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DOE OSTI2025

Navigating Epistemic Uncertainty in the Management of Flash Droughts

Abstract: Flash droughts, characterized by their rapid onset, sharply contrast with the typically gradual development of traditional droughts. These events are triggered by a combination of low rainfall and high evaporation rates, driven by elevated temperatures, making them particularly challenging to predict and prepare for. As a relatively new concept, flash drought is not well understood, which introduces significant epistemic uncertainties regarding their nature and detection methods. This under-detection hinders planners' ability to effectively manage these events. Despite these uncertainties, flash droughts can have significant impacts, raising the question: how can decision-makers prepare for such events given the current knowledge gaps? To address this, we propose a methodological framework aimed at enhancing flash drought preparedness by guiding the selection of appropriate indicators based on their detection capabilities and the decision-makers' level of risk aversion. Our approach involves evaluating six different flash drought indicators and analyzing the level of agreement among them. Additionally, we consider the decision-makers' risk aversion, distinguishing between those who require consensus across all methods (risk-takers) and those who act based on a single method's indication (risk-averse). The insights gained from this study offer a pathway towards more informed decision-making processes regarding flash droughts, potentially mitigating their adverse effects through better preparedness and response strategies.

climate resilience↗

DOE OSTI2025

Eucalyptus – An Analysis Suite for Fault Trees with Uncertainty Quantification

Eucalyptus is a novel code developed at Lawrence Livermore National Laboratory to incorporate uncertainty quantification into Fault Tree Analysis (FTA). This tool addresses the challenge of imperfect knowledge in “grey-box” systems by allowing analysts to incorporate and propagate uncertainty from component-level assessments to system-level effects. Eucalyptus facilitates a consistent evaluation of the impact of subject matter expert judgment and knowledge gaps on overall system response by Monte Carlo generation of possible system fault trees, sampling probabilities of the existence of subsystems and components. Here, the code supports the specification of fault trees through text and allows export to various formats, including auto-generated images, easing analysis and reducing errors. It has undergone extensive verification testing, demonstrating its reliability and readiness for deployment, and leverages on-node parallelism for rapid analysis. Example analyses are shown that include the identification of system failure paths and quantification of the value of further information about system components.

Fault Tree Analysis↗

NASA NTRS2012

Standardization of the Definitions of Vertical Resolution and Uncertainty in the NDACC-archived Ozone and Temperature Lidar Measurements

The international Network for the Detection of Atmospheric Composition Change (NDACC) is a global network of high-quality, remote-sensing research stations for observing and understanding the physical and chemical state of the Earth atmosphere. As part of NDACC, over 20 ground-based lidar instruments are dedicated to the long-term monitoring of atmospheric composition and to the validation of space-borne measurements of the atmosphere from environmental satellites such as Aura and ENVISAT. One caveat of large networks such as NDACC is the difficulty to archive measurement and analysis information consistently from one research group (or instrument) to another [1][2][3]. Yet the need for consistent definitions has strengthened as datasets of various origin (e.g., satellite and ground-based) are increasingly used for intercomparisons, validation, and ingested together in global assimilation systems.In the framework of the 2010 Call for Proposals by the International Space Science Institute (ISSI) located in Bern, Switzerland, a Team of lidar experts was created to address existing issues in three critical aspects of the NDACC lidar ozone and temperature data retrievals: signal filtering and the vertical filtering of the retrieved profiles, the quantification and propagation of the uncertainties, and the consistent definition and reporting of filtering and uncertainties in the NDACC- archived products. Additional experts from the satellite and global data standards communities complement the team to help address issues specific to the latter aspect.

Detection of Atmospheric Composition Change (NDACC↗

NASA NTRS2013

Advanced Methods for Determining Prediction Uncertainty in Model-Based Prognostics with Application to Planetary Rovers

Prognostics is centered on predicting the time of and time until adverse events in components, subsystems, and systems. It typically involves both a state estimation phase, in which the current health state of a system is identified, and a prediction phase, in which the state is projected forward in time. Since prognostics is mainly a prediction problem, prognostic approaches cannot avoid uncertainty, which arises due to several sources. Prognostics algorithms must both characterize this uncertainty and incorporate it into the predictions so that informed decisions can be made about the system. In this paper, we describe three methods to solve these problems, including Monte Carlo-, unscented transform-, and first-order reliability-based methods. Using a planetary rover as a case study, we demonstrate and compare the different methods in simulation for battery end-of-discharge prediction.

prognosis↗

NASA NTRS2015

Overview of Uncertainty Analysis Activities at NASA Glenn's Aeropropulsion Facilities

An overview of the measurement uncertainty analysis process utilized at GRC to determine the uncertainty of aeropropulstion facilities. Examples are given, but no real findings are presented.

Uncertainty↗

NASA NTRS2016

Simulation Credibility: Advances in Verification, Validation, and Uncertainty Quantification

Decision makers and other users of simulations need to know quantified simulation credibility to make simulation-based critical decisions and effectively use simulations, respectively. The credibility of a simulation is quantified by its accuracy in terms of uncertainty, and the responsibility of establishing credibility lies with the creator of the simulation. In this volume, we present some state-of-the-art philosophies, principles, and frameworks. The contributing authors involved in this publication have been dedicated to advancing simulation credibility. They detail and provide examples of key advances over the last 10 years in the processes used to quantify simulation credibility: verification, validation, and uncertainty quantification. The philosophies and assessment methods presented here are anticipated to be useful to other technical communities conducting continuum physics-based simulations; for example, issues related to the establishment of simulation credibility in the discipline of propulsion are discussed. We envision that simulation creators will find this volume very useful to guide and assist them in quantitatively conveying the credibility of their simulations.

Simulation Credibility↗

NASA NTRS2017

VIIRS Thermal Emissive Bands L1B Calibration Uncertainty

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key instrument on-board the Suomi National Polar-orbiting Partnership (S-NPP) spacecraft. The S-NPP launched in October 2011 and it has been collecting valuable Earth science data with VIIRS and four other instruments for more than five years. The VIIRS Characterization Support Team (VCST) of the National Aeronautics and Space Administration (NASA) Science Investigator-led Processing Systems (SIPS) is designed to support the VIIRS sensor pre-launch geometric and radiometric characterization and to access on-orbit long-term Level-1B (L1B) calibration and performance. This paper reviews the VIIRS thermal emissive bands (TEB), covering wavelengths from 3.7 to 12.0 μm, L1B radiometric calibration algorithms and presents the calibration uncertainty methodology which will be implanted in the L1B processing software. Discussions will be focused on the key uncertainty parameters and the application in L1B.

S-NPP VIIRS TEB Uncertainty↗

NASA NTRS2018

Adjustments and Uncertainty Quantification for SLS Aerodynamic Sectional Loads

This paper presents a method for adjusting sectional loads to match target values for integrated force and moment coefficients. In a typical application, the sectional load profile for one flight condition is calculated from Computational Fluid Dynamics (CFD) while the integrated forces and moments are measured in a wind tunnel experiment. These two methods do not generally result in identical predictions, and this leads to an inherent inconsistency between different data products. This paper aims to provide a procedure to remove that inconsistency. A sectional load profile for a launch vehicle splits the rocket into slices along its length and calculates the aerodynamic loading on each slice, which leads to a one-dimensional aerodynamic load profile that is used for structural analysis. Adjusting sectional loads, also known as line loads, is a nontrivial matter due to several consistency constraints. For example, the adjusted sectional normal force profile must be consistent with both the integrated normal force and pitching moment. To avoid such inconsistency issues, this paper presents a method using a Proper Orthogonal Decomposition (POD) to generate basis functions to adjust the sectional load profiles. As a corollary, this correction method enables the creation of an uncertainty quantification for sectional loads that is consistent with the dispersed integrated force and moment database and its uncertainty quantification. Several extensions to this technique, such as applying the method to the surface pressures, are considered.

Quantification↗

NASA NTRS2019

Fast-Time Simulation for Evaluating the Impact of Estimated Flight Ready Time Uncertainty on Surface Metering

NASA has been developing and demonstrating new concepts and technologies for Integrated Arrival, Departure, and Surface (IADS) traffic management capabilities under the Airspace Technology Demonstration 2 (ATD-2) project. One of the IADS capabilities in the ATD-2 project is surface metering enabled by a tactical surface scheduler to provide controllers with the pushback advisories for departures at gates. The tactical surface scheduler uses the estimated flight ready times provided by airlines, called Earliest Off-Block Times (EOBTs), as input to calculate the target off-block times for pushback advisories. However, the EOBTs are often inaccurate and deviate from the actual flight ready times, which may reduce the benefits of surface metering. In this paper, a linear regression model is developed to model the EOBT uncertainty distribution over time based on actual EOBT data collected at Charlotte airport. This EOBT model is integrated with a tactical surface scheduler and a fast-time simulation tool. To evaluate the impact of the EOBT accuracy on airport surface operations, fast-time simulations are implemented for selected traffic scenarios under different levels of modelled EOBT accuracy. The simulation results show that the EOBT uncertainty affects several performance metrics related to the surface metering, such as gate hold, taxi time reduction, and target takeoff time predictability, which in turn influences ATD-2's scheduler performance.

tactical surface scheduler↗

NASA NTRS2019

Fast-Time Simulation for Evaluating the Impact of Estimated Flight Ready Time Uncertainty on Surface Metering

tactical surface scheduler↗

NASA NTRSDate not supplied

Applying Sensor Uncertainty Mitigation Schemes to Detect-and-Avoid Systems

Impact of sensor noise on the performance of Detect-And-Avoid (DAA) systems can be reduced by implementing various mitigation schemes. This paper evaluates the Sensor Uncertainty Mitigation (SUM) method, implemented in the Detect and Avoid Alerting Logic for Unmanned Systems (DAIDALUS) algorithm, a reference implementation in the DAA minimum operational performance standards. DAIDALUS SUM performance is evaluated using a few safety and operational suitability metrics and compared with more traditional approaches using static safety buffers. A large number of encounters representative of low-speed unmanned aircraft against non-cooperative manned aircraft are simulated and evaluated. An air-to-air radar model produces representative sensor noise for the DAA system. Results show that increasing the tunable parameters for horizontal and vertical uncertainty in DAIDALUS SUM improves the safety metric at the cost of increasing the number of system alerts leading to increased workload. A range of SUM parameters is recommended as suitable values for the type of operations considered for this work. General trends and optimal SUM configurations were found to be nearly the same for two large and very different encounter data sets.

detect and avoid↗

NASA NTRSDate not supplied

Uncertainty Analysis of the GeoNEX Top-of-Atmospheric Reflectance Products Generated from the Third-Generation Geostationary Satellite Sensors

The GeoNEX (Geostationary-NASA Earth eXchange) Level-1G products consist of top-of-atmosphere (TOA) bi-directional reflectance factor (BRF) and brightness temperature generated with data streams from the latest geostationary (GEO) sensors including GOES-16/17 ABI, Himawari-8/9 AHI, and GK-2A AMI on a global tiled common grid (60oN-60o and 180oW-180oE) in geographic coordinates. With their 16 spectral bands, 0.01o/0.02o nadir spatial resolution, and 10-minute temporal resolutions, these products provide exciting opportunity to monitor Earth surface processes. However, the unique Sun-Target-Satellite geometry of geostationary sensors demands special attention in analyzing/interpreting these datasets. In this study we present a systematic analysis on the relationship between the radiometric uncertainties of the GeoNEX TOA reflectance and the corresponding solar/satellite zenith angles. We show that the signal-to-noise ratio (SNR) of the BRF are positively proportional to the square roots of the cosine of solar illuminating zenith angles. That is, the BRF data are noisier earlier in the morning or later in the afternoon than in the mid of the day. The cosine of satellite viewing zenith angles do not directly influence the SNR of the TOA BRF. However, they positively regulate the relative importance of the surface component in the TOA BRF. This means that variations in surface reflectance are more difficult to detect for pixels with larger view zenith angles, even when the SNR of the TOA BRF is the same. We are developing metrics to specify such illumination-view geometry related uncertainties in the GeoNEX L1G TOA BRF products so that this key information can be easily accessed by the user community.

Geostationary satellite↗

NASA NTRSDate not supplied

MAGE: Alleviating Uncertainty in Real-Time Decision-Making as a Function of Problem Complexity

In this paper, we discuss a critical aspect of uncertainty in the operation of complex systems, such as the future air traffic: the ability of agents in the system to arrive at satisfactory decisions and the attendant actions as a function of problem complexity. Intuitively, when the problem complexity is manageable, given an appropriate decision problem formulation and solution tools, an agent (computational or human) has no trouble arriving at a solution that yields good outcomes for the agent and the system. Growing problem complexity results in progressively larger computational problems that may yield suboptimal solutions or even be intractable within required time limits or at all. We propose a measurable representation of complexity in terms of problem tractability and quality of solutions. We also propose a computational scheme, MAGE (Monitor, Anticipate, Guide, Evolve), for detecting approaching transitions from efficient decision-making states to inefficient to unsafe ones, so that operations based on decision-making can be reconfigured to forestall unfavorable transitions, returning to efficient modes when complexity diminishes. Maintaining tractable complexity reduces the uncertainty in the outcomes of decision-making. We describe the general scheme, an outline of MAGE applied to managing airspace complexity, and initial examples of investigating the tractability of problem-solving schemes.

complexity management↗

NASA NTRS2022

NASA Engineering and Safety Center Technical Bulletin No. 22-04: Uncertainty Quantification of Reduced Order Structural Dynamic Models

Uncertainty quantification (UQ) provides statistical bounds on prediction accuracy based on finite element model (FEM) uncertainty. An alternate method for UQ, called the Hybrid Parametric Variation (HPV) combines a parametric variation of the Hurty/Craig-Bampton (HCB) fixed-interface (FI) modal frequencies with a nonparametric variation (NPV) method. This provides a UQ method that can be traced to test data, which can be updated as additional data and improved correlated models become available.

Uncertainty Quantification↗

NASA NTRS2022

Quantifying and Reducing Uncertainty in Microwave Vegetation Optical Depth and Soil Moisture Retrievals

Soil moisture and vegetation optical depth (VOD; related to vegetation water content) retrieved from SMAP and SMOS satellites are widely used for a range of hydrosphere and biosphere applications. However, while soil moisture has been globally well-validated, VOD validation has been sparse. Furthermore, simultaneously retrieval of these parameters results in uncertainties both individually in soil moisture and VOD retrievals as well as in compensation between the parameters. Here, we show global locations where soil moisture and VOD retrievals will have lower uncertainty, based on complementary brightness temperature information content and signal-to-noise ratio metrics. In these same locations, we show that error still propagates more into VOD. However, using VOD regularization algorithms, this error is greatly reduced, especially at sub-weekly timescales where algorithmic error can be most apparent. Despite these regularization approaches that reduce errors, there are yet vast differences in available global regularized retrievals originating from different algorithmic choices.

Passive microwave radiometry↗

NASA NTRS2023

Analysis of VFR Traffic Uncertainty and its Impact on Uncrewed Aircraft Operational Capacity at Regional Airports

This paper proposes a method to characterize Visual Flight Rules traffic around a regional airport. The applicability of the method is discussed in the context of Uncrewed Aircraft operations at a regional airport. The relation between traffic interaction uncertainty and operational capacity at the runway is also investigated. The spatio-temporal distribution of traffic operating under Visual Flight Rules is analyzed from historical track data and visualized as heat maps generated at different altitudes. These are used to characterize the spatio-temporal uncertainty associated with traffic density, around a given airport, down to the runway. The traffic patterns at the runway are used to compute the runway capacity as a function of the probability of interaction with traffic operating under visual flight rules. Fort Worth Alliance is used as a representative regional airport for the study. Applications of the traffic characterization methods developed in this paper are also discussed.

VFR traffic, uncertainty, air mobility↗

NASA NTRS2023

Analysis of VFR Traffic Uncertainty and its Impact on Uncrewed Aircraft Operational Capacity at Regional Airports

VFR traffic↗

NASA NTRSDate not supplied

Uncertainty in Servicing and Assembly Tasks for Space Robotic Manipulators

This presentation will discuss a subset of the sources of uncertainty that impact autonomous in-space servicing, assembly, and manufacturing missions. These include robotic manipulator modeling uncertainties in both kinematics and dynamics, perception error associated machine learning models for pose estimation, and sensor noise. Mitigation strategies will be discussed including the incorporation of capture envelopes in the design of robotic tools and selection of robot goal poses to minimize end-effector sensitivity in manipulators with redundant degrees of freedom.

robotics↗