Results for “predictive state estimation” — Page 42

NASA NTRS2023

Measurements and Computations of Natural Transition on the NASA Juncture-Flow Model with a Symmetric Wing

Experiments were performed in the 14- by 22-Foot Subsonic Tunnel to assess natural transition on the symmetric-airfoil wings of the NASA Juncture-Flow Model. Infrared thermography was used to visualize the heating on the upper surface of both wings of the full-span model, and on the fuselage, for angles of incidence ranging from -10° to 10° at a fixed Reynolds number of 2.4E6 based on the chord length at the wing planform break. The fuselage boundary layer transitioned well upstream of the wing-root leading edge for all conditions. Transition fronts were identified by a steep rise in the surface temperature, and the transition coordinates were transformed from an image-based to a body-fixed system. Additionally, the state of the boundary layer was estimated at pressure ports distributed on the wings through observation of the pressure coefficient as a function of the angle of incidence. For increasing angles of incidence, the transition front was observed to advance upstream, in a mostly spanwise-uniform fashion, from near midchord at α = 0°; however, for increasingly negative angles of incidence, the transition front first receded and then advanced in a nonuniform jagged manner that is typically observed with stationary crossflow. The transition wedges first appeared inboard of the wing break and then spread outboard to near the tip by α = -6°. The upstream shift in transition at positive angles of incidence and the outboard progression of crossflow-dominated transition at increasingly negative angles of incidence are consistent with trends identified in a computational assessment of the boundary-layer transition based on both linear stability analysis and Reynolds-averaged-Navier-Stokes-based transition models. The stability results obtained from the Langley Stability and Transition Analysis Code were used to recalibrate a dual N-factor criterion, which allowed for the prediction of transition fronts that showed excellent agreement with the experiment. The Reynolds-averaged-Navier-Stokes-based models, from the NASA OVERFLOW 2.3 solver, that accounted for the crossflow instability showed mixed results in comparison with the experiment, with the helicity-based Langtry-Menter model performing the best. The experimental data, particularly the cases involving strong influence from both Tollmien-Schlichting and crossflow instabilities, will be valuable for the continued validation and improvement of transition models.

boundary layer transition↗

NASA NTRS2023

Measurements and Computations of Natural Transition on the NASA Juncture-Flow Model with a Symmetric Wing

Experiments were performed in the 14- by 22-Foot Subsonic Tunnel to assess natural transition on the symmetric-airfoil wings of the NASA Juncture-Flow Model. Infrared thermography was used to visualize the heating on the upper surface of both wings of the full-span model, and on the fuselage, for angles of incidence ranging from -10° to 10° at a fixed Reynolds number of 2.4E6 based on the chord length at the wing planform break. The fuselage boundary layer transitioned well upstream of the wing-root leading edge for all conditions. Transition fronts were identified by a steep rise in the surface temperature, and the transition coordinates were transformed from an image-based to a body-fixed system. Additionally, the state of the boundary layer was estimated at pressure ports distributed on the wings through observation of the pressure coefficient as a function of the angle of incidence. For increasing angles of incidence, the transition front was observed to advance upstream, in a mostly spanwise-uniform fashion, from near midchord at α = 0°; however, for increasingly negative angles of incidence, the transition front first receded and then advanced in a nonuniform jagged manner that is typically observed with stationary crossflow. The transition wedges first appeared inboard of the wing break and then spread outboard to near the tip by α = -6°. The upstream shift in transition at positive angles of incidence and the outboard progression of crossflow-dominated transition at increasingly negative angles of incidence are consistent with trends identified in a computational assessment of the boundary-layer transition based on both linear stability analysis and Reynolds-averaged-Navier-Stokes-based transition models. The stability results obtained from the Langley Stability and Transition Analysis Code were used to recalibrate a dual N-factor criterion, which allowed for the prediction of transition fronts that showed excellent agreement with the experiment. The Reynolds-averaged-Navier-Stokes-based models, from the NASA OVERFLOW 2.3 solver, that accounted for the crossflow instability showed mixed results in comparison with the experiment, with the helicity-based Langtry-Menter model performing the best. The experimental data, particularly the cases involving strong influence from both Tollmien-Schlichting and crossflow instabilities, will be valuable for the continued validation and improvement of transition models.

boundary layer transition↗

NASA NTRS1980

Copernicus observations of C I and CO in diffuse interstellar clouds

Copernicus was used to observe absorption lines of C I in its ground state and excited fine structure levels and CO toward 29 stars. We use the C I data to infer densities and pressures within the observed clouds, and because our results are of higher precision than previous work, much more precise estimates of the physical conditions in clouds are obtained. In agreement with previous work, the interstellar thermal pressure appears to be variable, with most clouds having values of p/k between 1000/cu cm K and 10,000/cu cm K, but there are some clouds with p/k as high as 100,000/cu cm K. Our results are consistent with the view that the interstellar thermal pressure is so variable that the gas undergoes continuous dynamic evolution. Our observations provide useful constraints on the physical processes on the surfaces of grains. In particular, we find that grains are efficient catalysts of interstellar H2 in the sense that at least half of the hydrogen atoms that strike grains come off as part of H2. Results place strong constraints on models for the formation and destruction of interstellar CO. In many clouds, an order of magnitude less CO than predicted in some models was found.

Jenkins, E. B.↗

NASA NTRS1995

Solar Particle Induced Upsets in the TDRS-1 Attitude Control System RAM During the October 1989 Solar Particle Events

The three large solar particle events, beginning on October 19, 1989 and lasting approximately six days, were characterized by high fluences of solar protons and heavy ions at 1 AU. During these events, an abnormally large number of upsets (243) were observed in the random access memory of the attitude control system (ACS) control processing electronics (CPE) on-board the geosynchronous TDRS-1 (Telemetry and Data Relay Satellite). The RAM unit affected was composed of eight Fairchild 93L422 memory chips. The Galileo spacecraft, launched on October 18, 1989 (one day prior to the solar particle events) observed the fluxes of heavy ions experienced by TDRS-1. Two solid-state detector telescopes on-board Galileo, designed to measure heavy ion species and energy, were turned on during time periods within each of the three separate events. The heavy ion data have been modeled and the time history of the events reconstructed to estimate heavy ion fluences. These fluences were converted to effective LET spectra after transport through the estimated shielding distribution around the TDRS-1 ACS system. The number of single event upsets (SEU) expected was calculated by integrating the measured cross section for the Fairchild 93L422 memory chip with average effective LET spectrum. The expected number of heavy ion induced SEU's calculated was 176. GOES-7 proton data, observed during the solar particle events, were used to estimate the number of proton-induced SEU's by integrating the proton fluence spectrum incident on the memory chips, with the two-parameter Bendel cross section for proton SEU'S. The proton fluence spectrum at the device level was gotten by transporting the protons through the estimated shielding distribution. The number of calculated proton-induced SEU's was 72, yielding a total of 248 predicted SEU'S, very dose to the 243 observed SEU'S. These calculations uniquely demonstrate the roles that solar heavy ions and protons played in the production of SEU's during the October 1989 solar particle events.

Croley, D. R.↗

NASA NTRS2004

An Experimental Investigation Into the Temperature Profile of a Compliant Foil Air Bearing

A series of tests was performed to determine the internal temperature profile in a compliant bump-type foil journal air bearing operating at room temperature under various speeds and load conditions. The temperature profile was collected by instrumenting a foil bearing with nine, type K thermocouples arranged in the center and along the bearing s edges in order to measure local temperatures and estimate thermal gradients in the axial and circumferential directions. To facilitate the measurement of maximum temperatures from viscous shearing in the air film, the thermocouples were tack welded to the backside of the bumps that were in direct contact with the top foil. The mating journal was coated with a high temperature solid lubricant that, together with the bearing, underwent high temperature start-stop cycles to produce a smooth, steady-state run-in surface. Tests were conducted at speeds from 20 to 50 krpm and loads ranging from 9 to 222 N. The results indicate that, over the conditions tested, both journal rotational speed and radial load are responsible for heat generation with speed playing a more significant role in the magnitude of the temperatures. The temperature distribution was nearly symmetric about the bearing center at 20 and 30 krpm but became slightly skewed toward one side at 40 and 50 krpm. Surprisingly, the maximum temperatures did not occur at the bearing edge where the minimum film thickness is expected but rather in the middle of the bearing where analytical investigations have predicted the air film to be much thicker. Thermal gradients were common during testing and were strongest in the axial direction from the middle of the bearing to its edges, reaching 3.78 8C/mm. The temperature profile indicated the circumferential thermal gradients were negligible.

Radil, Kevin↗

NASA NTRS2005

Nonlinear Attitude Filtering Methods

This paper provides a survey of modern nonlinear filtering methods for attitude estimation. Early applications relied mostly on the extended Kalman filter for attitude estimation. Since these applications, several new approaches have been developed that have proven to be superior to the extended Kalman filter. Several of these approaches maintain the basic structure of the extended Kalman filter, but employ various modifications in order to provide better convergence or improve other performance characteristics. Examples of such approaches include: filter QUEST, extended QUEST, the super-iterated extended Kalman filter, the interlaced extended Kalman filter, and the second-order Kalman filter. Filters that propagate and update a discrete set of sigma points rather than using linearized equations for the mean and covariance are also reviewed. A two-step approach is discussed with a first-step state that linearizes the measurement model and an iterative second step to recover the desired attitude states. These approaches are all based on the Gaussian assumption that the probability density function is adequately specified by its mean and covariance. Other approaches that do not require this assumption are reviewed, including particle filters and a Bayesian filter based on a non-Gaussian, finite-parameter probability density function on SO(3). Finally, the predictive filter, nonlinear observers and adaptive approaches are shown. The strengths and weaknesses of the various approaches are discussed.

F Landis Markley↗

NASA NTRSDate not supplied

Measurement Techniques for the High-Contrast and in-Field Stray-Light Performance of Oci

Scheduled for launch in January 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records [1]. A key feature of PACE is the inclusion of an advanced satellite radiometer known as the Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. A critical requirement for OCI is the high-contrast or spatial cross-talk specification (also referred to as in-field stray-light response). The requirement states that for global top-of-atmosphere radiances based on measured MODIS radiances, the global average residual contamination shall be less than 0.15% for any multispectral band. Accurate resolution of high contrast in TOA radiance images is important to estimate stray light contamination due to clouds, for studying small scale features like ocean fronts and for working in coastal and estuarine areas where the scales are 1km. This occurs in all wavelengths in the spatial direction. Knowledge of high contrast resolution makes up part of the artifact budget. Accurate measurement of the high-contrast performance of OCI requires laboratory Ground Support Equipment (GSE) that projects a scene of sufficient quality that the unwanted stray light of the GSE itself is not confused with the stray light response of the telescope. This paper concerns the development, analyses and test of the GSE to ensure the quality of the projected image is sufficient to verify the OCI requirements. Optical models were developed for both the instrument as well as the GSE and laboratory environment. Simulation of various non-ideal parameters were critical to accurately predict performance. Measurements using COTS cameras and lenses were also made of the projected GSE image to reasonably verify the optical model predictions. Measured and modelled results from OCI are discussed.

OCI↗

NASA NTRSDate not supplied

Measurement Techniques for the High-Contrast and in-Field Stray-Light Performance of OCI

Scheduled for launch in January 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records [1]. A key feature of PACE is the inclusion of an advanced satellite radiometer known as the Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. A critical requirement for OCI is the high-contrast or spatial cross-talk specification (also referred to as in-field stray-light response). The requirement states that for global top-of-atmosphere radiances based on measured MODIS radiances, the global average residual contamination shall be less than 0.15% for any multispectral band. Accurate resolution of high contrast in TOA radiance images is important to estimate stray light contamination due to clouds, for studying small scale features like ocean fronts and for working in coastal and estuarine areas where the scales are 1km. This occurs in all wavelengths in the spatial direction. Knowledge of high contrast resolution makes up part of the artifact budget. Accurate measurement of the high-contrast performance of OCI requires laboratory Ground Support Equipment (GSE) that projects a scene of sufficient quality that the unwanted stray light of the GSE itself is not confused with the stray light response of the telescope. This paper concerns the development, analyses and test of the GSE to ensure the quality of the projected image is sufficient to verify the OCI requirements. Optical models were developed for both the instrument as well as the GSE and laboratory environment. Simulation of various non-ideal parameters were critical to accurately predict performance. Measurements using COTS cameras and lenses were also made of the projected GSE image to reasonably verify the optical model predictions. Measured and modelled results from OCI are discussed.

OCI, PACE, LSF, PSF, Stray-light, Contrast, Cross-↗

NASA NTRS2018

Optical Performance Prediction of the Thirty Meter Telescope after Initial Alignment Using Optical Modeling

We present an estimate of the optical performance of the Thirty Meter Telescope (TMT) after execution of the full telescope alignment plan. The TMT alignment is performed by the Global Metrology System (GMS) and the Alignment and Phasing System (APS). The GMS ﬁrst measures the locations of the telescope optics and instruments as a function of elevation angle. These initial measurements will be used to adjust the optics positions and build initial elevation look-up tables. Then the telescope is aligned using starlight as the input for the APS at multiple elevation angles. APS measurements are used to reﬁne the telescope alignment to build elevation and temperature dependent look-up tables. Due to the number of degrees of freedom in the telescope (over 10,000), the ability of the primary mirror to correct aberrations on other optics, the tight optical performance requirements and the multiple instrument locations, it is challenging to develop, test and validate these alignment procedures. In this paper, we consider several GMS and APS operational scenarios. We apply the alignment procedures to the model-generated TMT, which consists of various quasi-static errors such as polishing errors, passive supports errors, thermal and gravity deformations and installation position errors. Using an integrated optical model and Monte-Carlo framework, we evaluate the TMT’s aligned states using optical performance metrics at multiple instrument and ﬁeld of view locations. The optical performance metrics include the Normalized Point Source Sensitivity (PSSN), RMS wavefront error before and after Adaptive Optics (AO) correction, pupil position change, and plate scale distortion.

Rogers, John↗

NASA NTRS1994

Overview of the Oregon Transect Ecosystem Research Project

The Oregon Transect Ecosystem Research (OTTER) project is a study of ecosystem functions in coniferous forests using the methods of computer modeling, experimental and theoretical remote sensing, and ecological field and laboratory techniques. The study is focused on predicting the major fluxes of carbon, nitrogen, and water, and the factors that dynamically regulate them. The OTTER project was conceived to test two major questions: (1) Can a generalized ecosystem simulation model, designed to use mainly parameters available from remote sensing, predict the functioning of forests across an environmentally variable region? and (2) To what extent can the variables required by this model be derived from remotely sensed data? The scientific objectives and scope of the project demanded that a coordinated effort be made to link ground measurements with remote sensing and modeling requirements. OTTER was selected as a focus for a National Aeronautics and Space Administration (NASA)-sponsored Multi-sensor Aircraft Campaign (MAC; combining NASA aircraft and sensors with those of others) on the basis of experience gained in past ecosystem studies and remote-sensing projects, and the importance of the OTTER objectives to NASA's long-range science goals and plans. Having several independent approaches available, both on the ground and from various remote-sensing platforms, proved valuable in estimating and validating many of the critical variables. This experience and cross comparison should help simplify future studies of a similar nature. Edited data sets from the OTTER project are now available to the scientific community on optical disks or via on-line data banks at NASA (Washington, D.C., USA) and Oregon State University (Corvallis, Oregon, USA).

Peterson, David L.↗

NASA NTRS1991

Modeling the spatiotemporal organization of velocity storage in the vestibuloocular reflex by optokinetic studies

1. A generalized three-dimensional state space model of visual vestibular interaction was developed. Matrix and dynamical system operators associated with inputs from the semicircular canals, otolith velocity estimator, and the visual system have been incorporated into the model, which focus on their relationship to the velocity storage integrator. 2. A relationship was postulated between the eigenvalues and the direction of the eigenvectors of the system matrix and the orientation of the spatial vertical. It was assumed that the system matrix for a tilted position was a composition of two linear transformations of the system matrix for the upright position. One transformation modifies the eigenvalues of the system matrix, whereas another rotates the eigenvectors of the system matrix. The pitch and roll eigenvectors rotate with the head, whereas the yaw axis eigenvector remains approximately spatially invariant. 3. Based on the three-dimensional model, a computational procedure was formulated to identify the eigenvalues and eigenvectors of the system matrix with the use of a modification of the marquardt algorithm. With the use of data obtained from a monkey, it was shown that the three-dimensional behavior of velocity storage cannot be predicted solely in terms of its time constants, i.e., the inverse of its eigenvalues. With the use of the same eigenvalues the data could either be fit or not fit, depending on the eigenvector directions. Therefore, it is necessary to specify eigenvector directions when characterizing velocity storage in three dimensions. 4. Parameters found with the use of the Marquardt algorithm were incorporated into the model. Diagonal matrices in a head coordinate frame were introduced for coupling the visual system to the integrator and to the direct optokinetic pathway. Simulations of optokinetic nystagmus (OKN) and optokinetic after-nystagmus (OKAN) were run. The model predicted the behavior of yaw and pitch OKN and OKAN when the animal is upright. It also predicted the cross-coupling in the side down position. The trajectories in velocity space were also accurately simulated. 5. One of the predictions of the model is that when the stimulus direction is along an eigenvector, the trajectory in velocity space is a straight line. Using the "spectral width" of the residuals from a straight line sequence during OKAN, we developed a methodology to estimate how close the OKAN decay was to an eigenvector trajectory. 6. Thus we have developed a model-based approach for studying and interpreting the response characteristics of velocity storage in three dimensions.(ABSTRACT TRUNCATED AT 400 WORDS).

Non-NASA Center↗

NASA NTRS2013

Mission Data System Java Edition Version 7

The Mission Data System framework defines closed-loop control system abstractions from State Analysis including interfaces for state variables, goals, estimators, and controllers that can be adapted to implement a goal-oriented control system. The framework further provides an execution environment that includes a goal scheduler, execution engine, and fault monitor that support the expression of goal network activity plans. Using these frameworks, adapters can build a goal-oriented control system where activity coordination is verified before execution begins (plan time), and continually during execution. Plan failures including violations of safety constraints expressed in the plan can be handled through automatic re-planning. This version optimizes a number of key interfaces and features to minimize dependencies, performance overhead, and improve reliability. Fault diagnosis and real-time projection capabilities are incorporated. This version enhances earlier versions primarily through optimizations and quality improvements that raise the technology readiness level. Goals explicitly constrain system states over explicit time intervals to eliminate ambiguity about intent, as compared to command-oriented control that only implies persistent intent until another command is sent. A goal network scheduling and verification process ensures that all goals in the plan are achievable before starting execution. Goal failures at runtime can be detected (including predicted failures) and handled by adapted response logic. Responses can include plan repairs (try an alternate tactic to achieve the same goal), goal shedding, ignoring the fault, cancelling the plan, or safing the system.

Reinholtz, William K.↗

NASA NTRS2015

DSMC Simulations of Disturbance Torque to ISS During Airlock Depressurization

The primary attitude control system on the International Space Station (ISS) is part of the United States On-orbit Segment (USOS) and uses Control Moment Gyroscopes (CMG). The secondary system is part of the Russian On orbit Segment (RSOS) and uses a combination of gyroscopes and thrusters. Historically, events with significant disturbances such as the airlock depressurizations associated with extra-vehicular activity (EVA) have been performed using the RSOS attitude control system. This avoids excessive propulsive "de-saturations" of the CMGs. However, transfer of attitude control is labor intensive and requires significant propellant. Predictions employing NASA's DSMC Analysis Code (DAC) of the disturbance torque to the ISS for depressurization of the Pirs airlock on the RSOS will be presented [1]. These predictions were performed to assess the feasibility of using USOS control during these events. The ISS Pirs airlock is vented using a device known as a "T-vent" as shown in the inset in figure 1. By orienting two equal streams of gas in opposite directions, this device is intended to have no propulsive effect. However, disturbance force and torque to the ISS do occur due to plume impingement. The disturbance torque resulting from the Pirs depressurization during EVAs is estimated by using a loosely coupled CFD/DSMC technique [2]. CFD is used to simulate the flow field in the nozzle and the near field plume. DSMC is used to simulate the remaining flow field using the CFD results to create an in flow boundary to the DSMC simulation. Due to the highly continuum nature of flow field near the T-vent, two loosely coupled DSMC domains are employed. An 88.2 cubic meter inner domain contains the Pirs airlock and the T-vent. Inner domain results are used to create an in flow boundary for an outer domain containing the remaining portions of the ISS. Several orientations of the ISS solar arrays and radiators have been investigated to find cases that result in minimal disturbance torque. Figure 1 shows surface pressure contours on the ISS and a plane of number density contours for a particular case.

Lumpkin, F. E., III↗

NASA NTRSDate not supplied

Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the Howard County government in Maryland to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a prediction model capable of hindcasting the two severe flash flood events that devastated Ellicott City, and transitioned to an Long Short-Term Memory (LSTM) based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using the Nash-Sutcliffe Efficiency (NSE). The final product, called the Sequentially Trained Real-time EstimAted Model (STREAM), predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh (HRRR) model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate the OEM’s emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

Ryan Hammock↗

NASA NTRSDate not supplied

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗

NASA NTRS2009

Monitoring Top-of-Atmosphere Radiative Energy Imbalance for Climate Prediction

Large climate feedback uncertainties limit the prediction accuracy of the Earth s future climate with an increased CO2 atmosphere. One potential to reduce the feedback uncertainties using satellite observations of top-of-atmosphere (TOA) radiative energy imbalance is explored. Instead of solving the initial condition problem in previous energy balance analysis, current study focuses on the boundary condition problem with further considerations on climate system memory and deep ocean heat transport, which is more applicable for the climate. Along with surface temperature measurements of the present climate, the climate feedbacks are obtained based on the constraints of the TOA radiation imbalance. Comparing to the feedback factor of 3.3 W/sq m/K of the neutral climate system, the estimated feedback factor for the current climate system ranges from -1.3 to -1.0 W/sq m/K with an uncertainty of +/-0.26 W/sq m/K. That is, a positive climate feedback is found because of the measured TOA net radiative heating (0.85 W/sq m) to the climate system. The uncertainty is caused by the uncertainties in the climate memory length. The estimated time constant of the climate is large (70 to approx. 120 years), implying that the climate is not in an equilibrium state under the increasing CO2 forcing in the last century.

Lin, Bing↗

NASA NTRS2015

Completing the Feedback Loop: The Impact of Chlorophyll Data Assimilation on the Ocean State

In anticipation of the integration of a full biochemical model into the next generation GMAO coupled system, an intermediate solution has been implemented to estimate the penetration depth (1Kd_PAR) of ocean radiation based on the chlorophyll concentration. The chlorophyll is modeled as a tracer with sources-sinks coming from the assimilation of MODIS chlorophyll data. Two experiments were conducted with the coupled ocean-atmosphere model. In the first, climatological values of Kpar were used. In the second, retrieved daily chlorophyll concentrations were assimilated and Kd_PAR was derived according to Morel et al (2007). No other data was assimilated to isolate the effects of the time-evolving chlorophyll field. The daily MODIS Kd_PAR product was used to validate the skill of the penetration depth estimation and the MERRA-OCEAN re-analysis was used as a benchmark to study the sensitivity of the upper ocean heat content and vertical temperature distribution to the chlorophyll input. In the experiment with daily chlorophyll data assimilation, the penetration depth was estimated more accurately, especially in the tropics. As a result, the temperature bias of the model was reduced. A notably robust albeit small (2-5 percent) improvement was found across the equatorial Pacific ocean, which is a critical region for seasonal to inter-annual prediction.

MERRA-OCEAN↗

NASA NTRSDate not supplied

Global Variability in Sonic Boom Exposure due to Macroscopic Effects

Supersonic flight over land has been prohibited since 1973 due to the loudness of sonic booms. NASA is building the X-59 aircraft as part of its Quesst mission to demonstrate low-loudness shaped sonic booms, or “sonic thumps.” The Quesst mission will gather human perception data via a series of community noise surveys across the USA. The noise dose and perceptual response data will be provided to the International Civil Aviation Organization (ICAO) and the Federal Aviation Administration for use in determining potential future supersonic aircraft noise certification standards, effectively changing the prohibition from a speed limit to a noise limit. These noise regulations must be globally effective, as long travel distances see the largest benefit to supersonic flight. The state of the atmosphere through which a sonic boom travels affects the size of the region exposed to sound, the “carpet width” (CW), as well as the loudness. The focus of this dissertation is to understand and quantify the expected loudness and CW of sonic booms due to the macroscopic atmospheric effects around the world. A pair of large-scale propagation simulation studies were conducted using the NASA PCBoom code to compare predicted sonic boom loudness and CW statistics first across the USA and then across the world. For the USA study, near-field data of the X-59 in steady cruise was propagated at 4 cardinal headings at 138 locations through 5 years of Climate Forecast System Version 2 (CFSv2) atmospheric profiles. Results of a bootstrap forest predictor screening model indicated the importance of climate zone, latitude, ground elevation, season, and heading. It also noted the unimportance of time of day for predicting loudness and CW. The data is visualized in aggregate, and then broken out geographically, by season and heading, and by climate zone. Multiple linear regression models were fit to the data from the 138 locations so that estimates of the loudness and CW can be produced anywhere in the US. The results can aid in planning when and where to fly the X-59. For the global study, near-field data from three aircraft, the X-59 in a quiet and loud configuration, B-58, and Concorde, were propagated at four cardinal headings through data from three atmospheric models, the CFSv2, the Global Forecast System (GFS), and the ECMWF Reanalysis Version 5 (ERA5), at 100 global locations over 1 year. Results of a bootstrap forest predictor screening model indicated the importance of climate zone, ground elevation, season, and heading. Similar to the US study, the model indicated time of day was not an important predictor. The model also indicated that choice of weather model was not important, so the atmospheric model data are effectively interchangeable. The ERA5 model was chosen for use in an extension of the study to include 18 additional locations to ensure sampling of every climate zone. Loudness and CW results are shown in aggregate, and split geographically and by heading, season, and climate. Multiple linear regression models were fit to the data from the 118 locations so that estimates of loudness and CW can be produced around the world. N-waves and shaped booms did not have the same global variability. Koppen-Geiger climate zones were used as the climate zone definition for the global study. These are available as present-day and future climate projections. Making use of the multiple linear regression models, the future climate zones were input to estimate the effect of the changing climate on sonic boom loudness and CW. Results indicate that a changing climate would have little impact on the effectiveness of noise regulations.

X-59↗