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NASA NTRS2021

Improved Estimates of Pentad Precipitation through the Merging of Independent Precipitation Datasets

Three independent, quasi-global, gridded datasets of precipitation (a rain gauge-based dataset, the satellite-only component of the NASA Integrated Multi-satellitE Retrievals for Global Precipitation Measurement mission [IMERG] Final Run precipitation product, and precipitation estimates derived from NASA Soil Moisture Active Passive [SMAP] soil moisture retrievals), are objectively combined into a single pentad precipitation dataset at 36-km resolution using a unique approach based on extended triple collocation. The quality of each of the four datasets is then evaluated against independent observations. When a global land surface model at 36-km resolution is integrated four times, once utilizing the merged precipitation forcing and once with each of the three contributing datasets, the near-surface soil moisture variations produced with the merged forcing validate best against independent satellite-based soil moisture fields. In addition, the merged dataset is found to be more consistent, relative to each contributor, with estimates of air temperature variations across the globe. The merged dataset thus appears to draw successfully on the complementary strengths of each contributor: the particularly high quality of the rain gauge-based dataset in areas of high gauge density, the more uniform accuracy across the globe of the IMERG data, and the moderate accuracy, particularly in semi-arid regions, of the soil moisture retrieval-based data. Plain Language Summary Obtaining measurements of precipitation across the globe can be challenging. Rain gauges in some ways provide the most accurate measurements, but gauges are absent in many parts of the world, and even where they exist, they only measure precipitation at the gauge itself and therefore may not provide an accurate large-scale average. Satellite-based estimates of precipitation largely overcome these problems, but such data have their own issues, notably a “snapshot” (rather than a time-average) character of the measurements and difficulty associated with interpreting the measured radiances in the presence of complex land surfaces. In the present paper, we use a novel approach to generate a “merged” dataset, one that optimally combines the gauge precipitation information and the satellite-based precipitation information with a third set of estimates derived from soil moisture retrievals. The merged precipitation dataset and each of the three contributors (aggregated here to 5-day averages at a spatial resolution of about 36-km) are then evaluated for consistency with independent geophysical fields. The merged dataset is found to perform best, a clear indication that it takes proper advantage of the complementary strengths of each contributor and, accordingly, that the presented approach for merging the different contributors is indeed viable.

Precipitation↗
NASA NTRS2021

Retrieval of Snow Water Equivalent by the Precipitation Imaging Package (PIP) over Northern Great Lakes

Performance of the Precipitation Imaging Package (PIP) for estimating the snow water equivalent (SWE) is evaluated through a comparative study with the collocated National Oceanic and Atmospheric Administration National Weather Service snow stake field measurements. The PIP together with a vertically pointing radar, a weighing bucket gauge, and a laser-optical disdrometer was deployed at the NWS Marquette, Michigan office building for a long-term field study supported by the National Aeronautics and Space Administration’s Global Precipitation Measurement mission Ground Validation program. The site was also equipped with a weather station. During the 2017-18 winter, PIP functioned nearly uninterrupted at frigid temperatures accumulating 2345.8 mm of geometric snow depth over a total of 499 hours. This long record consists of 30 events, and the PIP-retrieved and snow stake field measured SWE differed less than 15% in every event. For the windy events, the wind corrected stake field record agreed better with the PIP-retrieved SWE. Two of the major events with the longest duration and the highest accumulation are examined in detail. The particle mass with a given diameter was much lower during a shallow, colder, uniform lake-effect event than in the deep, less cold, and variable synoptic event. This study demonstrated that PIP is a robust instrument for operational use, and is reliable for deriving the bulk properties of falling snow.

Ali Tokay↗
NASA NTRS2021

Calibration of the DSCOVR EPIC Visible and NIR Channels using Multiple LEO Radiometers

The first five years of operation of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) at the Lagrange one point have produced results that uniquely complement the data from currently operating low orbit Earth-observing instruments. In this paper we describe an updated unified approach to EPIC calibration. In this approach, calibration coefficients and their trends were obtained by comparing EPIC observations to the measurements from polar orbiting radiometers. In this study L1B reflectances from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua and Terra satellites, Multi-angle Imaging Spectroradiometer (MISR) onboard Terra and Visible Infrared Imaging Radiometer (VIIRS) onboard the Suomi National Polar-orbiting Partnership (Suomi NPP) spacecraft were used to infer calibration coefficients for four EPIC visible and near-infrared channels: 443 nm, 551 nm, 680 nm, and 780 nm. EPIC Version three measurements made between June 2015 and August 2020 were used for comparison. The calibration procedure identifies the most homogeneous low Earth orbit radiometer scenes matching scattering angles that are temporarily and spatially collocated with EPIC observations. These scenes are used to determine reflectance to count (R/C) ratios in spectrally analogous channels. Seasonal average R/C ratios were analyzed to obtain EPIC calibration gains and trends. The trends for the full dataset period are not statistically significant except in the 443 nm channel. No significant changes in calibration were found after the instrument’s exit from safe hold in March 2020. The R/C ratios were also used to determine the differences in EPIC gains resulting from separate calibrations: against MODIS Aqua or Terra, as well as against forward or aftward MISR cameras. Statistical tests indicate that the differences between the two datasets are not significant except in the 780 nm channels where Aqua-derived coefficients may be around 2% lower compared to Terra. The dependence of EPIC calibration gains on the instrument scattering angle and on DSCOVR-Earth distance were investigated. Lastly, model Low Earth Orbit (LEO) reflectances calculated to match the EPIC viewing geometry were employed to study how EPIC calibration coefficients depend on EPIC-LEO viewing geometry differences. The effect of LEO and EPIC angular mismatch on calibration was shown to be small.

deep space climate observatory/earth polychromatic↗
NASA NTRSDate not supplied

Polarization Difference - The Scientific Story Behind One Variable and Its Practical Applications

Cloud ice and snow microphysical characteristics play an important role in determining cloud radiative effect. They are also closely connected to the details of surface precipitation characteristics. Ice orientation is one microphysical property that is traditionally believed to have trivial impact on the weather systems/climate, and it is hard to measure from space. In this presentation, I will show how many scientific stories can be told from one variable – the brightness temperature difference between vertically-polarized and horizontally-polarized 166 GHz measurements from the Global Precipitation Measurement Microwave Imager (GPM-GMI). Through scrutinizing collocated CloudSat, GMI and GPM radar observations together with the aid of radiative transfer model simulations, we can not only tell apart the particle size, shape and orientation, but also understand the precipitation regime as well as the life stage of a a precipitation system. In the second half of my talk, I will showcase how this variable can be used for predicting precipitation flags and separating mixed-phase cloud from liquid and ice with the powerful machine learning/artificial intelligence (ML/AI) technique.

cloud ice and snow microphysical characteristics↗
NASA NTRS2021

First Attempt of Global-scale Assimilation of Subdaily Scale Soil Moisture Estimates from CYGNSS and SMAP into a Land Surface Model

Soil moisture performs a key function in the hydrologic process and understanding the global-scale water cycle. However, estimations of soil moisture taken from current sun-synchronous orbit (SSO) satellites are limited in that they are neither spatially nor temporally continuous. This limitation creates discontinuous soil moisture observation from space and hampers our understanding of the fundamental processes that control the surface hydrologic cycle across both time and space domains. Here, we propose to use frequent soil moisture observations from NASA’s constellation of eight micro-satellites called the Cyclone Global Navigation Satellite System(CYGNSS) together with the Soil Moisture Active Passive (SMAP) to assimilate subdaily-scale soil moisture intoa land surface model(LSM). Our results, which are based on triple collocation analysis(TCA), show how current scientific advances in satellite systems can fill previous gaps in soil moisture observations in subdaily scale bypast observations, and eventually adds value to improvements in global scale soil moisture estimates in LSMs. Overall, TCA-based fractional mean square errors (fMSE) of LSM soil moisture are improved by 61% with the synergetic assimilation of CYGNSS data with SMAP soil moisture observations. However, assimilating satellite-based soil moisture over dense vegetation areas can degrade the performance of LSMs as these areas propagate erroneous soil moisture information to LSMs. To our knowledge, this study isthe first global assimilation of GNSS-based soil moisture observations in land surface models.

Hyunglok Kim↗
NASA NTRSDate not supplied

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗
NASA NTRS2021

Variabilities in Pm2.5 and Black Carbon Surface Concentrations Reproduced by Aerosol Optical Properties Estimated by In-Situ Data, Ground Based Remote Sensing and Modeling

Because of the increased temporal and spatial resolutions of the sensors onboard recently launched satellites, satellite-based surface aerosol concentration, which is usually estimated from the aerosol optical depth (AOD), is expected to become a strategic tool for air quality studies in the future. By further exploring the relationships of aerosol concentrations and their optical properties using ground observations, the accuracies of these products can be improved. Here, we analyzed collocated observations of surface mass concentrations of fine particulate matter (PM2.5) and black carbon (BC), as well as columnar aerosol optical properties from a sky radiometer and aerosol extinction profiles obtained by multi-axis differential optical absorption spectroscopy (MAX-DOAS), during the 2019–2020 period. We focused the analyses on a daily scale, emphasizing the role of the ultraviolet (UV) spectral region. Generally, the correlation between the AOD of the fine fraction (i.e., fAOD) and the PM2.5 surface concentration was moderately strong, regardless of considerations of boundary layer humidity and altitude. In contrast, the fAOD of the partial column below 1 km, which was obtained by combining sky radiometer and MAX-DOAS retrievals, better reproduced the variability of the PM2.5 and resulted in a linear relationship. In the same manner, we demonstrated that the absorption AOD of the fine fraction (fAAOD) of the partial column was related to the variability of the BC concentration. Analogous analyses based on aerosol products from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) confirmed these findings and highlighted the importance of the shape of the aerosol profile. Overall, our results indicated a remarkable consistency among the retrieved datasets, and between the datasets and MERRA-2 products. These results confirmed the well-known sensitivity to aerosol absorption in the UV spectral region; they also highlighted the efficacy of combined MAX-DOAS and sky radiometer observations.

Alessandro Damiani↗
NASA NTRS2021

The Thermal Infrared Optical Depth of Mineral Dust Retrieved from Integrated CALIOP and IIR Observations

Recent studies reveal that a higher fraction of coarse mineral dust particles than that estimated by climate model simulations has been observed in the atmosphere, leading to a more significant positive (i.e., warming) longwave (LW) thermal infrared (TIR) direct radiative effect (DRE). However, the magnitude of this DRE remains highly uncertain because our understanding of the radiative properties, quantitatively represented by the optical depth of dust, especially information on the TIR, remains limited. This study presents a simple approach to retrieve the thermal infrared dust aerosol optical depth (DAODTIR) over oceans during nighttime using the observations from the Infrared Imaging Radiometer (IIR) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard CALIPSO. For each cloud-free dust-laden profile identified by the IIR-CALIOP observation, a Lookup-Table (LUT) of the 10.6 μm IIR band brightness temperatures difference (dBT) under different DAODTIR with respect to their dust-free BTs is constructed based on the CALIOP retrieved dust vertical profile and pre-assumed dust scattering properties using a fast radiative transfer model. Then the DAODTIR is retrieved by projecting the IIR-observed dBT on the LUT. Sensitivity studies show that the DAODTIR retrieval at 10.6 μm is more susceptible to the dust particle size distribution (PSD) assumption than dust refractive indices. To estimate the uncertainty caused by PSD assumption, two DAODTIR retrieval products, one based on the dust PSD from the AERONET at Cape Verde and the other on an in situ measured PSD from the recent Fennec campaign, are provided. The retrieval uncertainty is mainly contributed by the BT difference between the observation and simulation using auxiliary atmospheric data. The climatology of the retrieval from 2013 to 2019 shows confident spatiotemporal variations of DAODTIR with the global-averaged value of 0.006 and 0.008 based on different pre-assumed dust PSDs. Climatological results agree reasonably well with two independent DAODTIR retrieval products based on the Infrared Atmospheric Sounding Interferometer (IASI) over the active dust transport regions, such as North and Tropical Atlantic (r = 0.904 and 0.819) and Indian Ocean (r = 0.832). The seasonal and interannual variation is also well-compared (r = 0.758) with AERONET coarse-mode AOD at 97 selected sites. The synergic CALIOP observation allows the retrieved DAODTIR to directly compare with the extrapolated DAODTIR from DAOD in visible (i.e., 532 nm), which helps evaluate the observational constraints on DAODTIR. This study offers a unique prospect of collocating active lidar and passive IR observations for retrieving dust DAODTIR.

aerosol↗
NASA NTRSDate not supplied

NASA GLOBE CLOUD GAZE: Creating Data Quality Flags for Citizen Science Cloud Observations Matched to NASA Satellite Data

The GLOBE Program, NASA’s largest and longest lasting citizen science program about the Earth, has been collecting cloud observations matched to multiple satellite data daily. The program’s cloud protocol is historically the most popular protocol as your eyes are the only instruments you need to collect observations of the sky. This dataset includes over 3,300,000 cloud observations with variables like total cloud cover, cloud type and opacity that are collocated to the nearest overpass times of geostationary satellites (GOES-15, GOES-16, GOES-17, Meteosat-8, Meteosat-11, or Himawari-8), or to Clouds and the Earth’s Radiant Energy System (CERES) instruments onboard Aqua and Terra, or the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. In order to increase the usability of this dataset, the Community science project Leveraging Online and User Data through GLOBE And Zooniverse Engagement (CLOUD GAZE) has been developed to generate data quality flags of these ground-up and top-down perspectives of sky and clouds. Recently funded through NASA’s Citizen Science for Earth Systems Program, CLOUD GAZE has partnered with the Zooniverse online platform to obtain reference data and image tagging of sky photographs collected through The GLOBE Program’s clouds protocol. This paper will present the GLOBE Clouds dataset matched to NASA satellite data, integration of CLOUD GAZE to develop data quality flags, and research applications of the dataset (includes ground-up and top-down perspective comparisons, ground observations of dust storms and smoke plumes, and cloud observations in polar regions). The paper will also present on techniques and recommendations for classroom use and for community engagement, particularly for those looking to online resources.

Marilé Colón Robles↗
NASA NTRS2021

Deep Domain Adaptation based Cloud Type Detection using Active and Passive Satellite Data

Domain adaptation techniques have been developed to handle data from multiple sources or domains. Most existing domain adaptation models assume that source and target domains are homogeneous, i.e., they have the same feature space. Nevertheless, many real world applications often deal with data from heterogeneous domains that come from completely different feature spaces. In our remote sensing application, data in source domain (from an active spaceborne Lidar sensor CALIOP onboard CALIPSO satellite) contain 25 attributes, while data in target domain (from a passive spectroradiometer sensor VIIRS onboard Suomi-NPP satellite) contain 20 different attributes. CALIOP has better representation capability and sensitivity to aerosol types and cloud phase, while VIIRS has wide swaths and better spatial coverage but has inherent weakness in differentiating atmospheric objects on different vertical levels. To address this mismatch of features across the domains/sensors, we propose a novel end-to-end deep domain adaptation with domain mapping and correlation alignment (DAMA) to align the heterogeneous source and target domains in active and passive satellite remote sensing data. It can learn domain invariant representation from source and target domains by transferring knowledge across these domains, and achieve additional performance improvement by incorporating weak label information into the model (DAMA-WL). Our experiments on a collocated CALIOP and VIIRS dataset show that DAMA and DAMA-WL can achieve higher classification accuracy in predicting cloud types.

domain adaptation↗
NASA NTRS2021

Assessment of Tropospheric Concentrations of NO2 from the TROPOMI/Sentinel-5 Precursor for the Estimation of Long-Term Exposure to Surface NO2 over South Korea

Since April 2018, the TROPOspheric Monitoring Instrument (TROPOMI) has provided data on tropospheric NO2 column concentrations (CTROPOMI) with unprecedented spatial resolution. This study aims to assess the capability of TROPOMI to acquire high spatial resolution data regarding surface NO2 mixing ratios. In general, the instrument effectively detected major and moderate sources of NO2 over South Korea with a clear weekday–weekend distinction. We compared the CTROPOMI with surface NO2 mixing ratio measurements from an extensive ground-based network over South Korea operated by the Korean Ministry of Environment (SKME; more than 570 sites), for 2019. Spatiotemporally collocated CTROPOMI and SKME showed a moderate correlation (correlation coefficient, r = 0.67), whereas their annual mean values at each site showed a higher correlation (r = 0.84). The CTROPOMI and SKME were well correlated around the Seoul metropolitan area, where significant amounts of NO2 prevailed throughout the year, whereas they showed lower correlation at rural sites. We converted the tropospheric NO2 from TROPOMI to the surface mixing ratio (STROPOMI) using the EAC4 (ECMWF Atmospheric Composition Reanalysis 4) profile shape, for quantitative comparison with the SKME. The estimated STROPOMI generally underestimated the in-situ value obtained, SKME (slope = 0.64), as reported in previous studies.

TROPOMI↗
NASA NTRS2021

Applications of Dynamic Land Surface Information for Passive Microwave Precipitation Retrieval

Accurate, physically based precipitation retrieval over global land surfaces is an important goal of the NASA/JAXA Global Precipitation Measurement Mission (GPM). This is a difficult problem for the passive microwave constellation, as the signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used are indirect and typically require inferring some type of relationship between an observed scattering signal and precipitation at the surface. GPM, with collocated radiometer and dual-frequency radar, is an excellent tool for tackling this problem and improving global retrievals. In the years following the launch of the GPM Core Observatory satellite, physically based passive microwave retrieval of precipitation over land continues to be challenging. Validation efforts suggest that the operational GPM passive microwave algorithm, the Goddard profiling algorithm (GPROF), tends to overestimate precipitation at the low (<5 mm h−1) end of the distribution over land. In this work, retrieval sensitivities to dynamic surface conditions are explored through enhancement of the algorithm with dynamic, retrieved information from a GPM-derived optimal estimation scheme. The retrieved parameters describing surface and background characteristics replace current static or ancillary GPROF information including emissivity, water vapor, and snow cover. Results show that adding this information decreases probability of false detection by 50% and, most importantly, the enhancements with retrieved parameters move the retrieval away from dependence on ancillary datasets and lead to improved physical consistency.

Sarah Ringerud↗
NASA NTRSDate not supplied

SHERLOC Investigations at the Máaz and Séítah formations within Jezero crater

Introduction: The Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) instrument combines microscopic imaging, native fluorescence and Raman spectroscopy to better understand the mineral and chemical makeup of rocks on the martian surface. Native fluorescence emissions from aromatic organic species allow for detection and classification of aromatic organic molecules, whereas Raman scattered photons from molecules allow identification of functional groups of organics, chemicals, and minerals. These signatures are obtained on a 100 micron spatial scale and collocated to images so textures, minerals and chemicals can all be compared [1]. Results: SHERLOC has been operating on Mars since February 18, 2021. As of this writing, we have analyzed 3 natural surfaces, and 5 abraded rock patches created during the Crater Floor Campaign within Jezero crater [2]. The Guillaumes target (from the Roubion outcrop, Roubion member of the Máaz Formation) is dominated by Ca-sulfate with patches of perchlorate. The Bellegarde target (from the Rochette outcrop, Rochette member of the Máaz Formation) exhibits Raman peaks that match hydrated Ca-sulfate, amorphous/microcrystalline silicate (AMS), carbonate, and phosphate phases. A fluorescence doublet at ~305 and ~325 nm was detected and is most likely due to indigenous organic material in the sample. The Garde target (from the Bastide outcrop, Bastide member of the Séítah Formation) is dominated by olivine and carbonate with AMS occurring across the material. The Dourbes target (from the Brac outcrop, Bastide member of the Séítah Formation) is dominated by olivine and shows minor amounts of carbonate, hydrated Ca-sulfate, and AMS. The Quartier target exhibits a large sulfate feature, as well as carbonate, perchlorate, olivine and a fluorescence doublet at 305 and 325 nm and is very similar to that observed at Bellegarde. In each of these samples we have identified fluorescence features that are likely aromatic organics native to the rock interiors. Acknowledgments: This work was carried out at the Jet Propulsion Laboratory, The California Institute of Technology under a contract from NASA.

L W Beegle↗
NASA NTRS2022

Science Objectives for Flagship-Class Mission Concepts for the Search for Evidence of Life at Enceladus

Cassini revealed that Saturn's Moon Enceladus hosts a subsurface ocean that meets the accepted criteria for habitability with bio-essential elements and compounds, liquid water, and energy sources available in the environment. Whether these conditions are sufficiently abundant and collocated to support life remains unknown and cannot be determined from Cassini data. However, thanks to the plume of oceanic material emanating from Enceladus’ south pole, a new mission to Enceladus could search for evidence of life without having to descend through kilometers of ice. In this article, we outline the science motivations for such a successor to Cassini, choosing the primary science goal to be determining whether Enceladus is inhabited and assuming a resource level equivalent to NASA's Flagship-class missions. We selected a set of potential biosignature measurements that are complementary and orthogonal to build a robust case for any life detection result. This result would be further informed by quantifications of the habitability of the environment through geochemical and geophysical investigations into the ocean and ice shell crust. This study demonstrates that Enceladus’ plume offers an unparalleled opportunity for in situ exploration of an Ocean World and that the planetary science and astrobiology community is well equipped to take full advantage of it in the coming decades.

Enceladus↗
NASA NTRSDate not supplied

SHERLOC: Results of the first 350 sols of operations

On February 18th 2021, the Perseverance rover landed in Jezero crater, Mars. This site was chosen because orbiter data analysis provides evidence that the crater hosted a stream-fed lake during the Martian Noachian period. The Octavia Butler landing site is located ~1.9 km east of the remnants of a river delta. Deltaic and lacustrine sediments can preserve biosignatures, making Jezero crater a prime target for Mars sample return science. One of the seven instruments on Perseverance’s science payload is SHERLOC –Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals. SHERLOC combines fluorescence and Raman spectroscopy with microscopic imaging to analyze surface material to better understand the history of the aqueous environments recorded in the rocks of Jezero crater and to search for potential biosignatures. SHERLOC imaging obtains high spatial resolution images of geological targets to identify grain-scale structure and texture. SHERLOC spectroscopy enables high-sensitivity detection, characterization, and spatially resolved correlation of trace organic materials. Native fluorescence emissions from aromatic organic species allow for detection and classification of aromatic organic molecules, whereas Raman scattered photons from molecules allow identification of functional groups of organics, chemicals, and minerals. In the first 300 sols, SHERLOC has analyzed 3 natural surfaces, and 5 abraded rock patches created during the Crater Floor Campaign within Jezero crater. SHERLOC has been able to identify phosphates, amorphous/microcrystalline silicate (AMS), olivine, sulfates, and carbonates in abraded patches in the green zone campaign within Jezero Crater Máaz and Séítah formations. Within these detections we have begun to tell the story of what this crater was like when it was full of liquid water over 3 billion years ago. In each of these samples we have identified fluorescence features that are likely aromatic organics native to the rock interiors. We have identified multiple unique fluorescence signatures, within each of the abraded patches. The organic signatures have either been widely distributed over an extended area, which is probably due to planetary wide dust, or have spatially resolved locations that are collocated with different mineral signatures. Note: Additional information available on attachment.

L. W. Beegle↗
NASA NTRS2021

A Practical Guide to Writing a Radiative Transfer Code

Using our decades-long experience in radiative transfer (RT) code development for Earth science, we endeavor to reduce the knowledge gap of bringing RT from theory to code quickly. Despite numerous classic and recent literature, it is still hard to develop anRT code from scratch within a few weeks. It is equally hard to understand, not to mention modify, an existing “monster” RT code, for which the developer is either located remotely or has retired. Following the format of “Numerical Recipes” by Press et al., we collocate in this paper small pieces of necessary theory with corresponding small pieces of RT code. These are arranged in an order that is natural for code development, which is often opposite of the natural order for laying out the theoretical basis. We focus on the transfer of unpolarized monochromatic solar radiation in a plane-parallel atmosphere over a reflecting surface. Both the surface and the atmosphere are homogeneous (uniform) at all directions. The multiple scattering is numerically solved using the deterministic method of Gauss-Seidel iterations. Except for the presented Python-Numba open-source RT code gsit, the paper does not report any new scientific results, but rather serves as an academic demonstration. If development time is an issue or the reader is familiar with basic concepts of RT theory, we recommend proceeding directly to Sec.3 “RT code development.

multiple light scattering↗
NASA NTRS2020

Tidal Effects on the Longitudinal Structures of the Martian Thermosphere and Topside Ionosphere Observed by MAVEN

Longitudinal structures in the Martian thermosphere and topside ionosphere between 150 and 200 km altitudes are studied using in situ electron and neutral measurements from the NASA Mars Atmosphere and Volatile EvolutioN (MAVEN) mission. Four time intervals are selected for comparison, during which MAVEN sampled similar local time (9.3–10.3 h) and latitude (near 20°S) regions but at different solar longitude positions (two near northern summer solstice, one each at northern vernal and autumnal equinoxes). Persistent and pronounced tidal oscillations characterize the ionosphere and thermosphere, whose longitudinal variations in density are generally in-phase with each other. Our analysis of simultaneous and collocated neutral and electron data provides direct observational evidence for thermosphere-ionosphere coupling through atmospheric tides. We conclude that the ionosphere is subject to modulation by upward-propagating thermal tides, via both tide-induced vertical displacement and photochemical reactions. Atmospheric tides constitute a ubiquitous and significant perturbation source to the ionospheric electron density, up to ∼15% near 200 km.

Xiaohua Fang↗
NASA NTRSDate not supplied

Uncrewed Lunar Surface Operations and Support Activities

Sustained human presence on the surface of the Moon and future missions to Mars require increased independence from surface crews and Earth-based mission control to operate efficiently, safely, and reliably. The time for surface crews to perform tasks will be limited. Extravehicular activities by surface personnel are burdensome and time-consuming, even when a continuous human presence on the surface occurs. Identifying and balancing human/automation roles and tasks and infusing automation and autonomy practices early in a system’s lifecycle will be essential to achieve mission objectives. Among these objectives are attaining a sustained human presence, improving performance and mission effectiveness, reducing operations and maintenance (O&M) costs, and ensuring operations that are robust to communication delays. To achieve these objectives, an operational shift toward increased automation and autonomy with less reliance on humans is needed. Uncrewed lunar surface operations and support activities occur when surface crews are not present or are independent of surface crew timeline activities requiring no surface crew oversight or intervention. These uncrewed surface opportunities can also be planned to minimize crew workload that avoids routine maintenance and support tasks, thus maximizing crew exploration time. Uncrewed preparations such as staging and prepositioning equipment and materials before the crew arrives could improve crew task efficiency. Additional opportunities exist to conduct uncrewed science, exploration, and utilization. Uncrewed surface architecture functions can include science and exploration; habitation; launch and landing support; surface communication and navigation; surface power generation and distribution; human surface mobility; lifting, handling, manipulating; excavation, construction, and site preparation; logistics management; maintenance and repair; surface resource utilization; integrated site operations and shared support services (e.g., site scheduling/prioritization, dust mitigation/contamination control, and surface safety). Early robotic lunar surface campaigns will provide information on the availability of resources, such as oxygen and water, and demonstrate surface-based technologies. After the Artemis III human lunar return mission, a series of landers will deliver surface systems, cargo, supplies, science packages, spare parts, and commodities. A balance of crewed and uncrewed surface operations will enable a sustained lunar surface presence at the South Pole of the Moon at a site that will be known as the Artemis Base Camp (ABC). It is envisioned that base camp operations on and around the Moon will then help prepare for the mission durations and activities needed to support the first human mission to Mars. Before long-duration crew missions to the base camp can occur, the necessary surface infrastructure will be pre-deployed and verified operational. Surface assets will be teleoperated and remotely managed from Earth. Additionally, robotic and short-duration crewed missions to the ABC will ensure the site’s merit to achieve long-term science objectives, availability of usable resources, and that terrain, seasonal variations, and illumination conditions are acceptable. ABC will consist of different areas where specific functions and services are rendered, including: • Launch and Landing Area • Habitation Area • Power Production Area • Resource Areas Launch and Landing Area—The launch and landing area will support associated functions for the arrival and departure of vehicles, such as crewed landing and ascent and uncrewed cargo deliveries and offloading. It will evolve from an unimproved site at the beginning of the exploration campaign to a more sustainable landing and launch area that can support repeated arrivals and departures. Initial uncrewed Lunar Terrain Vehicle (LTV) surface operations may include emplacement of navigation beacons and communication equipment, real-time video and photography of landing/liftoff events, and element repositioning, such as portable utility power (PUP) (applicable for other landed assets at other areas). Site preparations, such as surface leveling, soil compaction, and berm/path construction, may be needed for a more sustainable launch and landing area capable of accommodating vehicles that are increasingly more reusable and reduce the effects of plume surface interactions and ejecta impacts on nearby surface assets. During the ABC missions, cargo and logistics will be delivered to the lunar surface via robotic cargo landers before the crew arrives. These shipments, which can arrive in pressurized logistics carriers, will deliver the logistics necessary to support a crewed mission and include items such as food, water, equipment spares, etc. Providing the capability to retrieve, offload, and transport the logistics closer to the ABC site before the arrival of the crew will increase the overall efficiency of crew operations once they arrive. In the sustained phase of exploration, other supporting services may be needed, such as lander propellant servicing, surface power services, commodity refreshes, and additional inspection, maintenance, and repair capabilities, to sustain a cadence of extended personnel stays and cargo arrivals and departures. Habitation Area—Uncrewed support to surface habitation could involve supporting activation and pre-entry operations of the habitat while the crew is in orbit at the Gateway outpost preparing for a surface landing. Surface Habitat (SH) uncrewed operations may include bringing the cabin environment to a habitable temperature and air mix and activating other critical crew support systems. Potential crop production uncrewed tasks in the SH could also include autonomous watering and tending. Additionally, when the crew departs, the SH enters dormancy for the long period of uncrewed operation. A logistical staging area could also be collocated near the SH. If so, staging operations for crew supplies, waste re-location, and recycling operations may be opportunities for uncrewed operations. Power Production Area—The Fission Surface Power (FSP) element and its supporting distribution equipment provide power to surface elements as needed across the ABC to supplement day-to-day operations and survive lunar nights. Uncrewed support of this power system includes any initial LTV-assisted deployments of cables and other distributed equipment, associated electrical connections, and system testing and activation operations. Robotically performing some inspections, maintenance, or repair tasks on the power distribution equipment could reduce the surface crew workload. Resource Area— Uncrewed resource prospecting, mapping, and characterizing possible resource sites is likely to be time-consuming and represents an opportunity for uncrewed operations between crewed missions. Uncrewed mobile equipment operations will be needed in the extreme environments of permanently shadowed locations where resource extractions occur. As In-Situ Resource Utilization (ISRU) pilot plant operations begin, uncrewed surface support activities with available mobile and portable assets (LTV, PUP, etc.) will better support these operations. Any produced commodities can be stored at a centralized storage location for future use. Also associated with these operations is the use of mobile robotic excavators for resource acquisition and robotic/autonomous regolith processing. The waste tailings generated during excavation and regolith processing would also need to be transported and deposited at a dedicated location. Surface assets will continue operating between crew visits to maintain surface capabilities, conduct lunar surface science, technology demonstrations, and public outreach opportunities. Additionally, certain sustaining tasks that would consume valuable crew time could be performed before crew arrival, or after their departure. This capability may offer more affordable options to construct, activate, test, and maintain a broad set of surface assets. Telerobotically operated human surface mobility systems, such as the LTV and Pressurized Rover (PR), can be utilized for various tasks. Surface environmental conditions pose a distinct challenge for all these activities. Surface illumination and localized shadows are one such factor. Night-survival operations could consist of thermal management, battery pre-charging, and load shedding. Some surface systems may hibernate through the night and then awake and continue nominal operations. Uncrewed mobile assets may use a more adaptive approach to optimize their power and operations; one method is to follow the sunlight. Night-survival operations may be initiated remotely by teleoperation, automated, or accomplished by supervised autonomous operation. The ability to pre-deploy and control remote assets in orbit or on Mars before the arrival of the mission crew is a key capability that can be simulated on the moon. The base camp provides a venue where these advanced operational concepts, technologies, and autonomous methods and techniques, including the incorporation of time delays to simulate Earth-Mars latency can be replicated to help buy down future Mars mission risks. This paper will examine the evolution of uncrewed lunar surface operations and support activities. It will also discuss the lunar surface environmental conditions (thermal, lighting, terrain, topography, communications) along with the challenges they pose on uncrewed surface operations, and the performance of these activities with limited to minimal human interaction and/or teleoperation. Since lunar missions include Mars mission analogs, such investigation provides the framework for future uncrewed Mars mission support.

Mark E Lewis↗