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NASA NTRS2016

Kepler Data Validation Time Series File: Description of File Format and Content

The Kepler space mission searches its time series data for periodic, transit-like signatures. The ephemerides of these events, called Threshold Crossing Events (TCEs), are reported in the TCE tables at the NASA Exoplanet Archive (NExScI). Those TCEs are then further evaluated to create planet candidates and populate the Kepler Objects of Interest (KOI) table, also hosted at the Exoplanet Archive. The search, evaluation and export of TCEs is performed by two pipeline modules, TPS (Transit Planet Search) and DV (Data Validation). TPS searches for the strongest, believable signal and then sends that information to DV to fit a transit model, compute various statistics, and remove the transit events so that the light curve can be searched for other TCEs. More on how this search is done and on the creation of the TCE table can be found in Tenenbaum et al. (2012), Seader et al. (2015), Jenkins (2002). For each star with at least one TCE, the pipeline exports a file that contains the light curves used by TPS and DV to find and evaluate the TCE(s). This document describes the content of these DV time series files, and this introduction provides a bit of context for how the data in these files are used by the pipeline.

DV Time Series↗
NASA NTRS2016

Vibrating-Wire, Supercooled Liquid Water Content Sensor Calibration and Characterization Progress

NASA conducted a winter 2015 field campaign at the NASA Glenn Research Center intended to generate a validation database for the NASA Icing Remote Sensing System. The weather balloons carried a specialized, disposable sensor designed to determine supercooled liquid water content aloft. Significant progress has been made to calibrate and characterize these specialized sensors. Calibration testing of these sensors was carried out in a specially developed, low-speed, icing wind tunnel. The sensor icing behavior was documented and analyzed. Finally, post-campaign evaluation of the balloon soundings revealed a gradual drift in the sensor data with increasing altitude. The behavior was analyzed and a method to account for the drift was developed.

Icing↗
NASA NTRS2019

Characteristics of Total Column CO2 Retrievals from the OCO Missions: Biases, Information Content and Implications for Flux Inversions

The Orbiting Carbon Observatory-2 and Orbiting Carbon Observatory-3, launched in 2015 and 2019, respectively, are intended to collect and deliver high-resolution observations of CO2 with unprecedented space and time coverage. Observations of CO2 from these remote-sensing missions (also known as XCO2, or column-based average, dry air mole fraction of CO2) are then used by the global carbon cycle community to answer a wide range of science questions, from the distribution and quantification of global and regional CO2 source-sink patterns to quantification of anthropogenic sources at urban scales. Even though we have had the OCO-2 mission flying for a few years now, the retrieval algorithms are continuously evolving and improving to deliver XCO2 retrievals with very high precision and high accuracy (or low biases). In this presentation, we will discuss a simple yet effective quantitative framework that has been developed by the OCO-2 flux team to evaluate the information content of these XCO2 retrievals as soon as they are released, i.e., with lower latency than full-scale flux inversions. This framework serves as a precursor to advanced inverse modeling frameworks and is intended to provide an early but accurate assessment of the signal present in the satellite retrievals, the robustness of that signal, and the ability of these retrievals to resolve patterns in CO2 surface fluxes that cannot be resolved by our current network of surface sites. Specific results will tackle a tiered set of questions that are being addressed using this framework: (a) what are the distribution of retrievals in the different modes of operation and how do they vary in space and time? (b) what is the information that is being given to the inverse modeling frameworks from the space-based data, information above and beyond what is provided by the in-situ data? and (c) how do these factors influence our choices for doing flux inversions with the satellite retrievals? While the primary focus of the results will be on application of this technique to mature OCO-2 retrievals, we will show early results for a couple of months of OCO-3 retrievals. For the time-period that the retrievals from the two missions overlap, we will highlight how this framework allows us to effortlessly put the information from OCO-3 and OCO-2 on an equal footing, thus enabling easy comparison between the two pioneering missions.

Chatterjee, Abhishek↗
NASA NTRS2020

A Multi-Sims Investigation of Water Content and D/H Ratios in Roberts Massif 04262 with Insight to Sources of Hydrogen in Maskelynite

We want to define the H2O content ([H2O]) and hydrogen (H) isotope composition of meteoritic material from Mars [1-3] with motivation to understand Mars’ volatile history, constrain geochemical signatures of interior water reservoirs (i.e. the Martian mantle) and explore effects of planetary (e.g. planet formation, magma ocean degassing) and local (e.g. volcanic degassing, impact melting and degassing) processes on H incorporated in minerals. Secondary ion mass spectrometry (SIMS) allows multiple avenues to address these questions. However, application to (1) precious astromaterials and (2) low level H measurements, pose specific challenges that are further complicated when combined. We present preliminary data of a multi-approach (SIMS vs. NanoSIMS) study of H in Roberts Massif 04262 (RBT 04262), an enriched lherzolitic shergottite with nonpoikilitic (NP) and poikilitic (P) lithologies [4]. We analyze olivine, pyrox-ene, and melt inclusions to compare indigenous mantle water, with impact-generated maskelynite to investigate H signatures due to shock.

Dudley, J. M.↗
NASA NTRS2020

Field Evaluation of Portable Soil Water Content Sensors in a Sandy Loam

Ground observations are critical in the validation of soil water content (SWC) estimates from both satellites and land surface models. Portable SWC sensors provide useful information to determine the amount of SWC in the topsoil layer for various applications; however, these probes are not accurate without site‐specific correction. In the present study, we examined and compared six different types of portable electromagnetic (EM) SWC sensors, including multiple sensors made by the same manufacturers, for a total of 16 EM‐based SWC probes equipped with portable data loggers. All SWC probes met the target accuracy after onsite correction—the RMSD was <0.025 m(exp 3) m(exp -3). Using the two‐sample t-tests, we observed that SWC data obtained from similar electrode lengths and from different manufacturers showed similar distributions over time with the same mean. Furthermore, using the maximize R method to combine SWC data from two different types of sensors increased the accuracy of the results. When datasets from two different types of sensors were combined, the Pearson's correlation coefficient (R value) and RMSD values were improved. The average R value improved from .930 to .945, and the RMSD decreased from 0.036 m(exp 3) m(exp -3) to 0.018 m(exp 3) m(exp −3). These results indicate that, along with site‐specific correction, synergetic use of multiple manufacturers’ EM‐based SWC probes can improve the R value and reduce systematic bias.

soil moisture data↗
NASA NTRS2020

Reconstructing Magma Storage Depths for the 2018 Kilauean Eruption from melt inclusion CO2 Contents: The importance of Vapor Bubbles

The 2018 Lower East Rift Zone (LERZ) eruption of Kīlauea Volcano and the accompanying collapse of the summit caldera marked the most destructive phase of activity on Hawai’i in the last 200 years. The integration of petrological data extracted from lava samples collected throughout the eruption with geodetic data examining the caldera collapse event, and estimates of the co-erupted flux of SO2 from the main eruptive fissure (Fissure 8), provides an exceptional opportunity to determine the reservoir geometry and magma transport paths supplying Kīlauea’s LERZ. The forsterite contents of erupted olivines and the degree of disequilibrium with their carrier melts indicate that two distinct olivine populations were erupted from Fissure 8. Melt inclusion entrapment pressures reveal that more evolved olivines (Fo<81.5) crystallized at ~2 km depth within the shallower Halema’uma’u reservoir, while more primitive olivines (Fo>81.5)crystallized within the deeper South Caldera reservoir at ~3–5 km depth. Crucially, primitive olivines experienced extensive post-entrapment crystallization, driving the growth of a vapor bubble. Raman spectroscopy reveals that this bubble contains up to 99% of the total inclusionCO2 budget (median=93%). Measurements of CO2 in only the glass phase would have underestimated entrapment depths by up to 60× (median=11×), and the importance of the SC reservoir as a source of magma to Fissure 8 would have been overlooked. Overall, we demonstrate that Raman measurements of bubbles, along with careful choice of suitably-calibrated H2O-CO2 solubility model, is vital to place accurate constraints on the depths of magma storage regions supplying volcanic eruptions.

SIMS↗
NASA NTRS2020

The Cl isotope composition and halogen contents of Apollo-return samples

Lunar mare basalts are depleted in F and Cl by approximately an order of magnitude relative to mid-ocean ridge basalts and contain two Cl-bearing components with elevated isotopic compositions relative to the bulk-Earth value of ∼0‰. The first is a watersoluble chloride constituting 65 ± 10% of total Cl with δ37Cl values averaging 3.0 ± 4.3‰. The second is structurally bound chloride with δ37Cl values averaging 7.3 ± 3.5‰. These high and distinctly different isotopic values are inconsistent with equilibrium fractionation processes and instead suggest early and extensive degassing of an isotopically light vapor. No relationship is observed between F/Cl ratios and δ37Cl values, which suggests that lunar halogen depletion largely resulted from the Moon-forming Giant Impact. The δ37Cl values of apatite are generally higher than the structurally bound Cl, and ubiquitously higher than the calculated bulk δ37Cl values of 4.1 ± 4.0‰. The apatite grains are not representative of the bulk rock, and instead record localized degassing during the final stages of lunar magma ocean (LMO) or later melt crystallization. The large variability in the δ37Cl values of apatite within individual thin sections further supports this conclusion. While urKREEP (primeval KREEP [potassium/rare-earth elements/phosphorus]) has been proposed to be the source of the Moon’s high Cl isotope values, the ferroan anorthosites (FANs) have the highest δ37Cl values and have a positive correlation with Cl content, and yet do not contain apatite, nor evidence of a KREEP component. The high δ37Cl values in this lithology are explained by the incorporation of a >30‰ HCl vapor from a highly evolved LMO.

The Moon↗
NASA NTRS2020

UV/Vis+ Photochemistry Database: Structure, Content and Applications

The “science-softCon UV/Vis+ Photochemistry Database” (www.photochemistry.org) is a large and comprehensive collection of EUV-VUV-UV–Vis-NIR spectral data and other photochemical information assembled from published peer-reviewed papers. The database contains photochemical data including absorption, fluorescence, photoelectron, and circular and linear dichroism spectra, as well as quantum yields and photolysis related data that are critically needed in many scientific disciplines. This manuscript gives an outline regarding the structure and content of the “science-softCon UV/Vis+ Photochemistry Database”. The accurate and reliable molecular level information provided in this database is fundamental in nature and helps in proceeding further to understand photon, electron and ion induced chemistry of molecules of interest not only in spectroscopy, astrochemistry, astrophysics, Earth and planetary sciences, environmental chemistry, plasma physics, combustion chemistry but also in applied fields such as medical diagnostics, pharmaceutical sciences, biochemistry, agriculture, and catalysis. In order to illustrate this, we illustrate the use of the UV/Vis+ Photochemistry Database in four different fields of scientific endeavor.

Photochemistry↗
NASA NTRS2017

Gaining the most utility from our geospace observational system: Network analysis of total electron content as a means to understand space weather to the point of prediction

We present the first network analysis of interplanetary magnetic field (IMF) clock angle dependent, high-latitude, hemispheric-specific total electron content (TEC) data. We examine network parameters to describe spatio-temporal correlations in the TEC data for January 2016. We find that significant network structure exists distinguishing the dayside and nightside ionosphere, and specific features in the high-latitudes (cusp/ionospheric footpoints of magnetospheric boundary layers, polar cap, and auroral zone), and that these features vary with IMF clock angle. In this brief summary paper, we provide proof of concept results and identify important areas of future research, providing a basis for the discussion of network analysis and machine learning approaches for space weather applications.

Malik, Nishant↗
NASA NTRS2020

COSMIC: Content-based Onboard Summarization to Monitor Infrequent Change

Interplanetary exploration occurs at vast distancesthat severely limit communication bandwidth to spacecraft exploringother planets. It is possible to collect much morescientific data than can ever be downlinked given current communicationcapabilities. Therefore, we are developing a systemcalled COSMIC (Content-based Onboard Summarization toMonitor Infrequent Change) that will opportunistically analyzedata onboard a Mars orbiter to alert scientists when meaningfulchanges have occurred. COSMIC will allow future spacecraftto continuously collect data to search for rare, transient phenomenasuch as fresh impacts or seasonally changing polarlandforms under a constrained downlink budget. In this paper,we describe the overall goals and architecture of COSMIC,plans to enable specific scientific studies, label acquisition toenable supervised approaches to surface landform classification,a new machine learning evaluation framework for analyzingthe trade-offs between classifier accuracy and computationalrequirements, and lessons learned about constraints that COSMICwill face operating onboard a spacecraft. In particular, wediscuss design considerations surrounding computational andstorage constraints, change detection strategies, and localizingdetected landforms of interest within a global coordinate frame.Finally, we describe challenges and open research questions thatmust be addressed prior to deploying COSMIC.

Trockman, Asher↗
NASA NTRS2019

Sensitvity analysis of SMAP-Reflectometry (SMAP-R) signals to vegetation water content

Global Navigation Satellite System – Reflectometry (GNSSR) techniques have proven successful to retrieve several geophysical parameters such as, ocean wind speed, soil moisture, altimetry, wetland dynamics, snow depth estimations. In this paper, the L2C GPS signals measured from the Soil Moisture Active Passive (SMAP) radar after its malfunction is used to investigate the effect of the vegetation water content on the electromagnetic signal. The SMAP Reflectometry (SMAP-R) measurements are obtained at V and H polarizations allowing for not only signal-to-noise ratio (SNR) analysis but also for polarimetric ratio (PR) studies.

Morris, Mary↗
NASA NTRS2020

Names Don't Fly: Smart Filters for Profanity Detection and Classification in User-Generated Content

Generally, names associate with a person’s identity. But what if in the pretext of a legitimate name and given the opportunity, users of software provide names to online web forms that carry along offensive language, slurs, and other profanity that is then sent to Mars ? The answer is simple: they don’t fly. In this paper,we perform model explorations to detect and classify inappropriate content in the names submitted from people across the world to ‘Send Your Names to MARS’ public engagement campaign.We propose a novel pipeline approach, that can effectively overcome the issues of lack of negative samples, noisy labels by gathering expert knowledge over time with human(s) in the loop and data augmentation, and achieve high accuracy in classifying inappropriate names with very little or no context. We describe cloud-based infrastructure to deploy our application and run predictions on large-scale data through our pipeline and achieve significant speedup over offline processes, with enhanced reliability and security.

Soderstrom, Tomas↗
NASA NTRS2022

Impact of Growth Phase, Pigment Adaptation, and Climate Change Conditions on the Cellular Pigment and Carbon Content of Fifty‐One Phytoplankton Isolates

Owing to their importance in aquatic ecosystems, the demand for models that estimate phytoplankton biomass and community composition in the global ocean has increased over the last decade. Moreover, the impacts of climate change, including elevated carbon dioxide (CO2), increased stratification, and warmer sea surface temperatures, will likely shape phytoplankton community composition in the global ocean. Chemotaxonomic methods are useful for modeling phytoplankton community composition from marker pigments normalized to chlorophyll a (Chl a). However, photosynthetic pigments, particularly Chl a, are sensitive to nutrient and light conditions. Cellular carbon is less sensitive, so using carbon biomass instead may provide an alternative approach. To this end, cellular pigment and carbon concentrations were measured in 51 strains of globally relevant, cultured phytoplankton. Pigment-to-Chl a and pigment-to-carbon ratios were computed for each strain. For 25 strains, measurements were taken during two growth phases. While some differences between growth phases were observed, they did not exceed within class differences. Multiple strains of Amphidinium carterae, Ditylum brightwellii and Heterosigma akashiwo were measured to determine whether time in culture influenced pigment and carbon composition. No appreciable trends in cellular pigment or carbon content were observed. Lastly, the potential impact of climate change conditions on the pigment ratios was assessed using a multistressor experiment that included increased mean light, temperature, and elevated pCO2 on three species: Thalassiosira oceanica, Ostreococcus lucimarinus, and Synechococcus. The largest differences were observed in the pigment-to-carbon ratios, while the marker pigments largely covaried with Chl a. The implications of these observations to chemotaxonomic applications are discussed.

Pigments↗
NASA NTRS2022

On the Effects of Cloud Water Content on Passive Microwave Snowfall Retrievals

The Bayesian passive microwave retrievals of snowfall often rely on mathematical matching of the observed vectors of brightness temperature with an a priori database of precipitation profiles and their corresponding brightness temperatures. Mathematical proximity does not necessarily lead to consistent retrievals due to limited information content of passive microwave observations. This paper defines imposter (genuine) vectors of brightness temperature as those that are mathematically close but physically inconsistent (consistent) and characterizes them through the Silhouette Coefficient (SC) analysis. The Neyman–Pearson (NP) hypothesis testing is used to separate the imposter and genuine brightness temperatures based on their associated values of cloud ice (IWP) and liquid water path (LWP), given by coincidences of CloudSat Profiling Radar (CPR) and the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The study determines thresholds for IWP and LWP that allow optimal identification of imposter brightness temperatures of non-snowing and snowing clouds, which can mislead the passive microwave retrieval algorithms to falsely detect or miss the snowfall events. It is demonstrated that emission signal of supercooled liquid water in snowing clouds can lead to improved passive microwave retrieval of snowfall and conditioning the retrievals to the cloud IWP and LWP can result in marginal correction of the snowfall detection probability; however, reduce the probability of false alarm by 6%–8% over sea ice and open oceans.

Snowfall↗
NASA NTRS2023

A Tuned Ocean Color Algorithm for the Arctic Ocean: A Solution for Waters With High CDM Content

The Arctic Ocean (AO) is the most river-influenced ocean. Located at the land-sea interface wherein phytoplankton blooms are common, Arctic coastal waterbodies are among the most affected regions by climate change. Given phytoplankton are critical for energy transfer supporting marine food webs, accurate estimation of chlorophyll a concentration (Chl), which is frequently used as a proxy of phytoplankton biomass, is critical for improving our knowledge of the Arctic marine ecosystem and its response to the ongoing climate change. Due to the unique and complex bio-optical properties of the AO, efforts are still needed to obtain more accurate Chl estimates, especially for coastal waters with high colored detrital material (CDM) content. In this study, we optimized the the Garver-Siegel-Maritorena (GSM) algorithm, using an Arctic bio-optical dataset comprised of seven wavelengths (the original GSM wavelengths plus 625 nm). Results suggested that our tuned algorithm, denoted GSMA, outperformed an alternative AO GSM algorithm denoted AO.GSM, but the accuracy of Chl estimates was only improved by 8%. In addition, GSMA showed appreciable robustness when assessed using a satellite image and two non-Arctic coastal datasets.

Arctic↗