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NASA NTRS2018

Collaborative Data Curation to Support the Multi-Mission Algorithm and Analysis Platform (MAAP)

Upcoming space-borne missions will offer unprecedented data about Earth but will also feature exponentially high data volumes. These high data volumes will change the way the scientific community works with data and will also create a unique need for improved data sharing and collaboration. NASA and ESA are working together to address these issues by collaboratively developing the Multi-Mission Algorithm and Analysis Platform (MAAP) to improve the understanding of global aboveground terrestrial carbon dynamics. The MAAP will support ESA’s BIOMASS mission, NASA’s GEDI mission and NASA/ISRO’s NISAR mission. The MAAP will be developed in two phases: a pilot phase and a full production phase. The pilot phase will demonstrate collaboration and basic capabilities. The pilot phase will focus on biomass relevant airborne and field campaign data. Two NASA teams are supporting the development of the MAAP. The MAAP engineering team is responsible for the development, maintenance and operations of the MAAP system while the MAAP data team ensures the ongoing quality of the data, metadata and other information provided in the MAAP. The MAAP data team also supports the ingest and archive of identified data to the MAAP platform. This poster describes the use case development process for the pilot MAAP and the data curated in support of those use cases. Additionally, this presentation will outline the pilot MAAP data ingest process and metadata curation effort along with efforts to ensure interoperability between ESA and NASA data and metadata.

Bugbee, Kaylin↗

NASA NTRS2021

Modeling Snow Ablation over the Western United States Mountains: Patterns and Controlling Factors

When compared with differences in snow accumulation predicted by widely used hydrological models, there is a much greater divergence among otherwise “good” models in their simulation of the snow ablation process. Here, we explore differences in the performance of the Variable Infiltration Capacity model (VIC), Noah land surface model with multiparameterization options (Noah-MP), the Catchment model, and the third-generation Simplified Simple Biosphere model (SiB3) in their ability to reproduce observed snow water equivalent (SWE) during the ablation season at 10 Snowpack Telemetry (SNOTEL) stations over 1992–2012. During the ablation period, net radiation generally has stronger correlations with observed melt rates than does air temperature. Average ablation rates tend to be higher (in both model predictions and observations) at stations with a large accumulation of SWE. The differences in the dates of last snow between models and observations range from several days to approximately a month (on average 5.1 days earlier than in observations). If the surface cover in the models is changed from observed vegetation to bare soil in all of the models, only the melt rate of the VIC model increases. The differences in responses of models to canopy removal are directly related to snowpack energy inputs, which are further affected by different algorithms for surface albedo and energy allocation across the models. We also find that the melt rates become higher in VIC and lower in Noah-MP if the shrub/grass present at the observation sites is switched to trees.

Snow↗

NASA NTRS2015

Aerosol Correction for Improving OMPS/LP Ozone Retrieval

The Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) on board the Suomi National Polar-orbiting Partnership (SNPP) satellite was launched on Oct. 28, 2011. Limb profilers measures the radiance scattered from the Earth's atmospheric in limb viewing mode from 290 to 1000 nm and infer ozone profiles from tropopause to 60 km. The recently released OMPS-LP Version 2 data product contains the first publicly released ozone profiles retrievals, and these are now available for the entire OMPS mission, which extends from April, 2012. The Version 2 data product retrievals incorporate several important improvements to the algorithm. One of the primary changes is to turn off the aerosol retrieval module. The aerosol profiles retrieved inside the ozone code was not helping the ozone retrieval and was adding noise and other artifacts. Aerosols including polar stratospheric cloud (PSC) and polar mesospheric clouds (PMC) have a detectable effect on OMPS-LP data. Our results show that ignoring the aerosol contribution would produce an ozone density bias of up to 10 percent in the region of maximum aerosol extinction. Therefore, aerosol correction is needed to improve the quality of the retrieved ozone concentration profile. We provide Aerosol Scattering Index (ASI) for detecting aerosols-PMC-PSC, defined as ln(Im-Ic) normalized at 45km, where Im is the measured radiance and Ic is the calculated radiance assuming no aerosols. Since ASI varies with wavelengths, latitude and altitude, we can start by assuming no aerosol profiles in calculating the ASIs and then use the aerosol profile to see if it significantly reduces the residuals. We also discuss the effect of aerosol size distribution on the ozone profile retrieval process. Finally, we present an aerosol-PMC-PSC correction scheme.

OMPS/LP↗

NASA NTRS2018

Atmospheric Chemistry Modeling Using a Regression Forest Model

Atmospheric chemistry is central to many environmental issues such as air pollution, climate change, and stratospheric ozone loss. Chemistry Transport Models (CTM) are a central tool for understanding these issues, whether for research or for forecasting. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a CTM. We have investigated a machine learning approach to solving the differential equations instead of solving them numerically. From an annual simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, centennial scale climate simulations etc. We discuss our approches' speed and accuracy, and highlight some potential future directions for improving this approach.

Keller, Christoph A.↗

NASA NTRS1985

Electroencephalogram (EEG) and Magnetoencephalogram (MEG) as Tools for Evaluation of Cognitive Function

We have developed computerized analysis and display techniques to help identify the origins of visually evoked scalped potentials (VESP). The potentials are recorded simultaneously from many electrodes (usually 40 to 48) spaced over the region of the scalp where appreciable evoked potentials are found in response to particular stimulus. Contour mapping algorithms are then used to display the time behavior of equipotential surfaces on the scalp during the VESP. We then use an optimization technique to select the parameters of arrays of current dipole sources within the model until the model equipotential field distribution closely fits the measured data. Computer graphics are then used to display, as a movie, the actual and model scalp potential fields and the parameters of the dipole generators within the model head during the course of VESP activity. We have devised reaction time tests that involve potentially separable stages of cognitive processing and utilize stimuli that produce measurable cognition-related features in the late component of the evoked potential. We have used these techniques to determine the loci in the brain where known cognition-related features in the evoked potential are generated and we have explored the extent to which each of these features can be related to the reaction time tasks. We have also examined the temporal-spatial aspects of their cerebral involvement. Our current work is planned to characterize the age-related changes in the processes performed by such sources. We also use a neuromagnetometer to measure the evoked magnetic fields in similar circumstances; we will discuss the relative merits of the two methodologies.

Fender, Derek H.↗

NASA NTRS2007

A Sequential Shifting Algorithm for Variable Rotor Speed Control

A proof of concept of a continuously variable rotor speed control methodology for rotorcraft is described. Variable rotor speed is desirable for several reasons including improved maneuverability, agility, and noise reduction. However, it has been difficult to implement because turboshaft engines are designed to operate within a narrow speed band, and a reliable drive train that can provide continuous power over a wide speed range does not exist. The new methodology proposed here is a sequential shifting control for twin-engine rotorcraft that coordinates the disengagement and engagement of the two turboshaft engines in such a way that the rotor speed may vary over a wide range, but the engines remain within their prescribed speed bands and provide continuous torque to the rotor; two multi-speed gearboxes facilitate the wide rotor speed variation. The shifting process begins when one engine slows down and disengages from the transmission by way of a standard freewheeling clutch mechanism; the other engine continues to apply torque to the rotor. Once one engine disengages, its gear shifts, the multi-speed gearbox output shaft speed resynchronizes and it re-engages. This process is then repeated with the other engine. By tailoring the sequential shifting, the rotor may perform large, rapid speed changes smoothly, as demonstrated in several examples. The emphasis of this effort is on the coordination and control aspects for proof of concept. The engines, rotor, and transmission are all simplified linear models, integrated to capture the basic dynamics of the problem.

Litt, Jonathan S.↗

NASA NTRSDate not supplied

Flow Characterization of the NASA Langley Unitary Plan Wind Tunnel, Test Section 2: Computational Results

This is an abstract for an invited paper at the AIAA Aviation Conference, June 2021. The work described here is part of an effort of coordinated experiments in the Unitary Plan Wind Tunnel (UPWT) facility at the NASA Langley Research Center (LaRC) and matching CFD simulations. The primary goal of the work is to assess the productivity and true predictive accuracy of CFD, absent any guidance from experiment, in the high supersonic speed range as compared to experiments performed at the NASA LaRC’s UPWT facility. This report concerns CFD simulation of the primary flow-path in the empty wind tunnel, including the settling chamber, nozzle, test section, and some of the tunnel downstream of the test section. The Mach number in the test section ranges from M~2.4 to M~4.6, and the required area ratio variation is achieved by translation of a nozzle block which constricts the area of a loosely S-shaped throat. Flow past protuberances in the settling chamber and into this S-bend throat are predicted by CFD to generate streamwise vorticity that may, or may not, persist through the throat and into the test section as coherent vortices. Some flow conditions are notably unsteady at frequencies well below those of turbulence, due to unsteady separated flow ahead of the nozzle block. The bulk flow moves at velocities ranging from 'walking speed' in the settling chamber to M~4.6 in the test section. Heat transfer to the settling chamber walls and buoyancy are significant at high Mach number. Subtle variations in surface curvature in the nozzle generate Mach waves that propagate into the test section. CFD of the empty tunnel serves two purposes. Firstly, the full-tunnel simulations are used to provide upstream boundary conditions for CFD of vehicle aerodynamics which are generally performed in a domain confined to the wind tunnel test section; these companion studies are addressed in other papers. Secondly, it is a challenging test for CFD to resolve all of the empty tunnel flow phenomena relevant to flow in the test section. It requires a more complete definition of geometry than was originally anticipated. In addition, it requires good spatial and temporal accuracy, and turbulence modeling that performs well on specific phenomena such as corner flows. The boundary conditions and solution algorithms must perform well from incompressible to almost hypersonic speeds. The final state of the pre-test CFD was a product of an iterative self-improvement process. The initial simulations of the empty tunnel were deficient in many respects, but hints to those deficiencies were recognized in the solutions, and remedies were implemented. Possible further improvements will be studied in the post-test phase when comparisons with experimental data are possible. The CFD was performed by five separate collaborative teams using four different flow solvers: FUN3D, Overflow, Star-CCM+ and USM3D. The level of effort of these teams varied significantly, but each made important contributions to the goals of the work. A concerted effort to use uncertainty quantification methods (UQ) in CFD is also a goal of this work. To this end, variations in CFD results due to grid refinement, turbulence modeling, and boundary conditions have been characterized. Code-to-code variation is another means of assessing CFD uncertainty. All CFD solvers predict similarity among the primary flow features; these include the variation of Mach number due to changes in Reynolds number, and the bulk flow angularity due to tunnel-wall curvature. All CFD solvers also predict similar trends in secondary flows, such as the downwash in the side-wall boundary layers. Three of the high-spatial resolution simulations give similar predictions of a complex secondary flow phenomena, streamwise vortices generated in the S-bend throat that persist into the side-wall boundary layers of the test section. Two of the highest-resolution simulations, run with the same turbulence model in different CFD solvers, gave encouragingly similar predictions of a complex tertiary flow phenomenon, small transient "sprites" of upwash flows, resulting from vortices that presumably originate in the separated flow near the leading edge of the nozzle block. While the CFD was done in a "blind pre-test" mode, requests from the experimental team for CFD results pertaining to unsteadiness and total temperature variations in the test section resulted in CFD runs that included a cooled wall in settling chamber. This then led to a change in the standard practice for running the Overflow results, which would not have occurred without this "release" of this experimental information. CFD was also used to guide some measurements. The paper will focus on establishing the consensus among CFD results and understanding differences among those results. Initial findings from the efforts to characterize CFD uncertainty have been done and will be included in the paper.

Robert Edward Childs↗

NASA NTRS2017

Operating Small Sat Swarms as a Single Entity: Introducing SODA

Swarm concepts are a growing topic of interest in the small satellite community. Compared to a small satellite constellation, a swarm has the distinction of being multiple spacecraft in close proximity, in approximately the same orbit. Furthermore, we envision swarms to have capabilities for cross-link communication and station-keeping. Of particular interest is a means to maintain operator-specified geometry, alignment, and/or separation.From NASA's decadal survey, it is clear that simultaneous measurements from a 3D volume of space are desired for a variety of Earth scientific studies. As this mission concept is ultimately extended to deep space, some degree of local control for the swarm to self-correct its configuration is required. We claim that the practicality of ground commanding each individual satellite in the swarm is simply not a feasible concept of operations. In other words, the current state-of-practice does not scale to very large swarms (e.g. 100 spacecraft or more) without becoming cost prohibitive. To contain the operations costs and complexity, a new approach is required: the swarm must be operated as a unit, responding to high-level specifications for relative position and velocity.The Mission Design Division at NASA Ames Research Center is looking to the near future for opportunities to develop satellite swarm technology. As part of this effort, we are developing SODA (Swarm Orbital Dynamics Advisor), a tool that provides the orbital maneuvers required to achieve a desired type of relative swarm motion. The purpose of SODA is two-fold. First, it encompasses the algorithms and orbital dynamics model to enable the desired relative motion of the swarm satellites. The process starts with the user specifying the properties of a swarm configuration. This could be as simple as varying in-track spacing of the swarm in one orbit, or as complex as maintaining a specified 3D geometrical orientation. We presume that science objectives will drive this choice. Given these inputs, the tool provides the most efficient maneuver(s) to achieve the objective.Second, SODA provides a variety of visualization tools. We acknowledge that the relationship between a desired relative motion amongst the swarm, and the corresponding orbital parameters for each individual satellite may not be immediately apparent for ground controllers and mission planners. The purpose of SODA's visualization tools is to illustrate this concept clearly with a variety of graphics and animations. After computing the optimal orbital maneuvers to modify the swarm, these results are simulated to demonstrate successful swarm control.Our emphasis in this paper is on the importance of relating the desired motion of the swarm satellites relative to one another with the required orbital element changes. One cannot joystick a drifting swarm satellite back into position; the underlying orbital mechanics dictate the most efficient recovery maneuvers. To illustrate this point, results from several case study simulations are presented. We conclude with our forward work for ongoing SODA development and potential science applications.

small satellites↗

NASA NTRS2003

Fractals and Spatial Methods for Mining Remote Sensing Imagery

The rapid increase in digital remote sensing and GIS data raises a critical problem -- how can such an enormous amount of data be handled and analyzed so that useful information can be derived quickly? Efficient handling and analysis of large spatial data sets is central to environmental research, particularly in global change studies that employ time series. Advances in large-scale environmental monitoring and modeling require not only high-quality data, but also reliable tools to analyze the various types of data. A major difficulty facing geographers and environmental scientists in environmental assessment and monitoring is that spatial analytical tools are not easily accessible. Although many spatial techniques have been described recently in the literature, they are typically presented in an analytical form and are difficult to transform to a numerical algorithm. Moreover, these spatial techniques are not necessarily designed for remote sensing and GIS applications, and research must be conducted to examine their applicability and effectiveness in different types of environmental applications. This poses a chicken-and-egg problem: on one hand we need more research to examine the usability of the newer techniques and tools, yet on the other hand, this type of research is difficult to conduct if the tools to be explored are not accessible. Another problem that is fundamental to environmental research are issues related to spatial scale. The scale issue is especially acute in the context of global change studies because of the need to integrate remote-sensing and other spatial data that are collected at different scales and resolutions. Extrapolation of results across broad spatial scales remains the most difficult problem in global environmental research. There is a need for basic characterization of the effects of scale on image data, and the techniques used to measure these effects must be developed and implemented to allow for a multiple scale assessment of the data before any useful process-oriented modeling involving scale-dependent data can be conducted. Through the support of research grants from NASA, we have developed a software module called ICAMS (Image Characterization And Modeling System) to address the need to develop innovative spatial techniques and make them available to the broader scientific communities. ICAMS provides new spatial techniques, such as fractal analysis, geostatistical functions, and multiscale analysis that are not easily available in commercial GIS/image processing software. By bundling newer spatial methods in a user-friendly software module, researchers can begin to test and experiment with the new spatial analysis methods and they can gauge scale effects using a variety of remote sensing imagery. In the following, we describe briefly the development of ICAMS and present application examples.

Lam, Nina↗

NASA NTRSDate not supplied

Application of Earth Observation Data for Improved Environmental and Disaster Monitoring in Central America

Since its establishment in 1991, the Central American Integration System (SICA, in Spanish) has served as a force for integrating the eight countries of Central America and the Dominican Republic, and in recent years, SICA’s General Secretariat and its various technical secretariats have focused on integrating Earth observations into various regional and national processes. In 2019, for instance, a joint statement was signed between SICA’s General Secretariat and NASA, toward strengthening the region’s use of Earth observations. SICA has also pursued cooperation with the Group on Earth Observations’ AmeriGEO regional initiative, the Japanese Aerospace Agency (JAXA), the United Nations Office of Outer Space Affairs (UNOOSA), and the Copernicus program, among others. Just in the framework of the NASA-SICA Joint Statement, and towards the goal of strengthening the region’s use of Earth observations across sectors, a large number of training webinars has been conducted, particularly benefiting national institutions, non-governmental organizations, and universities. Additionally, targeted support has been provided in the areas of environmental monitoring and disaster management. From 2019 through 2021, for instance, a number of feasibility studies were implemented by NASA’s DEVELOP program in collaboration with SICA technical secretariats. In 2020, in response to devastating Hurricanes Eta and Iota, the SERVIR program also provided support for extending the HYDRAFloods algorithm which had been originally developed for the Mekong to Central America. Support is also being provided toward monitoring land cover change across the Mesoamerican Biological Corridor. In the framework of the “observing the Earth for the benefit of all” theme, this presentation will also focus on how Central America can serve as a case study for the development of Earth observation capacity, applicable to other regions of the world.

applied sciences↗

NASA NTRS2019

An Approach for Uncertainty Quantification and Management of Unmanned Aerial Vehicle Health

The increasing interest in low-altitude unmanned aerial vehicle (UAV) operations is bringing along safety concerns. Performance of small, low-cost UAVs drastically changes with type, size and controller of the vehicle. Their reliability is lower when compared to reliability of commercial aircrafts, and the availability of on-board sensors for health and state awareness is extremely limited due to their size and propulsion capabilities. Uncertainty plays a dominant role in such a scenario, where a variety of UAVs of different size, propulsion systems, dynamic performance and reliability enters the low-altitude airspace. Unexpected failures could have dangerous consequences for both equipment and humans within that same airspace. As a result, a number of research works and methodologies are being proposed in the area of UAV dynamic modeling, health and safety monitoring, but uncertainty quantification is rarely addressed. Thus, this paper pro- poses a perspective towards uncertainty quantification for autonomous systems, giving special emphasis to a UAV health monitoring application. A formal approach to classify uncertainty is presented; it is utilized to identify the uncertainty sources in UAVs health and operations, and then map uncertainty within a predictive process. To show the application of the methodology proposed here, the design of a model-based powertrain health monitoring algorithm for small-size UAVs is used as case study. The example illustrates how the uncertainty quantification approach can help the modeling strategy, as well as the assessment of diagnostic and prognostic performance.

Health Monitoring↗

NASA NTRS2019

Negative extreme events in gross primary productivity and their drivers in China during the past three decades

Climate extremes have remarkable impacts on ecosystems and are expected to increase with future global warming. However, only few studies have focused on the ecological extreme events and their drivers in China. In this study, we carried out an analysis of negative extreme events in gross primary productivity (GPP) in China and the sub-regions during 1982–2015, using monthly GPP simulated by 12 process-based models (TRENDYv6) and an observation-based model (Yao-GPP). Extremes were defined as the negative 5th percentile of GPP anomalies, which were further merged into individual extreme events using a three-dimensional contiguous algorithm. Spatio-temporal patterns of negative GPP anomalies were analyzed by taking the 1000 largest extreme events into consideration. Results showed that the effects of extreme events decreased annual GPP by 2.8% (i.e. 208 TgC/year) in TRENDY models and 2.3% (i.e. 151 TgC/year) in Yao-GPP. Hotspots of extreme GPP deficits were mainly observed in North China (−53 gC/sq.m year) in TRENDY models and Northeast China (−42 gC/sq.m year) in Yao-GPP. For China as a whole, attribution analyses suggested that extreme low precipitation was associated with 40%–50% of extreme negative GPP events. Most events in northern and western China could be explained by meteorological droughts (i.e. low precipitation) while GPP extreme events in southern China were more associated with temperature extremes, in particular with cold spells. GPP was revealed to be much more sensitive to heat/drought than to cold/wet extreme events. Combined with projected changes in climate extremes in China, GPP negative anomalies caused by drought events in northern China and by temperature extremes in southern China might be more prominent in the future.

Climate change↗

NASA NTRS1987

A new technique to determine the correlation between the QT interval and heart-rate for control and SIDS babies

The ability of the autonomic nervous system to alter the QT interval in response to heart rate changes is essential to cardiovascular control. An accurate way to determine the relation between QT intervals and their corresponding RR intervals is described. A computer algorithm measures the RR intervals using digital filtering and cross-correlating the QRS sections of consecutive waveforms. The QT intervals is calculated by choosing a section of, the ECG that includes the T wave and cross-correlating it with all the consecutive T waves. At least 4000 pairs of QT-RR intervals are computed for each subject and a best fit correlation function determines the relations between the QT and RR intervals. This technique enables to establish a precise correlation between RR and QT in order to distinguish between control and SIDS babies.

Non-NASA Center↗

NASA NTRS1992

Efficient geometric rectification techniques for spectral analysis algorithm

The spectral analysis algorithm is a viable technique for processing synthetic aperture radar (SAR) data in near real time throughput rates by trading the image resolution. One major challenge of the spectral analysis algorithm is that the output image, often referred to as the range-Doppler image, is represented in the iso-range and iso-Doppler lines, a curved grid format. This phenomenon is known to be the fanshape effect. Therefore, resampling is required to convert the range-Doppler image into a rectangular grid format before the individual images can be overlaid together to form seamless multi-look strip imagery. An efficient algorithm for geometric rectification of the range-Doppler image is presented. The proposed algorithm, realized in two one-dimensional resampling steps, takes into consideration the fanshape phenomenon of the range-Doppler image as well as the high squint angle and updates of the cross-track and along-track Doppler parameters. No ground reference points are required.

Chang, C. Y.↗

NASA NTRS2000

Autonomous, Full-Time Cloud Profiling at Arm Sites with Micro Pulse Lidar

Since the early 1990's technology advances permit ground based lidar to operate full time and profile all significant aerosol and cloud structure of the atmosphere up to the limit of signal attenuation. These systems are known as Micro Pulse Lidars (MPL), as referenced by Spinhirne (1993), and were first in operation at DOE Atmospheric Radiation Measurement (ARM) sites. The objective of the ARM program is to improve the predictability of climate change, particularly as it relates to cloud-climate feedback. The fundamental application of the MPL systems is towards the detection of all significant hydrometeor layers, to the limit of signal attenuation. The heating and cooling of the atmosphere are effected by the distribution and characteristics of clouds and aerosol concentration. Aerosol and cloud retrievals in several important areas can only be adequately obtained with active remote sensing by lidar. For cloud cover, the height and related emissivity of thin clouds and the distribution of base height for all clouds are basic parameters for the surface radiation budget, and lidar is essetial for accurate measurements. The ARM MPL observing network represents the first long-term, global lidar study known within the community. MPL systems are now operational at four ARM sites. A six year data set has been obtained at the original Oklahoma site, and there are several years of observations at tropical and artic sites. Observational results include cloud base height distributions and aerosol profiles. These expanding data sets offer a significant new resource for cloud, aerosol and atmospheric radiation analysis. The nature of the data sets, data processing algorithms, derived parameters and application results are presented.

Spinhirne, James D.↗

NASA NTRS2012

Recent Progress on Deep Blue Aerosol Algorithm as Applied TO MODIS, SEA WIFS, and VIIRS, and Their Intercomparisons with Ground Based and Other Satellite Measurements

The impact of natural and anthropogenic sources of aerosols has gained increasing attention from scientific communities in recent years. Indeed, tropospheric aerosols not only perturb radiative energy balance by interacting with solar and terrestrial radiation, but also by changing cloud properties and lifetime. Furthermore, these anthropogenic and natural air particles, once generated over the source regions, can be transported out of the boundary layer into the free troposphere and can travel thousands of kilometers across oceans and continents resulting in important biogeochemical impacts on the ecosystem. With the launch of SeaWiFS in 1997, Terra/MODIS in 1999, and Aqua/MODIS in 2002, high quality comprehensive aerosol climatology is becoming feasible for the first time. As a result of these unprecedented data records, studies of the radiative and biogeochemical effects due to tropospheric aerosols are now possible. In this talk, we will demonstrate how this newly available SeaWiFS/MODIS aerosol climatology can provide an important piece of puzzles in reducing the uncertainty of estimated climatic forcing due to aerosols. We will start with the global distribution of aerosol loading and their variabilities over both land and ocean on short- and long-term temporal scales observed over the last decade. The recent progress made in Deep Blue aerosol algorithm on improving accuracy of these Sea WiFS / MODIS aerosol products in particular over land will be discussed. The impacts on quantifying physical and optical processes of aerosols over source regions of adding the Deep Blue products of aerosol properties over bright-reflecting surfaces into Sea WiFS / MODIS as well as VIIRS data suite will also be addressed. We will also show the intercomparison results of SeaWiFS/MODIS retrieved aerosol optical thickness with data from ground based AERONET sunphotometers over land and ocean as well as with other satellite measurements. The trends observed in global aerosol loadings of both natural and anthropogenic sources based upon more than a decade of combined MODIS/SeaWiFS data (1997-2011) will be discussed. We will also address the importance of various key issues such as differences in spatial-temporal sampling rates and observation time between different satellite measurements could potentially impact these intercomparisons results, especially for using the monthly mean data, and thus on estimates of long-term aerosol trends.

Hsu, N. Christina↗

NASA NTRS2020

Optical Navigation for Autonomous Approach of Small Unknown Bodies

State of the practice in navigation around small celestial bodies heavily relies on ground sup- port and human skill, in particular, for perception-based operations such as optical navigation and mapping. This leads to longer duration and more complex mission operations and sub- sequently higher cost. Furthermore, it imposes limitations for certain missions such as fast fly-bys or multi-agent operations. In this work, we present an autonomous navigation strat- egy suitable for approaching small unexplored bodies. During the approach, we estimate the body’s physical properties as well as the spacecraft’s relative trajectory and associated un- certainties. The autonomous navigation strategy, which is solely based on optical measure- ments, begins as soon as the body becomes resolved in the navigation camera and terminates at the start of proximity operations, when the spacecraft makes its first trajectory correction to stay in the vicinity of the body. Our strategy uses multiple image-processing algorithms: light-curve analysis for estimating the target body’s rotation rate, Shape-from-Silhouette for reconstructing the 3D shape and estimating its rotation pole, and feature tracking tailored to Small-Body images for estimating relative navigation parameters. We used the Mission Analysis, Operations, and Navigation Toolkit Environment (MONTE) developed by the Jet Propulsion Laboratory to evaluate the feasibility of this multi-phase navigation strategy using simulated images of an approach trajectory. We used the Rosetta mission data to generate photorealistic images to characterise the performance of this approach. This work is based on the assumptions that the spacecraft attitude is known, the body is a principal-axis rotator, a-priori estimates of ephemerides and scale are available, and the body is observed from a zero sun phase only during initial approach. Preliminary results show orbit determination performance that is on par with the human navigation from the Rosetta mission; albeit with a 1% bias in spacecraft-target radial distance estimate. The bias error is likely due to the robustness and accuracy of the visual tracking under dynamic lighting conditions and per- spective changes, which decrease accuracy.

Villa, Jacopo↗

NASA NTRS1992

Geodynamic contributions to global climatic change

Orbital and rotational variations perturb the latitudinal and seasonal pattern of incident solar radiation, producing major climatic change on time scales of 10(exp 4)-10(exp 6) years. The orbital variations are oblivious to internal structure and processes, but the rotational variations are not. A program of investigation whose objective would be to explore and quantify three aspects of orbital, rotational, and climatic interactions is described. An important premise of this investigation is the synergism between geodynamics and paleoclimate. Better geophysical models of precessional dynamics are needed in order to accurately reconstruct the radiative input to climate models. Some of the paleoclimate proxy records contain information relevant to solid Earth processes, on time scales which are difficult to constrain otherwise. Specific mechanisms which will be addressed include: (1) climatic consequences of deglacial polar motion; and (2) precessional and climatic consequences of glacially induced perturbations in the gravitational oblateness and partial decoupling of the mantle and core. The approach entails constructing theoretical models of the rotational, deformational, radiative, and climatic response of the Earth to known orbital perturbations, and comparing these with extensive records of paleoclimate proxy data. Several of the mechanisms of interest may participate in previously unrecognized feed-back loops in the climate dynamics system. A new algorithm for estimating climatically diagnostic locations and seasons from the paleoclimate time series is proposed.

Bills, Bruce G.↗