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NASA NTRS1993

A database for propagation models

The NASA Propagation Program supports academic research that models various propagation phenomena in the space research frequency bands. NASA supports such research via school and institutions prominent in the field. The products of such efforts are particularly useful for researchers in the field of propagation phenomena and telecommunications systems engineers. The systems engineer usually needs a few propagation parameter values for a system design. Published literature on the subject, such as the Cunsultative Committee for International Radio (CCIR) publications, may help somewhat, but often times, the parameter values given in such publications use a particular set of conditions which may not quite include the requirements of the system design. The systems engineer must resort to programming the propagation phenomena model of interest and to obtain the parameter values to be used in the project. Furthermore, the researcher in the propagation field must then program the propagation models either to substantiate the model or to generate a new model. The researcher or the systems engineer must either be a skillful computer programmer or hire a programmer, which of course increases the cost of the effort. An increase in cost due to the inevitable programming effort may seem particularly inappropriate if the data generated by the experiment is to be used to substantiate the already well-established models, or a slight variation thereof. To help researchers and the systems engineers, it was recommended by the participants of NASA Propagation Experimenters (NAPEX) 15 held in London, Ontario, Canada on 28-29 June 1991, that propagation software should be constructed which will contain models and prediction methods of most propagation phenomenon. Moreover, the software should be flexible enough for the user to make slight changes to the models without expending a substantial effort in programming.

Kantak, Anil V.↗
NASA NTRS2009

The ESA/NASA Multi-Aircraft ATV-1 Re-Entry Campaign: Analysis of Airborne Intensified Video Observations from the NASA/JSC Experiment

In September 2008, a joint ESA/NASA multi-instrument airborne observing campaign was conducted over the Southern Pacific ocean. The objective was the acquisition of data to support detailed atmospheric re-entry analysis for the first flight of the European Automated Transfer Vehicle (ATV)-1. Skilled observers were deployed aboard two aircraft which were flown at 12.8 km altitude within visible range of the ATV-1 re-entry zone. The observers operated a suite of instruments with low-light-level detection sensitivity including still cameras, high speed and 30 fps video cameras, and spectrographs. The collected data has provided valuable information regarding the dynamic time evolution of the ATV-1 re-entry fragmentation. Specifically, the data has satisfied the primary mission objective of recording the explosion of ATV-1's primary fuel tank and thereby validating predictions regarding the tanks demise and the altitude of its occurrence. Furthermore, the data contains the brightness and trajectories of several hundred ATV-1 fragments. It is the analysis of these properties, as recorded by the particular instrument set sponsored by NASA/Johnson Space Center, which we present here.

Barker, Ed↗
NASA NTRS2015

Completing the Feedback Loop: The Impact of Chlorophyll Data Assimilation on the Ocean State

In anticipation of the integration of a full biochemical model into the next generation GMAO coupled system, an intermediate solution has been implemented to estimate the penetration depth (1Kd_PAR) of ocean radiation based on the chlorophyll concentration. The chlorophyll is modeled as a tracer with sources-sinks coming from the assimilation of MODIS chlorophyll data. Two experiments were conducted with the coupled ocean-atmosphere model. In the first, climatological values of Kpar were used. In the second, retrieved daily chlorophyll concentrations were assimilated and Kd_PAR was derived according to Morel et al (2007). No other data was assimilated to isolate the effects of the time-evolving chlorophyll field. The daily MODIS Kd_PAR product was used to validate the skill of the penetration depth estimation and the MERRA-OCEAN re-analysis was used as a benchmark to study the sensitivity of the upper ocean heat content and vertical temperature distribution to the chlorophyll input. In the experiment with daily chlorophyll data assimilation, the penetration depth was estimated more accurately, especially in the tropics. As a result, the temperature bias of the model was reduced. A notably robust albeit small (2-5 percent) improvement was found across the equatorial Pacific ocean, which is a critical region for seasonal to inter-annual prediction.

MERRA-OCEAN↗
NASA NTRS2018

How Can TOLNet Help to Better Understand Tropospheric Ozone? A Satellite Perspective

Potential sources of a priori ozone (O3) profiles for use in Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite tropospheric O3 retrievals are evaluated with observations from multiple Tropospheric Ozone Lidar Network (TOLNet) systems in North America. An O3 profile climatology (tropopause-based O3 climatology (TB-Clim), currently proposed for use in the TEMPO O3 retrieval algorithm) derived from ozonesonde observations and O3 profiles from three separate models (operational Goddard Earth Observing System (GEOS-5) Forward Processing (FP) product, reanalysis product from Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), and the GEOS-Chem chemical transport model (CTM)) were: 1) evaluated with TOLNet measurements on various temporal scales (seasonally, daily, hourly) and 2) implemented as a priori information in theoretical TEMPO tropospheric O3 retrievals in order to determine how each a priori impacts the accuracy of retrieved tropospheric (0-10 km) and lowermost tropospheric (LMT, 0-2 km) O3 columns. We found that all sources of a priori O3 profiles evaluated in this study generally reproduced the vertical structure of summer-averaged observations. However, larger differences between the a priori profiles and lidar observations were observed when evaluating inter-daily and diurnal variability of tropospheric O3. The TB-Clim O3 profile climatology was unable to replicate observed inter-daily and diurnal variability of O3 while model products, in particular GEOS-Chem simulations, displayed more skill in reproducing these features. Due to the ability of models, primarily the CTM used in this study, on average to capture the inter-daily and diurnal variability of tropospheric and LMT O3 columns, using a priori profiles from CTM simulations resulted in TEMPO retrievals with the best statistical comparison with lidar observations. Furthermore, important from an air quality perspective, when high LMT O3 values were observed, using CTM a priori profiles resulted in TEMPO LMT O3 retrievals with the least bias. The application of time-specific (non-climatological) hourly/daily model predictions as the a priori profile in TEMPO O3 retrievals will be best suited when applying this data to study air quality or event-based processes as the standard retrieval algorithm will still need to use a climatology product. Follow-on studies to this work are currently being conducted to investigate the application of different CTM-predicted O3 climatology products in the standard TEMPO retrieval algorithm. Finally, similar methods to those used in this study can be easily applied by TEMPO data users to recalculate tropospheric O3 profiles provided from the standard retrieval using a different source of a priori.

Satellite↗
NASA NTRS2004

Seasonal-to-Interannual Variability and Land Surface Processes

Atmospheric chaos severely limits the predictability of precipitation on subseasonal to interannual timescales. Hope for accurate long-term precipitation forecasts lies with simulating atmospheric response to components of the Earth system, such as the ocean, that can be predicted beyond a couple of weeks. Indeed, seasonal forecasts centers now rely heavily on forecasts of ocean circulation. Soil moisture, another slow component of the Earth system, is relatively ignored by the operational seasonal forecasting community. It is starting, however, to garner more attention. Soil moisture anomalies can persist for months. Because these anomalies can have a strong impact on evaporation and other surface energy fluxes, and because the atmosphere may respond consistently to anomalies in the surface fluxes, an accurate soil moisture initialization in a forecast system has the potential to provide additional forecast skill. This potential has motivated a number of atmospheric general circulation model (AGCM) studies of soil moisture and its contribution to variability in the climate system. Some of these studies even suggest that in continental midlatitudes during summer, oceanic impacts on precipitation are quite small relative to soil moisture impacts. The model results, though, are strongly model-dependent, with some models showing large impacts and others showing almost none at all. A validation of the model results with observations thus naturally suggests itself, but this is exceedingly difficult. The necessary contemporaneous soil moisture, evaporation, and precipitation measurements at the large scale are virtually non-existent, and even if they did exist, showing statistically that soil moisture affects rainfall would be difficult because the other direction of causality - wherein rainfall affects soil moisture - is unquestionably active and is almost certainly dominant. Nevertheless, joint analyses of observations and AGCM results do reveal some suggestions of land-atmosphere feedback in the observational record, suggestions that soil moisture can affect precipitation over seasonal timescales and across certain large continental areas. The strength of this observed feedback in nature is not large but is still significant enough to be potentially useful, e.g., for forecasts. This talk will address all of these issues. It will begin with a brief overview of land surface modeling in atmospheric models but will then focus on recent research - using both observations and models - into the impact of land surface processes on variability in the climate system.

Koster, Randal↗
NASA NTRS2010

Aerosol Observability and Predictability: From Research to Operations for Chemical Weather Forecasting. Lagrangian Displacement Ensembles for Aerosol Data Assimilation

A challenge common to many constituent data assimilation applications is the fact that one observes a much smaller fraction of the phase space that one wishes to estimate. For example, remotely sensed estimates of the column average concentrations are available, while one is faced with the problem of estimating 3D concentrations for initializing a prognostic model. This problem is exacerbated in the case of aerosols because the observable Aerosol Optical Depth (AOD) is not only a column integrated quantity, but it also sums over a large number of species (dust, sea-salt, carbonaceous and sulfate aerosols. An aerosol transport model when driven by high-resolution, state-of-the-art analysis of meteorological fields and realistic emissions can produce skillful forecasts even when no aerosol data is assimilated. The main task of aerosol data assimilation is to address the bias arising from inaccurate emissions, and Lagrangian misplacement of plumes induced by errors in the driving meteorological fields. As long as one decouples the meteorological and aerosol assimilation as we do here, the classic baroclinic growth of error is no longer the main order of business. We will describe an aerosol data assimilation scheme in which the analysis update step is conducted in observation space, using an adaptive maximum-likelihood scheme for estimating background errors in AOD space. This scheme includes e explicit sequential bias estimation as in Dee and da Silva. Unlikely existing aerosol data assimilation schemes we do not obtain analysis increments of the 3D concentrations by scaling the background profiles. Instead we explore the Lagrangian characteristics of the problem for generating local displacement ensembles. These high-resolution state-dependent ensembles are then used to parameterize the background errors and generate 3D aerosol increments. The algorithm has computational complexity running at a resolution of 1/4 degree, globally. We will present the result of assimilating AOD retrievals from MODIS (on both Aqua and TERRA satellites) from AERONET for validation. The impact on the GEOS-5 Aerosol Forecasting will be fully documented.

da Silva, Arlindo↗
NASA NTRS1999

Seasonal Predictions with the GEOS GCM

A number of ensembles of seasonal forecasts have recently been completed as part of NASA's Seasonal to Interannual Prediction Project (NSIPP). The focus is on the extratropical response of the atmosphere to observed Surface Sea Temperature (SST) anomalies during boreal winter. The prediction experiments consist of nine forecasts starting from slightly different initial conditions for each year of the 15 year period 1981-95, employing version 2 of the Goddard Earth Observing System (GEOS) atmospheric Global Circulation Models (GCM). The initial conditions are obtained from the NASA GEOS-1 reanalysis data. Comparisons with a companion set of six long-term simulations with observed SST (starting in 1978, so they have no memory of the initial conditions for the periods of interest) are used to assess the relative contributions of the initial conditions and SST anomalies to forecast skill ranging from daily to seasonal time scales. The ensembles are used to isolate the signal, and to assess the nature of the inherent variability (noise) of the forecasts.

Schubert, Siegfried↗
NASA NTRSDate not supplied

Impact Assessment of All-Sky TROPICS Microwave Observations on the NASA GEOS Analyses and Forecasts and Progress to Use the Data in the JEDI-GEOS Analysis System

The NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission is a constellation of small satellites carrying state-of-art microwave temperature and humidity sounders with 12 channels between 91 GHz and 205 GHz frequency. Including TROPICS-pathfinder, launched on 30 June 2021, five TROPICS CubeSats operate and provide temperature and humidity data to NWP and atmospheric retrieval communities. This study is dedicated to evaluating the impact of the TROPICS satellite constellation microwave observations in numerical weather prediction (NWP) using the NASA Goddard Earth Observing System (GEOS). The TROPICS-01 (TROPICS-Pathfinder), TROPICS-03, TROPICS-05, and TROPICS-06 data in all-sky conditions over the ocean during the period of 25 July 2023 and 6 September 2023 are used for assessing forecast impacts on global NWP analysis and five-day forecasts. A series of experiments are carried out to measure the benefits of assimilating observations from only temperature sounders, water vapor sounders, and both sounders. Statistical analysis of the Observing System Experiments (OSEs) results has shown incremental improvements in global model forecast skills for critical geophysical parameters, including temperature, winds, and geopotential heights. The results demonstrate the potential of the TROPICS-like data to positively impact NWP by adding new information to the current observation and forecast system. In another set of experiments, the TROPICS-03, TROPICS-05, and TROPICS-06 data sets are added to the TROPICS-01 one by one to evaluate the impacts of increasing the revisit rate of TROPICS satellite measurements on NWP analysis for a tropical cyclone’s dynamical and microphysical structures. This study offers important insights into the capabilities of a new generation of small satellite microwave radiometers based on emerging technologies, including their unique measurements at 118 GHz and 205 GHz that are not available in traditional operational microwave sounders. Finally, the efforts to implement these new developments for TROPICS in the JEDI-GEOS atmospheric data assimilation system are in progress, and preliminary results from cycled JEDI-GEOS data assimilation experiments are presented.

Min-Jeong Kim↗
DOE OSTI2025

HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network

Machine learning (ML) is emerging as a promising tool for modeling hydro-ecological processes due to the increasing availability of large environmental data. However, the use of ML requires sufficient programming knowledge due to a lack of a graphical user interface (GUI). In this study, we introduced a GUI package, named HydroEcoLSTM, with the long short-term memory network (LSTM) as the core model, that allows non-ML experts to utilize their domain knowledge to construct complex ML models. We demonstrated the functionalities of HydroEcoLSTM with two practical examples, including (1) predictions of streamflow in both gauged and ungauged catchments and (2) predictions of multiple outputs (i.e., streamflow and isotope transport from two catchments). The simulation results obtained in both case experiments are satisfactory. In the first example, the average Nash–Sutcliffe Efficiency (NSE) for streamflow simulation during the testing period is 0.79 while the application of the trained model in two assumed ungauged catchments also achieves the average NSE of 0.68. In the second example, the average NSE for streamflow and instream isotope simulation during the testing period is 0.71. Ultimately, applications of HydroEcoLSTM with real-world examples demonstrate its potential use for practical applications and research without requiring extensive coding skills.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1998

Comparison Of Human Modelling Tools For Efficiency Of Prediction Of EVA Tasks

Construction of the International Space Station (ISS) will require extensive extravehicular activity (EVA, spacewalks), and estimates of the actual time needed continue to rise. As recently as September, 1996, the amount of time to be spent in EVA was believed to be about 400 hours, excluding spacewalks on the Russian segment. This estimate has recently risen to over 1100 hours, and it could go higher before assembly begins in the summer of 1998. These activities are extremely expensive and hazardous, so any design tools which help assure mission success and improve the efficiency of the astronaut in task completion can pay off in reduced design and EVA costs and increased astronaut safety. The tasks which astronauts can accomplish in EVA are limited by spacesuit mobility. They are therefore relatively simple, from an ergonomic standpoint, requiring gross movements rather than time motor skills. The actual tasks include driving bolts, mating and demating electric and fluid connectors, and actuating levers; the important characteristics to be considered in design improvement include the ability of the astronaut to see and reach the item to be manipulated and the clearance required to accomplish the manipulation. This makes the tasks amenable to simulation in a Computer-Assisted Design (CAD) environment. For EVA, the spacesuited astronaut must have his or her feet attached on a work platform called a foot restraint to obtain a purchase against which work forces may be actuated. An important component of the design is therefore the proper placement of foot restraints.

Dischinger, H. Charles, Jr.↗
NASA NTRS2026

Smart Process Planning for Automated Fiber Placement

Many industries, including aerospace, automotive, wind energy, maritime, and sporting goods, rely on strong, lightweight materials called composites. These materials are made by layering fibers, which can come in the form of narrow strips or wider sheets, and setting them in a polymer matrix. One of the most advanced ways to make these parts is through automated fiber placement, where a machine lays down the fibers in precise patterns. This method can create very efficient and strong designs, but it is complex, expensive, and often depends heavily on the experience of skilled engineers. Today, the design, manufacturing, and inspection stages of composite production are usually handled separately. This separation means that important information, such as how a part will be built or what defects might occur, is not always shared between stages. As a result, parts may not be as lightweight, strong, or defect-free as possible, and the process can take longer and cost more. This research develops a smart process planning system that connects design, manufacturing, and inspection into one continuous process. Built as software that works with existing tools, the system can automatically plan how the fibers are placed, predicting and reducing defects while improving both manufacturability and strength. The system optimizes not only individual layers but also how defects are distributed across all layers, preventing them from stacking up in ways that weaken the final part. It also uses inspection results from completed parts to improve future designs, creating a feedback loop where each stage informs the others. The system was tested by designing a composite panel using this new approach and comparing it to a panel made with state-of-the-art manual planning methods. The results showed that the system could intentionally control where defects appeared and increase the efficiency of the planning process. By unifying design, manufacturing, and inspection, this research shows a way to make advanced composite manufacturing more efficient, consistent, and cost-effective. This approach lowers the barrier to using automated fiber placement and opens the door for its wider adoption not only in aerospace but also in industries such as automotive, wind energy, maritime, and sporting goods, where strong and lightweight structures are essential.

Computer-Aided Process Planning↗
DOE OSTI2025

Evaluating mesoscale model predictions of diurnal speedup events in the Altamont Pass Wind Resource Area of California

Mesoscale model predictions of wind, turbulence, and wind energy capacity factors are evaluated in the Altamont Pass Wind Resource Area of California (APWRA), where the diurnal regional sea breeze and associated terrain-driven speedup flows drive wind energy production during the summer months. Results from the Weather Research and Forecasting model version 4.4 using a novel three-dimensional planetary boundary layer (3D PBL) scheme, which treats both vertical and horizontal turbulent mixing, are compared to those using a well-established one-dimensional (1D) scheme that treats only vertical turbulent mixing. Each configuration is evaluated over a nearly 3-month-long period during the Hill Flow Study, and due to the recurring nature of the observed speedup flows, diurnal composite averaging is used to capture robust trends in model performance. Both model configurations showed similar overall skill. The general timing and direction of the speedup flows is captured, but their magnitude is overestimated within a typical wind turbine rotor layer. Both also fail to capture a persistent observed near-surface jet-like flow, likely due to the limited grid resolution that is typical of mesoscale models. However, the 3D PBL configuration shows several minor improvements over the 1D PBL configuration, including improved wind speed and turbulence kinetic energy profiles during the accelerating phase of the speedup events, as well as reduced positive wind speed bias at surface stations across the APWRA region. Using a mesoscale wind farm parameterization, modeled capacity factors are also compared to monthly data reported to the US Energy Information Administration (EIA) during the study period. Although the monthly trend in the data is captured, both model configurations overestimate capacity factors by roughly 7 %–11 %. Through model evaluation, this study provides confidence in the 3D PBL scheme for wind energy applications in complex terrain and provides guidance for future testing.

17 WIND ENERGY↗
NASA NTRS2001

Psychosocial issues in long-term space flight: overview

Anecdotal evidence of the individual and interpersonal problems that occurred during the Shuttle-Mir Space Program (SMSP) and other long-duration Russian/Soviet missions, and studies of personnel in other isolated and confined extreme (ICE) environments suggest that psychosocial elements of behavior and performance are likely to have a significant impact on the outcome of long-duration missions in space. This impact may range from individual decrements in performance, health and well being, to catastrophic mission failure. This paper reviews our current understanding of the psychosocial issues related to long duration space missions according to three different domains of behavior: the individual domain, the interpersonal domain and the organizational domain. Individual issues include: personality characteristics that predict successful performance, stress due to isolation and confinement and its effect on emotions and cognitive performance, adaptive and maladaptive coping styles and strategies, and requirements for the psychological support of astronauts and their families during the mission. Interpersonal issues include: impact of crew diversity and leadership styles on small group dynamics, adaptive and maladaptive features of ground-crew interactions, and processes of crew cohesion, tension and conflict. Organizational issues include: the influence of organizational culture and mission duration on individual and group performance, and managerial requirements for long duration missions. Improved screening and selection of astronaut candidates, leadership, coping and interpersonal skills training of personnel, and organizational change are key elements in the prevention of performance decrements on long-duration missions.

Review, Tutorial↗
NASA NTRS2022

A Coupled Evaluation of Operational MODIS and Model Aerosol Products for Maritime Environments Using Sun Photometry: Evaluation of the Fine and Coarse Mode

Although satellite retrievals and data assimilation have progressed to where there is a good skill for monitoring maritime Aerosol Optical Depth (AOD), there remains uncertainty in achieving further degrees of freedom, such as distinguishing fine and coarse mode dominated species in maritime environments (e.g., coarse mode sea salt and dust versus fine mode terrestrial anthropogenic emissions, biomass burning, and maritime secondary production). For the years 2016 through 2019, we performed an analysis of 550 nm total AOD550, fine mode AOD (FAOD550; also known as FM AOD in the literature), coarse mode AOD (CAOD550), and fine mode fraction (η550) between Moderate Resolution Spectral Imaging Radiometer (MODIS) V6.1 MOD/MYD04 dark target aerosol retrievals and the International Cooperative for Aerosol Prediction (ICAP) core four multi-model consensus (C4C) of analyses/short term forecasts that assimilate total MODIS AOD550. Differences were adjudicated by the global shipboard Maritime Aerosol Network (MAN) and selected island AERONET sun photometer observations with the application of the spectral deconvolution algorithm (SDA). Through a series of conditional and regional analyses, we found divergence included regions of terrestrial influence and latitudinal dependencies in the remote oceans. Notably, MODIS and the C4C and its members, while having good correlations overall, have a persistent +0.04 to +0.02 biases relative to MAN and AERONET for typical AOD550 values (84th% < 0.28), with the C4C underestimating significant events thereafter. Second, high biases in AOD550 are largely associated with the attribution of the fine mode in satellites and models alike. Thus, both MODIS and C4C members are systematically overestimating AOD550 and FAOD550 but perform better in characterizing the CAOD550. Third, for MODIS, findings are consistent with previous reports of a high bias in the retrieved Ångström Exponent, and we diagnosed both the optical model and cloud masking as likely causal factors for the AOD550 and FAOD550 high bias, whereas for the C4C, it is likely from secondary overproduction and perhaps numerical diffusion. Fourth, while there is no wind-speed-dependent bias for surface winds <12 m s−1, the C4C and MODIS AOD550s also overestimate CAOD550 and FAOD550, respectively, for wind speeds above 12 m/s. Finally, sampling bias inherent in MAN, as well as other circumstantial evidence, suggests biases in MODIS are likely MODIS and the C4C products have their own strengths and challenges for a given climate application and discuss needed research. even larger than what was diagnosed here. We conclude with a discussion on how

Aerosols↗
DOE OSTI2024

Development of a Full-Scale Connected U-Net for Reflectivity Inpainting in Spaceborne Radar Blind Zones

CloudSat’s Cloud Profiling Radar is a valuable tool for remotely monitoring high-latitude snowfall, but its ability to observe hydrometeor activity near the Earth’s surface is limited by a radar blind zone caused by ground clutter contamination. This study presents the development of a deeply supervised U-Net-style convolutional neural network to predict cold season reflectivity profiles within the blind zone at two Arctic locations. The network learns to predict the presence and intensity of near-surface hydrometeors by coupling latent features encoded in blind zone-aloft clouds with additional context from collocated atmospheric state variables (i.e., temperature, specific humidity, and wind speed). Results show that the U-Net predictions outperform traditional linear extrapolation methods, with low mean absolute error, a 38% higher Sørensen–Dice coefficient, and vertical reflectivity distributions 60% closer to observed values. The U-Net is also able to detect the presence of near-surface cloud with a critical success index (CSI) of 72% and cases of shallow cumuliform snowfall and virga with 18% higher CSI values compared to linear methods. An explainability analysis shows that reflectivity information throughout the scene, especially at cloud edges and at the 1.2-km blind zone threshold, along with atmospheric state variables near the tropopause, are the most significant contributors to model skill. This surface-trained generative inpainting technique has the potential to enhance current and future remote sensing precipitation missions by providing a better understanding of the nonlinear relationship between blind zone reflectivity values and the surrounding atmospheric state.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

ASCENDS: Advanced data SCiENce toolkit for Non-Data Scientists

Recently, advances in machine learning and artificial intelligence have been playing more and more critical roles in a wide range of areas. For the last several years, industries have shown that how learning from data, identifying patterns and making decisions with minimal human intervention can be extremely useful to their business (e.g., image classification, recommending a product to a customer, finding friends in a social network, predicting customers actions, etc.). These success stories have been motivating scientists who study physics, chemistry, materials, medicine, and many other subjects, to explore a new pathway of utilizing machine learning techniques like regression and classification for their scientific activities. However, most existing machine learning tools, systems, and methodologies have been developed for programming experts but not for scientists (or any users) who have no or little knowledge of programming. ASCENDS is a toolkit that is developed to assist scientists (or any persons) who want to use their data for machine learning tasks, more specifically, correlation analysis, regression, and classification. ASCENDS does not require programming skills. Instead, it provides a set of simple but powerful CLI (Command Line Interface) and GUI (Graphic User Interface) tools for non-data scientists to be able to intuitively perform advanced data analysis and machine learning techniques. ASCENDS has been implemented by wrapping around opensource software including Keras, TensorFlow, and scikit-learn.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2003

Diabatic Initialization of Mesoscale Models in the Southeastern United States: Can 0 to 12h Warm Season QPF be Improved?

It is well known that numerical warm season quantitative precipitation forecasts lack significant skill for numerous reasons. Some are related to the model--it may lack physical processes required to realistically simulate convection or the numerical algorithms and dynamics employed may not be adequate. Others are related to initialization-mesoscale features play an important role in convective initialization and atmospheric observation systems are incapable of properly depicting the three-dimensional stability structure at the mesoscale. The purpose of this study is to determine if a mesoscale model initialized with a diabatic initialization scheme can improve short-term (0 to 12h) warm season quantitative precipitation forecasts in the Southeastern United States. The Local Analysis and Prediction System (LAPS) developed at the Forecast System Laboratory is used to diabatically initialize the Pennsylvania State University/National center for Atmospheric Research (PSUNCAR) Mesoscale Model version 5 (MM5). The SPORT Center runs LAPS operationally on an hourly cycle to produce analyses on a 15 km covering the eastern 2/3 of the United States. The 20 km National Centers for Environmental Prediction (NCEP) Rapid Update Cycle analyses are used for the background fields. Standard observational data are acquired from MADIS with GOES/CRAFT Nexrad data acquired from in-house feeds. The MM5 is configured on a 140 x 140 12 km grid centered on Huntsville Alabama. Preliminary results indicate that MM5 runs initialized with LAPS produce improved 6 and 12h QPF threat scores compared with those initialized with the NCEP RUC.

Lapenta, William M.↗
NASA NTRSDate not supplied

Clinical Decision Support - Concepts of Operation

We are entering a new era in space exploration to return to the moon and explore Mars. Crew members operating independently during long duration space exploration missions will require a clinical decision support system (CDSS) to increase autonomy by augmenting their knowledge, skills and abilities in different scenarios. Significant changes to in-flight and habitat medical care due to constraints on mass, volume, power, crew time and medical evacuation capabilities are needed to increase crew autonomy and self-reliance in decision making and task performance. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) pushes the boundary of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit by identifying and testing next-generation medical care and crew health maintenance technologies. Clinical decision support (CDS) presents knowledge and data in a context aware manner to augment a crew members’ knowledge, skills and abilities during the process of observation, orientation, decisions and action. A comprehensive crew health and performance CDSS is required to augment crew capability and will be used in different scenarios for several reasons. In general, the CDSS’s role is to assist the crew in prevention, detection, diagnosis and treatment of crew health and performance related conditions that may arise in exploration spaceflight. For example, CDSS would assist a high acuity scenario such as a heart attack by supplying clear instructions, vital signs and treatment reminders. A lower severity scenario such as kidney stone risk could interface to vehicle systems and display more complex predictive data during a diagnosis. A CDSS needs to contribute to successful missions by maintaining a high performing crew who can potentially exhibit countless medical conditions related to derangements from the space environment (sleep, cognition, nutrition and exercise) as well as conditions intrinsic to humans anywhere. While supporting the crew’s ability to make sound clinical decisions is desirable in any mission, it is essential for exploration missions with significant communication delays, no evacuation capability, and extended exposure to the flight environment. Such missions correspond with medical Level of Care V (LOC V), the highest level specified in NASA-STD-3001. The project focuses on CDS implementation research to derive requirements for LOC V, where the need for increased autonomy results in new practices and the inclusion of non-clinical data, such as vehicle environmental measures and physical exercise results, from other human and vehicle domains and advanced analytics. The CDS project describes how the CDSS is intended to be used by defining concepts of operations (ConOps). The process to derive ConOps focuses on increased autonomy that reduces the likelihood and consequences of accepted medical conditions. These crew health and performance inputs are grouped by common datasets and analysis models. Use cases are derived to research new clinical scenarios, architectural development and workflows. Implementation research is conducted with protypes to inform assumptions and derive requirements. The project also establishes how externally developed analysis and approaches can be added to expand a clinical decision support system and thus highlight how a comprehensive system can be globally developed with collaborators. This presentation will cover example scenarios from the CDS ConOps and the method to derive them. One example scenario will be CDSS alerting an increased kidney stone risk during a mission, with diagnosis and treatment options provided during the intervention.

clinical decision support↗