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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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129 records · Page 8

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NASA NTRS2006

3-D Structure of Sunspots using Imaging Spectroscopy

We use the Interferometric BIdimensional Spectrometer (IBIS) of the INAF/Arcetri Astrophysical Observatory and installed at the National Solar Observatory (NSO) Dunn Solar Telescope, to understand the structure of sunspots. Using the spectral lines FeI 6301.5 A, FeII 7224.4 A, and CaII 8542.6 A, we examine the spectroscopic variation of sunspot penumbral and umbral structures at the heights of formation of these lines. These high resolution observations were acquired on 2004 July 30-31, of active region NOAA 10654, using the high order NSO adaptive optics system. We map the spatio-temporal variation of Doppler signatures in these spectral lines, from the photosphere to the chromosphere. From a 70-minute temporal average of individual 32-second cadence Doppler observations we find that the averaged velocities decrease with height, about 3.5 times larger in the deeper photosphere (FeII 7224.4 A; height-of-formation approx. 50 km) than in the upper photosphere FeI 6301.5 A; height-of-formation approx. 350 km), There is a remarkable coherence of Doppler signals over the height difference of 300 km. From a high-speed animation of the Doppler sequence we find evidence for what appears to be ejection of high speed gas concentrations from edges of penumbral filaments into the surrounding granular photosphere. The Evershed flow persists a few arcseconds beyond the traditionally demarcated penumbra-granulation boundary. We present these and other results and discuss the implications of these measurements for sunspot models.

Balasubramaniam, K. S.↗

NASA NTRS2010

Integrating Enhanced Grace Terrestrial Water Storage Data Into the U.S. and North American Drought Monitors

NASA's Gravity Recovery and Climate Experiment (GRACE) satellites measure time variations nf the Earth's gravity field enabling reliable detection of spatio-temporal variations in total terrestrial water storage (TWS), including ground water. The U.S. and North American Drought Monitors are two of the premier drought monitoring products available to decision-makers for assessing and minimizing drought impacts, but they rely heavily on precipitation indices and do not currently incorporate systematic observations of deep soil moisture and groundwater storage conditions. Thus GRACE has great potential to improve the Drought Monitors hy filling this observational gap. Horizontal, vertical and temporal disaggregation of the coarse-resolution GRACE TWS data has been accomplished by assimilating GRACE TWS anomalies into the Catchment Land Surface Model using ensemble Kalman smoother. The Drought Monitors combine several short-term and long-term drought indices and indicators expressed in percentiles as a reference to their historical frequency of occurrence for the location and time of year in question. To be consistent, we are in the process of generating a climatology of estimated soil moisture and ground water based on m 60-year Catchment model simulation which will subsequently be used to convert seven years of GRACE assimilated fields into soil moisture and groundwater percentiles. for systematic incorporation into the objective blends that constitute Drought Monitor baselines. At this stage we provide a preliminary evaluation of GRACE assimilated Catchment model output against independent datasets including soil moisture observations from Aqua AMSR-E and groundwater level observations from the U.S. Geological Survey's Groundwater Climate Response Network.

Housborg, Rasmus↗

NASA NTRS2017

Limiting Data Friction by Reducing Data Download Using Spatiotemporally Aligned Data Organization Through STARE

Current data processing practice limits the volume and variety of relevant geoscience data that can practically be applied to important problems. File archives in centralized data centers are the principal means by which Earth Science data are accessed. This approach, however, requires laborious search, retrieval, and eventual customization/adaptation for the data to be used. Such fractionation makes it even more difficult to share outcomes, i.e. research artifacts and data products, hampering reusability and repeatability, since end users generally have their own research agenda and preferences as well as scarce resources. Thus, while finding and downloading data files from central data centers are already costly for end users working in their own field, using data products from other disciplines rapidly becomes prohibitive. This curtails scientific productivity, limits avenues of study, and endangers quality and reproducibility. The Spatio-Temporal Adaptive Resolution Encoding (STARE) is a unifying scheme that facilitates the indexing, access, and fusion of diverse Earth Science data. STARE implements an innovative encoding of geo-spatiotemporal information, originally developed for aligning datasets with diverse spatiotemporal characteristics in an array database. The spatial component of STARE recursively quadfurcates a root polyhedron, producing a hierarchical scheme for addressing geographic locations and regions. The temporal component of STARE uses conventional date-time units as an indexing hierarchy. The additional encoding of spatial and temporal resolution information in STARE enables comparisons and conditional selections across diverse datasets. Moreover, spatiotemporal set-operations, e.g. union and intersection, are mapped to efficient integer operations with STARE. Applied to existing data models (point, grid, spacecraft swath) and corresponding granules, STARE indexes provide a streamlined description usable as geo-spatiotemporal metadata. When coupled with large scale, distributed hardware and software, STARE-based data access reduces pre-analysis data preparation costs by offering a convenient means to align different datasets spatiotemporally without specialized effort in parallel computing or distributed data management.

Kuo, Kwo-Sen↗