Search NASA⌕ Search

SEARCH · Search NASA

Results for “cloud computing, data cloud”

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.

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

At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning

A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

Riihimaki, Laura↗
NASA NTRSDate not supplied

Retrieve Methane from IR sounder measurements Using Machine Learning-Enhanced Physical Inversion

The sensitivity of IR sounder measurements to atmospheric CH 4 is often limited due to interferences from signals of other trace gases, insufficient thermal contrast, and cloud blockage. In order to resolve the geographical and vertical distribution of atmospheric CH 4 profiles, accurate scene-dependent a priori information is critically needed to support an optimal estimation method-based physical inversion scheme. Following the principles of indexing, representation, and retrieval, a spectral fingerprinting methodology is developed to address the needs for both accuracy and computational efficiency in sounder-based CH 4 retrieval. Within this framework, a clustering method based on machine learning is first employed to stratify and identify the a priori state within the pre-constructed database, using optimized spectral radiances as predictors. The corresponding radiative kernel is then used to establish the physical inversion scheme for finding the solution. High-quality data from CH 4 data assimilation systems like the Carbon-Tracker and the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, as well as the state-of-art sounder products are used to build the training database, including radiative kernels. We will demonstrate the results retrieved from CrIS observations and the associated validation work.

Wan Wu↗
NASA NTRS1980

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

Separate holograms of horizontally (HH) and vertically (HV) polarized responses obtained by the APQ-102 side-looking radar were processed through an optical correlator and the resulting image was recorded on positive film from which black and white negative and positive prints were made. Visual comparison of the HH and HV images reveals a distinct dark band in the imagery which covers about 30% of the radar strip. Preliminary evaluaton of the flight line 1 date indicates that various features on the HH and HV images seem to have different response levels. The amount of sidelap due to the look angle between flight lines 1 and 2 is negligible. NASA mission #425 to obtain flightlines of NS-001 MSS data and supporting aerial photography was successfully flown. Flight line 3 data are of very good quality and virtually cloud-free. Results of data analysis for selection of test fields and for evaluation of waveband combination and spatial resolution are presented.

Hoffer, R. M.↗
DOE OSTI2017

Short-term solar irradiance forecasting via satellite/model coupling

Here, the short-term (0-3 h) prediction of solar insolation for renewable energy production is a problem well-suited to satellite-based techniques. The spatial, spectral, temporal and radiometric resolution of instrumentation hosted on the geostationary platform allows these satellites to describe the current cloud spatial distribution and optical properties. These properties relate directly to the transient properties of the downwelling solar irradiance at the surface, which come in the form of 'ramps' that pose a central challenge to energy load balancing in a spatially distributed network of solar farms. The short-term evolution of the cloud field may be approximated to first order simply as translational, but care must be taken in how the advection is handled and where the impacts are assigned. In this research, we describe how geostationary satellite observations are used with operational cloud masking and retrieval algorithms, wind field data from Numerical Weather Prediction (NWP), and radiative transfer calculations to produce short-term forecasts of solar insolation for applications in solar power generation. The scheme utilizes retrieved cloud properties to group pixels into contiguous cloud objects whose future positions are predicted using four-dimensional (space + time) model wind fields, selecting steering levels corresponding to the cloud height properties of each cloud group. The shadows associated with these clouds are adjusted for sensor viewing parallax displacement and combined with solar geometry and terrain height to determine the actual location of cloud shadows. For mid/high-level clouds at mid-latitudes and high solar zenith angles, the combined displacements from these geometric considerations are non-negligible. The cloud information is used to initialize a radiative transfer model that computes the direct and diffuse-sky solar insolation at both shadow locations and intervening clear-sky regions. Here, we describe the formulation of the algorithm and validate its performance against Surface Radiation (SURFRAD; Augustine et al., 2000, 2005) network observations. Typical errors range from 8.5% to 17.2% depending on the complexity of cloud regimes, and an operational demonstration outperformed persistence-based forecasting of Global Horizontal Irradiance (GHI) under all conditions by ~10 W/m 2 .

14 SOLAR ENERGY↗
NASA NTRS1975

Interstellar absorption lines in the spectrum of zeta Ophiuchi

Extensive high-resolution scans with the Copernicus UV spectrometer are combined with available ground-based data on interstellar lines to obtain temperatures, densities, and abundances in H I clouds and H II regions in the direction of zeta Oph. Column densities are obtained for 21 elements in various ionization stages, and upper limits are reported for 5 elements and 11 molecules. Analysis of radial velocities and growth curves indicates that H2, HD, and most neutral atoms are concentrated in one cloud with a heliocentric velocity of -14.4 km/s and that H II regions exist in addition to the Stroemgren sphere. The electron and proton densities are calculated for the -14.4 km/s cloud, and the temperatures required for the populations of the fine-structure levels in the C I ground state and the excited rotational levels of H2 are computed and indicate that the dense cloud must have some associated hotter regions. Relative to hydrogen, most of the elements in the H I clouds are found to be depleted by factors of 3 to 4000 as compared with solar system abundances, and several elements appear to be depleted in the H II regions as well.

Morton, D. C.↗
NASA NTRS1993

Radiative transfer for a three-dimensional raining cloud

Satellite-sensor-based microwave brightness temperatures for a three-dimensional raining cloud over a reflecting surface are computed by using a radiative transfer model based on the discrete-ordinates solution procedure. The three-dimensional model applied to a plane layer is validated by comparison with results from a one-dimensional model that is available in the literature. Results examining the effects of cloud height, rainfall rate, surface reflectance, rainfall footprint area, and satellite viewing position on one- and three-dimensional brightness temperature calculations are reported. The numerical experiments indicate that, under certain conditions, three-dimensional effects are significant in the analysis of satellite-sensor-based rainfall retrieval algorithms. The results point to the need to consider carefully three-dimensional effects as well as surface reflectance effects when interpreting satellite-measured radiation data.

Haferman, J. L.↗
NASA NTRS2011

The Relationship Between El Nino/La Nina Oscillations and Recent Anomaly Time Series of OLR Determined by CERES and AIRS

This paper compares recent spatial anomaly time series of OLR (Outgoing Longwave Radiation) and OLRCLR (Clear Sky OLR) as determined using CERES and AIRS observations over the time period September 2002 through June 2010. We find excellent agreement in OLR anomaly time series of both data sets in almost every detail, down to the 1 x 1 spatial grid point level. This extremely close agreement of OLR anomaly time series derived from observations by two different instruments implies that both sets of results must be highly stable. This agreement also validates to some extent the anomaly time series of the AIRS derived products used in the computation of the AIRS OLR product. The paper then examines anomaly time series of AIRS derived products over the extended time period September 2002 through April 2011. We show that OLR anomalies during this period are closely in phase with those of an El Nino index, and that the recent global and tropical mean decreases in OLR and OLRCLR are a result of a transition from an El Nino condition at the beginning of the data record to La Nina conditions toward the end of the data period. We show that the relationship between global mean, and especially tropical mean, OLR anomalies to the El Nino index can be explained by temporal changes of the distribution of mid-tropospheric water vapor and cloud cover in two spatial regions that are in direct response to El Nino/La Nina activity which occurs outside these spatial regions.

Susskind, Joel↗
NASA NTRSDate not supplied

Ice Shape Analysis of an eVTOL Propeller in Forward Flight at the NASA Glenn Icing Research Tunnel

Advanced Air Mobility (AAM) introduces many novel electric vertical takeoff and landing (eVTOL) aircraft configurations for which the effect of icing is not well understood. While icing computational tools have often aided in the design and certification of conventional aircraft, experimental data is needed to support the development and validation of such tools for eVTOL applications. To investigate the icing phenomenon relevant to eVTOL aircraft, NASA Glenn Research Center developed a general-purpose propeller test stand for conducting fundamental icing research on electrically driven propellers in the Icing Research Tunnel (IRT). A 10-day test entry in the IRT was completed in March, 2023, with the goal of generating ice accretions on a non-proprietary propeller geometry under well-characterized conditions. Three carbon fiber propellers of diameters 0.610, 0.711, and 0.914 m (24, 28, and 36 in) were used during the test. Various parametric sweeps of cloud and operating conditions were performed to evaluate the sensitivity of the parameters on the resulting ice accretion characteristics such as ice thickness, mass, location (impingement limits), and type (glaze vs rime). Ice shapes were reviewed for their repeatability, correctness of trends, and sensitivity to parameter variations. The data from the test is being used to support the development of the ice accretion solver, GlennICE.

Icing↗
NASA NTRS2024

Ice Shape Analysis of an eVTOL Propeller in Forward Flight at the NASA Glenn Icing Research Tunnel

Advanced Air Mobility (AAM) introduces many novel electric vertical takeoff and landing (eVTOL) aircraft configurations for which the effect of icing is not well understood. While icing computational tools have often aided in the design and certification of conventional aircraft, experimental data is needed to support the development and validation of such tools for eVTOL applications. To investigate the icing phenomenon relevant to eVTOL aircraft, NASA Glenn Research Center developed a general-purpose propeller test stand for conducting fundamental icing research on electrically driven propellers in the Icing Research Tunnel (IRT). A 10-day test entry in the IRT was completed in March, 2023, with the goal of generating ice accretions on a non-proprietary propeller geometry under well-characterized conditions. Three carbon fiber propellers of diameters 0.610, 0.711, and 0.914 m (24, 28, and 36 in) were used during the test. Various parametric sweeps of cloud and operating conditions were performed to evaluate the sensitivity of the parameters on the resulting ice accretion characteristics such as ice thickness, mass, location (impingement limits), and type (glaze vs rime). Ice shapes were reviewed for their repeatability, correctness of trends, and sensitivity to parameter variations. The data from the test is being used to support the development of the ice accretion solver, GlennICE.

Icing↗
DOE OSTI2015

NCAR Contribution to A U.S. National Multi-Model Ensemble (NMME) ISI Prediction System

NCAR brought the latest version of the Community Earth System Model (version 1, CESM1) into the mix of models in the NMME effort. This new version uses our newest atmospheric model CAM5 and produces a coupled climate and ENSO that are generally as good or better than those of the Community Climate System Model version 4 (CCSM4). Compared to CCSM4, the new coupled model has a superior climate response with respect to low clouds in both the subtropical stratus regimes and the Arctic. However, CESM1 has been run to date using a prognostic aerosol model that more than doubles its computational cost. We are currently evaluating a version of the new model using prescribed aerosols and expect it will be ready for integrations in summer 2012. Because of this NCAR has not been able to complete the hindcast integrations using the NCAR loosely-coupled ensemble Kalman filter assimilation method nor has it contributed to the current (Stage I) NMME operational utilization. The expectation is that this model will be included in the NMME in late 2012 or early 2013. The initialization method will utilize the Ensemble Kalman Filter Assimilation methods developed at NCAR using the Data Assimilation Research Testbed (DART) in conjunction with Jeff Anderson’s team in CISL. This methodology has been used in our decadal prediction contributions to CMIP5. During the course of this project, NCAR has setup and performed all the needed hindcast and forecast simulations and provide the requested fields to our collaborators. In addition, NCAR researchers have participated fully in research themes (i) and (ii). Specifically, i) we have begun to evaluate and optimize our system in hindcast mode, focusing on the optimal number of ensemble members, methodologies to recalibrate individual dynamical models, and accessing our forecasts across multiple time scales, i.e., beyond two weeks, and ii) we have begun investigation of the role of different ocean initial conditions in seasonal forecasts. The completion of the calibration hindcasts for Seasonal to Interannual (SI) predictions and the maintenance of the data archive associated with the NCAR portion of this effort has been the responsibility of the Project Scientist I (Alicia Karspeck) that was partially supported on this project.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Development of Detonation Modeling Capabilities for Rocket Test Facilities: Hydrogen-Oxygen-Nitrogen Mixtures

The objective of the presented work was to develop validated computational fluid dynamics (CFD) based methodologies for predicting propellant detonations and their associated blast environments. Applications of interest were scenarios relevant to rocket propulsion test and launch facilities. All model development was conducted within the framework of the Loci/CHEM CFD tool due to its reliability and robustness in predicting high-speed combusting flow-fields associated with rocket engines and plumes. During the course of the project, verification and validation studies were completed for hydrogen-fueled detonation phenomena such as shock-induced combustion, confined detonation waves, vapor cloud explosions, and deflagration-to-detonation transition (DDT) processes. The DDT validation cases included predicting flame acceleration mechanisms associated with turbulent flame-jets and flow-obstacles. Excellent comparison between test data and model predictions were observed. The proposed CFD methodology was then successfully applied to model a detonation event that occurred during liquid oxygen/gaseous hydrogen rocket diffuser testing at NASA Stennis Space Center.

Allgood, Daniel C.↗
NASA NTRS2019

Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements

The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloud-screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-field-deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty estimate for which average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of +0.002 with a ±0.02 SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases.

David M. Giles↗
DOE OSTI2021

A Grand Challenge "Uncertainty Project" to Accelerate Advances in Earth System Predictability: AI-Enabled Concepts and Applications

This proposal is emerging from GISS ModelE3 ESM development in the area of cloud physics, so we begin with an example of research needs/gaps from that work. Here, some of our greatest development concerns arise where we lack fundamental process-level understanding, as in ice formation. Namely, it is currently unclear what is the main process that is forming the majority of ice crystals in commonly occurring convection, apparently via secondary ice production at warm temperatures. We are keenly awaiting laboratory data for candidate mechanisms, which is not yet in hand to crucially establish their efficiency. Our progress is also hampered by a lack of uncertainty characterization in currently available measurements of ice crystal number size distributions. Furthermore, the same multiplication process may be responsible for a majority of ice crystals in many extratropical mixed-phase clouds, whose variable representation in CMIP6 ESMs may be a leading cause of differences in cloud phase feedback and ECS. Yet we have been required to deliver an ESM with the cloud physics knowledge at hand. The proposed grand challenge project is AI-enabled via application of machine learning (ML) to climate model and observational data streams (focal area 3), and applications include AI-guided observing system design and model/component/parameterization selection (areas 1 and 2). The project is structurally agnostic as to whether model or observing system components use AI approaches or not, but uncertainties must be estimated and propagatable in both.

58 GEOSCIENCES↗
DOE OSTI2024

The ATLAS experiment software on ARM

With an increased dataset obtained during the Run 3 of the LHC at CERN and the even larger expected increase of the dataset by more than one order of magnitude for the HL-LHC, the ATLAS experiment is reaching the limits of the current data processing model in terms of traditional CPU resources based on x86_64 architectures and an extensive program for software upgrades towards the HL-LHC has been set up. The ARM architecture is becoming a competitive and energy efficient alternative. Some surveys indicate its increased presence in HPCs and commercial clouds, and some WLCG sites have expressed their interest. Chip makers are also developing their next generation solutions on ARM architectures, sometimes combining ARM and GPU processors in the same chip. Consequently it is important that the ATLAS software embraces the change and is able to successfully exploit this architecture. We report on the successful porting to ARM of the Athena software framework, which is used by ATLAS for both online and offline computing operations. Furthermore we report on the successful validation of simulation workflows running on ARM resources. For this we have set up an ATLAS Grid site using ARM compatible middleware and containers on Amazon Web Services (AWS) ARM resources. The ARM version of Athena is fully integrated in the regular software build system and distributed in the same way as other software releases. In addition, the workflows have been integrated into the HEPscore benchmark suite which is the planned WLCG wide replacement of the HepSpec06 benchmark used for Grid site pledges. In the overall porting process we have used resources on AWS, Google Cloud Platform (GCP) and CERN. A performance comparison of different architectures and resources will be discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1994

How well was total ozone abundance inferred with Mariner 9?

Ozone is a key to understanding atmospheric chemistry on Mars. Over 20 photochemical models of the martian atmosphere have been published, and O3 is often used as a benchmark for these models. O3 abundance has been inferred from instrumentation on several spacecraft, with the most complete coverage provided by Mariner 9. The Mariner 9 UV spectrometer scanned from 2100 to 3500 Angstroms with a spectral resolution of 15 Angstroms and an effective field-of-view of approximately 300 sq km. The only atmospheric absorption in the 2000 to 3000 A wavelength region was previously assumed to come from the Hartley band system of ozone, which has an opacity of order unity at winter polar latitudes. Therefore, the amount of ozone was inferred by fitting this absorption feature with laboratory data of ozone absorption. Mars O3 shows strong seasonal and latitudinal variation, with column abundances ranging from 0.2 micron-atm at equatorial latitudes to 60 micron-atm over northern winter polar latitudes (1 micron-atm is a column abundance of 2.689 x 10 exp 15 molecules cm(exp -2)). However, the O3 abundance is never great enough to significantly affect atmospheric temperatures or surface temperatures and frost amounts. Some of the previously-inferred O3 abundances are shown. A radiative transfer computer model is used to re-examine the Mariner 9 UV spectra. Assuming a constant mixing ratio for O3 and no chemical or radiative interaction between O3 and clouds/dust, it is shown that when typical amounts of dust and cloud are present that significant underestimation of O3 abundance occurs. A factor of 3 times as much O3 is needed to generate the same spectrum the spacecraft would measure for a cloudy, dusty atmosphere as for a clear atmosphere. If the scattering properties of martian clouds and dust were well known, then their appearance would not be a problem, as a model would be capable of retrieving the O3 abundance. However, these properties are not well known, which raises doubts about the effectiveness of the UV reflectance spectroscopy technique for measuring O3 abundance on Mars.

Lindner, Bernhard Lee↗
NASA NTRS1995

Cloud Properties Derived From GOES-7 for Spring 1994 ARM Intensive Observing Period Using Version 1.0.0 of ARM Satellite Data Analysis Program

This document describes the initial formulation (Version 1.0.0) of the Atmospheric Radiation Measurement (ARM) program satellite data analysis procedures. Techniques are presented for calibrating geostationary satellite data with Sun synchronous satellite radiances and for converting narrowband radiances to top-of-the-atmosphere fluxes and albedos. A methodology is documented for combining geostationary visible and infrared radiances with surface-based temperature observations to derive cloud amount, optical depth, height, thickness, temperature, and albedo. The analysis is limited to two grids centered over the ARM Southern Great Plains central facility in north-central Oklahoma. Daytime data taken during 5 Apr. - 1 May 1994, were analyzed on the 0.3 deg and 0.5 deg latitude-longitude grids that cover areas of 0.9 deg x 0.9 deg and 10 deg x 14 deg, respectively. Conditions ranging from scattered low cumulus to thin cirrus and thick cumulonimbus occurred during the study period. Detailed comparisons with hourly surface observations indicate that the mean cloudiness is within a few percent of the surface-derived sky cover. Formats of the results are also provided. The data can be accessed through the World Wide Web computer network.

Minnis, Patrick↗
NASA NTRS2008

'Towers in the Tempest' Computer Animation Submission

The following describes a computer animation that has been submitted to the ACM/SIGGRAPH 2008 computer graphics conference: 'Towers in the Tempest' clearly communicates recent scientific research into how hurricanes intensify. This intensification can be caused by a phenomenon called a 'hot tower.' For the first time, research meteorologists have run complex atmospheric simulations at a very fine temporal resolution of 3 minutes. Combining this simulation data with satellite observations enables detailed study of 'hot towers.' The science of 'hot towers' is described using: satellite observation data, conceptual illustrations, and a volumetric atmospheric simulation data. The movie starts by showing a 'hot tower' observed by NASA's Tropical Rainfall Measuring Mission (TRMM) spacecraft's three dimensional precipitation radar data of Hurricane Bonnie. Next, the dynamics of a hurricane and the formation of 'hot towers' are briefly explained using conceptual illustrations. Finally, volumetric cloud, wind, and vorticity data from a supercomputer simulation of Hurricane Bonnie are shown using volume techniques such as ray marching.

Shirah, Greg↗
NASA NTRS2020

Connecting Users and Applications with Po.daac Hosted GHRSST Data

The 80+ GHRSST public datasets represent a rich resource for sea surface temperature research and applications given their time series length, resolution, spatial coverage, varying measurement types and processing levels, and availability in the full spectrum of PO.DAAC tools and services ecosystem. The PO.DAAC has created a publicly accessible recipe suite for the user community to perform straightforward yet powerful computations on GHRSST data using python recipes, Jupyter notebooks, R, Matlab, and the NCO programming language. These recipes include numerical computations for regional and global SST trends, anomaly derivations, EOF analysis, climate signal reproduction, and ocean phenology. For example, one recipe reproduces a famous SST based warming figure from the Fourth National Climate Assessment (USA) while another focuses on quantifying the regional changes in ocean SST phenology. Most are python-based while some contain hybrid calls and leverage the NCO programming interface too. All are available on the PO.DAAC user forum (https://podaac.jpl.nasa.gov/forum/) and/or via the open source NASA GitHub repository (https://github.com/nasa/podaac_tools_and_services). Several are available in the Jupyter notebook framework including podaacypy (https://github.com/nasa/podaacpy), a recipe for GHRSST granule metadata discovery and application, and more recently a Jupyter notebook developed to support data analysis and visualization of a cloud-based Zarr formatted Level 4 MUR dataset in the AWS Open Data Registry. Throughout the summer of 2020, the PO.DAAC intends to add and migrate more of its numerical recipes to the Jupyter notebook framework and publish them on its open source GitHub repository.

Gentemann, Chelle↗