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Results of investigations of returned Apollo 17 samples, and Apollo 15 and 17 photographs have provided a broad data base on which to interpret the southeastern Serenitatis region of the moon. Although many of the pre-Apollo 17 mission interpretations remain valid, detailed mapping of this region and correlation with earth-based and orbital remote-sensing data have resulted in a revision of the local mare stratigraphy.
This paper presents an introduction to a global campaign of simultaneous quasi-static electric field measurements from radars, balloons, and satellites at various places within the earth's environment for April 8-14, 1978. The 7-day time period encompassed both extended magnetically quiet times as well as two magnetic storms. These storms were related to SSCs that followed solar flares, one of which included a day-long solar proton event. The wide variety of instrumentation and associated operating modes involved in this campaign is described. Sample conjunctions between satellites and ionospheric measurements are shown that demonstrate that field line mapping is valid under certain circumstances. Some of the largest ionospheric electric fields ever reported with greater than 100-km scale size occurred on April 11, and these events are discussed in detail.
Geomorphic (or physiographic) provinces have been a well known and useful method of regional landform classification for a century. Every earth scientist will recognize a phrase such as Appalachian Plateau or Southern Rocky Mountains as defining a discrete region of consistent geologic structure that has experienced a similar interval of erosion by a similar process or set of processes. The geomorphic provinces formalized in the United States by Fenneman in the 1920's continue to be highly satisfactory even though some boundaries were only vaguely drawn. Mosaics of LANDSAT images illustrate better than any earlier maps the validity and coherence of Fenneman's provinces. The concept of geomorphic provinces has been used subconsciously or intuitively, to describe the relief of the ocean floor and the topography of the Moon and other planets.
A serious problem exists interfacing the output temperatures and temperature gradients from either the heat transfer codes or engine tests with the input to stress analysis codes. A thermal load transfer code was developed and was used in conjunction with a three-dimensional model of a combustor liner for verification. The 3D heat transfer and stress analysis models of combustor liners and turbine blades were used to validate the mapped temperature produced by the transfer module. Verification cases were made for both finite element and finite difference heat transfer codes. A user manual for the code was written and is available.
A method is presented which facilitates the detection and delineation of leads with single-channel Landsat data by coupling numeric and symbolic procedures. The procedure consists of three steps: (1) using the dynamic threshold method, an image is mapped to a lead/no lead binary image; (2) the likelihood of fragments to be real leads is examined with a set of numeric rules; and (3) pairs of objects are examined geometrically and merged where possible. The processing ends when all fragments are merged and statistical characteristics are determined, and a map of valid lead objects are left which summarizes useful physical in the lead complexes. Direct implementation of domain knowledge and rapid prototyping are two benefits of the rule-based system. The approach is found to be more successfully applied to mid- and high-level processing, and the system can retrieve statistics about sea-ice leads as well as detect the leads.
To understand the effects of land-surface heterogeneity and the interactions between the land-surface and the planetary boundary layer at different scales, we develop a multiscale data set. This data set, based on the Cooperative Atmosphere-Surface Exchange Study (CASES97) observations, includes atmospheric, surface, and sub-surface observations obtained from a dense observation network covering a large region on the order of 100 km. We use this data set to drive three land-surface models (LSMs) to generate multi-scale (with three resolutions of 1, 5, and 10 kilometers) gridded surface heat flux maps for the CASES area. Upon validating these flux maps with measurements from surface station and aircraft, we utilize them to investigate several approaches for estimating the area-integrated surface heat flux for the CASES97 domain of 71x74 square kilometers, which is crucial for land surface model development/validation and area water and energy budget studies. This research is aimed at understanding the relative contribution of random turbulence versus organized mesoscale circulations to the area-integrated surface flux at the scale of 100 kilometers, and identifying the most important effective parameters for characterizing the subgrid-scale variability for large-scale atmosphere-hydrology models.
One of the main health risks in human space exploration is central nervous system (CNS) damage by ionizing radiation due to exposure to the galactic cosmic rays (GCRs). In animal models, irradiation with simulated GCRs or their components has been shown to cause neuronal damage and neuroinflammation associated with cognitive and behavioral dysfunction. In general, the extent of CNS damage is partially regulated by the blood-brain barrier (BBB), which enables immune cells to enter the CNS. The main cellular regulators of BBB permeability are astrocytes, which also modulate neuronal death, immune responses and oxidative stress, and thus could serve as a robust CNS-specific target for countermeasure development. However, studies on BBB permeability and astrocyte functions in regulating CNS responses to ionizing radiation have been limited, especially in human tissue/organ analogs. Therefore, we established a high-throughput 3D organ-on-a-chip system to study human CNS and BBB impairments in response to ionizing radiation, based on commercially available OrganoPlates (Mimetas, Inc.) seeded with primary or induced pluripotent stem cell-derived human cells. We investigated both immediate and delayed CNS responses to major GCR components: 0.15-0.5 Gy 250MeV/n 4-He, and 0.3-0.8 Gy 600 MeV/n 56-Fe; as well as to 0.5-1 Gy X-rays. We observed ionizing radiation-mediated increases in BBB permeability that was exacerbated by astrocyte presence and accompanied by morphological changes in endothelial cells and tight junctions, altered cytokine profile including TNFa upregulation, and increased oxidative stress. We also quantified irradiation-mediated changes in astrocyte activation and neuronal functions, revealing major astrocyte damage mediated by 600MeV/n 56-Fe particles. Thus, we demonstrate that deep space radiation may contribute to CNS damage by disrupting both astrocyte and endothelial cell components of the blood-brain barrier. Our next steps include mapping and validating the transcriptomic changes induced by simulated GCRs and their components in human CNS models. Ultimately, we aim to uncover potential novel targets for countermeasure developments to mitigate CNS damage in long duration spaceflight.
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Surface emissivity is essential for many remote-sensing applications including the retrieval of surface skin temperature from satellite-based infrared measurements, the determination of cloud detection thresholds, and the estimation of the surface longwave radiation emission, an important component of the energy budget of the surface-atmosphere interface. The CERES (Clouds and the Earth’s Radiant Energy System) Project is measuring broadband shortwave and longwave radiances and deriving cloud properties from the MODIS on Terra and Aqua and from the VIIRS on NOAA-19 and NOAA-20 orbiters to produce combined global radiation and cloud property data sets. Zhou et al. (IEEE Trans. Geosci. Remote Sens., 49, 2011) used Infrared Atmospheric Sounding Interferometer (IASI) data to create a high spectral resolution surface emissivity atlas for remote sensing and modeling applications. The IASI measures spectral radiances between 3.62 and 15.5 μm. The VIIRS I4 channel width is from 3.55 to 3.93 μm, while MODIS Band 20 is from 3.66 to 3.84 m. Comparisons of top-of-atmosphere (TOA) radiance calculations with MODIS and VIIRS observations for these bands relative to bands in the mid-infrared suggest that the IASI emissivity atlas near 3.7 µm may not be suitable for CERES cloud retrievals. In this paper, the IASI emissivities for the VIIRS and MODIS bands centered near 11m are used to derive surface skin temperature from nighttime MODIS/VIIRS data. The Goddard Earth Observing System for Instrument Teams (GEOS-IT) numerical weather analyses provide temperature and water vapor profiles fused to correct the observed radiances for atmospheric absorption and emission. Global seasonal emissivity maps are then derived for the VIIRS and MODIS 3.7-m bands that are consistent with the derived skin temperatures and the observed TOA radiances. These seasonal climatology maps are validated and will be used in CERES Edition 5 and other CERES-related cloud retrieval algorithms to provide improved clear-sky radiances and derived cloud properties.
The LuNaMaps project seeks to advance mapping capabilities and understanding in preparation for lunar landing scenarios. In this presentation we outline the advancements made by the team over the last year.
The main contribution of this project is the combined knowledge of terrain relative navigation experts and lunar scientists who are familiar with both the lunar orbital imagery and the instruments that collected the data as well as how a TRN system utilizes map data. This knowledge comes in the form of published technical papers, benchmark map data sets, and software tools that can help others automate the process of creating the necessary maps for their own landing sites in the future. This document represents the project's plans to share all the lessons learned, processes developed, and applicable software tools with the public.
The main contribution of this project is the combined knowledge of terrain relative navigation experts and lunar scientists who are familiar with both the lunar orbital imagery and the instruments that collected the data as well as how a TRN system utilizes map data. This knowledge comes in the form of published technical papers, benchmark map data sets, and software tools that can help others automate the process of creating the necessary maps for their own landing sites in the future. This presentation provides a brief overview of the tools and processes developed by the project.
This presentation represents the final report for the NASA grant project. The goal of this project was to provide scientific analysis to aid in validation fo data sets used in detection of long term global trends of total ozone. Ozone data from the Earth Probe Total Ozone Mapping Spectrometer instrument was compared for validation purposes with features in previous TOMS data. Atmospheric dynamic concepts were used in the analysis. The publications sponsored by the grant are listed along with abstracts.
The well-known wave-one pattern seen in tropical total ozone [Shiotani, 1992; Ziemke et al., 1996, 1998] has been used to develop a modified-residual (MR) method for retrieving time-averaged stratospheric ozone and tropospheric ozone column amount from TOMS (Total Ozone Mapping Spectrometer) over the 14 complete calendar years of Nimbus 7 observations (1979-1992) and from TOMS on the Earth-Probe (1996-present) and ADEOS platforms (1996- 1997). Nine- to sixteen-day averaged tropical tropospheric ozone (TTO) maps, validated with ozonesondes, show a seasonality expected from dynamical and chemical influences. The maps may be viewed on a homepage: http://metosrv2.umd.edu/~tropo. Stratospheric column ozone, which is also derived by the modified-residual method, compares well with sondes (to within 6-7 DU) and with stratospheric ozone column derived from other satellites (within 8-10 DU). Validation of the TTO time-series is presently limited to ozonesonde comparisons with Atlantic stations and sites on the adjacent continents (Ascension Island, Natal, Brazil; Brazzaville); for the sounding periods, TTO at all locations agrees with the sonde record to +/-7 DU. TTO time-series and the magnitude of the wave-one pattern show ENSO signals in the strongest El Nifio periods from 1979-1998. From 12degN and 12degS, zonally averaged tropospheric ozone shows no significant trend from 1980-1990. Trends are also not significant during this period in localized regions, e.g. from just west of South America across to southern Africa. This is consistent with the ozonesonde record at Natal, Brazil (the only tropical ozone data publicly available for the 1980's), which shows a not statistically significant increase. The lack of trend in tropospheric ozone agrees with a statistical analysis based on another method for deriving TTO from TOMS, the so-called Convective-Cloud-Differential approach of Ziemke et al. [1998].
New data are used to revise the isopach of plains-forming material in the Lunae Planum region. The distribution of data points in the region has not increased; consequently, the new map is still valid for generalized thickness trends only. The isopach map is based on a total of 65 points; 32 points represent thickness estimates at partially buried craters. The remaining 33 points define the limits of plains-forming materials (zero thickness). Suitable buried craters are randomly distributed and sparse near the western edge and northern portion of the area. The overall thickness trend is similar to the previously mapped distribution, but there is a significant reduction in thickness values.
Mapping aboveground forest biomass is central for assessing the global carbon balance. However, current large-scale maps show strong disparities, despite good validation statistics of their underlying models. Here, we attribute this contradiction to a flaw in the validation methods, which ignore spatial autocorrelation (SAC) in data, leading to overoptimistic assessment of model predictive power. To illustrate this issue, we reproduce the approach of large-scale mapping studies using a massive forest inventory dataset of 11.8 million trees in central Africa to train and validate a random forest model based on multispectral and environmental variables. A standard nonspatial validation method suggests that the model predicts more than half of the forest biomass variation, while spatial validation methods accounting for SAC reveal quasi-null predictive power. This study underscores how a common practice in big data mapping studies shows an apparent high predictive power, even when predictors have poor relationships with the ecological variable of interest, thus possibly leading to erroneous maps and interpretations.
Spring snowmelt serves as the major hydrological contribution to many watersheds of the US West. Since the 1970s the conterminous western USA has seen an earlier arrival of spring snowmelt. The extremely low snowpack and early melt of 2015 in the Cascade Mountains may be a harbinger of winters to come, underscoring the interest in advancements in spring snowmelt monitoring. Target-of-opportunity and point measurements of snowmelt using meteorological stations or stream gauges are common sources of these data, however, there have been few attempts to identify snowmelt timing using remote sensing. In this study, we describe the creation of snowmelt timing maps (STMs) which identify the day of year that each pixel of a remotely sensed image transitions from "snow-covered" to "no snow" during the spring melt season, controlling for cloud coverage and ephemeral spring snow storms. Derived from the 500 meter MODerate-resolution Imaging Spectroradiometer (MODIS) standard snow map, MOD10A2, this new dataset provides annual maps of snowmelt timing, with corresponding maps of cloud interference and interannual variability in snow coverage from 2001-2015. We first show that the STMs agree strongly with in-situ snow telemetry (SNOTEL) meteorological station measurements in terms of snowmelt timing. We then use the STMs to investigate the early snowmelt event of 2015 in the Cascade Mountains, USA, highlighting the protected areas of Mt. Rainier, Crater Lake, and Lassen Volcanic National Parks. In 2015 the Cascade Mountains experienced snowmelt 41 days earlier than the 2001-2015 average, with 25 percent of its land area melting more than 65 days earlier than average. The upper elevations of the Cascade Mountains experienced the greatest snowmelt anomaly. Our results are relevant to land managers and biologists as they plan adaptation strategies for mitigating the effects of climate change throughout temperate mountains.
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