AbstractSeismic imaging is one of the most powerful tools available for constraining the internal structure and composition of planetary bodies as well as enabling our understanding planetary evolution, geology, and distribution of natural resources. However, traditional seismic instrumentation can be heavy and voluminous, expensive, and/or difficult to rapidly deploy in large numbers. Distributed acoustic sensing (DAS) provides a promising new alternative given the ease of deployment, light weight and simplicity of fiber optic cables. However, the feasibility and best operational practices for using DAS for planetary exploration are not well‐known. We examine the use of DAS with surface deployed fiber for planetary near‐surface seismic exploration at two lunar geophysical analogue sites in San Francisco Volcanic Field. We compare DAS recordings to 3‐component seismometer recordings and geophone shot recordings and determine empirical response functions for the DAS system with respect to the 3‐component recordings. Shot sections of DAS and traditional seismic equipment compare well visually, with similar moveout of identifiable phases. DAS records first arrivals in good agreement with seismometers making them suitable for refraction work. Multichannel analysis of surface waves is performed on DAS records to estimate shallow shear velocities. The DAS has high spectral coherence with the horizontal components of ∼0.7 in the frequency band of the seismic shot energy. The empirical response functions are stable with amplitudes of ∼1.0–3.0 × 10−10 m per strain. Finally, the phase response is linear but not flat or zero. Our experiment demonstrates that there is potential for surface deployed DAS in planetary landscapes.
As the volume of data collected at monitored volcanoes continues to expand, researchers will need quick, reliable, and automated methods of inverting those data into useful models of the underlying magma systems. Recently adapted from other fields for use in volcanology, the Ensemble Kalman Filter (EnKF) is one such inversion technique that has been used to produce several successful forecasts and hind-casts of volcanic unrest, correlating geodetic deformation with mechanical stresses around the magma reservoir. However, given the similarity in which changes to a reservoir's size and pressure are expressed at the surface, the filter can have trouble fully resolving magmatic conditions. In this study, we therefore test several different published variations of the EnKF workflow to produce an optimal configuration for use in future forecasting efforts. By generating synthetic observations of ground deformation under known conditions and then assimilating them through different implementations of the EnKF, we find that many variants favored in other fields underperform for this specific application. We conclude that correlations between model parameters that develop within the EnKF's Monte Carlo ensemble distort the filter's ability to correctly update the model state, causing the filter to systematically favor changes in some parameters over others and ultimately converge to a partially inaccurate solution. This effect can be somewhat mitigated by interrupting these parameter correlations, and the filter remains sensitive to many aspects of the magma system regardless. However, further research and novel approaches will be needed to truly optimize the EnKF for use in volcanology.
AbstractThe NASA Psyche mission will explore the structure, composition, and other properties of asteroid (16) Psyche to test hypotheses about its formation. Variations in radar reflectivity, density, thermal inertia, and visible to near‐infrared (VNIR) reflectance spectra of Psyche suggest a highly metallic composition with mafic silicate minerals (e.g., pyroxene) heterogeneously distributed on the surface in low abundance (<10 vol.%). The Psyche spacecraft's Multispectral Imager is designed to map ≥80% of the surface at high spatial resolution (≤20 m/pixel) through a panchromatic filter and provide compositional information for about ≥80% of the surface using seven narrowband filters at VNIR wavelengths (∼400–1,100 nm) and at spatial scales of ≤500 m/pixel. We analyzed 359 reflectance spectra from samples consistent with current uncertainties in Psyche's composition and compared them to published reflectance spectra of the asteroid using a chi‐square test for goodness of fit. The best matches for Psyche include iron meteorite powder, powders from the sulfide minerals troilite and pentlandite, and powder from the CH/CBb chondrite Isheyevo. Comparison of absorption features support the interpretation that Psyche's surface is a metal‐silicate mixture, although the exact abundance and chemistry of the silicate component remains poorly constrained. We convolve our spectra to the Imager's spectral throughput to demonstrate preliminary strategies for mapping the surface composition of the asteroid using filter ratios and reconstructed band parameters. Our results provide predictions of the kinds of surface compositional information that the Psyche mission could reveal on the solar system's largest M‐type asteroid.
Planetary caves are desirable environments for the search for biosignatures corresponding to extant or extinct extraterrestrial life due to the protection they offer from surface-level solar radiation and ionizing particles. Near-infrared (NIR) reflectance spectroscopy is one of a multitude of techniques that, when taken together, can provide a comprehensive understanding of the geomicrobiology in planetary subsurface regions. To that end, we developed two portable NIR spectrometers that employ acousto-optic tunable filters and demonstrated them in three geochemically distinct cave environments. The instruments were deployed both as stand-alone spectrometers positioned against the targets manually and as a component of an instrument payload mounted on a quadruped robot capable of vertical excursions of several meters. In situ measurements of calcium carbonates, sulfates, metal oxides, and microbial colonies and mats revealed spectral signatures that enable a distinction between the targets of interest and the underlying substrates. The ruggedness and portability of the instruments, and their low size, weight, and power, spectral agility, and active illumination make AOTF-based spectrometers ideally suited for studies of planetary caves.
Lightning is measured from space using optical instruments that detect transient changes in the illumination of the cloud top. How much of the flash (if any) is recorded by the instrument depends on the instrument detection threshold. NOAA's Geostationary Lightning Mapper (GLM) employs a dynamic threshold that varies across the imaging array and changes over time. This causes flashes in certain regions and at night to be recorded in greater detail than other flashes, and threshold inconsistencies will impose biases on all levels of GLM data products. In this study, we quantify the impact of the varying GLM threshold on event/group/flash detection, flash clustering, and gridded product generation by imposing artificial thresholds on the event data taken from a thunderstorm with a low instrument threshold (∼0.7 fJ). We find that even modest increases in threshold severely impact event (60% loss by 2 fJ, 90% loss by 10 fJ) and group (25% loss by 2 fJ, 81% loss by 10 fJ) detection by suppressing faint illumination of the cloud top from weak sources and scattering. Flash detection is impacted less by threshold increases (4% loss by 2 fJ), but reductions are still significant at higher thresholds (35% loss by 10 fJ, or 44% if single‐group flashes are removed). Undetected pulses cause individual flashes to be split and severely impact the construction of gridded products. All of these factors complicate the interpretation of GLM data, particularly when trended over time under a changing threshold.
Abstract Hyperspectral infrared sounding contains information about clouds, which plays an important role in modulating Earth's climate. However, there is a great deal of uncertainty in modeling the radiative effect of clouds due to its complex dependence on various parameters. Therefore, cloudy scenarios are often neglected in retrievals of infrared spectral measurements and in data assimilation. One‐dimensional radiative transfer (RT) models have a limited capability to represent the cloud three‐dimensional multilayer structure. This issue is typically resolved by using a multiple independent column approach, which is computationally demanding. Therefore, it is necessary to find a balance between computational speed and accuracy for infrared RT all‐sky radiance simulations. In this study, we utilize the Community Radiative Transfer Model with four different cloud overlap schemes and compare against observations made by the Atmospheric Infrared Sounder (AIRS) using a statistical metric called the first Wasserstein distance. Our results show that the average cloud overlap scheme performs the best and successfully predicts the overall probability distribution of brightness temperature over nonfrozen oceans for a wide range of wavelengths. The mean absolute differences are less than 0.7 K for 846 selected AIRS channels between 790 cm−1 and 1231 cm−1.
Ocean worlds such as Europa and Enceladus are high priority targets in the search for past or extant life beyond Earth. Evidence of life may be preserved in samples of surface ice by processes such as deposition from active plumes, hydrofracturing, or thermal convection. Terrestrial life produces unique distributions of organic molecules that translate into recognizable biosignatures. Identification and quantification of these organic compounds can be achieved by separation science such as capillary electrophoresis coupled to mass spectrometry (CE-MS). However, the data generated by such an instrument can be multiple orders of magnitude larger than what can be transmitted back to Earth during an ocean world's mission. This requires onboard science data analysis capabilities that summarize and prioritize CE-MS observations with limited computational resources. In response, the autonomous capillary electrophoresis mass-spectra examination (ACME) onboard science autonomy system was created for application to the ocean world's life surveyor (OWLS) instrument suite. ACME is able to compress raw mass spectra by two to three orders of magnitude while preserving most of its scientifically relevant information content. This summarization is achieved by the extraction of raw data surrounding autonomously identified ion peaks and the detection and parameterization of unique background regions. Prioritization of the summarized observations is then enabled by providing estimates of scientific utility, including presence of key target compounds, and the uniqueness of an observation relative to previous observations.
AbstractThe ROMA database (ROck reflectance for MArtian in situ exploration, https://roma.univ-lyon1.fr) provides the reflectance spectra between 0.4 and 3–4 μm of various terrestrial, Martian, and synthetic samples, as a means to document reference measurements for comparison with data acquired by visible and near‐infrared spectrometers on planetary surfaces, with a focus on current and future Martian observations by the Perseverance (Mars 2020 mission) and Rosalind Franklin (ExoMars) rovers. The main specificity of this database is to include a significant fraction of spectra of unprocessed rock, which are more realistic analogs and often have different spectral features than the fine powders more commonly analyzed in reflectance spectroscopy. Additionally, these measurements were acquired with a spectrometer whose spot size is similar to those of the SuperCam instrument (Mars 2020 mission) at a few meters from a target. Supplementary information are provided in the ROMA database: higher‐level data (such as absorption band parameters) as well as sample mineralogy estimated by whole‐rock X‐ray diffraction analyses. Future comparisons with this database will help improve the interpretation of spectral measurements acquired on the Martian surface. This work introduces the aim of the library and its current state, but additional data on intact natural rock surfaces will likely be added in the future.
Abstract Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two‐parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain‐hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude‐longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way.
Optical space‐based lightning sensors such as the Geostationary Lightning Mapper (GLM) detect and geolocate lightning by recording rapid changes in cloud top illumination. While lightning locations can be determined to within a pixel on the GLM imaging array, these instruments are not individually able to natively report lightning altitude. It has previously been shown that thunderclouds are illuminated differently based on the altitude of the optical source. In this study, we examine how altitude information can be extracted from the spatial distributions of GLM energy recorded from each optical pulse. We match GLM “groups” with Lightning Mapping Array (LMA) source data that accurately report the 3‐D positions of coincident Radio‐Frequency (RF) emitters. We then use machine learning methods to predict the mean LMA source altitudes matched to GLM groups using metrics from the optical data that describe the amplitude, breadth, and texture of the group spatial energy distribution. The resulting model can predict the LMA mean source altitude from GLM group data with a median absolute error of <1.5 km, which is sufficient to determine the location of the charge layer where the optical energy originated. This model is able to capture changes to the source altitude distribution in response to convective processes in the thunderstorm, and the GLM predictions can reveal the vertical structure of individual flashes ‐ enabling 3‐D flash geolocation with GLM for the first time. Future work will account for differences in thunderstorm charge/precipitation structures and viewing angle across the GLM Field of View.
To assist water managers in south-central Oklahoma prepare for future drought, reliable place-based drought forecasts are produced. Past-, present-, and future-forecasted climate indices (Multivariate ENSO Index, Pacific Decadal Oscillation index, and Atlantic Multidecadal Oscillation index) and past and present Palmer Drought Severity Index (PDSI) are employed as predictor variables to forecast PDSI using a multivariate regression technique. PDSI is forecasted 18 months in advance with sufficient skill to provide water managers early warning of drought. Using a training data set obtained from the period January 1901 to November 2021, a second-order model equation that contains, without any restriction, all the predictors and their interaction terms is built to predict drought intensity. Significant predictors are selected through stepwise regression, with cross-validation producing the simplest restricted model that describes the data well. PDSI values are predicted using 1000 fitted restricted models produced from bootstrapping, then averaged monthly. The technique found the best-fitting model and estimated the model coefficients that minimized the sum of squared deviations between the fitted model and the predictor variables. The adjusted R-squared value of the restricted model is large enough to explain an adequately accurate model, and relatively low values of error measures point to good predictive ability of the model. Although the model slightly overestimates the PDSI forecast maxima and minima, it necessarily captures the timing of the periods of severe to exceptional drought. Plain Language Summary Natural resource managers in south-central Oklahoma experienced severe-to-exceptional drought conditions during much of 2011-2014. Water managers from the Chickasaw Nation expressed the desire to obtain earlier advisories of impending drought. As a result, the author team developed an experimental predictive drought model using several statistical techniques. Results indicate that the model can adequately predict the timing and intensity of a common drought indicator (i.e., the Palmer Drought Severity Index) for south-central Oklahoma out to 18 months in advance. This lead time is useful for water managers to plan for the future risk of water quantity and quality problems.
When a lightning flash is propagating in the atmosphere it is known that especially the negative leaders emit a large number of very high frequency (VHF) radio pulses. It is thought that this is due to streamer activity at the tip of the growing negative leader. In this work, we have investigated the dependence of the strength of this VHF emission on the altitude of such emission for two lightning flashes as observed by the Low Frequency ARray (LOFAR) radio telescope. We find for these two flashes that the extracted amplitude distributions are consistent with a power‐law, and that the amplitude of the radio emissions decreases very strongly with source altitude, by more than a factor of 2 from 1 km altitude up to 5 km altitude. In addition, we do not find any dependence on the extracted power‐law with altitude, and that the extracted power‐law slope has an average around 3, for both flashes.
Abstract The COVID‐19 pandemic affected the scientific workforce in many ways. Many worried that stay‐at‐home orders would disproportionately harm the productivity and well‐being of women and early‐career scientists, who were expected to shoulder more childcare, homeschooling, and other domestic duties while also interrupting field and lab research, essential for career advancement. AGU journal submission and author and reviewer demographic data allowed us to investigate the effect the pandemic may have had on many Earth and space scientists, especially on women and early career scientists. However, we found that submissions to AGU journals increased during the pandemic as did total submissions from women (with no difference in the proportion). Although the rate at which women agreed to review decreased slightly (down 0.5%), women still made up a larger proportion of agreed reviewers during the pandemic compared to 2 years earlier. Little difference was seen overall in median times to complete reviews except with women in their 40s and 70s, suggesting that they were affected more during the pandemic than other age and gender groups. Although AGU's data do not show that the effects of the pandemic decreased women's participation in AGU journals, the lag between research and writing/submitting may still be seen in later months, which we will continue to report on as we analyze the data. The stay‐at‐home orders may also have allowed people to devote time to writing up research conducted prepandemic; writing too can be done during down‐time hours, which may have supported the increase in submissions to and reviews for AGU journals.
Abstract This article is composed of three independent commentaries of how “Integrated, Coordinated, Open, and Networked” (ICON), as well as “Findable, Accessible, Interoperable, and Reusable” (FAIR; under the umbrella of “Open” from ICON), principles are currently embedded in ocean sciences, and what are the opportunities and challenges by adopting them. Each commentary focuses on a different perspective as follows: (a) field sampling and experimentation to remote‐sensing and autonomous observations; (b) global collaboration, technology transfer and application, reproducibility, and data sharing and infrastructure; (c) increasing diversity and broadening participation in ocean sciences. Overall, there is a consensus that ocean sciences is well‐advanced in adopting many of the Integrated, Coordinated, Open, and Networked‐Findable, Accessible, Interoperable, and Reusable (ICON‐FAIR) principles regarding (a and b), although there are still plenty of opportunities to develop further by, for instance, making broader use of data‐of‐opportunity and citizen science, improving efforts toward standardized data organization and avoiding data fragmentation, and increasing training activities to implement good practices. On the other hand, fostering diversity, and broadening participation in ocean sciences is still in its infancy. Actions are needed to overcome under‐representation of women and other marginalized groups, create opportunities for early‐career scientists, and build bridges to support scientists from low‐income countries. We recognize that adopting ICON‐FAIR principles comes with high costs and efforts, but it has strong potential to develop ocean sciences in many aspects.
GeoHealth research both characterizes and predicts problems at the nexus of earth and human systems like climate change, pollution, and natural hazards. While GeoHealth excels in the area of integrated science, there is a need to improve coordinated and networked efforts to produce open science to enable environmental justice. There is a need to resource and empower frontline populations that are disproportionately marginalized by environmental injustice (i.e., the unequal protection from environmental harms and lack of access and meaningful engagement in decision making for a healthy environment; EPA, 2022, https://www.epa.gov/environmentaljustice). GeoHealth practice has the opportunity to advance environmental justice or the "fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income" with respect to how research and collaboration of GeoHealth professionals supports the "development, implementation, and enforcement of environmental laws, regulations, and policies" that produce equal protection from environmental and health hazards and access to the decision making for a health environment (EPA, 2022, https://www.epa.gov/environmentaljustice). Here we highlight barriers and opportunities to apply an equity-centered ICON framework to the field of GeoHealth to advance environmental justice and health equity.
Surface heterogeneities below the spatial resolution of thermal infrared (TIR) instruments result in anisothermality and can produce emissivity spectra with negative slopes toward longer wavelengths. Sloped spectra arise from an incorrect assumption of either a uniform surface temperature or a maximum emissivity during the temperature-emissivity separation of radiance data. Surface roughness and lateral mixing of different sub-pixel surface units result in distinct spectral slopes with magnitudes proportional to the degree of temperature mixing. Routine Off-nadir Targeted Observations (ROTO) of the Thermal Emission Imaging Spectrometer (THEMIS) are used here for the first time to investigate anisothermality below the spatial resolution of THEMIS. The southern flank of Apollinaris Mons and regions within the Medusae Fossae Formation are studied using THEMIS ROTO data acquired just after local sunset. We observe a range of sloped TIR emission spectra dependent on the magnitude of temperature differences within a THEMIS pixel. Spectral slopes and wavelength-dependent brightness temperature differences are forward-modeled for a series of two-component surfaces of varying thermal inertia values. Our results imply that differing relative proportions of rocky and unconsolidated surface units are observed at each ROTO viewing geometry and suggest a local rock abundance six times greater than published results that rely on nadir data. High-resolution visible images of these regions indicate a mixture of surface units from boulders to dunes, providing credence to the model.
The Earth-observing Aqua spacecraft was launched on 4 May 2002 and has now completed 20 years of collecting and transmitting data regarding the Earth's radiation budget, atmosphere, oceans, land, and ice. Although launched with a design life of 6 years, four of its instruments continue to operate and provide high-quality data streams more than 20 years after launch. The Aqua data are readily available to users worldwide and have been used in thousands of scientific publications and in numerous practical applications, including weather forecasting, air-quality assessments, and monitoring of forest fires, dust storms, volcanic ash plumes, oil spills, and crop yields.
Abstract Accurately measuring the variability of spectroscopic signatures of hydration (H2O + OH) on the illuminated lunar surface at 3 μm as a function of latitude, lunar time of day, and composition is crucial to determining the generation and destruction mechanisms of OH species and understanding the global water cycle. A prime complication in analysis of the spectroscopic feature is the accurate removal of thermal emission, which can modify or even eliminate the hydration feature depending on the data processing methods used and assumptions made. An orbital multispectral lidar, with laser illumination at key diagnostic wavelengths, would provide uniform, zero‐phase geometry, complete latitude and time of day coverage from a circular polar orbit, and is agnostic to the thermal state of the surface. We have performed measurement simulations of a four‐wavelength multispectral lidar using spectral mixtures of hydrated mid‐ocean‐ridge basalt (MORB) glasses and lunar regolith endmembers to assess the lidar performance in measuring hydration signatures on the lunar surface. Our results show a feasible system with wavelengths at 1.5 μm, 2.65 μm, 2.8 μm, and 3.1 μm can measure lunar hydration with a precision of 52 ppm (1σ) or better. These results, combined with the uniform measurement capabilities of multispectral lidar make it a valuable spectroscopic technique for elucidating mechanisms of OH/H2O generation, migration, and destruction.
AbstractGravity Recovery and Climate Experiment and its Follow On (GRACE (‐FO)) missions have resulted in a paradigm shift in understanding the temporal changes in the Earth's gravity field and its drivers. To provide continuous observations to the user community, missing monthly solutions within and between GRACE (‐FO) missions (33 solutions) need to be imputed. Here, we modeled GRACE (‐FO) data (196 solutions) between 04/2002–04/2021 to infer missing solutions and derive uncertainties in the existing and missing observations using Bayesian inference. First, we parametrized the GRACE (‐FO) time series using an additive generative model comprising long‐term variability (secular trend + interannual to decadal variations), annual, and semi‐annual cycles. Informative priors for each component were used and Markov Chain Monte Carlo (MCMC) was applied to generate 2,000 samples for each component to quantify the posterior distributions. Second, we reconstructed the new data (229 solutions) by joining medians of posterior distributions of all components and adding back the residuals to secure the variability of the original data. Results show that the reconstructed solutions explain 99% of the variability of the original data at the basin scale and 78% at the one‐degree grid scale. The results outperform other reconstructed data in terms of accuracy relative to land surface modeling. Our data‐driven approach relies only on GRACE (‐FO) observations and provides a total uncertainty over GRACE (‐FO) data from the data‐generation process perspective. Moreover, the predictive posterior distribution can be potentially used for “nowcasting” in GRACE (‐FO) near‐real‐time applications (e.g., data assimilations), which minimize the current mission data latency (40–60 days).
Previous studies suggested that the Amazon, the largest rainforest on Earth, changes from a CO2 sink to a CO2 source during the dry/fire season. However, the biospheric contributions to atmospheric CO2 are not well understood during the two main seasons, the dry/fire season and the wet season. In this article, we utilize Orbiting Carbon Observatory 2 (OCO-2) Solar-Induced Fluorescence (SIF) to explore photosynthetic activity during the different seasons. The spatiotemporal variability of OCO-2 SIF, OCO-2 CO2, precipitation, and burned area are investigated over the Amazon from September 2014 to December 2019. Averaging over the entire Amazon region, we found a positive temporal correlation (0.94) between OCO-2 SIF and Global Precipitation Climatology Project precipitation and a negative temporal correlation (-0.64) between OCO-2 SIF and OCO-2 CO2, consistent with the fact that precipitation enhances photosynthesis, which results in higher values for SIF and rate of removal of CO2 from the atmosphere above the Amazon region. We also observed seasonality in the spatial variability of these variables within the Amazon region. During the dry/fire (August-October) season, low SIF values, low precipitation, high vapor pressure deficit (VPD), large burned areas, and high atmospheric CO2 are mainly found over the southern Amazon region. In contrast, during the wet season (January-March), high SIF values, high precipitation, low VPD, smaller burned areas, and low CO2 are found over both the central and southern Amazon regions. The seasonal difference in SIF suggests that photosynthetic activity is reduced during the dry/fire season relative to the wet season as a result of low precipitation and high VPD, especially over the southern Amazon region, which will contribute to more CO2 in the atmosphere during the dry/fire season.
