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NASA NTRS2019

Implementation Recommendations and Usage Boundaries for the Two-Dimensional Probability of Collision Calculation

The two-dimensional (2D) probability of collision (𝑃𝑐) estimation method relies on several assumptions that must be satisfied for accurate results. Monte Carlo analysis of ~44,000 conjunctions indicates that 2D-𝑃(sub 𝑐) pro-vides accurate estimates for most typical conjunctions, but occasionally underestimates 𝑃(sub 𝑐) significantly, indicating an assumption violation. A test to detect large-amplitude underestimation inaccuracies can be based on how much “offset-from-TCA” 2D-𝑃(sub 𝑐) values vary during a well-defined time interval bracketing closest approach. The test successfully detects all large-amplitude 2D-𝑃(sub 𝑐) underestimations found to date, but with a high false-alarm rate. The analysis also provides implementation recommendations and usage boundaries for the 2D-𝑃(sub 𝑐) method.

Hall, Doyle T.↗
NASA NTRS2019

Implementation Recommendations and Usage Boundaries for the Two-Dimensional Probability of Collision Calculation

The two-dimensional (2D) probability of collision (𝑃𝑐) estimation method relies on several assumptions that must be satisfied for accurate results. Monte Carlo analysis of ~44,000 conjunctions indicates that 2D-𝑃(sub 𝑐) pro-vides accurate estimates for most typical conjunctions, but occasionally underestimates 𝑃(sub 𝑐) significantly, indicating an assumption violation. A test to detect large-amplitude underestimation inaccuracies can be based on how much “offset-from-TCA” 2D-𝑃(sub 𝑐) values vary during a well-defined time interval bracketing closest approach. The test successfully detects all large-amplitude 2D-𝑃(sub 𝑐) underestimations found to date, but with a high false-alarm rate. The analysis also provides implementation recommendations and usage boundaries for the 2D-𝑃(sub 𝑐) method.

Hall, Doyle T.↗
NASA NTRS2019

Satellite Conjunction "Probability," "Possibility," and "Plausibility": A Categorization of Competing Conjunction Assessment Risk Assessment Paradigms

A number of different conjunction assessment (CA) risk analysis methods and metrics have been proposed in the critical literature, and they vary widely in purport and form. However, they tend to be proposed individually and episodically, so that it is difficult for a CA practitioner to take stock of the possibilities, under- stand their fundamental differences, and make informed choices for their particular CA risk assessment enterprise. The present study seeks to collect the major proposals for risk assessment methods and parameters and organize them categorically, under the proposed divisions of “probability,” “plausibility,” and “possibility,” as well as formulate what appears for each to be its fundamental question and, where applicable, null hypothesis. This activity can, through a bottom-up approach, provide some of the building blocks for an overarching CA philosophy, as well as establish concepts and terminology potentially useful to the broader discussion of these topics.

Hejduk, M. D.↗
NASA NTRS2019

Satellite Conjunction “Probability,” “Plausibility,” and “Possibility”: A Categorization of Competing Satellite Conjunction Assessment Risk Analysis Paradigms

A number of different conjunction assessment (CA) risk analysis methods and metrics have been proposed in the critical literature, and they vary widely in purport and form. However, they tend to be proposed individually and episodically, so that it is difficult for a CA practitioner to take stock of the possibilities, under- stand their fundamental differences, and make informed choices for their particular CA risk assessment enterprise. The present study seeks to collect the major proposals for risk assessment methods and parameters and organize them categorically, under the proposed divisions of “probability,” “plausibility,” and “possibility,” as well as formulate what appears for each to be its fundamental question and, where applicable, null hypothesis. This activity can, through a bottom-up approach, provide some of the building blocks for an overarching CA philosophy, as well as establish concepts and terminology potentially useful to the broader discussion of these topics.

Hejduk, M. D.↗
NASA NTRS2018

An Interplay between Photons, Canopy Structure, and Recollision Probability: A Review of the Spectral Invariants Theory of 3D Canopy Radiative Transfer Processes

Earth observations collected by remote sensors provide unique information to our ever-growing knowledge of the terrestrial biosphere. Yet, retrieving information from remote sensing data requires sophisticated processing and demands a better understanding of the underlying physics. This paper reviews research efforts that lead to the developments of the stochastic radiative transfer equation (RTE) and the spectral invariants theory. The former simplifies the characteristics of canopy structures with a pair-correlation function so that the 3D information can be succinctly packed into a 1D equation. The latter indicates that the interactions between photons and canopy elements converge to certain invariant patterns quantifiable by a few wavelength independent parameters, which satisfy the law of energy conservation. By revealing the connections between plant structural characteristics and photon recollision probability, these developments significantly advance our understanding of the transportation of radiation within vegetation canopies. They enable a novel physically-based algorithm to simulate the “hot-spot” phenomenon of canopy bidirectional reflectance while conserving energy, a challenge known to the classic radiative transfer models. Therefore, these theoretical developments have a far-reaching influence in optical remote sensing of the biosphere.

vegetation remote sensing; stochastic radiative tr↗
NASA NTRSDate not supplied

Hopping with an Adaptive Hop Probability Distribution

Monotonic Basin Hopping (MBH) is a stochastic global search technique that may be used to design complex interplanetary trajectories. Previous research on MBH empirically demonstrated that a bi-polar Pareto distribution is an effective way to generate search “hops.” However, no analytical foundation exists to explain this performance. In this work we provide the beginning of that analytical foundation and also introduce a new variant of MBH that uses an adaptive probability distribution to generate the hops. The technique is demonstrated on historical interplanetary trajectory design problems.

A C Englander↗
NASA NTRS2020

PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018

We introduce the Precipitation Probability DISTribution (PPDIST) dataset, a collection of global high-resolution (0.1°) observation-based climatologies (1979–2018) of the occurrence and peak intensity of precipitation (P) at daily and 3-hourly time-scales. The climatologies were produced using neural networks trained with daily P observations from 93,138 gauges and hourly P observations (resampled to 3-hourly) from 11,881 gauges worldwide. Mean validation coefficient of determination (R^(2)) values ranged from 0.76 to 0.80 for the daily P occurrence indices, and from 0.44 to 0.84 for the daily peak P intensity indices. The neural networks performed significantly better than current state-of-the-art reanalysis (ERA5) and satellite (IMERG) products for all P indices. Using a 0.1 mm 3 per h threshold, P was estimated to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively. The highest P intensities were found over parts of Central America, India, and Southeast Asia, along the western equatorial coast of Africa, and in the intertropical convergence zone.

Hylke E. Beck↗
NASA NTRS2021

Generating Flood Probability Map Based on Combined Use of Synthetic Aperture Radar and Optical Imagery

Despite a lot of efforts to respond flood hazards with remote sensing data, it is still difficult to generate an accurate flood map using solely optical or radar imagery. While optical data is relatively high-resolution and does not suffer from speckle noise compared to radar data, it is very likely to be impacted by cloud and shadow. On the other hand, radar imagery can be used in all weather conditions due to its capability of penetrating clouds. Although a significant improvement of flood monitoring capability is achieved by using radar data, it is still challenging to map urban floods because of strong backscattering by man-made structures. Therefore, complementary use of optical and radar imagery in flood response is required, particularly in urban areas. In this study, we have adopted the Bayesian Joint Probability function to combine two different flood products generated from SAR and optical imagery. Flood detection with SAR data relies on the difference of backscatter signals between standing water and rougher land surface, while a Normalized Difference Water Index (NDWI) approach is used for optical data. Specifically, Planet Dove data with its 3m spatial resolution is used with higher weight values to detect flood extent in urban areas.

floods↗
NASA NTRSDate not supplied

A Practical Approach to Uncertainty Quantification Using Probability Boxes

To date, while the use of CFD for aerospace vehicle design and development is prevalent, the documentation of uncertainties associated with the simulations are rare. Instead, the current state-of-the-art relies heavily on the experience of the CFD practitioner to estimate the uncertainty associated with their simulations through simple sensitivity studies or subject matter expertise. This practice will have to be replaced with a formal uncertainty quantification (UQ) process if CFD is to play an expanded role in the research and engineering design community, test and evaluation community, and ultimately certification for flight. Accounting for uncertainties in a formal manner is a tedious process. Moreover, the typical CFD practitioner is not likely to be familiar with formal UQ methods. These factors have prevented the adoption of UQ methods in the engineering design and development cycle. This presentation will outline a credible approach to UQ using Probability Boxes that is straightforward to apply, and can readily be automated using existing UQ tool sets such as the DAKOTA packaged developed at Sandia. The added expense incurred when moving away from a deterministic CFD process to a stochastic one that captures uncertainties to enable risk-informed decision making will be discussed, as well as effective ways to reduce the computational costs.

Uncertainty Quantification↗
NASA NTRSDate not supplied

Coolant Leak from ISS External Active Thermal Control System (EATCS) – An Examination of Most Probable

The Port (P1) and Starboard (S1) External Active Thermal Control Systems (EATCS) are single phase, mechanically pumped ammonia loops that operate independently to cool the majority of the hardware and payloads onboard the International Space Station (ISS). A slow ammonia leak was detected using pressure and quantity telemetry five years after the P1 EATCS was activated. The leak gradually accelerated to a rate that locating and isolating the leak became imperative to maintain cooling capability. Partial pressure measurements from the Robotic External Leak Locator (RELL) scan surveys narrowed the search to the supply and return jumpers connecting one of three radiators to the system. Subsequently, the ISS crew performed high definition video surveys during an Extravehicular Activity (EVA), or spacewalk, and ammonia flakes were observed projecting from the jumpers. Thus, the ground teams were confident that the culprit for the ammonia leak was the jumpers. The ammonia leak stopped after ground teams remotely isolated and vented those jumpers and associated radiator. Both jumpers were removed and returned to the ground, and a root cause investigation was conducted. A calibrated leak test determined the bulk of the ammonia leaked through a pair of seals in a Quick Disconnect (QD), or connector, on one end of the return jumper. The return jumper QD was dissected, visually inspected, chemically tested and evaluated. The results indicated the most probable cause of the accelerating ammonia leak was due to defective seals, plating delamination underneath the seals, and on-orbit thermal cycles exacerbating the delamination. Both jumpers were refurbished, relaunched to the ISS, and scheduled to be reinstalled during an EVA in 2022. It appeared the issue was unique, but recently the S1 EATCS is showing signs of an accelerating ammonia leak, and RELL scans narrowed the source to a similar pair of radiator jumpers.

Thermal Control↗
NASA NTRSDate not supplied

Assessing Risk Due to Small Sample Size in Probability of Detection Analysis Using Tolerance Intervals

Small sample size (e.g.6-30) poses risk in results of probability of detection (POD) analysis using tolerance intervals. This method is also called as the limited sample or LS POD. The analysis is performed either during NDE procedure qualification or for assessment of reliability of an NDE procedure. The risk is primarily due to sampling error. Smaller samples are not likely to be random to the population or representative of the population. The small samples are likely to be biased. Biased samples have smaller standard deviation compared to the population. POD analysis with small biased sample can lead to overestimation of POD. Many sampling schemes are available in statistics to mitigate sampling risk. Primary objective of POD analysis is to determine a decision threshold from signal response measurements of a sample such that it is less than or equal to population decision threshold for 90% POD. Sampling error implies that this NDE reliability condition is violated. One of sampling types is called a representative sample. Representative samples reduce variance in POD estimates but also reduce magnitude of the error. Sampling sensitivity analysis for some sampling types is performed here using repetitive random sampling or Monte Carlo method. Six sampling types are considered for comparison. Some of the sampling types are similar to drawing a representative sample. LS POD model assumes random sampling. Therefore, random sampling is used as a basis for comparison with each sampling type. The sampling types used in the analysis are, A. Nominal and worst-case sampling, B. Worst-case sampling, C. Nominal case sampling, D. Random sampling, E. Random target, and sub-target sampling. F. Nominal target and sub-target sampling. Results of Monte Carlo simulation indicate that type F sampling can mitigate sampling risk and is also more practical to implement. Type A sampling may also mitigate the sampling risk, but it may be less practical to implement.

Ajay M Koshti↗
NASA NTRSDate not supplied

Coolant Leak from ISS External Active Thermal Control System (EATCS) – An Examination of Most Probable

The Port (P1) and Starboard (S1) External Active Thermal Control Systems (EATCS) are single phase, mechanically pumped ammonia loops that operate independently to cool majority of the hardware and payloads onboard the International Space Station (ISS). A slow ammonia leak was detected using pressure and quantity telemetry five years after the P1 EATCS was activated. The leak gradually accelerated to a rate that locating and isolating the leak became imperative to maintain cooling capability. Partial pressure measurements from the Robotic External Leak Locator (RELL) scan surveys narrowed the search to the supply and return jumpers connecting one of three radiators to the system. Subsequently, the ISS crew performed high definition video surveys during an Extravehicular Activity (EVA), or spacewalk, and ammonia flakes were observed projecting from the jumpers. Thus, the ground teams were confident that the culprit of the ammonia leak were the jumpers. The ammonia leak stopped after ground teams remotely isolated and vented those jumpers and associated radiator. Both jumpers were removed and returned to the ground, and a root cause investigation was conducted. A calibrated leak test determined the bulk of the ammonia leaked through a pair of seals in a Quick Disconnect (QD), or connector, on one end of the return jumper. The return jumper QD was dissected, visually inspected, chemically tested and evaluated. The results indicated the most probable cause of the accelerating ammonia leak was due to defective seals, plating delamination underneath the seals, and on-orbit thermal cycles exacerbating the delamination. Both jumpers were refurbished, relaunched to the ISS, and scheduled to be reinstalled during an EVA in 2022. It appeared the issue was unique, but recently the S1 EATCS is showing signs of an accelerating ammonia leak, and RELL scans narrowed the source to a similar pair of radiator jumpers.

Thermal Control↗
NASA NTRSDate not supplied

Effect of Cross-Correlation of Orbital Error on Probability of Collision Determination

This paper discusses the effect of global model error on probability of collision (Pc) determination. Modifications to the Pc formulation for cross-correlation of orbital error in prediction are developed and assessed for recent conjunctions. While specific geometries can be identified or constructed to produce significant change in Pc for the modified formulation, it is of operational interest to quanti- fy the relative occurrence of such cases for satellite conjunction risk assessment. Large-scale analysis is feasible per data collections in place over the past year.

Steve Casali↗
NASA NTRSDate not supplied

Probability of Obstacle Collision for UAVs in Presence of Wind

For incorporation of unmanned aerial vehicles into the National Airspace, ensuring safety of the airspace including the vehicles, people, and property on the ground is of utmost importance. One of the safety-critical factors for unmanned aviation flights is the risk of deviating from a planned trajectory resulting in a variety of hazards, including potential loss of separation between vehicle and obstacles or unexpected battery energy consumption. Off-nominal conditions introduced by component failures, degraded controllability and environmental disturbances such as wind gusts can lead to unacceptable unexpected deviations from the flight trajectory. It is essential to accurately model such effects on the flight trajectory while computing safety thresholds such as minimum separation from surrounding obstacles, available battery resource to complete the mission or determining delay in the expected time of arrival of flights. In this paper, a tool is presented based on Gaussian Process Regression for wind representation over a pre-defined trajectory for fast, yet approximated, in-time evaluation of possible trajectory deviations caused by wind gusts. The deviation in the planned trajectory caused by wind is further simulated utilizing a 6 degrees-of-freedom (DOF) UAV trajectory simulator comprising of a rotorcraft lumped-mass model with LQRI controller. Both steady-state wind and wind gust effects are investigated. The probability of collision with obstacle is computed and demonstrated on real flight data from experimental flights of an octocopter at NASA Langley Research Center in the presence of simulated obstacles and wind conditions. Effect of varying wind conditions and varying UAV airspeed is further demonstrated on experimental flights in the presence of wind measured by ground based weather service stations. The proposed approach would eventually benefit timely mitigation of current and future safety-critical events in autonomous systems by enabling risk-informed decision making.

Portia Banerjee↗
NASA NTRSDate not supplied

Coolant Leak from ISS External Active Thermal Control System (EATCS) – An Examination of Most Probable

The Port (P1) and Starboard (S1) External Active Thermal Control Systems (EATCS) are single phase, mechanically pumped ammonia loops that operate independently to cool majority of the hardware and payloads onboard the International Space Station (ISS). A slow ammonia leak was detected using pressure and quantity telemetry five years after the P1 EATCS was activated. The leak gradually accelerated to a rate that locating and isolating the leak became imperative to maintain cooling capability. Partial pressure measurements from the Robotic External Leak Locator (RELL) scan surveys narrowed the search to the supply and return jumpers connecting one of three radiators to the system. Subsequently, the ISS crew performed high-definition video surveys during an Extravehicular Activity (EVA), or spacewalk, and ammonia flakes were observed projecting from the jumpers. Thus, the ground teams were confident that the culprit of the ammonia leak were the jumpers. The ammonia leak stopped after ground teams remotely isolated and vented those jumpers and associated radiator. Both jumpers were removed and returned to the ground, and a root cause investigation was conducted. A calibrated leak test determined the bulk of the ammonia leaked through a pair of seals in a Quick Disconnect (QD), or connector, on one end of the return jumper. The return jumper QD was dissected, visually inspected, chemically tested and evaluated. The results indicated the most probable cause of the accelerating ammonia leak was due to defective seals, plating delamination underneath the seals, and on-orbit thermal cycles exacerbating the delamination. Both jumpers were refurbished, relaunched to the ISS, and scheduled to be reinstalled during an EVA in 2022. It appeared the issue was unique, but recently the S1 EATCS is showing signs of an accelerating ammonia leak, and RELL scans narrowed the source to a similar pair of radiator jumpers.

Darnell Cowan↗
NASA NTRSDate not supplied

Utilizing Earth Observations to Model Probable Coastal Wetland Extent, Sea-Level Rise Inundation Risk, and Assess Impacts on Historic Hawaiian Lands

Climate induced sea-level rise poses a risk to coastal areas on the Island of Hawai’i, and many of the island’s historic cultural lands are in danger of becoming overtaken by wetlands or inundation. In partnership with the County of Hawai’i, State of Hawai’i Department of Land and Natural Resources, and Arizona State University, NASA DEVELOP mapped wetland extent and short-term sea-level rise inundation risk. We utilized Earth observations over a 10-year span (2013 – 2022) that included the NASA MEaSUREs Gridded Sea Surface Height Anomalies and MEaSUREs Group for High Resolution Sea Surface Temperature datasets, United States Geological Survey (USGS) Hawaii Digital Elevation Models (DEM), and in situ tidal gauge data. Flood risk index values were acquired for 5 known Hawai’i flood events between 2019 – 2021 from the Global Flood Mapper tool on Google Earth Engine. We used a random forest model to predict short-term sea-level rise inundation risk along the entire coast of Hawai’i. Current wetland extents and probabilistic locations of new wetlands were modeled with the most recently available data from PlanetScope Surface Reflectance optical imagery (2022), USGS 3D Elevation Program (3DEP) 10m DEM (2020), temperature and precipitation data from the Hawai’i Climate Atlas, and soils data from the Hawai’i Soil Atlas (2014) using the Wetland Intrinsic Potential tool. Results indicated locations that had the highest probability of wetland creation. The end products aimed to help the partners prioritize efforts to meeting regulation requirements for wetlands protection, evaluate the inundation risk to historical features, and support decision-making for their Shoreline Setback and Climate Adaption plans.

Lisa Tanh↗
NASA NTRSDate not supplied

Creating Gridded Fire Probability Maps using NASA Data

Fire is a nationally and globally significant process that strongly affects human–dominated and wild landscapes. Even though fire can be devastating, wildland fire is a natural and integral force on our landscapes, providing value by decreasing fuels at the Wildland Urban Interface (WUI) to promote safe communities. However, uncontained wildfires can devastate communities, threaten our health, and result in substantial economic losses. There has been greater than a $50B increase in wildfire insurance claims from 2017-2021, which has been exacerbated by climate change. Our partners at Kettle reinsurance are focused on building a smarter reinsurance model for protecting today’s globalized world from the catastrophic effects of climate change. Our objective is to develop the world's first grid-based wildfire probability product using multiple sources of satellite data to determine whether a ‘conflagration' (fire larger than 999+ acres) has ‘breached’ a grid cell. This will substantially decrease the time it takes for homeowners to receive payouts, from over a year to a couple months. Working with our partners at Kettle reinsurance, we use multiple satellites and ancillary data to weigh the likelihood of fire, based on a number of sources that verify a fire burning in a grid cell and the level of confidence in the data source. For example, Sentinel-2 vegetation-change indices have a higher level of confidence than VIIRS (Visible Infrared Imaging Radiometer Suite) active-fire detection data; and VIIRS active-fire detection data have a higher-level of confidence than MODIS (Moderate Resolution Imaging Spectroradiometer) active-fire detection data. The first iteration has been developed for responding to wildfires in California, with the possibility to expand nationwide and globally.

Emily Gargulinski↗