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Remote sensing is being used more and more for decision-making and policy development. Specific examples are: (1) Providing constraints on climate models used in IPCC assessments (2) Framing discussions about greenhouse gas monitoring (3) Providing support for hazard assessment and recovery.
The Department of Energy (DOE) H-Canyon Facility at the Savannah River Site (SRS) is operated by Savannah River Nuclear Solutions (SRNS). The Facility Structural Integrity Program [1] provides a process to inspect, evaluate, and document the conditions of passive safety related structural systems and components, their degradation mechanisms and their impact on the safety envelope. The Structural Integrity Program performs periodic visual inspections to confirm the systems and components can perform their safety function and recommend corrective actions before the function is compromised. The H-Canyon Exhaust Tunnel is part of the H-Canyon Ventilation System and is periodically inspected under the Structural Integrity Program. The Tunnel performs a Safety Class passive design feature function that is available 100% of the time. The underground reinforced concrete Tunnel directs Canyon process air from the Canyon to the Sand Filter System. The radiological and chemical airborne activity coupled with the physical configuration of the tunnel precludes a manned entry into the tunnel to perform the inspection. Unlike active systems that can be subjected to physical testing to confirm capability, qualification calculations, including modeling, is performed to assess passive design feature capabilities relative to operational, accident and natural phenomena hazards events. Input for these calculations come from knowledge of field conditions, design drawings and conservative assumptions. The knowledge of field conditions comes from concrete and soil testing activities as well as inspection data. The use of cameras on crawlers to obtain inspection data and how that data is used in qualification calculations is the focus of this discussion. Remote Tunnel inspections have been performed using cameras on a stick, referred to as pole cameras, prior to Calendar Year 2003 (CY2003). Since CY2003, six camera equipped crawlers have been used. Crawlers have evolved and improved by incorporating lessons learned from the previous inspections. The latest 2019 crawler successfully traveled the entire length of the tunnel between the Canyon and Sand Filter System and captured images that were previously unseen, thus providing key visual data for the Structural Integrity Program report [2]. The inspection data serves as 1) input to qualification calculations, 2) validates current conditions remain bounded by qualification calculations, and 3) documents a record of change over time. This data is vital for the effective evaluation of the tunnel structure and aids with predicting the life cycle service life safety envelope to protect the public, environment, and facility worker. The goal of the 2019 deployment was to use a more robust vehicle to ensure travel over obstacles, perform the inspection with higher resolution cameras, and have the ability to elevate the cameras to view surfaces previously obscured by abandon-in-place ducts in the tunnel. The inspection information will serve to strengthen the input basis for qualification calculations, versus using excessive conservative calculation assumptions. This 2019 inspection was compared with previous crawler visual evidence to identify change over time to project remaining service life and confirm field conditions remain bounded by qualification calculations. The results of the 2019 Exhaust Tunnel inspection continue to improve and provide a firm basis to make sound engineering judgements regarding tunnel capability and approaches in projecting remaining service life with an aging system and component. (authors)
A major obstacle in the estimation of windspeed patterns associated with low-altitude windshear with an airborne pulsed Doppler radar system is the presence of strong levels of ground clutter which can strongly bias a windspeed estimate. Typical solutions attempt to remove the clutter energy from the return through clutter rejection filtering. Proposed is a method whereby both the weather and clutter modes present in a return spectrum can be identified to yield an unbiased estimate of the weather mode without the need for clutter rejection filtering. An attempt will be made to show that modeling through a second order extended Prony approach is sufficient for the identification of the weather mode. A pattern recognition approach to windspeed estimation from the identified modes is derived and applied to both simulated and actual flight data. Comparisons between windspeed estimates derived from modal analysis and the pulse-pair estimator are included as well as associated hazard factors. Also included is a computationally attractive method for estimating windspeeds directly from the coefficients of a second-order autoregressive model. Extensions and recommendations for further study are included.
Wildfires have a profound impact upon the biosphere and our society in general. They cause loss of life, destruction of personal property and natural resources and alter the chemistry of the atmosphere. In response to the concern over the consequences of wildland fire and to support the fire management community, the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data and Information Service (NESDIS) located in Camp Springs, Maryland gradually developed an operational system to routinely monitor wildland fire by satellite observations. The Hazard Mapping System, as it is known today, allows a team of trained fire analysts to examine and integrate, on a daily basis, remote sensing data from Geostationary Operational Environmental Satellite (GOES), Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors and generate a 24 hour fire product for the conterminous United States. Although assisted by automated fire detection algorithms, N O M has not been able to eliminate the human element from their fire detection procedures. As a consequence, the manually intensive effort has prevented NOAA from transitioning to a global fire product as urged particularly by climate modelers. NASA at Goddard Space Flight Center in Greenbelt, Maryland is helping N O M more fully automate the Hazard Mapping System by training neural networks to mimic the decision-making process of the frre analyst team as well as the automated algorithms.
The development of new technologies and the miniaturization of sensors bring new requirements for our ability to predict and forecast hazardous space weather conditions.
The meteoroid environment is often divided conceptually into meteor showers and the sporadic meteor background. It is commonly but incorrectly assumed that meteoroid impacts primarily occur during meteor showers; instead, the vast majority of hazardous meteoroids belong to the sporadic complex. Unlike meteor showers, which persist for a few hours to a few weeks, sporadic meteoroids impact the Earth's atmosphere and spacecraft throughout the year. The Meteoroid Environment Office (MEO) has produced two environment models to handle these cases: the Meteoroid Engineering Model (MEM) and an annual meteor shower forecast. The sporadic complex, despite its year-round activity, is not isotropic in its directionality. Instead, their apparent points of origin, or radiants, are organized into groups called "sources". The speed, directionality, and size distribution of these sporadic sources are modeled by the Meteoroid Engineering Model (MEM), which is currently in its second major release version (MEMR2) [Moorhead et al., 2015]. MEM provides the meteoroid flux relative to a user-provided spacecraft trajectory; it provides the total flux as well as the flux per angular bin, speed interval, and on specific surfaces (ram, wake, etc.). Because the sporadic complex dominates the meteoroid flux, MEM is the most appropriate model to use in spacecraft design. Although showers make up a small fraction of the meteoroid environment, they can produce significant short-term enhancements of the meteoroid flux. Thus, it can be valuable to consider showers when assessing risks associated with vehicle operations that are brief in duration. To assist with such assessments, the MEO issues an annual forecast that reports meteor shower fluxes as a function of time and compares showers with the time-averaged total meteoroid flux. This permits missions to do quick assessments of the increase in risk posed by meteor showers. Section II describes MEM in more detail and describes our current efforts to improve its characteristics for a future release. Section III describes the annual shower forecast and highlights recent improvements made to its algorithm and inputs.
After completion of this course, the analyst will: 1) Understand the differences between an unmitigated analysis and a mitigated analysis; 2) Know the key receptors that a radiological and hazardous chemical accident analysis must consider; 3) Understand how to calculate a radiological release source term and a toxic chemical release source term for various phenomenology; 4) Understand how to calculate a radiological and toxic chemical health insult to key receptors and compare to consequence thresholds; 5) understand the role of atmospheric dispersion in radiological and toxic consequence calculations; 6) understand atmospheric dispersion modeling and the inputs to and outputs from the MACCS/POSTMAX codes.
Ground-motion simulations for infrequent earthquake scenarios are gaining increasing interest in the engineering community for their potential to inform performance-based structural design and assessment, particularly in regions where major earthquakes are expected, but recordings from consistent historical events are not available. However, the absence of empirical data makes the evaluation of such scenarios and the interpretation of the simulation results very challenging. In this context, this paper presents the evaluation of the first EQSIM (v.1.1.0) ground-motion simulations generated for the San Francisco Bay Area (SFBA) region. The current database, which is at the first stages of development, includes eight realizations of a Hayward Fault M w 7 event. The evaluation of the simulated motions is first conducted on the average ground-motion amplitudes through comparison against the NGA-W2 GMPEs and a selected population of real records. A methodology for selecting a population of real records consistent with the simulated event is proposed. The objective of the proposed evaluation is twofold: (1) build confidence in the realistic character of the simulated motions for their use in engineering applications and (2) offer a critical physics-based interpretation of the simulation results that can help improve key features of the simulation models. To further investigate the implications of using the simulated ground motions for site-specific structural assessments, the ASCE7-16 approach is employed for the analysis of two hazard-consistent near-field sites in the SFBA. Results are discussed. Furthermore, this study offers a critical review of the EQSIM (v.1.1.0) SFBA ground-motion simulations and suggestions for improving the earthquake rupture models.
Processed LiDAR data and environmental covariates from 2015 and 2019 LiDAR scans in the Vicinity of Snodgrass Mountain (Western Colorado, USA), in a geographic subset used in primary analysis for the research paper.This package contains LiDAR-derived canopy height maps for 2015 and 2019, crown polygons derived from the height maps using a segmentation algorithm, and environmental covariates supporting the model of forest growth. Source datasets include August 2015 and August 2019 discrete-return LiDAR point clouds collected by Quantum Geospatial for terrain mapping purposes on behalf of the Colorado Hazard Mapping Program and the Colorado Water Conservation Board. Both datasets adhere to the USGS QL2 quality standard. The point cloud data were processed using the R package lidR to generate a canopy height model representing maximum vegetation height above the ground surface, using a pit-free algorithm.This dataset was compiled to assess how spatial patterns of tree growth in montane and subalpine forests are influenced by water and energy availability. Understanding these growth patterns can provide insight into forest dynamics in the Southern Rocky Mountains under changing climatic conditions.This dataset contains .tif, .csv, and .txt files. This dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.
This paper describes the results of a 3D super-resolution algorithm applied to the range data obtained from a recent Flash Lidar helicopter flight test. The flight test was conducted by the NASA's Autonomous Landing and Hazard Avoidance Technology (ALHAT) project over a simulated lunar terrain facility at NASA Kennedy Space Center. ALHAT is developing the technology for safe autonomous landing on the surface of celestial bodies: Moon, Mars, asteroids. One of the test objectives was to verify the ability of 3D super-resolution technique to generate high resolution digital elevation models (DEMs) and to determine time resolved relative positions and orientations of the vehicle. 3D super-resolution algorithm was developed earlier and tested in computational modeling, and laboratory experiments, and in a few dynamic experiments using a moving truck. Prior to the helicopter flight test campaign, a 100mX100m hazard field was constructed having most of the relevant extraterrestrial hazard: slopes, rocks, and craters with different sizes. Data were collected during the flight and then processed by the super-resolution code. The detailed DEM of the hazard field was constructed using independent measurement to be used for comparison. ALHAT navigation system data were used to verify abilities of super-resolution method to provide accurate relative navigation information. Namely, the 6 degree of freedom state vector of the instrument as a function of time was restored from super-resolution data. The results of comparisons show that the super-resolution method can construct high quality DEMs and allows for identifying hazards like rocks and craters within the accordance of ALHAT requirements.
Here, per- and polyfluoroalkyl substances (PFAS) represent a large group of contaminants of concern based on their widespread use, environmental persistence, and potential toxicity. Many traditional models for estimating toxicity, bioaccumulation, and other toxicological properties are not well suited for PFAS. Consequently, there is a need to generate hazard information for PFAS in an efficient and cost-effective manner. In the present study, Daphnia magna were exposed to multiple concentrations of 22 different PFAS for 24 h, in 96-well plate format. Following exposure, whole body RNA was extracted and extracts, each representing five exposed individuals, were subjected to RNA sequencing. Following analytical measurements to verify PFAS exposure concentrations, and quality control on processed cDNA libraries for sequencing, concentration-response modeling was applied to the data sets for 18 of the tested compounds, and the concentration at which a concerted molecular response occurred (transcriptomic point of departure; tPOD) was calculated. The tPODs, based on measured concentrations of PFAS, generally ranged from 0.03-0.58 µM (9.9-350 µg/L; interquartile range). In most cases, these concentrations were two orders of magnitude lower than similarly calculated tPODs for human cell lines exposed to PFAS. They were also lower than apical effect concentrations reported for seven PFAS for which some crustacean or invertebrate toxicity data were available, although there were a few exceptions. Despite being lower than most other available hazard benchmarks, Daphnia magna tPODs were, on average, four orders of magnitude greater than the maximum aqueous concentrations of PFAS measured in Great Lakes tributaries. Overall, this high throughput transcriptomics assay with Daphnia magna holds promise as a component of a tiered hazard evaluation strategy employing new approach methodologies.
INTRODUCTION: The meteoroid environment is often divided conceptually into meteor showers and the sporadic meteor background. It is commonly but incorrectly assumed that meteoroid impacts primarily occur during meteor showers; instead, the vast majority of hazardous meteoroids belong to the sporadic complex. Unlike meteor showers, which persist for a few hours to a few weeks, sporadic meteoroids impact the Earth's atmosphere and spacecraft throughout the year. The Meteoroid Environment Office (MEO) has produced two environment models to handle these cases: the Meteoroid Engineering Model (MEM) and an annual meteor shower forecast. The sporadic complex, despite its year-round activity, is not isotropic in its directionality. Instead, their apparent points of origin, or radiants, are organized into groups called "sources". The speed, directionality, and size distribution of these sporadic sources are modeled by the Meteoroid Engineering Model (MEM), which is currently in its second major release version (MEMR2) [Moorhead et al., 2015]. MEM provides the meteoroid flux relative to a user-provided spacecraft trajectory; it provides the total flux as well as the flux per angular bin, speed interval, and on specific surfaces (ram, wake, etc.). Because the sporadic complex dominates the meteoroid flux, MEM is the most appropriate model to use in spacecraft design. Although showers make up a small fraction of the meteoroid environment, they can produce significant short-term enhancements of the meteoroid flux. Thus, it can be valuable to consider showers when assessing risks associated with vehicle operations that are brief in duration. To assist with such assessments, the MEO issues an annual forecast that reports meteor shower fluxes as a function of time and compares showers with the time-averaged total meteoroid flux. This permits missions to do quick assessments of the increase in risk posed by meteor showers.
NASA Ames Research Center and the U.S. Army Aeroflight dynamics Directorate (AFDD) have performed initial flight evaluations of the Research Flight Control System (RFCS) integrated into the Army/NASA Rotorcraft Aircrew Systems Concepts Airborne Laboratory (RASCAL) JUH-GOA. The highly modified JUH-GOA Black Hawk helicopter is a full authority, high bandwidth, variable stability, in-flight simulator designed to support development of advanced flight control, sensor, and integrated display and control technologies in a fail safe environment. Preparation for flight test required an extensive hazard analysis and ground testing to ensure proper system operation. A hardware in the loop development facility was utilized to evaluate control law stability following software changes, assess servo hardover upset conditions during manual and monitor disengagements and provide pilot familiarization of test techniques and software changes prior to flight. First engagement of the RFCS was conducted on 31 Aug 2001. RFCS transfer system operation, envelope expansion and a limited rate monitor evaluation have been completed with low bandwidth and model following control laws. The presentation will discuss the following - System overview including aircraft modifications and integrated development facilities used with the RASCAL facility. - Preliminary hazard identification and mitigation prior to flight test. - Ground testing used to qualify the RFCS transfer system and verify fault monitor operation. - Flight test results of low-bandwidth and model following control law evaluations including maneuver agility, control limitations, fault monitor reliability, and recovery from manual and monitor disengagement. - Lessons learned including test techniques using a passive three-axis sidearm controller, the value of the development facility in reducing risk and crew coordination issues related to the operation of a full authority, variable stability platform. - Future research and modifications planned for the RASCAL aircraft.
High Mountain Asia has long been known as a hotspot for landslide risk, and studies have suggested that landslide hazard is likely to increase in this region over the coming decades. Extreme precipitation may become more frequent, with a nonlinear response relative to increasing global temperatures. However, these changes are geographically varied. This article maps probable changes to landslide hazard, as shown by a landslide hazard indicator (LHI) derived from downscaled precipitation and temperature. In order to capture the nonlinear response of slopes to extreme precipitation, a simple machine-learning model was trained on a database of landslides across High Mountain Asia to develop a regional LHI. This model was applied to statistically downscaled data from the 30 members of the Seamless System for Prediction and Earth System Research large ensembles to produce a range of possible outcomes under the Shared Socioeconomic Pathways 2-4.5 and 5-8.5. The LHI reveals that landslide hazard will increase in most parts of High Mountain Asia. Absolute increases will be highest in already hazardous areas such as the Central Himalaya, but relative change is greatest on the Tibetan Plateau. Even in regions where landslide hazard declines by year 2100, it will increase prior to the mid-century mark. However, the seasonal cycle of landslide occurrence will not change greatly across High Mountain Asia. Although substantial uncertainty remains in these projections, the overall direction of change seems reliable. These findings highlight the importance of continued analysis to inform disaster risk reduction strategies for stakeholders across High Mountain Asia.
In Linear Temporal Logic (LTL) model checking, we check LTL formulas representing desired behaviors against a formal model of the system designed to exhibit these behaviors. To accomplish this task, the LTL formulas must be translated into automata [21]. We focus on LTL compilation by investigating LTL satisfiability checking via a reduction to model checking. Having shown that symbolic LTL compilation algorithms are superior to explicit automata construction algorithms for this task [16], we concentrate here on seeking a better symbolic algorithm.We present experimental data comparing algorithmic variations such as normal forms, encoding methods, and variable ordering and examine their effects on performance metrics including processing time and scalability. Safety critical systems, such as air traffic control, life support systems, hazardous environment controls, and automotive control systems, pervade our daily lives, yet testing and simulation alone cannot adequately verify their reliability [3]. Model checking is a promising approach to formal verification for safety critical systems which involves creating a formal mathematical model of the system and translating desired safety properties into a formal specification for this model. The complement of the specification is then checked against the system model. When the model does not satisfy the specification, model-checking tools accompany this negative answer with a counterexample, which points to an inconsistency between the system and the desired behaviors and aids debugging efforts.
The Applied Meteorology Unit is conducting an evaluation of the Regional Atmospheric Modeling System (RAMS) contained within the Eastern Range Dispersion Assessment System (ERDAS). ERDAS provides emergency response guidance for operations at the Cape Canaveral Air Force Station and the Kennedy Space Center in the event of an accidental hazardous material release or aborted vehicle launch. The prognostic data from RAMS is available to ERDAS for display and is used to initialize the 45th Range Safety (45 SW/SE) dispersion model. Thus, the accuracy of the 45 SW/SE dispersion model is dependent upon the accuracy of RAMS forecasts. The RAMS evaluation task consists of an objective and subjective component for the Florida warm and cool seasons of 1999-2000. The objective evaluation includes gridded and point error statistics at surface and upper-level observational sites, a comparison of the model errors to a coarser grid configuration of RAMS, and a benchmark of RAMS against the widely accepted Eta model. The warm-season subjective evaluation involves a verification of the onset and movement of the Florida east coast sea breeze and RAMS forecast precipitation. This interim report provides a summary of the RAMS objective and subjective evaluation for the 1999 Florida warm season only.
Landslide susceptibility mapping is most effective if detailed surface and subsurface information can be combined with authoritative landslide catalogs or a deep understanding of local conditions. However, these types of homogeneous input data and catalogs are frequently not available over large areas. In this study, we model landslide susceptibility in Central America and the Caribbean islands by combining three globally available datasets and one regional dataset with fuzzy overlay. This primarily heuristic model provides the flexibility to test a range of different contributing variables and the capability to compare landslide inventories within the model framework that vary greatly in their size, spatiotemporal scope, and collection methods. We create a regional susceptibility map and evaluate its performance using receiver operating characteristics for both continuous and binned susceptibility values. This susceptibility map forms the basis for a near-real-time landslide hazard assessment system that couples susceptibility with rainfall and soil moisture triggers to estimate potential landslide activity at a regional scale. The application of this susceptibility model at the regional scale provides a foundation for transferring the methodology to other geographic areas.
Modeling efforts at NASA aim to advance scientific understanding of the Earth system and its response to natural and human-induced changes and to improve our ability to predict climate, weather, and natural hazards. These efforts are particularly relevant for communities, scientists and stakeholders working and living in the Arctic. Here, we highlight current and planned Earth system prediction and data assimilation products from the Global Modeling and Assimilation Office (GMAO). We invite feedback and discussion on GMAO’s model and reanalysis development in support of Arctic research, engagement, and policy.