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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.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

A Comparison of Multi-Angle Implementation of Atmospheric Correction and MOD09 Daily Surface Reflectance Products from MODIS

This study presents the first systematic comparison of MAIAC Collection 6 MCD19A1 daily surface reflectance (SR) product with standard MODIS SR (MOD/MYD09). The study was limited to four tiles located in mid-Atlantic United States (H11V05), Canada (H12V03),central Amazon (H11V09), and North-Eastern China (H27V05) and used over 5000 MODIS granules in 2018. Overall, there is a remarkable agreement between the best quality pixels of the two products, in particular in the Red and NIR bands. Over selected tiles, the evaluation found that MAIAC provides from 4 to 25% more high-quality retrievals thanMOD09 annually, with the largest difference in tropical regions, confirming results of the previous studies. The comparison of spectral characteristics showed a systematic MAIAC-MOD09 difference increasing from NIR to Blue, typical of biases of a Lambertian assumption in MOD09 algorithm. Over the North-Eastern China, MCD19A1 SR is found more stable at wide range of aerosol optical depth (AOD) variations, whereasMOD09 SR shows a consistent positive bias increasing with AOD and at shorter wavelengths. The observed SR differences can be attributed to differences in cloud detection, aerosol retrieval and in atmospheric correction which is performed using an accurate BRDF-coupled radiative transfer model in MAIAC and a Lambertian surface model in MOD09. While this study is not representative of the global performance because of its limited geographical coverage, it should help the land community to better understand the differences between the two products.

MAIAC↗
NASA NTRSDate not supplied

Atmospheric winds with deep optical flow

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Atmospheric winds↗
NASA NTRSDate not supplied

Toward Improving the Assimilation of IASI and CrIS Radiances Over Land Into the NASA GEOS: LST Inversion and Validation

Assimilating surface-sensitive radiances over land is still challenging for both infrared (IR) and microwave (WV) essentially because of the large uncertainties of the land physical surface emissivity model used in the CRTM and the uncertainties of land surface state properties. Currently very few IR radiances are assimilated over land in the NASA Goddard Earth Observing System (GEOS). Large number of radiances are rejected by the emissivity sensitivity check as well as the Cloud detection check. This study focuses on enhancing the assimilation of Infrared Atmospheric Sounding Interferometer (IASI) and Cross-track Infrared Sounder (CrIS) over land in the GEOS forecasting and data assimilation framework. To reach this goal, the Land surface Temperature (LST) is first inverted using IR radiances from IASI and CrIS selected channels to use it as surface boundary parameter for the assimilation of the rest of IASI and CrIS surface-sensitive channels. This work will present a full assessment of the quality of this LST by comparing it and its spatio-temporal variability to LST predicted by the GEOS model. The impacts on the quality of the resulting analysis and subsequent forecast will also be discussed.

Niama Boukachaba↗
NASA NTRS2024

3D Cloud Masking Across A Broad Swath Using Multi-Angle Polarimetry and Deep Learning

Understanding the 3-dimensional structure of clouds is of crucial importance to modeling our changing climate. Active sensors, such as radar and lidar, provide accurate vertical cloud profiles, but are mostly restricted to along-track sampling. Passive sensors can capture a wide swath, but struggle to see beneath cloud tops. In essence, both types of products are restricted to two dimensions: as a cross-section in the active case, and an image in the passive case. However, multi-angle sensor configurations contain implicit information about 3D structure, due to parallax and atmospheric path differences. Extracting that implicit information can be challenging, requiring computationally expensive radiative transfer techniques. Machine learning, as an alternative, may be able to capture some of the complexity of a full 3D radiative transfer solution with significantly less computational expense. In this work, we make three contributions towards understanding 3D cloud structure from multi-angle polarimetry. First, we introduce a large-scale, publicly available dataset that fuses existing cloud products into a format more amenable to machine learning. This dataset treats multi-angle polarimetry as an input, and radar-based vertical cloud profiles as an output. Second, we describe and evaluate strong baseline machine learning models based that predict these profiles from the passive imagery. Notably, these models are trained only on center-swath labels, but can predict cloud profiles over the entire passive imagery swath. Third, we leverage the information-theoretic nature of machine learning to draw conclusions about the relative utility of various sensor configurations, including spectral channels, viewing angles, and polarimetry. These findings have implications for Earth-observing missions such as NASA’s Plankton, Aerosol, Cloud-ocean Ecosystem (PACE) and Atmosphere Observing System (AOS) missions, as well as in informing future applications of computer vision to atmospheric remote sensing.

clouds↗
NASA NTRS2011

Enhancing a Simple MODIS Cloud Mask Algorithm for the Landsat Data Continuity Mission

The presence of clouds in images acquired by the Landsat series of satellites is usually an undesirable, but generally unavoidable fact. With the emphasis of the program being on land imaging, the suspended liquid/ice particles of which clouds are made of fully or partially obscure the desired observational target. Knowing the amount and location of clouds in a Landsat scene is therefore valuable information for scene selection, for making clear-sky composites from multiple scenes, and for scheduling future acquisitions. The two instruments in the upcoming Landsat Data Continuity Mission (LDCM) will include new channels that will enhance our ability to detect high clouds which are often also thin in the sense that a large fraction of solar radiation can pass through them. This work studies the potential impact of these new channels on enhancing LDCM's cloud detection capabilities compared to previous Landsat missions. We revisit a previously published scheme for cloud detection and add new tests to capture more of the thin clouds that are harder to detect with the more limited arsenal channels. Since there are no Landsat data yet that include the new LDCM channels, we resort to data from another instrument, MODIS, which has these bands, as well as the other bands of LDCM, to test the capabilities of our new algorithm. By comparing our revised scheme's performance against the performance of the official MODIS cloud detection scheme, we conclude that the new scheme performs better than the earlier scheme which was not very good at thin cloud detection.

Wilson, Michael J.↗
NASA NTRS2016

MPLNET V3 Cloud and Planetary Boundary Layer Detection

The NASA Micropulse Lidar Network Version 3 algorithms for planetary boundary layer and cloud detection are described and differences relative to the previous Version 2 algorithms are highlighted. A year of data from the Goddard Space Flight Center site in Greenbelt, MD consisting of diurnal and seasonal trends is used to demonstrate the results. Both the planetary boundary layer and cloud algorithms show significant improvement of the previous version.

cloud detection↗
NASA NTRS1996

Why HOC+ is detectable in interstellar clouds: the rate of the reaction between HOC+ and H2

The recent confirmation by Ziurys and Apponi of the detection of HOC+ toward Sgr B2 (OH), and their identification of the ion in Orion-KL and several other sources show that HOC+ is far more abundant than predicted by previous ion-molecule models. In these models, the reaction HOC(+) + H2 --> HCO(+) + H2 is assumed to rapidly destroy HOC+, based on the results of a prior calculation. We have recalculated the rate of this reaction as a function of temperature using a new ab initio potential surface and a phase space approach to the dynamics which includes tunneling. The newly calculated rate is small (< or = 1 x 10(-10) cm3 s-1) at temperatures under 100 K.

Non-NASA Center↗
NASA NTRS2023

The JWST Early-Release Science Program for Direct Observations of Exoplanetary Systems II: A 1 to 20 μM Spectrum of the Planetary-Mass Companion VHS 1256–1257 B

We present the highest fidelity spectrum to date of a planetary-mass object. VHS 1256 b is a <20 M Jup widely separated (∼8'', a = 150 au), young, planetary-mass companion that shares photometric colors and spectroscopic features with the directly imaged exoplanets HR 8799c, d, and e. As an L-to-T transition object, VHS 1256 b exists along the region of the color–magnitude diagram where substellar atmospheres transition from cloudy to clear. We observed VHS 1256 b with JWST's NIRSpec IFU and MIRI MRS modes for coverage from 1 to 20 μm at resolutions of ∼1000–3700. Water, methane, carbon monoxide, carbon dioxide, sodium, and potassium are observed in several portions of the JWST spectrum based on comparisons from template brown dwarf spectra, molecular opacities, and atmospheric models. The spectral shape of VHS 1256 b is influenced by disequilibrium chemistry and clouds. We directly detect silicate clouds, the first such detection reported for a planetary-mass companion.

Brown dwarfs↗
NASA NTRS1998

Evaluation of Clouds and the Earth's Radiant Energy System (CERES) Scanner Pointing Accuracy using a Coastline Detection System

Clouds and the Earth's Radiant Energy System (CERES) is a National Aeronautics and Space Administration (NASA) investigation to examine the role of clouds in the radiative energy flow through the Earth-atmosphere system. The first CERES scanning radiometer was launched on November 27, 1997 into a 35 inclination, 350 km altitude orbit, on the Tropical Rainfall Measuring Mission (TRMM) spacecraft. The CERES instrument consists of a three channel scanning broadband radiometer. The spectral bands measure shortwave (0.3 - 5 microns), window (8 - 12 microns), and total (0.3 - 100 microns) radiation reflected or emitted from the Earth-atmosphere system. Each Earth viewing measurement is geolocated to the Earth fixed coordinate system using satellite ephemeris, Earth rotation and geoid, and instrument pointing data. The interactive CERES coastline detection system is used to assess the accuracy of the CERES geolocation process. By analyzing radiative flux gradients at the boundaries of ocean and land masses, the accuracy of the scanner measurement locations may be derived for the CERES/TRMM instrument/satellite system. The resulting CERES measurement location errors are within 10% of the nadir footprint size. Precise pointing knowledge of the Visible and Infrared Scanner (VIRS) is required for convolution of cloud properties onto the CERES footprint; initial VIRS coastline results are included.

Currey, Chris↗
NASA NTRS2008

Results from Automated Cloud and Dust Devil Detection Onboard the MER

We describe a new capability to automatically detect dust devils and clouds in imagery onboard rovers, enabling downlink of just the images with the targets or only portions of the images containing the targets. Previously, the MER rovers conducted campaigns to image dust devils and clouds by commanding a set of images be collected at fixed times and downloading the entire image set. By increasing the efficiency of the campaigns, more campaigns can be executed. Software for these new capabilities was developed, tested, integrated, uploaded, and operationally checked out on both rovers as part of the R9.2 software upgrade. In April 2007 on Sol 1147 a dust devil was automatically detected onboard the Spirit rover for the first time. We discuss the operational usage of the capability and present initial dust devil results showing how this preliminary application has demonstrated the feasibility and potential benefits of the approach.

dust devils↗
DOE OSTI2022

When Will MISR Detect Rising High Clouds?

It is predicted by both theory and models that high-altitude clouds will occur higher in the atmosphere as a result of climate warming. This produces a positive longwave feedback and has a substantial impact on the Earth's response to warming. This effect is well established by theory, but is poorly constrained by observations, and there is large spread in the feedback strength between climate models. For this study, we use the NASA Multi-angle Imaging SpectroRadiometer (MISR) to examine changes in Cloud-Top-Height (CTH). MISR uses a stereo-imaging technique to determine CTH. This approach is geometric in nature and insensitive to instrument calibration and therefore is well suited for trend analysis and studies of variability on long time scales. In this article we show that the current MISR record does have an increase in CTH for high-altitude cloud over Southern Hemisphere (SH) oceans but not over Tropical or the Northern Hemisphere (NH) oceans. We use climate model simulations to estimate when MISR might be expected to detect trends in CTH, that include the NH. The analysis suggests that according to the models used in this study MISR should detect changes over the SH ocean earlier than the NH, and if the model predictions are correct should be capable of detecting a trend over the Tropics and NH very soon (3–10 years). This result highlights the potential value of a follow-on mission to MISR, which no longer maintains a fixed equator crossing time and is unlikely to be making observations for another 10 years.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

Cloud Thermodynamic Phase Detection with Polarimetrically Sensitive Passive Sky Radiometers

The primary goal of this project has been to investigate if ground-based visible and near-infrared passive radiometers that have polarization sensitivity can determine the thermodynamic phase of overlying clouds, i.e. if they are comprised of liquid droplets or ice particles. While this knowledge is important by itself for our understanding of the global climate, it can also help improve cloud property retrieval algorithms that use total (unpolarized) radiance to determine Cloud Optical Depth (COD). This is a potentially unexploited capability of some instruments in the NASA Aerosol Robotic Network (AERONET), which, if practical, could expand the products of that global instrument network at minimal additional cost. We performed simulations that found, for zenith observations, cloud thermodynamic phase is often expressed in the sign of the Q component of the Stokes polarization vector. We chose our reference frame as the plane containing solar and observation vectors, so the sign of Q indicates the polarization direction, parallel (negative) or perpendicular (positive) to that plane. Since the quantity of polarization is inversely proportional to COD, optically thin clouds are most likely to create a signal greater than instrument noise. Besides COD and instrument accuracy, other important factors for the determination of cloud thermodynamic phase are the solar and observation geometry (scattering angles between 40 and 60 degrees are best), and the properties of ice particles (pristine particles may have halos or other features that make them difficult to distinguish from water droplets at specific scattering angles, while extreme ice crystal aspect ratios polarize more than compact particles). We tested the conclusions of our simulations using data from polarimetrically sensitive versions of the Cimel 318 sun photometerradiometer that comprise AERONET. Most algorithms that exploit Cimel polarized observations use the Degree of Linear Polarization (DoLP), not the individual Stokes vector elements (such as Q). For this reason, we had no information about the accuracy of Cimel observed Q and the potential for cloud phase determination. Indeed, comparisons to ceilometer observations with a single polarized spectral channel version of the Cimel at a site in the Netherlands showed little correlation. Comparisons to Lidar observations with a more recently developed, multi-wavelength polarized Cimel in Maryland, USA, show more promise. This divergence between simulations and observations has prompted us to begin the development of a small test instrument called the Sky Polarization Radiometric Instrument for Test and Evaluation (SPRITE). This instrument is specifically devoted to the accurate observation of Q, and the testing of calibration and uncertainty assessment techniques, with the ultimate goal of understanding the practical feasibility of these measurements.

Knobelspiesse, Kirk D.↗
NASA NTRS1990

Detection of formic acid in the cold, dark cloud L134N

The detection of formic acid (HCOOH) in a cold dark interstellar cloud (L134N) is reported. The observed abundance of 3 x 10 to the 10th relative to H2 is between one and two orders of magnitude lower than that calculated by published ion-molecule models of dark-cloud chemistry, but is quite consistent with recent model revisions based on new reaction rates. Formic acid was not detected in the archetypical dark cloud TMC-1, and was tentatively detected in the region of massive star formation W51.

Irvine, W. M.↗
NASA NTRS2006

Multilayered Clouds Identification and Retrieval for CERES Using MODIS

Traditionally, analyses of satellite data have been limited to interpreting the radiances in terms of single layer clouds. Generally, this results in significant errors in the retrieved properties for multilayered cloud systems. Two techniques for detecting overlapped clouds and retrieving the cloud properties using satellite data are explored to help address the need for better quantification of cloud vertical structure. The first technique was developed using multispectral imager data with secondary imager products (infrared brightness temperature differences, BTD). The other method uses microwave (MWR) data. The use of BTD, the 11-12 micrometer brightness temperature difference, in conjunction with tau, the retrieved visible optical depth, was suggested by Kawamoto et al. (2001) and used by Pavlonis et al. (2004) as a means to detect multilayered clouds. Combining visible (VIS; 0.65 micrometer) and infrared (IR) retrievals of cloud properties with microwave (MW) retrievals of cloud water temperature Tw and liquid water path LWP retrieved from satellite microwave imagers appears to be a fruitful approach for detecting and retrieving overlapped clouds (Lin et al., 1998, Ho et al., 2003, Huang et al., 2005). The BTD method is limited to optically thin cirrus over low clouds, while the MWR method is limited to ocean areas only. With the availability of VIS and IR data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and MW data from the Advanced Microwave Scanning Radiometer EOS (AMSR-E), both on Aqua, it is now possible to examine both approaches simultaneously. This paper explores the use of the BTD method as applied to MODIS and AMSR-E data taken from the Aqua satellite over non-polar ocean surfaces.

Sun-Mack, Sunny↗
NASA NTRSDate not supplied

Evaluating Retrieval Algorithm Climate Stability: Estimating 3D Optical Thickness Bias Distributions by Cloud Type

Detecting climate trends on large spatiotemporal scales requires accurate, stable measurements and stable retrieval algorithms. We strive to estimate how time-variant retrieval algorithm biases may impact trend detection. Here we focus on the 3D cloud optical thickness (τc) bias, which is among the largest in passive cloud retrieval algorithms. If this bias is time dependent, a possibility with potential decadal changes in cloud morphology, it may obscure genuine trends in τc. Although previous studies have evaluated the cloud- and sun-view geometry-dependent 3D τc bias on small spatial scales, before our current study none have evaluated the stability of this well-known bias on climate-relevant large spatiotemporal scales. These studies must estimate large scale distributions of the 3D τc bias by cloud type and estimate how cloud type amount may change between two climate states. We employ a novel approach to estimate large scale distributions of 3D τc using a proxy of the bias that quantifies the departure of clouds from satisfying the 1D radiative transfer assumption used in passive τc retrievals. This existing globally-distributed proxy is an angular consistency metric that was developed using fused Moderate-Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR) measurements. Calculating the 3D τc bias and the proxy, for known cloud fields enables us to establish statistical relationships between these two quantities, which can be used to calculate large-scale distributions of the 3D τc bias. This approach limits the number of 3D radiative transfer simulations required to only those needed to estimate a statistical relationship between the 3D τc bias for known cloud fields and a proxy of the bias. It is likely that future studies will be needed to evaluate retrieval algorithm bias stability for other geophysical variables as the community develops climate data records from satellite observations and their retrievals. This must be done in addition to monitoring and correcting measurement errors and uncertainties and understanding their impact on retrieved essential climate variables.

Yolanda Shea↗
NASA NTRS2007

Interplanetary Dust Clouds Near 1AU Detected by STEREO

For a 21 day interval beginning on Jan 28, 2007, the radio and plasma wave instruments (S/WAVES) on the twin STEREO spacecraft detected intense impulse-like emissions in both the time-domain receivers and the low end of the frequency-domain receivers. These emissions were much more intense on the STEREO Ahead instrument, but were detected by both. During that interval, the two STEREO spacecraft were only separated by about 0.1deg in heliocentric longitude. All attempts to understand the emissions in terms of electrical disturbances onboard the spacecraft were unsuccessful and, since the impulsive events essentially ceased on Feb 18,2007, the entire study was put on lower priority. However, beginning abruptly on July 20, 2007, a very similar and more intense episode of impulses was detected exclusively by S/WAVES on the STEREO Ahead spacecraft, now separated from Behind by more than 20deg of heliocentric longitude. This new episode lasted nearly as long as the Jan-Feb episode, some 20 days, ending on Aug. 9. On Aug. 13,2007, the STEREO Behind spacecraft arrived at the same heliocentric longitude as the Ahead spacecraft was at on July 20 and the S/WAVES instrument abruptly started detecting impulsive events, although at lower intensities than those on Ahead. The individual waveform of the detected impulses is very similar to signals recorded by the Voyager plasma wave (PWS) and radio astronomy (PRA) instruments and more recently by the Cassini radio and plasma wave science (RPWS) instrument when those spacecraft plunged through Saturn's rings. Those Saturnian signals are attributed to micron-sized dust particle impacts on the spacecraft which creates a temporary plasma cloud which, in turn, induces an electrical signal on the antennas. We reanalyzed the STEREO episodes along the lines of the Saturnian ring plane analyses and have concluded that the STEREO impulses are very likely due to similar dust impacts with bands or clouds of dust of cometary or, more generally, zodiacal light material near 1 AU. The distribution of these clouds of small dust particles is significantly different than theoretical models of interplanetary dust distribution

Kaiser↗