Search NASA⌕ Search

SEARCH · Search NASA

Results for “uncertainty characterization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Validation of UARS Microwave Limb Sounder Temperature and Pressure Measurements

The accuracy and precision of the Upper Atmosphere Research Satellite (UARS) Microwave Limb Sounder (MLS) atmospheric temperature and tangent-point pressure measurements are described. Temperatures and tangent- point pressure (atmospheric pressure at the tangent height of the field of view boresight) are retrieved from a 15-channel 63-GHz radiometer measuring O2 microwave emissions from the stratosphere and mesosphere. The Version 3 data (first public release) contains scientifically useful temperatures from 22 to 0.46 hPa. Accuracy estimates are based on instrument performance, spectroscopic uncertainty and retrieval numerics, and range from 2.1 K at 22 hPa to 4.8 K at 0.46 hPa for temperature and from 200 m (equivalent log pressure) at 10 hPa to 300 m at 0.1 hPa. Temperature accuracy is limited mainly by uncertainty in instrument characterization, and tangent-point pressure accuracy is limited mainly by the accuracy of spectroscopic parameters. Precisions are around 1 K and 100 m. Comparisons are presented among temperatures from MLS, the National Meteorological Center (NMC) stratospheric analysis and lidar stations at Table Mountain, California, Observatory of Haute Provence (OHP), France, and Goddard Spaceflight Center, Maryland. MLS temperatures tend to be 1-2 K lower than NMC and lidar, but MLS is often 5 - 10 K lower than NMC in the winter at high latitudes, especially within the northern hemisphere vortex. Winter MLS and OHP (44 deg N) lidar temperatures generally agree and tend to be lower than NMC. Problems with Version 3 MLS temperatures and tangent-point pressures are identified, but the high precision of MLS radiances will allow improvements with better algorithms planned for the future.

Fishbein, E. F.↗
NASA NTRS2002

Cars Thermometry in a Supersonic Combustor for CFD Code Validation

An experiment has been conducted to acquire data for the validation of computational fluid dynamics (CFD) codes used in the design of supersonic combustors. The primary measurement technique is coherent anti-Stokes Raman spectroscopy (CARS), although surface pressures and temperatures have also been acquired. Modern- design- of-experiment techniques have been used to maximize the quality of the data set (for the given level of effort) and minimize systematic errors. The combustor consists of a diverging duct with single downstream- angled wall injector. Nominal entrance Mach number is 2 and enthalpy nominally corresponds to Mach 7 flight. Temperature maps are obtained at several planes in the flow for two cases: in one case the combustor is piloted by injecting fuel upstream of the main injector, the second is not. Boundary conditions and uncertainties are adequately characterized. Accurate CFD calculation of the flow will ultimately require accurate modeling of the chemical kinetics and turbulence-chemistry interactions as well as accurate modeling of the turbulent mixing

Cutler, A. D.↗
NASA NTRS2008

The Role of Trust and Interaction in Global Positioning System Related Accidents

The Global Positioning System (GPS) uses a network of satellites to calculate the position of a receiver over time. This technology has revolutionized a wide range of safety-critical industries and leisure applications. These systems provide diverse benefits; supplementing the users existing navigation skills and reducing the uncertainty that often characterizes many route planning tasks. GPS applications can also help to reduce workload by automating tasks that would otherwise require finite cognitive and perceptual resources. However, the operation of these systems has been identified as a contributory factor in a range of recent accidents. Users often come to rely on GPS applications and, therefore, fail to notice when they develop faults or when errors occur in the other systems that use the data from these systems. Further accidents can stem from the over confidence that arises when users assume automated warnings will be issued when they stray from an intended route. Unless greater attention is paid to the role of trust and interaction in GPS applications then there is a danger that we will see an increasing number of these failures as positioning technologies become integral in the functioning of increasing numbers of applications.

Johnson, Chris W.↗
NASA NTRS2016

Effects of Acute Exposures to Carbon Dioxide Upon Cognitive Functions

Large quantities of carbon dioxide (CO2) originate from human metabolism and typically, within spacecraft, remain about 10-fold higher in concentration than at the earth's surface. There have been recurring complaints by crew members of episodes of "mental viscosity" adversely affecting their performance, and there is evidence from the International Space Station (ISS) that associates CO2 levels with reports of headaches by crewmembers. Additionally, there is concern that CO2 may contribute to vision impairment and intracranial pressure that has been observed in some crewmembers. Consequently, flight rules have been employed to control the level of CO2 below 4 mm Hg, which is well below the existing Spacecraft Maximum Allowable Concentration (SMAC) of 10 mm Hg for 24-hour exposures, and 5.3 mm Hg for exposures of 7 to 180 days. However, the flight rule imposed limit, which places additional demands upon resources and current technology, still exceeds the lower bound of the threshold range for reportable headaches (2 - 5 mm Hg). Headaches, while sometime debilitating themselves, are also symptoms that can provide evidence that physiological defense mechanisms have been breached. The causes of the headaches may elicit other subtle adverse effects that occur at CO2 levels well below that for headaches. The concern that CO2 may have effects at levels below the threshold for headaches appears to be substantiated in unexpected findings that CO2 at concentrations below 2 mm Hg substantially reduced some cognitive functions that are associated with the ability to make complex decisions in conditions that are characterized by volatility, uncertainty, complexity, ambiguity, and delayed feedback. These are conditions that could be encountered by crews in off-nominal situations or during the first missions beyond low earth orbit. If findings of the earlier study are confirmed in crew-like subjects, our findings would provide additional evidence that CO2 may need to be controlled at levels that are well below current spacecraft limits. Our study will extend the earlier study to determine if crew-like subjects are similarly effected by CO2. In addition to employing the Strategic Management Simulation tool, we will use the Cognition battery of psychometric measures that are being utilized aboard the ISS. It will be important to learn, by using Cognition, if additional cognitive domains are sensitive to concentrations of CO2 at or below limits currently controlled by flight rules. While spaceflight Cognition data will greatly enhance the knowledge base related to inflight behavioral health and performance, some of the measures may be influenced by fatigue (related to sleep deprivation and or workload) and changes in circadian rhythms. Therefore our use of this battery of tests in a well-controlled, ground-based study that is free of these potential confounding influences will establish a baseline terrestrial data set against which Cognition data collected in flight may be assessed. The findings from this study will be useful to the NASA Toxicology Office and the National Research Council Committee on Toxicology, which assists NASA in setting environmental standards, for revision of the SMAC for CO2, and for designing further studies on effects of CO2 upon cognitive functions.

Scully, R. R.↗
NASA NTRS2017

Effects of Acute Exposures to Carbon Dioxide upon Cognitive Functions

Carbon dioxide (CO2) originates from human metabolism and typically remains about 10-fold higher in concentration on the International Space Station (ISS) than at the earth's surface. There have been recurring complaints by crew members of episodes of "mental viscosity" adversely affecting their performance, and there is evidence from the ISS that associates CO2 levels with reports of headaches by crewmembers. Consequently, flight rules have been employed to control CO2 below 3 mm Hg, which is well below the existing Spacecraft Maximum Allowable Concentration (SMAC) of 10 mm Hg for 24-hour exposures, and 5.3 mm Hg for exposures of 7 to 180 days. Headaches, while sometime debilitating themselves, are also symptoms that can provide evidence that physiological defense mechanisms have been breached, and there is evidence that CO2 has effects at levels below the threshold for headaches. This concern appears to be substantiated in reports that CO2 at concentrations below 2 mm Hg substantially reduced some cognitive functions that are associated with the ability to make complex decisions in conditions that are characterized by volatility, uncertainty, complexity, ambiguity, and delayed feedback. These are conditions that could be encountered by crews in off-nominal situations or during the first missions beyond low earth orbit. Therefore, we set out to determine if decision-making under volatile, uncertain, confusing and ambiguous circumstances, where feedback is delayed or absent, is correlated with low levels of CO2 during acute exposures (several hours) in crew-like subjects and to determine if additional cognitive domains are sensitive to concentrations of CO2 at, or below, current ISS levels by using a test battery that is currently available onboard ISS. We enrolled 22 volunteers (8 females, 14 males) between the ages of 30-55 (38.8 +/- 7.0) years whose training and professional experience reflect that of the astronaut corps. Subjects were divided among 4 study groups. Exposures occurred in the 20-foot environmental chamber in Bldg 7 at JSC. Each group was exposed to each of four concentrations of CO2 (600, 1200, 2500, and 5000 ppm), and the exposure order was balanced across the groups which were randomly assigned to 4 possible complete exposure orders. Study participants and investigators were blinded to the exposure order. Each exposure lasted 4 hours and occurred on the same day each week for 4 consecutive weeks. Testing included the Strategic Management Simulation (SMS) methodology and the Cognition battery of psychometric measures that are being utilized aboard the ISS. Subjects participated in a familiarization session one week prior to the start of exposure sessions. Each exposure session followed the schedule noted in Figure 1 below. On the morning of each exposure, subjects completed the Cognition battery in a conference room at indoor ambient CO2 levels. Subjects then entered the exposure chamber and were acclimated to the environment for 15 minutes before completing another round of the Cognition battery. After a 20 minute rest, subject completed the approx.80 minute SMS. Subjects were allowed another 20 minute rest period before completing the final Cognition test battery in the exposure chamber. Subjects then returned to the conference room where they completed one final round of Cognition. Data from both testing methodologies are currently being analyzed and will be presented. Study investigators have not yet been unblinded to the coded exposure concentrations, but preliminary results suggest differences.

Ryder, V. E.↗
NASA NTRS2016

Absorption Cross-Sections of Ozone in the Ultraviolet and Visible Spectral Regions: Status report 2015

The activity Absorption Cross-Sections of Ozone (ACSO) started in 2008 as a joint initiative of the International Ozone Commission (IO3C), the World Meteorological Organization (WMO) and the IGACO (Integrated Global Atmospheric Chemistry Observations) O3/UV subgroup to study, evaluate, and recommend the most suitable ozone absorption cross-section laboratory data to be used in atmospheric ozone measurements. The evaluation was basically restricted to ozone absorption cross-sections in the UV range with particular focus on the Huggins band. Up until now, the data of Bass and Paur published in 1985 (BP, 1985) are still officially recommended for such measurements. During the last decade it became obvious that BP (1985) cross-section data have deficits for use in advanced space-borne ozone measurements. At the same time, it was recognized that the origin of systematic differences in ground-based measurements of ozone required further investigation, in particular whether the BP (1985) cross-section data might contribute to these differences. In ACSO, different sets of laboratory ozone absorption cross-section data (including their dependence on temperature) of the group of Reims (France) (Brion et al., 1993, 1998, 1992, 1995, abbreviated as BDM, 1995) and those of Serdyuchenko et al. (2014), and Gorshelev et al. (2014), (abbreviated as SER, 2014) were examined for use in atmospheric ozone measurements in the Huggins band. In conclusion, ACSO recommends:(a) The spectroscopic data of BP (1985) should no longer be used for retrieval of atmospheric ozone measurements.(b) For retrieval of ground-based instruments of total ozone and ozone profile measurements by the Umkehr method performed by Brewer and Dobson instruments data of SER (2014) are recommended to be used. When SER (2014) is used, the difference between total ozone measurements of Brewer and Dobson instruments are very small and the difference between Dobson measurements at AD and CD wavelength pairs are diminished.(c) For ground-based Light Detection and Ranging (LIDAR) measurements the use of BDM (1995) or SER (2014) is recommended.(d) For satellite retrieval the presently widely used data of BDM (1995) should be used because SER (2014) seems less suitable for retrievals that use wavelengths close to 300 nm due to a deficiency in the signal-to-noise ratio in the SER (2014) dataset.The work of ACSO also showed: The need to continue laboratory cross-section measurements of ozone of highest quality. The importance of careful characterization of the uncertainties of the laboratory measurements. The need to extend the scope of such studies to other wavelength ranges (particularly to cover not only the Huggins band but also the comparison with the mid-infrared region). The need for regular cooperation of experts in spectral laboratory measurements and specialists in atmospheric (ozone) measurements.

Orphal, Johannes↗
NASA NTRSDate not supplied

Variability in Modelled Airborne Dust Mineralogy Derived from Global Soil Composition Uncertainties

Dust aerosols consist on a variety of minerals with different physic and chemical properties. As such, they interact with short and long wave radiation, potentially form clouds, act as nutrients modulating biogeochemical cycles, or influence atmospheric chemistry, differently. Most current state-of-the-art Earth System Models (ESMs) neglect the complexity in dust composition, mainly due to computational constraints, but also to the existing uncertainties in the size resolved composition of parent soils, the resulting distribution of minerals in airborne dust, and the scarcity of observations to constrain them. Within this work, we assess the variability of global dust composition due to uncertainties in the characterization of the parent soil mineralogy. To that end, we consider two available global soil mineralogy atlases, developed by Claquin et al. (1999) –C1999- and Journet et al. (2014) –J2014-, which represent respectively 8 and 12 relevant minerals for climate (namely: illite, smectite/montmorillonite, kaolinite, calcite, gypsum, hematite, quartz, and feldspars in C1999, and those plus chlorite, vermiculite, goethite, and mica in J2014). Thanks to a recently developed feature of the MONARCH atmospheric-chemistry model, we are able to explicitly resolve the minerals’ atmospheric cycle. Therefore, we define two global experiments to assess changes on airborne dust composition attributed to the soil mineralogy assumptions and provide a measure of their variability. We also perform a preliminary evaluation of the global mineralogy results against available observations of mineral fractions in surface dust concentration. Our results will inform the climate modelling community about the potential variability in dust composition, an aspect that will gain relevance as ESMs continue growing in complexity and new processes to better characterize aerosols’ forcing or biogeochemical cycles are added. Further observational constraints, such as those that will derive from the EMIT NASA mission on soil composition or the FRAGMENT experimental campaigns on airborne dust characterization, will be key in the near future to improve our understanding of the impact of dust mineralogy on fundamental climate features.

Dust aerosols↗
NASA NTRSDate not supplied

Upgrades to the Single Stage Axial Compressor and Fan Facility For Low Pressure Ratio and Boundary Layer Ingesting Fan Research

Next generation aircraft engine efficiency goals will be met through high bypass ratio engines. To achieve these goals, a combination of small core and low pressure ratio fan technologies will be necessary. Additionally, advanced propulsion air-frame integration concepts, such as boundary layer propulsion, and distributed electrically driven fans with short inlets or wing integration strategies, are beginning to be investigated for their potential fuel burn savings. In order to realize the potential fuel savings from these technologies, it is necessary to have testing capabilities for the components. The NASA Glenn Research Center (GRC) Single StageAxial Fan and Compressor Facility (also called the GRC W-8Facility) has recently undergone several significant upgrades in order to effectively test low pressure ratio fans and various inlet distortions. This paper will outline various challenges to testing low pressure ratio fans and the methods implemented to ensure that the W-8 facility is capable of performing these tests and acquiring high quality data. The upgrades include significant pip-ing upgrades to reduce pressure loss and turbulence, capability for inlet total pressure and swirl distortion, increased test section length including a longer shaft, a two component balance, an up-graded rotating data system, and a new temperature characterization to reduce uncertainties in thermocouple measurements

W-8 Facility↗
NASA NTRSDate not supplied

Upgrades to the Single Stage Axial Compressor and Fan Facility For Low Pressure Ratio and Boundary Layer Ingesting Fan Research

Next generation aircraft engine efficiency goals will be met through high bypass ratio engines. To achieve these goals, a combination of small core and low pressure ratio fan technologies will be necessary. Additionally, advanced propulsion airframe integration concepts, such as boundary layer propulsion, and distributed electrically driven fans with short inlets or wing integration strategies, are beginning to be investigated for their potential fuel burn savings. In order to realize the potential fuel savings from these technologies, it is necessary to have testing capabilities for the components. The NASA Glenn Research Center (GRC) Single Stage Axial Fan and Compressor Facility (also called the GRC W-8 Facility) has recently undergone several significant upgrades in order to effectively test low pressure ratio fans and various inlet distortions. This paper will outline various challenges to testing low pressure ratio fans and the methods implemented to ensure that the W-8 facility is capable of performing these tests and acquiring high quality data. The upgrades include significant piping upgrades to reduce pressure loss and turbulence, capability for inlet total pressure and swirl distortion, increased test section length including a longer shaft, a two component balance, an upgraded rotating data system, and a new temperature characterization to reduce uncertainties in thermocouple measurements.

W-8 facitlity↗
NASA NTRS2022

A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing

Numerical models based on physics represent the state of the art in Earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest generation computers, reducing the ability of modelers to generate simulations for understanding parameter sensitivities and characterizing variability and uncertainty. Thus, surrogate models are often developed to capture the essential attributes of the full-blown numerical models. Recent successes of machine learning methods, especially deep learning (DL), across many disciplines offer the possibility that complex nonlinear connectionist representations may be able to capture the underlying complex structures and nonlinear processes in Earth systems. A difficult test for DL-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner. A DL emulation that passes this test may be expected to perform even better than simple models with respect to capturing complex processes and spatiotemporal dependencies. Here, we examine, with a case study in satellite-based remote sensing, the hypothesis that DL approaches can credibly represent the simulations from a surrogate model with comparable computational efficiency. Our results are encouraging in that the DL emulation reproduces the results with acceptable accuracy and often even faster performance. We discuss the broader implications of our results in light of the pace of improvements in high-performance implementations of DL and the growing desire for higher resolution simulations in the Earth sciences.

Bayesian Deep Learning↗
NASA NTRS2011

Climate Change Impact Uncertainties for Maize in Panama: Farm Information, Climate Projections, and Yield Sensitivities

We present results from a pilot project to characterize and bound multi-disciplinary uncertainties around the assessment of maize (Zea mays) production impacts using the CERES-Maize crop model in a climate-sensitive region with a variety of farming systems (Panama). Segunda coa (autumn) maize yield in Panama currently suffers occasionally from high water stress at the end of the growing season, however under future climate conditions warmer temperatures accelerate crop maturation and elevated CO (sub 2) concentrations improve water retention. This combination reduces end-of-season water stresses and eventually leads to small mean yield gains according to median projections, although accelerated maturation reduces yields in seasons with low water stresses. Calibrations of cultivar traits, soil profile, and fertilizer amounts are most important for representing baseline yields, however sensitivity to all management factors is reduced in an assessment of future yield changes (most dramatically for fertilizers), suggesting that yield changes may be more generalizable than absolute yields. Uncertainty around General Circulation Model (GCM)s' projected changes in rainfall gain in importance throughout the century, with yield changes strongly correlated with growing season rainfall totals. Climate changes are expected to be obscured by the large inter-annual variations in Panamanian climate that will continue to be the dominant influence on seasonal maize yield into the coming decades. The relatively high (A2) and low (B1) emissions scenarios show little difference in their impact on future maize yields until the end of the century. Uncertainties related to the sensitivity of CERES-Maize to carbon dioxide concentrations have a substantial influence on projected changes, and remain a significant obstacle to climate change impacts assessment. Finally, an investigation into the potential of simple statistical yield emulators based upon key climate variables characterizes the important uncertainties behind the selection of climate change metrics and their performance against more complex process-based crop model simulations, revealing a danger in relying only on long-term mean quantities for crop impact assessment.

Maize↗
NASA NTRSDate not supplied

Uncertainty Improvement in the NASA Glenn Research Center 8- by 6-Foot Supersonic Wind Tunnel

Within the past decade, a measurement uncertainty analysis (MUA) team was assembled at NASA Glenn Research Center to assist the wind tunnel characterization team with quantification of uncertainty estimates for variables of interest in wind tunnels and test cells across the center. The initial analysis performed by the team was conducted on the 8- by 6-Foot Supersonic Wind Tunnel using results from the 1996/97 full calibration test entry. These MUA results were published in 2016 which included recommendations for methods to improve the uncertainty estimates for various variables of interest, such as changes to regression models, tunnel operation philosophy, and instrumentation choices. The wind tunnel characterization team utilized the proposed methods in 2019 during a full characterization test entry. Following publication of the 2019 test section characterization results, the MUA team pursued an update to the uncertainty estimates for the facility, which validated previous recommendations and revealed significantly reduced systematic uncertainties across most variables of interest. Inclusion of within-test repeat points in the 2019 test entry allowed for higher fidelity estimates of random uncertainty to be generated, as well. This collaboration between the facility, wind tunnel characterization, and MUA teams serves as an example of the data quality benefits that can be achieved through rigorous analysis of the sources of uncertainty in a ground-test facility.

Uncertainty↗
NASA NTRSDate not supplied

Uncertainty Improvement in the NASA Glenn Research Center 8- by 6-Foot Supersonic Wind Tunnel

Within the past decade, a measurement uncertainty analysis (MUA) team was assembled at the NASA Glenn Research Center to assist the wind tunnel characterization team with quantification of uncertainty estimates for variables of interest in wind tunnels and test cells across the center. The initial analysis performed by the team was conducted on the 8- by 6- Foot Supersonic Wind Tunnel using results from the 1996/97 full calibration test entry. These MUA results were published in 2016 which included recommendations for methods to improve the uncertainty estimates for various variables of interest, such as changes to regression models, tunnel operation philosophy, and instrumentation choices. The wind tunnel characterization team utilized the proposed methods in 2019 during a full characterization test entry. Following publication of the 2019 test section characterization results, the MUA team pursued an update to the uncertainty estimates for the facility, which validated previous recommendations and revealed significantly reduced systematic uncertainties across most variables of interest. Inclusion of within-test repeat points in the 2019 test entry allowed for higher fidelity estimates of random uncertainty to be generated, as well. This collaboration between the facility, wind tunnel characterization, and MUA teams serves as an example of the data quality benefits that can be achieved through rigorous analysis of the sources of uncertainty in a ground-test facility.

Uncertainty↗
NASA NTRS2016

Uncertainties in Coastal Ocean Color Products: Impacts of Spatial Sampling

With increasing demands for ocean color (OC) products with improved accuracy and well characterized, per-retrieval uncertainty budgets, it is vital to decompose overall estimated errors into their primary components. Amongst various contributing elements (e.g., instrument calibration, atmospheric correction, inversion algorithms) in the uncertainty of an OC observation, less attention has been paid to uncertainties associated with spatial sampling. In this paper, we simulate MODIS (aboard both Aqua and Terra) and VIIRS OC products using 30 m resolution OC products derived from the Operational Land Imager (OLI) aboard Landsat-8, to examine impacts of spatial sampling on both cross-sensor product intercomparisons and in-situ validations of R(sub rs) products in coastal waters. Various OLI OC products representing different productivity levels and in-water spatial features were scanned for one full orbital-repeat cycle of each ocean color satellite. While some view-angle dependent differences in simulated Aqua-MODIS and VIIRS were observed, the average uncertainties (absolute) in product intercomparisons (due to differences in spatial sampling) at regional scales are found to be 1.8%, 1.9%, 2.4%, 4.3%, 2.7%, 1.8%, and 4% for the R(sub rs)(443), R(sub rs)(482), R(sub rs)(561), R(sub rs)(655), Chla, K(sub d)(482), and b(sub bp)(655) products, respectively. It is also found that, depending on in-water spatial variability and the sensor's footprint size, the errors for an in-situ validation station in coastal areas can reach as high as +/- 18%. We conclude that a) expected biases induced by the spatial sampling in product intercomparisons are mitigated when products are averaged over at least 7 km × 7 km areas, b) VIIRS observations, with improved consistency in cross-track spatial sampling, yield more precise calibration/validation statistics than that of MODIS, and c) use of a single pixel centered on in-situ coastal stations provides an optimal sampling size for validation efforts. These findings will have implications for enhancing our understanding of uncertainties in ocean color retrievals and for planning of future ocean color missions and the associated calibration/validation exercises.

Coastal ocean color↗
NASA NTRS2005

Improvement of Raman lidar algorithm for quantifying aerosol extinction

Aerosols are particles of different composition and origin and influence the formation of clouds which are important in atmospheric radiative balance. At the present there is high uncertainty on the effect of aerosols on climate and this is mainly due to the fact that aerosol presence in the atmosphere can be highly variable in space and time. Monitoring of the aerosols in the atmosphere is necessary to better understanding many of these uncertainties. A lidar (an instrument that uses light to detect the extent of atmospheric aerosol loading) can be particularly useful to monitor aerosols in the atmosphere since it is capable to record the scattered intensity as a function of altitude from molecules and aerosols. One lidar method (the Raman lidar) makes use of the different wavelength changes that occur when light interacts with the varying chemistry and structure of atmospheric aerosols. One quantity that is indicative of aerosol presence is the aerosol extinction which quantifies the amount of attenuation (removal of photons), due to scattering, that light undergoes when propagating in the atmosphere. It can be directly measured with a Raman lidar using the wavelength dependence of the received signal. In order to calculate aerosol extinction from Raman scattering data it is necessary to evaluate the rate of change (derivative) of a Raman signal with respect to altitude. Since derivatives are defined for continuous functions, they cannot be performed directly on the experimental data which are not continuous. The most popular technique to find the functional behavior of experimental data is the least-square fit. This procedure allows finding a polynomial function which better approximate the experimental data. The typical approach in the lidar community is to make an a priori assumption about the functional behavior of the data in order to calculate the derivative. It has been shown in previous work that the use of the chi-square technique to determine the most likely functional behavior of the data prior to actually calculating the derivative eliminates the need for making a priori assumptions. We note that the a priori choice of a model itself can lead to larger uncertainties as compared to the method that is validated here. In this manuscript, the chi-square technique that determines the most likely functional behavior is validated through numerical simulation and by application to a large body of Raman lidar measurements. In general, we show that the chi-square approach to evaluate aerosol extinction yields lower extinction uncertainty than the traditional technique. We also use the technique to study the feasibility of developing a general characterization of the extinction uncertainty that could permit the uncertainty in Raman lidar aerosol extinction measurements to be estimated accurately without the use of the chi-square technique.

Russo, Felicita↗
NASA NTRSDate not supplied

Atmospheric Variability and Measurement Uncertainty: Pitfalls in Averaging in situ Data

Satellite measurements and atmospheric models, two essential components of the integrated global observing system, provide crucial tools for monitoring and predicting regional and global foci, spanning numerous Earth Science fields. Unfortunately, models can lack the spatial and temporal resolution needed to resolve finer scale structure. Satellite measurements also have similar tempo-spatial restriction issues, but additionally can only measure certain species and can have biases that must be evaluated. Ground measurement networks are critical components of this system, by providing both independent inputs of species needed by models (including those that satellites do not provide) and assisting with investigation of biases in satellite products. Similarly, aircraft measurements play a vital role in closing the gaps between satellite measurements, model products, and ground monitoring networks by providing high accuracy, high-resolution data on local to regional spatial scales. Therefore, both ground-based and airborne observations are widely used to assess model predictions and satellite observations. One of the great challenges in using both ground and aircraft data in this fashion is matching the data temporally and spatially to the model/satellite data. A meaningful comparison with model or satellite requires a solid assessment of the variability of the in-situ measurements, which include both the instrument uncertainty and the statistical uncertainty due to atmospheric variability. While instrument uncertainty is generally more straightforwardly characterized, it can be challenging to accurately capture this variability uncertainty as it often presents in a non-Gaussian manner (e.g. emission plumes, frontal passages). We will present results examining spatial and temporal variability over a selection of scales relevant to satellite measurements and models of several in situ measurement species spanning both airborne and ground measurements. The extent of non-Gaussian variability will be quantified, and we will discuss additional statistical parameters that help assess the fitness of gaussian variability assumption when temporally or spatially averaging.

satellite validation↗