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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Characterization of the 2012-044c Briz-M Upper Stage Breakup

On 6 August, 2012, Russia launched two commercial satellites aboard a Proton rocket, and attempted to place them in geosynchronous orbit using a Briz-M upper stage (2012-044C, SSN 38746). Unfortunately, the upper stage failed early in its burn and was left stranded in an elliptical orbit with a perigee in low Earth orbit (LEO). Because the stage failed with much of its fuel on board, it was deemed a significant breakup risk. These fears were confirmed when it broke up 16 October, creating a large cloud of debris with perigees below that of the International Space Station. The debris cloud was tracked by the US Space Surveillance Network (SSN), which can reliably detect and track objects down to about 10 cm in size. Because of the unusual geometry of the breakup, there was an opportunity for NASA Orbital Debris Program Office to request radar assets to characterize the extent of the debris cloud in sizes smaller than the standard debris tracked by the SSN. This paper will describe the observation campaign to measure the small particle distributions of this cloud, and presents the results of the analysis of the data. We shall compare the data to the modelled size distribution, number, and shape of the cloud, and what implications this may have for future breakup debris models. We shall conclude the paper with a discussion how this measurement process can be improved for future breakups.

Matney, M. J.↗
NASA NTRS2013

Characterization of the 2012-044C Briz-M Upper Stage Breakup

On 6 August, 2012, Russia launched two commercial satellites aboard a Proton rocket, and attempted to place them in geosynchronous orbit using a Briz-M upper stage (2012-044C, SSN 38746). Unfortunately, the upper stage failed early in its burn and was left stranded in an elliptical orbit with a perigee in low Earth orbit (LEO). Because the stage failed with much of its fuel on board, it was deemed a significant breakup risk. These fears were confirmed when it broke up 16 October, creating a large cloud of debris with perigees below that of the International Space Station. The debris cloud was tracked by the US Space Surveillance Network (SSN), which can reliably detect and track objects down to about 10 cm in size. Because of the unusual geometry of the breakup, there was an opportunity for NASA Orbital Debris Program Office to use specialized radar assets to characterize the extent of the debris cloud in sizes smaller than the standard debris tracked by the SSN. This paper will describe the observation campaign to measure the small particle distributions of this cloud, and presents the results of the analysis of the data. We shall compare the data to the modelled size distribution, number, and shape of the cloud, and what implications this may have for future breakup debris models. We shall conclude the paper with a discussion how this measurement process can be improved for future breakups.

Matney, M. J.↗
NASA NTRS2014

Understanding the Formation and Evolution of Water Ice Clouds in the Hellas Basin During NH Summer on Mars

Water in the atmosphere of Mars moves between the northern and southern hemispheres seasonally, with north-to-south transport during northern hemisphere (NH) summer and south-to-north transport during southern hemisphere (SH) summer. Understanding the processes that control this seasonal transport of water is critical for understanding the current climate of Mars. Because water ice clouds both track and influence atmospheric circulations on a range of spatial scales, investigations about when and why clouds form enhance our knowledge of the water cycle as a whole. We focus here on a population of clouds that has recently been observed by the MARs Color Imager (MARCI) instrument on the Mars Reconnaissance Orbiter (MRO) to persist in the Hellas Basin throughout the majority of NH summer. Centered at 321 nm, Band 7 of the MARCI instrument is ideally suited for mapping water ice clouds on Mars because water ice is bright in the UV. It is notable that the clouds observed by MARCI in Hellas during NH summer are not as easily observed by the Thermal Emission Spectrometer (TES) on Mars Global Surveyor (MGS), which observes in the IR. We use a Global Climate Model (GCM) to investigate the dynamical mechanisms that control the formation and evolution of Hellas water ice clouds.

water ice clouds↗
DOE OSTI2022

Toward data assimilation of ship-induced aerosol–cloud interactions

Satellite imagery can detect temporary cloud trails or ship tracks formed from aerosols emitted from large ships traversing our oceans, a phenomenon that global climate models cannot directly reproduce. Ship tracks are observable examples of marine cloud brightening, a potential solar climate intervention that shows promise in helping combat climate change. In this paper, we demonstrate a simulation-based approach in learning the behavior of ship tracks based upon a novel stochastic emulation mechanism. Our method uses wind fields to determine the movement of aerosol–cloud tracks and uses a stochastic partial differential equation (SPDE) to model their persistence behavior. This SPDE incorporates both a drift and diffusion term which describes the movement of aerosol particles via wind and their diffusivity through the atmosphere, respectively. We first present our proposed approach with examples using simulated wind fields and ship paths. We then successfully demonstrate our tool by applying the approximate Bayesian computation method-sequential Monte Carlo for data assimilation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Global High‐Resolution Mesoscale Convective System Database Using Satellite‐Derived Cloud Tops, Surface Precipitation, and Tracking

Abstract A new methodology is developed to construct a global (60°S–60°N) long‐term (2000–2019) high‐resolution (∼10‐km h) mesoscale convective system (MCS) database by tracking MCS jointly using geostationary satellite infrared brightness temperature ( T b ) and precipitation feature (PF) characteristics from the Integrated Multi‐satellitE Retrievals for GPM precipitation data sets. Independent validation shows that the satellite‐based MCS data set is able to reproduce important MCS statistics derived from ground‐based radar network observations in the United States and China. We show that by carefully considering key PF characteristics in addition to T b signatures, the new method significantly improves upon previous T b ‐only methods in detecting MCSs in the midlatitudes for all seasons. Results show that MCSs account for over 50% of annual total rainfall across most of the tropical belt and in selected regions of the midlatitudes, with a strong seasonality over many regions of the globe. The tracking database allows Lagrangian aspects such as MCS lifetime and translational speed and direction to be analyzed. The longest‐lived MCSs preferentially occur over the subtropical oceans. The land MCSs have higher cloud‐tops associated with more intense convection, and oceanic MCSs have much higher rainfall production. While MCSs are observed in many regions of the globe, there are fundamental differences in their dynamic and thermodynamic structures that warrant a better understanding of processes that control their evolution. This global database provides significant opportunities for observational and modeling studies of MCSs, their characteristics, and roles in regional and global water and energy cycles, as well as their hydrologic and other impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Automated nuclear cloud feature extraction from film

Chemical, biological, radiological, nuclear, and explosives incidents require rapid detection and characterization for appropriate response. For a nuclear detonation, visible-light cameras may be used to locate the cloud and characterize fallout deposition when coupled with numerical models. Films from the United States’ nuclear testing era compose the only sizeable collection of imagery depicting high-yield detonations. These films offer unique insights into characteristics of flows involving scales that are difficult to replicate experimentally, and they are a valuable source of data for the validation of models for nuclear fallout transport, either as part of emergency response or forensic activities. In this work, we implement modern computer vision and machine learning techniques to identify and track the cloud automatically and subsequently determine the time dependence of some of its features. We trained a ResNet-18 image classifier on hundreds of images to categorize nuclear cloud morphology. Each category or cloud regime is determined by early cloud evolution and is associated to constitutive properties of the flow, such as distribution of vorticity. Next, we identified keypoint features using the KAZE algorithm and tracked these keypoints in the images, allowing us to determine the dimensions and velocities of the cloud across film frames. These measurements converted to real-world units provide valuable experimental data that can be used in the development and validation of nuclear cloud models. We compared the results of this method against manual cloud rise measurements from two different films. In one, our automated method accelerated the feature extraction process without sacrificing measurement accuracy.

Khristy, Joel [ORNL] (ORCID:0000000209963060)↗
NASA NTRS2004

Cloud Detection from Satellite Imagery: A Comparison of Expert-Generated and Automatically-Generated Decision Trees

Automated cloud detection and tracking is an important step in assessing global climate change via remote sensing. Cloud masks, which indicate whether individual pixels depict clouds, are included in many of the data products that are based on data acquired on- board earth satellites. Many cloud-mask algorithms have the form of decision trees, which employ sequential tests that scientists designed based on empirical astrophysics studies and astrophysics simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In this study we explored the potential benefits of automatically-learned decision trees for detecting clouds from images acquired using the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA-14 weather satellite of the National Oceanic and Atmospheric Administration. We constructed three decision trees for a sample of 8km-daily AVHRR data from 2000 using a decision-tree learning procedure provided within MATLAB(R), and compared the accuracy of the decision trees to the accuracy of the cloud mask. We used ground observations collected by the National Aeronautics and Space Administration Clouds and the Earth s Radiant Energy Systems S COOL project as the gold standard. For the sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks included in the AVHRR data product.

Shiffman, Smadar↗
NASA NTRS1992

Infrared, microwave, and spaceborne radar simulations of a deep convective system using a 3-D cloud ensemble method

A 3D cloud model is used to simulate the storm structure, and the results are linked to microwave and infrared radiative transfer models for simulation of aircraft observations. Spaceborne radar data are also simulated along the aircraft flight track. The cloud and radiative model simulations are studied and compared with aircraft observations. The initial results indicate that the 3D cloud model is capable of simulating the major features of observed storm systems when given a representative atmospheric sounding to initialize the convective systems. The simulations of infrared and microwave radiances provide reasonably good comparisons with the observations.

Yeh, H.-Y. M.↗
NASA NTRS2020

Impact of Convectively Detrained Ice Crystals on the Tropical Upper Troposphere and Lower Stratosphere

The role of convectively detrained ice crystals on the humidity of the tropical upper troposphere and lower stratosphere (UTLS) is investigated in simulations of cirrus clouds along trajectories launched from the 378K potential temperature level in the tropics. The one-dimensional (vertical) cloud model tracks individual ice crystals through their lifecycle beginning with detrainment from convection, followed by deposition growth, sedimentation and sublimation. Convective influence of the parcels is diagnosed by tracing the trajectories through time-dependent fields of convective cloud-top height adjusted to match the CloudSAT and CALIPSO statistics. Model simulations of UTLS water vapor and cloud fields are evaluated and constrained by comparison with Aura MLS and CALIPSO measurements. Preliminary results indicate sensitivity of the detrained ice crystal lifecycle to atmospheric conditions downstream of convection. Specifically, cooling (high relative humidity and supersaturation) downstream of convection leads to deposition growth and sedimentation of detrained ice crystals, resulting in net dehydration of the UTLS. In contrast, warming (low relative humidity and subsaturation) downstream of convection leads to sublimation of detrained ice crystals and subsequent hydration. As such, the impact of detrained ice crystals on the humidity of the UTLS exhibits distinct spatial variability. Detrained ice crystals predominantly dehydrate the UTLS in the tropical mean. Sensitivities to the convectively detrained ice crystal size and concentration are also examined using measurements from the StatoClim aircraft campaign. The importance of convectively detrained ice crystals will be discussed within the context of the overall contribution of convection to the lower stratospheric humidity.

Ueyama, Rei↗
NASA NTRS2019

Influence of Deep Convection on Cirrus and Water Vapor Concentration in the Upper Troposphere and Lower Stratosphere

It is well known that stratospheric humidity is primarily controlled by freeze drying (ice crystal growth and sedimentation) of air ascending across the cold tropical tropopause. However, the suggestion of an important source of water vapor from deep convection that extends above the tropical tropopause has persisted. There exists much anecdotal evidence of direct convective hydration of the lower stratosphere based on measurements from high altitude aircraft campaigns, but quantifying the impact of deep convection on the overall budget of stratospheric water vapor has proven challenging. The role of convection on the humidity of the upper troposphere and lower stratosphere (UTLS) is investigated in simulations of cirrus clouds along trajectories launched from given potential temperature level surfaces. The one-dimensional (vertical) cloud model tracks individual ice crystals through their life cycles, beginning with nucleation or detrainment from convection, followed by deposition growth, sedimentation and sublimation. Convective influence of the parcels is diagnosed by tracing the trajectories through time-dependent fields of convective cloud-top height adjusted to match the CloudSAT and CALIPSO statistics. Model simulations of UTLS water vapor and cloud fields are evaluated and constrained by comparison with MLS and CALIPSO measurements. The simulation results indicate that the overall impact of convection on water vapor near the tropical tropopause is 10-15%, while the impact on lower stratospheric humidity is no more than a few percent. Ice crystals detrained from deep convection have relatively small effect. The general implications for the importance of deep convection on UTLS humidity and cirrus cloud fraction will be discussed.

Ueyama, Rei↗
NASA NTRS1991

Application of lightning data to satellite-based rainfall estimation

Information on lightning may improve rain estimates made from infrared images of a geostationary satellite. We address this proposition through a case from the Cooperative Huntsville Meteorological Experiment (COHMEX). During the afternoon and evening of 13 July 1986 waves of showers and thunderstorms developed over and near the lower Tennessee River Valley. For the shower and thunderstorm region within 200 km of the National Weather Service radar at Nashville, Tennessee, we measure cold-cloud area in a sequence of GOES infrared images covering all but the end of the shower and thunderstorm period. From observations of the NASA/Marshall direction-finding network in this small domain, we also count cloud-to-ground lightning flashes and, from scans of the Nashville radar, we calculate volume rain flux. Using a modified version of the Williams and Houze scheme, over an area within roughly 240 km of the radar (the large domain), we identify and track cold cloud systems. For these systems, over the large domain, we measure area and count flashes; over the small domain, we calculate volume rain flux. For a temperature threshold of 235K, peak cloud area over the small domain lags both peak rain flux and peak flash count by about four hours. At a threshold of 226K, the lag is about two hours. Flashes and flux are matched in phase. Over the large domain, nine storm systems occur. These range in size from 300 to 60,000 km(exp 2); in lifetime, from about 2 1/2 h to 6 h or more. Storm system area lags volume rain flux and flash count; nevertheless, it is linked with these variables. In essential respects the associations were the same when clouds were defined by a threshold of 226K. Tentatively, we conclude that flash counts complement infrared images in providing significant additional information on rain flux.

Martin, David W.↗
NASA NTRS2020

Tropopause and lower stratosphere winds and eddy fluxes on Saturn as seen by Cassini imaging

An automated cloud feature tracking algorithm is applied to 2004 Cassini Imaging Science Subsystem images of Saturn's southern hemisphere and equatorial region (5°N-70°S) in continuum, methane band, and ultraviolet filters to derive zonal wind profiles and eddy momentum fluxes from the middle troposphere cloud tops to the lower stratosphere. Zonal winds in the eastward and westward jets decrease in strength from the cloud tops (≳ 1 bar) to the upper troposphere (~60–600 mb), but do not measurably change in strength from the upper troposphere to the tropopause (~60 mb) to the lower stratosphere (≲ 60 mb). A narrow (~3°N-3°S) equatorial jet is detected at the cloud tops, but a previously reported strengthening of this jet from the cloud top level to the upper troposphere/tropopause region cannot be verified due to an absence of easily trackable features at the equator and conflicting results for the few features available. Eddy momentum fluxes on either side of the jet cores outside the tropics (poleward of ~20°S) converge into the eastward jets and diverge from the westward jets. Whether the flux convergence into the eastward jets weakens from the visible cloud level to the upper troposphere cannot be determined for this limited time period, but the eddy convergence appears to increase from the troposphere to the lower stratosphere. The divergence of the eddy flux from the westward jets seems to more clearly increase from the middle to the upper troposphere to the stratosphere, but this does not explain the slight weakening of the westward jets with height.

Saturn↗
DOE OSTI2023

The Joint Ninth Aerosol, Clouds, Precipitation and Climate (ACPC) initiative and TRacking Aerosol Convection interactions ExpeRiment (TRACER) Workshop

The joint ninth Aerosols-Clouds-Precipitation-and-Climate (ACPC; http://www.acpcinitiative.org) initiative and TRacking Aerosol Convection interactions ExpeRiment (TRACER; Jensen et al., 2022) workshop was convened from 16–19 May 2023 at Texas Southern University (TSU), Houston, TX, and online to discuss progress towards understanding the interactions among aerosols, clouds, precipitation, and climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

OPEN leaf : an open‐source cloud‐based phenotyping system for tracking dynamic changes at leaf‐specific resolution in Arabidopsis

SUMMARY The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., thedark genome). High‐throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation ofOPEN leaf, an open‐source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing.OPEN leaf, coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf‐specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high‐throughput screens to identify and characterize previously unidentified phenotypes in a leaf‐specific time‐dependent manner. Moreover, the modular and open‐source design ofOPEN leafallows seamless integration of additional sensors based on users and experimental needs.

Plant Sciences↗
NASA NTRS2006

Identification of a Debris Cloud from the Nuclear Powered SNAPSHOT Satellite with Haystack Radar Measurements

Data from the MIT Lincoln Laboratory (MIT/LL) Long Range Imaging Radar (known as the Haystack radar) have been used in the past to examine families of objects from individual satellite breakups or families of orbiting objects that can be isolated in altitude and inclination. This is possible because for some time after a breakup, the debris cloud of particles can remain grouped together in similar orbit planes. This cloud will be visible to the radar, in fixed staring mode, for a short time twice each day, as the orbit plane moves through the field of view. There should be a unique three-dimensional pattern in observation time, range, and range rate which can identify the cloud. Eventually, through slightly differing precession rates of the right ascension of ascending node of the debris cloud, the observation time becomes distributed so that event identification becomes much more difficult. Analyses of the patterns in observation time, range, and range rate have identified good debris candidates released from the polar orbiting SNAPSHOT satellite (International Identifier: 1965-027A). For orbits near 90o inclination, there is essentially no precession of the orbit plane. The SNAPSHOT satellite is a well known nuclear powered satellite launched in 1965 to a near circular 1300 km orbit with an inclination of 90.3o. This satellite began releasing debris in 1979 with new pieces being discovered and cataloged over the years. 51 objects are still being tracked by the United States Space Surveillance Network. An analysis of the Haystack data has identified at least 60 pieces of debris separate from the 51 known tracked debris pieces, where all but 2 of the 60 pieces have a size less than 10cm. The altitude and inclination (derived from range-rate with a circular orbit assumption) are consistent with the SNAPSHOT satellite and its tracked debris cloud.

Stokely, C.↗
NASA NTRS2018

NASA's GMAO Atmospheric Motion Vectors Simulator: Description and Application to the MISTiC Winds Concept

An atmospheric wind vectors (AMVs) simulator was developed by NASA's GMAO to simulate observations from future satellite constellation concepts. The synthetic AMVs can then be used in OSSEs to estimate and quantify the potential added value of new observations to the present Earth observing system and, ultimately, the expected impact on the current weather forecasting skill. The GMAO AMV simulator is a tunable and flexible computer code that is able to simulate AMVs expected to be derived from different instruments and satellite orbit configurations. As a case study and example of the usefulness of this tool, the GMAO AMV simulator was used to simulate AMVs envisioned to be provided by the MISTiC Winds, a NASA mission concept consisting of a constellation of satellites equipped with infrared spectral midwave spectrometers, expected to provide high spatial and temporal resolution temperature and humidity soundings of the troposphere that can be used to derive AMVs from the tracking of clouds and water vapor features. The GMAO AMV simulator identifies trackable clouds and water vapor features in the G5NR and employs a probabilistic function to draw a subset of the identified trackable features. Before the simulator is applied to the MISTiC Winds concept, the simulator was calibrated to yield realistic observations counts and spatial distributions and validated considering as a proxy instrument to the MISTiC Winds the Himawari-8 Advanced Imager (AHI). The simulated AHI AMVs showed a close match with the real AHI AMVs in terms of observation counts and spatial distributions, showing that the GMAO AMVs simulator synthesizes AMVs observations with enough quality and realism to produce a response from the DAS equivalent to the one produced with real observations. When applied to the MISTiC Winds scanning points, it can be expected that the MISTiC Winds will be able to collect approximately 60,000 wind observations every 6 hours, if considering a constellation composed of 12 satellites (4 orbital planes). In addition, one of the main expected impacts of the MISTiC Winds concept is the ability to derive water vapor feature tracking AMVs below 500-400 hPa, an unique feature among the water vapor AMVs derived from the current Earth observing system.

Carvalho, David↗
NASA NTRS2018

NASA's GMAO Atmospheric Motion Vectors Simulator: Description and Application to the MISTiC Winds Concept

An atmospheric wind vectors (AMVs) simulator was developed by NASA's GMAO to simulate observations from future satellite constellation concepts. The synthetic AMVs can then be used in OSSEs to estimate and quantify the potential added value of new observations to the present Earth observing system and, ultimately, the expected impact on the current weather forecasting skill. The GMAO AMV simulator is a tunable and flexible computer code that is able to simulate AMVs expected to be derived from different instruments and satellite orbit configurations. As a case study and example of the usefulness of this tool, the GMAO AMV simulator was used to simulate AMVs envisioned to be provided by the MISTiC Winds, a NASA mission concept consisting of a constellation of satellites equipped with infrared spectral midwave spectrometers, expected to provide high spatial and temporal resolution temperature and humidity soundings of the troposphere that can be used to derive AMVs from the tracking of clouds and water vapor features. The GMAO AMV simulator identifies trackable clouds and water vapor features in the G5NR and employs a probabilistic function to draw a subset of the identified trackable features. Before the simulator is applied to the MISTiC Winds concept, the simulator was calibrated to yield realistic observations counts and spatial distributions and validated considering as a proxy instrument to the MISTiC Winds the Himawari-8 Advanced Imager (AHI). The simulated AHI AMVs showed a close match with the real AHI AMVs in terms of observation counts and spatial distributions, showing that the GMAO AMVs simulator synthesizes AMVs observations with enough quality and realism to produce a response from the DAS equivalent to the one produced with real observations. When applied to the MISTiC Winds scanning points, it can be expected that the MISTiC Winds will be able to collect approximately 60,000 wind observations every 6 hours, if considering a constellation composed of 12 satellites (4 orbital planes). In addition, one of the main expected impacts of the MISTiC Winds concept is the ability to derive water vapor feature tracking AMVs below 500-400 hPa, an unique feature among the water vapor AMVs derived from the current Earth observing system.

Carvalho, David↗