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NASA NTRS2007

Virtual Diagnostics Interface: Real Time Comparison of Experimental Data and CFD Predictions for a NASA Ares I-Like Vehicle

Virtual Diagnostics Interface technology, or ViDI, is a suite of techniques utilizing image processing, data handling and three-dimensional computer graphics. These techniques aid in the design, implementation, and analysis of complex aerospace experiments. LiveView3D is a software application component of ViDI used to display experimental wind tunnel data in real-time within an interactive, three-dimensional virtual environment. The LiveView3D software application was under development at NASA Langley Research Center (LaRC) for nearly three years. LiveView3D recently was upgraded to perform real-time (as well as post-test) comparisons of experimental data with pre-computed Computational Fluid Dynamics (CFD) predictions. This capability was utilized to compare experimental measurements with CFD predictions of the surface pressure distribution of the NASA Ares I Crew Launch Vehicle (CLV) - like vehicle when tested in the NASA LaRC Unitary Plan Wind Tunnel (UPWT) in December 2006 - January 2007 timeframe. The wind tunnel tests were conducted to develop a database of experimentally-measured aerodynamic performance of the CLV-like configuration for validation of CFD predictive codes.

Schwartz, Richard J.↗

NASA NTRS2007

Virtual Diagnostics Interface: Real Time Comparison of Experimental Data and CFD Predictions for a NASA Ares I-Like Vehicle

Schwartz, Richard J.↗

NASA NTRS2006

Overview of the Solar-B Mission

The Solar-B mission is a collaboration between the Japan Aerospace Exploration Agency, Institute of Space and Astronautical Science, the National Aeronautics and Space Administration (NASA) and the Particle Physics and Astronomy Research Council (PPARC) of the United Kingdom and the European Space Agency. The principal scientific goals of the mission are to understand the processes of magnetic field generation, transport and ultimate dissipation of solar magnetic fields and how the release of magnetic energy is responsible for the heating and structuring of the chromosphere and corona. The scientific payload consists of three instruments: the Solar Optical Telescope that consists of the Optical Telescope Assembly and the Focal Plane Package (FPP), the X-ray Telescope and the EUV Imaging Spectrometer Each instrument is a result of the combined talents of all the members of the international team and their design and performance is described in separate papers in this session. The instruments are designed to work together as an 'observatory' simultaneously studying the target, at which the spacecraft is pointed, at different levels in the atmosphere. The spacecraft is scheduled for launch in September 2006 from the Uchinoura Space Center into a 600 km circular, sun-synchronous, polar orbit with a nominal elevation of 97.9 degrees. The orbit provides at least two morning and two evening contacts in Japan. Morning contacts are used for recovering quick look science data and the evening contacts for uploading commands. In addition ESA will provide 15 contacts per day from the Norwegian high latitude (78deg 14' N) ground station at Svalbard. The data downloads are transmitted to the ISAS Sirius database. They will be reformatted into FITS files and archived as Level 0 data on the ISAS DARTS system and made available to the scientific community. Scientific operations will be conducted from the IS AS facility located in Sagamihara, Japan. They are separated into planning, implementation and archiving. The planning process involves monthly, weekly and daily planning meetings. All scientific data will be made available after the first six month approximately one week after its collection.

Davis, John M.↗

NASA NTRSDate not supplied

Assessing Stratospheric Aerosols Contamination Due to Space Activities

The life cycle of a spacecraft starts and ends in the atmosphere: it interacts with the atmosphere right after the launch and during the atmospheric reentry when it usually mostly ablates. Both phases induce emissions of gases and solid particles, providing a source of these components in the middle atmosphere. Little is known about the exact nature, composition and effects of these emissions on the atmosphere and climate, but their impact is expected to rise as more and more orbiting satellites are launched. Ever since the years 2000, the number of space rockets launched per year has increased by a factor 3 globally. At the same time, the number of satellites launched in orbit around the Earth per year has been multiplied by about 30. One may wonder whether changes in the anthropogenic material injected in the terrestrial stratosphere can be detected and what its influence may be. In order to study the cosmic dust particles arriving on Earth, the NASA Johnson Space Center (JSC) has been systematically collecting solid dust particles from the Earth’s stratosphere by aircraft equipped with dedicated particle collectors since 1981. So far, 25 catalogs have been published, covering campaigns of collection from 1981 to 2020, with a total of 5071 solid particles that have been preliminary characterized and curated. In this work, we use the preliminary classification of the dust particles. Based on SEM images and X-ray EDS composition the collected dust is separated into four groups: C (Cosmic), TCN (Terrestrial Contaminant Natural), TCA (Terrestrial Contaminant Artificial) and AOS (Aluminum Oxide Sphere). The AOS being mostly generated by solid rocket propellant, they also belong to the TCA class. Our analysis of the data published indicates that from 1980 to 2009 the cosmic dust particles typically represent on average 40% of the collection with TCA and TCN corresponding to about 30% each. In the recent years, the TCA fraction has doubled to about 60% of the collection (2010-2020). This increase in anthropogenic particles is likely due to the overall human space activity and its recent increase. We will present the properties of the solid stratospheric dust particles collected and their evolution with time. Future work will be dedicated to better classify the natural and anthropogenic particles collected and described in the existing databases. We will use numerical modelling to produce quantitative estimates of the injected mass, the lifetime of particles in the middle atmosphere (stratosphere) and the relative abundance of the anthropogenic particles with respect to the stratospheric background particle population.

J Lasue↗

DOE OSTI2024

Optimized Viewing Techniques to Minimize Radiation Damage From X-ray Imaging Systems

X-ray inspection of ball grid arrays (BGAs) is typically performed at one or more viewing angles to examine adhesion sites for errors such as voids, joint cracking, or head-in-pillow. During this inspection process, the cir cuit board assembly is subject to ionizing radiation exposure, which can cause trapped charge within oxide layers of semiconductor devices. Some x-ray machines allow for programmable inspection routines, which could be used to optimize radiation exposure to semiconductor components. Using Monte Carlo methods, x-ray inspection of a BGA was simulated to determine a range of acceptable viewing angles. Dose rates to circuit board components were estimated at each inspection angle to determine the view resulting in optimized radiation exposure. Results showed that for each BGA, the maximum unobstructed viewing times without exceeding a 5 Gy dose limit to a single part ranged from 82 to 94 minutes. Using a radiation cost function method, optimized viewing across all components was found. Here, it was observed that for a consistent dose limit applied to silicon-based components, performing inspection with BGAs facing the x-ray source was optimal. A third method was applied, assigning individual dose limits based on empirical data from the NASA Goddard Space Flight Center radiation database. This method showed that optimized viewing maximizes the distance between the radiation source and highly sensitive components. It was also observed that cumulative effects from viewing two BGAs will influence viewing angles, causing the optimal view of one BGA to exist nearly 180° from the other.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2023

Sub-Continental-Scale Carbon Stocks of Individual Trees in African Drylands

The distribution of dryland trees and their density, cover, size, mass and carbon content are not well known at sub-continental to continental scales. This information is important for ecological protection, carbon accounting, climate mitigation and restoration efforts of dryland ecosystems. We assessed more than 9.9 billion trees derived from more than 300,000 satellite images, covering semi-arid sub-Saharan Africa north of the Equator. We attributed wood, foliage and root carbon to every tree in the 0–1,000 mm year −1 rainfall zone by coupling field data, machine learning, satellite data and high-performance computing. Average carbon stocks of individual trees ranged from 0.54 Mg C ha −1 and 63 kg C tree −1 in the arid zone to 3.7 Mg C ha −1 and 98 kg tree −1 in the sub-humid zone. Overall, we estimated the total carbon for our study area to be 0.84 (±19.8%) Pg C. Comparisons with 14 previous TRENDY numerical simulation studies23 for our area found that the density and carbon stocks of scattered trees have been underestimated by three models and overestimated by 11 models, respectively. This benchmarking can help understand the carbon cycle and address concerns about land degradation. We make available a linked database of wood mass, foliage mass, root mass and carbon stock of each tree for scientists, policymakers, dryland-restoration practitioners and farmers, who can use it to estimate farmland tree carbon stocks from tablets or laptops.

Compton Tucker↗

DOE OSTI2021

Beyond Taylor’s hypothesis: a novel volumetric reconstruction of velocity and density fields for variable‑density and shear flows

Abstract This work presents a novel numerical procedure for reconstructing volumetric density and velocity fields from planar laser-induced fluorescence (PLIF) and stereoscopic particle image velocimetry (SPIV) data. This new method is theoretically and practically demonstrated to provide more accurate 3D vortical structures and density fields in high shear flows than reconstruction methods based on the mean convective velocity. While Taylor’s hypothesis of frozen turbulence is commonly applied by using the local mean streamwise velocity, the proposed algorithm uses the measured local instantaneous velocity for data convection. It consists of a step-by-step reconstruction based on a mixed Lagrangian–Eulerian solver that includes the 3D interpolation of scattered flow data and that relaxes the Taylor’s hypothesis by iterative enforcement of the incompressibility constraint on the velocity field. This methodology provides 3D fields with temporal resolution, spatial resolution, and accuracy comparable to that of real 3D snapshots, thus providing a practical alternative to tomographic measurements. The procedure is validated using numerical data of the constant-density channel flow available on the Johns Hopkins University Turbulence Database (JHTDB), showing the accurate reconstruction of the 3D velocity field. The algorithm is applied to an experimental dataset of PLIF and SPIV measurements of a variable-density jet flow, demonstrating its capability to provide 3D velocity and density fields that are more consistent with the Navier–Stokes equations compared to the mean flow convective method. Graphic abstract

42 ENGINEERING↗

DOE OSTI2025

First observations of edge instabilities in strongly shaped negative triangularity plasmas on DIII-D

Throughout the 2023 DIII-D negative triangularity (NT) campaign, unexpected magnetic fluctuations indicative of an edge localized instability were observed in the majority of strongly shaped (–0.55 ≤ δ ≤ –0.45) discharges. Moreover, these same fluctuations were found to be absent in weaker NT plasma shapes. Based on a database study, three separate mode behaviors are observed based primarily on the edge safety factor and normalized plasma beta β N . The main regime is the bursty state, in which short (~5 ms) magnetic bursts occur every 5–10 ms. Each burst drives a small reduction in the edge electron temperature (T e ) as measured by electron cyclotron emissions (ECE), which then rapidly recovers between the bursts. The quiescent regime appears to have governing mechanics similar to the bursty regime, but with multiple unique mode signatures observed in the magnetics. These additional modes greatly reduce the bursty nature of the plasma. Finally, a saturated state is observed in a number of discharges, particularly at early time slices. The saturated regime is often short-lived (maximum 500 ms), but can have a large impact on the dynamics of the plasma edge region, causing significant drops in electron temperature as observed in the ECE diagnostic. 2D ECE imaging (ECE-I) is utilized, showing large kink-like perturbations localized near the last closed flux surface that extend into the SOL indicative of an external kink/peeling behavior. Additionally, evidence of phase mixing and observed tearing-parity perturbations localized near the edge rational surfaces is observed in the quiescent regime plasmas. As a result, the various modes do not appear to have a large impact on global confinement but may play a role in regulating the transport of the NT edge and the observed edge gradients.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

A deep learning approach to real-time HIV outbreak detection using genetic data

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0 ≥ 2.5, and overall a specificity exceeding 98% and sensitivity better than 92%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2022

Snow Microphysical Retrieval from the NASA D3R Radar During ICE-POP 2018

A method is developed to use both polarimetric and dual-frequency radar measurements to retrieve microphysical properties of falling snow. It is applied to the Ku- and Ka-band measurements of the NASA dual-polarization, dual-frequency Doppler radar (D3R) obtained during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018) field campaign and incorporates the Atmospheric Radiative Transfer Simulator (ARTS) microwave singles cattering property database for oriented particles. The retrieval uses optimal estimation to solve for several parameters that describe the particle size distribution (PSD), relative contribution of pristine, aggregate, and rimed ice species, and the orientation distribution along an entire radial simultaneously. Examination of Jacobian matrices and averaging kernels shows that the dual-wavelength ratio (DWR) measurements provide information regarding the characteristic particle size, and to a lesser extent, the rime fraction and shape parameter of the size distribution, whereas the polarimetric measurements provide information regarding the mass fraction of pristine particles and their characteristic size and orientation distribution. Thus, by combining the dual-frequency and polarimetric measurements, some ambiguities can be resolved that should allow a better determination of the PSD and bulk microphysical properties (e.g., snowfall rate) than can be retrieved from single-frequency polarimetric measurements or dual-frequency, single-polarization measurements. The D3R ICE-POP retrievals were validated using Precipitation Imaging Package (PIP) and Pluvio weighing gauge measurements taken nearby at the May Hills ground site. The PIP measures the snow PSD directly, and its measurements can be used to derived the snowfall rate (volumetric and water equivalent), mean volume-weighted particle size, and effective density, as well as particle aspect ratio and orientation. Four retrieval experiments were performed to evaluate the utility of different measurement combinations: Kuonly, DWR-only, Ku-pol, and All-obs. In terms of correlation, the volumetric snowfall rate (r D 0:95) and snow water equivalent rate (r D 0:92) were best retrieved by the Ku-pol method, while the DWR-only method had the lowest magnitude bias for these parameters (-31% and -8 %, respectively). The methods that incorporated DWR also had the best correlation to particle size (r D 0:74 and r D 0:71 for DWR-only and All-obs, respectively), although none of the methods retrieved density particularly well (r D 0:43 for Allobs). The ability of the measurements to retrieve mean aspect ratio was also inconclusive, although the polarimetric methods (Ku-pol and All-obs) had reduced biases and mean absolute error (MAE) relative to the Ku-only and DWR-only methods. The significant biases in particle size and snowfall rate appeared to be related to biases in the measured DWR, emphasizing the need for accurate DWR measurements and frequent calibration in future D3R deployments.

S Joseph Munchak↗

DOE OSTI2023

Comparison of Zirconium Redistribution in BISON EBR-II Models Using FIPD and IMIS Databases with Experimental Post Irradiation Examination

Metallic fuels have seen increased interest for future sodium fast reactors due to their material properties: high thermal conductivities and advantageous neutronic properties allow for greater fission densities. One drawback to typical metallic fuels is zirconium redistribution, which impacts this advantageous material and its neutronic properties. Unfortunately, the processes behind zirconium migration behavior are understood using first principles, so before these fuels are implemented in future fast reactors, characterization and fuel qualification regimes must be completed. These activities can be supported through the use of robust modeling using the most accurate empirical models currently available to fuel researchers around the world. The tool that allows researchers to model this complex coupled thermo-mechanical behavior and nuclear properties is BISON. Additionally, BISON model parameters need to be compared against PIE measurements. The current work utilizes two fuel pins from EBR-II experiment X441 to optimize various model parameters, including porosity correction factor, thermal conductivity, phase transition temperature, and diffusion coefficient multipliers, before implementing the final model for seven fuel pins with differing characteristics. To properly evaluate the BISON simulations, the results are compared to PIE metallography data for each fuel pin, to ensure the zirconium redistribution is properly reflected in the simulation results. Six out of seven analyzed fuel pins demonstrate good agreement between the metallography images and BISON results, showing alignment of the Zr-rich, Zr-depleted, and moderately Zr-enriched zones at various axial heights along the fuel pins. Further work is needed to refine the model parameters for general pin use.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1989

Cloud field classification based on textural features

An essential component in global climate research is accurate cloud cover and type determination. Of the two approaches to texture-based classification (statistical and textural), only the former is effective in the classification of natural scenes such as land, ocean, and atmosphere. In the statistical approach that was adopted, parameters characterizing the stochastic properties of the spatial distribution of grey levels in an image are estimated and then used as features for cloud classification. Two types of textural measures were used. One is based on the distribution of the grey level difference vector (GLDV), and the other on a set of textural features derived from the MaxMin cooccurrence matrix (MMCM). The GLDV method looks at the difference D of grey levels at pixels separated by a horizontal distance d and computes several statistics based on this distribution. These are then used as features in subsequent classification. The MaxMin tectural features on the other hand are based on the MMCM, a matrix whose (I,J)th entry give the relative frequency of occurrences of the grey level pair (I,J) that are consecutive and thresholded local extremes separated by a given pixel distance d. Textural measures are then computed based on this matrix in much the same manner as is done in texture computation using the grey level cooccurrence matrix. The database consists of 37 cloud field scenes from LANDSAT imagery using a near IR visible channel. The classification algorithm used is the well known Stepwise Discriminant Analysis. The overall accuracy was estimated by the percentage or correct classifications in each case. It turns out that both types of classifiers, at their best combination of features, and at any given spatial resolution give approximately the same classification accuracy. A neural network based classifier with a feed forward architecture and a back propagation training algorithm is used to increase the classification accuracy, using these two classes of features. Preliminary results based on the GLDV textural features alone look promising.

Sengupta, Sailes Kumar↗

NASA NTRS1992

Lunar base activities and the lunar environment

The Moon is an attractive site for astronomical observatories and other facilities because of the absence of a substantial lunar atmosphere and the stability of the lunar surface. The present lunar atmosphere is sufficiently transparent that there is no significant image distortion due to absorption or refraction. This thin atmosphere results from a combination of small sources and prompt losses. The major source that has been identified is the solar wind, whose total mass input into the lunar atmosphere is approximately 50 gm/sec. The major components of the solar wind are light elements (H and He) that promptly escape from the lunar surface by exospheric evaporation (Jeans' escape). The principal atmospheric loss mechanism for heavier gases is photoionization within a period of weeks to months, followed by immediate loss to the solar wind. Lunar base activities will modify the lunar atmosphere if gas is released at a larger rate than that now occurring naturally. Possible gas sources are rocket exhaust, processing of lunar materials, venting of pressurized volumes, and astronaut life support systems. For even modest lunar base activity, such sources will substantially exceed natural sources, although effects are expected to be localized and transient. The Apollo database serves as a useful reference for both measurements of the natural lunar environment and its modification by lunar base activities.

Vondrak, Richard R.↗

NASA NTRS2004

Microgravity Investigation of Crew Reactions in 0-G (MICRO-G)

There is a need for a human factors, technology-based bioastronautics research effort to develop an integrated system that reduces risk and provides scientific knowledge of astronaut-induced loads and motions during long-duration missions on the International Space Station (ISS), which will lead to appropriate countermeasures. The primary objectives of the Microgravity Investigation of Crew Reactions in 0-G (MICRO-GI research effort are to quantify astronaut adaptation and movement as well as to model motor strategies for differing gravity environments. The overall goal of this research program is to improve astronaut performance and efficiency through the use of rigorous quantitative dynamic analysis, simulation and experimentation. The MICRO-G research effort provides a modular, kinetic and kinematic capability for the ISS. The collection and evaluation of kinematics (whole-body motion) and dynamics (reacting forces and torques) of astronauts within the ISS will allow for quantification of human motion and performance in weightlessness, gathering fundamental human factors information for design, scientific investigation in the field of dynamics and motor control, technological assessment of microgravity disturbances, and the design of miniaturized, real-time space systems. The proposed research effort builds on a strong foundation of successful microgravity experiments, namely, the EDLS (Enhanced Dynamics Load Sensors) flown aboard the Russian Mir space station (19961998) and the DLS (Dynamic Load Sensors) flown on Space Shuttle Mission STS-62. In addition, previously funded NASA ground-based research into sensor technology development and development of algorithms to produce three-dimensional (3-0) kinematics from video images have come to fruition and these efforts culminate in the proposed collaborative MICRO-G flight experiment. The required technology and hardware capitalize on previous sensor design, fabrication, and testing and can be flight qualified for a fraction of the cost of an initial spaceflight experiment. Four dynamic load sensors/restraints are envisioned for measurement of astronaut forces and torques. Two standard ISS video cameras record typical astronaut operations and prescribed IVA motions for 3-D kinematics. Forces and kinematics are combined for dynamic analysis of astronaut motion, exploiting the results of the detailed dynamic modeling effort for the quantitative verification of astronaut IVA performance, induced-loads, and adaptive control strategies for crewmember whole-body motion in microgravity. This comprehensive effort, provides an enhanced human factors approach based on physics-based modeling to identify adaptive performance during long-duration spaceflight, which is critically important for astronaut training as well as providing a spaceflight database to drive countermeasure design.

Newman, Dava↗

NASA NTRS2016

New Methods for Retrieval of Chlorophyll Red Fluorescence from Hyperspectral Satellite Instruments: Simulations and Application to GOME-2 and SCIAMACHY

Global satellite measurements of solar-induced fluorescence (SIF) from chlorophyll over land and ocean have proven useful for a number of different applications related to physiology, phenology, and productivity of plants and phytoplankton. Terrestrial chlorophyll fluorescence is emitted throughout the red and far-red spectrum, producing two broad peaks near 683 and 736nm. From ocean surfaces, phytoplankton fluorescence emissions are entirely from the red region (683nm peak). Studies using satellite-derived SIF over land have focused almost exclusively on measurements in the far red (wavelengths greater than 712nm), since those are the most easily obtained with existing instrumentation. Here, we examine new ways to use existing hyperspectral satellite data sets to retrieve red SIF (wavelengths less than 712nm) over both land and ocean. Red SIF is thought to provide complementary information to that from the far red for terrestrial vegetation. The satellite instruments that we use were designed to make atmospheric trace-gas measurements and are therefore not optimal for observing SIF; they have coarse spatial resolution and only moderate spectral resolution (0.5nm). Nevertheless, these instruments, the Global Ozone Monitoring Instrument 2 (GOME-2) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), offer a unique opportunity to compare red and far-red terrestrial SIF at regional spatial scales. Terrestrial SIF has been estimated with ground-, aircraft-, or satellite-based instruments by measuring the filling-in of atmospheric andor solar absorption spectral features by SIF. Our approach makes use of the oxygen (O2) gamma band that is not affected by SIF. The SIF-free O2 gamma band helps to estimate absorption within the spectrally variable O2 B band, which is filled in by red SIF. SIF also fills in the spectrally stable solar Fraunhofer lines (SFLs) at wavelengths both inside and just outside the O2 B band, which further helps to estimate red SIF emission. Our approach is then an extension of previous approaches applied to satellite data that utilized only the filling-in of SFLs by red SIF. We conducted retrievals of red SIF using an extensive database of simulated radiances covering a wide range of conditions. Our new algorithm produces good agreement between the simulated truth and retrievals and shows the potential of the O2 bands for noise reduction in red SIF retrievals as compared with approaches that rely solely on SFL filling. Biases seen with existing satellite data, most likely due to instrumental artifacts that vary in time, space, and with instrument, must be addressed in order to obtain reasonable results. Our 8-year record of red SIF observations over land with the GOME-2 allows for the first time reliable global mapping of monthly anomalies. These anomalies are shown to have similar spatiotemporal structure as those in the far red, particularly for drought-prone regions. There is a somewhat larger percentage response in the red as compared with the far red for these areas that are drought sensitive. We also demonstrate that good-quality ocean fluorescence line height retrievals can be achieved with GOME-2, SCIAMACHY, and similar instruments by utilizing the full complement of radiance measurements that span the red SIF emission feature.

spatiotemporal structure↗

NASA NTRS2021

Satellite imaging reveals increased proportion of population exposed to floods

Flooding affects more people than any other environmental hazard and hinders sustainable development. Investing in flood adaptation strategies may reduce the loss of life and livelihood caused by floods. Where and how floods occur and who is exposed are changing as a result of rapid urbanization4, flood mitigation infrastructure and increasing settlements in floodplains6. Previous estimates of the global flood-exposed population have been limited by a lack of observational data, relying instead on models, which have high uncertainty. Here we use daily satellite imagery at 250-metre resolution to estimate flood extent and population exposure for 913 large flood events from 2000 to 2018. We determine a total inundation area of 2.23 million square kilometres, with 255–290 million people directly affected by floods. We estimate that the total population in locations with satellite-observed inundation grew by 58–86 million from 2000 to 2015. This represents an increase of 20 to 24 per cent in the proportion of the global population exposed to floods, ten times higher than previous estimates. Climate change projections for 2030 indicate that the proportion of the population exposed to floods will increase further. The high spatial and temporal resolution of the satellite observations will improve our understanding of where floods are changing and how best to adapt. The global flood database generated from these observations will help to improve vulnerability assessments, the accuracy of global and local flood models, the efficacy of adaptation interventions and our understanding of the interactions between landcover change, climate and floods.

B.Tellman↗

NASA NTRS2005

Combined Radar and Radiometer Analysis of Precipitation Profiles for a Parametric Retrieval Algorithm

A methodology to analyze precipitation profiles using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) is proposed. Rainfall profiles are retrieved from PR measurements, defined as the best-fit solution selected from precalculated profiles by cloud-resolving models (CRMs), under explicitly defined assumptions of drop size distribution (DSD) and ice hydrometeor models. The PR path-integrated attenuation (PIA), where available, is further used to adjust DSD in a manner that is similar to the PR operational algorithm. Combined with the TMI-retrieved nonraining geophysical parameters, the three-dimensional structure of the geophysical parameters is obtained across the satellite-observed domains. Microwave brightness temperatures are then computed for a comparison with TMI observations to examine if the radar-retrieved rainfall is consistent in the radiometric measurement space. The inconsistency in microwave brightness temperatures is reduced by iterating the retrieval procedure with updated assumptions of the DSD and ice-density models. The proposed methodology is expected to refine the a priori rain profile database and error models for use by parametric passive microwave algorithms, aimed at the Global Precipitation Measurement (GPM) mission, as well as a future TRMM algorithms.

Masunaga, Hirohiko↗

DOE OSTI2025

System Engineers and Decisions: It?s All about Knowledge

In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).

97 - MATHEMATICS AND COMPUTING↗