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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2022

Soil Moisture Estimation in South Asia via Assimilation of SMAP Retrievals

A soil moisture retrieval assimilation framework is implemented across South Asia in an attempt to improve regional soil moisture estimation as well as to provide a consistent regional soil moisture dataset. This study aims to improve the spatiotemporal variability of soil moisture estimates by assimilating Soil Moisture Active Passive (SMAP) near-surface soil moisture retrievals into a land surface model. The Noah-MP (v4.0.1) land surface model is run within the NASA Land Information System software framework to model regional land surface processes. NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA2) and Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals (IMERG) provide the meteorological boundary conditions to the land surface model. Assimilation is carried out using both cumulative distribution function (CDF)-corrected (DA-CDF) and uncorrected SMAP retrievals (DA-NoCDF). CDF matching is applied to correct the statistical moments of the SMAP soil moisture retrieval relative to the land surface model. Comparison of assimilated and model-only soil moisture estimates with publicly available in situ measurements highlights the relative improvement in soil moisture estimates by assimilating SMAP retrievals. Across the Tibetan Plateau, DA-NoCDF reduced the mean bias and RMSE by 8.4 % and 9.4 %, even though assimilation only occurred during less than 10 % of the study period due to frozen (or partially frozen) soil conditions. The best goodness-of-fit statistics were achieved for the IMERG DA-NoCDF soil moisture experiment. The general lack of publicly available in situ measurements across irrigated areas limited a domain-wide direct model validation. However, comparison with regional irrigation patterns suggested correction of biases associated with an unmodeled hydrologic phenomenon (i.e., anthropogenic influence via irrigation) as a result of SMAP soil moisture retrieval assimilation. The greatest sensitivity to assimilation was observed in cropland areas. Improvements in soil moisture potentially translate into improved spatiotemporal patterns of modeled evapotranspiration, although limited influence from soil moisture assimilation was observed on modeled processes within the carbon cycle such as gross primary production. Improvement in fine-scale modeled estimates by assimilating coarse-scale retrievals highlights the potential of this approach for soil moisture estimation over data-scarce regions.

Jawairia Ahmad↗
NASA NTRS2023

Reports From EXPORTS Modeling and Data-Mining Activities

EXPORTS -EXport Processes in the Ocean from RemoTe Sensing is NASA’s large field campaign focusing on development of a predictive understanding of the export, fate and carbon cycle impacts of the global net primary production. This co-funded program (NSF, private funding1, and international participation) was conceived in 2013, with EXPORTS Science plan published in 2016 (Siegel et al 2016, EXPORTS Writing Team 2015) and its implementation plan finalized in 2016 (EXPORTS Science Definition Team 2016). More details are presented in Siegel et al (2021). EXPORTS campaign is structured as a multiyear effort (Figure 1). It started with “Pre-EXPORTS” modeling and data-mining activity followed by a first phase with two major field programs and a second synthesis and modeling phase. The “Pre-EXPORTS” projects, total of 6 of them (Table 1), funded under A.3 Ocean Biology and Biogeochemistry 2015 call, helped to plan the field campaign (Resplandy et al 2019, Rousseaux & Gregg 2017), and supporting further global synthesis with datasets mined from the literature (e.g. Bisson et al (2020), Bisson et al (2018), Kramer and Siegel (2019)), directly responding to objectives outlined in Science Plan (see section 6 in Team (2015)) and Implementation team (see Figure 1 in Team (2016)). This document presents a compilation of the final reports of the Pre-EXPORTS funded projects, in hope of synthesizing the outcomes, and insuring the legacy of this program. Each of these reports contains a list of published papers, and reader should refer to them to see results in details.

Brandi J. McCarty↗
NASA NTRS2022

Instantaneous Photosynthetically Available Radiation (IPAR) prediction models based on Neural Network for ocean waters.

Instantaneous photosynthetically available radiation (IPAR) at the ocean surface and its vertical profile below the surface play a critical role in models to calculate net primary productivity of marine phytoplankton. In this work, we report two IPAR prediction models based on neural network (NN) approach, one for open ocean and the other for coastal waters. These models are trained, validated, and tested using a large volume of synthetic datasets for open ocean and coastal waters simulated by a radiative transfer model. Our NN models are designed to predict IPAR under a large range of atmospheric and oceanic conditions. The NN models can compute subsurface IPAR profile very accurately up to euphotic zone depth. The root mean square errors associated with the diffuse attenuation coefficient of IPAR are less than 0.011 𝑚−1 and 0.036 𝑚−1 for open ocean and coastal waters respectively. The performance of the NN models is better than presently available semi analytical models, with significant superiority in coastal waters.

PACE↗
NASA NTRS2023

Phytoplankton Diversity and Chemotaxonomy in Contrasting North Pacific Ecosystems

Background. Phytoplankton is the base of majority of ocean ecosystems. It is responsible for half of the global primary production, and different phytoplankton taxa have a unique role in global biogeochemical cycles. In addition, phytoplankton abundance and diversity are highly susceptible to climate induced changes, hence monitoring of phytoplankton and its diversity is important and necessary. Methods. Water samples for phytoplankton and photosynthetic pigment analyses were collected in boreal winter 2017, along transect in the North Pacific Subtropical Gyre (NPSG) and the California Current System (CCS). Phytoplankton community was analyzed using light and scanning electron microscopy and photosynthetic pigments by high-performance liquid chromatography. To describe distinct ecosystems, monthly average satellite data of MODIS Aqua Sea Surface temperature and Chlorophyll a concentration, as well as Apparent Visible Wavelength were used. Results. A total of 207 taxa have been determined, mostly comprised of coccolithophores (35.5%), diatoms (25.2%) and dinoflagellates (19.5%) while cryptophytes, phytoflagellates and silicoflagellates were included in the group ``others" (19.8%). Phytoplankton spatial distribution was distinct, indicating variable planktonic dispersal rates and specific adaptation to ecosystems. Dinoflagellates, and nano-scale coccolithophores dominated NPSG, while micro-scale diatoms, and cryptophytes prevailed in CCS. A clear split between CCS and NPSG is evident in dendogram visualising LINKTREE constrained binary divisive clustering analysis done on phytoplankton counts and pigment oncentrations. Of all pigments determined, alloxanthin, zeaxanthin, divinyl chlorophyll b and lutein have highest correlation to phytoplankton counts. Conclusion. Combining chemotaxonomy and microscopy is an optimal method to determine phytoplankton diversity on a large-scale transect. Distinct communities between the two contrasting ecosystems of North Pacific reveal phytoplankton groups specific adaptations to trophic state, and support the hypothesis of shift from microto nano-scale taxa due to sea surface temperatures rising, favoring stratification and oligotrophic conditions.

oceanography↗
NASA NTRS2021

Geological Factors Impacted Cadmium Availability and use as an Alternative Cofactor for Zinc in the Carbon Fixation Pathways of Marine Diatoms

Transition metal cofactors are crucial for many biological processes. Despite being primarily considered to be toxic, the transition metal cadmium (Cd) was discovered to be a substitute cofactor for zinc (Zn) in photosynthetic carbon fixation pathways of marine diatoms. However, it is not known how conditions in the geosphere impacted Cd availability and its incorporation as an alternative metal cofactor for phytoplankton. We employed mineral chemistry network analysis to investigate which geochemical factors may have influenced the availability of Cd and Zn during the putative time period that the alternative Cd-based pathway evolved. Our results show that Zn minerals are more chemically diverse than are Cd minerals, but Zn- and Cd-containing minerals have similar network centrality values when specifically considering sulfur (S)-containing species. Cadmium and Zn sulfides are the most common Cd- and Zn-containing mineral species over the past 500 million years. In particular, the Cd and Zn sulfides, respectively greenockite and sphalerite, were highly abundant during this time period. Furthermore, S-containing Cd and Zn minerals are commonly co-located in geologic time, allowing them to be weathered and transported to the ocean in tandem, rather than from separate sources. We suggest that the simultaneous weathering of Cd and Zn sulfides allowed for Cd to be a bioavailable direct substitute for Zn in protein complexes during periods of Zn depletion. The biogeochemical cycles of Zn and Cd exemplify the importance of the coevolution of the geosphere and biosphere in shaping primary production in the modern ocean.

Naman Srivastava↗
NASA NTRSDate not supplied

Constraints on Triple Oxygen Isotope Kinetics

Triple oxygen isotope methods (e.g., using δ 18 O, and Δ′ 17 O) have been used to classify meteorites, to constrain atmospheric chemistry, and to estimate primary productivity, among other uses. Such utility relies on the mass dependency of most chemical reactions: δ 17 O is proportional to δ 18 O by roughly a factor of 0.5. However, in detail, mass-dependency factors vary. While constraints on mass dependency are well established for equilibrium isotope effects, similar constraints for kinetic isotope effects are lacking. Using an ab initio Monte Carlo approach (see envelopes in Figure 1), we find the range of kinetic isotope effects (i.e., resulting from unidirectional chemical reactions) contains and exceeds the range of accessible equilibrium isotope effects for the 16 O 17 O 18 O system (Hayles and Killingsworth, 2022). Accessible kinetic isotope effects can yield Δ′ 17 O variations as large 0.8 ‰ from a single kinetic fractionation step. Reactions that yield the largest deviations from equilibrium are those with the largest molecular mass difference between decomposition fragments, typically the two products such as a gas and solid generated from the thermal decomposition of an initial solid. Comparison of DFT transition state model results against previously published experiments on the thermal decomposition of calcite (CaCO 3 ) (Miller et al., 2002) and dehydroxylation of brucite (Mg(OH) 2 ) (Clayton and Mayeda, 2009) show they are matched with mass-dependent kinetic isotope effects. This approach can be applied to any system with more than two isotopes.

Oxygen isotopes↗
NASA NTRS2023

Ocean Carbon From Space: Current Status and Priorities for the Next Decade

The ocean plays a central role in modulating the Earth’s carbon cycle. Monitoring how the ocean carbon cycle is changing is fundamental to managing climate change. Satellite remote sensing is currently our best tool for viewing the ocean surface globally and systematically, at high spatial and temporal resolutions, and the past few decades have seen an exponential growth in studies utilising satellite data for ocean carbon research. Satellite-based observations must be combined with in-situ observations and models, to obtain a comprehensive view of ocean carbon pools and fluxes. To help prioritise future research in this area, a workshop was organised that assembled leading experts working on the topic, from around the world, including remote-sensing scientists, field scientists and modellers, with the goal to articulate a collective view of the current status of ocean carbon research, identify gaps in knowledge, and formulate a scientific roadmap for the next decade, with an emphasis on evaluating where satellite remote sensing may contribute. A total of 449 scientists and stakeholders participated (with balanced gender representation), from North and South America, Europe, Asia, Africa, and Oceania. Sessions targeted both inorganic and organic pools of carbon in the ocean, in both dissolved and particulate form, as well as major fluxes of carbon between reservoirs (e.g., primary production) and at interfaces (e.g., air-sea and land–ocean). Extreme events, blue carbon and carbon budgeting were also key topics discussed. Emerging priorities identified include: expanding the networks and quality of in-situ observations; improved satellite retrievals; improved uncertainty quantification; improved understanding of vertical distributions; integration with models; improved techniques to bridge spatial and temporal scales of the different data sources; and improved fundamental understanding of the ocean carbon cycle, and of the interactions among pools of carbon and light. We also report on priorities for the specific pools and fluxes studied, and highlight issues and concerns that arose during discussions, such as the need to consider the environmental impact of satellites or space activities; the role satellites can play in monitoring ocean carbon dioxide removal approaches; economic valuation of the satellite based information; to consider how satellites can contribute to monitoring cycles of other important climatically-relevant compounds and elements; to promote diversity and inclusivity in ocean carbon research; to bring together communities working on different aspects of planetary carbon; maximising use of international bodies; to follow an open science approach; to explore new and innovative ways to remotely monitor ocean carbon; and to harness quantum computing. Overall, this paper provides a comprehensive scientific roadmap for the next decade on how satellite remote sensing could help monitor the ocean carbon cycle, and its links to the other domains, such as terrestrial and atmosphere.

oceanography↗
NASA NTRS2021

Hybrid phenology matching model for robust crop phenological retrieval

Crop phenology regulates seasonal agroecosystem carbon, water, and energy exchanges, and is a key component in empirical and process-based crop models for simulating biogeochemical cycles of farmlands, assessing gross and net primary production, and forecasting the crop yield. The advances in phenology matching models provide a feasible means to monitor crop phenological progress using remote sensing observations, with a priori information of reference shapes and reference phenological transition dates. Yet the underlying geometrical scaling assumption of models, together with the challenge in defining phenological references, hinders the applicability of phenology matching in crop phenological studies. The objective of this study is to develop a novel hybrid phenology matching model to robustly retrieve a diverse spectrum of crop phenological stages using satellite time series. The devised hybrid model leverages the complementary strengths of phenometric extraction methods and phenology matching models. It relaxes the geometrical scaling assumption and can characterize key phenological stages of crop cycles, ranging from farming practice-relevant stages (e.g., planted and harvested) to crop development stages (e.g., emerged and mature). To systematically evaluate the influence of phenological references on phenology matching, four representative phenological reference scenarios under varying levels of phenological calibrations in terms of time and space are further designed with publicly accessible phenological information. The results indicate that the hybrid phenology matching model can achieve high accuracies for estimating corn and soybean phenological growth stages in Illinois, particularly with the year- and region-adjusted phenological reference (R-squared higher than 0.9 and RMSE less than 5 days for most phenological stages). The inter-annual and regional phenological patterns characterized by the hybrid model correspond well with those in the crop progress reports (CPRs) from the USDA National Agricultural Statistics Service (NASS). Compared to the benchmark phenology matching model, the hybrid model is more robust to the decreasing levels of phenological reference calibrations, and is particularly advantageous in retrieving crop early phenological stages (e.g., planted and emerged stages) when the phenological reference information is limited. This innovative hybrid phenology matching model, together with CPR-enabled phenological reference calibrations, holds 3 considerable promise in revealing spatio-temporal patterns of crop phenology over extended geographical regions.

Phenology↗
NASA NTRSDate not supplied

Observing Supraglacial Lakes Using Deep Learning and PlanetScope Imagery

Supraglacial lakes (SGL)s result from melt water accumulation in topographic depressions on the surface of glaciers. SGLs primarily affect glacial dynamics through a positive feedback loop in which the albedo-lowering effect of SGLs can escalate surface melt leading to increases in lake extent and depth, amplifying the afore mentioned albedo-lowering effect. The implications of accelerated glacial melt include increased sea level rise and modifications to ocean primary productivity. SGLs are critical indicators of surface melt and its downstream impacts and should be monitored efficiently. In situ observations and measurements of SGLs are time consuming, cost-prohibitive and difficult to scale. Earth observation data and machine learning enable scalable monitoring of SGLs through pattern detection and quantification of lake evolution over time [1]. This work presents a model developed by training a convolutional neural network with imagery and labels from NASA Operation IceBridge and predicting SGLs in high temporal and spatial resolution PlanetScope imagery.

Supraglacial lake↗
NASA NTRSDate not supplied

Sampling the ENSO response to tropical volcanic eruptions in the GISS-E2-1 model: physical & biogeochemical ocean effects

Despite observational evidence that an El-Niño is likely to follow tropical volcanic eruptions (Predybaylo et al, 2017), modelling studies have so far reached no consensus on either the sign or physical mechanism of El Niño response to volcanism (Khodri et al, 2017) . A previous modelling study found that following the Pinatubo eruption, the tropical Pacific developed El Niño-like conditions that triggered a decrease in the upwelling of DIC and increased carbon uptake (Eddebbar et al., 2019). However, the carbon cycle response to volcanisms as it depends on ENSO phase remains unclear. Here we perform thirty ensemble control simulations using the GISS-E2-1-G model to sample the pre-industrial control ENSO cycle and thirty more to superimpose a Pinatubo-like volcanic eruption. We find that the model tends to favor the development of a weak El-Niño following the volcanic eruption only in the case of a volcano that erupts at the start of a neutral ENSO phase. In response to the weak El-Niño, some anomalies have the same sign as those that develop from a strong El-Niño, such as weaker winds, shallower mixed layer depths, less direct but more diffuse radiation at the surface of the ocean, less nitrate, and lower primary productivity. However, other anomalies show the opposite sign as those from a strong El-Niño, such as more precipitation, less surface DIC, and less surface ocean pCO 2 . In contrast to the neutral phase, the volcanic response during positive ENSO and negative ENSO phases are swamped by the model’s internal variability.

ENSO↗
NASA NTRS2023

The northernmost hyperspectral FLoX sensor dataset for monitoring of high-Arctic tundra vegetation phenology and Sun-Induced Fluorescence (SIF)

A hyperspectral field sensor (FloX) was installed in Adventdalen (Svalbard, Norway) in 2019 as part of the Svalbard Integrated Arctic Earth Observing System (SIOS) for monitoring vegetation phenology and Sun-Induced Chlorophyll Fluorescence (SIF) of high-Arctic tundra. This northernmost hyperspectral sensor is located within the footprint of a tower for long-term eddy covariance flux measurements and is an integral part of an automatic environmental monitoring system on Svalbard (AsMovEn), which is also a part of SIOS. One of the measurements that this hyperspectral instrument can capture is SIF, which serves as a proxy of gross primary production (GPP) and carbon flux rates. This paper presents an overview of the data collection and processing, and the 4-year (2019–2021) datasets in processed format are available at: https://thredds.met.no/thredds/catalog/arcticdata/infranor/NINA-FLOX/raw/catalog.html associated with https://doi.org/10.21343/ZDM7-JD72 under a CC-BY-4.0 license. Results obtained from the first three years in operation showed interannual variation in SIF and other spectral vegetation indices including MERIS Terrestrial Chlorophyll Index (MTCI), EVI and NDVI. Synergistic uses of the measurements from this northernmost hyperspectral FLoX sensor, in conjunction with other monitoring systems, will advance our understanding of how tundra vegetation responds to changing climate and the resulting implications on carbon and energy balance.

hyperspectral field sensor↗
NASA NTRS2024

Noise Reduction for Solar-Induced Fluorescence Retrievals Using Machine Learning and Principal Component Analysis: Simulations and Applications to GOME-2 Satellite Retrievals

We use a spectral-based approach that employs principal component analysis along with a relatively shallow artificial neural network (NN) to substantially reduce noise and other artifacts in terrestrial chlorophyll solar-induced fluorescence (SIF) retrievals. SIF is a very small emission at red and far-red wavelengths that is difficult to measure and is highly sensitive to random errors and systematic artifacts. Our approach relies upon an assumption that a trained NN can effectively reconstruct the total SIF signal from a relatively small number of leading principal components of the satellite-observed far-red radiance spectra without using information from the trailing modes that contain most of the random errors. We test the approach with simulated reflectance spectra produced with a full atmospheric and surface radiative transfer model using different observing and geophysical parameters and various noise levels. Resulting noisy and noise-reduced retrieved SIF values are compared with true values to assess performance. We then apply our noise reduction approach to SIF derived from two different satellite spectrometers. For evaluation, since the truth in this case is unknown, we compare SIF retrievals from two independent sensors with each other. We also compare the noise-reduced SIF temporal variations with those from an independent gross primary product (GPP) product that should display similar variations. Results show that our noise reduction approach improves capture of SIF seasonal and inter-annual variability. Our approach should be applicable to many noisy data products derived from spectral measurements. Our methodology does not replace the original retrieval algorithms; rather the original noisy retrievals are needed as the target for the NN training process.

noise reduction, solar-induced fluorescence, SIF, ↗
NASA NTRS2024

Integrating State Data Assimilation and Innovative Model Parameterization Reduces Simulated Carbon Uptake in the Arctic and Boreal Region

Model representation of carbon uptake and storage is essential for accurate projection of the response of the arctic‐boreal zone to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of Gross Primary Productivity (GPP). To understand and correct bias of LAI and aboveground biomass in the Community Land Model (CLM), we assimilated the 8‐day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product of annual aboveground biomass into CLM using an Ensemble Adjustment Kalman Filter (EAKF) in an experimental region including Alaska and Western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels. The International Land Model Benchmarking system assessment showed that data assimilation significantly improved CLM's performance in simulating the carbon and hydrological cycles, as well as in representing the functional relationships between LAI and other variables. To further reduce the remaining bias in GPP after LAI bias correction, we re‐parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in LSMs.

land data assimilation↗
NASA NTRS1992

An isotopic biogeochemical study of the Green River oil shale

Thirty-five different samples from three different sulfur cycles were examined in this stratigraphically oriented study of the Shell 22x-l well (U.S.G.S. C177 core) in the Piceance Basin, Colorado. Carbon isotopic compositions of constituents of Green River bitumens indicate mixing of three main components: products of primary photoautotrophs and their immediate consumers (delta approximately -30% vs PDB), products of methanotrophic bacteria (delta approximately -85%), and products of unknown bacteria (delta approximately -40%). For individual compounds synthesized by primary producers, delta-values ranged from -28 to -32%. 13C contents of individual primary products (beta-carotane, steranes, acyclic isoprenoids, tricyclic triterpenoids) were not closely correlated, suggesting diverse origins for these materials. 13C contents of numerous hopanoids were inversely related to sulfur abundance, indicating that they derived both from methanotrophs and from other bacteria, with abundances of methanotrophs depressed when sulfur was plentiful in the paleoenvironment. gamma-Cerane coeluted with 3 beta(CH3),17 alpha(H),21 beta(H)-hopane, but delta-values could be determined after deconvolution. gamma-Cerane (delta approximately -25%) probably derives from a eukaryotic heterotroph grazing on primary materials, the latter compound (delta approximately -90%) must derive from methanotrophic organisms. 13C contents of n-alkanes in bitumen differed markedly from those of paraffins generated pyrolytically. Isotopic and quantitative relationships suggest that alkanes released by pyrolysis derived from a resistant biopolymer of eukaryotic origin and that this was a dominant constituent of total organic carbon.

Non-NASA Center↗
NASA NTRS1991

Sulfate reduction in deep-sea sediments

Sulfate reduction rates calculated from about 200 DSDP pore water sulfate profiles have been contoured and plotted on a map covering most areas of the world ocean. Rates show a remarkable spatial consistency, with high rates observed near the continental margins, becoming progressively lower toward the central ocean basins. Relatively elevated rates are also found in the eastern equatorial Pacific, a site of upwelling and correspondingly high rates of primary organic production. Overall, the distribution of sulfate reduction in pelagic sediments looks very similar to the distribution of primary organic carbon production. When rates are directly compared, however, the correlation between sulfate reduction and primary production is only moderately strong. Perhaps the most important influence on sulfate reduction is sediment deposition rate and the control this has over the fraction of the sedimentary organic carbon flux that becomes available for sulfate reduction. The slower the rate of sediment deposition the more time for oxic respiration and the less organic carbon that escapes to the zone of sulfate reduction. To predict most accurately sulfate reduction rates, however, the variables of primary production, water depth, and sediment deposition rate must all be integrated.

NASA Program Exobiology↗
NASA NTRS1986

Calcium ion dependency of ethylene production in segments of primary roots of Zea mays

We investigated the effect of Ca2+ on ethylene production in 2-cm long apical segments from primary roots of corn (Zea mays L., B73 x Missouri 17) seedlings. The seedlings were raised under different conditions of Ca2+ availability. Low-Ca and high-Ca seedlings were raised by soaking the grains and watering the seedlings with distilled water or 10 mM CaCl2, respectively. Segments from high-Ca roots produced more than twice as much ethylene as segments from low-Ca roots. Indoleacetic acid (IAA; 1 micromole) enhanced ethylene production in segments from both low-Ca and high-Ca roots but auxin-induced promotion of ethylene production was consistently higher in segments from high-Ca roots. Addition of 1-aminocyclopropane-1-carboxylic acid (ACC) to root segments from low-Ca seedlings doubled total ethylene production and the rate of production remained fairly constant during a 24 h period of monitoring. In segments from high-Ca seedlings ACC also increased total ethylene production but most of the ethylene was produced within the first 6 h. The data suggest that Ca2+ enhances the conversion of ACC to ethylene. The terminal 2 mm of the root tip were found to be especially important to ethylene biosynthesis by apical segments and, experiments using 45Ca2+ as tracer indicated that the apical 2 mm of the root is the region of strongest Ca2+ accumulation. Other cations such as Mn2+, Mg2+, and K+ could largely substitute for Ca2+. The significance of these findings is discussed with respect to recent evidence for gravity-induced Ca2+ redistribution and its relationship to the establishment of asymmetric growth during gravitropic curvature.

NASA Discipline Plant Biology↗
NASA NTRS2016

Understanding Oceanic Heavy Precipitation Using Scatterometer, Satellite Precipitation, and Reanalysis Products

The primary aim of this study is to understand the heavy precipitation events over Oceanic regions using vector wind retrievals from space based scatterometers in combination with precipitation products from satellite and model reanalysis products. Heavy precipitation over oceans is a less understood phenomenon and this study tries to fill in the gaps which may lead us to a better understanding of heavy precipitation over oceans. Various phenomenon may lead to intense precipitation viz. MJO (Madden-Julian Oscillation), Extratropical cyclones, MCSs (Mesoscale Convective Systems), that occur inside or outside the tropics and if we can decipher the physical mechanisms behind occurrence of heavy precipitation, then it may lead us to a better understanding of such events which further may help us in building more robust weather and climate models. During a heavy precipitation event, scatterometer wind observations may lead us to understand the governing dynamics behind that event near the surface. We hypothesize that scatterometer winds can observe significant changes in the near-surface circulation and that there are global relationships among these quantities. To the degree to which this hypothesis fails, we will learn about the regional behavior of heavy precipitation-producing systems over the ocean. We use a "precipitation feature" (PF) approach to enable statistical analysis of a large database of raining features.

Winds↗
NASA NTRS2014

Phosphates at the Surface of Mars: Primary Deposits and Alteration Products

Phosphorus is an essential element in terrestrial organisms and thus characterizing the occurrences of phosphate phases at the martian surface is crucial in the assessment of habitability. The Alpha Particle X-Ray Spectrometers onboard Spirit, Opportunity and Curiosity discovered a variety of primary and secondary phosphate phases allowing direct comparisons across the three landing sites. The Spirit rover at Gusev Crater encountered the "Wishstone/Watchtower" class of P-rich (up to 5.2 wt% P2O5) rocks interpreted to be alkaline volcanic rocks with a physical admixture of approximately 10 to 20% merrillite [Usui et al 2008]. These rocks are characterized by elevated Ti and Y and anomalously low Cr and Ni, which could largely reflect the nature of the protoliths: Evolved magmatic rocks. Many of these chemical signatures are also found in pyroclastic deposits at nearby "Home Plate" and in phosphate precipitates derived from fluid interactions with these rocks ("Paso Robles" soils). The Opportunity rover at Meridiani Planum recently analyzed approximately 4 cm clast in a fine-grained matrix, one of numerous rocks of similar appearance at the rim of Endeavour Crater. This clast, "Sarcobatus," has minor enrichments in Ca and P relative to the matrix, and like the P-rich rocks at Gusev, Sarcobatus also shows elevated Al and Ti. On the same segment of the Endeavour rim, subsurface samples were found with exceptional levels of Mn (approximately 3.5 wt% MnO). These secondary and likely aqueous deposits contain strong evidence for associated Mg-sulfate and Ca-phosphate phases. Finally, the Curiosity traverse at Gale crater encountered P-rich rocks compositionally comparable to Wishstone at Gusev, including elevated Y. Phosphorous-rich rocks with similar chemical characteristics are prevalent on Mars, and the trace and minor element signatures provide constraints on whether these are primary deposits, secondary products of physical weathering or secondary products of chemical weathering.

Yen, Albert S.↗