after completion of NSTX operation. These observations initiated diagnostic efforts during the Upgrade project to monitor the severity of the disruptions that were causing the damage, and planned instrumentation to trend the behavior of the mounting hardware. The Recovery Project mission for the passive plate hardware is to appropriately reinforce the passive plates with stainless steel ribs mounted on the back of the plate to reduce their flexure and stress and reinforcements of the “biscuit” components of the bracketry to transfer loads without damage to bolts, biscuits, and bracket plates and welds. Mid-plane disruptions and quenches were manageable with NSTX vintage hardware. The Vertical Displacement Event (VDE) disruptions position the plasma closer to the passive plates and are more severe than the centered plasma. Earlier work pointed to the P1-P5 VDE as limiting. This has been confirmed by A. Brooks work in support of the load generation effort in reference. During vertical displacement large counter currents are generated in the plate as the plasma approaches it. - as would be expected from passive plates. When the plasma disrupts the currents in the plates are reversed, which also reverses the loading on the plates. This report documents the methodology and results of transient structural analysis on the plasma, passive plates support brackets. The loads induced by eddy and halo currents have been considered, and were generated using a separate electromagnetic analysis (NSTXU_1_11_2_1_CALC_051). The structural model also incorporates the effects from preload. Calculation was performed with the assumption that existing biscuits are not participating in the load bearing except the biscuits connected to jumper bolts, which are fully engaged. Results show that with the planned reinforcements, Passive plates and mounting structures satisfy static and fatigue criteria in all areas except welds, where separate analysis was performed. Both symmetric and asymmetric (with locating block) designs were analyzed, and although stresses were higher without locating block they still satisfy NSTX design criteria.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2013

Probabilistic Modeling of the Renal Stone Formation Module

The Integrated Medical Model (IMM) is a probabilistic tool, used in mission planning decision making and medical systems risk assessments. The IMM project maintains a database of over 80 medical conditions that could occur during a spaceflight, documenting an incidence rate and end case scenarios for each. In some cases, where observational data are insufficient to adequately define the inflight medical risk, the IMM utilizes external probabilistic modules to model and estimate the event likelihoods. One such medical event of interest is an unpassed renal stone. Due to a high salt diet and high concentrations of calcium in the blood (due to bone depletion caused by unloading in the microgravity environment) astronauts are at a considerable elevated risk for developing renal calculi (nephrolithiasis) while in space. Lack of observed incidences of nephrolithiasis has led HRP to initiate the development of the Renal Stone Formation Module (RSFM) to create a probabilistic simulator capable of estimating the likelihood of symptomatic renal stone presentation in astronauts on exploration missions. The model consists of two major parts. The first is the probabilistic component, which utilizes probability distributions to assess the range of urine electrolyte parameters and a multivariate regression to transform estimated crystal density and size distributions to the likelihood of the presentation of nephrolithiasis symptoms. The second is a deterministic physical and chemical model of renal stone growth in the kidney developed by Kassemi et al. The probabilistic component of the renal stone model couples the input probability distributions describing the urine chemistry, astronaut physiology, and system parameters with the physical and chemical outputs and inputs to the deterministic stone growth model. These two parts of the model are necessary to capture the uncertainty in the likelihood estimate. The model will be driven by Monte Carlo simulations, continuously randomly sampling the probability distributions of the electrolyte concentrations and system parameters that are inputs into the deterministic model. The total urine chemistry concentrations are used to determine the urine chemistry activity using the Joint Expert Speciation System (JESS), a biochemistry model. Information used from JESS is then fed into the deterministic growth model. Outputs from JESS and the deterministic model are passed back to the probabilistic model where a multivariate regression is used to assess the likelihood of a stone forming and the likelihood of a stone requiring clinical intervention. The parameters used to determine to quantify these risks include: relative supersaturation (RS) of calcium oxalate, citrate/calcium ratio, crystal number density, total urine volume, pH, magnesium excretion, maximum stone width, and ureteral location. Methods and Validation: The RSFM is designed to perform a Monte Carlo simulation to generate probability distributions of clinically significant renal stones, as well as provide an associated uncertainty in the estimate. Initially, early versions will be used to test integration of the components and assess component validation and verification (V&V), with later versions used to address questions regarding design reference mission scenarios. Once integrated with the deterministic component, the credibility assessment of the integrated model will follow NASA STD 7009 requirements.

kidney stones↗
DOE OSTI2026

Measuring the neutrino-oxygen neutral current quasielastic cross section using the accelerator neutrino neutron interaction experiment

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton gadolinium-doped water Cherenkov detector located on-axis to Fermilab’s Booster Neutrino Beam (BNB). ANNIE is uniquely positioned to perform high-statistics measurements of neutrino-nucleus interactions in water, benefiting from a large neutrino flux due to a short (100-meter) baseline. A central focus of ANNIE’s physics program is the measurement of both charged current (CC) and neutral current (NC) cross sections on water, including neutral current quasielastic (NCQE) and CC-inclusive channels. The NCQE measurement is particularly critical for constraining uncertainties in rare-event searches such as the Diffuse Supernova Neutrino Background (DSNB), where atmospheric $\nu$NCQE interactions constitute a significant and poorly constrained background. This dissertation presents a measurement of the flux-averaged neutrino-oxygen neutral current quasielastic ($\nu$NCQE) cross section using $2.573 \times 10^{20}$~POT of BNB exposure from the 2022 and 2023 beam years. The $\nu$NCQE interaction is identified through the primary $\gamma$-rays produced by nuclear de-excitation of the residual $^{15}$N$^*$ or $^{15}$O$^*$ nucleus following nucleon knockout from $^{16}$O. A dedicated Monte Carlo (MC) re-tuning campaign was conducted using an americium-beryllium (AmBe) calibration source, Michel electrons from stopped muons, and throughgoing dirt muons originating upstream of the detector. This multi-sample approach provided a wide-ranging $\mathcal{O}(\text{MeV})$--$\mathcal{O}(\text{GeV})$ dataset for tuning the simulated detector response, which was subsequently validated against AmBe neutron and Michel electron data for use in the $\nu$NCQE analysis. A dedicated laser calibration campaign was carried out to reduce timing uncertainties across the PMT system, enabling reconstruction of the BNB bunch substructure with sufficient resolution to serve as a background rejection tool. By selecting events in-time with individual neutrino bunches, beam-correlated $\nu$NCQE events are separated from diffuse and accelerator-induced backgrounds, notably skyshine neutrons and externally-originating events, that would otherwise dominate traditional charge-based selections within a small-scale, surface-level, short-baseline detector. A data-driven estimation of the skyshine neutron and external background rates was performed and incorporated into the systematic uncertainty budget. The flux-averaged $\nu$NCQE cross section on oxygen is measured to be $1.57 \pm 0.06\,(\text{stat.})$ $^{+0.91}_{-0.67}\,(\text{syst.})$ $\times 10^{-38}\ \text{cm}^{2}$. A full systematic budget is constructed by propagating uncertainties in the secondary hadronic interaction modeling, background cross section normalizations, detector response, neutrino flux, and the primary $\gamma$-ray emission probabilities from oxygen nuclear de-excitation. An idealized de-excitation model, constructed from existing measurements in the literature is developed to benchmark the predictions of the \textsc{GENIE} event generator. A comparison reveals that \textsc{GENIE} systematically overpredicts the primary $\gamma$-ray emission probability from oxygen de-excitation by a factor of $1.49\times$ for $E_\gamma > 6$~MeV and $3.07\times$ in the $3$--$6$~MeV band. This comparison motivates the dominant systematic uncertainty in this analysis, where a conservative uncertainty of $^{+39.9\%}_{-0\%}$ on the primary $\gamma$-ray signal prediction is assigned. The ANNIE result is consistent with and complementary to existing flux-averaged $\nu$NCQE cross section measurements from T2K and Super-Kamiokande, providing an independent measurement with a different detector, neutrino beam, and analysis methodology. Looking ahead, an upgrade to the ANNIE DAQ infrastructure enabling continuous extended readout will allow a complementary $\nu$NCQE neutron multiplicity measurement, directly relevant to constraining the NCQE background in DSNB searches, competitive with the recent T2K measurement at SK-Gd. The planned Super-SANDI upgrade, deploying a large Water-based Liquid Scintillator (WbLS) volume, will further extend ANNIE's reach to hadronic final states and exclusive NC channels, and enable joint measurements with liquid argon detectors sharing the BNB beamline ahead of DUNE and Hyper-Kamiokande.

Doran, Steven [Iowa State U.]↗
DOE OSTI2023

Meteorological, reference evapotranspiration and estimated transpiration data, July 2020-Dec 2021, Slate River Floodplain, Crested Butte, Colorado

This data package includes a time series of meteorological data (air temperature, relative humidity, barometric pressure, precipitation, wind speed, soil heat flux, incoming shortwave radiation, incoming longwave radiation, outgoing shortwave radiation and outgoing longwave radiation) from the Slate River floodplain outside Crested Butte, Colorado, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The package also includes reference evapotranspiration calculated via the Penman-Monteith equation and an estimate of willow (Salix spp.) evapotranspiration using the method of Irmak et al., J. Hydrology, 2013. Due to two malfunctioning sensors (incoming shortwave radiation from Oct 2020-May 2021 and incoming longwave radiation from May 2021-March 2022), observed net radiation and calculated evapotranspiration are only included from July-October 2020. However, users may be able to extend calculated evapotranspiration using estimates of these two parameters from other sources. Both sensors have been replaced and data beyond 2021 be uploaded to this repository when it is available.Within the data package, "FLMD.csv" describes file-level metadata and "dd.csv" defines column headers and universal terms across the dataset. The data package includes 3 "*data.csv" files, two for meteorological data for 2020 and 2021 and one for reference evapotranspiration and transpiration data from 2020 ("SLT_OBJ2_PenmanMonteith_2020_data.csv"). Each "*data.csv" file of meteorological data has a corresponding "*_InstallationMethods.csv" file that describes the location, sensor model, sensor serial number and other metadata corresponding to each measured parameter. We also include a Jupyter notebook ("PenmanMonteithET_Calculations.ipynb") that details the assumptions and equations used to calculate daily and hourly reference evapotranspiration and daily willow transpiration from raw meteorological data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning

Stream temperature (Ts) is an important water quality parameter that affects ecosystem health and human water use for beneficial purposes. Accurate Ts predictions at different spatial and temporal scales can inform water management decisions that account for the effects of changing climate and extreme events. In particular, widespread predictions of Ts in unmonitored stream reaches can enable decision makers to be responsive to changes caused by unforeseen disturbances. In this study, we demonstrate the use of classical machine learning (ML) models, support vector regression and gradient boosted trees (XGBoost), for monthly Ts predictions in 78 pristine and human-impacted catchments of the Mid-Atlantic and Pacific Northwest hydrologic regions spanning different geologies, climate, and land use. The ML models were trained using long-term monitoring data from 1980–2020 for three scenarios: (1) temporal predictions at a single site, (2) temporal predictions for multiple sites within a region, and (3) spatiotemporal predictions in unmonitored basins (PUB). In the first two scenarios, the ML models predicted Ts with median root mean squared errors (RMSE) of 0.69–0.84 °C and 0.92–1.02 °C across different model types for the temporal predictions at single and multiple sites respectively. For the PUB scenario, we used a bootstrap aggregation approach using models trained with different subsets of data, for which an ensemble XGBoost implementation outperformed all other modeling configurations (median RMSE 0.62 °C).The ML models improved median monthly Ts estimates compared to baseline statistical multi-linear regression models by 15–48% depending on the site and scenario. Air temperature was found to be the primary driver of monthly Ts for all sites, with secondary influence of month of the year (seasonality) and solar radiation, while discharge was a significant predictor at only 10 sites. The predictive performance of the ML models was robust to configuration changes in model setup and inputs, but was influenced by the distance to the nearest dam with RMSE <1 °C at sites situated greater than 16 and 44 km from a dam for the temporal single site and regional scenarios, and over 1.4 km from a dam for the PUB scenario. Our results show that classical ML models with solely meteorological inputs can be used for spatial and temporal predictions of monthly Ts in pristine and managed basins with reasonable (<1 °C) accuracy for most locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Assessment of Model Hub Height Wind Speed Performance Using DOE Lidar Buoy Data

As the offshore wind industry continues to gain momentum both in the U.S. and globally, reanalysis products remain essential tools for the lifecycle of a wind farm. From project siting and production estimates to determining construction and maintenance windows, reanalysis products play an important role. These regional and global long-term models also serve as inputs to higher resolution models. With such wide-ranging impacts on the offshore wind industry, the need for validation of reanalysis products is strong, particularly at typical turbine hub heights. Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinelTM buoys for the U.S. Department of Energy (DOE) in order to collect meteorological and oceanographic data, including hub height wind speed, in areas of interest for offshore wind development. This work uses DOE buoy observations off the coasts of New Jersey and Virginia to evaluate the performance of Modern Era Retrospective Analysis for Research and Applications-2 (MERRA-2), the North American Regional Reanalysis (NARR), the European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th generation (ERA5), and the National Oceanic and Atmospheric Administration (NOAA) assimilation system Rapid Refresh (RAP). Biases and degrees of correspondence are determined for each reanalysis product in order to provide insights on the performance and uncertainty for long-term wind resource characterization. With the baseline metrics determined, this work proceeds with an investigation into the sources of large deviations between the modeled and observed hub height wind speeds during the East Coast lidar buoy deployments. Consistent sources of reanalysis model error include stably stratified flow conditions, high wind shear, flow parallel to the coastline, and tropical and winter storms. For the near shore New Jersey location, offshore winds (i.e. blowing from land to water) tend to be associated with model overestimation of observed hub height wind speed, while winds parallel to the coastline are correlated with model underestimation of wind speed. Similarly, near the Virginia buoy, during southerly winds (parallel to the coastline) models underestimate the observed wind speed due to coastal upwelling. Reanalysis model wind speeds underestimate the hub height winds with increasing stable atmospheric stratification. Reanalysis model biases are seen when the observed wind speeds approach or exceed typical turbine cut-out speeds (around 20 m s-1). Large model errors during winter and tropical storm events, high wind speed events, and ramp events are often the result of wind speed timing and magnitude offsets. Overall, ERA5 provides the most successful representation of observed offshore hub height wind speeds at the U.S. East Coast buoy locations and is therefore best suited as an input boundary condition for model case studies. ERA5 performs well for a variety of atmospheric phenomena, including storms and sea breezes, however RAP, the next most successful reanalysis model, performs best in capturing the frequency of ramp events.

17 WIND ENERGY↗
NASA NTRSDate not supplied

The INSTEP Monitoring Network: Merging High-and-Low Cost Measurements to Characterize California Wildfires

Despite challenges with data quality and scope, low-cost sensor networks have skyrocketed in popularity over the last 15 years, making air quality data available on refined spatial scales. More recently, studies have leveraged both high and low-quality instruments to create stronger “hybrid” models, with most studies focusing on particulate matter. Low-cost measurements typically represent ground-level emissions only, providing context for human health issues from climate change-driven events such as wildfires. Since low-cost sensors’ capabilities are localized, daily events and microclimates tend to dominate the data rather than larger regional or atmospheric trends. Likewise, their low cost explains their high uncertainty. In contrast, some regulatory-grade instruments produce column measurements as well, providing reliable information on a broader scope. To bridge this gap while expanding into gas-phase measurements, we deployed 12 air quality sensor packages in California, USA during the 2022 wildfire season. These INSTEP (Inexpensive Network Sensor Technology Exploring Pollution) monitors measure carbon monoxide (CO), carbon dioxide (CO2), ozone (O3), nitrogen dioxide (NO2), and several hydrocarbons including methane (CH4) and formaldehyde (HCHO). Half of the monitors were co-located with remote sensing spectrometers: NASA Pandora and Total Column Carbon Observing Network (TCCON). The overlap in pollutants includes NO2, O3, and HCHO between the INSTEP monitors and the Pandora column measurements. TCCON covers column CO, CO2, and CH4, rounding out our comparison. Most of the monitors were distributed throughout the San Francisco Bay area, and an additional three were located within 100 km of Los Angeles. The sites ranged in geographic and population characteristics, including desert, mountainous, coastal, and urban locations. Since varying environmental conditions such as temperature and pressure are known to challenge sensor performance, we will apply newer sensor “calibration” techniques meant to combat this. We will normalize our sensor signals by z-scoring them prior to applying a single calibration model in the form of multivariate linear regression or an artificial neural network. While this technique has been validated for the hydrocarbon and ozone sensor types (metal oxide), it has not yet been tested on electrochemical and non-dispersive infrared sensors, which are also used in the INSTEP monitors. This will serve as a test to see if this normalization technique – or another – is most effective in accounting for environmental differences among sensors. Related data analysis efforts have found success with a variety of geospatial analysis techniques, including weighted network models in which high-quality instruments are given higher weights than their low-cost counterparts. Our preliminary analysis will focus on kriging, which uses a Gaussian algorithm to assign weights, providing estimated pollution levels at locations between monitors. Smoke trajectory and evolution will also be considered using both measurement types. We also aim to baseline subtract our emission estimates from each region to determine which portion of emissions are regional and local, further characterizing burn differences in northern and southern California fires. Future directions include using INSTEP jointly with TEMPO satellite data, and mobile deployments on aircraft and uncrewed aerial vehicles (UAV).

Low-cost sensors↗
DOE OSTI2021

Lifetime risk of suicide among survivors of the atomic bombings of Japan

Abstract Aims The long-term physical health effects of the atomic bombings of Hiroshima and Nagasaki are well characterised, but the psychological effects remain unclear. Therefore, we sought to determine whether measures of exposure severity, as indirect measures of psychological trauma arising from exposure to the atomic bombings, are associated with suicide mortality among atomic bomb survivors. Methods The Life Span Study is a prospective cohort study of 93 741 Japanese atomic bomb survivors who were located within 10 km of the hypocentre in Hiroshima or Nagasaki at the time of the bombings in 1945, and 26 579 residents of Hiroshima and Nagasaki who were not in either city at the time of the bombings, matched to survivors on city, sex and age. Measures of exposure severity included: proximity to the hypocentre, type of shielding between the survivor and the blast and self-reported occurrence of acute radiation and thermal injuries. Date of death was obtained from the Japanese National Family Registry system. Cause of death was obtained from death certificates. Adjusted hazard ratios (HRs) were estimated from Cox regression models overall and stratified by sex and age. Results During the 60-year follow-up period (1950–2009), 1150 suicide deaths were recorded among 120 231 participants (23.6 per 100 000 person-years): 510 among 70 092 women (17.2 per 100 000 person-years) and 640 among 50 139 men (33.6 per 100 000 person-years). Overall, there was no association of proximity, type of shielding or the occurrence of acute injuries with suicide mortality. Among those <25 years of age at the time of the bombings, increased suicide risk was observed for survivors outside v . shielded inside any structure (HR: 1.24; 95% confidence interval (CI): 1.03, 1.48; interaction p = 0.054) and for those who reported flash burns (HR: 1.32; 95% CI: 1.00, 1.73; interaction p = 0.025). Sex-stratified analyses indicated that these associations were limited to men. Among women, closer proximity to the hypocentre was associated with a non-significant increase in suicide risk, with a positive association between proximity and suicide risk observed among women <15 years of age (HR: 1.09 per km; 95% CI: 1.00, 1.18; interaction p = 0.067). Conclusions Proximity to the hypocentre, shielding and acute injury presence do not generally appear to influence suicide mortality among atomic bomb survivors. However, heterogeneity may exist by age and sex, with younger survivors potentially more sensitive to psychological trauma. Coupled with other studies, our results suggest the importance of long-term monitoring of mental health among young populations exposed to catastrophic events or mass trauma.

Psychiatry↗
NASA NTRS2022

Wilson Corners Solid Waste Management Unit (SWMU) 001: 2021 Annual Long-Term Monitoring Report, Kennedy Space Center, Florida

This report presents a summary of the long-term monitoring (LTM) activities that occurred in 2021 at Wilson Corners, Solid Waste Management Unit (SWMU) 001, at Kennedy Space Center (KSC), Florida. The site is monitored under KSC’s Resource Conservation and Recovery Act (RCRA) Corrective Action Program. Adaptive site management is being utilized through ongoing assessment, design, and interim measures (IM). Annual LTM of the groundwater is also being conducted at the site. This approach also meets the requirements of Chapter 62-780, Florida Administrative Code (F.A.C.). The goal of LTM at this site is threefold: to determine groundwater flow characteristics, monitor the downgradient concentration trends, and monitor select locations internal to the groundwater plume. Every 5 years, upgradient and side-gradient monitoring wells are sampled to verify delineation. The last time this was performed was in 2015. The sampling of these wells in 2020 was replaced with the direct push technology (DPT) investigations completed in October 2020 and April 2021. This DPT groundwater data was presented in an Advance Data Package (ADP) in September 2021 and discussed in the Implementation Work Plan (IWP) dated November 2021 for installation of an air sparge (AS) system. Based on results from groundwater sampling activities performed during the previous reporting period, including the 2020 and 2021 DPT groundwater sampling, it was determined that the LTM sampling plan was no longer meeting the goal of LTM because delineation was not verified. The 2021 LTM sampling plan was modified to include the sampling of monitoring wells located around the perimeter of the low concentration plume (LCP); the area with concentrations of contaminants of concern [COCs] greater than Groundwater Cleanup Target Levels [GCTLs]), and sampling of 10 monitoring wells proposed for installation (April 2021 KSC Remediation Team (KSCRT) Meeting, Decision 2104-D32). The modified LTM plan received team consensus at the September 2021 KSCRT Meeting (Decision Number 2109-D03), and sampling of the existing monitoring wells was completed in December 2021. The proposed monitoring wells are planned for installation in late 2022, concurrent with ongoing IM construction activities. December 2021 LTM data was presented at the May 2022 KSCRT Meeting, and activities are summarized in this report. The activities presented in this report include the December 2021 groundwater gauging of 42 monitoring wells and sampling of 44 monitoring wells. During the December 2021 event, the low flow sampling method was used, and samples were analyzed for a select list of volatile organic compounds (VOCs), including 1,1,2-trichloro-1,2,2-trifluoroethane (Freon 113). The following conclusions can be made based on the 2021 LTM results: - In December 2021, groundwater flow for the site was generally to the west at all intervals. This is generally consistent with historical observations at the site, with the exception of a southwest and southeast flow component observed at 34 to 48 feet below land surface (bls). - The vertical extent of VOCs was historically delineated by monitoring wells screened greater than 48 feet bls. The results from the two vertical extent monitoring wells, WILC-MW0078 (screened 65 to 70 feet bls) and WILC-MW0130 (screened 56 to 66 feet bls) that were sampled during the 2021 LTM indicate that groundwater vinyl chloride (VC) concentrations in both wells were greater than the GCTL. The Remediation Team has previously agreed to delay deeper DPT investigations in this area to prevent the creation of additional pathways for vertical migration. - The LCP continues to extend both horizontally, predominantly to the west, and vertically beyond the current monitoring well network, with some retraction observed to the southeast. Evaluation of this data combined with data from the 2020 and 2021 DPT sampling event indicate that the LCP encompasses an estimated 20.7 acres based on an expanded sampling area, as compared to the 2020 LCP footprint of 17.0 acres. - Freon 113 was not detected above GCTLs during the 2021 LTM event. Based on groundwater sampling activities performed in 2021, including April 2021 DPT groundwater sampling, the following recommendations are provided: - Perform the next LTM sampling event, targeted to occur in 2023, concurrently with the IM baseline sampling prior to AS system installation; - Include sampling from nine monitoring wells that are planned to be installed in late 2022, concurrent with upcoming IM construction activities. Installation of one deep vertical well, screened 70 to 80 feet bls, will be delayed to prevent the creation of an additional pathway for vertical migration; - Continue to sample under the modified annual LTM plan as presented in Table 4-1 concurrently with IM baseline sampling; and - Once the AS system install and start-up is complete, select monitoring wells from the LTM program will transition into the performance monitoring plan, and LTM will be temporarily discontinued. Performance monitoring will be performed quarterly, and the monitoring well network will be evaluated following the first performance monitoring sampling event.

long-term monitoring (LTM)↗
NASA NTRSDate not supplied

Uranus Probe Entry and Descent Mission Concept

Introduction: Uranus was identified as the third highest priority flagship mission in the 2012-2022 Planetary Science Decadal Survey. This latest concept study was requested by the Decadal Survey panel to determine NASA’s planetary science priorities from 2022-2032. This study focused on the probe’s entry and descent aspects and associated trades for viable trajectory options. Uranus Mission and Descent Probe: The proposed Uranus Orbiter and Probe (UOP) Flagship mission will investigate Uranus and its surrounding moons using an orbiting spacecraft with a Uranus descent probe. Unlike previous studies the probe release will occur after orbit insertion allowing sufficient separation of critical events during the orbit insertion burn. The probe will be released at an altitude that allows one hour of in situ atmospheric readings that will be relayed to the orbiter. Afterwards the orbiter will transition to the moon tour phase of the mission. The configuration chosen for the entry aeroshell was a 45° sphere-cone. This shape has been used in the past in the Pioneer Venus Galileo missions. However, the nose radius considered in the present study differed from the values used in either of the previous configurations, primarily to reduce the heat flux at the stagnation point. A two-step approach was used in the development of flight trajectories for the chosen configuration. In the first step, the trajectory code POST2 was used to screen the thousands of entry states provided by interplanetary trajectory simulations, which were terminated at an altitude of 2000 km from the reference surface (1 bar) of Uranus. The screening criteria were: (i) optimization of the communication geometry between the entry probe and orbiter to ensure at least 1 hour of science measurements, (ii) peak stagnation point pressures to be less than six bar, (iii) peak heat fluxes to be less than 5 kW/cm2; the latter two constraints being the limits of ground-test capabilities of the arc jets at NASA Ames Research Center. The entry team investigated two trajectories that met the criteria above, a shallow entry (high heat load ~44 kJ/cm2) and a steeper entry (high heat rate ~1950 W/cm2). In the second step, the two bounding candidate entry states from the POST2 screening process were used in developing flight trajectories using TRAJ coupled with FIAT (a materials thermal response and sizing code) and a margins policy to determine a margined uniform thickness (hence mass) of the forward heatshield material based on the aerothermal environments at the stagnation point. Since the combination of TRAJ and FIAT size the TPS based on stagnation point environments only, flow field computations using DPLR were necessary to determine turbulent aerothermal environments on the conical flank, and the augmentation of these environments due to surface roughness. The environments at select locations on the forward heatshield were then used to size the thermal protection material, with the largest thickness value then used to estimate the mass. The newly developed woven thermal protection material –HEEET (Heatshield for Extreme Entry Environments Technology) –was considered for the forward heatshield and PICA (Phenolic-Impregnated Carbon Ablator) was considered for the backshell. These NASA-developed materials are at TRL 6 and TRL 9, respectively. Furthermore, two options were considered for the HEEET material: (i) a dual-layer option with a denser recession layer on top and an insulative layer underneath it, and (ii) a single layer option consisting of the insulative layer alone. Results: It is clear that probe entry states are feasible and the selected TPS options are able to perform in the predicted aerothermal environments thus enabling the mission to meet of the descent probe portion of this flagship mission

Entry Descent and Landing↗
DOE OSTI2021

Phasor-Measurement-Unit-Based Data Analytics Using Digital Twin and PhasorAnalytics Software

A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2020

Feasibility Study of SDAS Instrumentation's Ability to Identify Mobile Launcher (ML)/Crawler-Transporter (CT) Modes During Rollout Operations

The Space Launch System (SLS) and its Mobile Launcher (ML) will be transported to the launch pad via the Crawler-Transporter (CT) system. Rollout (i.e., transportation) loads produce structural loads on the integrated SLS/Orion Multi-Purpose Crew Vehicle (MPCV) launch vehicle which are of a concern with respect to fatigue. As part of the risk reduction process and in addition to the modal building block test approach that has been adopted by the SLS Program, acceleration data will be obtained during rollout for use in modal parameter estimation. There are several occurrences where the ML/CT will be transported either into the Vertical Assembly Building (VAB) or to the launch pad and back without the SLS stack as part of the Kennedy Space Center (KSC) Exploration Ground Systems (EGS) Integrated Test and Checkout (ITCO). NASA KSC EGS has instrumentation installed on both the ML and CT to record data during rollout, at the launch pad, and during liftoff. The EGS instrumentation on the ML, which includes accelerometers, is referred to as the Sensor Data Acquisition System (SDAS). The EGS instrumentation on the CT, which also includes accelerometers, is referred to as the CT Data Acquisition System (CTDAS). The forces and accelerations applied to the ML and CT during a rollout event will be higher than any of the planned building block modal tests. This can be very beneficial in helping identify nonlinear behavior in the structure. Developing modal parameters from the same test hardware in multiple boundary conditions and under multiple levels of excitation is a key step in developing a well correlated FEM. The purpose of this study was three fold. First, determine the target modes of the ML/CT in its rollout configuration. Second, determine if the test degrees of freedom (DOF) corresponding to the layout of the SDAS/CTDAS accelerometers (i.e. position and orientation) is sufficient to identify the target modes. Third, determine if the Generic Rollout Forcing Functions (GRFF's) is sufficient for identifying the ML/CT target modes accounting for variations in CT speed, modal damping, and sensor/ambient background noise levels. The finding from the first part of this study identified 28 target modes of the ML/CT rollout configuration based upon Modal Effective Mass Fractions (MEFF) and engineering judgement. The finding from the second part of this study showed that the SDAS/CTDAS accelerometers (i.e. position and orientation) are able to identify a sufficient number of the target modes to support model correlation of the ML/CT FEM. The finding from the third part of this study confirms the GRFFs sufficiently excite the ML/CT such that varying quantities of the defined target modes should be able to be extracted when utilizing an Experimental Modal Analysis (EMA) Multi-Input Multi-Output (MIMO) analysis approach. An EMA analysis approach was used because Operational Modal Analysis (OMA) tools were not available and the GRFFs were sufficiently uncorrelated. Two key findings from this third part of the study are that the CT speed does not show a significant impact on the ability to extract the modal parameters and that keeping the ambient background noise observed at each accelerometer location at or below 30 µgrms is essential to the success of this approach.

Winkel, James P.↗
NASA NTRS1998

A Satellite-Derived Upper-Tropospheric Water Vapor Transport Index for Climate Studies

A new approach is presented to quantify upper-level moisture transport from geostationary satellite data. Daily time sequences of Geostationary Operational Environmental Satellite GOES-7 water vapor imagery were used to produce estimates of winds and water vapor mixing ratio in the cloud-free region of the upper troposphere sensed by the 6.7- microns water vapor channel. The winds and mixing ratio values were gridded and then combined to produce a parameter called the water vapor transport index (WVTI), which represents the magnitude of the two-dimensional transport of water vapor in the upper troposphere. Daily grids of WVTI, meridional moisture transport, mixing ratio, pressure, and other associated parameters were averaged to produce monthly fields for June, July, and August (JJA) of 1987 and 1988 over the Americas and surrounding oceanic regions, The WVTI was used to compare upper-tropospheric moisture transport between the summers of 1987 and 1988, contrasting the latter part of the 1986/87 El Nino event and the La Nina period of 1988. A similar product derived from the National Centers for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) 40-Year Reanalysis Project was used to help to validate the index. Although the goal of this research was to describe the formulation and utility of the WVTI, considerable insight was obtained into the interannual variability of upper-level water vapor transport. Both datasets showed large upper-level water vapor transport associated with synoptic features over the Americas and with outflow from tropical convective systems. Minimal transport occurred over tropical and subtropical high pressure regions where winds were light. Index values from NCEP-NCAR were 2-3 times larger than that determined from GOES. This difference resulted from large zonal wind differences and an apparent overestimate of upper-tropospheric moisture in the reanalysis model. A comparison of the satellite-derived monthly values between the summers of 1987 and 1988 provided some insight into the impact of the ENSO event on upper-level moisture and its transport during the period. During July 1987, a large portion of the Tropics in the eastern Pacific Ocean and Caribbean Sea was dominated by strong vapor transport in excess of 4.0 g/kg m/s, with relatively small amounts in the other months. JJA 1988 transport values reached similar magnitude and showed similar patterns for all three months. The meridional transport of upper-level water vapor indicated large poleward transport from the Tropics to the higher latitudes. This transport favored the Southern Hemisphere, with large transport occurring south of the ITCZ, which extended across the eastern Pacific and northern South America. Zonally averaged monthly transport values were shown to provide a simple way to quantify the monthly and interannual changes in water vapor transport. Zonally averaged WVTI values peaked in the Southern Hemisphere subtropics during both austral winters. In the Tropics, a single, more- pronounced peak located over the equator and south latitudes occurred in 1988 as opposed to a dual peak in 1987. The second peak around 20'N latitude is consistent with findings of others in which upper-tropospheric winds were noted to be stronger in this region during warm ENSO events. Zonally averaged meridional transport was southward for all summer months and was stronger in 1988. The asymmetric nature of the zonally averaged meridional transport (more southerly water vapor transport) was enhanced during JJA 1988, thus indicating a stronger upper- level branch of the Hadley circulation during this notably strong La Nina period.

Jedlovec, Gray J.↗
NASA NTRS2017

NASA's Space Launch System: Deep-Space Delivery for Smallsats

Designed for human exploration missions into deep space, NASA's Space Launch System (SLS) represents a new spaceflight infrastructure asset, enabling a wide variety of unique utilization opportunities. While primarily focused on launching the large systems needed for crewed spaceflight beyond Earth orbit, SLS also offers a game-changing capability for the deployment of small satellites to deep-space destinations, beginning with its first flight. Currently, SLS is making rapid progress toward readiness for its first launch in two years, using the initial configuration of the vehicle, which is capable of delivering 70 metric tons (t) to Low Earth Orbit (LEO). On its first flight test of the Orion spacecraft around the moon, accompanying Orion on SLS will be small-satellite secondary payloads, which will deploy in cislunar space. The deployment berths are sized for "6U" CubeSats, and on EM-1 the spacecraft will be deployed into cislunar space following Orion separate from the SLS Interim Cryogenic Propulsion Stage. Payloads in 6U class will be limited to 14 kg maximum mass. Secondary payloads on EM-1 will be launched in the Orion Stage Adapter (OSA). Payload dispensers will be mounted on specially designed brackets, each attached to the interior wall of the OSA. For the EM-1 mission, a total of fourteen brackets will be installed, allowing for thirteen payload locations. The final location will be used for mounting an avionics unit, which will include a battery and sequencer for executing the mission deployment sequence. Following the launch of EM-1, deployments of the secondary payloads will commence after sufficient separation of the Orion spacecraft to the upper stage vehicle to minimize any possible contact of the deployed CubeSats to Orion. Currently this is estimated to require approximately 4 hours. The allowed deployment window for the CubeSats will be from the time the upper stage disposal maneuvers are complete to up to 10 days after launch. The upper stage will fly past the moon at a perigee of approximately 100km, and this closest approach will occur about 5 days after launch. The limiting factor for the latest deployment time is the available power in the sequencer system. Several NASA Mission Directorates were involved in the development of programs for the competition, selection, and development of EM-1 payloads that support directorate priorities. CubeSat payloads on EM-1 will include both NASA research experiments and spacecraft developed by industry, international and potentially academia partners. The Human Exploration and Operations Mission Directorate (HEOMD) Advanced Exploration Systems (AES) Division was allocated five payload opportunities on the EM-1 mission. Near Earth Asteroid (NEA) Scout is designed to rendezvous with and characterize a candidate NEA. A solar sail, an innovation the spacecraft will demonstrated for the CubeSat class, will provide propulsion. Lunar Flashlight will use a green propellant system and will search for potential ice deposits in the moon's permanently shadowed craters. BioSentinel is a yeast radiation biosensor, planned to measure the effects of space radiation on deoxyribonucleic acid (DNA). Lunar Icecube, a collaboration with Morehead State University, will prospect for water in ice, liquid, and vapor forms as well as other lunar volatiles from a low-perigee, highly inclined lunar orbit using a compact Infrared spectrometer. Skyfire, a partnership with Lockheed Martin, is a technology demonstration mission that will perform a lunar flyby, collecting spectroscopy, and thermography data to address questions related to surface characterization, remote sensing, and site selection. NASA's Space Technology Mission Directorate (STMD) was allocated three payload opportunities on the EM-1 mission. These slots will be filled via the Centennial Challenges Program, NASA's flagship program for technology prize competitions, which directly engages the public, academia, and industry in open prize competitions to stimulate innovation. The NASA Science Mission Directorate (SMD) was allocated two payload opportunities on the EM-1 mission. The CubeSat Mission to Study Solar Particles (CuSP) payload will study the sources and acceleration mechanisms of solar and interplanetary particles in near-Earth orbit, support space weather research by determining proton radiation levels during Solar Energetic Particle (SEP) events and identifying suprathermal properties that could help predict geomagnetic storms. The LunaH-Map payload will help scientists understand the quantity of H-bearing materials in lunar cold traps (~10 km), determine the concentration of H-bearing materials with 1m depth, and constrain the vertical distribution of H-bearing materials. The final three payload opportunities for the EM-1 mission were allocated for NASA's international space agency counterparts. The flight opportunities are intended to benefit the international space agency and NASA as well as further the collective space exploration goals. ArgoMoon is sponsored by ESA/ASI and will fly along with the ICPS on its disposal trajectory to perform proximity operations with the ICPS post-disposal, take external imagery of engineering and historical significance, and perform an optical communications demonstration. EQUULEUS, sponsored by JAXA, will fly to a libration orbit around the Earth-Moon L2 point and demonstrate trajectory control techniques within the Sun-Earth- Moon region for the first time by a nano spacecraft. The mission will also contribute to the future human exploration scenario by understanding the radiation environment in geospace and deep space, characterizing the flux of impacting meteors on the far side of the moon, and demonstrating the future deep space exploration scenario using the "deep space port" at Lagrange points. OMOTENASHI, also sponsored by JAXA, will land the smallest lunar lander to date on the lunar surface to demonstrate the feasibility of the hardware for distributed cooperative exploration system. Small landers will enable multi-point exploration, which is complimentary with large-scale human exploration. Once on the lunar surface, the OMOTENASHI spacecraft will observe the radiation and soil environments of the lunar surface by active radiation measurements and soil shear measurements. Following EM-1, Space Launch System will evolve to the more-powerful Block 1B configuration, which uses a new Exploration Upper Stage to increase the vehicle's LEO payload capability from 70 t to 105 t. With that transition, the Orion Stage Adapter, which will carry the secondary payloads on EM-1, will be phased out, and a new Universal Stage Adapter will be introduced, creating opportunities for flying larger secondary payloads. This paper will provide a brief status of SLS progress toward first launch; an overview of smallsat accommodations, integration, and operations on EM-1; information about the specific payloads flying on that launch; and a discussion of future accommodations and opportunities for secondary payloads on SLS for Exploration Mission-2 and beyond.

Robinson, Kimberly F.↗