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NASA NTRS2020

Quantifying Spatiotemporal Variability of Glacier Algal Blooms and the Impact on Surface Albedo in Southwestern Greenland

Albedo reduction due to light-absorbing impurities can substantially enhance ice sheet surface melt by increasing surface absorption of solar energy. Glacier algae have been suggested to play a critical role in darkening the ablation zone in southwestern Greenland. It was very recently found that the Sentinel-3 Ocean and Land Colour Instrument (OLCI) band ratio R709 nm∕R673 nm can characterize the spatial patterns of glacier algal blooms. However, Sentinel-3 was launched in 2016, and current data are only available over three melting seasons (2016–2019). Here, we demonstrate the capability of the MEdium Resolution Imaging Spectrometer (MERIS) for mapping glacier algae from space and extend the quantification of glacier algal blooms over southwestern Greenland back to the period 2004–2011. Several band ratio indices (MERIS chlorophyll a indices and the impurity index) were computed and compared with each other. The results indicate that the MERIS two-band ratio index (2BDA) R709 nm∕R665 nm is very effective in capturing the spatial distribution and temporal dynamics of glacier algal growth on bare ice in July and August. We analyzed the interannual (2004–2011) and summer (July–August) trends of algal distribution and found significant seasonal and interannual increases in glacier algae close to the Jakobshavn Isbrae Glacier and along the middle dark zone between the altitudes of 1200 and 1400 m. Using broadband albedo data from the Moderate Resolution Imaging Spectroradiometer (MODIS), we quantified the impact of glacier algal growth on bare ice albedo, finding a significant correlation between algal development and albedo reduction over algae-abundant areas. Our analysis indicates the strong potential for the satellite algal index to be used to reduce bare ice albedo biases in regional climate model simulations.

surface albedo↗

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↗

DOE OSTI2025

Arctic extreme precipitation changes from 1980 to 2022 in response to sea ice decline and enhanced atmospheric rivers

Arctic extreme precipitation (EP) broadly impacts permafrost degradation, glacier and snow cover changes, and ice sheet mass balance as well as ecosystems. However, investigation of EP spatiotemporal variations over the Arctic remains challenging, and their primary drivers are still poorly understood. Performance estimation of three state-of-the-art reanalysis products (Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA-5), and Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2)) against gauge-based precipitation observations reveals that MERRA-2 outperforms other reanalysis for Arctic EP changes. Based on MERRA-2 data, both annual EP amount and occurrence days averaged over the Arctic show statistically significant positive trends during 1980–2022 (3.37 ± 1.03 mm dec −1 and 0.42 ± 0.17 d dec −1 , respectively), with the most pronounced increase in the autumn. Spatial heterogeneity in annual and seasonal EP trends is found across the Arctic, with the largest positive annual trends of 30 mm dec −1 over the Bering Sea and the Denmark Strait. The significant EP increase is closely associated with intensified atmospheric rivers (ARs) and widespread decline in sea ice concentration (SIC). Specially, SIC and ARs are responsible for 12% and 50% of Arctic EP inter-annual variance, respectively, while ARs directly contribute 28.3% of the total annual EP amounts. These findings explain the mechanistic controls on Arctic EP, providing critical insights for projecting polar weather and climate extremes and their impacts on the Arctic environment.

atmospheric rivers↗

DOE OSTI2021

Monitoring changes in human activity during the COVID-19 shutdown in Las Vegas using infrasound microbarometers

While studies of urban acoustics are typically restricted to the audio range, anthropogenic activity also generates infrasound (<20 Hz, roughly at the lower end of the range of human hearing). Shutdowns related to the COVID-19 pandemic unintentionally created ideal conditions for the study of urban infrasound and low frequency audio (20–500 Hz), as closures reduced human-generated ambient noise, while natural signals remained relatively unaffected. An array of infrasound sensors deployed in Las Vegas, NV, provides data for a case study in monitoring human activity during the pandemic through urban acoustics. The array records a sharp decline in acoustic power following the temporary shutdown of businesses deemed nonessential by the state of Nevada. This decline varies spatially across the array, with stations close to McCarran International Airport generally recording the greatest declines in acoustic power. Further, declines in acoustic power fluctuate with the time of day. As only signals associated with anthropogenic activity are expected to decline, this gives a rough indication of periodicities in urban acoustics throughout Las Vegas. This study's results reflect the city's response to the pandemic and suggest spatiotemporal trends in acoustics outside of shutdowns.

58 GEOSCIENCES↗

DOE OSTI2021

Upregulation of PPPDE1 contributes to anorectal malformations via the mitochondrial apoptosis pathway during hindgut development in rats

Congenital anorectal malformations (ARMs) are among the most prominent deformities of the gastrointestinal tract; however, their precise aetiology remains obscure. Immunohistochemistry demonstrated that, in the ARM group, the PPPDE1-positive cells were widely distributed in the hindgut epithelial tissue from GD13 to GD16. Immunofluorescence revealed that most TUNEL-, Bax-, and Cytochrome C (Cyt C)-positive cells overlapped with PPPDE1-positive cells in the urorectal septum (URS). Western blotting and quantitative real-time RT-PCR revealed that PPPDE1 levels were significantly higher in the ARM group from GD13 to GD14 (p < 0.05). IEC-6 cells were transfected with PPPDE1 overexpression plasmid/NC (negative control) or si-PPPDE1/si-NC. Flow cytometry analysis and CCK-8 assay (used to detect apoptosis and proliferation, respectively), as well as western blotting, showed that the levels of PPPDE1 were positively correlated with the pro-apoptotic molecules Bax and Cyt C. Accordingly, aberrantly high expression of PPPDE1 caused a spatiotemporal imbalance in foetal rats with ARMs during hindgut development. Therefore, the upregulation of PPPDE1 may promote epithelial apoptosis and reduce proliferation in the hindgut via the mitochondrial apoptotic pathway. This could affect the fusion of the URS and cloacal membrane, ultimately inhibiting the hindgut development and resulting in ARMs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Nonlinear propagation of chirped laser pulses through a dispersive and turbulent atmosphere

The evolution of ultrashort laser pulses in dispersive, turbulent, nonlinear, and dissipative media is discussed in connection with nonlinear self-focusing collapse and the onset of laser filamentation. In quiescent air, a laser pulse propagating with a peak power greater than a critical power for self-focusing will undergo a catastrophic, transverse collapse until the intensity is large enough for photoionization. At this point, self-focusing is arrested and balanced by plasma refraction, forming a laser filament. By applying an appropriate chirp, the dispersive properties of the medium can be used to enhance this process and control its onset, and to counter dissipative effects such as molecular absorption and atmospheric scattering. This paper presents an analysis of the effect of atmospheric turbulence on the propagation of nonlinear pulses with dispersion compensation (chirp). Furthermore, the analytical results are compared with wave optics simulations and found to be in reasonable agreement as long as the pulse maintains a near-Gaussian spatiotemporal profile.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

A co-kurtosis PCA based dimensionality reduction with nonlinear reconstruction using neural networks

For turbulent reacting flow systems, identification of low-dimensional representations of the thermo-chemical state space is vitally important, primarily to significantly reduce the computational cost of device-scale simulations. Principal component analysis (PCA), and its variants, are a widely employed class of methods. Recently, an alternative technique that focuses on higher-order statistical interactions, co-kurtosis PCA (CoK-PCA), has been shown to effectively provide a low-dimensional representation by capturing the stiff chemical dynamics associated with spatiotemporally localized reaction zones. While its effectiveness has only been demonstrated based on a priori analyses with linear reconstruction, in this work, we employ nonlinear techniques to reconstruct the full thermo-chemical state and evaluate the efficacy of CoK-PCA compared to PCA. Specifically, we combine a CoK-PCA-/PCA-based dimensionality reduction (encoding) with an artificial neural network (ANN) based reconstruction (decoding) and examine, a priori, the reconstruction errors of the thermo-chemical state. In addition, we evaluate the errors in species production rates and heat release rates, which are nonlinear functions of the reconstructed state, as a measure of the overall accuracy of the dimensionality reduction technique. We employ four datasets to assess CoK-PCA/PCA coupled with ANN-based reconstruction: zero-dimensional (homogeneous) reactor for autoignition of an ethylene/air mixture that has conventional single-stage ignition kinetics, a dimethyl ether (DME)/air mixture which has two-stage (low and high temperature) ignition kinetics, a one-dimensional freely propagating premixed ethylene/air laminar flame, and a two-dimensional dataset representing turbulent autoignition of ethanol in a homogeneous charge compression ignition (HCCI) engine. Finally, results from the analyses demonstrate the robustness of the CoK-PCA based low-dimensional manifold with ANN reconstruction in accurately capturing the data, specifically from the reaction zones.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Evaluation and modification of ELM seasonal deciduous phenology against observations in a southern boreal peatland forest

Phenological transitions determine the timing of changes in land surface properties and the seasonality of exchanges of biosphere-atmosphere energy, water, and carbon. Accurate mechanistic modeling of phenological processes is therefore critical to understand and correctly predict terrestrial ecosystem feedbacks with changing atmospheric and climate conditions. However, the phenological components in the land model of the US Department of Energy's (DOE) Energy Exascale Earth System Model (ELM of E3SM) were previously unable to accurately capture the observed phenological responses to environmental conditions in a well-studied boreal peatland forest. In this research, we introduced new seasonal-deciduous phenology schemes into version 1.0 of ELM and evaluated their performance against the PhenoCam observations at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment in northern Minnesota from 2015 to 2018. We found that phenology simulated by the revised ELM (i.e., earlier spring onsets and stronger warming responses of spring onsets and autumn senescence) was closer to observations than simulations from the original algorithms for both the deciduous conifer (Larix laricina) and mixed shrub layers. Moreover, the revised ELM generally produced higher carbon and water fluxes (e.g., photosynthesis and evapotranspiration) during the growing season and stronger flux responses to warming than the default ELM. A parameter sensitivity analysis further indicated the significant contribution of phenology parameters to uncertainty in key carbon and water cycle variables, underscoring the importance of precise phenology parameterization. Furthermore, this phenological modeling effort demonstrates the potential to enhance the E3SM representation of land-climate interactions at broader spatiotemporal scales, especially under anticipated elevated CO 2 and warming conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Distribution System Resilience Assessment Considering PV Vulnerabilities for Hurricane Events

Distribution networks are increasingly vulnerable to damage and outages from extreme weather events. The integration of solar photovoltaics (PVs) further complicates resilience analysis due to its weather-dependent nature. However, limited research has examined the impacts of weather on PVs under severe events like hurricanes. This paper proposes a probabilistic framework to assess distribution system resilience considering PV vulnerabilities during hurricanes. The framework incorporates (i) a spatiotemporal fragility model to evaluate failure probabilities for distribution lines and PVs, and (ii) resilience indices at both system and component levels. The approach offers valuable insights into the resilience of modern distribution grids under extreme weather conditions. Numerical results on the unbalanced IEEE 123-bus test system validate the effectiveness of the framework.

Vahedi, Soroush [University of Connecticut, Storrs↗

DOE OSTI2022

Few-femtosecond resolved imaging of laser-driven nanoplasma expansion

Abstract The free expansion of a planar plasma surface is a fundamental non-equilibrium process relevant for various fields but as-yet experimentally still difficult to capture. The significance of the associated spatiotemporal plasma motion ranges from astrophysics and controlled fusion to laser machining, surface high-harmonic generation, plasma mirrors, and laser-driven particle acceleration. Here, we show that x-ray coherent diffractive imaging can surpass existing approaches and enables the quantitative real-time analysis of the sudden free expansion of laser-heated nanoplasmas. For laser-ionized SiO 2 nanospheres, we resolve the formation of the emerging nearly self-similar plasma profile evolution and expose the so far inaccessible shell-wise expansion dynamics including the associated startup delay and rarefaction front velocity. Our results establish time-resolved diffractive imaging as an accurate quantitative diagnostic platform for tracing and characterizing plasma expansion and indicate the possibility to resolve various laser-driven processes including shock formation and wave-breaking phenomena with unprecedented resolution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity

Despite the rapid growth of solar energy, we still lack a dynamic, high-fidelity database that tracks the spatiotemporal variations of solar PVs and their associated infrastructures across different places at a spatially resolved scale. The absence of such data presents a barrier to various applications such as solar PV growth projection, solar energy integration, solar incentive design, and climate risk assessment. In this project, we aim to bridge this gap by developing AI-based algorithms to extract granular information about solar PV installations and their associated infrastructures (i.e., distribution grids) from widely available unstructured data like remote sensing images and street views. As a result, we have built the Solar Energy Atlas, a fine-grained, large-scale geospatial overlay of distributed solar PVs and distribution grids. On top of it, we have advanced the understanding of solar adoption and distribution grid vulnerability to climate-induced extremes. Our major contributions can be summarized as follow: (1) By developing new AI algorithms, we have built the most comprehensive solar PV spatiotemporal database covering the entire US. This is the first time we obtained the exact GPS locations, size, subtype, and installation year information for rooftop solar PVs across the US. This database can be used for solar PV growth projection, solar energy integration, solar energy policy analysis and design, and spatially-resolved climate risk assessment. (2) Leveraging this database, we have uncovered the socioeconomic driving factors that are correlated with earlier onset of solar adoption and higher saturated adoption levels. We have identified the heterogeneity in the effects of different types of financial incentives on solar adoption and provided implications for tailoring incentive design based on local income levels to promote equitable solar adoption. (3) We have developed a distribution grid GIS mapping algorithm which can obtain granular geospatial and topology information about distribution grids using multi-modal open data, reducing the dependency on hard-to-obtain smart meter data of conventional approaches. It shows effectiveness in both the U.S. and Sub-Saharan Africa. Using this algorithm, we have uncovered the non-uniform vulnerability of distribution grids to wildfires in California in the aspects of undergrounding protection and Distributed Energy Resources (DER) preparedness. This has provided important implications for improving the affordability and equity of grid adaptation approaches. (3) We have made our produced database publicly available and provided user-friendly interface to enable various stakeholders and the general public to interact with the data. We have also integrated the produced data into the Data Commons platform to enable the public to access the data and correlate it with other location-specific characteristics simply using natural language as queries. The impact of our project is three-fold: (1) New algorithms for mapping solar PVs and distribution grids across space and time, which are open source to facilitate researchers and industry; (2) New databases of solar PVs and distribution grids that have been made publicly available for engineering, social, and policy applications; (3) New understandings and actionable insights on the potential approaches to promoting solar adoption and reducing energy infrastructure vulnerabilities. In this report, we start by discussing the project background and motivation (section 5), followed by the overview of project objectives (section 6). Results and discussion for each task are presented in section 7. Significant accomplishments are summarized in section 8. This report will be concluded by discussing the paths forwards (section 9), products (section 10), and team roles (section 11).

14 SOLAR ENERGY↗

NASA NTRS2012

Characterization of Turbulent Latent and Sensible Heat Flux Exchange Between the Atmosphere and Ocean in MERRA

Turbulent fluxes of heat and moisture across the atmosphere-ocean interface are fundamental components of the Earth's energy and water balance. Characterizing both the spatiotemporal variability and the fidelity of these exchanges of heat and moisture is critical to understanding the global water and energy cycle variations, quantifying atmosphere-ocean feedbacks, and improving model predictability. This study examines the veracity of the recently completed NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) product with respect to its representation of the surface turbulent heat fluxes. A validation of MERRA turbulent heat fluxes and near-surface bulk variables at local, high-resolution space and time scales is achieved by making comparisons to a large suite of direct observations. Both in situ and satellite-observed gridded surface heat flux estimates are employed to investigate the spatial and temporal variability of the surface fluxes with respect to their annual mean climatologies, their seasonal covariability of near-surface bulk parameters, and their representation of extremes. The impact of data assimilation on the near-surface parameters is assessed through evaluation of incremental analysis update tendencies produced by the assimilation procedure. It is found that MERRA turbulent surface heat fluxes are relatively accurate for typical conditions but have systematically weak vertical gradients in moisture and temperature and have a weaker covariability between the near-surface gradients and wind speed than found in observations. This results in an underestimate of the surface latent and sensible heat fluxes over the western boundary current and storm track regions. The assimilation of observations mostly acts to bring MERRA closer to observational products by increasing moisture and temperature near the surface and decreasing the near-surface wind speeds. The major patterns of spatial and temporal variability of the turbulent heat fluxes produced by MERRA compare favorably to observationally based estimates. However, MERRA is distinct in terms of amplitude. These results suggest that MERRA is likely to be a valuable resource for a number of research applications though, as with all turbulent flux estimates, systematic issues should be taken into account

Robert, J. Brent↗

NASA NTRS2012

Characterization of Turbulent Latent and Sensible Heat Flux Exchange Between the Atmosphere and Ocean in MERRA

Turbulent fluxes of heat and moisture across the atmosphere-ocean interface are fundamental components of the Earth s energy and water balance. Characterizing both the spatiotemporal variability and the fidelity of these exchanges of heat and moisture is critical to understanding the global water and energy cycle variations, quantifying atmosphere-ocean feedbacks, and improving model predictability. This study examines the veracity of the recently completed NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) product with respect to its representation of the surface turbulent heat fluxes. A validation of MERRA turbulent heat fluxes and near-surface bulk variables at local, high-resolution space and time scales is achieved by making comparisons to a large suite of direct observations. Both in situ and satellite-observed gridded surface heat flux estimates are employed to investigate the spatial and temporal variability of the surface fluxes with respect to their annual mean climatologies, their seasonal covariability of near-surface bulk parameters, and their representation of extremes. The impact of data assimilation on the near-surface parameters is assessed through evaluation of incremental analysis update tendencies produced by the assimilation procedure. It is found that MERRA turbulent surface heat fluxes are relatively accurate for typical conditions but have systematically weak vertical gradients in moisture and temperature and have a weaker covariability between the near-surface gradients and wind speed than found in observations. This results in an underestimate of the surface latent and sensible heat fluxes over the western boundary current and storm track regions. The assimilation of observations mostly acts to bring MERRA closer to observational products by increasing moisture and temperature near the surface and decreasing the near-surface wind speeds. The major patterns of spatial and temporal variability of the turbulent heat fluxes produced by MERRA compare favorably to observationally based estimates. However, MERRA is distinct in terms of amplitude. These results suggest that MERRA is likely to be a valuable resource for a number of research applications though, as with all turbulent flux estimates, systematic issues should be taken into account.

Roberts, J. Brent↗

NASA NTRS2022

Summer Greenland Blocking Diversity and Its Impact on the Surface Mass Balance of the Greenland Ice Sheet

The increase in Greenland Ice Sheet (GrIS) surface runoff since the turn of the century has been linked to a rise in Greenland blocking frequency. However, a range of synoptic patterns can be considered blocked flow and efforts that summarize all blocking types indiscriminately likely fail to capture consequential differences in GrIS response. To account for these differences, we employ ERA5 reanalysis to identify summer blocking using two independent blocking metrics: the Greenland Blocking Index (GBI) and the blocking index of Pelly and Hoskins (2003, https://doi.org/10.1175/1520-0469(2003)060<0743:ANPOB>2.0.CO;2). We then conduct a self-organizing map analysis to objectively classify synoptic conditions during Greenland blocking episodes and identify three primary blocking types: (a) a high-amplitude Omega block, (b) a lower-amplitude, stationary summer ridge, and (c) a cyclonic wave breaking pattern. Using Modèle Atmosphérique Régional output, we document the spatiotemporal progression of the surface energy and mass balance for each blocking type. Relative to all blocking episodes, summer ridge patterns produce more melt over the southern ice sheet, Omega blocks produce more melt across the northern ice sheet, and cyclonic wave breaking patterns produce more melt in northeast Greenland. Our results indicate that the recent trend in summer Greenland blocking was largely driven by an increase in Omega patterns and suggest that Omega blocks have played a central role in the recent acceleration of GrIS mass loss. Furthermore, the GBI exhibited a relative bias toward Omega patterns, which may help explain why it has measured stronger trends in summer Greenland blocking than other blocking metrics.

atmospheric blocking↗

DOE OSTI2020

Leveraging Fine-Grained Occupancy Estimation Patterns for Effective HVAC Control

As occupancy sensing technologies become mature, various occupancy sensors are increasingly deployed in commercial buildings for pervasive occupancy monitoring. These sensors provide occupant-count data, which contains rich spatiotemporal information about occupancy patterns. With long-term occupant-count data collected from a commercial building, we design three different predictive models that capture the occupancy dynamics and examine how a model predictive control of the HVAC system benefits from actual occupancy count prediction. Our analysis reveals that mispredictions of occupancy states, especially false positives and false negatives, may introduce inefficient control that leads to energy waste or user discomfort. To address this issue, we take a step further to design an adaptive model predictive controller that minimizes inefficient control actions according to misprediction types and distributions. A comprehensive evaluation is performed in OpenBuild and EnergyPlus simulators to study the effectiveness of the proposed end-to-end control strategy. The evaluation shows that the proposed solution reduces energy consumption by 29.5% while improving the average weighted occupants comfort by 86.7% in Predicted Mean Vote (PMV) over the fixed schedule strategy.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Identifying Indicators of Harmful Algal Blooms in Coastal Atacama Using Satellite Image Processing Techniques to Improve Industry and Authority Response

Over the past decade, Chile has experienced a significant rise in both the frequency and intensity of coastal Harmful Algal Blooms (HABs). These bloom events are a growing concern for the Atacama Region especially, with potential impacts on human health, aquaculture, and the environment. HABs are caused by an excess proliferation of microalgae, with certain algae species commonly found in Chilean HABs capable of producing toxins which can poison fish, toxify shellfish, and cause illness or death when ingested by humans. Collaborating with the Ministry of Health of Chile, Centro de Información de Recursos Naturales, the University of Atacama, and the Embassy of Chile’s Agricultural Office, this study identifies potential indicators of HABs in the Coastal Atacama Region using NASA Earth observations. Satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard NASA’s Aqua satellite was used for the identification of chlorophyll-a which provides direct estimates of the algae pigment’s concentration in water bodies. In addition, the spatiotemporal patterns of several other parameters were examined to identify correlations with occurrences of HABs, including sea surface temperature and normalized fluorescence line height using Aqua MODIS, Normalized Difference Turbidity Index (NDTI) using Landsat 8, and ocean wind speed measurements from the NOAA Joint Polar Satellite System. Analysis of these patterns revealed hotspots of high chlorophyll-a concentrations from the years 2014 to 2022, the most major of which occurring in the coastal zones of Chañaral, Bahía Inglesa, and Huasco. Also, through the use of a similarity search conducted, the seasonal variation of these indicators was mapped to highlight areas with great likelihood of experiencing HABs in the future. The results can guide future efforts for in-situ water sampling, toxin analysis and assessment, and HAB monitoring and prevention.

Cody O'Ferrall↗

NASA NTRS2020

Building a landslide hazard indicator with machine learning and land surface models

The U.S. Pacific Northwest has a history of frequent and occasionally deadly landslides caused by various factors. Using a multivariate, machine-learning approach, we combined a Pacific Northwest Landslide Inventory with a 36-year gridded hydrologic dataset from the National Climate Assessment – Land Data Assimilation System to produce a landslide hazard indicator (LHI) on a daily 0.125-degree grid. The LHI identified where and when landslides were most probable over the years 1979–2016, addressing issues of bias and completeness that muddy the analysis of multi-decadal landslide inventories. The seasonal cycle was strong along the west coast, with a peak in the winter, but weaker east of the Cascade Range. This lagging indicator can fill gaps in the observational record to identify the seasonality of landslides over a large spatiotemporal domain and show how landslide hazard has responded to a changing climate.

XGBoost↗

DOE OSTI2024

Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition

Abstract Vibrational spectroscopy allows us to understand complex physical and chemical interactions of molecular crystals and liquids such as ammonia, which has recently emerged as a strong hydrogen fuel candidate to support a sustainable society. We report inelastic neutron scattering measurement of vibrational properties of ammonia along the solid-to-liquid phase transition with high enough resolution for direct comparisons to ab-initio simulations. Theoretical analysis reveals the essential role of nuclear quantum effects (NQEs) for correctly describing the intermolecular spectrum as well as high energy intramolecular N-H stretching modes. This is achieved by training neural network models using ab-initio path-integral molecular dynamics (PIMD) simulations, thereby encompassing large spatiotemporal trajectories required to resolve low energy dynamics while retaining NQEs. Our results not only establish the role of NQEs in ammonia but also provide general computational frameworks to study complex molecular systems with NQEs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗