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Performance tests of variable camber guide vane and stator to extend range of axial flow compressors
Polarimetric and multifrequency SAR data are used to construct a multi-tier estimation algorithm for successive derivation of the variables describing the entire forest canopy.
Health effects of air pollution, especially particulate matter (PM), have been widely investigated. However, most of the studies rely on few monitors located in urban areas for short-term assessments, or land use/dispersion modelling for long-term evaluations, again mostly in cities. Recently, the availability of finely resolved satellite data provides an opportunity to estimate daily concentrations of air pollutants over wide spatio-temporal domains. Italy lacks a robust and validated high resolution spatio-temporally resolved model of particulate matter. The complex topography and the air mixture from both natural and anthropogenic sources are great challenges difficult to be addressed. We combined finely resolved data on Aerosol Optical Depth (AOD) from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, ground-level PM10measurements, land-use variables and meteorological parameters into a four-stage mixed model framework to derive estimates of daily PM10concentrations at 1-km2 grid over Italy, for the years 2006-2012. We checked performance of our models by applying 10-fold cross-validation (CV) for each year. Our models displayed good fitting, with mean CV-R2=0.65 and little bias (average slope of predicted VS observed PM10=0.99). Out-of-sample predictions were more accurate in Northern Italy (Po valley) and large conurbations (e.g. Rome), for background monitoring stations, and in the winter season. Resulting concentration maps showed highest average PM10levels in specific areas (Po river valley, main industrial and metropolitan areas) with decreasing trends over time. Our daily predictions of PM10concentrations across the whole Italy will allow, for the first time, estimation of long-term and short-term effects of air pollution nationwide, even in areas lacking monitoring data. Copyright © 2016 Elsevier Ltd. All rights reserved.
A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.
The number of variables measured during airborne field campaigns has increased more than tenfold over the last thirty years. With this increase in measurements, the complexity for distributed active archive centers (DAACs) to distribute the data and for data users to search for and find measurements of interest has also increased. Part of this complexity arises from the unique variable names in suborbital atmospheric composition field studies. With limited guidelines related to variable naming, variable names and structures can vary significantly, even for the same type of variable. It is common for instrument scientists to use their intended measurable quantity as the data variable name. This can make it difficult for users to locate and interact with a particular variable across multiple data sets. One effective solution to this problem, identified by the Earth Science Data System (ESDS) ICARTT Refresh Working Group [1], was to introduce variable standard names that can be used as tags for each data variable. This allows similar measurements (e.g., dew point) to be categorized and located across field campaigns, regardless of what variable name the instrument scientist has used. From this the atmospheric composition variable standard names were developed with the goal to use Findable, Accessible, Interoperable, and Reusable (FAIR) principles [2] and provide context for all users, while remaining connected to those in the subject area. These standard names have been successfully implemented in FIREX-AQ, CAMP 2EX, ACTIVATE, and DCOTSS field campaigns.
A variable-conductance heat-pipe system (VCHPS) with methanol as the working fluid and a nitrogen and helium mixture as the control gas was used for the thermal control of a 200 W RF traveling wave tube of the Communication Technology Satellite. Three stainless steel heat pipes (one redundant) and an aluminum radiator were designed to transfer 196 watts for an evaporator temperature of 50 C. The system has operated for three years with no noticeable change in performance. On four occasions the heat pipes apparently deprimed. A short time after reducing the tube power, the heat pipes reprimed and the system continued to operate normally. The description, qualification testing, and orbit data of the VCHPS are presented.
The transfer, processing, and use of satellite data are discussed. Topics relating to simulation and processing techniques include computer design assessments through simulation, data system dynamic simulation, and future timing accuracy requirements and procedures for data processing. Subjects relevant to data base management systems and users and their needs include an image-based information system architecture for correlation satellite and topological data base, efficient searching and sorting applications using an associative array processor, analysis of user's needs for a large technical data base, and variable length data formats. The technology development outlook was considered with attention to data systems technology outlook for supporting NASA programs, a user-oriented interactive information extraction system, and flow control and sorting techniques for telemetry packets.
The decreased instantaneous field of view (IFOV) is one of the principal advances noted for the Thematic Mapper (TM) sensor. The 42.5 microradian IFOV of TM and the 710 km nominal orbit altitude result in a 30 m nominal spatial resolution at the earth surface. This is a considerable decrease in the projected pixel area when compared to the 79 m nominal spatial resolution of the Landsat Multispectral Scanner (MSS). An experiment was conducted which allowed a rigorous test of the influence of classifier design, with data spatial resolution of TM (30 m) and approximately that of the Landsat MSS (90 m), on classification performance for a particular TM scene. The experiment involved evaluation of the results for the per-point Gaussian maximum likelihood (GML) classifier and the supervised ECHO (Extraction and Classification of Homogeneous Objects) classifier.
Analysis of meteorological, chemical, and microphysical data from the airborne SUCCESS mission is reported. Careful analysis of the complex DC-8 flight pattern of May 2, 1996 reveals 19 linear or nearly linear flight segments within six main geographical areas, which we have analyzed. Significant mountain wave activity is revealed in the data from the MMS and MTP instruments on the DC-8, which resembles previous observations of mountain wave structures near Boulder, CO. Strong mountain-wave-induced upwelling downwind of the Rockies is noted. Turbulence is also noted in regions of the mountain wave consistent with overturning near the tropopause. Zonal winds recorded on the ER-2 are shown to consistent with mountain wave breaking at or near critical levels in the stratosphere, consistent with the strong turbulence reported by the pilot during the ER-2 flight. Those observations have been supported with spectral analyses and modeling studies. "Postcasts" of mountain wave activity on May 2, 1996, using the Naval Research Laboratory Mountain Wave Forecast Model (NRL/MWFM) predicts both strong mountain wave activity near the tropopause (as measured by the DC-8) and strong mountain-wave-induced turbulence in the stratosphere (as encountered by the ER-2). Two-dimensional simulations of fluid flow over topography reveal similar isentropic structures to observations.
Analysis of meteorological, chemical and microphysical data from the airborne SUCCESS (SUbsonic aircraft Contrail and Cloud Effects Special Study) mission is reported. Careful analysis of the complex DC-8 flight pattern of May 2, 1996 reveals 19 linear flight segments within six main geographical areas, which we have analyzed. Significant mountain wave activity is revealed in the data from the MMS (Meteorology Measurement System) and MTP (Microwave Temperature Profiler) instruments on the DC-8, which resembles previous observations of mountain wave structures near Boulder, Colorado. Strong mountain-wave-induced upwelling downwind of the Rockies is noted. Turbulence is also noted in regions of the mountain wave consistent with overturning near the tropopause. Zonal winds recorded on the ER-2 are shown to be consistent with mountain wave breaking at or near critical levels in the stratosphere, consistent with the strong turbulence reported by the pilot during the ER-2 flight. These observations have been supported with spectral analyses and modeling studies. 'Postcasts' of mountain wave activity on May 2, 1996 using the Naval Research Laboratory Mountain Wave Forecast Model predicts both strong mountain wave activity near the tropopause and strong mountain-wave-induced turbulence in the stratosphere.
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Experimental data from Ariel 5 on the spectral shape and time variability of Ser X-1 are presented, and possible explanations for the observations are discussed in terms of current theoretical suggestions for source emission. The observations are summarized in the form of a light curve for 3-7.6-keV photons. The data are fitted with a power law of index -2.3, which yields a hydrogen column density of (1.1 + or - 0.4) x 10 to the 22nd power atoms/sq cm. No persistent periodicity of amplitude greater than 5% of the steady flux is found, but evidence of statistically significant burst activity is obtained. Various emission mechanisms are considered for the time-averaged spectrum and the X-ray bursts. It is suggested that the inverse Compton mechanism is a likely cause for the emission from this source and that the source is radiating near the Eddington limit.
Variable-rate, asynchronous data signals from up to four measuring instruments or other sources combined in first-in/first-out (FIFO) buffer for transmission on single channel. Constructed in complementary metal-oxide-semiconductor (CMOS) logic, buffer consumes low power (only 125 mW at 5V) and conforms to aerospace standards of reliability and maintainability.
The variable resolution stretched grid (SG) version of the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) incorporating the GEOS SG-GCM, has been developed and tested. The area/region of interest used in experiments is a rectangle over the U.S. with -60 km horizontal resolution and 70 layers extending from the surface to 0.1 hPa. The forecast error statistics has been reassessed for finer regional resolution. The experiments are performed for winter and summer seasons. The SG-GCM and SG-DAS experiments show that a definite down-scaling takes place over the area of interest. The SG-DAS is capable of reproducing regional mesoscale patterns and diagnostics that are not produced by coarser uniform resolution runs. The SG-DAS fields and diagnostics are used for regional forecasting, new instrument impact studies, and for validation of regional climate simulation experiments.
Satellite Level 2 data presents unique challenges for tools and services. From nonlinear spatial geometry to inhomogeneous file data structure to inconsistent temporal variables to complex data variable dimensionality to multiple file formats, there are many difficulties in creating general tools for Level 2 data support. At NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we are implementing a general Level 2 Subsetting service for Level 2 data to a user-specified spatio-temporal region of interest (ROI). In this presentation, we will unravel some of the challenges faced in creating this service and the strategies we used to surmount them.
Accurate soil moisture information has proved to be important to climate simulations and climate and weather forecasts. However, many difficulties exist that limit our understanding of soil moisture distribution and variability. One of them is the lack of accurate precipitation with appropriate spatial and temporal resolution. Precipitation as an input forcing to the land surface greatly influences soil moisture characteristics and variability. To improve precipitation data quality, an algorithm has been developed to generate a spatially and temporally continuous 3-hourly global precipitation data for the period of 1987 to present. This precipitation product is a combination of the precipitation from Special Sensor Microwave Imager (SSMI) with the Goddard Earth Observing System-1 Data Assimilation System (GEOS-1 DAS) employing a Physical-space Statistical Analysis System (PSAS). In this study we investigate the impact of this merged/analyzed precipitation data on the global soil moisture variability using an Off-line Land-surface GEOS Assimilation (OLGA) system. Two OLGA integrations starting from 1987 to 1993 are performed forced with the analyzed and GEOS-1 DAS precipitation respectively. We examine the spatial and temporal characteristics of soil moisture variability in response to the analyzed precipitation. The influence of this merged precipitation on the soil moisture variability and regional hydrological budget is estimated throughout the comparison with the results forced with the GEOS-1 DAS precipitation only. In the OLGA the sut@-grid scale horizontal heterogeneity is explicitly represented on the tile space. This provides a means to assess the role of the surface moisture heterogeneity in the interaction with the surface atmosphere and surface hydrological budget, and to validate OLGA results at tile space with in situ observation. ABRACOS (Anglo-Brazilian Amazonian Climate Observation Study), FIFE (First ISLSCP Field Experiment) I and HAPEX data will be used in the validation.
For abstract, see N74-15350.