Publications

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In September of 2020, Arctic sea ice extent was the second-lowest on record. State of the art climate prediction uses Earth system models (ESMs), driven by systems of differential equations representing the laws of physics. Previously, these models have tended to underestimate Arctic sea ice loss. The issue is grave because accurate modeling is critical for economic, ecological, and geopolitical planning. We use machine learning techniques, including random forest regression and Gini importance, to show that the Energy Exascale Earth System Model (E3SM) relies too heavily on just one of the ten chosen climatological quantities to predict September sea ice averages. Furthermore, E3SM gives too much importance to six of those quantities when compared to observed data. Identifying the features that climate models incorrectly rely on should allow climatologists to improve prediction accuracy.

We use a nascent data-driven causal discovery method to find and compare causal relationships in observed data and climate model output. We consider ten different features in the Arctic climate collected from public databases on observational and Energy Exascale Earth System Model (E3SM) data. In identifying and analyzing the resulting causal networks, we make meaningful comparisons between observed and climate model interdependencies. This work demonstrates our ability to apply the PCMCI causal discovery algorithm to Arctic climate data, that there are noticeable similarities between observed and simulated Arctic climate dynamics, and that further work is needed to identify specific areas for improvement to better align models with natural observations.

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Signal arrival-time estimation plays a critical role in a variety of downstream seismic analyses, including location estimation and source characterization. Any arrival-time errors propagate through subsequent data-processing results. In this article, we detail a general framework for refining estimated seismic signal arrival times along with full estimation of their associated uncertainty. Using the standard short-term average/long-term average threshold algorithm to identify a search window, we demonstrate how to refine the pick estimate through two different approaches. In both cases, new waveform realizations are generated through bootstrap algorithms to produce full a posteriori estimates of uncertainty of onset arrival time of the seismic signal. The onset arrival uncertainty estimates provide additional data-derived information from the signal and have the potential to influence seismic analysis along several fronts.

The Arctic is warming and feedbacks in the coupled Earth system may be driving the Arctic to tipping events that could have critical downstream impacts for the rest of the globe. In this project we have focused on analyzing sea ice variability and loss in the coupled Earth system Summer sea ice loss is happening rapidly and although the loss may be smooth and reversible, it has significant consequences for other Arctic systems as well as geopolitical and economic implications. Accurate seasonal predictions of sea ice minimum extent and long-term estimates of timing for a seasonally ice-free Arctic depend on a better understanding of the factors influencing sea ice dynamics and variation in this strongly coupled system. Under this project we have investigated the most influential factors in accurate predictions of September Arctic sea ice extent using machine learning models trained separately on observational data and on simulation data from five E3SM historical ensembles. Monthly averaged data from June, July, and August for a selection of ice, ocean, and atmosphere variables were used to train a random forest regression model. Gini importance measures were computed for each input feature with the testing data. We found that sea ice volume is most important earlier in the season (June) and sea ice extent became a more important predictor closer to September. Results from this study provide insight into how feature importance changes with forecast length and illustrates differences between observational data and simulated Earth system data. We have additionally performed a global sensitivity analysis (GSA) using a fully coupled ultra- low resolution configuration E3SM. To our knowledge, this is the first global sensitivity analysis involving the fully-coupled E3SM Earth system model. We have found that parameter variations show significant impact on the Arctic climate state and atmospheric parameters related to cloud parameterizations are the most significant. We also find significant interactions between parameters from different components of E3SM. The results of this study provide invaluable insight into the relative importance of various parameters from the sea ice, atmosphere and ocean components of the E3SM (including cross-component parameter interactions) on various Arctic-focused quantities of interest (QOIs).

This report summarizes the results of an LDRD focused on developing and demonstrating statistically rigorous methods for analyzing and comparing complex activities from remote sensing data. Identifying activity from remote sensing data, particularly those that play out over time and span multiple locations, often requires extensive manual effort because of the variety of features that describe the activity and the required domain expertise. Our results suggest that there are some hidden challenges in extracting and representing activities in sensor data. In particular, we found that the variability in the underlying behaviors can be difficult to overcome statistically, and the report identifies several examples of the issue. We discuss key lessons learned in the context of the project, and finally conclude with recommendations on next steps and future work.

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Data-driven modeling, including machine learning methods, continue to play an increasing role in society. Data-driven methods impact decision making for applications ranging from everyday determinations about which news people see and control of self-driving cars to high-consequence national security situations related to cyber security and analysis of nuclear weapons reliability. Although modern machine learning methods have made great strides in model induction and show excellent performance in a broad variety of complex domains, uncertainty remains an inherent aspect of any data-driven model. In this report, we provide an update to the preliminary results on uncertainty quantification for machine learning presented in SAND2017-6776. Specifically, we improve upon the general problem definition and expand upon the experiments conducted for the earlier re- port. Most importantly, we summarize key lessons learned about how and when uncertainty quantification can inform decision making and provide valuable insights into the quality of learned models and potential improvements to them.

The quality of automatic signal detections from sensor networks depends on individual detector trigger levels (TLs) from each sensor. The largely manual process of identifying effective TLs is painstaking and does not guarantee optimal configuration settings, yet achieving superior automatic detection of signals and ultimately, events, is closely related to these parameters. We present a Dynamic Detector Tuning (DDT) system that automatically adjusts effective TL settings for signal detectors to the current state of the environment by leveraging cooperation within a local neighborhood of network sensors. After a stabilization period, the DDT algorithm can adapt in near-real time to changing conditions and automatically tune a signal detector to identify (detect) signals from only events of interest. Our current work focuses on reducing false signal detections early in the seismic signal processing pipeline, which leads to fewer false events and has a significant impact on reducing analyst time and effort. This system provides an important new method to automatically tune detector TLs for a network of sensors and is applicable to both existing sensor performance boosting and new sensor deployment. With ground truth on detections from a local neighborhood of seismic sensors within a network monitoring the Mount Erebus volcano in Antarctica, we show that DDT reduces the number of false detections by 18% and the number of missed detections by 11% when compared with optimal fixed TLs for all sensors.

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We discuss uncertainty quantification in multisensor data integration and analysis, including estimation methods and the role of uncertainty in decision making and trust in automated analytics. The challenges associated with automatically aggregating information across multiple images, identifying subtle contextual cues, and detecting small changes in noisy activity patterns are well-established in the intelligence, surveillance, and reconnaissance (ISR) community. In practice, such questions cannot be adequately addressed with discrete counting, hard classifications, or yes/no answers. For a variety of reasons ranging from data quality to modeling assumptions to inadequate definitions of what constitutes "interesting" activity, variability is inherent in the output of automated analytics, yet it is rarely reported. Consideration of these uncertainties can provide nuance to automated analyses and engender trust in their results. In this work, we assert the importance of uncertainty quantification for automated data analytics and outline a research agenda. We begin by defining uncertainty in the context of machine learning and statistical data analysis, identify its sources, and motivate the importance and impact of its quantification. We then illustrate these issues and discuss methods for data-driven uncertainty quantification in the context of a multi-source image analysis example. We conclude by identifying several specific research issues and by discussing the potential long-term implications of uncertainty quantification for data analytics, including sensor tasking and analyst trust in automated analytics.

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The quality of automatic detections from sensor networks depends on a large number of data processing parameters that interact in complex ways. The largely manual process of identifying effective parameters is painstaking and does not guarantee that the resulting controls are the optimal configuration settings, yet achieving superior automatic detection of events is closely related to these parameters. We present an automated sensor tuning (AST) system that tunes effective parameter settings for each sensor detector to the current state of the environment by leveraging cooperation within a neighborhood of sensors. After a stabilization period, the AST algorithm can adapt in near real-time to changing conditions and automatically self-tune a signal detector to identify (detect) only signals from events of interest. The overall goal is to reduce the number of missed legitimate event detections and the number of false event detections. Our current work focuses on reducing false signal detections early in the seismic signal processing pipeline, which leads to fewer false events and has a significant impact on reducing analyst time and effort. Applicable both for existing sensor performance boosting and new sensor deployment, this system provides an important new method to automatically tune complex remote sensing systems. Systems tuned in this way will achieve better performance than is currently possible by manual tuning, and with much less time and effort devoted to the tuning process. With ground truth on detections from a seismic sensor network monitoring the Mount Erebus Volcano in Antarctica, we show that AST increases the probability of detection while decreasing false alarms.

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In this paper, we assert the importance of uncertainty quantification for machine learning and sketch an initial research agenda. We define uncertainty in the context of machine learning, identify its sources, and motivate the importance and impact of its quantification. We then illustrate these issues with an image analysis example. The paper concludes by identifying several specific research issues and by discussing the potential long-term implications of uncertainty quantification for data analytics in general.

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This report summarizes preliminary research into uncertainty quantification for pattern ana- lytics within the context of the Pattern Analytics to Support High-Performance Exploitation and Reasoning (PANTHER) project. The primary focus of PANTHER was to make large quantities of remote sensing data searchable by analysts. The work described in this re- port adds nuance to both the initial data preparation steps and the search process. Search queries are transformed from does the specified pattern exist in the data? to how certain is the system that the returned results match the query? We show example results for both data processing and search, and discuss a number of possible improvements for each.

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