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NASA NTRS2007

NASA Tech Briefs, January 2007

Topics covered include: Flexible Skins Containing Integrated Sensors and Circuitry; Artificial Hair Cells for Sensing Flows; Video Guidance Sensor and Time-of-Flight Rangefinder; Optical Beam-Shear Sensors; Multiple-Agent Air/Ground Autonomous Exploration Systems; A 640 512-Pixel Portable Long-Wavelength Infrared Camera; An Array of Optical Receivers for Deep-Space Communications; Microstrip Antenna Arrays on Multilayer LCP Substrates; Applications for Subvocal Speech; Multiloop Rapid-Rise/Rapid Fall High-Voltage Power Supply; The PICWidget; Fusing Symbolic and Numerical Diagnostic Computations; Probabilistic Reasoning for Robustness in Automated Planning; Short-Term Forecasting of Radiation Belt and Ring Current; JMS Proxy and C/C++ Client SDK; XML Flight/Ground Data Dictionary Management; Cross-Compiler for Modeling Space-Flight Systems; Composite Elastic Skins for Shape-Changing Structures; Glass/Ceramic Composites for Sealing Solid Oxide Fuel Cells; Aligning Optical Fibers by Means of Actuated MEMS Wedges; Manufacturing Large Membrane Mirrors at Low Cost; Double-Vacuum-Bag Process for Making Resin- Matrix Composites; Surface Bacterial-Spore Assay Using Tb3+/DPA Luminescence; Simplified Microarray Technique for Identifying mRNA in Rare Samples; High-Resolution, Wide-Field-of-View Scanning Telescope; Multispectral Imager With Improved Filter Wheel and Optics; Integral Radiator and Storage Tank; Compensation for Phase Anisotropy of a Metal Reflector; Optical Characterization of Molecular Contaminant Films; Integrated Hardware and Software for No-Loss Computing; Decision-Tree Formulation With Order-1 Lateral Execution; GIS Methodology for Planning Planetary-Rover Operations; Optimal Calibration of the Spitzer Space Telescope; Automated Detection of Events of Scientific Interest; Representation-Independent Iteration of Sparse Data Arrays; Mission Operations of the Mars Exploration Rovers; and More About Software for No-Loss Computing.

Source record↗

DOE OSTI2024

Automating Rabi & Ramsey Measurements via ML

As quantum computers scale up, the manual process of qubit tune-up becomes increasingly impractical due to its time-consuming and repetitive nature. While existing research has explored some automation techniques, many models remain underutilized for this purpose. This research aims to answer the question: can qubit tune-up be automated using the Long Short-Term Memory (LSTM) model? For the purposes of this project, only the rabi and ramsey measurement cycle was automated. These measurements are used to fine-tune a rough qubit frequency by repeating them until the optimal qubit frequency is obtained. The LSTM model uses the qubit frequency at one time step to forecast the qubit frequency at the next time step. A rabi-ramsey simulation was made to fabricate a dataset to train and test the LSTM model. As the model was trained, the error of the model decreased. Although there wasn t enough training data to generate perfect predictions, this shows it is possible to utilize forecasting models in automating the tune-up process.

Roberts, Rachel↗

DOE OSTI2024

Automating Rabi & Ramsey Measurements via Machine Learning

As quantum computers scale up, the manual process of qubit tune-up becomes increasingly impractical due to its time-consuming and repetitive nature. While existing research has explored some automation techniques, many models remain underutilized for this purpose. This research aims to answer the question: can qubit tune-up be automated using the Long Short-Term Memory (LSTM) model? For the purposes of this project, only the rabi and ramsey measurement cycle was automated. These measurements are used to fine-tune a rough qubit frequency by repeating them until the optimal qubit frequency is obtained. The LSTM model uses the qubit frequency at one time step to forecast the qubit frequency at the next time step. A rabi-ramsey simulation was made to fabricate a dataset to train and test the LSTM model. As the model was trained, the error of the model decreased. Although there wasn't enough training data to generate perfect predictions, this shows it is possible to utilize forecasting models in automating the tune-up process.

Roberts, Rachel↗

NASA NTRS1980

Tropical cyclone intensities from satellite microwave data

Radial profiles of mean 1000 mb to 250 mb temperature from the Nimbus 6 scanning microwave spectrometer (SCAMS) were constructed around eight intensifying tropical storms in the western Pacific. Seven storms showed distinct inward temperature gradients required for intensification; the eighth displayed no inward gradient and was decaying 24 hours later. The possibility that satellite data might be used to forecast tropical cyclone turning motion was investigated using estimates obtained from Nimbus 6 SCAMS data tapes of the mean 1000 mb to 250 mb temperature field around eleven tropical storms in 1975. Analysis of these data show that for turning storms, in all but one case, the turn was signaled 24 hours in advance by a significant temperature gradient perpendicular to the storm's path, at a distance of 9 deg to 13 deg in front of the storm. A thresholding technique was applied to the North Central U.S. during the summer to estimate precipitation frequency. except

Vonderhaar, T. H.↗

NASA NTRS2009

Multi-Scale Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System

The Land Information System (LIS; http://lis.gsfc.nasa.gov; Kumar et al., 2006; Peters- Lidard et al.,2007) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. As such, LIS represents a step towards the next generation land component of an integrated Earth system model. In recognition of LIS object-oriented software design, use and impact in the land surface and hydrometeorological modeling community, the LIS software was selected ase co-winner of NASA's 2005 Software of the Year award. LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has evolved from two earlier efforts North American Land Data Assimilation System (NLDAS; Mitchell et al. 2004) and Global Land Data Assimilation System (GLDAS; Rodell al. 2004) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of GLDAS and NLDAS now use specific configurations of the LIS software in their current implementations. In addition, LIS was recently transitioned into operations at the US Air Force Weather Agency (AFWA) to ultimately replace their Agricultural Meteorology (AGRMET) system, and is also used routinely by NOAA's National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) for their land data assimilation systems to support weather and climate modeling. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through "plugins,". As described in Kumar et al., 2007, and demonstrated in Case et al., 2008, and Santanello et al., 2009, LIS has been coupled to the Weather Research and Forecasting (WRF) model to support studies of land-atmosphere coupling the enabling ensembles of land surface states to be tested against multiple representations of the atmospheric boundary layer. LIS has also been demonstrated for parameter estimation as described in Peters-Lidard et al. (2008) and Santanello et al. (2007), who showed that the use of sequential remotely sensed soil moisture products can be used to derive soil hydraulic and texture properties given a sufficient dynamic range in the soil moisture retrievals and accurate precipitation inputs. LIS has also recently been demonstrated for multi-model data assimilation (Kumar et al., 2008) using an Ensemble Kalman Filter for sequential assimilation of soil moisture, snow, and temperature. Ongoing work has demonstrated the value of bias correction as part of the filter, and also that of joint calibration and assimilation. Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeoroogical modeling, assimilation and parameter estimation will be presented as advancements towards the next generation of integrated observation and modeling systems.

Peters-Lidard, Christa D.↗

NASA NTRS2011

Multi-Scale Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System

The Land Information System (LIS; http://lis.gsfc.nasa.gov) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite-and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. As such, LIS represents a step towards the next generation land component of an integrated Earth system model. In recognition of LIS object-oriented software design, use and impact in the land surface and hydrometeorological modeling community, the LIS software was selected as a co-winner of NASA?s 2005 Software of the Year award.LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has e volved from two earlier efforts -- North American Land Data Assimilation System (NLDAS) and Global Land Data Assimilation System (GLDAS) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of GLDAS and NLDAS now use specific configurations of the LIS software in their current implementations.In addition, LIS was recently transitioned into operations at the US Air Force Weather Agency (AFWA) to ultimately replace their Agricultural Meteorology (AGRMET) system, and is also used routinely by NOAA's National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) for their land data assimilation systems to support weather and climate modeling. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through "plugins". LIS has been coupled to the Weather Research and Forecasting (WRF) model to support studies of land-atmosphere coupling be enabling ensembles of land surface states to be tested against multiple representations of the atmospheric boundary layer. LIS has also been demonstrated for parameter estimation, who showed that the use of sequential remotely sensed soil moisture products can be used to derive soil hydraulic and texture properties given a sufficient dynamic range in the soil moisture retrievals and accurate precipitation inputs.LIS has also recently been demonstrated for multi-model data assimilation using an Ensemble Kalman Filter for sequential assimilation of soil moisture, snow, and temperature.Ongoing work has demonstrated the value of bias correction as part of the filter, and also that of joint calibration and assimilation.Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeorological modeling, assimilation and parameter estimation will be presented as advancements towards the next generation of integrated observation and modeling systems

Peters-Lidard, Christa D.↗

DOE OSTI2024

H ∞ Control for Energy Dispatch in Autonomous Nanogrid With Communication Delays

This paper proposes an optimal controller and estimator for energy dispatch to balance the power supply and demand considering communication delays. The proposed algorithm involves modeling an autonomous nanogrid (ANG) consisting of distributed energy resources, energy storage systems, loads, an $H$ ∞ controller with a reference power modulation technique, and a state estimator. The ANG was developed to express the dynamic supply-demand energy balance of a nanogird system. Reference power modulation was designed to generate the desired ESS power based on the imbalanced energy. Random communication delays were modeled using a stochastic variable satisfying the Bernoulli random binary distribution. The optimal $H$ ∞ controller and estimator were developed using a linear matrix inequality approach to exponentially stabilize the closed-loop system. Simulations were performed using real daily demand forecasts obtained from the Korea Meteorological Administration to demonstrate the effectiveness of the proposed real-time optimization algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Automating Rabi & Ramsey Measurements via ML

As quantum computers scale up, the manual process of qubit tune-up becomes increasingly impractical due to its time-consuming and repetitive nature. While existing research has explored some automation techniques, many models remain underutilized for this purpose. This research aims to answer the question: is qubit tune-up able to be automated using the Long Short-Term Memory (LSTM) model? For the purposes of this project, only the rabi and ramsey measurement cycle was automated. These measurements are used to fine-tune a rough qubit frequency by repeating them until the optimal qubit frequency is obtained. The LSTM model uses the qubit frequency at one time step to forecast the qubit frequency at the next time step. A rabi-ramsey simulation was made to fabricate a dataset to train and test the LSTM model. As the model was trained, the error of the model decreased. Although there wasn't enough training data to generate perfect predictions, this shows it is possible to utilize forecasting models in automating the tune-up process.

Roberts, Rachel↗

NASA NTRS1973

An investigation of the Sutcliffe development theory

Two case studies were used to test the Sutcliffe-Peterssen development theory for both cyclonic and anticyclonic development over the eastern United States. Each term was examined to determine when and where it made a significant contribution to the development process. Results indicate the advection of vorticity at the level of non-divergence exerts the dominant influence for initial cyclone development, and that the thermal terms (advection of thickness, stability, and diabatic influence) become important after development has begun. Anticyclonic development, however, depends primarily on the stability term throughout the life cycle of the anticyclone. Simple procedures for forecasting the development and movement of cyclones and anticyclones are listed. These rules indicate that routine National Meteorological Center analyses may be used to locate areas where the positive advection of 500-mb vorticity, indicative of cyclonic development, coincides with regions of severe weather activity. The development of anticyclones also is predicted easily. Regions of increasing stability, indicating anticyclonic development, may be located by use of National Meteorological Center radar summaries and analyses for 1000-500-mb thickness. A test of these techniques found them to be satisfactory for the case examined.

Dushan, J. D.↗

NASA NTRS1990

Microwave (SSM/I) Estimates of the Precipitation Rate to Improve Numerical Atmospheric Model Forecasts

Delay in the spin-up of precipitation early in numerical atmospheric forecasts is a deficiency correctable by diabatic initialization combined with diabatic forcing. For either to be effective requires some knowledge of the magnitude and vertical placement of the latent heating fields. Until recently the best source of cloud and rain water data was the remotely sensed vertical integrated precipitation rate or liquid water content. Vertical placement of the condensation remains unknown. Some information about the vertical distribution of the heating rates and precipitating liquid water and ice can be obtained from retrieval techniques that use a physical model of precipitating clouds to refine and improve the interpretation of the remotely sensed data. A description of this procedure and an examination of its 3-D liquid water products, along with improved modeling methods that enhance or speed-up storm development is discussed.

Raymond, William H.↗

NASA NTRS1999

"An On-Line Access System for TRMM Data"

The Tropical Rainfall Measuring Mission (TRMM), jointly sponsored by the National Aeronautics and Space Administration (NASA) of United States and the National Space Development Agency (NASDA) of Japan, provides visible, infrared, and microwave observations of tropical and subtropical rain system. The satellite observations are complemented by ground radar and rain gauge measurements to validate satellite rain estimation techniques. TRMM satellite was launched on November 27, 1997(EST). Data from TRMM are being archived, processed, and disseminated by DAAC at the Goddard Space Flight Center (GSFC) of NASA. The Goddard DAAC has been archiving Tropical Rainfall Measuring Mission (TRMM) data since the launch. The TRMM level 1 and level 2 products first have been released to public on June 15, 1998. Now, most of TRMM products, including some ground validation products are released to public. TRMM data are useful for forecast model research, disaster mitigation, climatological studies, agricultural predictions, and many other applications. An online system or user interface is designed for easy, friendly, and quick display, access, and ordering of the TRMM data. Components, features, and limitations of the system will be discussed and demonstrated. The TRMM standard products and variety of the subsets and ancillary data will be shown, and their availability will be discussed.

Rui, Hualan↗

NASA NTRS2014

Development and Implementation of Dynamic Scripts to Execute Cycled GSI/WRF Forecasts

The Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model and Gridpoint Statistical Interpolation (GSI) data assimilation (DA) are the operational systems that make up the North American Mesoscale (NAM) model and the NAM Data Assimilation System (NDAS) analysis used by National Weather Service forecasters. The Developmental Testbed Center (DTC) manages and distributes the code for the WRF and GSI, but it is up to individual researchers to link the systems together and write scripts to run the systems, which can take considerable time for those not familiar with the code. The objective of this project is to develop and disseminate a set of dynamic scripts that mimic the unique cycling configuration of the operational NAM to enable researchers to develop new modeling and data assimilation techniques that can be easily transferred to operations. The current version of the SPoRT GSI/WRF Scripts (v3.0.1) is compatible with WRF v3.3 and GSI v3.0.

Zavodsky, Bradley↗

NASA NTRS2014

Development and Implementation of Dynamic Scripts to Execute Cycled GSI/WRF Forecasts

Zavodsky, Bradley↗

NASA NTRS2011

Performance of Airborne Precision Spacing Under Realistic Wind Conditions and Limited Surveillance Range

With the expected worldwide increase of air traffic during the coming decade, both the Federal Aviation Administration's (FAA's) Next Generation Air Transportation System (NextGen), as well as Eurocontrol's Single European Sky ATM Research (SESAR) program have, as part of their plans, air traffic management (ATM) solutions that can increase performance without requiring time-consuming and expensive infrastructure changes. One such solution involves the ability of both controllers and flight crews to deliver aircraft to the runway with greater accuracy than they can today. Previous research has shown that time-based spacing techniques, wherein the controller assigns a time spacing to each pair of arriving aircraft, can achieve this goal by providing greater runway delivery accuracy and producing a concomitant increase in system-wide performance. The research described herein focuses on one specific application of time-based spacing, called Airborne Precision Spacing (APS), which has evolved over the past ten years. This research furthers APS understanding by studying its performance with realistic wind conditions obtained from atmospheric sounding data and with realistic wind forecasts obtained from the Rapid Update Cycle (RUC) short-range weather forecast. In addition, this study investigates APS performance with limited surveillance range, as provided by the Automatic Dependent Surveillance-Broadcast (ADS-B) system, and with an algorithm designed to improve APS performance when ADS-B surveillance data is unavailable. The results presented herein quantify the runway threshold delivery accuracy of APS under these conditions, and also quantify resulting workload metrics such as the number of speed changes required to maintain spacing.

Wieland, Frederick↗

NASA NTRS1985

Comparison of Land Cover Information from LANDSAT Multispectral Scanner (MSS) and Airborne Thematic Mapper Simulator (TMS) Data for Hydrologic Applications

Thematic mapper simulator (TMS) data produced a more accurate and spatially contiguous classification than MSS for the Clinton River Basin in Michigan. While the accuracy of the 4-band TMS data set was as good as the 7-band, the 3-band TMS data sets were also better than the MSS. The combination of bands selected based on the transformed divergence technique provided one band in each of the major regions of the spectrum: visible (band 3), near IR (band 4), middle IR (band 5) and thermal IR (band 7). These results should be viewed with some caution, since the data are from a TMS rather than the actual TM and the MSS data were obtained in early summer while the TMS was flown in late summer. The higher accuracies for the developed categories (residential and commercial) should improve the predictions of runoff in flood forecasting models and of flood damage for damage calculation models appreciably.

Gervin, J. C.↗

NASA NTRS1974

The economic value of remote sensing of earth resources from space: An ERTS overview and the value of continuity of service. Volume 3: Intensive use of living resources, agriculture. Part 2: Distribution effects

The results of an investigation of the value of improving information for forecasting future crop harvests are described. A theoretical model is developed to calculate the value of increased speed of availablitiy of that information. The analysis of U.S. domestic wheat consumption was implemented. New estimates of a demand function for wheat and of a cost of storage function were involved, along with a Monte Carlo simulation for the wheat spot and future markets and a model of market determinations of wheat inventories. Results are shown to depend critically on the accuracy of current and proposed measurement techniques.

Bradford, D. F.↗

NASA NTRS1980

Weather analysis and interpretation procedures developed for the US/Canada wheat and barley exploratory experiment

Procedures and techniques for providing analyses of meteorological conditions at segments during the growing season were developed for the U.S./Canada Wheat and Barley Exploratory Experiment. The main product and analysis tool is the segment-level climagraph which depicts temporally meteorological variables for the current year compared with climatological normals. The variable values for the segment are estimates derived through objective analysis of values obtained at first-order station in the region. The procedures and products documented represent a baseline for future Foreign Commodity Production Forecasting experiments.

Trenchard, M. H.↗

NASA NTRS2011

Incorporating JULES into NASA's Land Information System (LIS) and Investigations of Land-Atmosphere Coupling

NASA's Land Information System (LIS; lis.gsfc.nasa.gov) is a flexible land surface modeling and data assimilation framework developed over the past decade with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. LIS features a high performance and flexible design, and operates on an ensemble of land surface models for extension over user-specified regional or global domains. The extensible interfaces of LIS allow the incorporation of new domains, land surface models (LSMs), land surface parameters, meteorological inputs, data assimilation and optimization algorithms. In addition, LIS has also been demonstrated for parameter estimation and uncertainty estimation, and has been coupled to the Weather Research and Forecasting (WRF) mesoscale model. A visiting fellowship is currently underway to implement JULES into LIS and to undertake some fundamental science on the feedbacks between the land surface and the atmosphere. An overview of the LIS system, features, and sample results will be presented in an effort to engage the community in the potential advantages of LIS-JULES for a range of applications. Ongoing efforts to develop a framework for diagnosing land-atmosphere coupling will also be presented using the suite of LSM and PBL schemes available in LIS and WRF along with observations from the U. S .. Southern Great Plains. This methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

Santanello, Joseph↗