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All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Mn-53 in the Apollo 15 and 16 drill stems - Evidence for surface mixing

The activity of cosmic ray produced Mn-53 has been measured in a series of samples from the upper 10 cm of the Apollo 15 and 16 drill stems. The activity profiles for both cores indicate disturbance to depths of about 3 g/sq cm within the last 6 m.y. In at least one case (Apollo 16) the soil has been gardened to at least 14 g/sq cm within the last 10 m.y., and material from the upper less than 2 g/sq cm has been buried to 14 g/sq cm by this gardening. The results for the Apollo 15 core are compatible with a wide variety of possible histories including loss or gain of material.

Nishiizumi, K.↗
NASA NTRS1987

The thermal properties of Martian surface materials at high lattitudes: Possible evidence for permafrost

Evidence is presented from analysis of Viking thermal mapping results that suggests a transition to high thermal inertial material at a depth of approx. 12 cm at +75 deg latitude. This was interpreted as reflecting the transition from ice poor soil to hard frozen permafrost. It was pointed out that such a transition would be expected on the basis of theoretical models of hard frozen permafrost distribution. Although permafrost is not the only plausible martian surface material with high thermal inertia, polar thermal mapping may turn out to be a powerful tool for determining the distribution of permafrost deposits and understanding their behavior. At this point, the circumstantial case for permafrost deposits in the north pole region of Mars is very strong.

Paige, D. A.↗
NASA NTRS2003

Prospecting for Martian Ice from Orbit

Recent data from the Gamma-Ray Spectrometer (GRS) on Mars Odyssey indicate the presence of a hydrogen-rich layer tens of centimeters thick in high latitudes on Mars. This hydrogen-rich layer correlates to previously determined regions of ice stability. It has been suggested that the subsurface hydrogen is ice and constitutes 35 plus or minus 15% by weight near the north and south polar regions. This study constrains the location of subsurface ice deposits on the scale of kilometers or smaller by combining GRS data with surface features indicative of subsurface ice. The most recognizable terrestrial geomorphic indicators of subsurface ice, formed in permafrost and periglacial environments, include thermokarst pits, pingos, pseudocraters and patterned ground. Patterned ground features have geometric forms such as circles, polygons, stripes and nets. This study focuses on the polygonal form of patterned ground, selected for its discernable shape and subsurface implications. Polygonal features are typically demarcated by troughs, beneath which grow vertical ice-wedges. Ice-wedges form in thermal contraction cracks in ice-rich soil and grow with annual freezing and thawing events repeated over tens of years. Ice wedges exist below the depth of seasonal freeze-thaw. Terrestrial ice wedges can be several meters deep and polygons can be tens of meters apart, and, on rare occasions, up to 1 km. The crack spacing of terrestrial polygons is typically 3 to 10 times the crack depth.

Kanner, L. C.↗
NASA NTRS1997

Lithological Variation with Depth and Decoupling of Maturity Parameters in Apollo 16 Regolith Core 68001/2

Using FerroMagnetic Resonance (FMR) and Instrumental Neutron Activation Analysis (INAA), we have determined the maturity (surface exposure) parameter I(sub s)/FeO and concentrations of twenty- five chemical elements on samples taken every half centimeter down the 61-cm length of the 68001/2 regolith core (double drive tube) collected at station 8 on the Apollo 16 mission to the Moon. Contrary to premission expectations, no ejecta or other influence from South Ray crater is evident in the core, although a small inflection in the I(sub s)/FeO profile at 3 cm depth may be related the South Ray crater impact. Regolith maturity generally decreases with depth, as in several previously studied cores. We recognize five compositionally distinct units in the core, which we designate A through E, although all are similar in composition to each other and to other soils from the Cayley plains at the Apollo 16 site. Unit A (0-33 cm) is mature to submature throughout (I(sub s)/FeO: 89-34 units) and is indistinguishable in composition from surface soils collected at station 8. Unit B (33-37 cm) is enriched slightly in a component of anorthositic norite composition. Unit D (42-53 cm) is compositionally equivalent to 80 wt% Unit-A soil plus 20 wt% Apollo-16-type dimict breccia consisting of subequal parts anorthosite and impact-melt breccia. Compared to Unit A, Unit E (53-61 cm) contains a small proportion (up to 4%) of some component compositionally similar to Apollo 14 sample 14321. Unit C (37-42 cm) is unusual. For lithophile and siderophile elements, it is similar to Units A and D. However, I(sub s)/FeO is low throughout the unit (less than 30 units) and in a bluish-gray zone at 41 cm depth I(sub s)/FeO drops to 1.6 units, the lowest value that we have observed in several hundred Apollo 16 soil samples. Samples from the bluish-gray zone also have low Zn concentrations, less than 10 micro g/g, compared to 20-30 micro g/g for the rest of the core. Although both values are consistent with fragmented rock material that has received virtually no surface exposure, the abundance of agglutinates in the bluish-gray soil of Unit C is moderately high, typical of a submature soil that would ordinarily have I(sub s)/FeO - 30. We believe that the anomalously low values of I(sub s)/FeO and Zn concentration result because the soil was heated to -800-1000 'C, probably during an impact. This temperature range is sufficient to volatize the surface-correlated Zn and agglomerate the nanophase metal giving rise to the FMR signal but is not great enough to sinter the soil. Alternatively, the unusual soil interval may represent a disaggregated or incipient regolith breccia, although there is no significant difference in the texture or clast-matrix relationships between Unit C and adjacent units.

Korotev, Randy L.↗
NASA NTRS2004

Subsurface Sampling and Sensing Using Burrowing Moles

Finding evidence for life on Mars will likely require accessing the subsurface since the Martian surface is both hostile to life and to preservation of biosignatures due to the cold dry conditions, the strong W environment, and the presence of strong oxidants. Systems are needed to probe beneath the sun and oxidant baked surface of Mars and return samples to the surface for analysis or to bring the instrument sensing underground. Recognizing this need, the European Space Agency incorporated a small subsurface penetrometer or Mole onto the Beagle 2 Mars lander. Had the 2003 landing been successful, the Mole would have collected samples from 1-1.5 m depth and delivered them to an organic analysis instrument on the surface. The de- vice called the Planetary Underground Tool (PLUTO), also measured soil mechanical and thermophysical properties. Constrained by the small mass and volume allowance of the Beagle lander, the PLUTO mole was a slender cylinder only 2 cm diameter and 28 cm long equipped with a small sampling device designed to collect samples and bring them to the surface for analysis by other instrument. The mass of the entire system including deployment mechanism and tether was 1/2 kg. sensor package underground to make in situ measurements. The Mars Underground Mole (MUM) is a larger Mole based on the PLUTO design but incorporating light collection optics that interface to a fiber optic cable in the tether that transmits light to a combined stimulated emission Raman Spectrometer and Short Wave Infrared (SWIR) reflectance Spectrometer with sensitivity from 0.7 to 2.5 micrometers. This instrument is called the Dual Spectral Sensor and uses a Digital Array Scanning Interferometer as the sensor technology, a type of fourier transform interferometer that uses fixed element prisms and thus is highly rugged compared to a Michaelson interferometer. Due to the size limitations of an on-Mole instrument compartment, and the availability of a tether, the sensor head, light sources, and control electronics for the instrument are on the surface. The DSS sensor is capable of sensing a wide range of minerals relevant to Mars Astrobiology objectives including hydrated minerals, clays, carbonates, sulfates, and ice. Additionally, Raman spectroscopy is effective for detecting organics. The MUM is designed to achieve a maximum depth of penetration of 5 m in Mars regolith and can be repeatedly deployed and retrieved. The ability to perform repeated sampling, combined with the low mass and power requirements, means that Moles could be incorporated into a rover mission as well as used on a stationary platform. The Mole mechanism is a pointed slender cylinder that advances into soil by way of an internal sliding hammer mechanism. Part of the energy released by the spring-loaded hammer with each shock is transferred to the Mole casing and from there to the soil, resulting in penetration by displacing and compressing the surrounding soil. A backwards-directed impulse as a re- action to each forward shock is transferred via a suppressor mass against a second weaker spring allowing forward motion without requiring reactive forces provided by the lander. The Mole tip can be opened to collect soil samples. The Mole casing is tethered to a supporting mechanism that supplies power. Components supporting the Mole on the surface include a launch tube, tether reel and winch for pulling in tether, in addition to the tether itself.

Stoker, C. R.↗
DOE OSTI2024

Geophysical and Environmental Monitoring Data, and Subsurface Flow Modelling Results for Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO

This dataset includes geoelectrical monitoring data acquired between October 2021 and November 2022, soil moisture and temperature data, groundwater data obtained from borehole SNIB covering the period from June 2021 to September 2022, and hydrological modelling results. The data were acquired to investigate how variations in bedrock type and topography, and vegetation cover control subsurface flow dynamics. To provide insights into the subsurface flow dynamics and their controls, a monitoring transect was installed at the Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO, measuring the spatio-temporal variations of soil moisture, soil and snow temperature, subsurface electrical resistivity variations, and groundwater dynamics. Field data are organized in a folder structure, with Electrical Resistivity Tomography (ERT) data being provided as one file per measurement, and data of the soil moisture and temperature sensors being provided as text files covering the entire monitoring period. The ‘Locations.csv’ file contains the location of all sensors, given in NAD83 – UTM Zone 13N. ERT monitoring data has been processed to filter data based on reciprocal errors (data with errors > 30% were removed), a linear error model was fitted to each survey, and to ensure a constant set of measurements for time-lapse inversion, filtered data were interpolated and assigned a 100% measurement error. Soil moisture and temperature data were acquired at 15 min intervals, and averaged to provide 1h data. Weather data and borehole data (groundwater depth, conductivity and temperature) were acquired at 30 min intervals, and are provided as daily measurements; all measurements are averaged, except of precipitation values, which are given as daily accumulation. The hydrological model was set up along the ERT monitoring transect, and net infiltration was used as surface boundary condition and derived from the weather data. Four different results are provided, (1) results for a parameterization using hydraulic permeability and porosity as derived from the ERT data through petrophysical relationships, and (2) three simplified model results, using 1 to 3 geological layers above the bedrock. Modelling was performed using PFLOTRAN, and for each model the PFLOTRAN input files are provided. The result files include weekly hydrological modelling results (e.g., saturation, velocities, pressures), as well as the model parameterization. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Water and cheese from the lunar desert: Abundances and accessibility of H, C, and N on the Moon

The Moon has been underrated as a source of H, N, C, and other elements essential to support life and to provide fuel for rockets. There is enough of these elements in each cubic meter of typical lunar soil to provide a substantial lunch for two, if converted to edible forms. The average amount of C per square meter of the lunar surface to a depth of 2 m is some 35 percent of the average amount per square meter tied up in living organisms on Earth. The water equivalent of H in the upper 2 m of the regolith averages at least 1.3 million liters per square kilometer. Mining of H from a small fraction of the regolith would provide all the rocket fuel needed for thousands of years. These elements can be removed from the soil by heating it to high temperature. Some favor the unproven resources of Phobos, Deimos, or near-Earth asteroids instead of the Moon as a source of extraterrestrial material for use in space, or Mars over the Moon as a site for habitation, partly on the basis that the chemical elements needed for life support and propellant are readily abundant on those bodies, but not on the Moon. Well, the Moon is not as barren of H, C, and N as is commonly perceived. In fact, the elements needed for life support and for rocket fuel are plentiful there, although the ore grades are low. Furthermore, the proximity of the Moon and consequent lower cost of transportation and shorter trip and communication times favor that body as the logical site for early acquisition of resources and extraterrestrial living.

Haskin, L. A.↗
DOE OSTI2024

Global Geo-processed Data of Aquifer Properties by 0.5° Grid, Country and Water Basins

This repository of global hydrogeologic datasets contains aquifer properties on 0.5° scale, including depth to groundwater (Fan et al., 2013), aquifer thickness (de Graaf et al., 2015), WHYMap aquifer classes (Richts et al., 2011), porosity and permeability (Gleeson et al., 2014), digitized and geo-processed from their respective sources. Globally gridded aquifer properties could be used independently to estimate global groundwater availability or used as critical inputs to the superwell model to simulate groundwater extraction and provide estimates of pumped volumes and unit costs under user-specific scenarios. Key resources related to this data are: Niazi, H., Ferencz, S., Graham, N., Yoon, J., Wild, T., Hejazi, M., Watson, D., & Vernon, C. (2024; In-prep). Long-term Hydro-economic Assessment Tool for Evaluating Global Groundwater Cost and Supply: Superwell v1. Geoscientific Model Development. superwell model repository which uses this data to simulate groundwater extraction and provides estimates of the global extractable volumes and unit-costs ($/km3) of accessible groundwater production under user-specified extraction scenarios. Repository Overview Main output: aquifer_properties.csv contains all processed outputs, including aquifer properties like porosity, permeability, aquifer thickness, and depth to groundwater. shapefiles.zip: contains all digitized GIS databases and shapefile for all aquifer properties prep_inputs.R: R script that processes the shapefiles to produce the aquifer_properties file plot_inputs.R: R script for plotting the maps and conducting preliminary analysis on the available groundwater volume basin_to_country_mapping.csv, basin_country_region_mapping.csv and continent_county_mapping.csv provide the mapping between continents, 32 energy-economic macro regions, countries, and water basins for post-processing aquifer_properties.csv Maps: Each map visualizes the spatial distribution of one of the aquifer properties across the globe map_in_Porosity.png map_in_Permeability.png map_in_Aquifer_thickness.png map_in_Depth_to_water.png map_in_Grid_area_km.png map_in_WHYClass.png Sample inputs sample_inputs.py: this script samples inputs from the aquifer_properties dataset, ensuring the sampled and original inputs maintain the same distributions sampled_data_100.csv contains 100 sampled data points and sampled_data_100.png compares their distributions Dataset Overview The main outputs are consolidated in a comprehensive aquifer_properties.csv file and include the following fields: GridCellID: Unique identifier for each (roughly 0.5°) grid cell Continent: Continent name Country: Country name GCAM_basin_ID: Identifier for GCAM hydrologic basin Basin_long_name: Full name of the basin WHYClass: Hydrogeologic classification based on WHYMap aquifer classes (Richts et al., 2011) Porosity: Soil porosity (%) (Gleeson et al., 2014) Permeability: Soil permeability (in square meters; Gleeson et al., 2014) Aquifer_thickness: Thickness of the aquifer (in meters; de Graaf et al., 2015) Depth_to_water: Depth to groundwater (in meters; Fan et al., 2013) Grid_area: Area of the grid cell (in square meters) Key References The datasets are digitized versions of global hydrogeologic properties from the following key literature sources: Depth to Groundwater: Fan, Y., Li, H., & Miguez-Macho, G. (2013). Global Patterns of Groundwater Table Depth. Science, 339(6122), 940-943. https://doi.org/10.1126/science.1229881 Aquifer Thickness: de Graaf, I. E. M., Sutanudjaja, E. H., van Beek, L. P. H., & Bierkens, M. F. P. (2015). A high-resolution global-scale groundwater model. Hydrol. Earth Syst. Sci., 19(2), 823-837. https://doi.org/10.5194/hess-19-823-2015 Porosity and Permeability: Gleeson, T., Moosdorf, N., Hartmann, J., & van Beek, L. P. H. (2014). A glimpse beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity. Geophysical Research Letters, 41(11), 3891-3898. https://doi.org/10.1002/2014GL059856 Aquifer classes: Richts, A., Struckmeier, W. F., & Zaepke, M. (2011). WHYMAP and the Groundwater Resources Map of the World 1:25,000,000. In J. A. A. Jones (Ed.), Sustaining Groundwater Resources: A Critical Element in the Global Water Crisis (pp. 159-173). Springer Netherlands. https://doi.org/10.1007/978-90-481-3426-7_10 Cite as Niazi, H., Watson, D., Hejazi, M., Yonkofski, C., Ferencz, S., Vernon, C., Graham, N., Wild, T., & Yoon, J. (2024). Global Geo-processed Data of Aquifer Properties by 0.5° Grid, Country and Water Basins. MSD-LIVE Data Repository. https://doi.org/10.57931/2307831 Contact Reach out to Hassan Niazi or open an issue in superwell repository for questions or suggestions.

aquifer thickness↗
DOE OSTI2024

Global Geo-processed Data of Aquifer Properties by 0.5° Grid, Country and Water Basins

This repository of global hydrogeologic datasets contains aquifer properties on 0.5° scale, including depth to groundwater (Fan et al., 2013), aquifer thickness (de Graaf et al., 2015), WHYMap aquifer classes (Richts et al., 2011), recharge (Döll and Fiedler, 2008; Gleeson et al., 2016), lakes (Messager et al., 2016), porosity and permeability (Gleeson et al., 2014), digitized and geo-processed from their respective sources. Globally gridded aquifer properties could be used independently to estimate global groundwater availability or used as critical inputs to the superwell model to simulate groundwater extraction and provide estimates of pumped volumes and unit costs under user-specific scenarios. Key resources related to this data are: Niazi, H., Ferencz, S. B., Graham, N. T., Yoon, J., Wild, T. B., Hejazi, M., Watson, D. J., & Vernon, C. R. (2025). Long-term hydro-economic analysis tool for evaluating global groundwater cost and supply: Superwell v1.1. Geoscientific Model Development, 18(5), 1737-1767. https://doi.org/10.5194/gmd-18-1737-2025 superwell model repository which uses this data to simulate groundwater extraction and provides estimates of the global extractable volumes and unit-costs ($/km3) of accessible groundwater production under user-specified extraction scenarios. Repository Overview Main output: aquifer_properties_rec.csv contains all processed outputs, including aquifer properties like porosity, permeability, recharge, lake areas, aquifer thickness, and depth to groundwater. shapefiles.zip: contains all digitized GIS databases and shapefile for all aquifer properties prep_inputs.R and prep_inputs_recharge_lakes.R: R scripts that process the shapefiles to produce the aquifer_properties_rec.csv file plot_inputs.R: R script for plotting the maps and conducting preliminary analysis on the available groundwater volume basin_to_country_mapping.csv, basin_country_region_mapping.csv and continent_county_mapping.csv provide the mapping between continents, 32 energy-economic macro regions, countries, and water basins for post-processing aquifer_properties_rec.csv Maps: Each map visualizes the spatial distribution of one of the aquifer properties across the globe map_in_Porosity.png map_in_Permeability.png map_in_Aquifer_thickness.png map_in_Depth_to_water.png map_in_Recharge.png map_in_Grid_area_km.png map_in_Lake_area_km.png map_in_WHYClass.png Sample inputs sample_inputs.py: this script samples inputs from the aquifer_properties_rec dataset, ensuring the sampled and original inputs maintain the same distributions sampled_data_100.csv contains 100 sampled data points and sampled_data_100.png compares their distributions Dataset Overview The main outputs are consolidated in a comprehensive aquifer_properties_rec.csv file and include the following fields: GridCellID: Unique identifier for each (roughly 0.5°) grid cell Continent: Continent name Country: Country name GCAM_basin_ID: Identifier for GCAM hydrologic basin Basin_long_name: Full name of the basin WHYClass: Hydrogeologic classification based on WHYMap aquifer classes (Richts et al., 2011) Porosity: Soil porosity (%) (Gleeson et al., 2014) Permeability: Soil permeability (in square meters; Gleeson et al., 2014) Aquifer_thickness: Thickness of the aquifer (in meters; de Graaf et al., 2015) Depth_to_water: Depth to groundwater (in meters; Fan et al., 2013) Recharge: long-term annual averaged recharge rates (in m/yr; Döll and Fiedler, 2008; Gleeson et al., 2016) Grid_area: Area of the grid cell (in square meters) Lakes_area: Area of inland lakes (in square meters; Messager et al., 2016) Key References The datasets are digitized versions of global hydrogeologic properties from the following key literature sources: Depth to Groundwater: Fan, Y., Li, H., & Miguez-Macho, G. (2013). Global Patterns of Groundwater Table Depth. Science, 339(6122), 940-943. https://doi.org/10.1126/science.1229881 Aquifer Thickness: de Graaf, I. E. M., Sutanudjaja, E. H., van Beek, L. P. H., & Bierkens, M. F. P. (2015). A high-resolution global-scale groundwater model. Hydrol. Earth Syst. Sci., 19(2), 823-837. https://doi.org/10.5194/hess-19-823-2015 Porosity and Permeability: Gleeson, T., Moosdorf, N., Hartmann, J., & van Beek, L. P. H. (2014). A glimpse beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity. Geophysical Research Letters, 41(11), 3891-3898. https://doi.org/10.1002/2014GL059856 Aquifer classes: Richts, A., Struckmeier, W. F., & Zaepke, M. (2011). WHYMAP and the Groundwater Resources Map of the World 1:25,000,000. In J. A. A. Jones (Ed.), Sustaining Groundwater Resources: A Critical Element in the Global Water Crisis (pp. 159-173). Springer Netherlands. https://doi.org/10.1007/978-90-481-3426-7_10 Recharge: Döll, P., & Fiedler, K. (2008). Global-scale modeling of groundwater recharge. Hydrol. Earth Syst. Sci., 12(3), 863-885. https://doi.org/10.5194/hess-12-863-2008; Gleeson, T., Befus, K. M., Jasechko, S., Luijendijk, E., & Cardenas, M. B. (2016). The global volume and distribution of modern groundwater. Nature Geoscience, 9(2), 161-167. https://doi.org/10.1038/ngeo2590 Inland Lakes: Messager, M. L., Lehner, B., Grill, G., Nedeva, I., & Schmitt, O. (2016). Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications, 7(1), 13603. https://doi.org/10.1038/ncomms13603 Cite as Niazi, H., Watson, D., Hejazi, M., Yonkofski, C., Ferencz, S., Vernon, C., Graham, N., Wild, T., & Yoon, J. (2024). Global Geo-processed Data of Aquifer Properties by 0.5° Grid, Country and Water Basins. MultiSector Dynamics-Living, Intuitive, Value-adding, Environment. https://doi.org/10.57931/2484226 Contact Reach out to Hassan Niazi or open an issue in the superwell repository for questions or suggestions.

aquifer thickness↗
NASA NTRS1984

Ice-Soil Mixtures: Visual and Near-Infrared Remote Sensing Techniques

The spectral properties of ice soil mixtures are reviewed. Water ice has an absorption coefficient which varies by several orders of magnitude in the visual and near infrared, and has several prominent overtone absorptions. Different wavelengths can be used to probe to different depths in the surface as well as for different mineral impurity concentrations. Empirical and/or theoretical models might be used to derive characteristics grain size of the ice or of the impurity minerals and to derive abundance of the ice and rock or soil components. Quantitative analysis of remotely obtained reflectance spectra can only be performed by using absorption features in the spectra of ice and soil, and not by broadband response. It is suggested that this might be done by special selection of several narrow band filters in the near infrared which will adequately define the ice absorptions.

Clark, R. N.↗
DOE OSTI2024

Selected micrometeorological and soil data from the Manaus ZF2 K34 eddy covariance tower for the 2015/16 El Niño event

The K34 tower site (2.609 S, 60.209 W, 130 masl), located within the Tropical Silviculture Experimental Station (EEST, also known as ZF2) research station, in an area of undisturbed tropical forest (Araújo et al. 2002). It features long term measurements using micrometeorological, eddy covariance, and soil instrumentation, among others. This dataset covers January 2012 to December 2017, and includes air temperature (TA, ℃) and air relative humidity (RH, %), measured with thermohygrometers (HC2S3, Campbell Scientific, Logan, UT, USA) deployed above the canopy at the top of the tower (at 51.1 m height), and net radiation (NETRAD, W m-2) using an NR-LITE sensor (Kipp & Zonen, Delft, Netherlands), also at the top of the tower (at 35.0 m above the canopy). The soil moisture data included here is from a soil profile located approximately 12 m from the tower, and covers the period between February 2015 and September 2016. Volumetric soil water content (SWC, m3 m-3) measurements used Water Content Reflectometers (CS655 Campbell Scientific, Logan, UT, USA) at six depths: 10 cm, 20 cm, 30 cm, 40 cm, 60 cm, and 100 cm. Data are included in .csv files, and the text file ZF2-K34_Tower_headers.txt provides descriptions of data file headers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstrated in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which constitutes the core content of the metrics and diagnostics package in section 2, and a user's guide documenting the workflow/structure of the version 1.0 codes, and including step-by-step instruction for running the package in section 3.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

The nature of lunar soil

Characteristics of lunar soil are examined. The paper includes topics such as lunar soil genesis, particle types, grain size distribution, grain shape distribution, specific gravity, minimum and maximum density, and relative density. Lunar soil, consisting of complex mixtures of mineral fragments, miscellaneous glasses, agglutinates, and lithic fragments, is produced primarily by meteorite impacts. The grain size distributions for soils exposed to meteorite reworking for 100 million yr or more are classified as well-graded silty sands and sandy silts. The specific gravity of submillimeter lunar soil samples varies from 2.9 to 3.24 and individual particles range from 1.0 to greater than 3.32. Differences in specific gravity, intragranular porosity, particle shape, surface texture, and grain arrangements result in wide ranges in minimum and maximum densities. Meteorite impacts cause in situ lunar soil to have a low relative density at the surface which increases rapidly to a very high relative density at depths greater than 10 cm to 20 cm.

Carrier, W., III↗
NASA NTRS1981

The DMSP imager (SSMI): Unfinished plans

The basic theory relating microwave emissivity to soil and snow moisture is presented along with data from field and aircraft measurements to support the theory. Data from the ESMR on Nimbus-5 and the S-194 L Band radiometer on Skylab were compared with Antecedent Precipitation indices (APT) to show the sensitivity of spaceborne observations to soil moisture. Similarly, data from the ESMR and SMMR on the Nimbus spacecraft were compared with surface measurements of snow depth with good results.

Savage, R. C.↗
NASA NTRS1982

A numerical simulation of soil temperature and moisture variations for a bare field

The diurnal variations of soil temperature and moisture content were simulated for a bare agricultural field in the San Joaquin Valley in California. The simulation pertained to the first 72 hours of drying, from saturation, of a sandy, clay loam soil. The results were compared with measurements of soil temperature and moisture content made at the field. Calculated and measured values of soil temperature trends agreed in general, but model results of moisture trends did not replicate observed diurnal effects evident at depths 4 centimeters or more below the surface.

Schieldge, J. P.↗
NASA NTRS1982

Microwave remote sensing: Active and passive. Volume 2 - Radar remote sensing and surface scattering and emission theory

The fundamental principles of radar backscattering measurements are presented, including measurement statistics, Doppler and pulse discrimination techniques, and associated ambiguity functions. The operation of real and synthetic aperture sidelooking airborne radar systems is described, along with the internal and external calibration techniques employed in scattering measurements. Attention is given to the physical mechanisms responsible for the scattering emission behavior of homogeneous and inhomogeneous media, through a discussion of surface roughness, dielectric properties and inhomogeneity, and penetration depth. Simple semiempirical models are presented. Theoretical models involving greater mathematical sophistication are also given for extended ocean and bare soil surfaces, and the more general case of a vegetation canopy over a rough surface.

Ulaby, F. T.↗
NASA NTRS1978

Petrology, chemistry, age and irradiation history of Luna 24 samples

The results of petrological, chemical, isotopic age determination and irradiation studies of sample 24170 from the 170 cm depth of the regolith core returned from Mare Crisium by Luna 24 are presented. The sample is found to be comprised of fragments from a single igneous rock, with mineralogical evidence indicating it to be a mare basalt. The crystallization age is determined by Sm-Nd and Ar(40)-Ar(39) ages to be 3.30 AE, establishing the presence of relatively young flows. All soil samples show low trace element compositions with minimum contamination by KREEPUTh-rich materials. Rb-Sr and Sm-Nd relations reflect the absence of significant fractionation at ages younger than 4.5 AE. One soil sample shows extremely large neutron capture effects, imposing a new lower limit to the neutron production rate in the regolith and requiring the addition of irradiated materials from depth.

Wasserburg, G. J.↗