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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Uncertainty in Electric Propulsion Erosion Measurements

Uncertainty in erosion rates as measured by different methods is discussed and quantified. The work focuses on case studies from components on the Hall Effect Rocket with Magnetic Shielding (HERMeS) Hall thruster, but the methods can be extended for many electric propulsion applications. The primary method used for evaluating erosion is non-contact profilometry of masked and exposed components. Accurate quantification of the erosion rates of components is critical to determining lifetime and is therefore critical to mission planning purposes.

Jonathan A Mackey↗
NASA NTRS2021

J. E. Nichols and D. M. Peteet reply

The quantification of northern peatland carbon stocks is vital to our understanding of the global carbon cycle, and we welcome the opportunity to respond to the comments submitted in the preceding “Matters Arising” (Yu et al., Ratcliffe et al.). In response to Yu et al., we provide additional clarification of how data from the Neotoma Paleoecological Database (NPD) are stored and used. We also repeated our quantification of northern peat carbon stocks using only radiocarbon data vetted by other publications. However, our conclusions are unchanged by the elimination of the additional data. We respond to Ratcliffe et al. by addressing the conflict between the “time-history” and “inventory methods.” We calculated northern peatland carbon stocks by the inventory method using our dataset and carefully quantify the uncertainties. We find that while the mean values for carbon stock differ by twofold, the uncertainties around those means are larger than previously acknowledged and are overlapping. This result emphasizes the need for continued work by the carbon cycle science community to understand and quantify uncertainty in terrestrial carbon stock estimates.

Peatlands↗
NASA NTRS1992

LDEF yaw and pitch angle estimates

Quantification of the LDEF yaw and pitch misorientations is crucial to the knowledge of atomic oxygen exposure of samples placed on LDEF. Video camera documentation of the LDEF spacecraft prior to grapple attachment, atomic oxygen shadows on experiment trays and longerons, and a pinhole atomic oxygen camera placed on LDEF provided sources of documentation of the yaw and pitch misorientation. Based on uncertainty-weighted averaging of data, the LDEF yaw offset was found to be 8.1 plus or minus 0.6 degrees, allowing higher atomic oxygen exposure of row 12 than initially anticipated. The LDEF pitch angle offset was found to be 0.8 plus or minus 0.4 degrees, such that the space end was tipped forward toward the direction of travel. The resulting consequences of the yaw and pitch misorientation of LDEF on the atomic oxygen fluence is a factor of 2.16 increase for samples located on row 12, and a factor of 1.18 increase for samples located on the space end compared to that which would be expected for perfect orientation.

Banks, Bruce A.↗
NASA NTRS2013

Northern Eurasian Heat Waves and Droughts

This article reviews our understanding of the characteristics and causes of northern Eurasian summertime heat waves and droughts. Additional insights into the nature of temperature and precipitation variability in Eurasia on monthly to decadal time scales and into the causes and predictability of the most extreme events are gained from the latest generation of reanalyses and from supplemental simulations with the NASA GEOS-5 AGCM. Key new results are: 1) the identification of the important role of summertime stationary Rossby waves in the development of the leading patterns of monthly Eurasian surface temperature and precipitation variability (including the development of extreme events such as the 2010 Russian heat wave), 2) an assessment of the mean temperature and precipitation changes that have occurred over northern Eurasia in the last three decades and their connections to decadal variability and global trends in SST, and 3) the quantification (via a case study) of the predictability of the most extreme simulated heat wave/drought events, with some focus on the role of soil moisture in the development and maintenance of such events. A literature survey indicates a general consensus that the future holds an enhanced probability of heat waves across northern Eurasia, while there is less agreement regarding future drought, reflecting a greater uncertainty in soil moisture and precipitation projections. Substantial uncertainties remain in our understanding of heat waves and drought, including the nature of the interactions between the short-term atmospheric variability associated with such extremes and the longer-term variability and trends associated with soil moisture feedbacks, SST anomalies, and an overall warming world.

SST↗
NASA NTRS1992

Fuzzy sets, rough sets, and modeling evidence: Theory and Application. A Dempster-Shafer based approach to compromise decision making with multiattributes applied to product selection

The Dempster-Shafer theory of evidence is applied to a multiattribute decision making problem whereby the decision maker (DM) must compromise with available alternatives, none of which exactly satisfies his ideal. The decision mechanism is constrained by the uncertainty inherent in the determination of the relative importance of each attribute element and the classification of existing alternatives. The classification of alternatives is addressed through expert evaluation of the degree to which each element is contained in each available alternative. The relative importance of each attribute element is determined through pairwise comparisons of the elements by the decision maker and implementation of a ratio scale quantification method. Then the 'belief' and 'plausibility' that an alternative will satisfy the decision maker's ideal are calculated and combined to rank order the available alternatives. Application to the problem of selecting computer software is given.

Dekorvin, Andre↗
NASA NTRS1991

Structural dynamics: Probabilistic structural analysis methods. Program overview

A brief description is provided of the fundamental aspects of a quantification process. Progress since the last structural durability conference in 1989 is summarized. The methodology to date and that to be developed during the life of the program is presented. The uncertain factors are presented. The approach is outlined that is required to achieve component and/or system certification in the shortest possible time for affordable reliability risk. Two new elements appear in a block diagram: (1) uncertainties in human factor, and (2) uncertainties in the computer code. Research to quantify the uncertainties in the human factor was initiated and is discussed.

Chamis, Christos C.↗
NASA NTRS2013

Taking Stock of Circumboreal Forest Carbon With Ground Measurements, Airborne and Spaceborne LiDAR

The boreal forest accounts for one-third of global forests, but remains largely inaccessible to ground-based measurements and monitoring. It contains large quantities of carbon in its vegetation and soils, and research suggests that it will be subject to increasingly severe climate-driven disturbance. We employ a suite of ground-, airborne- and space-based measurement techniques to derive the first satellite LiDAR-based estimates of aboveground carbon for the entire circumboreal forest biome. Incorporating these inventory techniques with uncertainty analysis, we estimate total aboveground carbon of 38 +/- 3.1 Pg. This boreal forest carbon is mostly concentrated from 50 to 55degN in eastern Canada and from 55 to 60degN in eastern Eurasia. Both of these regions are expected to warm >3 C by 2100, and monitoring the effects of warming on these stocks is important to understanding its future carbon balance. Our maps establish a baseline for future quantification of circumboreal carbon and the described technique should provide a robust method for future monitoring of the spatial and temporal changes of the aboveground carbon content.

Biomass↗
NASA NTRS2004

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Method and Uncertainties - Part 1

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating/drying profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and non-convective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud resolving model simulations, and from the Bayesian formulation itself. Synthetic rain rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in instantaneous rain rate estimates at 0.5 deg resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. These errors represent about 70-90% of the mean random deviation between collocated passive microwave and spaceborne radar rain rate estimates. The cumulative algorithm error in TMI estimates at monthly, 2.5 deg resolution is relatively small (less than 6% at 5 mm/day) compared to the random error due to infrequent satellite temporal sampling (8-35% at the same rain rate).

Olson, William S.↗
NASA NTRS2006

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Improved Method and Uncertainties - Part 1

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5 -resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%-80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5deg resolution is relatively small (less than 6% at 5 mm day.1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%-35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%-15% at 5 mm day.1, with proportionate reductions in latent heating sampling errors.

Olson, William S.↗
NASA NTRS2019

Quantification of CO2 and CH4 emissions over Sacramento, California, based on divergence theorem using aircraft measurements

Emission estimates of carbon dioxide (CO2) and methane (CH4) and the meteorological factors affecting them are investigated over Sacramento, California, using an aircraft equipped with a cavity ring-down greenhouse gas sensor as part of the Alpha Jet Atmospheric eXperiment (AJAX) project. To better constrain the emission fluxes, we designed flights in a cylindrical pattern and computed the emission fluxes from two flights using a kriging method and Gauss’s divergence theorem. Differences in wind treatment and assumptions about background concentrations affect the emission estimates by a factor of 1.5 to 7. The uncertainty is also impacted by meteorological conditions and distance from the emission sources. The vertical layer averaging affects the flux estimate, but the choice of raw wind or mass-balanced wind is more important than the thickness of the vertical averaging for massbalanced wind for both urban and local scales. The importance of vertical mass transfer for flux estimates is examined, and the difference in the total emission estimate with and without vertical mass transfer is found to be small, especially at the local scale. The total flux estimates accounting for the entire circumference are larger than those based solely on measurements made in the downwind region. This indicates that a closed-shape flight profile can better contain total emissions relative to a one-sided curtain flight because most cities have more than one point source and wind direction can change with time and altitude. To reduce the uncertainty of the emission estimate, it is important that the sampling strategy account not only for known source locations but also possible unidentified sources around the city. Our results highlight that aircraft-based measurements using a closed-shape flight pattern are an efficient and useful strategy for identifying emission sources and estimating local- and city-scale greenhouse gas emission fluxes.

Ju-Mee Ryoo↗
NASA NTRSDate not supplied

A LaGrangian Perspective of the Surface Energy Budget Contributions to Sea Ice Evolution in the ‘New Arctic’

Recent Arctic warming outpaces global warming and is characterized by dramatic changes in sea ice (record sea ice loss, thinner and more seasonal ice cover), warming SSTs, and a lengthening melt season. These changes impact the surface energy budget and the sensitivity of sea ice to radiative forcing, namely an increased sensitivity of thinner ice to local radiative forcings and a weaker buffering of surface-atmosphere exchanges of heat and moisture. However, these processes are a large source of uncertainty in global climate models with prior studies finding that poor representations of downwelling longwave radiation and sea ice albedo lead to model spread in sea ice conditions. A limitation of prior work is examining sea ice-atmosphere interactions through an Eulerian framework. This study utilizes a sea ice parcel dataset in a LaGrangian framework, allowing for tracking of surface energy budget-sea ice interactions over the lifespan of individual sea ice parcels; this framework also allows for the quantification of the sea ice response to episodic weather events along with seasonal drivers, including the drivers of rapid growth and melt events. First, lag-lead relationships between relevant surface energy budget terms and sea ice thickness changes are explored to understand the atmospheric conditions preceding rapid growth and melt. The impact of episodic events- moisture intrusion, warm air outbreaks, cyclones- on sea ice growth are also investigated by stratifying first-year parcels by sea ice thickness at the end of the growth season and analyzing the effect of atmospheric variability (number and intensity of episodic events) on parcel thickness change. Lastly, parcel growth and melt and the associated radiative anomalies are compared to snowfall events/snow thickness to determine how snowfall modifies the interaction between the local surface energy budget and sea ice mass balance.

Patrick Taylor↗
NASA NTRS2017

Impact of a Regional Drought on Terrestrial Carbon Fluxes and Atmospheric Carbon: Results from a Coupled Carbon Cycle Model

Understanding the underlying processes that control the carbon cycle is key to predicting future global change. Much of the uncertainty in the magnitude and variability of the atmospheric carbon dioxide (CO2) stems from uncertainty in terrestrial carbon fluxes, and the relative impacts of temperature and moisture variations on regional and global scales are poorly understood. Here we investigate the impact of a regional drought on terrestrial carbon fluxes and CO2 mixing ratios over North America using the NASA Goddard Earth Observing System (GEOS) Model. Results show a sequence of changes in carbon fluxes and atmospheric CO2, induced by the drought. The relative contributions of meteorological changes to the neighboring carbon dynamics are also presented. The coupled modeling approach allows a direct quantification of the impact of the regional drought on local and proximate carbon exchange at the land surface via the carbon-water feedback processes.

CO↗
NASA NTRS2014

Far-Reaching Impacts of African Dust- A Calipso Perspective

African dust can transport across the tropical Atlantic and reach the Amazon basin, exerting far-reaching impacts on climate in downwind regions. The transported dust influences the surface-atmosphere interactions and cloud and precipitation processes through perturbing the surface radiative budget and atmospheric radiative heating and acting as cloud condensation nuclei and ice nuclei. Dust also influences biogeochemical cycle and climate through providing nutrients vital to the productivity of ocean biomass and Amazon forests. Assessing these climate impacts relies on an accurate quantification of dust transport and deposition. Currently model simulations show extremely large diversity, which calls for a need of observational constraints. Kaufman et al. (2005) estimated from MODIS aerosol measurements that about 144 Tg of dust is deposited into the tropical Atlantic and 50 Tg of dust into the Amazon in 2001. This estimated dust import to Amazon is a factor of 3-4 higher than other observations and models. However, several studies have argued that the oversimplified characterization of dust vertical profile in the study would have introduced large uncertainty and very likely a high bias. In this study we quantify the trans-Atlantic dust transport and deposition by using 7 years (2007-2013) observations from CALIPSO lidar. CALIPSO acquires high-resolution aerosol extinction and depolarization profiles in both cloud-free and above-cloud conditions. The unique CALIPSO capability of profiling aerosols above clouds offers an unprecedented opportunity of examining uncertainties associated with the use of MODIS clear-sky data. Dust is separated from other types of aerosols using the depolarization measurements. We estimated that on the basis of 7-year average, 118142 Tg of dust is deposited into the tropical Atlantic and 3860 Tg of dust into the Amazon basin. Substantial interannual variations are observed during the period, with the maximum to minimum ratio of about 1.6 and 2.5 for the deposition to the tropical Atlantic and Amazon, respectively. The MODIS-based estimates appear to fall within the range of CALIPSO-based estimates; and the difference between MODIS and CALIPSO estimates can be largely attributed to the interannual variability, which is corroborated by long-term surface dust concentration observations in the tropical Atlantic. Considering that CALIPSO generally tends to underestimate the aerosol loading, our estimate is likely to represent a low bound for the dust transport and deposition estimate. The finding suggests that models have substantial biases and considerable effort is needed to improve model simulations of dust cycle.

dust↗
NASA NTRSDate not supplied

Illuminating Albedo: Using MOSAiC Data to Assess the CERES Cloud Radiative Swath (CRS) Albedo Quantification Process

Increasing surface and lower tropospheric air temperatures as a result of rising greenhouse gases are expected to be most pronounced over the Arctic. Such rapid changes alter the surface climate of the region, and impacts can be observed atmospherically, oceanographically, and biogeophysically. Accurately quantifying the impact of decreasing surface albedo on the surface energy budget with satellite observations alone is complicated by a lack of shortwave radiation during winter and seasonal/spatial heterogeneity of surface type and associated spectral albedo. NASA’s Clouds and the Earth’s Radiant Energy System (CERES) project features the Cloud Radiative Swath (CRS) product, which builds upon the Single Scanner Footprint (SSF) product by using the NASA Langley Fu-Liou radiative transfer model to calculate a robust and high-quality array of surface and atmospheric radiative fluxes on an instantaneous, footprint-level scale. This study aims to use MOSAiC and CRS data to illuminate potential uncertainties in the CERES albedo production process, with goals of determining 1) spectral albedo under clear sky conditions when stratified by ice concentration, 2) the uncertainty associated with CERES surface albedo “history maps” when compared against observations captured during MOSAiC, and 3) the magnitude of variation between meteorological inputs compared to those from MOSAiC.

Emily Monroe↗
NASA NTRS2018

The Challenges of Measuring Methane from Space with a Lidar

The global and regional quantification of methane fluxes and identification of its sources and sinks has been highlighted as one of the goals of the NASA 2017 Earth Science Decadal Survey. Detecting methane from space and airborne platforms with an active (laser) remote sensing instrument presents several unique technology and measurement challenges. The instrument must have a single frequency, narrow-linewidth light source, and photon-sensitive detector at the right spectral region to make continuous measurements from orbit, day and night, all seasons and at all latitudes. It must have a high signal to noise ratio and must be relatively immune to biases from aerosol/cloud scattering, spectroscopic and meteorological data uncertainties, and instrument systematic errors. At Goddard Space Flight Center (GSFC), in collaboration with industry, we have developed an airborne instrument to measure methane. Our instrument is a nadir-viewing lidar that uses Integrated Path Differential Absorption (IPDA), to measure methane near 1.65 µm. We sample the absorption line using multiple wavelengths from a narrow linewidth laser source and a sensitive photodetector. This measurement approach provides maximum information content about the CH4 column, and minimizes biases in the XCH4 retrieval. In this paper, we will review our progress to date and discuss the technology challenges, options and tradeoffs to measure methane from space and airborne platforms.

airborne instruments↗
NASA NTRSDate not supplied

Ground Risk Informed Operational Planning for Small Unmanned Aerial Systems

Increasing quantities of small Unmanned Aerial Systems (sUAS) operations present many challenges in terms of safe adoption and integration into existing airspace. The ability to study and quantify the risk to third parties on the ground prior to flight is an important step toward enabling Beyond Visual Line of Sight (BVLOS) operations. The Ground Risk Assessment Service Provider (GRASP) software is a capability developed by NASA to assist with third-party risk quantification and risk-informed flight planning. In this paper, two nominal flight paths intended to represent an infrastructure inspection mission are evaluated using the software to demonstrate its utility. A method is also introduced for adding other NASA-developed capabilities into a single architecture to assess a broader set of operational risks associated with BVLOS operations. These capabilities include a navigation system performance prediction tool, a high fidelity vehicle dynamics model, high resolution wind field data, and other information pertinent to operators. Data produced by these capabilities are combined to enable use of the Performance Based Navigation (PBN) concept borrowed from conventional aviation, providing quantified flight path uncertainty for where the sUAS is likely to be relative to its nominal flight plan. Ground risk is assessed within this region of uncertainty, giving a higher level of confidence in the solution compared to an analysis of only the nominal flight path.

Ground Risk↗
NASA NTRS2015

Quantification of Trans-Atlantic Dust Transport from Seven-year (2007-2013) Record of CALIPSO Lidar Measurements

The trans-Atlantic dust transport has important implications for human and ecosystem health, the terrestrial and oceanic biogeochemical cycle,weather systems, and climate. This study provides an observation-based multiyear estimate of trans-Atlantic dust transport using a 7-year (2007-2013) record of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements of the three dimensional distribution of aerosol backscatter, extinction and depolarization ratio in both cloud-free and above-cloud conditions. We estimate that on a basis of the 7-year average and integration over 10 deg S-30 deg N, 182 Tg a(exp -1) dust leaves the coast of North Africa at 15 deg W, of which 132 Tg a(exp -1) and 43 Tg a(exp -1) reaches 35 deg W and 75 deg W, respectively. These flux estimates have an overall known uncertainty of +/-(45-70)%. Because of lack of reliable observations, uncertainties associated with the diurnal variation of dust and the missing below-cloud dust cannot be quantified. Significant seasonal variations are observed in both the magnitude of total dust mass flux and its meridional and vertical distributions. The interannual variability of annual dust mass flux is highly anti-correlated with the prior-year Sahel Precipitation Index. Using only cloud-free aerosol observations to calculate dust mass flux could introduce a high bias when compared with all-sky conditions that include both cloud-free and above-cloud aerosol observations. The bias is about 20% at 35 deg W and 75 deg W in boreal winter and spring based on the 7-year average, as long as dust within and below low-level clouds is negligible.

trans-Atlantic dust transport↗
NASA NTRS2018

Attribution of Flux Partitioning Variations Between Land Surface Models over the Continental U.S.

Accurate quantification of the terrestrial evapotranspiration (ET) components of plant transpiration (T), soil evaporation (E) and evaporation of the intercepted water (I) is necessary for improving our understanding of the links between the carbon and water cycles. Recent studies have noted that, among the modeled estimates, large disagreements exist in the relative contributions of T, E and I to the total ET. As these models are often used in data assimilation environments for incorporating and extending ET relevant remote sensing measurements, understanding the sources of inter-model differences in ET components is also necessary for improving the utilization of such remote sensing measurements. This study quantifies the contributions of two key factors explaining inter-model disagreements to the uncertainty in total ET: (1) contribution of the local partitioning and (2) regional distribution of ET. The analysis is conducted by using outputs from a suite of land surface models in the North American Land Data Assimilation System (NLDAS) configuration. For most of these models, transpiration is the dominant component of the ET partition. The results indicate that the uncertainty in local partitioning dominates the inter-model spread in modeled soil evaporation E. The inter-model differences in T are dominated by the uncertainty in the distribution of ET over the Eastern U.S. and the local partitioning uncertainty in the Western U.S. The results also indicate that uncertainty in the T estimates is the primary driver of total ET errors. Over the majority of the U.S., the contribution of the two factors of uncertainty to the overall uncertainty is non-trivial.

uncertainty↗