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At least 217 records · Page 12

Allophane on Mars: Significance for Chemical Weathering and Soil Development

It has been suggested that allophane or related poorly crystalline aluminosilicates are present on Mars, and that they comprise the high-silica phase detected by the Thermal Emission Spectrometer (TES) in Surface Type 2 materials (Michalski et al., 2005). Using new laboratory spectra of allophanic materials, we (Rampe et al., this meeting) have detected allophane on the Martian surface via spectral modeling of TES data. We find that ST2 materials in the Northern Plains are consistent with a significant amount of high-silica allophane-like materials. In addition, we find that allophane may be present in some areas of ancient highlands (TES surface type 1), but spectra of those regions are more consistent with aluminous allophane. The presence of allophane and its chemical variability have important implications for chemical weathering and soil development on Mars. Allophane-like materials are amorphous or poorly crystalline hydrous aluminosilicates formed from chemical weathering of glasses, feldspars, and other silicates (cf. Parfitt, 2009). True allophane is a combination of SiO2, Al2O3 and H2O where Al:Si ranges from ~0.5-2. Aluminosilicate gels are amorphous and chemically similar to allophane but can have higher SiO2 contents. The presence of allophane indicates low-temperature chemical weathering and provides constraints on alteration conditions, limiting pH to circum-neutral (~4.5-8). Our model results indicate that weathering occurred in the relatively young northern plains of Mars. The high-silica allophane-like material present there implies little silica mobility through the soil column, which suggests that weathering involved small amounts of liquid water, consistent with our previous models of weathering in ice-rich soils (Kraft et al., 2007). The aluminous allophane indicated by our spectral models to be present in the highlands suggest that those regions experienced greater amounts of SiO2 leaching and weathering in those soils may have involved much larger amounts of water. The presence of allophane-like materials suggests that these weathering regimes were not influenced by the acidic weathering that appears to have affected other areas of Mars and has been proposed as a planetwide alteration process (Hurowitz and McLennan, 2007). Soil development in basaltic material (typically tephra) on Earth usually leads to formation of andosols. Although we do not suggest a one-to-one analogy between dark basaltic Martian soils and andosols, there may be important similarities, as andosols are typified by significant production of allophane as well as poorly crystalline Fe-hydroxides. The detection of allophane on Mars suggests a positive utility of an andosol model for Martian soils, particularly when coupled with the ubiquitous presence of Feoxide materials on Mars. An andosol model of soil formation is mineralogically consistent with palagonite models for the formation of Martian dust (cf. Banin et al., 1992; Morris et al., 2001), which suggests a possible genetic relationship of dust and bright soils to the broader soil layer of Mars.

Kraft, M. D.↗

An Evaluation of Antarctica as a Calibration Target for Passive Microwave Satellite Missions with Climate Data Record Applications

Passive microwave remote sensing at L-band (1.4 GHz) is sensitive to soil moisture and sea surface salinity, both important climate variables. Science studies involving these variables can now take advantage of new satellite L-band observations. The first mission with regular global passive microwave observations at L-band is the European Space Agency's Soil Moisture and Ocean Salinity (SMOS), launched November, 2009. A second mission, NASA's Aquarius, was launched June, 201 I. A third mission, NASA's Soil Moisture Active Passive (SMAP) is scheduled to launch in 2014. Together, these three missions may provide a decade-long data record-provided that they are intercalibrated. The intercalibration is best performed at the radiance (brightness temperature) level, and Antarctica is proving to be a key calibration target. However, Antarctica has thus far not been fully characterized as a potential target. This paper will present evaluations of Antarctica as a microwave calibration target for the above satellite missions. Preliminary analyses have identified likely target areas, such as the vicinity of Dome-C and larger areas within East Antarctica. Physical sources of temporal and spatial variability of polar firn are key to assessing calibration uncertainty. These sources include spatial variability of accumulation rate, compaction, surface characteristics (dunes, micro-topography), wind patterns, and vertical profiles of density and temperature. Using primarily SMOS data, variability is being empirically characterized and attempts are being made to attribute observed variability to physical sources. One expected outcome of these studies is the potential discovery of techniques for remotely sensing--over all of Antarctica-parameters such as surface temperature.

Kim, Edward↗

Modeling and Assimilating Ocean Color Radiances

Radiances are the source of information from ocean color sensors to produce estimates of biological and geochemical constituents. They potentially provide information on various other aspects of global biological and chemical systems, and there is considerable work involved in deriving new information from these signals. Each derived product, however, contains errors that are derived from the application of the radiances, above and beyond the radiance errors. A global biogeochemical model with an explicit spectral radiative transfer model is used to investigate the potential of assimilating radiances. The results indicate gaps in our understanding of radiative processes in the oceans and their relationships with biogeochemical variables. Most important, detritus optical properties are not well characterized and produce important effects of the simulated radiances. Specifically, there does not appear to be a relationship between detrital biomass and its optical properties, as there is for chlorophyll. Approximations are necessary to get beyond this problem. In this reprt we will discuss the challenges in modeling and assimilation water-leaving radiances and the prospects for improving our understanding of biogeochemical process by utilizing these signals.

Gregg, Watson↗

An Evaluation of Antarctica as a Calibration Target for Passive Microwave Satellite Missions

Passive microwave remote sensing at L-band (1.4 GHz) is sensitive to soil moisture and sea surface salinity, both important climate variables. Science studies involving these variables can now take advantage of new satellite L-band observations. The first mission with regular global passive microwave observations at L-band is the European Space Agency's Soil Moisture and Ocean Salinity (SMOS), launched November, 2009. A second mission, NASA's Aquarius, was launched June, 201l. A third mission, NASA's Soil Moisture Active Passive (SMAP) is scheduled to launch in 2014. Together, these three missions may provide a decade-long data record -- provided that they are intercalibrated. The intercalibration is best performed at the radiance (brightness temperature) level, and Antarctica is proving to be a key calibration target. However, Antarctica has thus far not been fully characterized as a potential target. This paper will present evaluations of Antarctica as a microwave calibration target for the above satellite missions. Preliminary analyses have identified likely target areas, such as the vicinity of Dome-C and larger areas within East Antarctica. Physical sources of temporal and spatial variability of polar firn are key to assessing calibration uncertainty. These sources include spatial variability of accumulation rate, compaction, surface characteristics (dunes, micro-topography), wind patterns, and vertical profiles of density and temperature. Using primarily SMOS data, variability is being empirically characterized and attempts are being made to attribute observed variability to physical sources. One expected outcome of these studies is the potential discovery of techniques for remotely sensing--over all of Antarctica--parameters such as surface temperature.

Kim, Edward↗

An Alternative Inter-Satellite Calibration of the UMD HIRS OLR Retrievals

Outgoing Longwave Radiation (OLR) at the top-of-atmosphere (TOA) is a fundamental component of Earth's energy balance and represents the heat energy in the thermal bands rejected to space by the planet. Determination of OLR from satellites has a long and storied history, but the observational record remains largely fragmented with gaps in satellite measurements over the past three decades. Perhaps the most semi-continuous set of retrievals comes from the University of Maryland (UMD) algorithm that uses four HIRS (High Resolution Infrared Sounder) channels on the NOAA polar orbiting satellites to estimate OLR. This data set shows great promise in helping to bridge the discontinuous ERBS (Earth Radiation Budget Satellite) and CERES (Clouds and the Earth s Radiant Energy System) measurements. However, significant satellite inter-calibration biases persist with the present UMD data, principally outside the tropics. Difficulties relate to the combination of drift of the satellite equator crossing time through the diurnal cycle and changes in HIRS channel response function design. Here we show how an ad hoc recalibration of the UMD retrievals among the different satellites removes much of the remaining uncertainty due to diurnal drift of the satellite orbit. The adjusted HIRS data (using no other external information) show much better agreement with OLR from the European Center Interim Reanalysis (EC-Int), longer-term signals in the Global Energy and Water Cycle Experiment / Surface Radiation Budget (GEWEX/SRB) retrievals, and also agree well with ERBS and CERES OLR measurements. These results augur well for narrowing the uncertainties in multi-decadal estimates of this important climate variable.

Robertson, Franklin R.↗

Improve Problem Solving Skills through Adapting Programming Tools

There are numerous ways for engineers and students to become better problem-solvers. The use of command line and visual programming tools can help to model a problem and formulate a solution through visualization. The analysis of problem attributes and constraints provide insight into the scope and complexity of the problem. The visualization aspect of the problem-solving approach tends to make students and engineers more systematic in their thought process and help them catch errors before proceeding too far in the wrong direction. The problem-solver identifies and defines important terms, variables, rules, and procedures required for solving a problem. Every step required to construct the problem solution can be defined in program commands that produce intermediate output. This paper advocates improved problem solving skills through using a programming tool. MatLab created by MathWorks, is an interactive numerical computing environment and programming language. It is a matrix-based system that easily lends itself to matrix manipulation, and plotting of functions and data. MatLab can be used as an interactive command line or a sequence of commands that can be saved in a file as a script or named functions. Prior programming experience is not required to use MatLab commands. The GNU Octave, part of the GNU project, a free computer program for performing numerical computations, is comparable to MatLab. MatLab visual and command programming are presented here.

Shaykhian, Linda H.↗

LDAS Land Data Assimilation Systems

The land-surface component of the hydrological cycle is fundamental to the overall functioning of the atmospheric and climate processes. The characterization of the spatial and temporal variability of water and energy cycles is critical to improve our understanding of the land-surface-atmosphere interaction and the impact of land-surface processes on climate extremes. Because the accurate knowledge of these processes and their variability is important for climate predictions, most Numerical Weather Prediction (NWP) centers have incorporated land-surface schemes in their models. However, errors in the NWP forcing accumulate in the surface and energy stores, leading to incorrect surface water and energy partitioning and related processes.

data↗

On the Origin of Multidecadal to Centennial Greenland Temperature Anomalies Over the Past 800 yr

The surface temperature of the Greenland ice sheet is among the most important climate variables for assessing how climate change may impact human societies due to its association with sea level rise. However, the causes of multidecadal-to-centennial temperature changes in Greenland temperatures are not well understood, largely owing to short observational records. To examine these, we calculated the Greenland temperature anomalies (GTA[G−NH]) over the past 800 yr by subtracting the standardized northern hemispheric (NH) temperature from the standardized Greenland temperature. This decomposes the Greenland temperature variation into background climate (NH); polar amplification; and regional variability (GTA[G−NH]). The central Greenland polar amplification factor as expressed by the variance ratio Greenland/NH is 2.6 over the past 161 yr, and 3.3-4.2 over the past 800 yr. The GTA[G−NH] explains 31-35%of the variation of Greenland temperature in the multidecadal-to-centennial time scale over the past 800 yr. We found that the GTA[G−NH] has been influenced by solar-induced changes in atmospheric circulation patterns such as those produced by the North Atlantic Oscillation/Arctic Oscillation (NAO/AO). Climate modeling and proxy temperature records indicate that the anomaly is also likely linked to solar-paced changes in the Atlantic meridional overturning circulation (AMOC) and associated changes in northward oceanic heat transport.

Kobashi, T.↗

Jarosite in Gale Crater, Mars: The Importance of Temporal and Spatial Variability and Implications for Habitiability

The Curiosity rover has recently found evidence for small amounts of jarosite, a ferric sulfate, in the Pahrump Hills region at the base of Aeolis Mons (Mount Sharp), Gale crater. While jarosite has been described previously at other locations on Mars, including several sites at Meridiani Planum (explored by the Opportunity rover; and Mawrth Vallis (by remote MRO-CRISM observations; this is the first identification in Gale. Jarosite is interpreted to be a mineral indicator of acidic conditions (pH less than 4; on Earth, it is most commonly found in acid rock-drainage or acid sulfate soil environments. However, jarosite has also been described from a number of terrestrial environments where widespread acidic conditions are not prevalent. As a case study, we describe here an occurrence of sedimentary pyrite nodules that have been variably oxidized in situ to gypsum, schwertmannite, K-/Na-jarosite and iron oxides in a polar desert environment on Devon Island, Nunavut, Canada. Remarkably, these nodules occur in loosely consolidated carbonate sediments, which would have required a higher pH environment at their time of formation and deposition. Thus, acidic conditions may only exist at a small (sub-cm) scale or in a restricted temporal window in an otherwise well-buffered environment. On Devon Island, the jarosite occurs in the most oxidized nodules and is never associated directly with pyrite. Schwertmannite, a metastable iron oxyhydroxysulfate that can form at pH higher than that required for jarosite, occurs in association with partially oxidized pyrite. The paragenetic sequence observed here suggests initial formation of schwertmannite and late-stage precipitation of jarosite in restricted micro-environments, possibly forming via transformation of an amorphous schwertmannite-like phase. While the carbonate environment on Devon Island differs significantly from that of Gale crater, i.e., where we find predominantly basaltic sedimentary rocks, this terrestrial analog provides insight into the significance of jarosite with respect to habitability. For example, the variable abundance of jarosite on Mars and possibly in Gale crater points to potentially localized conditions favorable for jarosite formation. Interestingly, small amounts of sulfide minerals have also been detected by Curiosity at Yellowknife Bay; oxidation of sulfide minerals at Pahrump could explain the presence of small amounts of jarosite. The iron-rich rocks at Pahrump may also represent relatively altered basaltic sediments, or they could be sediments that were altered further by a fluid with a distinct, possibly more acidic, composition. In addition, the abundance of iron-rich amorphous material in Gale rocks allows for the possibility that pre-cursor, iron-bearing phases transform to jarosite post-depositionally. Thus, the occurrence of jarosite at Pahrump could reflect changing paleoenvironmental conditions, though continuing study of its context and textural relationships should provide a fuller understanding of the significance of this mineral to past fluid compositions and past habitability at Gale crater.

Leveille, R. J.↗

AIRS-Only Product in Giovanni for Exploring Up-to-Date AIRS Observation and Comparing with AIRS+AMSU Product

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has been the home of processing, archiving, and distribution services for the Atmospheric Infrared Sounder (AIRS) mission since its launch in 2002 for global observations of the atmospheric state. Giovanni, a Web-based application developed by the GES DISC, provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data without having to download the data. Most important AIRS variables, including temperature and humidity profiles, outgoing longwave radiation, cloud properties, and trace gases, are available in Giovanni. AIRS is an instrument suite comprised of a hyperspectral infrared instrument (AIRS) and two multichannel microwave instruments, the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB). As HSB ceased operation in the very early stages of the AIRS mission, the AIRS project operates two parallel retrieval algorithms: one using both IR and MW measurements (AIRS+AMSU) and the other using only IR measurements (AIRS-only), which covers most of the mission duration. The AIRS+AMSU product is better quality, and the variables in Giovanni are from this product. However, generation of the AIRS+AMSU product has been suspended since the AMSU instrument anomaly occurred in late September 2016. To continue exploring up-to-date AIRS observations, the same set of variables from the AIRS-only product have been added to Giovanni by the GES DSIC. This will also support comparison of AIRS-only with AIRS+AMSU retrievals. In this presentation, we demonstrate the visualization of the AIRS-only product and plots/statistics of comparison with AIRS+AMSU product using Giovanni.

AIRS↗

PRELIMINARY MODEL FOR SOIL MOISTURE RETRIEVAL USING P-BAND RADIOMETER OBSERVATIONS

Soil Moisture is an important geophysical variable that needs reliable quantification for applications in hydrology, meteorology and agriculture. L-band radiometry has proved to be one of the best methods in soil moisture estimation using microwave signals. However, they provide measurements that correspond to a shallow depth of 5 cm and are also affected by the presence of overlaying vegetation and roughness. In contrast, P-band radiometry is expected to provide moisture information on a deeper layer of soil. Moreover, these lower frequency measurements are expected to be less affected by soil roughness and vegetation contributions. Consequently, this pilot study uses the Polarimetric P-band Multi beam Radiometer(PPMR) at 740 MHz to evaluate the response of theP-band radiometer over a realistic range of surface condition sat the field scale. A preliminary framework of P-band MicrowaveEmission of theBiosphere (P-MEB) has been developed as a forward model that simulates brightness temperature from soil moisture and other ancillary data collected from the field. This paper presents the model for the bare soil condition observed during June 2018 to August 2018. The results show that H-polarized PPMR data has better correlation to the soil moisture over a depth of 10 cm than the V-polarized PPMR data. A model is under improvement by incorporating a more suitable effective temperature formulation.

Edward J Kim↗

DroughtCast: A Machine Learning Forecast of the United States Drought Monitor

Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods toforecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.

Machine Learning↗

Western Montana Ecological Forecasting II: Enhancing Habitat Suitability Modeling of Mustelid Species and Contaminant Monitoring in Northern Montana Using NASA Earth Observations

Environmental contaminants in aquatic ecosystems threaten both human and ecosystem health. Western Montana’s rivers possess great economic and ecological value, yet the status of contaminants in these systems can be unclear. Contaminants such as brominated flame-retardants, heavy metals, and pharmaceuticals negatively affect ecosystem health by traveling through the food chain and bioaccumulating in apex predators. Exposure to these contaminants can result in neurological, endocrine, and reproductive impairments in wildlife and humans. American mink (Neovison vison) and North American river otter (Lontra canadensis) have a predominantly aquatic diet and serve as reliable indicator species of environmental health. Working Dogs for Conservation (WD4C) uses detection dogs to locate scat samples of these species which are assessed for contaminants by the Virginia Institute of Marine Science (VIMS). With Software for Assisted Habitat Modeling (SAHM), the team generated current (2013-2020) and projected (2021-2040) habitat suitability models for mink and otter utilizing NASA Earth observations from Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Global Precipitation Measurement Integrated Multi-Satellite Retrievals for GPM (GPM IMERG), Shuttle Radar Topography Mission (SRTM), and Soil Moisture Active Passive (SMAP). The habitat suitability maps’ study region encompassed Flathead National Forest and Blackfeet Indian Reservation. Additionally, the team created site accessibility and precipitation anomaly maps that display the viability of survey locations. The current habitat suitability models performed well (AUC-PR=0.88). The most important predictor variables for suitable habitat were distance to rivers, elevation, and land cover. These end products will further inform WD4C survey site selection and contaminant monitoring.

Chelsea Morton↗

Experiment Design and Visualization Techniques for an X-59 Low-boom Variability Study

This presentation outlines the design of experiments approach and data visualization techniques for a simulation study of sonic booms from NASA’s X-59 supersonic aircraft. The X-59 will soon be flown over communities across the contiguous USA as it produces a low-loudness sonic boom, or low-boom. Survey data on human perception of low-booms will be collected to support development of potential future commercial supersonic aircraft noise regulatory standards. The macroscopic atmosphere plays a critical role in the loudness of sonic booms. The extensive sonic boom simulation study presented herein was completed to assess climatological, geographical, and seasonal effects on the variability of the X-59’s low-boom loudness and noise exposure region size in order to inform X-59 community test planning. The loudness and extent of the noise exposure region make up the “sonic boom carpet.” Two spatial and temporal resolutions of atmospheric input data to the simulation were investigated. A Fast Flexible Space-Filling Design was used to select the locations across the USA for the two spatial resolutions. Analysis of simulated X-59 low-boom loudness data within a regional subset of the northeast USA was completed using a bootstrap forest to determine the final spatial and temporal resolution of the countrywide simulation study. Atmospheric profiles from NOAA’s Climate Forecast System Version 2 database were used to generate over one million simulated X-59 carpets at the final selected 138 locations across the USA. Effects of aircraft heading, season, geography, and climate zone on low-boom levels and noise exposure region size were analyzed. Models were developed to estimate loudness metrics throughout the USA for X-59 supersonic cruise overflight, and results were visualized on maps to show geographical and seasonal trends. These results inform regulators and mission planners on expected variations in boom levels and carpet extent from atmospheric variations. Understanding potential carpet variability is important when planning community noise surveys using the X-59.

X-59↗

Simulation and Regression Modeling of Nasa'S X-59 Low-Boom Carpets Across America

NASA’s X-59 aircraft is predicted to produce a significantly quieter cruise sonic boom than traditional N-wave-producing aircraft. A propagation simulation study was undertaken to quantify loudness levels, exposure size, and variability of the X-59’s low-boom carpet using realistic atmospheric profiles across the contiguous United States of America (CONUS). Near-field pressure data of the X-59 in supersonic cruise from NASA’s fully unstructured Navier–Stokes three-dimensional (known as FUN3D) computational fluid dynamics code were propagated using NASA’s PCBoom code, which solves an enhanced Burgers equation along acoustic rays. Atmospheric profiles from the National Oceanic and Atmospheric Administration’s Climate Forecast System Version 2 database were used for propagation at 138 locations across the CONUS. Carpets at each location were generated for aircraft headings in the four cardinal directions. Over one million X-59 carpets were generated in total. The effects of the heading, season, geography, and climate zone on boom levels and exposure size are presented. Multiple linear regression models were developed to estimate carpet width and loudness metrics across the CONUS. These results inform regulators and mission planners on expected variations in boom levels and carpet extent from atmospheric variations. Understanding potential carpet variability is important when planning community noise surveys using the X-59.

X-59↗

Simulation and Regression Modeling of X-59 Low-Boom Carpets Across America

The NASA X-59 aircraft is predicted to produce a significantly quieter cruise sonic boom than traditional N-wave-producing aircraft. A propagation simulation study was undertaken to quantify loudness levels, exposure size, and variability of the X-59 low-boom carpet using realistic atmospheric profiles across the contiguous United States of America (CONUS). Near-field pressure data of the X-59 in supersonic cruise from NASA’s fully unstructured Navier–Stokes three-dimensional (known as FUN3D) computational fluid dynamics code were propagated using NASA’s PCBoom code, which solves an enhanced Burgers equation along acoustic rays. Atmospheric profiles from the National Oceanic and Atmospheric Administration’s Climate Forecast System Version 2 database were used for propagation at 138 locations across the CONUS. Carpets at each location were generated for aircraft headings in the four cardinal directions. Over one million X-59 carpets were generated in total. The effects of the heading, season, geography, and climate zone on boom levels and exposure size are presented. Multiple linear regression models were developed to estimate carpet width and loudness metrics across the CONUS. These results inform regulators and mission planners on expected variations in boom levels and carpet extent from atmospheric variations. Understanding potential carpet variability is important when planning community noise surveys using the X-59.

X-59↗

Quantifying the Importance of Selected Drought Indicators for the United States Drought Monitor

Using information theory, our study quantifies the importance of selected indicators for the U.S. Drought Monitor (USDM) maps. We use the technique of mutual information (MI) to measure the importance of any indicator to the USDM, and because MI is derived solely from the data, our findings are independent of any model structure (conceptual, physically-based, or empirical). We also compare these MIs against the drought representation effectiveness ratings in the North America Drought Indices and Indicators Assessment (NADIIA) survey for Koeppen climate zones. This reveals: [1] agreement between some ratings and our MI values (high for example indicators like Standardized Precipitation-Evapotranspiration Index or SPEI); [2] some divergences (for example, soil moisture has high ratings but near-zero MIs for ESA-CCI soil moisture in the Western U.S., indicating the need of another remotely sensed soil moisture source); and [3] new insights into the importance of variables such as Snow Water Equivalent (SWE) that are not included in sources like NADIIA. Further analysis of the MI results yields findings related to: [1] hydrological mechanisms (summertime SWE domination during individual drought events through snowmelt into the water-scarce soil); [2] hydroclimatic types (the top pair of inputs in the Western and non-Western regions are SPEIs and soil moistures respectively); and [3] predictability (high for the California 2012-2017 event, with longer-timescale indicators dominating). Finally, the high MIs between multiple indicators jointly and the USDM indicate potentially high drought forecasting accuracies achievable using only model-based inputs, and the potential for global drought monitoring using only remotely sensed inputs, especially for locations having insufficient in situ observations.

Drought↗

Capturing Complex Multivariate Time Series Interactions to Detect High-Risk Adverse Events During Flight

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗