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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 NTRS1999

Validation of Surface Skin Temperature and Moisture Profiles Using Satellite Data

New validation techniques and metrics using satellite data have been developed to evaluate the quality of model-based estimates of surface skin temperature (Tg) and moisture profiles (q). The satellite data consist of clear sky outgoing long-wave radiation (CLR), broadband radiances from 8 to 12 mu (RadWn), brightness temperature centered around 10.8 mu (Tbb), and total precipitable water (TPW) from microwave radiometry. We show that CLR can be used to diagnose Tg. Furthermore, by using a combination of CLR and RadWn from CERES-TRMM measurements and TPW from SSM/I, we are able to identify errors in the moisture profile. Finally, three-hourly Tbb from the International Satellite Cloud Climatology Project can be used to evaluate the amplitude and diurnal variation of Tg. For purpose of illustration, Tg and q are evaluated from runs with an early version of the Goddard Earth Observing System Data Assimilation System (GEOS-2). It is found that, in general, Tg is too cold in the winter hemisphere and q is too wet in the upper atmosphere. In order to address these deficiencies, several improvements have been implemented into GEOS-2, including a Land-Surface-Model, a Moist Turbulence Scheme, and the assimilation of new TOVS retrievals. Preliminary results indicate positive impacts from each of these implementations.

Wu, Man Li C.↗
NASA NTRS2009

Using LIDAR and Quickbird Data to Model Plant Production and Quantify Uncertainties Associated with Wetland Detection and Land Cover Generalizations

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the MOderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Cook, Bruce D.↗
NASA NTRSDate not supplied

The Equilibrium Response of Climate and Composition to Lightning

Climate change can affect atmospheric composition through perturbation of natural processes, leading to complex feedbacks. The primary atmospheric oxidants OH and ozone are very sensitive to emissions of nitrogen oxides (NO x ) from lightning, and therefore so are the subsequent chemical perturbations to long-lived greenhouse gases (e.g., methane) and aerosol chemistry and physics. Meanwhile, cloud electrification responds to both meteorology and composition (aerosol particles). Key to understanding the ultimate impact of lightning on air quality and climate is the long-term methane feedback. Here, we present simulations from the GISS ModelE2.1 chemistry-climate model in which we isolate the response of Earth’s radiative budget and composition to lightning NO x in the present day and at the end of this century by allowing the model to re-equilibrate following removal of the source. Whereas lightning initially contributes to surface ozone enhancements, longer-term responses amplified by the methane lifetime feedback reduce surface ozone on multi-decadal time scales in the northern midlatitudes. Lightning consequently contributes a strong negative radiative effect in the present day (-0.5 W m -2 ), stronger than that estimated for the anthropogenic NOx source. In addition to influencing tropospheric composition, we find significant changes in stratospheric dynamics and composition. We test the sensitivity of our results to multiple parameterizations for global lightning activity.

Lightning↗
NASA NTRS2022

Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ)

The NOAA/NASA Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) experiment was a multi-agency, inter-disciplinary research effort to: (a) obtain detailed measurements of trace gas and aerosol emissions from wildfires and prescribed fires using aircraft, satellites and ground-based instruments, (b) make extensive suborbital remote sensing measurements of fire dynamics, (c) assess local, regional, and global modeling of fires, and (d) strengthen connections to observables on the ground such as fuels and fuel consumption and satellite products such as burned area and fire radiative power. From Boise, ID western wildfires were studied with the NASA DC-8 and two NOAA Twin Otter aircraft. The high-altitude NASA ER-2 was deployed from Palmdale, CA to observe some of these fires in conjunction with satellite overpasses and the other aircraft. Further research was conducted on three mobile laboratories and ground sites, and 17 different modeling forecast and analyses products for fire, fuels and air quality and climate implications. From Salina, KS the DC-8 investigated 87 smaller fires in the Southeast with remote and in-situ data collection. Sampling by all platforms was designed to measure emissions of trace gases and aerosols with multiple transects to capture the chemical transformation of these emissions and perform remote sensing observations of fire and smoke plumes under day and night conditions. The emissions were linked to fuels consumed and fire radiative power using orbital and suborbital remote sensing observations collected during overflights of the fires and smoke plumes and ground sampling of fuels.

Carsten Warneke↗
NASA NTRSDate not supplied

20-Years of Atmospheric Temperature, Water Vapor, Cloud, and Surface Temperature Anomalies and Trends Derived From Operational Hyperspectral Ir Sounders

Hyperspectral IR sounders such as AIRS on Aqua, CrIS on S-NPP, NOAA20 and JPSS-2, IASI on Metop A, B, and C provide high-quality atmospheric temperature, water, vapor, and greenhouse gas vertical profiles. Additionally, they provide atmospheric cloud properties, surface emissivity, and surface skin temperatures. We have developed two algorithms which can consistently derive these products from multiple IR sounders. The first one is a Single Field-of-view Sounder Atmospheric Product (SIFSAP) algorithm and the second one is a Climate Fingerprinting Sounder Product (ClimFiSP) algorithm. Compared to current operational AIRS and CrIS Level-2 (L2) algorithms, which perform one retrieval for each 3 by 3 field of views (FOVs) using a cloud-clearing approach, the SiFSAP algorithm, on the other hand, performs one retrieval for each FOV using an all-sky optimal estimation approach. The SiFSAP algorithm retrieves all the above-mentioned atmosphere and surface properties simultaneously including cloud properties with 3-time higher spatial resolution and 9-times more products. The core of the SiFSAP algorithm is an accurate and fast Principal Component-based Radiative Transfer Model (PCRTM), which can calculate hyperspectral radiance spectra under both clear and cloudy conditions. The PCRTM was developed in the past decade using consistent reference line-by-line radiative transfer model and spectroscopy for hyperspectral sounders such as AIRS, CrIS, IASI, NAST-I, and S-HIS. The SiFSAP retrieval algorithm also uses the same climatology a priori and associated covariances, which makes it ideal for generating high quality products for both weather and climate applications. Climate products are typically derived by performing spatial and temporal averaging of L2 products. It is a time-consuming process to generate L2 data products since AIRS, CrIS, and IASI have millions of observations each day with thousands of spectral channels for each observation. Additionally, differences in L2 retrieval algorithms for different satellite sensors can lead to errors in the climate products. Our ClimFiSP algorithm, which performs retrievals from spatiotemporally averaged L1 hyperspectral radiances directly, will be orders of magnitude faster than traditional method. he ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides accurate data climate data fusion products from multiple satellite sensors. We have applied this method to both AIRS and CrIS (on SNPP and on NOAA 20) data and generated two decades climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. Both SiFSAP and ClimFiSP will be available at NASA GES DISC data center for public access.

Xu Liu↗
NASA NTRS2012

Impact of Emissions and Long-Range Transport on Multi-Decadal Aerosol Trends: Implications for Air Quality and Climate

We present a global model analysis of the impact of long-range transport and anthropogenic emissions on the aerosol trends in the major pollution regions in the northern hemisphere and in the Arctic in the past three decades. We will use the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model to analyze the multi-spatial and temporal scale data, including observations from Terra, Aqua, and CALIPSO satellites and from the long-term surface monitoring stations. We will analyze the source attribution (SA) and source-receptor (SR) relationships in North America, Europe, East Asia, South Asia, and the Arctic at the surface and free troposphere and establish the quantitative linkages between emissions from different source regions. We will discuss the implications for regional air quality and climate change.

Chin, Mian↗
NASA NTRS2007

Finding Uncertainties that Cause the Age Dependence of Dose Limits to Be Immature

Space radiation permissible exposure limits (PEL) are intended to set acceptable levels of cancer risks, and avoid any clinical significant non-cancer effects. The 1989 recommendation of the National Council of Radiation Protection and Measurements (NCRP) recommended a strong age dependence of dose limits that departed drastically from the then mature 1970 dose limits recommendations from the National Academy of Science, which were independent of age. In 2000, the NCRP recommended revised limits that showed a similar trend of risk with age to the 1989 report. In this model, the cancer risk per Sv varies by more than 2-fold for ages between 30- and 50-yr. Therefore for galactic cosmic rays exposure, astronaut age has a larger influence on risk then radiation shielding mass or material composition, vehicle propulsion method, or position in the solar cycle. For considering the control of mission costs and resources, the possibility of using astronaut age as a trade variable in mission design could be considered. However, the uncertainties in describing the age dependence on risk have not been fully explored. We discuss biological factors that influence the age dependence of radiation risks, including susceptibility, expression and latency, and radiation quality. These factors depend not only on the individual s age, but also their genetic sensitivity and interaction with other environmental factors. Epidemiological data is limited in describing the age dependence on risk. The 2005, BEIR VII report recommends an age dependence for cancer risk attributable solely to the life-table disagreeing strongly with the NCRP model. However, BEIR VII also noted the limited power of human data for concomitantly describing both age and age after exposure dependences of cancer risks. Many experimental studies have shown that high LET radiation (e.g., high charge and energy (HZE) nuclei and neutrons) display reduced latency compared to low LET radiation, suggesting distinct biological factors are important. We discuss potential molecular mechanisms that would influence the age dependence of radiation risks. A probability distribution function for the uncertainties in age-dependence of risk models is described and predictions for Mars missions discussed. Our report suggests that theoretical considerations based on new experimental studies are needed to ensure the correct age dependence in space radiation risk models and the resulting Astronaut PEL.

Cucinotta, Francis A.↗
NASA NTRS2006

Comparisons of Integrated Radiation Transport Models with Microdosimetry Data in Spaceflight

Astronauts are exposed to galactic cosmic rays (GCR), trapped protons, and possible solar particle events (SPE) during spaceflight. For such complicated mixtures of radiation types and kinetic energies, tissue equivalent proportional counters (TEPC's) represent a simple time-dependent approach for radiation monitoring. Of interest in radiation protection is the average quality factor of a radiation field defined as a function of linear energy transfer, LET, Q(sub ave)(LET). However TEPC's measure the average quality factors as a function of lineal energy (y), Q(sub ave)(y) defined as the average energy deposition in a volume divided by the average chord length of the volume. Lineal energy, y deviates from LET due to energy straggling, delta-ray escape or entry, and nuclear fragments produced in the detector. Using integrated space radiation models that includes the transport code HZETRN/BRYNTRN, the quantum nuclear interaction model, QMSFRG, and results from Monte-Carlo track simulations of TEPC's response to ions, we consider comparisons of model calculations to TEPC results from NASA missions in low Earth orbit and make predictions for lunar and Mars missions. Good agreement between the model and measured spectra from past NASA missions is found. A finding of this work is that TEPC's values for trapped or solar protons of Q(sub ave)(y) range from 1.9-2.5, overestimating Q(sub ave)(LET), which ranges from 1.4-1.6 with both quantities increasing with shielding depth due to nuclear secondaries Comparisons for the complete GCR spectra show that Q(sub ave)(LET) for GCR is approximately 3.5-4.5, while TEPC's measure 2.9-3.4 for Q(sub ave)(y) with the GCR values decreasing with depth as heavy ions are absorbed in shielding material. Our results support the use of TEPC's for space radiation environmental monitoring when computational analysis is used for proper data interpretation.

Cucinotta, Francis A.↗
NASA NTRS1987

Modelling and design of high efficiency radiation tolerant indium phosphide space solar cells

Using a fairly comprehensive model, a parametric variation study was performed of the InP shallow homojunction solar cell with a view to determining the maximum realistically achievable efficiency and an optimum design that would yield this efficiency. Calculations show that with good-quality epitaxial material, a beginning-of-life efficiency of about 20.3 precent at 1AM0, 25 C may be possible. The design parameters of the near-optimum cell are given. Also presented are the expected effect on the performance parameters of radiation damage by 1-MeV electrons and a possible explanation of the high radiation tolerance of InP solar cells.

Goradia, Chandra↗
NASA NTRS1992

A numerical model including PID control of a multizone crystal growth furnace

This paper presents a 2D axisymmetric combined conduction and radiation model of a multizone crystal growth furnace. The model is based on a programmable multizone furnace (PMZF) designed and built at NASA Lewis Research Center for growing high quality semiconductor crystals. A novel feature of this model is a control algorithm which automatically adjusts the power in any number of independently controlled heaters to establish the desired crystal temperatures in the furnace model. The control algorithm eliminates the need for numerous trial and error runs previously required to obtain the same results. The finite element code, FIDAP, used to develop the furnace model, was modified to directly incorporate the control algorithm. This algorithm, which presently uses PID control, and the associated heat transfer model are briefly discussed. Together, they have been used to predict the heater power distributions for a variety of furnace configurations and desired temperature profiles. Examples are included to demonstrate the effectiveness of the PID controlled model in establishing isothermal, Bridgman, and other complicated temperature profies in the sample. Finally, an example is given to show how the algorithm can be used to change the desired profile with time according to a prescribed temperature-time evolution.

Panzarella, Charles H.↗
NASA NTRS1999

Ionizing radiation-induced mutagenesis: radiation studies in Neurospora predictive for results in mammalian cells

Ionizing radiation was the first mutagen discovered and was used to develop the first mutagenicity assay. In the ensuing 70+ years, ionizing radiation became a fundamental tool in understanding mutagenesis and is still a subject of intensive research. Frederick de Serres et al. developed and used the Neurospora crassa ad-3 system initially to explore the mutagenic effects of ionizing radiation. Using this system, de Serres et al. demonstrated the dependence of the frequency and spectra of mutations induced by ionizing radiation on the dose, dose rate, radiation quality, repair capabilities of the cells, and the target gene employed. This work in Neurospora predicted the subsequent observations of the mutagenic effects of ionizing radiation in mammalian cells. Modeled originally on the mouse specific-locus system developed by William L. Russell, the N. crassa ad-3 system developed by de Serres has itself served as a model for interpreting the results in subsequent systems in mammalian cells. This review describes the primary findings on the nature of ionizing radiation-induced mutagenesis in the N. crassa ad-3 system and the parallel observations made years later in mammalian cells.

NASA Discipline Radiation Health↗
NASA NTRS2014

Investigating the Aerosol Optical and Radiative Characteristics of Heavy Haze Episodes in Beijing During January of 2013

Several heavy atmospheric haze pollution episodes occurred over eastern and northern China during January of 2013. The pollution covered more than 100 sq km and caused serious impacts on environmental quality, human health, and transportation. In this study, we characterize aerosol microphysical, optical, and radiative characteristics using a combination of ground-based Sun/sky radiometer retrievals and a radiative transfer model. Our results show that during about half of the total number of days, daily PM2.5 and PM10 concentrations are larger than 100 microg/cu m, with maxima of 462 and 433 microg/cu m, respectively, during the haze events. Fine-mode (PM2.5) particles dominated the aerosol size during the episodes. The volume size distribution and median radius of fine-mode particles generally increase as aerosol optical depth at 440 nm (AOD440) increases. The median effective radius of fine-mode particles increases from 0.15 microns at low AOD value (AOD440~0.3) to a radius of 0.25-0.30 microns at high AOD value (AOD440> or = 1.0). The daily mean single-scattering albedo (SSA), imaginary part of refractive index (RI), and asymmetry factor display pronounced spectral behaviors. The overall mean SSA440 and SSA675 are 0.892 and 0.905, respectively. The corresponding RI440 and RI675 are 0.016 and 0.011, respectively. This indicates that a significant amount of absorption occurred under the haze event in Beijing during January 2013. Approximately half of the incident solar radiation energy went into heating the atmosphere as a result of strong aerosol loading and absorption. The daily averaged heating rate in the haze particle layer (0-3.2 km) varies from 0.12 to 0.81 K/day in Beijing, which might exert profound impact on the atmospheric thermodynamic and dynamical structures and cloud development, which should be further studied.

bejing↗
NASA NTRS2022

An Evaluation of Biomass Burning Aerosol Mass, Extinction, and Size Distribution in Geos Using Observations From Camp2EX

Biomass burning aerosol impacts aspects of the atmosphere and Earth system through radiative forcing, serving as cloud condensation nuclei, and air quality. Despite its importance, the representation of biomass burning aerosol is not always accurate in numerical weather prediction and climate models or reanalysis products. Using observations collected as part of the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) in August through October of 2019, aerosol concentration and optical properties are evaluated within the Goddard Earth Observing System (GEOS) and its underlying aerosol module, GOCART. In the operational configuration, GEOS assimilates aerosol optical depth observations at 550 nm to constrain aerosol fields. Particularly for biomass burning aerosol, without the assimilation of aerosol optical depth, aerosol extinction is underestimated compared to observations collected in the Philippines region during the CAMP2Ex campaign. The assimilation process adds excessive amounts of carbon to account for the underestimated extinction, resulting in positive biases in the mass of black and organic carbon, especially within the boundary layer, relative to in situ observations from the Langley Aerosol Research Group Experiment. Counteracting this, GEOS is deficient in sulphate and nitrate aerosol just above the boundary layer. Aside from aerosol mass, extinction within GEOS is a function of ambient relative humidity and an assumed particle size distribution. The relationship between dry and ambient extinction in GEOS reveals that hygroscopic growth is too aggressive within the model for biomass burning aerosol. An additional concern lies in the assumed particle size distribution for GEOS, which has a mode radius that is too small for organic carbon. Variability in the observed particle size distribution for biomass burning aerosol within a single flight also illuminates the fact that a single assumed particle size distribution is not sufficient and that for a proper representation, a more advanced aerosol module with GEOS may be necessary.

Aerosol Mass↗
NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗
NASA NTRS2019

Analysis of A Severe Dust Storm and Its Impact on Air Quality Conditions Using Wrf-Chem Modeling, Satellite Imagery, and Ground Observations

This study presents a comprehensive analysis of an extreme dust event recorded in the Arabian Peninsula and the United Arab Emirates (UAE) between 31 March and 3 April 2015. Simulations of the dust event with the Weather Research and Forecasting model coupled with the Chemistry module (WRF-Chem) were analyzed and verified using MSG-SEVIRI imagery and aerosol optical depth (AOD) from the recent 1-km Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm for MODIS Terra/Aqua. Data from the National Centers for Atmospheric Prediction/National Center for Atmospheric Research (NCEP/NCAR) and the upper-air radiosonde observations were used to understand the synoptic of the event. In addition, the impact of the event on atmospheric and air quality conditions is investigated. The Air Quality Index (AQI) was calculated prior, during, and after the event to assess the degradation of air quality conditions. Simulated temperature, relative humidity, wind speed, and surface radiation were compared to observations at six monitoring stations in the UAE giving R2 values of 0.84, 0.63, 0.60, and 0.84, respectively. From 1 to 2 April 2015, both observations and simulations showed an average drop in temperature from 33 to 26 °C and radiance reduction from about 950 to 520 Wm−2. The AOD modeled by WRF-Chem showed a good correlation with Aerosol Robotic Network (AERONET) measurements in the UAE with R2 of 0.83. The AQI over the UAE reached hazardous levels during the peak of the dust event before rapidly decreasing to moderate–good air quality levels. This work is the first attempt to demonstrate the potential of using WRF-Chem to estimate AQI over the UAE along with two satellite products (MODIS-MAIAC and MSG-SEVIRI) for dust detection and tracking.

dust storm↗
NASA NTRS2000

Solar UV Radiation and the Origin of Life on Earth

We have started a comprehensive, interdisciplinary study of the influence of solar ultraviolet radiation on the atmosphere of of the early Earth. We plan to model the chemistry of the Earth atmosphere during its evolution, using observed UV flux distributions of early solar analogs as boundary conditions in photochemical models of the Earth's atmosphere. The study has four distinct but interlinked parts: (1) Establishing the radiation of the early Sun; (2) Determining the photochemistry of the early Earth's atmosphere; (3) Estimating the rates of H2 loss from the atmosphere; and (4) Ascertaining how sensitive is the photochemistry to the metallicity of the Sun. We are currently using STIS and EUVE to obtain high-quality far-UV and extreme-UV observations of three early-solar analogs. We will perform a detailed non-LTE study of each stars, and construct theoretical model photosphere, and an empirical model chromospheres, which can be used to extrapolate the continuum to the Lyman continuum region. Given a realistic flux distribution of the early Sun, we will perform photochemical modeling of weakly reducing primitive atmospheres to determine the lifetime and photochemistry of CH4. In particular, we will make estimates of the amount of CH4 present in the prebiotic atmosphere, and estimate the atmospheric CH4 concentration during the Late Archean (2.5-3.0 b.y. ago) and determine whether it would have been sufficiently abundant to help offset reduced solar luminosity at that time. Having obtained a photochemical model, we will solve for the concentrations of greenhouse gasses and important pre-biotic molecules, and perform a detailed radiative transfer calculations to compute the UV flux reaching the surface.

Heap, Sara R.↗
NASA NTRSDate not supplied

SWIPE: Spectral Water Inversion Processor and Emulator

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will be discussing the progress made developing SWIPE: Spectral Water Inversion Processor and Emulator. SWIPE is a platform for advanced modeling of coastal and inland aquatic habitats. The goal is create a comprehensive and cohesive system to leverage recent advancements in computation and machine learning to develop a synthetic training ground for sensitivity studies and algorithm development. The four principal facets of SWIPE include: 1. Advanced two-layer coated sphere bio-optical modeling and GPU radiative transfer modeling, 2. Big Data involving massive synthetic spectral libraries of optical properties of various global aquatic particles, surface reflectance, and top-of-atmosphere reflectance, all at hyperspectral resolution leveraging high-end computing systems at NASA Ames Research Center, 3. Deep Learning for algorithm development for water quality inversion of concentrations of common biogeophysical variables as well as optics, full uncertainty characterization by water type, and forward emulation, and lastly, 4. Image Processing for application of developed retrieval algorithms for both hyperspectral and multispectral sensors with experimental corrections for global adjacency, noise, sunglint, and benthic reflectance. This presentation will demonstrate the Equivalent Algal Populations (EAP) two-layer coated sphere scattering model which has been used develop spectral libraries of hyperspectral inherent optical properties of roughly 80 species of phytoplankton, covering 15 different classes and nine taxonomic functional types. The EAP model was also used to derive spectral properties of 10 different non-algal particle functional types. Examples of how the SMART-G (Speed-up Monte-carlo Advanced Radiative Transfer using GPU) radiative transfer code is used to model optically complex aquatic signals will be presented and discussed in the context of creating a massive synthetic database which can leverage the full power of next generation machine learning techniques and high end computing for water quality inversion. We will discuss our active investigation in things like appropriate model architectures, dimensionality reduction techniques such as PCA and autoencoders, uncertainty quantification and abstaining, and which variables actually benefit most from hyperspectral information versus multispectral resolution. We are also curious about questions relating to cost/benefit analysis in terms of computation resources, neural network complexity, and data volumes. Answers to these questions will hopefully elaborate on cost efficiency for potential future sensor design considerations.

SWIPE↗
NASA NTRS2021

Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Automatic Differentiation

Remote sensing measurements from multi-angle polarimeters (MAPs) contain rich aerosol microphysical property information, and these sensors have been used to perform retrievals in optically complex atmosphere and ocean systems. Previous studies have concluded that, generally, five moderately separated viewing angles in each spectral band provide sufficient accuracy for aerosol property retrievals, with performance gradually saturating as angles are added above that threshold. The Hyper-Angular Rainbow Polarimeter (HARP) instruments provide high angular sampling with a total of 90-120 unique angles across four bands, a capability developed mainly for liquid cloud retrievals. In practice, not all view angles are optimal for aerosol retrievals due to impacts of clouds, sun glint, and other impediments. The many viewing angles of HARP can provide resilience to these effects, if the impacted views are screened from the dataset, as the remaining views may be sufficient for successful analysis. In this study, we discuss how the number of available viewing angles impacts aerosol and ocean color retrieval uncertainties, as applied to two versions of the HARP instrument. AirHARP is an airborne prototype that was deployed in the ACEPOL field campaign, while HARP2 is an instrument in development for the upcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Based on synthetic data, we find that a total of 20-30 angles across all bands (i.e. five to eight viewing angles per band) are sufficient to achieve good retrieval performance. Following from this result, we develop an adaptive multi-angle polarimetric data screening (MAPDS) approach to evaluate data quality by comparing measurements with their best-fitted forward model. The FastMAPOL retrieval algorithm is used to retrieve scene geophysical values, by matching an efficient, deep learning-based, radiative transfer emulator to observations. The data screening method effectively identifies and removes viewing angles affected by thin cirrus clouds and other anomalies, improving retrieval performance. This was tested with AirHARP data, and we found agreement with the High Spectral Resolution Lidar-2 (HSRL-2) aerosol data. The data screening approach can be applied to modern satellite remote sensing missions, such as PACE, where a large amount of multi-angle, hyperspectral, polarimetric measurements will be collected.

multi-angle polarimeter↗