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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Advanced Geosynchronous Imager

For improved understanding of chaotic processes and the diurnal cycle, an advanced GOES imager must also have the multi-spectral spectral bands used by low earth orbit (LEO) imagers, with on-orbit calibration for all bands. A synergy between GEO and LEO radiometry would enable earth system scientists to fuse the remote sensing data from all the spaceborne platforms. These additional radiometric capabilities are designed to observe important physical processes that vary rapidly and unpredicably: smoke, fires, precipitation, ozone, volcanic ash, cloud phase and height, and surface temperature. We believe the technology now exists to develop an imaging system that can meet future weather reporting and earth system science needs. To meet this need, we propose a design for a comprehensive geosynchronous atmospheric imager. This imager is envisioned to fly on a GOES-N class spacecraft, within the volume, weight and power constraints of a platform similar to GOES-N while delivering 100 times more data and radiometric quality than the GOES-N imager. The higher data rate probably requires its own ground station, which could serve as a systems prototype for NOAA's next generation of operational satellites. For operational compatibility, our proposed advanced GOES imaging system contains the GOES-R requirements as a subset, and the GOES-N imager capabilities (and the sounder's imaging channels) as a further subset.

Chesters, Dennis↗
NASA NTRS2000

Recent Advances in Interstellar Astrochemistry

This presentation will address the possibility that molecules created in the interstellar medium may play a role in the origin and evolution of life on planetary surfaces. The argument will be presented in three main parts. First, I will review the various organic and volatile compounds that are now known or suspected to exist in a variety of circumstellar and interstellar environments. This information is largely the result of the combined applications of observational infrared and radio spectroscopy, laboratory astrophysical simulations, and theoretical astrochemistry. Second, I will devote additional attention to those materials that exhibit chemical properties that are of biological interest. Finally, I will discuss the evidence, largely gathered from the laboratory study of extraterrestrial materials (meteorites and cosmic dust), that interstellar materials, including organics, can survive the transition from the dense cloud phase into forming stellar systems. Once there, some of this material can be delivered largely unaltered to planetary surfaces where it can play key roles in the origin and subsequent evolution of life. Thus, the guiding principle of this presentation will be to attempt to 'put the astro into astrobiology.'

Sandford, Scott A.↗
NASA NTRS2004

Comparison of Satellite and Aircraft Measurements of Cloud Microphysical Properties in Icing Conditions During ATREC/AIRS-II

Satellites are ideal for continuous monitoring of aircraft icing conditions in many situations over extensive areas. The satellite imager data are used to diagnose a number of cloud properties that can be used to develop icing intensity indices. Developing and validating these indices requires comparison with objective "cloud truth" data in addition to conventional pilot reports (PIREPS) of icing conditions. Minnis et al. examined the relationships between PIREPS icing and satellite-derived cloud properties. The Atlantic-THORPEX Regional Campaign (ATReC) and the second Alliance Icing Research Study (AIRS-II) field programs were conducted over the northeastern USA and southeastern Canada during late 2003 and early 2004. The aircraft and surface measurements are concerned primarily with the icing characteristics of clouds and, thus, are ideal for providing some validation information for the satellite remote sensing product. This paper starts the process of comparing cloud properties and icing indices derived from the Geostationary Operational Environmental Satellite (GOES) with the aircraft in situ measurements of several cloud properties during campaigns and some of the The comparisons include cloud phase, particle size, icing intensity, base and top altitudes, temperatures, and liquid water path. The results of this study are crucial for developing a more reliable and objective icing product from satellite data. This icing product, currently being derived from GOES data over the USA, is an important complement to more conventional products based on forecasts, and PIREPS.

Nguyen, Louis↗
NASA NTRS2014

The Atmospheric Infrared Sounder Version 6 Cloud Products

The version 6 cloud products of the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) instrument suite are described. The cloud top temperature, pressure, and height and effective cloud fraction are now reported at the AIRS field-of-view (FOV) resolution. Significant improvements in cloud height assignment over version 5 are shown with FOV-scale comparisons to cloud vertical structure observed by the CloudSat 94 GHz radar and the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP). Cloud thermodynamic phase (ice, liquid, and unknown phase), ice cloud effective diameter D(sub e), and ice cloud optical thickness (τ) are derived using an optimal estimation methodology for AIRS FOVs, and global distributions for 2007 are presented. The largest values of tau are found in the storm tracks and near convection in the tropics, while D(sub e) is largest on the equatorial side of the midlatitude storm tracks in both hemispheres, and lowest in tropical thin cirrus and the winter polar atmosphere. Over the Maritime Continent the diurnal variability of tau is significantly larger than for the total cloud fraction, ice cloud frequency, and D(sub e), and is anchored to the island archipelago morphology. Important differences are described between northern and southern hemispheric midlatitude cyclones using storm center composites. The infrared-based cloud retrievals of AIRS provide unique, decadal-scale and global observations of clouds over portions of the diurnal and annual cycles, and capture variability within the mesoscale and synoptic scales at all latitudes.

infrared instruments↗
NASA NTRS2015

Dome Degradation Characterization of Wide-Field-of-View Nonscanner Aboard ERBE and Its Reprocessing

Earth Radiation Budget Experiment (ERBE) wide-field-of-view (WFOV) nonscanners aboard ERBS and NOAA- 9/NOAA-10 provided broadband shortwave and longwave irradiances from 1985 to 1999. The previous analysis showed dome degradation in the shortwave nonscanner instruments. The correction was performed with a constant spectral (gray assumption) degradation. We suspect that the gray assumption affected daytime longwave irradiance and led to a day-minus-night longwave flux differences (little change in night time longwave) increase over time. Based on knowledge from the CERES process, we will reprocess entire ERBE nonscanner radiation dataset by characterizing shortwave dome transmissivity with spectral dependent degradation using the solar data observed by these instruments. Once spectral dependent degradation is derived, imager derived cloud fraction and the cloud phase as well as surface type over the FOV of nonscanner instruments will be used to model unfiltering coefficients. This poster primarily explains the reprocessing techniques and includes initial comparison of several months of data processed with existing and our recent methods.

Shrestha, Alok K.↗
NASA NTRS2015

Tropical and Arctic Cloud Observations From CALIPSO Between 2008-2014 and its Implications

There has been studies (e.g., Schweiger, 2004) suggest significant increase of Arctic clouds during the last three decades, especially in the western Arctic region. Such studies are based on passive remote sensing that are not highly reliable due to the lack of contrasts in temperature and reflectance between clouds and snow/ice surfaces. Changes in the Arctic clouds can be evaluated more accurately using the space-based lidar measurements from CALIPSO during the last nine years since CALIPSO can provide much more accurate detection and classification of both water and ice clouds in the Arctic. Time series of Arctic cloud properties (e.g., cloud fraction, cloud thermodynamic phase, water cloud depolarization ratio and droplet number concentration) from CALIPSO data are analyzed in this study. This study reveals the changes in both cloud fraction and cloud microphysical properties during the last nine years when CALIPSO data are available. We will evaluate the changes in Arctic cloud fraction and cloud microphysical properties, their seasonal and spatial characteristics and the potential impact on the energy budget and the climate of the Arctic.

Hu, Yongxiang↗
NASA NTRS2016

A New, Low Braking Index for the LMC Pulsar B0540-69

We report the results of a 16-month monitoring campaign using the Swift satellite of PSR 0540, a young pulsar in the Large Magellanic Cloud. Phase connection was maintained throughout the campaign so that a reliable ephemeris could be determined, and the length of the campaign is adequate to accurately determine the spin frequency and its first and second derivatives. The braking index n is 0.031 +/- 0.013 (90% confidence), a value much lower than previously reported for 0 540 and almost all other young pulsars. We use data from the extensive monitoring campaign with RXTE to showt hat timing noise is unlikely to significantly affect the measurement. This is the first measurement of the braking index in the pulsars recently discovered high spin-down state. We discuss possible mechanisms for producing the low braking index.

pulsars individual (PSR B0540−69)↗
NASA NTRS2017

Development of Multi-Sensor Global Cloud and Radiance Composites for Earth Radiation Budget Monitoring from DSCOVR

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). Radiance observations and cloud property retrievals from low earth orbit and geostationary satellite imagers have to be co-located with EPIC pixels to provide scene identification in order to select anisotropic directional models needed to calculate shortwave and longwave fluxes. A new algorithm is proposed for optimal merging of selected radiances and cloud properties derived from multiple satellite imagers to obtain seamless global hourly composites at 5-kilometer resolution. An aggregated rating is employed to incorporate several factors and to select the best observation at the time nearest to the EPIC measurement. Spatial accuracy is improved using inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling. The composite data are subsequently remapped into EPIC-view domain by convolving composite pixels with the EPIC point spread function (PSF) defined with a half-pixel accuracy. PSF-weighted average radiances and cloud properties are computed separately for each cloud phase. The algorithm has demonstrated contiguous global coverage for any requested time of day with a temporal lag of under 2 hours in over 95 percent of the globe.

Khlopenkov, Konstantin↗
NASA NTRS2017

How the Emitted Size Distribution and Mixing State of Feldspar Affect Ice Nucleating Particles in a Global Model

The effect of aerosol particles on ice nucleation and, in turn, the formation of ice and mixed phase clouds is recognized as one of the largest sources of uncertainty in climate prediction. We apply an improved dust mineral specific aerosol module in the NASA GISS Earth System ModelE, which takes into account soil aggregates and their fragmentation at emission as well as the emission of large particles. We calculate ice nucleating particle concentrations from K-feldspar abundance for an active site parameterization for a range of activation temperatures and external and internal mixing assumption. We find that the globally averaged INP concentration is reduced by a factor of two to three, compared to a simple assumption on the size distribution of emitted dust minerals. The decrease can amount to a factor of five in some geographical regions. The results vary little between external and internal mixing and different activation temperatures, except for the coldest temperatures. In the sectional size distribution, the size range 24 micrometer contributes the largest INP number.

Perlwitz, Jan P.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-29931-DND

The ACE science working group satellite concept calls for a combination of high spectral resolution aerosol lidar and a polarimeter able to determine vertical profiles of aerosol extinction and clouds. Here we discuss why an HSRL lidar is required to achieve these requirements. The primary advantage of an HSRL over an elastic backscatter lidar is that the HSRL channel allows for the independent retrieval of vertically resolved aerosol extinction without constraint. Not requiring a constraint means greater availability of retrievals in polar regions, on the night side, and in broken cloud situations, plus greater accuracy particularly in the lowest part of the atmosphere and in scenes with aerosol heterogeneity. Advantages go beyond the extinction products. Unlike attenuated backscatter, which is an integrated quantity, the aerosol backscatter coefficient depends only on the aerosol at a specific level in the atmosphere, making it preferable for use in quantitative applications. The aerosol backscatter coefficient is more accurate from an HSRL system compared to the retrieval from a single-channel system. This is because errors in both extinction and backscatter from a single-channel system increase with decreasing altitude, due to the need to accurately characterize the overlying attenuation. This means that an HSRL will also have more valid data in and near the boundary layer where aerosol is of primary interest for air quality and weather applications and for many climate objectives including aerosol-cloud interaction studies. Even cloud properties that do not directly use the HSRL capability (cloud phase and thin cirrus retrievals) benefit from the more accurate calibration of an HSRL system. The improved accuracy of backscatter and extinction also lead to better capabilities for aerosol typing, improved retrievals of aerosol radiative effect, and increased information content for combined lidar + polarimeter retrievals of profiles of microphysical properties such as vertically resolved effective radius.

S P Burton↗
NASA NTRSDate not supplied

Comparison of INP Parameterizations for Dust Minerals in Climatological Simulations With a Global Model

The effect of aerosol particles on ice nucleation and, in turn, the formation of ice and mixed phase clouds is recognized as one of the largest sources of uncertainty in weather and climate prediction. We utilize an improved sectional dust module in NASA GISS Earth System ModelE2.1, which distinguishes eight different mineral species and accretions between iron oxides and the other minerals. Simulations over a period of 20 years have been carried out with this model, and the mineral fields and other model variables (temperature, relative humidity) are used to calculate the ice nucleating particle (INP) number concentration, applying time-independent and time-dependent INP parameterizations, such as active site parameterization and water activity based immersion freezing model (ABIFM). We study how the dependence of the parameterizations on different model variables affects the mean INP number concentration. The sensitivity of the INP number concentration to fundamental dust properties such as emitted mineral size distributions and mixing state between minerals is also investigated. Results show that the sensitivity of the total INP number concentration to the emitted dust size distribution is rather small, but the sensitivity over the whole size range obscures offsetting differences in the magnitude and the sign of the sensitivity between smaller and larger particles.

INP Parameterizations↗
NASA NTRSDate not supplied

Towards a more realistic representation of NASA CERES-derived surface radiative fluxes during polar night: a comparison with the MOSAiC field campaign

The Arctic is one of the most sensitive regions of Earth to climate change, and yet remains one of the more difficult regions to both observe and simulate. The high latitude of the Arctic gives it some unusual features compared with much of Earth, such as large and variable sea ice cover, extensive mixed phase cloud cover, and the polar day/night cycle. These features significantly influence the radiative environment of the Arctic as well as the ability to observe it. The remoteness of the Arctic from most of civilization makes large volumes of in situ observations difficult to collect, and so satellite observations are a critical tool for observing the region, such as those from Clouds and the Earth’s Radiant Energy System (CERES). But validating the satellite radiative flux estimates is difficult because of the lack of in situ measurements. A useful remedy is utilizing measurements from field campaigns, such as the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition during 2019-2020. The extensive high-quality surface radiative flux and meteorological measurements collected from MOSAiC provide a useful check on flux retrievals from CERES instruments. Previous work compared MOSAiC and CERES surface radiative fluxes during the 2020 melt season (April-September). Errors in CERES fluxes were related to errors in estimated atmospheric optical depth and surface albedo. We conduct a similar assessment of CERES surface flux retrievals using MOSAiC data, but for the months of October through March instead. As this time period is mostly during polar night and twilight, the focus of the study is on longwave surface fluxes. To better understand the reasons for errors in CERES fluxes, we use the large set of meteorological measurements also collected by MOSAiC, including surface temperature, thermodynamic vertical profiles, surface turbulent fluxes, and cloud properties. For example, one notable possible source of error in the CERES estimate of surface downwelling longwave flux is the estimate of cloud base height when compared with MOSAiC W-band radar measurements – an overestimate of cloud base height yields an underestimate of the downwelling flux on the order of tens of W m2.

J. Brant Dodson↗
NASA NTRSDate not supplied

A Convolutional Neural Network for Removing GOES-17 Image Anomalies to Improve CERES Broadband Flux Measurement

Background - CERES provides satellite-based global climate data record of Earth's radiation budget and clouds - CERES = Clouds and the Earth's Radiant Energy System - Measurement anomalies impact cloud retrieval - Incorrect Cloud Phase = Incorrect Flux - Unmitigated bad scanlines will impact climate data records - GOES-17 ABI cooling system anomaly = many bad scanlines at night (~10:30 - 16:30 UTC) - Cleaning imagery of bad scanlines is laborious but necessary - A convolution neural network (CNN) can identify and clean bad scanlines as effectively as a human

Benjamin Scarino↗
NASA NTRS1993

Comparison of cloud microphysical parameters derived from surface and satellite measurements during FIRE phase 2

Cloud microphysical properties are an important component in climate model parameterizations of water transport, cloud radiative exchange, and latent heat processes. Estimation of effective cloud particle size, liquid or ice water content, and optical depth from satellite-based instrumentation is needed to develop a climatology of cloud microphysical properties and to better understand and model cloud processes in atmospheric circulation. These parameters are estimated from two different surface data sets taken at Coffeyville, Kansas, during the First ISCCP Regional Experiment (FIRE) Phase-2 Intensive Field Observation (IFO) period (November 13 - December 7, 1991). Satellite data can also provide information about optical depth and effective particle size. This paper explores the combination of the FIRE-2 surface and satellite data to determine each of the cloud microphysical properties.

Young, David F.↗
DOE OSTI2016

On the relationships among cloud cover, mixed‐phase partitioning, and planetary albedo in GCMs

Abstract In this study, it is shown that CMIP5 global climate models (GCMs) that convert supercooled water to ice at relatively warm temperatures tend to have a greater mean‐state cloud fraction and more negative cloud feedback in the middle and high latitude Southern Hemisphere. We investigate possible reasons for these relationships by analyzing the mixed‐phase parameterizations in 26 GCMs. The atmospheric temperature where ice and liquid are equally prevalent (T5050) is used to characterize the mixed‐phase parameterization in each GCM. Liquid clouds have a higher albedo than ice clouds, so, all else being equal, models with more supercooled liquid water would also have a higher planetary albedo. The lower cloud fraction in these models compensates the higher cloud reflectivity and results in clouds that reflect shortwave radiation (SW) in reasonable agreement with observations, but gives clouds that are too bright and too few. The temperature at which supercooled liquid can remain unfrozen is strongly anti‐correlated with cloud fraction in the climate mean state across the model ensemble, but we know of no robust physical mechanism to explain this behavior, especially because this anti‐correlation extends through the subtropics. A set of perturbed physics simulations with the Community Atmospheric Model Version 4 (CAM4) shows that, if its temperature‐dependent phase partitioning is varied and the critical relative humidity for cloud formation in each model run is also tuned to bring reflected SW into agreement with observations, then cloud fraction increases and liquid water path (LWP) decreases with T5050, as in the CMIP5 ensemble.

58 GEOSCIENCES↗
NASA NTRS2011

Remote Sensing the Vertical Profile of Cloud Droplet Effective Radius, Thermodynamic Phase, and Temperature

Cloud-aerosol interaction is a key issue in the climate system, affecting the water cycle, the weather, and the total energy balance including the spatial and temporal distribution of latent heat release. Information on the vertical distribution of cloud droplet microphysics and thermodynamic phase as a function of temperature or height, can be correlated with details of the aerosol field to provide insight on how these particles are affecting cloud properties and their consequences to cloud lifetime, precipitation, water cycle, and general energy balance. Unfortunately, today's experimental methods still lack the observational tools that can characterize the true evolution of the cloud microphysical, spatial and temporal structure in the cloud droplet scale, and then link these characteristics to environmental factors and properties of the cloud condensation nuclei. Here we propose and demonstrate a new experimental approach (the cloud scanner instrument) that provides the microphysical information missed in current experiments and remote sensing options. Cloud scanner measurements can be performed from aircraft, ground, or satellite by scanning the side of the clouds from the base to the top, providing us with the unique opportunity of obtaining snapshots of the cloud droplet microphysical and thermodynamic states as a function of height and brightness temperature in clouds at several development stages. The brightness temperature profile of the cloud side can be directly associated with the thermodynamic phase of the droplets to provide information on the glaciation temperature as a function of different ambient conditions, aerosol concentration, and type. An aircraft prototype of the cloud scanner was built and flew in a field campaign in Brazil.

Martins, J. V.↗
NASA NTRS2020

A Machine Learning-Based Cloud Detection and Thermodynamic Phase Classification Algorithm using Passive Spectral Observations

We trained two Random Forest (RF) machine-learning models for cloud mask and cloud thermodynamic phase detection using spectral observations from VIIRS on Suomi NPP (SNPP). Observations from CALIOP were carefully selected to provide reference labels. The two RF models were trained for all-day and daytime-only conditions using a 4-year collocated VIIRS/CALIOP dataset from 2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPP VIIRS training samples cover a broad viewing zenith angle range, which is a great benefit to overall model performance. The all-day model uses 3 VIIRS infrared (IR) bands (8.6,11, and 12 μm) and the daytime model uses 5 Near-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64 and 2.25 μm) together with the 3 IR bands to detect clear, liquid water, and ice cloud pixels. Up to 7 surface types, namely, ocean/water, forest, cropland, grassland, snow/ice, barren/desert, and shrubland, were considered separately to enhance performance for both models. Detection of cloudy pixels and thermodynamic phase with the two RF models were compared against collocated CALIOP products from 2017. It is shown that, with a conservative screening process that excludes the most challenging cloudy pixels for passive remote sensing, the two RF models have high accuracy rates in comparison with the CALIOP reference for both cloud detection and thermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask and phase products are also evaluated, with results showing that the two RF models and the MODIS MYD06 optical property phase product are the top 3 algorithms with respect to lidar observations during the daytime. During the nighttime, the RF all-day model works best for both cloud detection and phase, in particular for pixels over snow/ice surfaces. The present RF models can be extended to other similar passive instruments if training samples can be collected from CALIOP or other lidars. However, the quality of reference labels and potential sampling issues that may impact model performance would need further attention.

cloud detection↗
NASA NTRS1983

Aqueous-phase source of formic acid in clouds

The coupled gas- and aqueous-phase cloud chemistry of HCOOH were examined for controlling factors in the acidity of cloud and rainwater. Attention was given to the aqueous OH/HO2 system that yields an OH species that is highly reactive with other species, notably SO2 and the formaldehyde/formic acid complex. A numerical model was developed to simulate the cloud chemistry in the remote troposphere, with considerations given to CH4-CO-NO(x)-O3-H(x)O(y) system. It was determined that aqueous phase OH radicals can produce and destroy formic acid droplets in daylight conditions, as well as control formic acid levels in rainwater. It is sugested that the same types of reactions may be involved in the control of acetic acid and other organic acids.

Chameides, W. L.↗