SEARCH · Engineering Papers

Results for “Improv”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

NASA NTRS2016

Crop Model Improvement Reduces the Uncertainty of the Response to Temperature of Multi-Model Ensembles

To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT worldwide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures greater than 24 C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively.

damage assessment↗

NASA NTRS2019

Orion Heat Shield Manufacturing Producibility Improvements for the EM-1 Flight Test Program

This paper describes how the Orion program is incorporating improvements in the heat shield design and manufacturing processes reducing programmatic risk and ensuring crew safety in support of NASA’s Exploration Missions. The Orion program successfully completed the EFT-1 flight test in 2014 and is currently developing the EM-1 spacecraft to meet the test objectives of an orbital mission to the moon and return to earth in 2019. Lessons learned from the EFT-1 manufacturing and flight test experience are being incorporated into a wide variety of vehicle systems and manufacturing processes to reduce risk to the Orion missions and flight crew. A critical contributor to crew safety is the heat shield that protects the crew capsule during re-entry through the earth’s atmosphere for return from deep space. The first flight test vehicle, EFT-1, was manufactured and tested in the Neil Armstrong Operations and Checkout (O&C) facility at KSC to demonstrate early risk reduction including the functionality of the Thermal Protection Systems (TPS) for capsule safe return to earth. The approach for the EFT-1 heat shield utilized a low risk approach using Apollo heritage design and manufacturing processes using an Avcoat TPS ablator with a honeycomb substrate to provide a one piece heat shield to meet the mission re-entry heating environments. The manufacturing processes used honeycomb cell injection guns which were redeveloped from the Apollo Lunar Program processes to build the EFT-1 heat shield. The completed heat shield was transported across the country by aircraft to the O&C at KSC for installation onto the capsule. The EFT-1 heat shield successfully performed its mission and experienced ~80% of the re-entry velocity (50% heating rate) for a lunar return for an Exploration Mission. The second flight test vehicle is the EM-1 mission which will have additional flight systems installed to fly to the moon and return. Heat shield design and producibility improvements have been incorporated in the EM-1 vehicle to meet deep space mission and programmatic requirements. The design continues to use the Avcoat material, but in a “block” configuration to enable improvements in the application processes as well as additional improvements in the carrier structure design and manufacturing operations. Incorporating flight test results and producibility improvements from EFT-1 for the heat shield system design and processes have improved the thermal protection capability, improved the producibility, and cost for EM-1 flight test.

Producibility↗

NASA NTRS2022

Cross-Cutting Flight Infrastructure Improvements on M2020

Mars2020 (M2020) was formulated as a mission that leveraged as much Mars Science Laboratory (MSL) heritage as possible, while focusing major new development efforts on the original and unique elements needed to accomplish the different mission objectives. Well publicized examples of high profile new developments include precision landing, the sampling and caching system, the specific instrument suite, improved mobility via Autonomous Navigation, and later the addition of the Ingenuity helicopter. Less well known are the refinements to the core flight infrastructure, primarily in the cross-cutting functions of Telecom, Avionics, Data Management, Communications Behaviors, and Parameter Management. These enhancements are introduced predominately via flight software, and represent increases in capability that justified their inclusion in an otherwise heritage-focused project environment.Perseverance’s cross-cutting flight infrastructure improvements fall into and across the following five categories. First is a trimming of the software footprint of infrastructure modules, in order to make room for memory demands elsewhere in the system. Second is the minimization of data volume to be downlinked, through various methods such as the incorporation of new compression options. Third is the maximization of the available downlink bandwidth for data, by curtailing content-less data (fill) and introducing an improved UHF proximity link protocol. Fourth is a reduction in vulnerabilities, through increased file system redundancy, robustness, and software process monitoring. Fifth is an increase in operations efficiency by lowering file system mount times, improving parallelism between simultaneous events, minimizing the time to recover from file system errors, streamlining the purging of obsolete data, and reducing the number of commands to service parameters by a factor of 100.Individually, none of the cross-cutting infrastructure improvements are likely to garner headlines, but collectively they appreciably improve the safety and operability of Perseverance over its predecessor. This paper will describe the improvements, their promise, and where applicable, their actual impact in operations.

Bohannon, Emily↗

NASA NTRSDate not supplied

Improving Ocean Reanalyses and ENSO Forecasts By Assimilation of Rain-Corrected Satellite Sea Surface Salinity Using the GMOA S2s Forecast System

The ENSO phenomenon has a significant global socio-economic impact and has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of satellite altimetry and subsurface temperature and salinity from (mostly) Argo help improve the initialization of the thermocline, while satellite SST aids in constraining surface heat-fluxes, leading to improved coupled system sub-seasonal to seasonal forecasts. However, few studies have focused on improving the near-surface density and mixing through satellite sea surface salinity (SSS) assimilation. The few ocean models that assimilate satellite SSS, bias correct to normalize towards the near-surface Argo data for expediency. This assumption is likely inadequate in rainy regions, where buoyant water forms a fresh surface lens. In previous work, we showed that adjusting SSS to bulk salinity (Sb) using the Rain Impact Model (RIM) of Santos-Garcia et al., 2014 improves the near-surface density and mixed layer depth, leading to deeper thermocline and improved NINO3.4 SST forecasts. We now utilize the Soil Moisture and Ocean Salinity rain-corrected (SMOS_RC) SSS, available in SMOS-CATDS products, to represent Sb more accurately at the first model layer (e.g., 5 m). Rather than a diffusivity model as RIM, SMOS_RC uses a statistical correction dependent on Integrated Multi-satellitE Retrievals for GPM (IMERG) rain rates, established on observed SMOS SSS decreases related to Sb in the presence of rain (Supply et al., 2020). For all experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF scheme (Penny et al., 2013). One reanalysis additionally assimilates SMOS SSS data as is, and a separate reanalysis assimilates SMOS_RC. We assess the impact on near-surface and subsurface dynamics by validating against observations and explore how SSS assimilation (SMOS vs SMOS_RC) impacts ENSO forecasts using the NASA GMAO Sub-seasonal to Seasonal coupled forecast system (S2S-v3, Molod et al. 2020). We show that improved estimates of density and near-surface mixing led to more accurate coupled air/sea interaction and better ENSO forecasts. The increased SSS, resulting from the removal of the instantaneous rain effect, modifies the ocean state by enhancing mixing and deepening the thermocline.

Veronica Ruiz Xomchuk↗

NASA NTRSDate not supplied

Improving Ocean Reanalyses and ENSO Forecasts By Assimilation of Rain Corrected Satellite Sea Surface Salinity Using the GMAO S2S Forecast System

During the past years, we have seen that the La Nina to El Nino transition has had a significant global socio-economic impact and so has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of satellite altimetry and subsurface temperature and salinity from (mostly) Argo help to improve the initialization of the thermocline, while satellite Sea Surface Temperature (SST) aids in constraining surface heat-fluxes, leading to improved subseasonal to seasonal forecasts of the coupled system. However, few studies have focused on improving the fresh-water flux and near-surface density and mixing through assimilation of satellite sea surface salinity (SSS). For expediency, the few ocean models that do assimilate SSS bias-correct the satellite SSS data to normalize towards the near-surface Argo data. However, in rainy regions, where buoyant water sits as a fresh lens at the surface, this assumption is likely inadequate. In previous work, we have shown that adjusting SSS data to bulk salinity (Sb) using the Rain Impact Model (RIM) of Santos-Garcia et al., 2014 has improved the near-surface density and mixed layer depth, leading to deeper thermocline and improved the NINO3.4 SST forecasts. Now we utilize the Soil Moisture/Ocean Salinity, Rain Corrected (SMOS_RC) SSS product provided by the Centre Aval de Traitement des données SMOS (CATDS CPDC) to represent the Sb more accurately at first model layer (in our case 5 m). Rather than using a diffusivity model as with RIM, SMOS_RC relies on an observed relationship between the spatial heterogeneity of SMOS SSS and instantaneous rain rate (RR) (Supply et al., 2020). In order to test the impact of SMOS_RC versus SMOS, we compare two reanalyses over the period 2014 to 2021. For both reanalysis experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF scheme (Penny et al., 2013). One reanalysis additionally assimilates SMOS SSS data as is (i.e., with the fresh bias), and a separate reanalysis is performed assimilating the SMOS_RC data. We assess the impact for near-surface and subsurface dynamics within ocean reanalyses by validating against observations and explore how SSS assimilation (SMOS versus SMOS_RC) impacts dynamical ENSO forecasts using the NASA GMAO Sub-seasonal to Seasonal coupled forecast system (GEOS S2S-3, Molod et al., 2020, Hackert et al., 2023). We will show that improved SSS estimates and near-surface density and mixing led to more accurate coupled air/sea interaction and better ENSO forecasts.

Eric Hackert↗

DOE OSTI2025

Better Climate Challenge Working Groups Non-Energy Benefits of Energy Projects-Improving Financial Payback

Energy efficiency is a key strategy recently identified by the United States Department of Energy as a pillar of industrial decarbonization. For manufacturing companies, improving energy efficiency will reduce money spent on energy utilities such as gas, electricity, and oil. Energy improvement projects also provide valuable benefits outside of simple operating cost reductions, such as reducing the carbon footprint, improving safety metrics and even enhancing quality and productivity. Unfortunately, energy efficiency projects have typically faced an adoption gap, even when they meet criteria such as payback period for capital projects. The inclusion and quantification of non-energy benefits (NEBs), also known as co-benefits, in the decision-making process for energy efficiency projects can improve the overall financial payback periods for those projects as well as potentially improve the company's key performance metrics aligned with business strategies. There are no readily available tools that facilitate this, however, and the most used tools for energy audits address NEBs in a perfunctory way if at all. We integrated research for finding and quantifying non-energy benefits of energy efficiency projects into a commonly recognized continuous improvement practice, the Define, Measure, Analyze, Improve and Control (DMAIC) Process. This process, along with software and supplemental materials, guides energy assessments to find and to quantify NEBs associated with energy conservation opportunities. Our aim is to deliver an easy to use and effective process and software tool and to maximize return on investment for energy efficiency projects as well as contribute to companies' strategic performance goals.

DMAIC↗

DOE OSTI2025

Projection-to-Born-improved subtractions at NNLO

While the current frontier in fixed-order precision for collider observables is N 3 LO, important steps are necessary to consolidate NNLO cross-section predictions with improved stability and efficiency. Slicing methods have been successfully applied to obtain NNLO and N 3 LO predictions, but have shown poor performance in the presence of fiducial cuts due to large kinematical power corrections. In this paper we implement Projection-to-Born-improved q T (P2B q T ) and jettiness (P2Bτ 0 ) subtractions for a large class of color singlet processes in MCFM. This method allows for the efficient evaluation of fiducial power corrections in any non-local subtraction scheme using a Projection-to-Born subtraction. We demonstrate the significant numerical improvements of this method based on fiducial Drell-Yan and Higgs cross-sections. Moreover, with fiducial power corrections removed via this method, the leading-logarithmic power corrections that have only been calculated without fiducial cuts can be included, further improving the calculations. For di-photon production with photon isolation, we devise a novel method in combination with P2B-improved subtractions, which we name P2B γ τ 0 , and P2B γ q T for the two subtraction schemes, respectively. This method allows the inclusion of both fiducial power corrections due to kinematic cuts on the photons and a set of isolation power corrections in the fragmentation channel where a quark may enter the isolation cone. We find significant improvements in the convergence of NNLO di-photon cross-sections with photon isolation cuts, demonstrating that it is possible to achieve a stable and efficient calculation of di-photon cross-sections using slicing methods.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Noble-Metal-Free, Nickel-Based Dual Functional Materials for Improved Methane Production from In Situ Carbon Dioxide Capture and Conversion

Promoters for dual functional materials have not been well explored, but promoters could improve the efficiency of the process by improving the selectivity of the CO 2 methanation process. Utilizing integrated capture and conversion, where CO 2 is captured and converted to useful products, would allow for a useful avenue to control CO 2 emissions. One such way to accomplish this would be to utilize materials that can both capture and convert CO 2 to useful products. However, these materials are often based on costly noble metals, like ruthenium and platinum, decreasing their viability on an industrial scale. Less expensive metals, for example, nickel, would allow for dual functional materials to be more readily utilized in industrial settings. Nickel-based dual functional materials often do not react with the captured CO 2 and merely desorb the CO 2 rather than form a useful product. However, promoters have not been well explored for these types of materials to improve the catalytic properties, which would be beneficial to improve nickel-based materials. Herein, we report the addition of ytterbium on a nickel-based dual functional material and the improvements to the production of methane from captured CO 2 with the incorporated ytterbium promoter. The ytterbium promoter improves the selectivity of the catalysts for the hydrogenation of captured CO 2 to methane and increases the ability for the material to capture CO 2 due to additional basic sites being formed on the surface of alumina. As a result, the 12%Ni/4%Yb/6%Na 2 O/Al 2 O 3 catalyst was utilized to capture carbon dioxide and then convert the captured CO 2 to methane over five cycles, where both the amount captured and the amount converted remained stable, indicating the stability of the material over long-term use.

Catalysts↗

DOE OSTI2025

Improving the Transportability of a Deep Learning Denoising Model Using Transfer Learning Techniques

The adoption of machine learning techniques in the seismology community has led to great performance improvements in several areas, including signal processing. Specifically, the development of deep learning–based seismic waveform denoising models has the potential to yield improvements in signal detection capabilities for networks operating in particularly noisy environments. Recent advancements in the design of these deep learning denoising models have included the incorporation of continuous and discrete wavelet transform functions into the network architecture to improve the learning capabilities and efficiency of said models. These wavelet transform–based seismic denoising models have shown improved denoising capabilities in regions where there is good agreement between the data features present in the training and evaluation datasets. However, questions remain about the overall transportability of these models to other monitoring regions. Here, in this study, we will determine the baseline transportability of a newly developed multilevel wavelet‐transform convolutional neural network (MWCNN) seismic denoising model. We accomplish this by taking a version of the MWCNN denoising model trained on data collected from the Utah region and evaluating its denoising performance on datasets collected from the neighboring Nevada region, which differ with regard to monitoring sensor types and event histories. We find that there is a notable variability in denoising performance related to the degree of similarity between the initial and new target datasets. The most notable difference in denoising performance is the ability of the denoising model to preserve accurate amplitude information associated with the signal energy present in the waveform data. Finally, we evaluate the ability of transfer learning techniques to improve the transportability of the MWCNN denoising model. We find that although there is still a performance gap present in the denoising results of the MWCNN model, transfer learning did yield improved results.

Quinones, Louis [Sandia National Laboratories (SNL↗

DOE OSTI2026

Prediction of non-intuitive metabolic targets with bayesian metabolic control analysis to improve 3-hydroxypropionic acid production in Aspergillus niger

Development of efficient bioconversion processes is limited by the ability to predictably improve metabolic flux. Here we deployed Bayesian Metabolic Control Analysis as a platform to integrate multi-omics data with metabolic modeling and evaluated its ability to predict genetic interventions that improve metabolic flux. Global Metabolomics and proteomics data was collected from 17 Aspergillus niger strains engineered to produce the platform biochemical 3-hydroxypropionic acid from which seven actional genetic interventions were predicted from significant flux control coefficients. Of the suggested genetic interventions, two were present within the intuitively designed strains used for training (malonic semialdehyde dehydrogenase and pyruvate carboxylase) while five predicted targets were present within non-intuitive areas of the metabolic network including 5-formyltetrahydrofolate deformylase and four mitochondrial enzymes, alcohol dehydrogenase, succinyl-CoA ligase, aspartate aminotransferase, and malate dehydrogenase. Six of the targets were validated in the highest performing 3-HP strain used for multi-omics data generation which contained a prior disruption of the highest scoring target malonic semialdehyde dehydrogenase. Predicted directional perturbation of five of the six tested targets significantly improved titer and rate of 3-HP production and two significantly improved yield. The greatest improvements were observed following disruption of the non-intuitive target succinyl-CoA ligase which increased titer by 39% and yield by 29% (to 20.4 g/L 3-HP and 0.31 g 3-HP/g glucose) over the strains used for training. This study demonstrates the utility of Bayesian Metabolic Control Analysis and highlights the ability to predict meaningful genetic targets in unexpected areas of metabolism to improve engineered strains for bioconversion.

3-hydroxypropionic acid↗

NASA NTRS1978

Fuel consumption improvement in current transport engines

A review is conducted of improvements which can be made with respect to the fuel consumption of current engines and new production versions of current engines. A description is presented of an engine diagnostics program which has the objective to identify and quantify the causes and sources of performance deterioration in the JT9D turbofan engine and to develop basic data which will be applied to minimize performance degradation of current and future engines. General areas where performance losses occur are examined, taking into account seals, blades and vanes, and cases. Potential performance improvement concepts are related to improved component aerodynamics, improved flowpath sealing, blade tip clearance control, improved turbine cooling effectiveness, improved turbine materials and coatings, duct and nozzle aerodynamic refinements, nacelle aerodynamic refinements, forced exhaust mixers, advanced nacelle materials, and advanced fuel control.

Hines, R. W.↗

NASA NTRS1978

NASA/General Electric Engine Component Improvement Program

The Engine Component Improvement (ECI) Program has been initiated in connection with projects designed to reduce the impact of the world-wide energy crisis in the area of aviation. The two parts of the ECI program have the overall objective to identify and quantify the sources and causes of CF6 engine performance deterioration, and to reduce the fuel consumption of CF6 engines through the development and the incorporation of various performance improvement concepts. The CF6 high-bypass turbofan engine was selected as a basis for this effort, since it is expected to be a significant fuel user in commercial revenue service for the next 15 to 20 years. The first part of the ECI program represents the initial step in an effort to achieve a goal of five percent reduction in fuel usage for CF6 engines in the 1979-82 time period. The first performance improvement concept selected is an improved efficiency fan blade. Other improvements are related to a short core exhaust system and an improved high pressure turbine.

Albright, A. J.↗

NASA NTRS1989

Tailless aircraft performance improvements with relaxed static stability

The purpose is to determine the tailless aircraft performance improvements gained from relaxed static stability, to quantify this potential in terms of range-payload improvements, and to identify other possible operational and handling benefits or problems. Two configurations were chosen for the study: a modern high aspect ratio, short-chord wing proposed as a high-altitude long endurance (HALE) remotely piloted vehicle; a wider, lower aspect ratio, high volume wing suitable for internal stowage of all fuel and payload required for a manned long-range reconnaissance mission. Flying at best cruise altitude, both unstable configurations were found to have a 14 percent improvement in range and a 7 to 9 percent improvement in maximum endurance compared to the stable configurations. The unstable manned configuration also shows a 15 percent improvement in the 50 ft takeoff obstacle distance and an improved height response to elevator control. However, it is generally more deficient in control power due to its larger adverse aileron yaw and its higher takeoff and landing lift coefficient C(sub L), both due to the downward trimmed (vs. upward trimmed for stable configurations) trailing edge surfaces.

Ashkenas, Irving L.↗

NASA NTRS1990

Current Emergency Locator Transmitter (ELT) deficiencies and potential improvements utilizing TSO-C91a ELTs

An analysis was conducted of current ELT problems and potential improvements that could be made by employing the TSO-C91a ELTs to replace the current TSO-C91 ELTs. The scope of the study included the following: (1) validate the problems; (2) determine specific failure causes; (3) determine false alarm causes; (4) estimate improvements from TSO-C91a; (5) estimate benefits from replacement of the current ELTs; and (6) determine need and benefits for improved ELT inspection and maintenance. A detailed comparison between the two requirements documents (TSO-C91 and -91a) was made to assess improved performance of the ELT in each category of failure cause and each cause of false alarms. The comparison and analysis resulted in projecting a success of operation rate approximately 3 times the current rate and a reduction in false alarms to 0.25 of those generated by TSO-C91 ELTs. These improvements led to a projection of benefits of approximately 25 additional lives to be saved each year with TSO-C91a ELTs and an improved inspection and maintenance program.

Trudell, Bernard J.↗

NASA NTRS1993

Investigation for improving Global Positioning System (GPS) orbits using a discrete sequential estimator and stochastic models of selected physical processes

GEODYNII is a conventional batch least-squares differential corrector computer program with deterministic models of the physical environment. Conventional algorithms were used to process differenced phase and pseudorange data to determine eight-day Global Positioning system (GPS) orbits with several meter accuracy. However, random physical processes drive the errors whose magnitudes prevent improving the GPS orbit accuracy. To improve the orbit accuracy, these random processes should be modeled stochastically. The conventional batch least-squares algorithm cannot accommodate stochastic models, only a stochastic estimation algorithm is suitable, such as a sequential filter/smoother. Also, GEODYNII cannot currently model the correlation among data values. Differenced pseudorange, and especially differenced phase, are precise data types that can be used to improve the GPS orbit precision. To overcome these limitations and improve the accuracy of GPS orbits computed using GEODYNII, we proposed to develop a sequential stochastic filter/smoother processor by using GEODYNII as a type of trajectory preprocessor. Our proposed processor is now completed. It contains a correlated double difference range processing capability, first order Gauss Markov models for the solar radiation pressure scale coefficient and y-bias acceleration, and a random walk model for the tropospheric refraction correction. The development approach was to interface the standard GEODYNII output files (measurement partials and variationals) with software modules containing the stochastic estimator, the stochastic models, and a double differenced phase range processing routine. Thus, no modifications to the original GEODYNII software were required. A schematic of the development is shown. The observational data are edited in the preprocessor and the data are passed to GEODYNII as one of its standard data types. A reference orbit is determined using GEODYNII as a batch least-squares processor and the GEODYNII measurement partial (FTN90) and variational (FTN80, V-matrix) files are generated. These two files along with a control statement file and a satellite identification and mass file are passed to the filter/smoother to estimate time-varying parameter states at each epoch, improved satellite initial elements, and improved estimates of constant parameters.

Goad, Clyde C.↗

NASA NTRS1993

The maturing of the quality improvement paradigm in the SEL

The Software Engineering Laboratory uses a paradigm for improving the software process and product, called the quality improvement paradigm. This paradigm has evolved over the past 18 years, along with our software development processes and product. Since 1976, when we first began the SEL, we have learned a great deal about improving the software process and product, making a great many mistakes along the way. Quality improvement paradigm, as it is currently defined, can be broken up into six steps: characterize the current project and its environment with respect to the appropriate models and metrics; set the quantifiable goals for successful project performance and improvement; choose the appropriate process model and supporting methods and tools for this project; execute the processes, construct the products, and collect, validate, and analyze the data to provide real-time feedback for corrective action; analyze the data to evaluate the current practices, determine problems, record findings, and make recommendations for future project improvements; and package the experience gained in the form of updated and refined models and other forms of structured knowledge gained from this and prior projects and save it in an experience base to be reused on future projects.

Basili, Victor R.↗

NASA NTRS1994

Continual improvement plan

NASA's approach to continual improvement (CI) is a systems-oriented, agency-wide approach that builds on the past accomplishments of NASA Headquarters and its field installations and helps achieve NASA's vision, mission, and values. The NASA of the future will fully use the principles of continual improvement in every aspect of its operations. This NASA CI plan defines a systematic approach and a model for continual improvement throughout NASA, stressing systems integration and optimization. It demonstrates NASA's constancy of purpose for improvement - a consistent vision of NASA as a worldwide leader in top-quality science, technology, and management practices. The CI plan provides the rationale, structures, methods, and steps, and it defines NASA's short term (1-year) objectives for improvement. The CI plan presents the deployment strategies necessary for cascading the goals and objectives throughout the agency. It also provides guidance on implementing continual improvement with participation from top leadership and all levels of employees.

Source record↗

NASA NTRS1994

Improvements to TOVS retrievals over sea ice and applications to estimating Arctic energy fluxes

Modeling studies suggest that polar regions play a major role in modulating the Earth's climate and that they may be more sensitive than lower latitudes to climate change. Until recently, however, data from meteorological stations poleward of 70 degs have been sparse, and consequently, our understanding of air-sea-ice interaction processes is relatively poor. Satellite-borne sensors now offer a promising opportunity to observe polar regions and ultimately to improve parameterizations of energy transfer processes in climate models. This study focuses on the application of the TIROS-N operational vertical sounder (TOVS) to sea-ice-covered regions in the nonmelt season. TOVS radiances are processed with the improved initialization inversion ('3I') algorithm, providng estimates of layer-average temperature and moisture, cloud conditions, and surface characteristics at a horizontal resolution of approximately 100 km x 100 km. Although TOVS has flown continuously on polar-orbiting satellites since 1978, its potential has not been realized in high latitudes because the quality of retrievals is often significantly lower over sea ice and snow than over the surfaces. The recent availability of three Arctic data sets has provided an opportunity to validate TOVS retrievals: the first from the Coordinated Eastern Arctic Experiment (CEAREX) in winter 1988/1989, the second from the LeadEx field program in spring 1992, and the third from Russian drifting ice stations. Comparisons with these data reveal deficiencies in TOVS retrievals over sea ice during the cold season; e.g., ice surface temperature is often 5 to 15 K too warm, microwave emissivity is approximately 15% too low at large view angles, clear/cloudy scenes are sometimes misidentified, and low-level inversions are often not captured. In this study, methods to reduce these errors are investigated. Improvements to the ice surface temperature retrieval have reduced rms errors from approximately 7 K to 3 K; correction of microwaves (50 GHz) brightness temperatures for view angle dependence now allows the surface type (open water versus sea ice) to be determined; modifications to the clear/cloud tests have improved cloud detection over sea ice, especially where it is inhomogeneous; and improved surface temperature estimates have resulted in more successful diagnoses of low-level stratification. Applications of improved retrievals to studies of the Arctic energy budget are encouraging. Preliminary calculations of air-ice stress vectors, 10-m wind speed, and horizontal advection of heat and moisture are presented.

Francis, Jennifer A.↗