Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “cloud platform”

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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Assimilation of global cloud imagery from multiple satellites

Synoptic images of the global cloud field are created from measurements of IR radiation taken aboard four geostationary and two polar-orbiting platforms simultaneously observing the earth's cloud field. A series of spatial and temporal interpolations, together with data reliability criteria, are used to map synoptic measurements from the geostationary satellites and asynoptic measurements from the polar-orbiting satellites. The synoptic global cloud imagery (GCI) that results has a horizontal resolution of 0.7 deg in longitude by 0.35 deg in latitude and a temporal resolution of 3 h, providing an unprecedented view of the global cloud pattern. Each composite image represents a nearly instantaneous snapshot of the entire earth's cloud field. Collectively, the composite imagery resolve, on a global basis, most of the variability inherent to organized convection, including several harmonics of the diurnal cycle. Because of its homogeneous properties, the GCI lends itself easily to a variety of space-time analyses useful for studying global cloud behavior.

Tanaka, Ken↗
NASA NTRS2010

Observed Differences in Spectral Microphysical Retrievals from MODIS

The microphysical structure of clouds is of fundamental importance for understanding a variety of cloud radiation and physical processes. With the advent of MODIS (Moderate Resolution Imaging Spectroradiometer) on the NASA EOS Terra and Aqua platforms, simultaneous global/daily 1km retrievals of cloud effective particle size are available using the heritage 3.7 an band from AVHRR as well as the 1.6 and 2.1 m shortwave IR bands. The MODIS cloud product (MOD06/MYD06 for MODIS Terra and Aqua, respectively) provides separate effective radii results using each of these spectral bands. It has been found that significant differences can occur between the three size retrievals, mainly for liquid water marine boundary layer clouds and especially in broken (low cloud fraction) regimes. In particular, for such regimes, effective radii derived from the MODIS 2.1 lim band can be substantially larger than retrievals from the Heritage 3.7 lam band. In this paper, we present global and regional results of the differences, including correlations, view angle dependencies, and algorithm sensitivities for the existing MODIS Collection 5 product.

Platnick, Steven E.↗
NASA NTRS2012

Sensitivity of Marine Warm Cloud Retrieval Statistics to Algorithm Choices: Examples from MODIS Collection 6

The optical and microphysical structure of warm boundary layer marine clouds is of fundamental importance for understanding a variety of cloud radiation and precipitation processes. With the advent of MODIS (Moderate Resolution Imaging Spectroradiometer) on the NASA EOS Terra and Aqua platforms, simultaneous global/daily 1km retrievals of cloud optical thickness and effective particle size are provided, as well as the derived water path. In addition, the cloud product (MOD06/MYD06 for MODIS Terra and Aqua, respectively) provides separate effective radii results using the l.6, 2.1, and 3.7 ~m spectral channels. Cloud retrieval statistics are highly sensitive to how a pixel identified as being "notclear" by a cloud mask (e.g., the MOD35/MYD35 product) is determined to be useful for an optical retrieval based on a 1-D cloud model. The Collection 5 MODIS retrieval algorithm removed pixels associated with cloud'edges as well as ocean pixels with partly cloudy elements in the 250m MODIS cloud mask - part of the so-called Clear Sky Restoral (CSR) algorithm. Collection 6 attempts retrievals for those two pixel populations, but allows a user to isolate or filter out the populations via CSR pixel-level Quality Assessment (QA) assignments. In this paper, using the preliminary Collection 6 MOD06 product, we present global and regional statistical results of marine warm cloud retrieval sensitivities to the cloud edge and 250m partly cloudy pixel populations. As expected, retrievals for these pixels are generally consistent with a breakdown of the ID cloud model. While optical thickness for these suspect pixel populations may have some utility for radiative studies, the retrievals should be used with extreme caution for process and microphysical studies.

Platnick, Steven↗
NASA NTRS2005

Comparison of Cloud Resolving Model Simulations to Remote Sensing Data

The purpose of this research was to evaluate the ability of a cloud-resolving model (CRM) to simulate the dynamical, radiative, and microphysical properties of deep convective cloud objects identified using CERES (Clouds and the Earth s Radiant Energy System) on board the Tropical Rainfall Measuring Mission (TRMM) satellite platform, for many cases. A deep convective cloud object is a contiguous region that is composed of satellite footprints that fulfill the following selection criteria: 100% cloud fraction, cloud optical depth > 10, and a cloud top height of at least 10 km. Selection criteria have also been formed for different types of boundary-layer clouds, as described in Xu et al. (2005). The purpose of the cloud object approach is to identify specific areas of where the cloud properties simulated by the CRM systematically differ from the observed cloud properties. Where these systematic differences exist, concrete steps can be made to improve the CRM s simulation of an entire class of clouds, rather than by tuning the model to correctly simulate a single case study, as is often done. Additional information regarding detailed approaches and findings are presented.

Randall, David A.↗
NASA NTRS2011

Airborne Observation of the Hayabusa Sample Return Capsule Re-Entry

The Japan Aerospace Exploration Agency (JAXA) recently completed their Hayabusa asteroid exploration mission. Launched in 2003, Hayabusa made contact with, and retrieved a sample from, the near-Earth asteroid Itokawa in 2005. The sample return capsule (SRC) re-entered over the Woomera Test Range (WTR) in southern Australia on June 13, 2010, at approximately 11:21 pm local time (09:51 UTC). The SRC re-entry velocity was 12.2 km/s, making it the second-fastest Earth return velocity behind NASA s Stardust sample return capsule re-entry in 2006. From a space technology development perspective, Hayabusa s re-entry functioned as a rare flight experiment of an entry vehicle and its thermal protection system. In collaboration with the SETI Institute, NASA deployed its DC-8 airborne laboratory and a team of international researchers to Australia to observe the re-entry of the SRC. The use of an airborne platform enables observation above most clouds and weather and greatly diminishes atmospheric absorption of the optical signals. The DC-8 s flight path was engineered and flown to provide a view of the spacecraft that bracketed the heat pulse to the capsule. A suite of imaging instruments on board the DC-8 successfully recorded the luminous portion of the re-entry event. For approximately 70 seconds, the spectroscopic and radiometric instruments acquired images and spectra of the capsule, its wake, and destructive re-entry of the spacecraft bus. Figure 1 shows a perspective view of the WTR, the SRC re-entry trajectory, and the flight path of the DC-8. The SRC was jettisoned from the spacecraft bus approximately 3 hours prior to entry interface. Due to thruster failures on the spacecraft, it could not be diverted from the entry path and followed the trajectory of the SRC, where it burned up in the atmosphere between approximately 100 and 50 km altitude. Fortuitously, the separation distance between the spacecraft and SRC was sufficient to clearly resolve the SRC from the debris field of the burning spacecraft. Figure 2 shows a frame from a high-definition television camera on board the aircraft and denotes the locations of the SRC and spacecraft bus debris.

Grinstead, Jay H.↗
NASA NTRS2018

NASA Earth eXchange (NEX) App Store

NASA Earth Exchange (NEX), and her public cloud version OpenNEX, have become platforms supporting scientific collaboration, knowledge sharing and research for the entire Earth science community. To date, a number of custom tools and capabilities have been integrated into the platforms. However, such integration has to undergo a case-by-case manual process thus lacks scalability. This timely project builds an App Store onto OpenNEX as a building block. Climate data analytics tools/programs can be easily uploaded, shared, organized, searched, and recommended like photos and videos on the YouTube. The foundation of our App Store is a provenance server, which not only records metadata but also execution history of climate data analytics apps including the input data and parameters, output data and products, who runs the app for which purpose, and how apps may be chained into workflows. Researchers can thus understand, reproduce, and repurpose existing apps and workflows. Machine learning approaches are applied to mine provenance to provide recommend-as-you-go services for Earth scientists, such as to recommend suitable apps and workflow snippets. A browser-based workflow tool is also provided for researchers to explore the provenance server and design value-added workflows. Scalability, sustainability, extensibility, usability, adaptability, security and privacy are considered in the App Store.

eXchange↗
NASA NTRS2020

Air Traffic Management TestBed Traffic Viewer: Developer's Guide

The Air Traffic Management (ATM) TestBed is a Platform as a Service that is being developed by the National Aeronautics and Space Administration (NASA) to help design, configure, integrate, run, and monitor air traffic simulations. The platform is designed to provide cloud services including back-end, big-data analytics tools, on-demand computing resource management, data storage, and communication middleware. The ATM TestBed reduces the time to test concepts and technologies, supports interactions among various methods such as human-in-the-loop and automation-in-the-loop simulations, and enables collaborative simulations by sharing technologies and tools in the ATM community. The Traffic Viewer application provides a graphical user interface tool for visualizing real and simulated air traffic as well as airspace definition in two-dimensional space. This guide describes a high-level design and implementation of Traffic Viewer and provides information for a new developer or a user to add new capabilities by following the software design and leveraging existing capabilities.

Lai, Chok Fung↗
NASA NTRS2020

Air Traffic Management TestBed Data Exchange Model

The Air Traffic Management (ATM) TestBed is a Platform as a Service that is being developed by the National Aeronautics and Space Administration (NASA) to help design, configure, integrate, run, and monitor air traffic simulations. The platform is designed to provide cloud services including back-end, big-data analytics tools, on-demand computing resource management, data storage, and communication middleware. The ATM TestBed reduces the time to test concepts and technologies, supports interactions among various methods such as human-in-the-loop and automation-in-the-loop simulations, and enables collaborative simulations by sharing technologies and tools in the ATM community. In order to allow easier access to simulation components, TestBed provides a messaging support layer for connectivity using a consistent set of input/output interfaces. In addition, a standard data format is introduced to facilitate communication between the components. The data exchange model, supported in the messaging support layer, standardizes the format of the information to be exchanged among the components. This document describes the messaging data model currently developed in TestBed and provides data dictionaries for references to component developers as well as simulation engineers.

air traffic simulation↗
NASA NTRSDate not supplied

Venus Sample Return Using In-situ Propellant

This project will pioneer a new approach to return a sample from the surface of Venus. At 450°C and 92 bar pressure, the Venus surface is the most hostile environment in the solar system. This project merges an innovative carbon monoxide rocket concept using propellant made from the Venus atmosphere with innovations in high-temperature technology and solar aircraft. We turn an ambitious mission into a reality. The mission will return a sample from the scorching surface of Venus, using a novel multi-step approach incorporating new technology. The high-temperature solar aircraft collects the sample from the surface and carries it above the main cloud deck to a balloon platform with a launch vehicle & propellant manufacturing plant. The carbon monoxide/oxygen monopropellant is manufactured from the Venus atmosphere.

Venus↗
NASA NTRS1983

Possible effects of the El Chichon volcanic cloud on the radiation budget of the northern tropics

A series of calculations with a one-dimensional, time-marching, radiative-convective model are performed to assess the impact of the El Chichon volcanic cloud on the radiation budget of the northern tropics during the 6-month period following the injection of volcanic material into the stratosphere. Extensive measurement of the cloud obtained from airborne, spacecraft, and ground platforms were used to define the model parameters and to test the predictions of the model. The El Chichon cloud is predicted to have caused an increase in planetary albedo of 10 percent, a decrease in total solar radiation of 2-3 percent at the ground on cloudless days, and an increase in temperature of 3.5 K at the 24-km (30-mb) level. These predictions are compatible with relevant observations, within their respective error bars.

Pollack, J. B.↗
NASA NTRS2016

A Ten-Year Global Record of Absorbing Aerosols Above Clouds from OMI's Near-UV Observations

Aerosol-cloud interaction continues to be one of the leading uncertain components of climate models, primarily due to the lack of an adequate knowledge of the complex microphysical and radiative processes associated with the aerosol-cloud system. The situations when aerosols and clouds are found in the same atmospheric column, for instance, when light-absorbing aerosols such as biomass burning generated carbonaceous particles or wind-blown dust overlay low-level cloud decks, are commonly found over several regional of the world. Contrary to the cloud-free scenario over dark surface, for which aerosols are known to produce a net cooling effect (negative radiative forcing) on climate, the overlapping situation of absorbing aerosols over cloud can potentially exert a significant level of atmospheric absorption and produces a positive radiative forcing at top-of-atmosphere. The magnitude of direct radiative effects of aerosols above cloud depends directly on the aerosol loading, microphysical-optical properties of the aerosol layer and the underlying cloud deck, and geometric cloud fraction. We help in addressing this problem by introducing a novel product of optical depth of absorbing aerosols above clouds retrieved from near-UV observations made by the Ozone Monitoring Instrument (OMI) on board NASA's Aura platform. The presence of absorbing aerosols above cloud reduces the upwelling radiation reflected by cloud and produces a strong 'color ratio' effect in the near-UV region, which can be unambiguously detected in the OMI measurements. Physically based on this effect, the OMACA algorithm retrieves the optical depths of aerosols and clouds simultaneously under a prescribed state of atmosphere. The algorithm architecture and results from a ten-year global record including global climatology of frequency of occurrence and above-cloud aerosol optical depth, and a discussion on related future field campaigns are presented.

Ozone Monitoring Instrument↗
NASA NTRS2019

Leveraging Thousands of Contrail Observations from GLOBE Citizen Scientists

The GLOBE (Global Learning and Observations to Benefit the Environment) Program is NASA's largest and longest-operating citizen science program contributing Earth observations. Over 800,000 cloud observations have been reported worldwide since YEAR that include reports of short-lived, persistent, and persistent-spreading contrails. While contrails can be challenging to observe with space-borne platforms, humans are adept at spotting contrails from the ground. The NASA GLOBE Clouds team at NASA Langley Research Center in Hampton, Virginia matches cloud observations to multiple satellite platforms for comparison, including: NASA's CERES (Clouds and Earth's Radiant Energy System) instrument onboard Terra and Aqua, CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation), and geostationary satellites. A pilot project was started with select students in the United States to track airplanes above 25,000 ft and report airplane type, altitude, and report if a contrail was being or was not being produced. The objective of the pilot project was to establish if this is a scalable approach for building an international observational dataset documenting what types of airplanes are creating what types of contrails (short-lived, persistent, spreading) under what atmospheric conditions. Preliminary results of this pilot project will be presented.

Colón Robles, Marilé↗
NASA NTRSDate not supplied

Development of a Consistent MODIS and VIIRS Cloud Detection Approach for CERES

A consistent cloud fraction record across various satellite platforms is essential for maintaining a long-term and stable climate data record of Earth's energy budget. With the Aqua satellite nearing the end of its operational lifetime, the continuation of this record relies on utilizing VIIRS observations from NOAA20 for cloud detection in NASA’s Clouds and Earth’s Radiant Energy System (CERES) project. However, integrating data from VIIRS and MODIS instruments poses challenges due to their distinct characteristics, such as varying spatial resolutions and different spectral channels. As a result, deriving consistent cloud properties from these two sensors without introducing artificial discontinuities in the time series remains a complex and challenging task. This paper will present progress toward developing a unified MODIS and VIIRS cloud mask using common channels to produce consistent cloud properties for CERES next edition (Ed5) Earth radiation budget data products. The fundamental approach taken in the CERES cloud mask is to compare the observed radiances to the expected background clear sky radiances. Therefore, one vital step is to compute clear sky radiances with a radiative transfer model that accurately accounts for satellite-specific, spectrally dependent surface reflectance, surface emission, and atmospheric absorption. Refined radiative transfer models and updated ancillary data inputs including surface emissivity maps, IGBP, snow and ice maps are incorporated into the processing framework to improve cloud detection consistency and accuracy. Pixel level cloud mask results and monthly global cloud fraction comparisons between MODIS and VIIRS will be presented to evaluate their consistency. Remaining challenges will be discussed. It is expected that this work will contribute consistent cloud properties for CERES that adequately bridges the MODIS and VIIRS imager data records.

CERES↗
NASA NTRS2023

Easy, Scalable Subsetting of GEDI Point Clouds

The GEDI Subsetter, a Python tool developed for NASA’s Multi-mission Algorithm and Analysis Platform (MAAP), optimizes the accessibility and visualization of GEDI point clouds by enabling users to efficiently subset data in a convenient, scalable manner. Complex science data often requires users to learn new software skills and handle many large files. Handling and cleaning large data sets is tedious and error-prone. These challenges significantly impede analysis. One of the goals of NASA's MAAP is to provide a platform that lowers the barrier to conducting research and analysis at scale. When a group of MAAP users wanted to conduct above-ground biomass estimation using GEDI data, we found that their existing workflow for leveraging GEDI data suffered from the barriers mentioned above. Furthermore, their workflow did not scale easily beyond a small number of granules. We found that existing tools related to GEDI data retrieval and subsetting were too limiting, so the GEDI Subsetter was written to support MAAP users’ needs. Being able to run many subsetting jobs simultaneously in the MAAP, and parallelizing the code itself, has led to significant speed improvements in obtaining relevant data, reducing subsetting time from hours to minutes. MAAP users can now more quickly and easily obtain only the data relevant to their research, by choosing which GEDI collection they want to work with (L1A, L2A, L2B, or L4A), and how they want to subset it, by specifying an area of interest, a temporal range, and relevant attributes. This has significantly reduced the feedback loop for users, allowing them to much more quickly subset GEDI data and begin their analysis. Although the GEDI Subsetter originally targeted users of the MAAP, it is generalized such that it can also be used outside of the MAAP and includes a command-line interface for convenience. Furthermore, with minor modifications, it should be possible to use it with non-GEDI data as the general pattern should be applicable to other sparse/track-based sensors.

Charles Daniels↗
NASA NTRSDate not supplied

Entwine Point Tiles for 3D Visualization and Querying of ICESat-2

Point Cloud data from non-optical sensors present challenges in scientific computing in both volume of data and files, even for cloud services environments. As part of the Multi-Mission Algorithm and Analysis Platform (MAAP), a joint open science platform for global biomass modelling, we’ve developed a cloud optimized workflow for using ATL08 (ICESat-2) data as a point cloud. For MAAP, the ATL08 data product is published as Entwine Point Tiles (EPT), allowing users to visualize and query the full extent of this collection interactively without pre-downloading, or preprocessing. The EPT format is a cloud-optimized point cloud data format which re-organizes points into a cloud friendly spatially indexed data structure. MAAP uses AWS S3 to store these point clouds and serves them over OGC specified APIs, 3DTiles for visualization, and WFS for querying. This workflow allows for interactive 3D visualizations in a web browser, including notebook environments and facilitates on the fly subsetting for interactive data exploration, all of which can be applied to other similar sensors.

Alex Mandel↗
NASA NTRS1983

Theoretical performance of a vortex generator type of cloud droplet sampler

One of the goals of the NASA global tropospheric research program is understanding the chemical processes which influence the chemistry of the natural troposphere. An important element of this program is the study of the role of heterogenous chemistry in global tropospheric chemical cycles. Any study of the part which clouds play in the chemistry of tropospheric trace constituents requires a determination of cloud water chemical composition. There exists, therefore, a need for highly efficient collector devices which can obtain unbiased samples of cloud water in sufficient quantities aboard an aircraft platform. A promising concept which could meet the dual requirements of accurate sampling and efficient collection of cloud water droplets in the vortex generator. This concept involves an inertial separator which uses the centrifugal force of a rotating airstream. The separator is built into a commercial device which has been used for many years as a dust separator for air breathing engines. Attention is given to the optimization of the sampler design.

Miller, I. M.↗
NASA NTRSDate not supplied

NASA GLOBE CLOUD GAZE: Creating Data Quality Flags for Citizen Science Cloud Observations Matched to NASA Satellite Data

The GLOBE Program, NASA’s largest and longest lasting citizen science program about the Earth, has been collecting cloud observations matched to multiple satellite data daily. The program’s cloud protocol is historically the most popular protocol as your eyes are the only instruments you need to collect observations of the sky. This dataset includes over 3,300,000 cloud observations with variables like total cloud cover, cloud type and opacity that are collocated to the nearest overpass times of geostationary satellites (GOES-15, GOES-16, GOES-17, Meteosat-8, Meteosat-11, or Himawari-8), or to Clouds and the Earth’s Radiant Energy System (CERES) instruments onboard Aqua and Terra, or the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. In order to increase the usability of this dataset, the Community science project Leveraging Online and User Data through GLOBE And Zooniverse Engagement (CLOUD GAZE) has been developed to generate data quality flags of these ground-up and top-down perspectives of sky and clouds. Recently funded through NASA’s Citizen Science for Earth Systems Program, CLOUD GAZE has partnered with the Zooniverse online platform to obtain reference data and image tagging of sky photographs collected through The GLOBE Program’s clouds protocol. This paper will present the GLOBE Clouds dataset matched to NASA satellite data, integration of CLOUD GAZE to develop data quality flags, and research applications of the dataset (includes ground-up and top-down perspective comparisons, ground observations of dust storms and smoke plumes, and cloud observations in polar regions). The paper will also present on techniques and recommendations for classroom use and for community engagement, particularly for those looking to online resources.

Marilé Colón Robles↗