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NASA NTRS2015

Exploration Architecture with Quantum Inertial Gravimetry and In-Situ ChipSat Sensors

The Dual Exploration Architecture is a mission concept that combines remote sensing and in-situ observations into a single mission to answer planetary science questions that can only be answered with both types of data. Adoption of dual exploration architectures may short circuit the long, slow cycle of missions to inaccessible bodies by eliminating the need for separate precursor and follow-up missions. Additionally, the dual architecture possesses inherent flexibility that enables the design of adaptive, event-driven missions that are very different from traditional, largely pre-planned missions. Five key observations about the state and trends of planetary science exploration lead us to the dual architecture: increasing complexity of observations; scarcity of future mission opportunities; desire to capture transitory events; continued miniaturization of spacecraft components; and the Mars exploration cycle. Our goal in this study is to explore missions that can only happen using the dual architecture concept and find technology development needs that must be filled for those missions to compete. A survey of historical and current missions finds that opportunities for exploration are becoming less frequent, causing the flexibility and dual-nature elements of each mission to become more common. The dual exploration architecture takes these trends to their far conclusion, attempting to eliminate precursor and follow-up missions while still returning more scientific payoff. A study of the future of planetary science goals through the decadal survey reveals broad applicability of dual missions to solve mysteries that cannot be answered with a traditional mission architecture. These missions fall into three broad classes: choosing a local target from a global survey; dynamic/reactive science; and global in-situ networks. Two example missions of each class are notionally described. A deeper look at these dual architecture classes reveals four technology development needs that must be addressed for wide adoption of dual missions: passive landers; guided atmospheric probes; robust sensing packages; and small, precise orbital instruments. This study pursues a specific focus on two examples of such enabling technologies: the ChipSat and cold atom gravimetry. The ChipSat is a fully functional spacecraft-on-a-chip system that has broad versatility in the dual architecture mission space. Initial studies show that ChipSats could survive as passive impactor landers on bodies up to the size of Europa. Furthermore, COTS (Commercial Off-The-Shelf) components could provide an in-situ sensor suite that readily answers a number of pressing planetary science questions. Cold atom gravimetry uses inertial sensors based on light-pulse atom interferometry in a small form factor to map the gravity field of a body to precision equaling what would normally require two full spacecraft to achieve. The cold atom gravimeter provides an example of how advanced remote sensing capability can enable dual missions by providing greater returns in a significantly smaller package. Using the above two technologies, we study an example dual-architecture mission to both characterize and sample the subsurface oceans at Europa. The greatest scientific return in terms of detecting extraterrestrial life is in those regions where Europa’s ice crust is thin. The identification of regions with thin ice should therefore precede the selection of surface targets and dispatch of probes to those targets. This two-step process, if accomplished by separate flagship-scale missions, might take decades. As a result, a combined mission to both identify thin areas of Europa’s ice and follow up with surface observations at those regions is a good candidate for the dual-exploration architecture. This example mission consists of an orbiter spacecraft carrying a cold atom gravimeter capable of sensing or inferring the ice thickness on regional to local scales, along with a number of ChipSat probes capable of landing on the moon. The small size and weight of the ChipSats allows large numbers of them to be carried, ensuring that enough can be dropped to ensure survival of a minimum number of probes and potentially allowing for the in-situ sampling of multiple locations on the moon. The example missions and Europa case study show that amazing scientific return can obtained from dual-exploration architecture missions with a single launch by breaking the long timescales of planetary exploration and providing the flexibility to capturing transitory events and collect data across the local, regional, and global scales.

Exploration↗
NASA NTRS2022

Estimating groundwater use and demand in arid Kenya through assimilation of satellite data and in-situ sensors with machine learning toward drought early action

Groundwater is an important source of water for people, livestock, and agriculture during drought in the Horn of Africa. In this work, areas of high groundwater use and demand in drought-prone Kenya were identified and forecasted prior to the dry season. Estimates of groundwater use were extended from a sentinel network of 69 in-situ sensored mechanical boreholes to the region with satellite data and a machine learning model. The sensors contributed 756 site-month observations from June 2017 to September 2021 for model building and validation at a density of approximately one sensor per 3700 sq.km. An ensemble of 19 parameterized algorithms was informed by features including satellite-derived precipitation, surface water availability, vegetation indices, hydrologic land surface modeling, and site characteristics to dichotomize high groundwater pump utilization. Three operational definitions of high demand on groundwater infrastructure were considered: 1) mechanical runtime of pumps greater than a quarter of a day (6+ hr) and daily per capita volume extractions indicative of 2) domestic water needs (35+ L), and 3) intermediate needs including livestock (75+ L). Gridded interpolation of localized groundwater use and demand was provided from 2017 to 2020 and forecasted for the 2021 dry season, June–September 2021. Cross-validated skill for contemporary estimates of daily pump runtime and daily volume extraction to meet domestic and intermediate water needs was 68%, 69%, and 75%, respectively. Forecasts were externally validated with an accuracy of at least 56%, 70%, or 72% for each groundwater use definition. The groundwater maps are accessible to stakeholders including the Kenya National Drought Management Authority (NDMA) and the Famine Early Warning Systems Network (FEWS NET). These maps represent the first operational spatially-explicit sub-seasonal to seasonal (S2S) estimates of groundwater use and demand in the literature. Knowledge of historical and forecasted groundwater use is anticipated to improve decision-making and resource allocation for a range of early warning early action applications.

Katie Fankhauser↗
NASA NTRS2008

Comparison of Satellite-Derived and In-Situ Observations of Ice and Snow Surface Temperatures over Greenland

The most practical way to get a spatially broad and continuous measurements of the surface temperature in the data-sparse cryosphere is by satellite remote sensing. The uncertainties in satellite-derived LSTs must be understood to develop internally-consistent decade-scale land-surface temperature (LST) records needed for climate studies. In this work we assess satellite-derived "clear-sky" LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and LSTs derived from the Enhanced Thematic Mapper Plus (ETM+) over snow and ice on Greenland. When possible, we compare satellite-derived LSTs with in-situ air-temperature observations from Greenland Climate Network (GC-Net) automatic-weather stations (AWS). We find that MODIS, ASTER and ETM+ provide reliable and consistent LSTs under clear-sky conditions and relatively-flat terrain over snow and ice targets over a range of temperatures from -40 to 0 C. The satellite-derived LSTs agree within a relative RMS uncertainty of approx.0.5 C. The good agreement among the LSTs derived from the various satellite instruments is especially notable since different spectral channels and different retrieval algorithms are used to calculate LST from the raw satellite data. The AWS record in-situ data at a "point" while the satellite instruments record data over an area varying in size from: 57 X 57 m (ETM+), 90 X 90 m (ASTER), or to 1 X 1 km (MODIS). Surface topography and other factors contribute to variability of LST within a pixel, thus the AWS measurements may not be representative of the LST of the pixel. Without more information on the local spatial patterns of LST, the AWS LST cannot be considered valid ground truth for the satellite measurements, with RMS uncertainty approx.2 C. Despite the relatively large AWS-derived uncertainty, we find LST data are characterized by high accuracy but have uncertain absolute precision.

Hall, Dorothy K.↗
NASA NTRS2023

Lifetimes of Overshooting Convective Events using High-Frequency Gridded Radar Composites

Deep convection that penetrates the tropopause, referred to here as overshooting convection, is capable of lifting tropospheric air well into the stratosphere. In addition to water, these overshoots also transport various chemical species, affecting chemistry and radiation in the stratosphere. It is not currently known, however, how much transport is a result of this mechanism. To better understand overshooting convection, this study aims to characterize the durations of overshooting events. To achieve this, radar data from the Next Generation Weather Radar (NEXRAD) network is composited onto a three-dimensional grid at 5-min intervals. Overshoots are identified by comparing echo-top heights with tropopause estimates derived from ERA5 reanalysis data. These overshoots are linked in space from one analysis time to the next to form tracks. This process is performed for 12 four-day sample windows in the months May–August of 2017–19. Track characteristics such as duration, overshoot area, tropopause-relative altitude, and column-maximum reflectivity are investigated. Positive correlations are found between track duration and other track characteristics. Integrated track volume is found as a product of the overshoot area, depth, and duration, and provides a measure of the potential stratospheric impact of each track. Short-lived tracks are observed to contribute the most total integrated volume when considering track duration, while tracks that overshoot by 2–3 km show the largest contribution when considering overshoot depth. A diurnal cycle is observed, with peak track initiation around 1600–1700 local time. Track-mean duration peaks a few hours earlier, while track-mean area and tropopause-relative height peak a few hours later.

Atmosphere↗
NASA NTRS2004

Anthropogenic Land-use Change and the Dynamics of Amazon Forest Biomass

This project was focused on assessing the effects of prevailing land uses, such as habitat fragmentation, selective logging, and fire, on biomass and carbon storage in Amazonian forests, and on the dynamics of carbon sequestration in regenerating forests. Ancillary goals included developing GIs models to help predict the future condition of Amazonian forests, and assessing the effects of anthropogenic climate change and ENS0 droughts on intact and fragmented forests. Ground-based studies using networks of permanent plots were linked with remote-sensing data (including Landsat TM and AVHRR) at regional scales, and higher-resolution techniques (IKONOS imagery, videography, LIDAR, aerial photographs) at landscape and local scales. The project s specific goals were quite eclectic and included: Determining the effects of habitat fragmentation on forest dynamics, floristic composition, and the various components of above- and below-ground biomass. Assessing historical and physical factors that affect trajectories of forest regeneration and carbon sequestration on abandoned lands. Extrapolating results from local studies of biomass dynamics in fragmented and regenerating forests to landscape and regional scales in Amazonia, using remote sensing and GIS. Testing the hypothesis that intact Amazonian forests are functioning as a significant carbon sink. Examining destructive synergisms between forest fragmentation and fire. Assessing the short-term impacts of selective logging on aboveground biomass. Developing GIS models that integrate current spatial data on forest cover, deforestation, logging, mining, highway and roads, navigable rivers, vulnerability to wild fires, protected areas, and existing and planned infrastructure projects, in an effort to predict the future condition of Brazilian Amazonian forests over the next 20-25 years. Devising predictive spatial models to assess the influence of varied biophysical and anthropogenic predictors on Amazonian deforestation.

Laurance, William F.↗
NASA NTRS2020

PHALANX: Expendable Projectile Sensor Networks for Planetary Exploration

Technologies enabling long-term, wide-ranging measurement in hard-to-reach areas are a critical need for planetary science inquiry. Phenomena of interest include flows or variations in volatiles, gas composition or concentration, particulate density, or even simply temperature. Improved measurement of these processes enables understanding of exotic geologies and distributions or correlating indicators of trapped water or biological activity. However, such data is often needed in unsafe areas such as caves, lava tubes, or steep ravines not easily reached by current spacecraft and planetary robots. To address this capability gap, we have developed miniaturized, expendable sensors which can be ballistically lobbed from a robotic rover or static lander - or even dropped during a flyover. These projectiles can perform sensing during flight and after anchoring to terrain features. By augmenting exploration systems with these sensors, we can extend situational awareness, perform long-duration monitoring, and reduce utilization of primary mobility resources, all of which are crucial in surface missions. We call the integrated payload that includes a cold gas launcher, smart projectiles, planning software, network discovery, and science sensing: PHALANX. In this paper, we introduce the mission architecture for PHALANX and describe an exploration concept that pairs projectile sensors with a rover “mothership.” Science use cases explored include reconnaissance using ballistic cameras, volatiles detection, and building timelapse maps of temperature and illumination conditions. Strategies to autonomously coordinate constellations of deployed sensors to self-discover and localize with peer ranging (i.e. a “local GPS”) are summarized, thus providing communications infrastructure beyond-line-of-sight (BLOS) of the rover. Capabilities were demonstrated through both simulation and physical testing with a terrestrial prototype. The approach to developing a terrestrial prototype is discussed, including design of the launching mechanism, projectile optimization, micro-electronics fabrication, and sensor selection. Results from early testing and characterization of commercial-off-the-shelf (COTS) components are reported. Nodes were subjected to successful burn-in tests over 48 hours at full logging duty cycle. Integrated field tests were conducted in the Roverscape, a half-acre planetary analog environment at NASA Ames, where we tested up to 10 sensor nodes simultaneously coordinating with an exploration rover. Ranging accuracy has been demonstrated to be within +/-10cm over 20m using commodity radios when compared to high-resolution laser scanner ground truthing. Evolution of the design, including progressive miniaturization of the electronics and iterated modifications of the enclosure housing for streamlining and optimized radio performance are described. Finally, lessons learned to date, gaps toward eventual flight mission implementation, and continuing future development plans are discussed.

Dille, Michael↗
NASA NTRS2021

Colorado River Basin Water Resources: Pairing Phenology Citizen Science Observations with NASA Earth Observations to Monitor Riparian Vegetation

Native plant species in the Colorado River Basin are currently threatened by non-native species that are changing resource competition dynamics. Monitoring native species is essential for understanding changes in plant health throughout the year as well as how species’ distributions change inter-annually. However, current monitoring of species is limited to local efforts and cannot be efficiently conducted across more extensive regions of the basin. Project partners, including the USA National Phenology Network, United States Geological Survey, and Educating Children Outdoors, provided data and on-the-ground expertise to assist the project. NASA Earth observations, including Landsat 8 Operational Land Imager (OLI), Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and Sentinel-2 MultiSpectral Instrument (MSI) from the European Space Agency, provided remote sensing data that covers the large spatial extent of the basin. This study combined both data sources over the study period of 2013 – 2019 and explored the relationship between on-the-ground phenology observations and remotely sensed phenology signals. The team found that pairing the datasets required spatial coordinates to be accurately logged and that it was necessary to have consistent observations throughout the year by citizen scientists. The team found promising relationships between phenology observations and the two vegetation indices, normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), when study locations were in an area with a high proportion of vegetation within a satellite pixel. The results and methods outlined in this study create new opportunities to leverage remote sensing alongside citizen science data for widespread monitoring of non-native species.

Tyler Pantle↗
NASA NTRS2018

Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies

Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihood–based classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrence–based texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms.

Pasolli, Edoardo↗
NASA NTRS2023

Tracking NO2 Pollution Changes Over Texas: Synthesis of In Situ and Satellite Observations

Nitrogen oxides (NO x ) are major air pollutants that play a crucial role in atmospheric chemistry. We compare Ozone Measuring Instrument's (OMI) NO 2 records with the in situ surface measurements from the Air Quality System of the US Environmental Protection Agency and the Texas Commission on Environmental Quality network in the state of Texas with the goal of understanding the correspondence of satellite and in situ surface observations and identifying the potential synergies between the two observing systems. Our analysis of over 40 in situ daily surface site observations, mostly from urban areas, and OMI daily observed data suggests a correlation ( r ) ranging between 0.2 and 0.8. The correlation improves considerably ( r > 0.5) for monthly average data. Weekly variation of surface NO 2 with a Sunday minimum is well captured by OMI tropospheric NO 2 column observations with similar weekend reductions. NO 2 trend in Texas during 2005–2019 is characterized by significant reductions of 20%–36% in highly populated cities and urban centers. However, a significant (up to 80%) increase was observed in oil and gas producing regions of the Permian and Eagle Ford Basins over the same period. In March–April of 2020, like the other US and global cities, Texas experienced up to 60% reduction in NO 2 levels in major cities due to travel restrictions imposed at local and national levels to contain the spread of COVID-19. Though such reduction is temporary, these reductions were significantly larger than those achieved in the past 16 years of OMI record suggesting that technological advancement can curtail NO x emissions.

Nitrogen dioxide (NO2)↗
NASA NTRS1978

Emergency Communications Console

NASA has applied its communications equipment expertise to development of a communications console that provides, in a compact package only slightly larger than an electric typewriter, all the emergency medical services communications functions needed for a regional hospital. A prototype unit, built by Johnson Space Center, has been installed in the Odessa (Texas) Medical Center Hospital. The hospital is the medical control center for the 17-county Permian Basin Emergency Medical System in west Texas. The console project originated in response to a request to NASA from the Texas governor's office, which sought a better way of providing emergency medical care in rural areas. Because ambulance travel time is frequently long in remote areas of west Texas, it is important that treatment begin at the scene of the emergency rather than at the hospital emergency room. A radio and telephone system linking ambulance emergency technicians and hospital staff makes this possible. But earlier equipment was complex, requiring specialized operators. A highly reliable system was needed to minimize breakdowns and provide controls of utmost simplicity, so that the system could be operated by physicians and nurses rather than by communications specialists. The resulting console has both radio and telephone sections. With the radio equipment, hospital personnel can communicate with ambulance drivers and paramedics, receive incoming electrocardiagrams, consult with other hospitals, page hospital staff and set up a radio-to-telephone "patch." The telephone portion of the system includes a hotline from the Permian Basin Emergency Medical Service's resource control center, an automatic dialer for contacting special care facilities in the Permian Basin network, a hospital intercom terminal and a means of relaying cardioscope displays and other data between hospitals. The integrated system also provides links with local disaster and civil defense organizations and with emergency "Dial 911" control points.

Source record↗
NASA NTRS2005

Forecasting Lightning at Kennedy Space Center/Cape Canaveral Air Force Station, Florida

The Applied Meteorology Unit (AMU) developed a set of statistical forecast equations that provide a probability of lightning occurrence on Kennedy Space Center (KSC) I Cape Canaveral Air Force Station (CCAFS) for the day during the warm season (May September). The 45th Weather Squadron (45 WS) forecasters at CCAFS in Florida include a probability of lightning occurrence in their daily 24-hour and weekly planning forecasts, which are briefed at 1100 UTC (0700 EDT). This information is used for general scheduling of operations at CCAFS and KSC. Forecasters at the Spaceflight Meteorology Group also make thunderstorm forecasts for the KSC/CCAFS area during Shuttle flight operations. Much of the current lightning probability forecast at both groups is based on a subjective analysis of model and observational data. The objective tool currently available is the Neumann-Pfeffer Thunderstorm Index (NPTI, Neumann 1971), developed specifically for the KSCICCAFS area over 30 years ago. However, recent studies have shown that 1-day persistence provides a better forecast than the NPTI, indicating that the NPTI needed to be upgraded or replaced. Because they require a tool that provides a reliable estimate of the daily thunderstorm probability forecast, the 45 WS forecasters requested that the AMU develop a new lightning probability forecast tool using recent data and more sophisticated techniques now possible through more computing power than that available over 30 years ago. The equation development incorporated results from two research projects that investigated causes of lightning occurrence near KSCICCAFS and over the Florida peninsula. One proved that logistic regression outperformed the linear regression method used in NPTI, even when the same predictors were used. The other study found relationships between large scale flow regimes and spatial lightning distributions over Florida. Lightning, probabilities based on these flow regimes were used as candidate predictors in the equation development. Fifteen years (1 989-2003) of warm season data were used to develop the forecast equations. The data sources included a local network of cloud-to-ground lightning sensors called the Cloud-to-Ground Lightning Surveillance System (CGLSS), 1200 UTC Florida synoptic soundings, and the 1000 UTC CCAFS sounding. Data from CGLSS were used to determine lightning occurrence for each day. The 1200 UTC soundings were used to calculate the synoptic-scale flow regimes and the 1000 UTC soundings were used to calculate local stability parameters, which were used as candidate predictors of lightning occurrence. Five logistic regression forecast equations were created through careful selection and elimination of the candidate predictors. The resulting equations contain five to six predictors each. Results from four performance tests indicated that the equations showed an increase in skill over several standard forecasting methods, good reliability, an ability to distinguish between non-lightning and lightning days, and good accuracy measures and skill scores. Given the overall good performance the 45 WS requested that the equations be transitioned to operations and added to the current set of tools used to determine the daily lightning probability of occurrence.

Lambert, Winfred↗
NASA NTRS1988

Geodetic Measurement of Deformation East of the San Andreas Fault in Central California

Triangulation and trilateration data from two geodetic networks located between the western edge of the Great Valley and the San Andreas fault have been used to calculate shear strain rates in the Diablo Range and to estimate the slip rate along the Calaveras and Paicines faults in Central California. Within the Diablo Range the average shear strain rate was determined for the time period between 1962 and 1982 to be 0.15 + or - 0.08 microrad/yr, with the orientation of the most compressive strain at N 16 deg E + or - 14 deg. The orientation of the principal compressive strain predicted from the azimuth of the major structures in the region is N 25 deg E. It is inferred that the measured strain is due to compression across the folds of this area: the average shear straining corresponds to a relative shortening rate of 4.5 + or - 2.4 mm/yr. From an examination of wellbore breakout orientations and the azimuths of P-axes from earthquake focal mechanisms the inferred orientation of maximum compressive stress was found to be similar to the direction of maximum compressive strain implied by the trend of local fold structures. Results do not support the hypothesis of uniform fault-normal compression within the Coast Ranges. From trilateration measurements made between 1972 and 1987 on lines that are within 10 km of the San Andreas fault, a slip rate of 10 to 12 mm/yr was calculated for the Calaveras-Paicines fault south of Hollister. The slip rate of the Paicines fault decreases to 4 mm/yr near Bitter.

Sauber, Jeanne M.↗
NASA NTRS1997

Algorithms for Performance, Dependability, and Performability Evaluation using Stochastic Activity Networks

Modeling tools and technologies are important for aerospace development. At the University of Illinois, we have worked on advancing the state of the art in modeling by Markov reward models in two important areas: reducing the memory necessary to numerically solve systems represented as stochastic activity networks and other stochastic Petri net extensions while still obtaining solutions in a reasonable amount of time, and finding numerically stable and memory-efficient methods to solve for the reward accumulated during a finite mission time. A long standing problem when modeling with high level formalisms such as stochastic activity networks is the so-called state space explosion, where the number of states increases exponentially with size of the high level model. Thus, the corresponding Markov model becomes prohibitively large and solution is constrained by the the size of primary memory. To reduce the memory necessary to numerically solve complex systems, we propose new methods that can tolerate such large state spaces that do not require any special structure in the model (as many other techniques do). First, we develop methods that generate row and columns of the state transition-rate-matrix on-the-fly, eliminating the need to explicitly store the matrix at all. Next, we introduce a new iterative solution method, called modified adaptive Gauss-Seidel, that exhibits locality in its use of data from the state transition-rate-matrix, permitting us to cache portions of the matrix and hence reduce the solution time. Finally, we develop a new memory and computationally efficient technique for Gauss-Seidel based solvers that avoids the need for generating rows of A in order to solve Ax = b. This is a significant performance improvement for on-the-fly methods as well as other recent solution techniques based on Kronecker operators. Taken together, these new results show that one can solve very large models without any special structure.

Deavours, Daniel D.↗
NASA NTRS2014

Validation of TRMM Multi-Satellite Precipitation Analysis (TMPA) Products in the Peruvian Andes

The relevance of accurate and timely rainfall estimates cannot be overstated. The rainfall gauge network is still insufficient across significant areas worldwide. Rainfall estimates fromspaceborne sensors present an opportunity to supplement the existing network and enable the development of critical, near real time applications. However, the societal benefits of such systems can only be realized if the estimates are properly validated and the performance of existing products accurately described. In this study, two products generated by the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) are validated for the Peruvian Andes. This is a region of complex topography that poses significant challenges to the retrieval of rainfall values fromspace. The TMPA products, both research grade (3B42V7) and near real time (3B42RT), are compared against in situ data. Different observation lengths are studied and the results are analyzed in light of geographic, topographic and climatic constraints. The Time Series of the science grade product were also studied under Dynamic TimeWarping and Hierarchical Clustering to streamline intertile comparisons. The TMPA products showa good agreementwith the gauge values, especially formore prolonged observation periods (over 8 days). The validation results display a strong regional dependence as a consequence of differences in the climate and topography. This region-specific performance calls for additional, detailed case studies and localized validation efforts. Overall the TMPAwas found to performadequately and provide quality information for a number of applications requiring timely estimates in convenient formats.

satellites↗
NASA NTRS2017

Classification of Meteorological Influences Surrounding Extreme Precipitation Events in the United States using the MERRA-2 Reanalysis

Extreme precipitation events can have a large impact on society through flooding that can result in property destruction, crop losses, economic losses, the spread of water-borne diseases, and fatalities. Observations indicate there has been a statistically significant increase in extreme precipitation events over the past 15 years in the Northeastern United States and other localized regions of the country have become crippled with record flooding events, for example, the flooding that occurred in the Southeast United States associated with Hurricane Matthew in October 2016. Extreme precipitation events in the United States can be caused by various meteorological influences such as extratropical cyclones, tropical cyclones, mesoscale convective complexes, general air mass thunderstorms, upslope flow, fronts, and the North American Monsoon. Reanalyses, such as the Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), have become a pivotal tool to study the meteorology surrounding extreme precipitation events. Using days classified as an extreme precipitation events based on a combination of observational gauge and radar data, two techniques for the classification of these events are used to gather additional information that can be used to determine how events have changed over time using atmospheric data from MERRA-2. The first is self organizing maps, which is an artificial neural network that uses unsupervised learning to cluster like patterns and the second is an automated detection technique that searches for characteristics in the atmosphere that define a meteorological phenomena. For example, the automated detection for tropical cycles searches for a defined area of suppressed sea level pressure, alongside thickness anomalies aloft, indicating the presence of a warm core. These techniques are employed for extreme precipitation events in preselected regions that were chosen based an analysis of the climatology of precipitation.

Monsoon↗
NASA NTRS1998

Analyzing Space-Based Interferometric Measurements of Stars and Network Measurements of Gamma-Ray Bursts

Since the announcement of the discovery of sources of bursts of gamma-ray radiation in 1973, hundreds more reports of such bursts have now been published. Numerous artificial satellites have been equipped with gamma-ray detectors including the very successful Compton Gamma Ray Observatory BATSE instrument. Unfortunately, we have made no progress in identifying the source(s) of this high energy radiation. We suspected that this was a consequence of the method used to define gamma-ray burst source "error boxes." An alternative procedure to compute gamma-ray burst source positions, with a purely physical underpinning, was proposed in 1988 by Taff. Since then we have also made significant progress in understanding the analytical nature of the triangulation problem and in computing actual gamma-ray burst positions and their corresponding error boxes. For the former, we can now mathematically illustrate the crucial role of the area occupied by the detectors, while for the latter, the Atteia et al. (1987) catalog has been completely re-reduced. There are very few discrepancies in locations between our results and those of the customary "time difference of arrival" procedure. Thus, we have numerically demonstrated that the end result, for the positions, of these two very different-looking procedures is the same. Finally, for the first time, we provide a sample of realistic "error boxes" whose non-simple shapes vividly portray the difficulty of burst source localization.

Taff, L. G.↗
NASA NTRS2013

The Interplanetary Network Supplement to the Fermi Gbm Catalog of Cosmic Gamma-Ray Bursts

We present Interplanetary Network (IPN) data for the gamma-ray bursts in the first Fermi Gamma-Ray BurstMonitor (GBM) catalog. Of the 491 bursts in that catalog, covering 2008 July 12 to 2010 July 11, 427 wereobserved by at least one other instrument in the nine-spacecraft IPN. Of the 427, the localizations of 149 could beimproved by arrival time analysis (or triangulation). For any given burst observed by the GBM and one otherdistant spacecraft, triangulation gives an annulus of possible arrival directions whose half-width varies betweenabout 0. 4 and 32, depending on the intensity, time history, and arrival direction of the burst, as well as the distancebetween the spacecraft. We find that the IPN localizations intersect the 1 GBM error circles in only 52 of thecases, if no systematic uncertainty is assumed for the latter. If a 6 systematic uncertainty is assumed and added inquadrature, the two localization samples agree about 87 of the time, as would be expected. If we then multiply theresulting error radii by a factor of three, the two samples agree in slightly over 98 of the cases, providing a goodestimate of the GBM 3 error radius. The IPN 3 error boxes have areas between about 1 arcmin2 and 110 deg2,and are, on the average, a factor of 180 smaller than the corresponding GBM localizations. We identify two burstsin the IPNGBM sample that did not appear in the GBM catalog. In one case, the GBM triggered on a terrestrialgamma flash, and in the other, its origin was given as uncertain. We also discuss the sensitivity and calibration ofthe IPN.

INTERPLANET↗
NASA NTRS2021

Tracking Smoke from a Prescribed Fire and its Impacts on Local Air Quality using Temporally Resolved GOES-16 ABI Aerosol Optical Depth (AOD)

Aerosol optical depth (AOD) retrieved from the GOES-16 Advanced Baseline Imager (ABI) was used to track a smoke plume from a prescribed fire in northeastern Virginia on 8 March 2020. Weather and atmospheric conditions created a favorable environment to transport the plume through the Washington, D.C., and Baltimore, Maryland, metro areas in the afternoon and concentrate smoke near the surface, degrading air quality for several hours. ABI AOD with 5-min temporal resolution and 2-km spatial resolution definitively identified the timing and geographic extent of the plume during daylight hours. Comparison to AERONET AOD indicates that ABI AOD captured the relative change in AOD due to passage of the smoke, with a mean absolute error of 0.047. Ground-based measurements of fine particulate matter (PM2.5) confirm deteriorations in air quality coincident with the progression of the smoke. Ceilometer aerosol backscatter profiles verify plume transport timing and indicate that smoke aerosols were well mixed in a shallow boundary layer. This event illustrates the advantages of using multiple datasets to analyze the impacts of aerosols on ambient air quality. Given the quickly evolving nature of the event over several hours, ABI AOD provided information for the public and decision-makers that was not available from any other source, including polar-orbiting satellite sensors. This study suggests that PM2.5 concentrations estimated from ABI AOD can be used to fill in the gaps in nationwide regulatory PM2.5 monitor networks and may be a valuable addition to EPA’s PM2.5 NowCast of current air quality conditions.

satellite observations↗