Results for “Modeling the Quality of Radiation” — Page 3

NASA NTRS2004

The Average Quality Factors by TEPC for Charged Particles

The quality factor used in radiation protection is defined as a function of LET, Q(sub ave)(LET). However, tissue equivalent proportional counters (TEPC) measure the average quality factors as a function of lineal energy (y), Q(sub ave)(Y). A model of the TEPC response for charged particles considers energy deposition as a function of impact parameter from the ion s path to the volume, and describes the escape of energy out of sensitive volume by delta-rays and the entry of delta rays from the high-density wall into the low-density gas-volume. A common goal for operational detectors is to measure the average radiation quality to within accuracy of 25%. Using our TEPC response model and the NASA space radiation transport model we show that this accuracy is obtained by a properly calibrated TEPC. However, when the individual contributions from trapped protons and galactic cosmic rays (GCR) are considered; the average quality factor obtained by TEPC is overestimated for trapped protons and underestimated for GCR by about 30%, i.e., a compensating error. Using TEPC's values for trapped protons for Q(sub ave)(y), we obtained average quality factors in the 2.07-2.32 range. However, Q(sub ave)(LET) ranges from 1.5-1.65 as spacecraft shielding depth increases. The average quality factors for trapped protons on STS-89 demonstrate that the model of the TEPC response is in good agreement with flight TEPC data for Q(sub ave)(y), and thus Q(sub ave)(LET) for trapped protons is overestimated by TEPC. Preliminary comparisons for the complete GCR spectra show that Q(sub ave)(LET) for GCR is approximately 3.2-4.1, while TEPC measures 2.9-3.4 for QQ(sub ave)(y), indicating that QQ(sub ave)(LET) for GCR is underestimated by TEPC.

Kim, Myung-Hee Y.↗

NASA NTRS1978

Simplified multiple scattering model for radiative transfer in turbid water

Quantitative analytical procedures for relating selected water quality parameters to the characteristics of the backscattered signals, measured by remote sensors, require the solution of the radiative transport equation in turbid media. Presented is an approximate closed form solution of this equation and based on this solution, the remote sensing of sediments is discussed. The results are compared with other standard closed form solutions such as quasi-single scattering approximations.

Ghovanlou, A. H.↗

NASA NTRS2019

The Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) Mission: Status, Science, Advances

The PACE mission represents NASA’s next investment in ocean color, cloud, and aerosol data records to enable continued and advanced insight into oceanographic and atmospheric responses to Earth’s changing climate. The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission represents NASA’s next investment in satellite ocean color and the study of Earth’s ocean-atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth's changing climate. PACE objectives include extending systematic cloud, aerosol, and ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles, food web processes, and ecosystem responses to a changing climate, and improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and air and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument is a spectrometer that spans the ultraviolet to shortwave infrared, with a ground sample distance of 1-km at nadir. This payload is complemented by two multi-angle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022-to-early 2023, the PACE observatory will enable significant advances in the study of Earth’s biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean-atmosphere-land system. Here, we present an overview of the PACE mission, including its developmental history, science objectives, instrument payload, observatory characteristics, and data products.

P Jeremy Werdell↗

NASA NTRS2019

The NASA Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) Mission: an Emerging Era of Global, Hyperspectral Earth System Remote Sensing

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission represents NASA's next investment in satellite ocean color and the study of Earth's ocean-atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth's changing climate. PACE objectives include extending systematic cloud, aerosol, and ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles and ecosystem responses to a changing climate, and improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will also provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument - the Ocean Color Instrument (OCI) - is a hyperspectral imaging radiometer that spans the ultraviolet to shortwave infrared, with a ground sample distance of 1-kilometer at nadir. This includes continuous collection of spectra from 340 to 890 nanometers in 5-nanometer steps. The PACE payload is complemented by two multi-angle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022-to-early 2023, the PACE observatory will enable significant advances in the study of Earth's biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean-atmosphere system. We present a brief overview of the PACE mission, followed by a detailed discussion of the capabilities and design concept of OCI.

Passive Remote Sensing↗

NASA NTRS2019

The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission: An Emerging Era of Global, Hyperspectral Earth System Remote Sensing

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission represents NASA’s next investment in satellite ocean color and the study of Earth’s ocean-atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth's changing climate. PACE objectives include extending systematic cloud, aerosol, ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles and ecosystem responses to a changing climate, as well as improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will also provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument – the Ocean Color Instrument (OCI) – is a hyperspectral imaging radiometer that spans the ultraviolet to shortwave infrared, with a ground sample distance of 1-km at nadir. This includes continuous collection of spectra from 340 nm to 890 nm in 5 nm steps. The PACE payload is complemented by two multi-angle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022-to-early 2023, the PACE observatory will enable significant advances in the study of Earth’s biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean-atmosphere system. We present a brief overview of the PACE mission, followed by a discussion of the capabilities and design concept of OCI.

hyperspectral radiometer↗

NASA NTRSDate not supplied

Near Real Time Air Quality Forecasts Using the NASA GEOS Model

This presentation provides an overview of NASA's Global Modeling and Assimilation Office (GMAO) high-resolution global forecast and reanalysis products for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model radiatively coupled to GOCART aerosol module assimilates 2-dimensional column-integrated aerosol optical depth (AOD) at one wavelength (550 nm) in order to constrain the model's background AOD in order to have the best historical estimate and forecasts of AOD and particulate matter. Furthermore, the GEOS model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5).

K Emma Knowland↗

NASA NTRSDate not supplied

Near Real Time Air Quality Forecasts Using the NASA GEOS Model

This presentation provides an overview of NASA's Global Modeling and Assimilation Office (GMAO) high-resolution global forecast and reanalysis products for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model radiatively coupled to GOCART aerosol module assimilates 2-dimensional column-integrated aerosol optical depth (AOD) at one wavelength (550 nm) in order to constrain the model's background AOD in order to have the best historical estimate and forecasts of AOD and particulate matter. Furthermore, the GEOS model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5).

K Emma Knowland↗

NASA NTRS1981

Application of digital terrain data to quantify and reduce the topographic effect on Landsat data

Landsat multispectral scanner (MSS) data are integrated with 30 m US Geological Survey digital terrain data to quantify and reduce topographic effects on imagery of a forested mountain ridge in central Pennsylvania. Spectral band ratioing slightly decreased variation of Landsat data for 50% of the transects and could not eliminate direct-light topographic effects. A Lambertian model increased topographic effect, and a modified Lambertian model decreased variation produced by the original model, but gave higher variances than found in the raw Landsat data. A non-Lambertian model, however, decreased variation by 86%, reducing topographic effects. The study demonstrated that high quality digital terrain data can be used to develop and test improved radiative transfer models, and enhance the utility of MSS data.

Justice, C. O.↗

NASA NTRS2015

Comparison of GFED3, QFED2 and FEER1 Biomass Burning Emissions Datasets in a Global Model

Biomass burning contributes about 40% of the global loading of carbonaceous aerosols, significantly affecting air quality and the climate system by modulating solar radiation and cloud properties. However, fire emissions are poorly constrained in models on global and regional levels. In this study, we investigate 3 global biomass burning emission datasets in NASA GEOS5, namely: (1) GFEDv3.1 (Global Fire Emissions Database version 3.1); (2) QFEDv2.4 (Quick Fire Emissions Dataset version 2.4); (3) FEERv1 (Fire Energetics and Emissions Research version 1.0). The simulated aerosol optical depth (AOD), absorption AOD (AAOD), angstrom exponent and surface concentrations of aerosol plumes dominated by fire emissions are evaluated and compared to MODIS, OMI, AERONET, and IMPROVE data over different regions. In general, the spatial patterns of biomass burning emissions from these inventories are similar, although the strength of the emissions can be noticeably different. The emissions estimates from QFED are generally larger than those of FEER, which are in turn larger than those of GFED. AOD simulated with all these 3 databases are lower than the corresponding observations in Southern Africa and South America, two of the major biomass burning regions in the world.

biomass burning↗

NASA NTRS1983

Radiation protection for manned space activities

The Earth's natural radiation environment poses a hazard to manned space activities directly through biological effects and indirectly through effects on materials and electronics. The following standard practices are indicated that address: (1) environment models for all radiation species including uncertainties and temporal variations; (2) upper bound and nominal quality factors for biological radiation effects that include dose, dose rate, critical organ, and linear energy transfer variations; (3) particle transport and shielding methodology including system and man modeling and uncertainty analysis; (4) mission planning that includes active dosimetry, minimizes exposure during extravehicular activities, subjects every mission to a radiation review, and specifies operational procedures for forecasting, recognizing, and dealing with large solar flaes.

Jordan, T. M.↗

NASA NTRS2016

Nitrogen Dioxide Observations from the Geostationary Trace Gas and Aerosol Sensor Optimization (GeoTaso) Airborne Instrument: Retrieval Algorithm and Measurements During DISCOVER-AQ Texas 2013

The Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument is a test bed for upcoming air quality satellite instruments that will measure backscattered ultraviolet, visible and near-infrared light from geostationary orbit. GeoTASO flew on the NASA Falcon aircraft in its first intensive field measurement campaign during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) Earth Venture Mission over Houston, Texas, in September 2013. Measurements of backscattered solar radiation between 420 and 465 nm collected on 4 days during the campaign are used to determine slant column amounts of NO2 at 250 m x 250 m spatial resolution with a fitting precision of 2.2 x 10(exp 15) molecules/sq cm. These slant columns are converted to tropospheric NO2 vertical columns using a radiative transfer model and trace gas profiles from the Community Multiscale Air Quality (CMAQ) model. Total column NO2 from GeoTASO is well correlated with ground-based Pandora observations (r = 0.90 on the most polluted and cloud-free day of measurements and r = 0.74 overall), with GeoTASO NO2 slightly higher for the most polluted observations. Surface NO2 mixing ratios inferred from GeoTASO using the CMAQ model show good correlation with NO2 measured in situ at the surface during the campaign (r = 0.85). NO2 slant columns from GeoTASO also agree well with preliminary retrievals from the GEO-CAPE Airborne Simulator (GCAS) which flew on the NASA King Air B200 (r = 0.81, slope = 0.91). Enhanced NO2 is resolvable over areas of traffic NOx emissions and near individual petrochemical facilities.

Aerosol Sensor Optimization↗

NASA NTRS2003

Dust Emissions, Transport, and Deposition Simulated with the NASA Finite-Volume General Circulation Model

Mineral dust aerosols have radiative impacts on Earth's atmosphere, have been implicated in local and regional air quality issues, and have been identified as vectors for transporting disease pathogens and bringing mineral nutrients to terrestrial and oceanic ecosystems. We present for the first time dust simulations using online transport and meteorological analysis in the NASA Finite-Volume General Circulation Model (FVGCM). Our dust formulation follows the formulation in the offline Georgia Institute of Technology-Goddard Global Ozone Chemistry Aerosol Radiation and Transport Model (GOCART) using a topographical source for dust emissions. We compare results of the FVGCM simulations with GOCART, as well as with in situ and remotely sensed observations. Additionally, we estimate budgets of dust emission and transport into various regions.

Colarco, Peter↗

NASA NTRS2019

Impact of GMI All-Sky Radiance Assimilation in the NASA GEOS Forecast System

The assimilation of cloud- and precipitation-affected ("all-sky") radiances has become an important focus of development at most numerical weather prediction centers. Efforts at the Global Modeling and Assimilation Office (GMAO) have focused on all-sky assimilation of GPM Microwave Imager (GMI) radiances, which became operational in the GEOS real-time production system in July 2018. Implementation of the all-sky capability required several upgrades to the GEOS hybrid 4D-EnVar assimilation infrastructure including the addition of control variables for cloud liquid, cloud ice, rain and snow, enhancements to the radiative transfer model, new hybrid background and observational error models, and modified quality control and bias correction procedures. This talk describes the impact of GMI all-sky radiance assimilation on GEOS analyses and forecasts as determined from examination of various metrics including statistics of background departures and analysis increments, forecast skill scores, and forecast sensitivity observation impact (FSOI) calculations. It is shown that in addition to the hydrometeors themselves, the initial wind, temperature and pressure fields all undergo significant dynamic adjustment in response to the analyzed cloud and precipitation features. Assimilation of GMI radiances leads to improved forecasts of lower tropospheric wind, temperature and humidity, especially in the tropics. The largest forecast improvements occur during the first 48 hours, with diminishing impact thereafter. However, combining GMI all-sky assimilation with improvements to the GEOS model physics as in the recent implementation of the real-time production system, extends these forecast improvements well in to the medium range. FSOI results based on a 24-hr moist global energy norm show that GMI radiances provide nearly uniform beneficial impact throughout the tropics, with more mixed impacts in the subtropics. While the overall impact of GMI is smaller than that of other, much more numerous microwave and hyperspectral infrared radiance types, its impact is among the largest of all radiance types on a per-observation basis.

Gelaro, Ron↗

NASA NTRS2021

The First Global 883 GHz Cloud Ice Survey: IceCube Level 1 Data Calibration, Processing and Analysis

Sub-millimeter (sub-mm, 200 - 1000 GHz) wavelengths contribute a unique capability to fill-in the sensitivity gap between operational visible/infrared (VIS/IR) and microwave (MW) remote sensing for atmosphere cloud ice and snow. Being able of penetrating cloud to measure cloud ice mass and microphysical properties in the middle to upper troposphere, this is a critical spectrum range for us to understand the connection between cloud ice and precipitation processes.5As the first space-borne 883 GHz radiometer, IceCube mission was NASA’s latest effort in spaceflight demonstration of a commercial sub-mm radiometer technology. Successfully launched from the International Space Station, IceCube is essentially a free-running radiometer and collected valuable 15-month measurements of atmosphere and cloud ice. This paper describes the detailed procedures for Level 1 data calibration, processing and validation. The scientific quality and values of IceCube data are then discussed, including radiative transfer model validation and evaluation, as well as the unique spatial distribution10and diurnal cycle of cloud ice that are revealed for the first time on a quasi-global scale at this frequency.

IceCube↗

NASA NTRS2021

The First Global 883 GHz Cloud Ice Survey: IceCube Level 1 Data Calibration, Processing and Analysis

Sub-millimeter (sub-mm, 200 - 1000 GHz) wavelengths contribute a unique capability to fill-in the sensitivity gap between operational visible/infrared (VIS/IR) and microwave (MW) remote sensing for atmosphere cloud ice and snow. Being able of penetrating cloud to measure cloud ice mass and microphysical properties in the middle to upper troposphere, this is a critical spectrum range for us to understand the connection between cloud ice and precipitation processes.5As the first space-borne 883 GHz radiometer, IceCube mission was NASA’s latest effort in spaceflight demonstration of a commercial sub-mm radiometer technology. Successfully launched from the International Space Station, IceCube is essentially a free-running radiometer and collected valuable 15-month measurements of atmosphere and cloud ice. This paper describes the detailed procedures for Level 1 data calibration, processing and validation. The scientific quality and values of IceCube data are then discussed, including radiative transfer model validation and evaluation, as well as the unique spatial distribution10and diurnal cycle of cloud ice that are revealed for the first time on a quasi-global scale at this frequency

Jie Gong↗

NASA NTRS2024

A Uniform Analysis of Debris Discs with the Gemini Planet Imager II: Constraints on Dust Density Distribution Using Empirically Informed Scattering Phase Functions

Spatially resolved images of debris discs are necessary to determine disc morphological properties and the scattering phase function (SPF) thatantifies the brightness of scattered light as a function of phase angle. Current high-contrast imaging instruments have successfully resolved several dozens of debris discs around other stars, but few studies have investigated trends in the scattered-light, resolved population of debris discs in a uniform and consistent manner. We have combined Karhunen-Loeve Image Projection (KLIP) with radiative-transfer disc forward modelling in order to obtain the highest-quality image reductions and constrain disc morphological properties of eight debris discs imaged by the Gemini Planet Imager at H -band with a consistent and uniformly applied approach. In describing the scattering properties of our models, we assume a common SPF informed from solar system dust scattering measurements and apply it to all systems. We identify a diverse range of dust density properties among the sample, including critical radius, radial width, and vertical width. We also identify radially narrow and vertically extended discs that may have resulted from substellar companion perturbations, along with a tentative positive trend in disc eccentricity with relative disc width. We also find that using a common SPF can achieve reasonable model fits for discs that are axisymmetric and asymmetric when fitting models to each side of the disc independently, suggesting that scattering behaviour from debris discs may be similar to Solar system dust.

scattering↗

NASA NTRS2014

The DMRT-ML Model: Numerical Simulations of the Microwave Emission of Snowpacks Based on the Dense Media Radiative Transfer Theory

Microwave radiometer observations have been used to retrieve snow depth and snow water equivalent on both land and sea ice, snow accumulation on ice sheets, melt events, snow temperature, and snow grain size. Modeling the microwave emission from snow and ice physical properties is crucial to improve the quality of these retrievals. It also is crucial to improve our understanding of the radiative transfer processes within the snow cover, and the snow properties most relevant in microwave remote sensing. Our objective is to present a recent microwave emission model and its validation. The model is named DMRT-ML (DMRT Multi-Layer).

Microwave radiometer↗

NASA NTRS2014

The DMRT-ML Model: Numerical Simulations of the Microwave Emission of Snowpacks Based on the Dense Media Radiative Transfer Theory

Microwave radiometer observations have been used to retrieve snow depth and snow water equivalent on both land and sea ice, snow accumulation on ice sheets, melt events, snow temperature, and snow grain size. Modeling the microwave emission from snow and ice physical properties is crucial to improve the quality of these retrievals. It also is crucial to improve our understanding of the radiative transfer processes within the snow cover, and the snow properties most relevant in microwave remote sensing. Our objective is to present a recent microwave emission model and its validation. The model is named DMRT-ML (DMRT Multi-Layer), and is available at http:lgge.osug.frpicarddmrtml.

ice sheet↗