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At least 1,063 records · Page 59

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NASA NTRS2014

Design of a K/Q-band Beacon Receiver for the Alphasat TDP#5 Experiment

This paper describes the design and performance of a coherent K/Q-band (20/40GHz) beacon receiver developed at NASA Glenn Research Center (GRC) that will be installed at the Politecnico di Milano (POLIMI) for use in the Alphasat Technology Demonstration Payload #5 (TDP#5) beacon experiment. The goal of this experiment is to characterize rain fade attenuation at 40GHz to improve the performance of existing statistical rain attenuation models in the Q-band. The ground terminal developed by NASA GRC utilizes an FFT-based frequency estimation receiver capable of characterizing total path attenuation effects due to gaseous absorption, clouds, rain, and scintillation. The receiver system has been characterized in the lab and demonstrates a system dynamic range performance of better than 58dB at 1Hz and better than 48dB at 10Hz rates.

beacon receiver↗

NASA NTRS2013

Phylogenetic Conservatism in Plant Phenology

Phenological events defined points in the life cycle of a plant or animal have been regarded as highly plastic traits, reflecting flexible responses to various environmental cues. The ability of a species to track, via shifts in phenological events, the abiotic environment through time might dictate its vulnerability to future climate change. Understanding the predictors and drivers of phenological change is therefore critical. Here, we evaluated evidence for phylogenetic conservatism the tendency for closely related species to share similar ecological and biological attributes in phenological traits across flowering plants. We aggregated published and unpublished data on timing of first flower and first leaf, encompassing 4000 species at 23 sites across the Northern Hemisphere. We reconstructed the phylogeny for the set of included species, first, using the software program Phylomatic, and second, from DNA data. We then quantified phylogenetic conservatism in plant phenology within and across sites. We show that more closely related species tend to flower and leaf at similar times. By contrasting mean flowering times within and across sites, however, we illustrate that it is not the time of year that is conserved, but rather the phenological responses to a common set of abiotic cues. Our findings suggest that species cannot be treated as statistically independent when modelling phenological responses.Closely related species tend to resemble each other in the timing of their life-history events, a likely product of evolutionarily conserved responses to environmental cues. The search for the underlying drivers of phenology must therefore account for species' shared evolutionary histories.

phylogenetic conservatism↗

NASA NTRS2014

Design of a K/Q-Band Beacon Receiver for the Alphasat Technology Demonstration Payload (TDP) #5 Experiment

This paper describes the design and performance of a coherent KQ-band (2040 GHz) beacon receiver developed at NASA Glenn Research Center (GRC) that will be installed at the Politecnico di Milano (POLIMI) for use in the Alphasat Technology Demonstration Payload 5 (TDP5) beacon experiment. The goal of this experiment is to characterize rain fade attenuation at 40 GHz to improve the performance of existing statistical rain attenuation models in the Q-band. The ground terminal developed by NASA GRC utilizes an FFT-based frequency estimation receiver capable of characterizing total path attenuation effects due to gaseous absorption, clouds, rain, and scintillation. The receiver system has been characterized in the lab and demonstrates a system dynamic range performance of better than 58 dB at 1 Hz and better than 48 dB at 10 Hz rates.

rain fade↗

NASA NTRS2015

Experiment Design for Complex VTOL Aircraft with Distributed Propulsion and Tilt Wing

Selected experimental results from a wind tunnel study of a subscale VTOL concept with distributed propulsion and tilt lifting surfaces are presented. The vehicle complexity and automated test facility were ideal for use with a randomized designed experiment. Design of Experiments and Response Surface Methods were invoked to produce run efficient, statistically rigorous regression models with minimized prediction error. Static tests were conducted at the NASA Langley 12-Foot Low-Speed Tunnel to model all six aerodynamic coefficients over a large flight envelope. This work supports investigations at NASA Langley in developing advanced configurations, simulations, and advanced control systems.

Murphy, Patrick C.↗

NASA NTRS2014

Design of a K/Q-Band Beacon Receiver for the Alphasat TDP#5 Experiment

beacon receiver↗

NASA NTRS2016

Statistically Based Approach to Broadband Liner Design and Assessment

A broadband liner design optimization includes utilizing in-duct attenuation predictions with a statistical fan source model to obtain optimum impedance spectra over a number of flow conditions for one or more liner locations in a bypass duct. The predicted optimum impedance information is then used with acoustic liner modeling tools to design liners having impedance spectra that most closely match the predicted optimum values. Design selection is based on an acceptance criterion that provides the ability to apply increasing weighting to specific frequencies and/or operating conditions. One or more broadband design approaches are utilized to produce a broadband liner that targets a full range of frequencies and operating conditions.

Nark, Douglas M.↗

NASA NTRS2016

Design of a Ka-Band Propagation Terminal for Atmospheric Measurements in Polar Regions

This paper describes the design and performance of a Ka-Band beacon receiver developed at NASA Glenn Research Center (GRC) that will be installed alongside an existing Ka-Band Radiometer [2] located at the east end of the Svalbard Near Earth Network (NEN) complex. The goal of this experiment is to characterize rain fade attenuation to improve the performance of existing statistical rain attenuation models. The ground terminal developed by NASA GRC utilizes an FFT-based frequency estimation [3] receiver capable of characterizing total path attenuation effects due to gaseous absorption, clouds, rain, and scintillation by directly measuring the propagated signal from the satellite Thor 7.

propagation↗

NASA NTRS2016

Design of a Ka-band Propagation Terminal for Atmospheric Measurements in Polar Regions

This paper describes the design and performance of a Ka-Band beacon receiver developed at NASA Glenn Research Center (GRC) that will be installed alongside an existing Ka-Band Radiometer located at the east end of the Svalbard Near Earth Network (NEN) complex. The goal of this experiment is to characterize rain fade attenuation to improve the performance of existing statistical rain attenuation models. The ground terminal developed by NASA GRC utilizes an FFT-based frequency estimation receiver capable of characterizing total path attenuation effects due to gaseous absorption, clouds, rain, and scintillation by directly measuring the propagated signal from the satellite Thor 7.

propagation↗

NASA NTRS2015

Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

Decomposition↗

NASA NTRS2016

Kepler Data Validation Time Series File: Description of File Format and Content

The Kepler space mission searches its time series data for periodic, transit-like signatures. The ephemerides of these events, called Threshold Crossing Events (TCEs), are reported in the TCE tables at the NASA Exoplanet Archive (NExScI). Those TCEs are then further evaluated to create planet candidates and populate the Kepler Objects of Interest (KOI) table, also hosted at the Exoplanet Archive. The search, evaluation and export of TCEs is performed by two pipeline modules, TPS (Transit Planet Search) and DV (Data Validation). TPS searches for the strongest, believable signal and then sends that information to DV to fit a transit model, compute various statistics, and remove the transit events so that the light curve can be searched for other TCEs. More on how this search is done and on the creation of the TCE table can be found in Tenenbaum et al. (2012), Seader et al. (2015), Jenkins (2002). For each star with at least one TCE, the pipeline exports a file that contains the light curves used by TPS and DV to find and evaluate the TCE(s). This document describes the content of these DV time series files, and this introduction provides a bit of context for how the data in these files are used by the pipeline.

DV Time Series↗

NASA NTRS2016

Threats to North American Forests from Southern Pine Beetle with Warming Winters

In coming decades, warmer winters are likely to lift range constraints on many cold-limited forest insects. Recent unprecedented expansion of the southern pine beetle (SPB, Dendroctonus frontalis) into New Jersey, New York, Connecticut, and Massachusetts in concert with warming annual temperature minima highlights the risk that this insect pest poses to the pine forests of the northern United States and Canada under continued climate change. Here we present the first projections of northward expansion in SPB-suitable climates using a statistical bioclimatic range modeling approach and current-generation general circulation model (GCM) output under the RCP 4.5 and 8.5 emissions scenarios. Our results show that by the middle of the 21st century, the climate is likely to be suitable for SPB expansion into vast areas of previously unaffected forests throughout the northeastern United States and into southeastern Canada. This scenario would pose a significant economic and ecological risk to the affected regions, including disruption oflocal ecosystem services, dramatic shifts in forest structure, and threats to native biodiversity.

risk↗

NASA NTRS2018

Examining Dense Data Usage near the Regions with Severe Storms in All-Sky Microwave Radiance Data Assimilation and Impacts on GEOS Hurricane Analyses

Many numerical weather prediction (NWP) centers assimilate radiances affected by clouds and precipitation from microwave sensors, with the expectation that these data can provide critical constraints on meteorological parameters in dynamically sensitive regions to make significant impacts on forecast accuracy for precipitation. The Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center assimilates all-sky microwave radiance data from various microwave sensors such as all-sky GPM Microwave Imager (GMI) radiance in the Goddard Earth Observing System (GEOS) atmospheric data assimilation system (ADAS), which includes the GEOS atmospheric model, the Gridpoint Statistical Interpolation (GSI) atmospheric analysis system, and the Goddard Aerosol Assimilation System (GAAS). So far, most of NWP centers apply same large data thinning distances, that are used in clear-sky radiance data to avoid correlated observation errors, to all-sky microwave radiance data. For example, NASA GMAO is applying 145 km thinning distances for most of satellite radiance data including microwave radiance data in which all-sky approach is implemented. Even with these coarse observation data usage in all-sky assimilation approach, noticeable positive impacts from all-sky microwave data on hurricane track forecasts were identified in GEOS-5 system. The motivation of this study is based on the dynamic thinning distance method developed in our all-sky framework to use of denser data in cloudy and precipitating regions due to relatively small spatial correlations of observation errors. To investigate the benefits of all-sky microwave radiance on hurricane forecasts, several hurricane cases selected between 2016-2017 are examined. The dynamic thinning distance method is utilized in our all-sky approach to understand the sources and mechanisms to explain the benefits of all-sky microwave radiance data from various microwave radiance sensors like Advanced Microwave Sounder Unit (AMSU-A), Microwave Humidity Sounder (MHS), and GMI on GEOS-5 analyses and forecasts of various hurricanes.

Kim, Min-Jeong↗

NASA NTRS2018

Stochastic Reduced Order Models with Python (SROMPy)

Stochastic Reduced Order Models with Python (SROMPy) is a software package developed to enable user-friendly utilization of the stochastic reduced order model (SROM) approach for uncertainty quantification. A SROM is a low dimensional, discrete approximation to a random quantity that enables efficient and non-intrusive stochastic computations. With SROMPy, a user can easily generate a SROM to approximate a random variable or vector described by several different types of probability distributions using the Python programming language. Once a SROM is constructed, the software can be used to propagate uncertainty through a user-defined computational model to estimate statistics of a given quantity of interest. This report is meant to introduce the SROMPy module and brie y demonstrate its capabilities. A simple example of a spring-mass system with a random input is included to illustrate the practicality of the SROM approach to uncertainty quantification and relative ease of applying it with SROMPy. The example includes a comparison with a solution obtained using classical Monte Carlo simulation, demonstrating the similarities and advantages of using the SROM approach.

Warner, James E.↗

NASA NTRS2018

Integrating Cloud-Based Workflows in Continental-Scale Cropland Extent Classification

Accurate information on cropland spatial distribution is required for global-scale assessments and agricultural land use policies. Cloud computing platforms such as Google Earth Engine (GEE) provide unprecedented opportunities for large-scale classifications of Landsat data. We developed a novel method to fuse pixel-based random forest classification of continental-scale Landsat data on GEE and an object-based segmentation approach known as recursive hierarchical segmentation (RHSeg). Using our fusion method, we produced a continental-scale cropland extent map for North America at 30m spatial resolution for the nominal year 2010. The total cropland area for North America was estimated at 275.18 million hectares (Mha). The overall accuracies of the map are>90% across the continent. This map also compares well with the United States Department of Agriculture (USDA) cropland data layer (CDL), Agriculture and Agri-food Canada (AAFC) annual crop inventory (ACI), and the Mexican government agency Servicio de Informacion Agroalimentaria y Pesquera (SIAP)'s agricultural boundaries. Furthermore, our map compared well with sub-country statistics including state-wise and county-wise cropland statistics in regression models resulting in R2 > 0.84. This key contribution paves the way for more detailed products such as crop intensity, crop type, and crop irrigation, and provides a method for creating high-resolution cropland extent maps for other countries where spatial information about croplands are not as prevalent.

Massey, Richard↗

NASA NTRS2019

Radar Cross Section of Orbital Debris Objects

This discussion is concerned with the radar-data analysis and usage involved in the building of model orbital debris (OD) populations in the near-Earth environment, focusing on radar cross section (RCS). While varying with radar wavelength, physical dimension, material composition, overall shape and structure, the RCS of an irregular object is also strongly dependent on its spatial orientation. The historical records of observed RCSs for cataloged OD objects in the Space Surveillance Network are usually distributed over an RCS range, forming respective characteristic patterns. The National Aeronautics and Space Administration (NASA) Size Estimation Model provides an empirical probability-density function of RCS as a function of “effective diameter” (or “characteristic length”), which makes it feasible to predict possible RCS distributions for a given model OD population and to link data with model from a statistical perspective. The discussion also includes application of the widely used method of moments (MoM) and the Generalized Multi-particle Mie-solution (GMM) in the prediction of the RCS of arbitrarily shaped objects. Theoretical calculation results for an aluminum cube are compared with corresponding experimental measurements.

Xu, Yu-Lin↗

NASA NTRS2020

Confirmation of the Stellar Binary Microlensing Event, Macho 97-BLG-28

The high-magnification microlensing event MACHO-97-BLG-28 was previously determined to be a binary system composed of either two M dwarfs or an M dwarf and a brown dwarf. We present a revised light-curve model using additional data from the Mt. Stromlo 74″telescope, model estimates of stellar limb darkening, and fitting of the blend separately for each telescope and passband. We find a lensing system with a larger mass ratio, q=0.28±0.01, and smaller projected separation, s=0.61±0.01, than those presented in the original study. We revise the estimate of the lens–source relative proper motion to μ(rel)=2.8±0.5 mas/yr, which indicates that 16.07 yr after the event maximum the lens and source should have separated by 46±8 mas. We revise the radius of the source star using more recent reddening maps and angular diameter–color relations to R(*)=(10.3±1.9) R(ʘ). K- and J-band adaptive optics images of the field taken at this epoch using the NIRC2 imager on the Keck telescope show that the source and lens are still blended, consistent with our light-curve model. With no statistically significant excess flux detection we constrain the mass, M(L)=0.24 (+0.28, -0.12) M(ʘ), and distance, D(L)=7.0±1.0 kpc, of the lensing system. This supports the interpretation of this event as a stellar binary in the Galactic bulge. This lens mass gives a companion mass of M= 0.07(+0.08,-0.04) M(ʘ), close to the boundary between being a star and a brown dwarf.

W. Blackman↗

NASA NTRSDate not supplied

Hail Storm Risk Assessment Using Space-Borne Remote Sensing Observations and Reanalyses

Much of the world is impacted by severe thunderstorms, but whether they become disasters depends upon resilience--our capacity to prepare, mitigate, respond, and recover. Hail is the costliest severe weather hazard for the insurance industry, generating ~70% of severe convective storm losses due to damage to assets such as homes, businesses, agriculture, and infrastructure. Most insurance companies do not reserve enough capital to cover catastrophes, so they acquire reinsurance. The reinsurance industry uses catastrophe models (CatModels) to statistically estimate risk to an insurer’s portfolio. Hail CatModels are developed with climatologies that define hailstorm frequency and severity. Hail-prone areas can be defined using hail reports from trained spotters, the media, and the general public. Extremely severe hail (2+ inch diameter) occurs nearly every day across the world. Weather radars can detect hail because hailstones strongly reflect microwave signals that they emit. However, hail climatologies are difficult to derive because hail covers small areas and there are neither hail reporting mechanisms (e.g. website or mobile app) nor radar networks in most places outside the US and Europe. This lack of ground truth on severe hail puts society and economies at risk. Hail is generated within storms by strong updrafts. These updrafts exhibit unique signatures in NASA and other agency satellite observations, offering new opportunities for hailstorm analysis. Geostationary (GEO) visible and infrared imagery has been collected for ~15-25 years across the world (region dependent) and methods have been developed at NASA Langley Research Center (LaRC) to detect hailstorm updrafts using GEO imagery. Climatological GEO updraft data has been used by Willis Towers Watson (WTW), a leader in catastrophe risk assessment for the insurance industry, and Karlsruhe Institute of Technology to develop CatModels over Europe and Australia. Hail can also be inferred with passive microwave imagery collected by low-Earth-orbiting sensors such as the GPM GMI, TRMM TMI, AMSR-E, AMSR-2, SSM/I, and SSMIS over the last 20+ years using methods developed at the Marshall Space Flight Center (MSFC). Hailstorms generate enhanced lightning flash rates that can be tracked using new GOES-R series GEO Lightning Mapping (GLM) imagery. Atmospheric reanalyses can be used to define favorable hailstorm environments for combination with the satellite-based storm detections. This presentation will describe a framework for developing continental to global hail climatologies and CatModels based on NASA satellite data and capabilities. This is a collaboration between LaRC and MSFC, WTW, and partners in Brazil, Argentina, and South Africa. This project seeks to mitigate hail disasters by aiding development of new satellite-based severe storm nowcasting tools by regional partners and developing climatologies to improve societal understanding of hail frequency. GEOO visible and infrared metrics of storm intensity, environmental conditions based on reanalyses, spotter hail reports and radar MESH observations are intercompared to quantify the detectability of hailstorms, and our ability to discriminate hailstorms from other severe storms. We are also maturing methods using land surface imaging satellite data (e.g. MODIS, Landsat, Sentinel 1 and 2) to identify hail damage to agriculture. Work with WTW will improve socioeconomic resilience through development of new CatModels. Southern Brazil, Uruguay, Paraguay, and Argentina feature some of the most intense thunderstorms on Earth. South America and South Africa are developing insurance markets of interest to WTW clients, and is similar to other regions routinely impacted by hail that do not have comprehensive hail reporting or radars to assess hailstorm frequency. Project datasets will be made available via online GIS-enabled tools developed at the LaRC Atmospheric Science Data Center (ASDC) which will visualize data and provide it in multiple formats for use in a wide range of open source and commercial tools.

Kristopher Michael Bedka↗

NASA NTRS2020

Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales

Fire emissions of gases and aerosols alter atmospheric composition and have substantial impacts on climate, ecosystem function, and human health. Warming climate and human expansion in fire‐prone landscapes exacerbate fire impacts and call for more effective management tools. Here we developed a global fire forecasting system that predicts monthly emissions using past fire data and climate variables for lead times of 1 to 6 months. Using monthly fire emissions from the Global Fire Emissions Database (GFED) as the prediction target, we fit a statistical time series model, the Autoregressive Integrated Moving Average model with eXogenous variables (ARIMAX), in over 1,300 different fire regions. Optimized parameters were then used to forecast future emissions. The forecast system took into account information about region‐specific seasonality, long‐term trends, recent fire observations, and climate drivers representing both large‐scale climate variability and local fire weather. We cross‐validated the forecast skill of the system with different combinations of predictors and forecast lead times. The reference model, which combined endogenous and exogenous predictors with a 1 month forecast lead time, explained 52% of the variability in the global fire emissions anomaly, considerably exceeding the performance of a reference model that assumed persistent emissions during the forecast period. The system also successfully resolved detailed spatial patterns of fire emissions anomalies in regions with significant fire activity. This study bridges the gap between the efforts of near‐real‐time fire forecasts and seasonal fire outlooks and represents a step toward establishing an operational global fire, smoke, and carbon cycle forecasting system.

vapor pressure↗