Results for “uncertainty relation” — Page 7

NASA NTRS2016

Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors

We estimated global fine particulate matter (PM(sub 2.5)) concentrations using information from satellite-, simulation- and monitor-based sources by applying a Geographically Weighted Regression (GWR) to global geophysically-based satellite-derived PM(sub 2.5) estimates. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations for 1998−2014. The GWR predictors included simulated aerosol composition and land use information. The resultant PM(sub 2.5) estimates were highly consistent (R(sup 2) equals 0.81) with out-of-sample cross-validated PM(sub 2.5) concentrations from monitors. The global population-weighted annual average PM(sub 2.5) concentrations were 3-fold higher than the 10 micrograms per cubic meter WHO guideline, driven by exposures in Asian and African regions. Estimates in regions with high contributions from mineral dust were associated with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM(sub 2.5) data sources can yield valuable improvements to PM(sub 2.5) characterization on a global scale.

aerosols↗

NASA NTRS2017

Version 3 of the SMAP Level 4 Soil Moisture Product

The NASA Soil Moisture Active Passive (SMAP) Level 4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root zone (0-100 cm) soil moisture as well as related land surface states and fluxes from 31 March 2015 to present with a latency of 2.5 days. The ensemble-based L4_SM algorithm is a variant of the Goddard Earth Observing System version 5 (GEOS-5) land data assimilation system and ingests SMAP L-band (1.4 GHz) Level 1 brightness temperature observations into the Catchment land surface model. The soil moisture analysis is non-local (spatially distributed), performs downscaling from the 36-km resolution of the observations to that of the model, and respects the relative uncertainties of the modeled and observed brightness temperatures. Prior to assimilation, a climatological rescaling is applied to the assimilated brightness temperatures using a 6 year record of SMOS observations. A new feature in Version 3 of the L4_SM data product is the use of 2 years of SMAP observations for rescaling where SMOS observations are not available because of radio frequency interference, which expands the impact of SMAP observations on the L4_SM estimates into large regions of northern Africa and Asia. This presentation investigates the performance and data assimilation diagnostics of the Version 3 L4_SM data product. The L4_SM soil moisture estimates meet the 0.04 m3m3 (unbiased) RMSE requirement. We further demonstrate that there is little bias in the soil moisture analysis. Finally, we illustrate where the assimilation system overestimates or underestimates the actual errors in the system.

GEOS-↗

NASA NTRS2020

Understanding the Effects of Climate on Ocean Carbon Pools

The role of oceans as a carbon sink has long been accepted. The uncertainties related to the physical and biological pump and their role in the carbon cycle remain however high. In this talk, we dive into the various pathways of carbon in the oceans, the effects that climate has on the ocean carbon cycle and the methodologies used to assess these effects.

Rousseaux, Cecile S.↗

NASA NTRS2020

On the Role of Trend and Variability of Hydroxyl Radical (OH) in the Global Methane Budget

Decadal trends and interannual variations in the hydroxyl radical (OH), while poorly constrained at present, are critical for understanding the observed evolution of atmospheric methane (CH4). Through analyzing the OH fields simulated by the model ensemble of the Chemistry-Climate Model Initiative (CCMI), we find (1) the negative OH anomalies during the El Niño years mainly corresponding to the enhanced carbon monoxide (CO) emissions from biomass burning and (2) a positive OH trend during 1980-2010 dominated by the elevated primary production and the reduced loss of OH due to decreasing CO after 2000. Both two-box model inversions and variational 4D inversions suggest that ignoring the negative anomaly of OH during the El Niño years leads to a large overestimation of the increase in global CH4 emissions by up to10±3Tg yr-1to match the observed CH4 increase over these years. Not accounting for the increasing OH trends given by the CCMI models leads to an underestimation of the CH4 emission increase by 23±9Tg yr-1from 1986 to 2010. The variational inversion estimated CH4 emissions show that the tropical regions contribute most to the uncertainties related to OH. This study highlights the significant impact of climate and chemical feedbacks related to OH on the top-down estimates of the global CH4 budget.

Hydroxyl radical (OH)↗

NASA NTRS2021

Indirect Dark Matter Searches at Ultrahigh Energy Neutrino Detectors

High to ultrahigh energy neutrino detectors can uniquely probe the properties of dark matterχby searching for the secondary products produced through annihilation and/or decay processes. We evaluate the sensitivities to dark matter thermally averaged annihilation cross section〈σv〉and partial decay width into neutrinos Γ χ→ν ̄ν (in the mass scale 10 7 ≤ m χ /GeV ≤ 10 15 ) for next generation observatories like POEMMA and GRAND. We show that in the range 10 7 ≤ m χ /GeV ≤ 10 11 , space-based Cherenkov detectors like POEMMA have the advantage of full-sky coverage and rapid slewing, enabling an optimized dark matter observation strategy focusing on the Galactic center. We also show that ground-based radio detectors such as GRAND can achieve high sensitivities and high duty cycles in radio quiet areas. We compare the sensitivities of next generation neutrino experiments with existing constraints from IceCube and updated 90% C.L. upper limits on〈σv〉and Γ χ→ν ̄ν using results from the Pierre Auger Collaboration and ANITA. We show that in the range 10 7 ≤ m χ /GeV ≤ 10 11 POEMMA and GRAND10k will improve the neutrino sensitivity to particle dark matter by factors of 2 to 10 over existing limits, whereas GRAND200k will improve this sensitivity by two orders of magnitude. In the range 10 11 ≤ m χ /GeV ≤ 10 15 , POEMMA’s fluorescence observation mode will achieve an unprecedented sensitivity to dark matter properties. Finally, we highlight the importance of the uncertainties related to the dark matter distribution in the Galactic halo, using the latest fit and estimates of the Galactic parameters.

Particle astrophysics↗

NASA NTRSDate not supplied

Probing the Properties of Superheavy Dark Matter Annihilating or Decaying into Neutrinos With Ultra-High Energy Neutrino Experiments

The evidence for dark matter particles,x, is compelling based on Galactic to cosmological scale observations. Thus far, the promising weakly interacting massive particle scenario have eluded detection, motivating alternative models of dark matter. We consider scenarios involving super-heavy dark matter (SHDM) that potentially can decay or annihilate to neutrinos and antineutrinos. In the mass range m(sub x) =10(exp 7) −10(exp 15) GeV, we evaluate the sensitivities of future observatories PO-EMMA and GRAND for indirect dark matter detection via the measurement of neutrino-induced extensive air showers (EAS), compute the Auger and ANITA limits using their last up-to-date sensitivities, and compare them with IceCube limits. We also show that the uncertainties related to the dark matter distribution in the Galactic halo have a large impact on the neutrino flux. We show that a ground-based radio detector such as GRAND can achieve high sensitivities due to its large effective area and high duty cycle. Space-based Cherenkov detectors such as POEMMA that measure the EAS optical Cherenkov signal have the advantage of full-sky coverage and rapid slewing, enabling an optimized SHDM observation strategy focusing on the Galactic Center. We show that increasing the field of view of the Cherenkov detectors can significantly enhance the sensitivity. Moreover, POEMMA’s fluorescence observation mode that measures EAS above20EeV will achieve state-of-the-art sensitivity to SHDM properties at the highest mass scales.

Claire Guepin↗

NASA NTRS2020

MODIS Reflective Solar Bands Lunar Calibration Update and Improvements

The MODerate resolution Imaging Spectroradiometer (MODIS) instruments on-board the Terra and Aqua spacecrafts were launched on December 18, 1999 and May 4, 2002, respectively. Each instrument has been scheduled to view the Moon through its Space View (SV) port approximately once per month in order to monitor the long-term radiometric stability of their reflective solar bands (RSB). The lunar calibration has played a key role in tracking the change in the response versus scan angle (RVS) of the instrument’s scan mirror, which is an essential part of the RSB calibration. The lunar irradiance observed by MODIS depends on the view geometry and it is crucial to remove the geometric effects in order to derive accurate RSB calibration coefficients. With a scheduled roll maneuver, the lunar phase angles are kept in a narrow range of 1° for each MODIS instrument such that the impact of these geometric effects is minimized. Nevertheless, it remains a challenge to completely remove the impact of geometric effects in the calibration coefficients derived from the lunar observations, considering the high expectations for the accuracy and quality of the results. In the current MODIS lunar calibration, the geometric effects on the lunar irradiance are corrected by the RObotic Lunar Observatory (ROLO) model. The overall relative uncertainty of the ROLO model for the MODIS calibration has been assessed to be about 1% in the selected lunar phase angle range of 1°. It could be as large as 4% beyond this small phase angle range, especially for the shortest wavelengths. This uncertainty induces noticeable oscillations in the calibration coefficients derived from the lunar observations. We developed a simple lunar model, which is based on MODIS lunar measurements, derived from scheduled lunar observations, as well as those obtained from intrusion of the Moon in the SV, referred to as unscheduled lunar observations, in the time period from 2005 to 2012. Here, the scheduled lunar observations for the entire mission are reprocessed with the new lunar model applied to correct the view geometry effects for each MODIS instrument. New calibration coefficients for the RSBs are calculated. We show that oscillations and noise in the derived lunar calibration coefficients are significantly reduced for both MODIS instruments.

Terra↗

NASA NTRS2021

Design, Calibration, and Application of A Cryogenic Low-Background Infrared Radiometer for Spectral Irradiance and Radiance Measurements From 4- to 20-μm Wavelength

We have constructed, calibrated, and tested a cryogenic low-background infrared (IR) radiometer for both spectral radiance and irradiance measurements over the 4- to 20-μm wavelength range. The primary purpose of the Missile Defense Transfer Radiometer (MDXR) is to measure absolute irradiance or radiance from cryogenic IR test chamber sources using a photoconductive Si:As blocked-impurity band (BIB) detector and a set of spectral filters. The MDXR also includes an absolute cryogenic radiometer (ACR) and a Fourier-transform spectrometer (FTS). For irradiance measurements, the ACR is used to provide the primary power scale for the BIB detector in conjunction with spectral filters, whereas the FTS/BIB configuration derives its scale from an internal blackbody source. The two measurement scales show agreement for the irradiance of highly collimated (<1 mrad) IR beams from 10 -13 to 10 -8 W / μm / cm 2 within the combined relative uncertainties of 2.6% (coverage factor k = 1.) We have also calibrated the radiometer for radiance measurements using a large cavity fluid bath blackbody that overfills the spatial and angular extent of the radiometer entrance pupil. The radiometric calibration uncertainty analysis of the radiometer and its maintenance and stability are discussed.

radiometry↗

NASA NTRSDate not supplied

New Global Ocean Color Sensor: OCI on PACE

- OCI will provide TOA radiances at ~1km spatial resolution, from 340nm (315nm?) to 2260nm, hyperspectral from 340nm to 890nm, 2 day global coverage - OCI will continue and enhance NASA’s earth system data records for ocean color (heritage sensors: SeaWiFS, MODIS, VIIRS) - OCI flight unit is close to being ready for testing (planned for March 2022 to September 2022) - OCI ETU (Engineering Test Unit) completed testing summer 2021, results look promising (see next presentation) - On-orbit calibration will combine successful trending approaches from previous sensors (2 solar diffusers, QVD, lunar gain trending, spectral trending) - New calibration approaches for OCI: large QVD, dim diffuser for linearity trending, lunar hysteresis trending - OCI will be characterized prelaunch with an ambitious goal of 0.5% relative uncertainty; absolute uncertainty will be about 2% (before vicarious calibration); expected on-orbit gain trending accuracy is 0.2% or better - More info on PACE and OCI can be found at https://pace.oceansciences.org/

radiometer↗

NASA NTRSDate not supplied

Clouds in the Arctic Climate System: A Force for Change?

Clouds play a central role in the global climate system through the modulation of Earth’s energy flows and as a mediator of precipitation. In the Arctic, clouds are also a major player; however, the processes that govern cloud evolution in the Arctic differ from most of the other regions of the globe. Thus, clouds are a key “wildcard” within the Arctic climate system that could have a substantial influence on the Arctic climate system response to anthropogenic forcing. What is the role of clouds within the phenomenon known as Arctic Amplification? This is question does not have a clear answer. Clouds seem to be a center to the important processes driving Arctic Amplification (e.g., the atmospheric response to sea ice loss and airmass transformation), however feedback analysis studies indicate that net cloud feedback in the Arctic is small. This seminar discusses the role of clouds within the Arctic Amplification processes reviewing aspects of what we know about Arctic clouds and the uncertainties that limit our ability to model them. Results from recently published and ongoing work are presented that provide an observationally-based estimate of the cloud-sea ice feedback and evaluate cloud properties within models. The goal of this presentation is to ignite a discussion and new collaborations around the best approaches to resolving uncertainties related to the role of clouds with the Arctic system and how to better represent Arctic clouds in models.

Patrick C Taylor↗

NASA NTRSDate not supplied

Pretest Simulations of a Supersonic Mixing and Combustion Validation Experiment to Assess Sensitivities

The reliance on CFD simulations to develop, design, and optimize scramjet systems (or components) has become commonplace. This reliance inevitably hinges on the ability of the computational analyst to quantify the level of confidence in their computational results. Unfortunately, nearly all the measured data available for this assessment comes from antiquated experimental datasets, or from tests that focused on the extraction of scramjet system (or component) performance. The objective of a CFD validation experiment is to quantify the predictive accuracy of one or more of the CFD physics submodels, implying that other uncertainties related to replicating the facility flow environment (e.g., knowledge of boundary conditions) must be minimized to the extent possible. This inevitably places stringent requirements on the quality and quantity of measurements taken to accurately specify inflow, outflow, and surface conditions for the CFD simulations; in addition to the measurements taken for the validation of physics submodels. This places additional demands on the experimental process above and beyond those for test article performance assessment. A recent high speed code credibility workshop series sponsored by AFRL identified a gap in existing validation data for fundamental assessments of turbulent mixing and combustion CFD closure models at scramjet engine relevant conditions. To address this gap, engineers at AFRL have designed a coaxial jet flame configuration that will be tested at two facilities (Research Cell 19 at the Air Force Research Lab, and at Purdue University). The effort described here documents pretest simulations of this validation experiment with the goal of fleshing out the extent of the facility flowpath that must be included to adequately reproduce the facility test section flow environment. The findings indicate that the flow around the support structure for the fuel injection centerbody upstream of the facility nozzle generates disturbances that persist throughout the nozzle expansion process; corrupting the azimuthal symmetry that was desired in the fuel/air mixing region of the test section. Simulations without this support structure maintained a high degree of azimuthal symmetry up until the fuel injection plane. However, even in this scenario the azimuthal symmetry was not maintained once the centerbody boundary layer transitioned to a wake flow downstream of the fuel injection plane.

CFD↗

NASA NTRSDate not supplied

Air Quality Data Fusion with Sensors, Satellites, and Models

Global forecasting models, satellite remote sensing, and ground-based regulatory and low-cost monitors all have strengths and weaknesses with respect to providing locally relevant information about air quality. This presentation will give a brief overview of these data sources and then discuss a method for combining them via data fusion to support near-real-time air quality estimation and forecasting at sub-city scales. The basic idea behind the approach will be summarized, followed by an update on recent developments towards creating an operational system using Google Earth Engine and on quantifying uncertainties related to data fusion outputs.

Carl Malings↗

NASA NTRSDate not supplied

Pretest Simulations of a Supersonic Mixing and Combustion Validation Experiment to Assess Sensitivities

The reliance on CFD simulations to develop, design, and optimize scramjet systems (or components) has become commonplace. This reliance inevitably hinges on the ability of the computational analyst to quantify the level of confidence in their computational results. Unfortunately, nearly all the measured data available for this assessment comes from antiquated experimental datasets, or from tests that focused on the extraction of scramjet system (or component) performance. The objective of a CFD validation experiment is to quantify the predictive accuracy of one or more of the CFD physics submodels, implying that other uncertainties related to replicating the facility flow environment (e.g., knowledge of boundary conditions) must be minimized to the extent possible. This inevitably places stringent requirements on the quality and quantity of measurements taken to accurately specify inflow, outflow, and surface conditions for the CFD simulations; in addition to the measurements taken for the validation of physics submodels. This places additional demands on the experimental process above and beyond those for test article performance assessment. A recent high speed code credibility workshop series sponsored by AFRL identified a gap in existing validation data for fundamental assessments of turbulent mixing and combustion CFD closure models at scramjet engine relevant conditions. To address this gap, engineers at AFRL have designed a coaxial jet flame configuration that will be tested at two facilities (Research Cell 19 at the Air Force Research Lab, and at Purdue University). The effort described here documents pretest simulations of this validation experiment with the goal of fleshing out the extent of the facility flowpath that must be included to adequately reproduce the facility test section flow environment. The findings indicate that the flow around the support structure for the fuel injection centerbody upstream of the facility nozzle generates disturbances that persist throughout the nozzle expansion process; corrupting the azimuthal symmetry that was desired in the fuel/air mixing region of the test section. Simulations without this support structure maintained a high degree of azimuthal symmetry up until the fuel injection plane. However, even in this scenario the azimuthal symmetry was not maintained once the centerbody boundary layer transitioned to a wake flow downstream of the fuel injection plane.

CFD↗

NASA NTRS2024

Modeling Impacts of Dust Mineralogy on Fast Climate Response

Mineralogical composition drives dust impacts on Earth's climate systems. However, most climate models still use homogeneous dust, without accounting for the temporal and spatial variation in mineralogy. To quantify the radiative impact of resolving dust mineralogy on Earth's climate, we implement and simulate the distribution of dust minerals (i.e., illite, kaolinite, smectite, hematite, calcite, feldspar, quartz, and gypsum) from Claquin et al. (1999) (C1999) and activate their interaction with radiation in the GFDL AM4.0 model. Resolving mineralogy reduces dust absorption compared to the homogeneous dust used in the standard GFDL AM4.0 model that assumes a globally uniform hematite volume content of 2.7 % (HD27). The reduction in dust absorption results in improved agreement with observation-based single-scattering albedo (SSA), radiative fluxes from CERES (the Clouds and the Earth's Radiant Energy System), and land surface temperature from the CRU (Climatic Research Unit) compared to the baseline HD27 model version. It also results in distinct radiative impacts on Earth's climate over North Africa. Over the 19-year (from 2001 to 2019) modeled period during JJA (June–July–August), the reduction in dust absorption in AM4.0 leads to a reduction of over 50 % in net downward radiation across the Sahara and approximately 20 % over the Sahel at the top of the atmosphere (TOA) compared to the baseline HD27 model version. The reduced dust absorption weakens the atmospheric warming effect of dust aerosols and leads to an alteration in land surface temperature, resulting in a decrease of 0.66 K over the Sahara and an increase of 0.7 K over the Sahel. The less warming in the atmosphere suppresses ascent and weakens the monsoon inflow from the Gulf of Guinea. This brings less moisture to the Sahel, which combined with decreased ascent induces a reduction of precipitation. To isolate the effect of reduced absorption compared to resolving spatial and temporal mineralogy, we carry out a simulation where the hematite volume content of homogeneous dust is reduced from 2.7 % to 0.9 % (HD09). The dust absorption (e.g., single-scattering albedo) of HD09 is comparable to that of the mineralogically speciated model on a global mean scale, albeit with a lower spatial variation that arises solely from particle size. Comparison of the two models indicates that the spatial inhomogeneity in dust absorption resulting from resolving mineralogy does not have significant impacts on Earth's radiation and climate, provided there is a similar level of dust absorption on a global mean scale before and after resolving dust mineralogy. However, uncertainties related to emission and distribution of minerals may blur the advantages of resolving minerals to study their impact on radiation, cloud properties, ocean biogeochemistry, air quality, and photochemistry. On the other hand, lumping together clay minerals (i.e., illite, kaolinite, and smectite), but excluding externally mixed hematite and gypsum, appears to provide both computational efficiency and relative accuracy. Nevertheless, for specific research, it may be necessary to fully resolve mineralogy to achieve accuracy.

Soil dust↗

NASA NTRSDate not supplied

Understanding Relationships Between Satellite, Model, and Ground-Based Surface Temperature Characterizations From Overcast to Clear Conditions in Support of Satellite Remote Sensing of Clouds and Radiation

Accurate and consistent global estimates of cloud coverage and their properties are fundamental to long-term Earth radiation budget (ERB) monitoring efforts like the Clouds and the Earth’s Radiant Energy System (CERES) project. Cloud detection algorithms often apply thresholding approaches to identify where clouds occur by comparing satellite-measured radiances with those that are expected under cloud-free conditions. In addition, once a cloud is detected, the derivation of cloud optical and microphysical properties also requires knowledge of the background radiances below the cloud. In the infrared, knowledge of the surface emissivity and the expected skin temperature under both cloudy and cloud-free conditions is needed. These traits are generally well known over the oceans. Over land, however, comparisons between satellite-derived land surface temperature (LST) with that characterized in numerical weather analyses reveal large differences in many parts of the world, often exceeding 5 K, which can lead to significant satellite cloud detection and cloud property retrieval errors. Furthermore, clouds have a dramatic influence on the LST, and therefore characterization of that model parameter also depends on the capability of the model to accurately resolve clouds. Thus, the LST characterized in models is, at times, a poor approximation for what would otherwise be observed, thereby impeding accurate satellite cloud retrievals. As a result, we seek to develop a more robust method for estimating the LST required for satellite cloud characterizations. This effort is accomplished through a combination of surface emission/air temperature relationship studies in all-sky conditions using ground measurement stations, along with deep neural network (DNN) estimates of expected LST under overcast and cloud-free conditions. We demonstrate that substituting DNN-predicted LST for that generated by numerical models can mitigate model-inherent diurnal dependencies and reduce overall bias and uncertainty relative to satellite/ground observations by 0.5–4 K and 0.5–2 K, respectively. It is expected that this work will lead to improved satellite cloud retrievals that enhance ERB monitoring efforts.

B Scarino↗

NASA NTRS2014

NASA Team 2 Sea Ice Concentration Algorithm Retrieval Uncertainty

Satellite microwave radiometers are widely used to estimate sea ice cover properties (concentration, extent, and area) through the use of sea ice concentration (IC) algorithms. Rare are the algorithms providing associated IC uncertainty estimates. Algorithm uncertainty estimates are needed to assess accurately global and regional trends in IC (and thus extent and area), and to improve sea ice predictions on seasonal to interannual timescales using data assimilation approaches. This paper presents a method to provide relative IC uncertainty estimates using the enhanced NASA Team (NT2) IC algorithm. The proposed approach takes advantage of the NT2 calculations and solely relies on the brightness temperatures (TBs) used as input. NT2 IC and its associated relative uncertainty are obtained for both the Northern and Southern Hemispheres using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) TB. NT2 IC relative uncertainties estimated on a footprint-by-footprint swath-by-swath basis were averaged daily over each 12.5-km grid cell of the polar stereographic grid. For both hemispheres and throughout the year, the NT2 relative uncertainty is less than 5%. In the Southern Hemisphere, it is low in the interior ice pack, and it increases in the marginal ice zone up to 5%. In the Northern Hemisphere, areas with high uncertainties are also found in the high IC area of the Central Arctic. Retrieval uncertainties are greater in areas corresponding to NT2 ice types associated with deep snow and new ice. Seasonal variations in uncertainty show larger values in summer as a result of melt conditions and greater atmospheric contributions. Our analysis also includes an evaluation of the NT2 algorithm sensitivity to AMSR-E sensor noise. There is a 60% probability that the IC does not change (to within the computed retrieval precision of 1%) due to sensor noise, and the cumulated probability shows that there is a 90% chance that the IC varies by less than +/-3%. We also examined the daily IC variability, which is dominated by sea ice drift and ice formation/melt. Daily IC variability is the highest, year round, in the MIZ (often up to 20%, locally 30%). The temporal and spatial distributions of the retrieval uncertainties and the daily IC variability is expected to be useful for algorithm intercomparisons, climate trend assessments, and possibly IC assimilation in models.

Sea Ice↗

NASA NTRSDate not supplied

Quantifying Effects of Departure and Flight Time Uncertainty on Urban Air Mobility Operations

Demand capacity balancing is a key mechanism for maintaining safe and efficient Urban Air Mobility (UAM) operations. However, uncertainties such as departure delays and flight time variation may reduce the effectiveness of algorithms used for balancing and detrimentally impact the safety and efficiency of UAM operations. In this paper, the effects of these uncertainties on UAM operations are quantified by modeling a distribution of departure and flight time errors. A route network in the Dallas/Fort Worth metropolitan area was used to simulate traffic demand with and without uncertainty. Simulations were conducted with three main models of uncertainty – first uncertainty in departure time delay resulting in late takeoffs, second with uncertainty in flight times in addition to departure delays, and finally, uncertainty in departure times that cause either late or early takeoffs. Each of these simulations were performed using varied standard deviations to fully understand the effects of uncertainties. Results from these simulations were compared to a baseline simulation using the same parameters, but without any uncertainty. The results suggest that both safety and efficiency are significantly impacted by uncertainty even with relatively low uncertainty introduced. These results work towards quantifying the effects of uncertainty in flight scheduling for UAM. They will also aid in the further development of the demand capacity balancing algorithms for UAM operations and associated air traffic management.

Urban Air Mobility↗

NASA NTRSDate not supplied

Quantifying the Effects of Departure and Flight Time Uncertainty on Urban Air Mobility Operations

Demand capacity balancing is a key mechanism for maintaining safe and efficient Urban Air Mobility (UAM) operations. However, uncertainties such as departure delays and flight time variation may reduce the effectiveness of algorithms used for balancing and detrimentally impact the safety and efficiency of UAM operations. In this paper, the effects of these uncertainties on UAM operations are quantified by modeling a distribution of departure and flight time errors. A route network in the Dallas/Fort Worth metropolitan area was used to simulate traffic demand with and without uncertainty. Simulations were conducted with three main models of uncertainty – first uncertainty in departure time delay resulting in late takeoffs, second with uncertainty in flight times in addition to departure delays, and finally, uncertainty in departure times that cause either late or early takeoffs. Each of these simulations were performed using varied standard deviations to fully understand the effects of uncertainties. Results from these simulations were compared to a baseline simulation using the same parameters, but without any uncertainty. The results suggest that both safety and efficiency are significantly impacted by uncertainty even with relatively low uncertainty introduced. These results work towards quantifying the effects of uncertainty in flight scheduling for UAM. They will also aid in the further development of the demand capacity balancing algorithms for UAM operations and associated air traffic management.

Urban Air Mobility↗