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NASA NTRS1987

The predictive information obtained by testing multiple software versions

Multiversion programming is a redundancy approach to developing highly reliable software. In applications of this method, two or more versions of a program are developed independently by different programmers and the versions are combined to form a redundant system. One variation of this approach consists of developing a set of n program versions and testing the versions to predict the failure probability of a particular program or a system formed from a subset of the programs. The precision that might be obtained, and also the effect of programmer variability if predictions are made over repetitions of the process of generating different program versions, are examined.

Lee, Larry D.↗

NASA NTRS1999

Variance and Predictability of Precipitation at Seasonal-to-Interannual Timescales

A series of atmospheric general circulation model (AGCM) simulations, spanning a total of several thousand years, is used to assess the impact of land-surface and ocean boundary conditions on the seasonal-to-interannual variability and predictability of precipitation in a coupled modeling system. In the first half of the analysis, which focuses on precipitation variance, we show that the contributions of ocean, atmosphere, and land processes to this variance can be characterized, to first order, with a simple linear model. This allows a clean separation of the contributions, from which we find: (1) land and ocean processes have essentially different domains of influence, i.e., the amplification of precipitation variance by land-atmosphere feedback is most important outside of the regions (mainly in the tropics) that are most affected by sea surface temperatures; and (2) the strength of land-atmosphere feedback in a given region is largely controlled by the relative availability of energy and water there. In the second half of the analysis, the potential for seasonal-to-interannual predictability of precipitation is quantified under the assumption that all relevant surface boundary conditions (in the ocean and on land) are known perfectly into the future. We find that the chaotic nature of the atmospheric circulation imposes fundamental limits on predictability in many extratropical regions. Associated with this result is an indication that soil moisture initialization or assimilation in a seasonal-to-interannual forecasting system would be beneficial mainly in transition zones between dry and humid regions.

Koster, Randal D.↗

NASA NTRS2020

Ellicott City Disasters II - 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 uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗

NASA NTRS2020

Ellicott City Disasters III: Building a Real-Time Statistical Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

Erika Munshi↗

NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term. The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗

NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission TRMM Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗

NASA NTRS1993

A predictability study of Lorenz's 28-variable model as a dynamical system

The dynamics of error growth in a two-layer nonlinear quasi-geostrophic model has been studied to gain an understanding of the mathematical theory of atmospheric predictability. The growth of random errors of varying initial magnitudes has been studied, and the relation between this classical approach and the concepts of the nonlinear dynamical systems theory has been explored. The local and global growths of random errors have been expressed partly in terms of the properties of an error ellipsoid and the Liapunov exponents determined by linear error dynamics. The local growth of small errors is initially governed by several modes of the evolving error ellipsoid but soon becomes dominated by the longest axis. The average global growth of small errors is exponential with a growth rate consistent with the largest Liapunov exponent. The duration of the exponential growth phase depends on the initial magnitude of the errors. The subsequent large errors undergo a nonlinear growth with a steadily decreasing growth rate and attain saturation that defines the limit of predictability. The degree of chaos and the largest Liapunov exponent show considerable variation with change in the forcing, which implies that the time variation in the external forcing can introduce variable character to the predictability.

Krishnamurthy, V.↗

NASA NTRS2011

Observation of Hydrological Processes Using Remote Sensing: Volume 2: The Science of Hydrology - Chapter 2.14

Improving water management can make a significant contribution to achieving most of the Millennium Development Goals established by the UN General Assembly in 2000, especially those related to poverty, hunger, and major diseases. The World Summit on Sustainable Development (WSSD) in 2002 recognized this need. Water and sanitation in particular received great attention from the Summit. The Johannesburg Plan of Implementation recommended to improve water resources management and scientific understanding of the water cycle through joint cooperation and research. For this purpose, it is recommended to promote knowledge sharing, provide capacity building, and facilitate the transfer of technology including remote-sensing (RS) and satellite technologies, especially to developing countries and countries with economies in transition, and to support these countries in their efforts to monitor and assess the quantity and quality of water resources, for example, by establishing and/or further developing national monitoring networks and water resources databases and by developing relevant national indicators. The Johannesburg Plan also adopted integrated water resources management as the overarching concept in addressing and solving water-related issues. As a result of the commitments made in the Johannesburg Plan of Implementation, several global and regional initiatives have emerged. Current international initiatives such as the Global Monitoring for Environment and Security (GMES) program of the European Commission and the European Space Agency (ESA), and the Global Earth Observation System of Systems (GEOSS) 10-Year Implementation Plan, have all identified Earth observation (EO) of the water cycle as the key in helping to solve the world s water problems. The availability of spatial information on water quantity and quality will also enable closure of the water budget at river basin and continental scales to the point where effective water management is essential (e.g., as requested by the European Union s Water Framework Directive (WFD), as well as national policies). Geo-information science and EO are vital in achieving a better understanding of the water cycle and better monitoring, analysis, prediction, and management of the world s water resources. The major components of the water cycle of the Earth system and their possible observations are presented. Such observations are essential to understand the global water cycle and its variability, both spatially and temporally, and can only be achieved consistently by means of EOs. Additionally, such observations are essential to advance our understanding of coupling between the terrestrial, atmospheric, and oceanic branches of the water cycle, and how this coupling may influence climate variability and predictability. Water resources management directly interferes with the natural water cycle in the forms of building dams, reservoirs, water transfer systems, and irrigation systems that divert and redistribute part of the water storages and fluxes on land. The water cycle is mainly driven and coupled to the energy cycle in terms of phase changes of water (changes among liquid, water vapor, and solid phases) and transport of water by winds in addition to gravity and diffusion processes. The water-cycle components can be observed with in situ sensors as well as airborne and satellite sensors in terms of radiative quantities. Processing and conversion of these radiative signals are necessary to retrieve the water-cycle components.

Wilder, Peter↗

NASA NTRS1995

Experimental Verification of a Progressive Damage Model for IM7/5260 Laminates Subjected to Tension-Tension Fatigue

The durability and damage tolerance of laminated composites are critical design considerations for airframe composite structures. Therefore, the ability to model damage initiation and growth and predict the life of laminated composites is necessary to achieve structurally efficient and economical designs. The purpose of this research is to experimentally verify the application of a continuum damage model to predict progressive damage development in a toughened material system. Damage due to monotonic and tension-tension fatigue was documented for IM7/5260 graphite/bismaleimide laminates. Crack density and delamination surface area were used to calculate matrix cracking and delamination internal state variables to predict stiffness loss in unnotched laminates. A damage dependent finite element code predicted the stiffness loss for notched laminates with good agreement to experimental data. It was concluded that the continuum damage model can adequately predict matrix damage progression in notched and unnotched laminates as a function of loading history and laminate stacking sequence.

Coats, Timothy W.↗

NASA NTRS2016

Assessment of Arctic and Antarctic Sea Ice Predictability in CMIP5 Decadal Hindcasts

This paper examines the ability of coupled global climate models to predict decadal variability of Arctic and Antarctic sea ice. We analyze decadal hindcasts/predictions of 11 Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Decadal hindcasts exhibit a large multimodel spread in the simulated sea ice extent, with some models deviating significantly from the observations as the predicted ice extent quickly drifts away from the initial constraint. The anomaly correlation analysis between the decadal hindcast and observed sea ice suggests that in the Arctic, for most models, the areas showing significant predictive skill become broader associated with increasing lead times. This area expansion is largely because nearly all the models are capable of predicting the observed decreasing Arctic sea ice cover. Sea ice extent in the North Pacific has better predictive skill than that in the North Atlantic (particularly at a lead time of 3-7 years), but there is a reemerging predictive skill in the North Atlantic at a lead time of 6-8 years. In contrast to the Arctic, Antarctic sea ice decadal hindcasts do not show broad predictive skill at any timescales, and there is no obvious improvement linking the areal extent of significant predictive skill to lead time increase. This might be because nearly all the models predict a retreating Antarctic sea ice cover, opposite to the observations. For the Arctic, the predictive skill of the multi-model ensemble mean outperforms most models and the persistence prediction at longer timescales, which is not the case for the Antarctic. Overall, for the Arctic, initialized decadal hindcasts show improved predictive skill compared to uninitialized simulations, although this improvement is not present in the Antarctic.

sea ice↗

NASA NTRS2016

An Assessment of Multimodel Simulations for the Variability of Western North Pacific Tropical Cyclones and Its Association with ENSO

An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño-Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation. Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Niño-namely, eastern Pacific (EP) and central Pacific (CP) El Niño-and weaker activity during La Niña. However, none of the models capture the differences in TC activity between EP and CP El Niño as are shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Niño events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Niño.

climate models↗

NASA NTRS2020

Impacts of Water Clarity Variability on Temperature and Biogeochemistry in the Chesapeake Bay

Estuarine water clarity depends on the concentrations of aquatic constituents, such as colored dissolved organic matter, phytoplankton, inorganic suspended solids, and detritus, which are influenced by variations in riverine inputs. These constituents directly affect temperature because when water is opaque, sunlight heats a shallower layer of the water compared to when it is clear. Despite the importance of accurately predicting temperature variability, many numerical modeling studies do not adequately account for this key process. In this study, we quantify the effect of water clarity on heating by comparing two simulations of a hydrodynamic-biogeochemical model of the Chesapeake Bay for the years 2001–2005, in which (1) water clarity is constant in space and time for the computation of solar heating, compared to (2) a simulation where water clarity varies with modeled concentrations of light-attenuating materials. In the variable water clarity simulation, the water is more opaque, particularly in the northern region of the Bay. This decrease in water clarity reduces the total heat, phytoplankton, and nitrate throughout the Bay. During the spring and summer months, surface temperatures in the northern Bay are warmer by 0.1 °C and bottom temperatures are colder by 0.2 °C in the variable light attenuation simulation. Warmer surface temperatures encourage phytoplankton growth and nutrient uptake near the head of the Bay, and fewer nutrients are transported downstream. These impacts are greater during higher river flow years, when differences in temperature, nutrients, phytoplankton, and zooplankton extend further seaward compared to other years. This study demonstrates the consequences of utilizing different light calculations for estuarine heating and biogeochemistry.

water clarity↗

NASA NTRS2003

Evaluating Corn (Zea Mays L.) N Variability Via Remote Sensed Data

Transformations and losses of nitrogen (N) throughout the growing season can be costly. Methods in place to improve N management and facilitate split N applications during the growing season can be time consuming and logistically difficult. Remote sensing (RS) may be a method to rapidly assess temporal changes in crop N status and promote more efficient N management. This study was designed to evaluate the ability of three different RS platforms to predict N variability in corn (Zea mays L.) leaves during vegetative and early reproductive growth stages. Plots (15 x 15m) were established in the Coastal Plain (CP) and Appalachian Plateau (AP) physiographic regions each spring from 2000 to 2002 in a completely randomized design. Treatments consisted of four N rates (0, 56, 112, and 168 kg N/ha) applied as ammonium nitrate (NH4N03) replicated four time. Spectral measurements were acquired via spectroradiometer (lambda = 350 - 1050 nm), Airborne Terrestrial Applications Sensor (ATLAS) (lambda = 400 - 12,500 nm), and the IKONOS satellite (lambda = 450 - 900 nm). Spectroradiometer data were collected on a biweekly basis from V4 through R1. Due to the nature of - satellite and aircraft acquisitions, these data were acquired per availability. Chlorophyll meter (SPAD) and tissue N were collected as ancillary data along with each RS acquisition. Results showed vegetation indices derived from hand-held spectroradiometer measurements as early as V6-V8 were linearly related to yield and tissue N content. ATLAS data was correlated with tissue N at the AP site during the V6 stage (r2 = 0.66), but no significant relationships were observed at the CP site. No significant relationships were observed between plant N and IKONOS imagery. Using a combination of the greenness vegetation index (GNDVI) and the normalized difference vegetation index (NDVI), RS data acquired via ATLAS and the spectroradiometer could be used to evaluate tissue N variability and estimate corn yield variability under ideal growing conditions.

Sullivan, D. G.↗

NASA NTRS1976

A new interpretation of luminous blue stars

A major revision of current theoretical ideas about the brightest blue stars must be made if Carson's (1976) radiative opacities are adopted in stellar models. Unlike earlier opacities, these exhibit a large 'bump' due to CNO ionization, which leads to very strong central condensation, convective instability, and pulsational instability in hot diffuse stellar envelopes. Despite a number of theoretical uncertainties, the new picture of the structure of very luminous stars is reasonably successful in accounting for a variety of previously unexplained observations. The stellar models for the phase of core hydrogen burning predict large radii and rather cool effective temperatures for O stars and a spreading out of the main-sequence band in the H-R diagram toward luminous cool supergiants for masses higher than about 20 solar masses. In massive X-ray binary systems, circular orbits and supergiant-like visual companions are expected to be quite common. Long-period variability is predicted to exist for massive blue supergiants of luminosity class Ia. The models for helium stars predict large radii and rather cool effective temperatures for Wolf-Rayet stars, as well as multimodal pulsational instability and, possibly, surface turbulence for these stars.

Stothers, R.↗

NASA NTRS1982

Evaluation of the Williams-type spring wheat model in North Dakota and Minnesota

The Williams type model, developed similarly to previous models of C.V.D. Williams, uses monthly temperature and precipitation data as well as soil and topological variables to predict the yield of the spring wheat crop. The models are statistically developed using the regression technique. Eight model characteristics are examined in the evaluation of the model. Evaluation is at the crop reporting district level, the state level and for the entire region. A ten year bootstrap test was the basis of the statistical evaluation. The accuracy and current indication of modeled yield reliability could show improvement. There is great variability in the bias measured over the districts, but there is a slight overall positive bias. The model estimates for the east central crop reporting district in Minnesota are not accurate. The estimate of yield for 1974 were inaccurate for all of the models.

Leduc, S.↗

NASA NTRS1992

Subsonic flight test evaluation of a propulsion system parameter estimation process for the F100 engine

Integrated engine-airframe optimal control technology may significantly improve aircraft performance. This technology requires a reliable and accurate parameter estimator to predict unmeasured variables. To develop this technology base, NASA Dryden Flight Research Facility (Edwards, CA), McDonnell Aircraft Company (St. Louis, MO), and Pratt & Whitney (West Palm Beach, FL) have developed and flight-tested an adaptive performance seeking control system which optimizes the quasi-steady-state performance of the F-15 propulsion system. This paper presents flight and ground test evaluations of the propulsion system parameter estimation process used by the performance seeking control system. The estimator consists of a compact propulsion system model and an extended Kalman filter. The extended Laman filter estimates five engine component deviation parameters from measured inputs. The compact model uses measurements and Kalman-filter estimates as inputs to predict unmeasured propulsion parameters such as net propulsive force and fan stall margin. The ability to track trends and estimate absolute values of propulsion system parameters was demonstrated. For example, thrust stand results show a good correlation, especially in trends, between the performance seeking control estimated and measured thrust.

Orme, John S.↗

NASA NTRS2016

Tropical Cyclones in the GISS ModelE2

The authors describe the characteristics of tropical cyclone (TC) activity in the GISS general circulation ModelE2 with a horizontal resolution 1deg x 1deg. Four model simulations are analyzed. In the first, the model is forced with sea surface temperature (SST) from the recent historical climatology. The other three have different idealized climate change simulations, namely (1) a uniform increase of SST by 2 deg., (2) doubling of the CO2 concentration and (3) a combination of the two. These simulations were performed as part of the US Climate Variability and Predictability Program Hurricane Working Group. Diagnostics of standard measures of TC activity are computed from the recent historical climatological SST simulation and compared with the same measures computed from observations. The changes in TC activity in the three idealized climate change simulations, by comparison with that in the historical climatological SST simulation, are also described. Similar to previous results in the literature, the changes in TC frequency in the simulation with a doubling CO2 and an increase in SST are approximately the linear sum of the TC frequency in the other two simulations. However, in contrast with previous results, in these simulations the effects of CO2 and SST on TC frequency oppose each other. Large-scale environmental variables associated with TC activity are then analyzed for the present and future simulations. Model biases in the large-scale fields are identified through a comparison with ERA-Interim reanalysis. Changes in the environmental fields in the future climate simulations are shown and their association with changes in TC activity discussed.

Hurricanes↗

NASA NTRS1992

The prediction of nonlinear dynamic loads on helicopters from flight variables using artificial neural networks

A method of indirectly monitoring component loads through common flight variables is proposed which requires an accurate model of the underlying nonlinear relationships. An artificial neural network (ANN) model learns relationships through exposure to a database of flight variable records and corresponding load histories from an instrumented military helicopter undergoing standard maneuvers. The ANN model, utilizing eight standard flight variables as inputs, is trained to predict normalized time-varying mean and oscillatory loads on two critical components over a range of seven maneuvers. Both interpolative and extrapolative capabilities are demonstrated with agreement between predicted and measured loads on the order of 90 percent to 95 percent. This work justifies pursuing the ANN method of predicting loads from flight variables.

Cook, A. B.↗