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DOE OSTI2014

Climate data, analysis and models for the study of natural variability and anthropogenic change

Gridded Temperature Under prior/current support, we completed and published (Jones et al., 2012) the fourth major update to our global land dataset of near-surface air temperatures, CRUTEM4. This is one of the most widely used records of the climate system, having been updated, maintained and further developed with DoE support since the 1980s. We have continued to update the CRUTEM4 (Jones et al., 2012) database that is combined with marine data to produce HadCRUT4 (Morice et al., 2012). The emphasis in our use of station temperature data is to access as many land series that have been homogenized by National Meteorological Services (NMSs, including NCDC/NOAA, Asheville, NC). Unlike the three US groups monitoring surface temperatures in a similar way, we do not infill areas that have no or missing data. We can only infill such regions in CRUTEM4 by accessing more station temperature series. During early 2014, we have begun the extensive task of updating as many of these series as possible using data provided by some NMSs and also through a number of research projects and programs around the world. All the station data used in CRUTEM4 have been available since 2009, but in Osborn and Jones (2014) we have made this more usable using a Google Earth interface (http://www.cru.uea.ac.uk/cru/data/crutem/ge/ ). We have recently completed the update of our infilled land multi-variable dataset (CRU TS 3.10, Harris et al., 2014). This additionally produces complete land fields (except for the Antarctic) for temperature, precipitation, diurnal temperature range, vapour pressure and sunshine/cloud. Using this dataset we have calculated sc-PDSI (self-calibrating Palmer Drought Severity Index) data and compared with other PDSI datasets (Trenberth et al., 2014). Also using CRU TS 3.10 and Reanalysis datasets, we showed no overall increase in global temperature variability despite changing regional patterns (Huntingford et al., 2013). Harris et al. (2014) is an update of an earlier dataset (Mitchell and Jones, 2005) which also had earlier DoE support. The earlier dataset has been cited over 1700 times according to ResearcherID on 31/July/2014 and the recent paper has already been cited 22 times. Analyses of Temperature Data Using the ERA-Interim estimate of the absolute surface air temperature of the Earth (instead of in the more normal form of anomalies) we compared the result against estimates we produced in 1999 with earlier DoE support. The two estimates are surprisingly close (differing by a couple of tenths of a degree Celsius), with the average temperature of the world (for 1981-2010) being very close to 14°C (Jones and Harpham, 2013). We have assessed ERA-Interim against station temperatures from manned and automatic weather station measurements across the Antarctic (Jones and Lister, 2014). Agreement is generally excellent across the Antarctic Peninsula and the sparsely sampled western parts of Antarctica. Differences tend to occur over eastern Antarctica where ERA-Interim is biased warm (up to 6°C) in the interior of the continent and biased cool (up to 6°C) for some of the coastal locations. Opportunities presented themselves during 2012 for collaborative work with a couple of Chinese groups. Three papers develop new temperature series for China as a whole and also for the eastern third of China (Wang et al., 2014, Cao et al., 2013 and Zhao et al., 2014). A dataset of ~400 daily Chinese temperature stations has been added to the CRU datasets. The latter paper finds that urban effects are generally about 10% of the long-term warming trend across eastern China. A fourth paper (Wang et al., 2013) illustrates issues with comparisons between reanalyses and surface temperatures across China, a method that has been widely used by some to suggest urban heating effects are much larger in the region. ERA-Interim can be used but NCEP/NCAR comparisons are very dependent on the period analysed. Earlier a new temperature dataset of homogenized records was developed for China (Li et al., 2009). Urbanization has also been addressed for London (Jones and Lister, 2009) where two rural sites have not warmed more than a city centre site since 1900. Additionally, in Ethymiadis and Jones (2010) we show that land air temperatures agree with marine data around coastal areas, further illustrating that urbanization is not a major component of large-scale surface air temperature change. Early instrumental data (before the development of modern thermometer screens) have always been suspected of being biased warm in summer, due to possible direct exposure to the sun. Two studies (Böhm et al., 2010 and Brunet et al., 2010) show this for the Greater Alpine Region (GAR) and for mainland Spain respectively. The issue is important before about 1870 in the GAR and before about 1900 in Spain. After correction for the problems, summer temperature estimates before these dates are cooler by about 0.4°C. In Jones and Wigley (2010), we discussed the importance of the biases in global temperature estimation. Exposure and to a lesser extent urbanization are the most important biases for the land areas, but both are dwarfed by the necessary adjustments for bucket SST measurements before about 1950. Individual station homogeneity is only important at the local scale. This was additionally illustrated by Hawkins and Jones (2013) where we replicated the temperature record developed by Guy Stewart Callendar in papers in 1938 and 1961. Analyses of Daily Climate Data Work here indicates that ERA-Interim (at least in Europe, Cornes and Jones, 2013, discussed in more detail in this proposal) can be used to monitor extremes (using the ETCCDI software – see Zhang et al., 2011). Additionally, also as a result of Chinese collaboration, a new method of daily temperature homogenization has been developed (Li et al., 2014). In Cornes and Jones (2011) we assessed storm activity in the northeast Atlantic region using daily gridded data. Even though the grid resolution is coarse (5° by 5° lat/long) the changes in storm activity are similar to those developed from the pressure triangle approach with station data. Analyses of humidity and pressure data In Simmons et al. (2010) we showed a reduction in relative humidity over low-latitude and mid-latitude land areas for the 10 years to 2008, based on monthly anomalies of surface air temperature and humidity from ECMWF reanalyses (ERA-40 and ERA-Interim) and our earlier land-only dataset (CRUTEM3) and synoptic humidity observations (HadCRUH). Updates of this station-based humidity dataset (now called HadISDH) extend the record, showing continued reductions (Willett et al., 2013). Analyses of Proxy Temperature Data In Vinther et al. (2010), relationships between the seasonal stable isotope data from Greenland Ice Cores and Greenland and Icelandic instrumental temperatures were investigated for the past 150-200 years. The winter season stable isotope data are found to be influenced by the North Atlantic Oscillation (NAO) and very closely related to SW Greenland temperatures. The summer season stable isotope data display higher correlations with Icelandic summer temperatures and North Atlantic SST conditions than with local SW Greenland temperatures. In Jones et al. (2014) we use these winter isotope reconstructions to show the expected inverse correlation (due to the NAO) with winter-season documentary reconstructions from the Netherlands and Sweden over the last 800 years. Finally, in this section Jones et al. (2013) shows the agreement between tree-ring width measurements from Northern Sweden and Finland and an assessment of the link to explosive volcanic eruptions. An instrumental record for the region in the early 19th century indicates that the summer of 1816 was only slightly below normal, explaining why this year has normal growth for both ring width and density. GCM/RCM/Reanalysis Evaluation In this section we have intercompared daily temperature extremes across Europe in Cornes and Jones (2013) using station data, E-OBS and ERA-Interim. We have additionally considered the impact of the urban issue on the global scale using the results of the Compo et al. (2011) Reanalyses, 20CR. These only make use of SST and station pressure data. Across the world’s land areas, they indicate similar warming since 1900 to that which has occurred (Compo et al., 2013), again illustrating that urbanization is not the cause of the long-term warming. Changes in HadCRUH global land surface specific humidity and CRUTEM3 surface temperatures from 1973 to 1999 were compared to the CMIP3 archive of climate model simulations with 20th Century forcings (Willett et al., 2010). The models reproduce the magnitude of observed interannual variance over all large regions. Observed and modelled trends and temperature-humidity relationships are comparable with the exception of the extra-tropical Southern Hemisphere where observations exhibit no trend but models exhibit moistening.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1986

Examples of mesoscale structures and short-term wind variations detected by VHF Doppler radar

The first of three wind profilers planned for operation in central and western Pennsylvania began full-time, high-quality operation during July 1985. It is located about 20 km south-southeast of University Park and operates at 50 MHz. Another 50-MHz radar and a 400-MHz radar are to be installed over the next few months, to complete a mesoscale triangle with sides of 120 to 160 km. During the period since early July, a number of weather systems have passed over the wind profiler. Those accompanied by thunderstorms caused data losses either because the Department computer system lost power or because power went out at the profiler site. A backup power supply and an automatic re-start program will be added to the profiler system to minimize such future losses. Data have normally been averaged over a one-hour period, although there have been some investigations of shorter-period averaging. In each case, preliminary examinations reveal that the profiler winds are indicative of meteorological phenomena. The only occasions of bad or missing data are obtained when airplane noise is occasionally experienced and when the returned power is nearly at the noise level, at the upper few gates, where a consensus wind cannot be determined. Jets streams, clouds, and diurnal variations of winds are discussed.

Forbes, G. S.↗

DOE OSTI2014

Inferring the gravitational potential of the Milky Way with a few precisely measured stars

The dark matter halo of the Milky Way is expected to be triaxial and filled with substructure. It is hoped that streams or shells of stars produced by tidal disruption of stellar systems will provide precise measures of the gravitational potential to test these predictions. We develop a method for inferring the Galactic potential with tidal streams based on the idea that the stream stars were once close in phase space. Our method can flexibly adapt to any form for the Galactic potential: it works in phase-space rather than action-space and hence relies neither on our ability to derive actions nor on the integrability of the potential. Our model is probabilistic, with a likelihood function and priors on the parameters. The method can properly account for finite observational uncertainties and missing data dimensions. We test our method on synthetic data sets generated from N-body simulations of satellite disruption in a static, multi-component Milky Way, including a triaxial dark matter halo with observational uncertainties chosen to mimic current and near-future surveys of various stars. We find that with just eight well-measured stream stars, we can infer properties of a triaxial potential with precisions of the order of 5%-7%. Without proper motions, we obtain 10% constraints on most potential parameters and precisions around 5%-10% for recovering missing phase-space coordinates. These results are encouraging for the goal of using flexible, time-dependent potential models combined with larger data sets to unravel the detailed shape of the dark matter distribution around the Milky Way.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Long-Term Daily and Monthly Climate Records from Stations Across the Contiguous United States (U.S. Historical Climatology Network)

The United States Historical Climatology Network (USHCN) is a high-quality data set of daily and monthly records of basic meteorological variables from 1218 observing stations across the 48 contiguous United States. Daily data include observations of maximum and minimum temperature, precipitation amount, snowfall amount, and snow depth; monthly data consist of monthly-averaged maximum, minimum, and mean temperature and total monthly precipitation. Most of these stations are U.S. Cooperative Observing Network stations located generally in rural locations, while some are National Weather Service First-Order stations that are often located in more urbanized environments. The USHCN has been developed over the years at the National Oceanic and Atmospheric Administration's (NOAA) National Climatic Data Center (NCDC) to assist in the detection of regional climate change. Furthermore, it has been widely used in analyzing U.S. climte. The period of record varies for each station. USHCN stations were chosen using a number of criteria including length of record, percent of missing data, number of station moves and other station changes that may affect data homogeneity, and resulting network spatial coverage. Collaboration between NCDC and CDIAC on the USHCN project dates to the 1980s (Quinlan et al. 1987). At that time, in response to the need for an accurate, unbiased, modern historical climate record for the United States, the Global Change Research Program of the U.S. Department of Energy and NCDC chose a network of 1219 stations in the contiguous United States that would become a key baseline data set for monitoring U.S. climate. This initial USHCN data set contained monthly data and was made available free of charge from CDIAC. Since then it has been comprehensively updated several times [e.g., Karl et al. (1990) and Easterling et al. (1996)]. The initial USHCN daily data set was made available through CDIAC via Hughes et al. (1992) and contained a 138-station subset of the USHCN. This product was updated by Easterling et al. (1999) and expanded to include 1062 stations. In 2009 the daily USHCN dataset was expanded to include all 1218 stations in the USHCN.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

ORNL FACE Tree Height, with data on height of a subset of trees measured every year, 1997-2009. Free-Air CO2 Enrichment (FACE) Experiment Synthesis Activities.

Height of 20 sweetgum trees in three rows of each of the plots in the Oak Ridge National Laboratory (ORNL) free-air CO2 enrichment (FACE) experiment was measured each year of the experiment (1997-2009). The trees were exposed to ambient or elevated CO2. Tree height data contribute to the overall project objectives to determine the integrated response of a deciduous forest to elevated CO2. Average tree height per plot was used in allometric equations to calculate tree dry mass each year and to test whether there were effects of elevated CO2 on canopy development. Height was derived from trigonometric calculation from measurements of horizontal distance of observer from the bole and the angles from observer’s eye to top and base of tree, using a Haglof sonic hypsometer. The data are presented in a csv file, with plot number in the first column, tree number in the second column, and height recorded to 0.1 m in 1997 to 2009 in remaining columns. Plots 1 and 2 had elevated CO2, and plots 3-5 were in ambient CO2. Missing data occur after the tree died. The experiment and the height measurement are described in: Norby, R. J., D. E. Todd, J. Fults, and D. W. Johnson. 2001. Allometric determination of tree growth in a CO2-enriched sweetgum stand. New Phytologist 150: 477-487. The ORNL FACE experiment is described at: https://richnorby.org/home/face-home/.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A Representation Fusion Framework for Decoupling Diagnostic Information in Multimodal Learning

Modern medicine increasingly relies on multimodal data, ranging from clinical notes to imaging and genomics, to guide diagnosis and treatment. However, integrating these heterogeneous data sources in a principled and interpretable manner remains a major challenge. We present MODES (Multi-mOdal Disentangled Embedding Space), a representation fusion framework that explicitly separates shared and modality-specific factors of variation, offering a structured latent space for multimodal information that improves both prediction and interpretability. By leveraging pre-trained unimodal foundation models, MODES mitigates the dependency on extensive paired datasets, crucial in data-scarce clinical settings. We introduce a masking strategy that optimizes representation dimensionality by eliminating low-information dimensions, to achieve compact, information-rich representations. Our framework demonstrates superior performance in predicting diagnoses and phenotypes compared to unimodal and conventional fusion models. MODES also enables robust diagnostic inference in missing data scenarios, offering an opportunity toward interpretable and efficient multimodal diagnostics in personalized healthcare.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Considerations for AMI-Based Operations for Distribution Feeders

More than $5 billion in investments in advanced metering infrastructure (AMI) technologies, AMI deployments, as pervasive secondary network voltage monitoring systems, provide opportunities for utility operations and controls. This paper focuses on the considerations for AMI-based tools and techniques as the industry moves toward operationalizing such large data sets. Phase identification is a first such tool. Numerous distribution network analysis, monitoring, and control applications - including volt/volt-ampere reactive control, state estimation, and distribution automation - require accurate phase connectivity information in the system models. The phase connectivity database maintained by utilities is inaccurate because of a significant amount of missing data, restoration activities, and network reconfiguration. Existing phase identification techniques that estimate phase connectivity work well in distribution feeders that have low or no photovoltaic (PV) generation; however, they fail to identify the phases accurately when considerable PV generation is present. This work addresses the phase identification problem in the presence of high PV generation using statistical analysis methods. Further, insights into the AMI data requirements for this application in terms of data window length and resolution are provided using sensitivity analysis performed on an actual distribution feeder model of San Diego Gas & Electric Company. The results of this study show that the phase connectivity, even in the presence of high PV generation, can be accurately identified using statistical analysis of AMI data of 1 day.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

Considerations for AMI-Based Operations for Distribution Feeders: Preprint

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Learning of networked spreading models from noisy and incomplete data

Recent years have seen a lot of progress in algorithms for learning parameters of spreading dynamics from both full and partial data. Some of the remaining challenges include model selection under the scenarios of unknown network structure, noisy data, missing observations in time, as well as an efficient incorporation of prior information to minimize the number of samples required for an accurate learning. Here, in this work, we introduce a universal learning method based on a scalable dynamic message-passing technique that addresses these challenges often encountered in real data. The algorithm leverages available prior knowledge on the model and on the data, and reconstructs both network structure and parameters of a spreading model. We show that a linear computational complexity of the method with the key model parameters makes the algorithm scalable to large network instances.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Addressing the pervasive scarcity of structural annotation in eukaryotic algae

Abstract Despite a continuous increase in algal genome sequencing, structural annotations of most algal genome assemblies remain unavailable. This pervasive scarcity of genome annotation has restricted rigorous investigation of these genomic resources and may have precipitated misleading biological interpretations. However, the annotation process for eukaryotic algal species is often challenging as genomic resources and transcriptomic evidence are not always available. To address this challenge, we benchmark the cutting-edge gene prediction methods that can be generalized for a broad range of non-model eukaryotes. Using the most accurate methods selected based on high-quality algal genomes, we predict structural annotations for 135 unannotated algal genomes. Using previously available genomic data pooled together with new data obtained in this study, we identified the core orthologous genes and the multi-gene phylogeny of eukaryotic algae, including of previously unexplored algal species. This study not only provides a benchmark for the use of structural annotation methods on a variety of non-model eukaryotes, but also compensates for missing data in the current spectrum of algal genomic resources. These results bring us one step closer to the full potential of eukaryotic algal genomics.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRSDate not supplied

Quality Assurance of ISS LIS lightning Data

Optical lighting detection from space has been ongoing since 1995 from the Optical Transient detector (1995-2000), then later by the Lightning Imaging Sensors (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite (1997-2015) and the International Space Station (ISS) (ISS 2017-present). A rapid readout (2 ms) 128x128 pixel CCD (Charge-coupled Device) array detects optical transients from lightning (events) and transmits the observations to ground. Ground processing determines whether individual events are likely due to lightning or noise. Likely lightning events are then grouped into groups, flashes, and areas and their location on the earth are determined. The lightning data are saved in orbit files; each file containing the time, location, and intensity of the lightning events, groups, flashes, and areas detected during one orbit. Even after this processing anomalies may exist in some of the orbits. A manual quality assurance (QA) procedure is performed monthly to assess the data and omit orbits with obvious anomalies from the dataset. These files are not included in the final dataset that has undergone QA. The manual QA was developed during the OTD mission to identify artifacts that made it through ground processing algorithms. The manual QA process was continued for the TRMM and ISS LIS missions. Many anomalies have been identified and software updates now address many of them. However, some anomalies still occur in the processed datasets. The manual QA process examines the data for obvious issues in the lightning files that passed through the processing algorithms. These files are considered anomalous and are not included in the final QA dataset. Some issues that may cause an orbit to be considered anomalous include: obvious noise (excess events that make it through the processing), suspect geolocation, and excessive missing data packets. As an example, one way noise manifests is as “streaks” in lightning climatology plots. These streaks are especially apparent in low lightning rate regions (eg., over oceans). Since there is no method at present to extract these anomalies during processing, orbit files with significant anomalies are identified and removed from the final QA ISS LIS dataset. The final QA dataset can be obtained files from the Global Hydrometeorological Resource Center (GHRC). This presentation will provide examples of the various anomalies and examine how often the various anomalies occur and how much data is lost due to the omission of the anomalous orbits. Comparisons with OTD and TRMM LIS will also be examined.

ISS↗

DOE OSTI2015

SU-E-T-23: A Developing Australian Network for Datamining and Modelling Routine Radiotherapy Clinical Data and Radiomics Information for Rapid Learning and Clinical Decision Support

Purpose: Large amounts of routine radiotherapy (RT) data are available, which can potentially add clinical evidence to support better decisions. A developing collaborative Australian network, with a leading European partner, aims to validate, implement and extend European predictive models (PMs) for Australian practice and assess their impact on future patient decisions. Wider objectives include: developing multi-institutional rapid learning, using distributed learning approaches; and assessing and incorporating radiomics information into PMs. Methods: Two initial standalone pilots were conducted; one on NSCLC, the other on larynx, patient datasets in two different centres. Open-source rapid learning systems were installed, for data extraction and mining to collect relevant clinical parameters from the centres’ databases. The European DSSs were learned (“training cohort”) and validated against local data sets (“clinical cohort”). Further NSCLC studies are underway in three more centres to pilot a wider distributed learning network. Initial radiomics work is underway. Results: For the NSCLC pilot, 159/419 patient datasets were identified meeting the PM criteria, and hence eligible for inclusion in the curative clinical cohort (for the larynx pilot, 109/125). Some missing data were imputed using Bayesian methods. For both, the European PMs successfully predicted prognosis groups, but with some differences in practice reflected. For example, the PM-predicted good prognosis NSCLC group was differentiated from a combined medium/poor prognosis group (2YOS 69% vs. 27%, p<0.001). Stage was less discriminatory in identifying prognostic groups. In the good prognosis group two-year overall survival was 65% in curatively and 18% in palliatively treated patients. Conclusion: The technical infrastructure and basic European PMs support prognosis prediction for these Australian patient groups, showing promise for supporting future personalized treatment decisions, improved treatment quality and potential practice changes. The early indications from the distributed learning and radiomics pilots strengthen this. Improved routine patient data quality should strengthen such rapid learning systems.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2007

Bayesian Analysis of the Cosmic Microwave Background

There is a wealth of cosmological information encoded in the spatial power spectrum of temperature anisotropies of the cosmic microwave background! Experiments designed to map the microwave sky are returning a flood of data (time streams of instrument response as a beam is swept over the sky) at several different frequencies (from 30 to 900 GHz), all with different resolutions and noise properties. The resulting analysis challenge is to estimate, and quantify our uncertainty in, the spatial power spectrum of the cosmic microwave background given the complexities of "missing data", foreground emission, and complicated instrumental noise. Bayesian formulation of this problem allows consistent treatment of many complexities including complicated instrumental noise and foregrounds, and can be numerically implemented with Gibbs sampling. Gibbs sampling has now been validated as an efficient, statistically exact, and practically useful method for low-resolution (as demonstrated on WMAP 1 and 3 year temperature and polarization data). Continuing development for Planck - the goal is to exploit the unique capabilities of Gibbs sampling to directly propagate uncertainties in both foreground and instrument models to total uncertainty in cosmological parameters.

methods - statistical↗

DOE OSTI2020

A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers

The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, Tg, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure–property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.

quantitative structure–property relationship (QSPR↗

NASA NTRS2014

Functional Sensory-Motor Performance Following Long Term Space Flight: The First Results of "Field Test" Experiment

The effect that extended-duration space flights may have on human space travelers, including exploration missions, is widely discussed at the present time. Specifically, there is an increasing amount of evidence showing that the physical capacity of cosmonauts is significantly reduced after long-duration space flights. It is evident that the most impaired functions are those that rely on gravity, particularly up right posture and gait. Because of the sensorimotor disturbances manifested in the neurology of the posture and gait space flight and postflight changes may also be observed in debilitating motion sickness. While the severity of particular symptoms varies, disturbances in spatial orientation and alterations in the accuracy of voluntary movements are persistently observed after long-duration space flights. At this time most of the currently available data are primarily descriptive and not yet suitable for predicting operational impacts of most sensorimotor decrements observed upon landing on planetary surfaces or asteroids. In particular there are no existing data on the recovery dynamics or functionality of neurological, cardiovascular or muscle performance making it difficult to model or simulate the cosmonauts' activity after landing and develop the appropriate countermeasure that will ensure the rapid and safe recovery of crewmembers immediately after landing in what could be hostile environments. However and as a starting position, the videos we have acquired during recent data collection following the long duration flights of cosmonauts and astronauts walking and performing other tasks shortly after return from space flight speak volumes about their level of deconditioning. A joint Russian-American team has developed a new study specifically to address the changes in crewmembers performance and the recovery of performance with the intent of filling the missing data gaps. The first (pilot) phase of this study includes recording body kinematics and quantifying the coordination and timing of relatively simple basic movements - transition from seated and prone positions to standing, walking, stepping over obstacles, tandem walking, muscle compliance, as well as characteristics of postural sway and orthostatic tolerance. Testing for changes in these parameters have been initiated in the medical tent at the landing site. The first set of experiments showed that during the first hour after landing, cosmonauts and astronauts were able to execute (although slower and with more effort than preflight) simple movements such as egress from a seated or prone position and also to remain standing for 3.5 minutes without exhibiting pronounced cardiovascular changes. More challenging tests, however, demonstrated a prominent reduction in coordination - the obstacle task, for example, was performed at much slower speed and with a marked overestimation of the obstacle height and tandem walking was greatly degraded suggesting significant changes in proprioception, brainstem and vestibular function. There is some speculation that the neural changes, either from the bottom-up or top down may be long lasting; requiring compensatory responses that will modify or mask the adverse responses we have observed. Furthermore, these compensatory responses may actually be beneficial, helping achieve a more rapid adaptation to both weightlessness and a return to earth.

Tomilovskaya, E. S.↗

NASA NTRS1993

Tomographic methods in flow diagnostics

This report presents a viewpoint of tomography that should be well adapted to currently available optical measurement technology as well as the needs of computational and experimental fluid dynamists. The goals in mind are to record data with the fastest optical array sensors; process the data with the fastest parallel processing technology available for small computers; and generate results for both experimental and theoretical data. An in-depth example treats interferometric data as it might be recorded in an aeronautics test facility, but the results are applicable whenever fluid properties are to be measured or applied from projections of those properties. The paper discusses both computed and neural net calibration tomography. The report also contains an overview of key definitions and computational methods, key references, computational problems such as ill-posedness, artifacts, missing data, and some possible and current research topics.

Decker, Arthur J.↗

DOE OSTI2020

A Hybrid Framework Combining Model-Based and Data-Driven Methods for Hierarchical Decentralized Robust Dynamic State Estimation

This paper combines model-based and data-driven methods to develop a hierarchical, decentralized, robust dynamic state estimator (DSE). A two-level hierarchy is proposed, where the lower level consists of robust, model-based, decentralized DSEs. The state estimates sent from the lower level are received at the upper level, where they are filtered by a robust data-driven DSE after a principled sparse selection. This selection allows us to shrink the dimension of the problem at the upper level and hence significantly speed up the computational time. The proposed hybrid framework does not depend on the centralized infrastructure of the control centers; thus it can be completely embedded into the wide-area measurement systems. This feature will ultimately facilitate the placement of hierarchical decentralized control schemes at the phasor data concentrator locations. Also, the network model is not necessary; thus, a topology processor is not required. Finally, there is no assumption on the dynamics of the electric loads. The proposed framework is tested on the 2,000-bus synthetic Texas system, and shown to be capable of reconstructing the dynamic states of the generators with high accuracy, and of forecasting in the advent of missing data.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Ground surface temperature derived Snow Cover Properties, Seward Peninsula, Alaska, 2019-2023

Snow-ground interface temperatures have been collected at the Teller mile marker 27 and Kougarok mile marker 64 field sites on the Seward Peninsula, Alaska from 2019 through 2023 (with data missing from Fall 2020 through Summer 2021 due to COVID). Temperatures were measured using iButton Link DS1921G-F5# Thermochron miniature temperature sensors and Tinytag TGP-4017 internal sensors deployed across the Kougarok 64 and Teller 27 field sites. These sensors are a cost-efficient way to collect snow-ground interface temperatures at a high spatial resolution, and when paired with air temperature data these measurements can provide insight into fine-scale variability in snowpack characteristics across the study sites. From this data, snow process metrics were calculated at each sensor location based on the methods outlined in Staub and Delaloye, 2017. Metrics are calculated daily for each sensor as well as over the entire season. These metrics include ground surface temperature (°C), the number of days under snow cover (number of days), the insulation effect of snow (unitless), the length of the transitional snow periods (number of days), as well as intermediaries such as temperature variability. Calculating these snow processes relies on the assumption that when snow covers a temperature sensor, it is buffered from diurnal fluctuations in air temperature by the insulating snow layer. More information on the calculated metrics can be found in the User Guide of this dataset, as well as in Staub and Delaloye’s 2017 publication Using Near-Surface Ground Temperature Data to Derive Snow Insulation and Melt Indices for Mountain Permafrost Applications. This dataset includes one daily and one seasonal *.csv file of metrics for every year of data, a daily and a seasonal *.csv data dictionary, and one User Guide document (*.pdf) describing data collection and processing.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗