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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Measurement of Atmospheric CO2 Column Concentrations to Cloud Tops With a Pulsed Multi-Wavelength Airborne Lidar

We have measured the column-averaged atmospheric CO2 mixing ratio to a variety of cloud tops by using an airborne pulsed multi-wavelength integrated-path differential absorption (IPDA) lidar. Airborne measurements were made at altitudes up to 13 km during the 2011, 2013 and 2014 NASA Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) science campaigns flown in the United States West and Midwest and were compared to those from an in situ sensor. Analysis of the lidar backscatter profiles shows the average cloud top reflectance was approx. 5% for the CO2 measurement at 1572.335 nm except to cirrus clouds, which had lower reflectance. The energies for 1 micro-s wide laser pulses reflected from cloud tops were sufficient to allow clear identification of CO2 absorption line shape and then to allow retrievals of atmospheric column CO2 from the aircraft to cloud tops more than 90% of the time. Retrievals from the CO2 measurements to cloud tops had minimal bias but larger standard deviations when compared to those made to the ground, depending on cloud top roughness and reflectance. The measurements show this new capability helps resolve CO2 horizontal and vertical gradients in the atmosphere. When used with nearby full-column measurements to ground, the CO2 measurements to cloud tops can be used to estimate the partial-column CO2 concentration below clouds, which should lead to better estimates of surface carbon sources and sinks. This additional capability of the range-resolved CO2 IPDA lidar technique provides a new benefit for studying the carbon cycle in future airborne and space-based CO2 missions.

atmospheric↗
NASA NTRS2017

RFI Statistical Distribution and Missed Detection in Aquarius Radiometer Measurements

Aquarius is an microwave active/passive sensor whose main goal is to globally estimate sea surface salinity from space. Two instruments, a radar scatterometer and a radiometer, operate at L-band observing the same surface footprint almost simultaneously. The sensitivity to sea surface salinity (SSS) is given by the radiometer, while the scatterometer measurements provide a correction for sea surface roughness. Although the primary objective is the measurement of SSS, the instrument combination operates continuously, acquiring data over land and sea ice as well. Radio Frequency Interference (RFI) can occur in both the radiometer and the scatterometer bands of operation, and for this reason detection and mitigation of RFI was included in the data processing of both active and passive instruments. This paper will focus on the RFI processing for the Aquarius radiometer only and provide an update on the efforts to reduce the amount of missed RFI detection.

RFI↗
NASA NTRS2018

PIV and Rotational Raman-Based Temperature Measurements for CFD Validation in a Single Injector Cooling Flow

Film cooling is used in a wide variety of engineering applications for protection of surfaces from hot or combusting gases. The design of more efficient thin film cooling geometries/configurations could be facilitated by an ability to accurately model and predict the effectiveness of current designs using computational fluid dynamics (CFD) code predictions. Hence, a benchmark set of flow field property data were obtained for use in assessing current CFD capabilities and for development of better turbulence models. Both Particle Image Velocimetry (PIV) and spontaneous rotational Raman scattering (SRS) spectroscopy were used to acquire high quality, spatially-resolved measurements of the mean velocity, turbulence intensity and also the mean temperature and normalized root mean square (rms) temperatures in a single injector cooling flow arrangement. In addition to flowfield measurements, thermocouple measurements on the plate surface enabled estimates of the film effectiveness. Raman spectra in air were obtained across a matrix of radial and axial locations downstream from a 68.07 mm square nozzle blowing heated air over a range of temperatures and Mach numbers, across a 30.48cm long plate equipped with a single injector cooling hole. In addition, both centerline streamwise 2-component PIV and cross-stream 3-component Stereo PIV data at 15 axial stations were collected in the same flows. The velocity and temperature data were then compared against Wind-US CFD code predictions for the same flow conditions. The results of this and planned follow-on studies will support NASA's development and assessment of turbulence models for heated flows.

Turbulence Modeling↗
NASA NTRS2018

Recent Updates in the SMAP Level-4 Soil Moisture Algorithm

The NASA Soil Moisture Active Passive (SMAP) mission generates, among other data sets, the Level-4 Soil Moisture (L4_SM) product. The L4_SM data are published with a mean latency of ~2.5 days from the time of observation and provide global, three-hourly, 9-km resolution estimates of surface and root-zone soil moisture and related land surface states and fluxes. The L4_SM algorithm is based on the assimilation of SMAP radiometer brightness temperature observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter (EnKF). In 2018, the L4_SM algorithm was upgraded from Version 3 to Version 4. Underlying the new version is a revised modeling system that includes improved input parameter datasets for land cover, topography, and vegetation height that are based on recent, high-quality, space-borne remote sensing observations. Additionally, SMAP Level-2 soil moisture retrievals and in situ soil moisture measurements were used to calibrate a particular Catchment model parameter that governs the recharge of surface soil moisture from below under non-equilibrium conditions, which brings the model's surface soil moisture more in line with the SMAP Level-2 and in situ soil moisture. Moreover, the calibration of the assimilated SMAP brightness temperatures changed substantially from Version 3 to Version 4, and the "catchment deficit" model variable was removed from the EnKF state vector to avoid degrading the model's groundwater estimates.Considerable effort went into the version upgrade, creating an expectation that the new version is improved over the old version. Indeed, some aspects of the new version are clearly better. However, other aspects are not. In this presentation we summarize the skill of the new and old versions vs. independent in situ measurements and in terms of data assimilation diagnostics, including, for example, the statistics of the (soil moisture) analysis increments and the observation-minus-forecast (brightness temperatures) residuals. We share our experience with trying to improve to the L4_SM product and the lessons learned from the effort.

Reichle, Rolf H.↗
NASA NTRS2019

PIV and Rotational Raman-Based Temperature Measurements for CFD Validation of a Perforated Plate Cooling Flow: Part I

Film cooling is used in a wide variety of engineering applications for protection of surfaces from hot or combusting gases. The design of more efficient film cooling geometries/configurations could be facilitated by an ability to accurately model and predict the effectiveness of current designs using computational fluid dynamics (CFD) code predictions. Hence, a benchmark set of flow field property data were obtained for use in assessing current CFD capabilities and for development of better modeling approaches for these turbulent flow fields where accurate calculation of turbulent heat flux is important. Both Particle Image Velocimetry (PIV) and spontaneous rotational Raman scattering (SRS) spectroscopy were used to acquire high quality, spatially-resolved measurements of the mean velocity, turbulence intensity as well as the mean temperature and root mean square (rms) temperatures in a film cooling flow field. In addition to off-body flow field measurements, infrared thermography (IR) and thermocouple measurements on the plate surface enabled estimates of the film effectiveness. Raman spectra in air were obtained across a matrix of axial locations downstream from a 68.07 mm square nozzle blowing heated air over a range of temperatures (up to TR = 2.7) and Mach numbers (up to M0.9), across a 30.48 cm long plate equipped with three patches of 45 small (~1 mm) diameter cooling holes arranged in a staggered configuration. In addition, both centerline streamwise 2-component PIV and cross-stream 3-component Stereo PIV data at 14 axial stations were collected in the same flows. Only a subset of the data collected in the test program is included in this Part I report and are available from the NASA STI office. The final portion of the data will be published in a future report, Part II, along with CFD predictions of the complex cooling film flow.

IR camera Measurments↗
NASA NTRS2019

PIV and Rotational Raman-Based Temperature Measurements for CFD Validation of a Perforated Plate Cooling Flow: Part 1

Film cooling is used in a wide variety of engineering applications for protection of surfaces from hot or combusting gases. The design of more efficient film cooling geometries/configurations could be facilitated by an ability to accurately model and predict the effectiveness of current designs using computational fluid dynamics (CFD) code predictions. Hence, a benchmark set of flow field property data were obtained for use in assessing current CFD capabilities and for development of better modeling approaches for these turbulent flow fields where accurate calculation of turbulent heat flux is important. Both Particle Image Velocimetry (PIV) and spontaneous rotational Raman scattering (SRS) spectroscopy were used to acquire high quality, spatially-resolved measurements of the mean velocity, turbulence intensity as well as the mean temperature and root mean square (rms) temperatures in a film cooling flow field. In addition to off-body flow field measurements, infrared thermography (IR) and thermocouple measurements on the plate surface enabled estimates of the film effectiveness. Raman spectra in air were obtained across a matrix of axial locations downstream from a 68.07 mm square nozzle blowing heated air over a range of temperatures (up to TR = 2.7) and Mach numbers (up to M0.9), across a 30.48 cm long plate equipped with three patches of 45 small (~1 mm) diameter cooling holes arranged in a staggered configuration. In addition, both centerline streamwise 2-component PIV and cross-stream 3-component Stereo PIV data at 14 axial stations were collected in the same flows. Only a subset of the data collected in the test program is included in this Part I report and are available from the NASA STI office. The final portion of the data will be published in a future report, Part II, along with CFD predictions of the complex cooling film flow.

Raman Scattering↗
NASA NTRS2020

PIV and Rotational Raman-Based Temperature Measurements for CFD Validation of a Perforated Plate Cooling Flow: Part I

Film cooling is used in a wide variety of engineering applications for protection of surfaces from hot or combusting gases. The design of more efficient film cooling geometries/configurations could be facilitated by an ability to accurately model and predict the effectiveness of current designs using computational fluid dynamics (CFD) code predictions. Hence, a benchmark set of flow field property data was obtained to assess current CFD capabilities and develop better modeling approaches for these turbulent flow fields where accurate calculation of turbulent heat flux is important. Both Particle Image Velocimetry (PIV) and spontaneous rotational Raman scattering (SRS) spectroscopy were used to acquire high quality, spatially-resolved measurements of the mean and root mean square (rms) velocities as well as the mean and rms temperatures in a film cooling flow field. In addition to off-body flow field measurements, infrared thermography (IR) and thermocouple measurements on the plate surface enabled estimates of the film effectiveness. Raman spectra in air were obtained across a matrix of axial locations downstream from a 68.07 mm square nozzle blowing heated air over a range of temperatures (up to TR = 2.7) and Mach numbers (up to Mach 0.9), across a 30.48 cm long plate equipped with three patches of 45 small (~1 mm) diameter cooling holes arranged in a staggered configuration. In addition, both streamwise 2-component PIV (on the plate centerline) and cross-stream 3-component Stereo PIV data at 14 axial stations were collected in the same flows. Only a subset of the data collected in the test program is included in this Part I report. The rest of the data will be published in a future report, Part II, along with planned CFD predictions of the complex cooling film flow. The entire data set of Raman temperature data, PIV velocity data and IR camera data covering the Set Points 23 and 49 in the test matrix in Table 1 is available in an accompanying DVD (available online from www.sti.nasa.gov) for those interested in further analysis.

Nozzle↗
NASA NTRS2020

PIV and Rotational Raman-Based Temperature Measurements for CFD Validation of a Perforated Plate Cooling Flow Part I

Film cooling is used in a wide variety of engineering applications for protection of surfaces from hotor combusting gases. The design of more efficient film cooling geometries/configurations could be facilitated by an ability to accurately model and predict the effectiveness of current designs using computational fluid dynamics (CFD) code predictions. Hence, a benchmark set of flow field property data were obtained to assess current CFD capabilities and develop better modeling approaches for these turbulent flow fields where accurate calculation of turbulent heat flux is important. Both Particle Image Velocimetry (PIV) and spontaneous rotational Raman scattering (SRS) spectroscopy were used to acquire high quality, spatially-resolved measurements of the mean and root mean square (rms) velocities as well as the mean and rms temperatures in a film cooling flow field. In addition to off-body flow field measurements, infrared thermography (IR) and thermocouple measurements on the plate surface enabled estimates of the film effectiveness. Raman spectra in air were obtained across a matrix of axial locations downstream from a 68.07 mm square nozzle blowing heated air over a range of temperatures (up to TR =2.7) and Mach numbers (up to Mach 0.9), across a 30.48 cm long plate equipped with three patches of 45 small (~1 mm) diameter cooling holes arranged in a staggered configuration. In addition, both streamwise 2-component PIV (along the plate centerline) and cross-stream 3-component Stereo PIV data at 14 axial stations were collected in the same flows. Only a subset of the data collected in the test program is included in this Part 1 report. The rest of the data will be published in a future report, Part 2, along with planned CFD predictions of the complex cooling film flow.The entire data set of Raman temperature data, PIV velocity data and IR camera data covering the Set Points 23 and 49 in the test matrix in Table 1 is available in an accompanying DVD (available online from www.sti.nasa.gov) for those interested in further analysis.

Mark P Wernet↗
NASA NTRS2020

Radiometric approach for the detection of picophytoplankton assemblages across oceanic fronts

Cell abundances of Prochlorococcus, Synechococcus, and autotrophic picoeukaryotes were estimated in surface waters using principal component analysis (PCA) of hyperspectral and multispectral remote-sensing reflectance data. This involved the development of models that employed multilinear correlations between cell abundances across the Atlantic Ocean and a combination of PCA scores and sea surface temperatures. The models retrieve high Prochlorococcus abundances in the Equatorial Convergence Zone and show their numerical dominance in oceanic gyres, with decreases in Prochlorococcus abundances towards temperate waters where Synechococcus flourishes, and an emergence of picoeukaryotes in temperate waters. Fine-scale in-situ sampling across ocean fronts provided a large dynamic range of measurements for the training dataset, which resulted in the successful detection of fine-scale Synechococcus patches. Satellite implementation of the models showed good performance (R(exp 2) > 0.50) when validated against in-situ data from six Atlantic Meridional Transect cruises. The improved relative performance of the hyperspectral models highlights the importance of future high spectral resolution satellite instruments, such as the NASA PACE mission’s Ocean Color Instrument, to extend our spatiotemporal knowledge about ecologically relevant phytoplankton assemblages.

Priscila Kienteca Lange↗
NASA NTRS1996

Implementation of a Very Large Atmospheric Correction Lookup Table for ASTER Using a Relational Database Management System

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)is designed to provide a high resolution map of the Earth in both visible, near-infrared, and thermal spectral regions of the electromagnetic spectrum. The ASTER Science Team has developed several standard data product algorithms, but the most complex aod computing-intensive of these is the estimation of surface radiance and reflectance values, which is done by modeling and correcting for the effects of the atmosphere. The algorithm for atmospheric correction in the visible bands sensed by ASTER calls for use of a very large Atmospheric Correction Look Up Table (ACLUT).

ASTER↗
NASA NTRS1996

Implementation of the ASTER Science Standard Data Product Requirements in the EOSDIS System

The Advanced Spaceborne Thermal Emmission and Reflection Radiometer (ASTER) is designed to provide a high-resolution map of the Earth in both visible, near-infrared, and thermal spectral regions of the electromagnetic spectrum. The ASTER Science Team has developed standard data product algorithms to permit the estimation of surface radiances and reflectance values, to calculate surface temperatures both over water and land, to provide a color enhanced product with a high degree of surface discriminability, in addition to other functions. The ASTER Product Generation System (PGS) Team is implementing these requirements within the constraints of the EOSDIS system, using a rapid development methodology that emphasizes open lines of communication in a team approach using concurrent engineering techniques. This paper describes the critical elements in the development and integration of the ASTER PGS system.

ASTER↗
NASA NTRS2019

Validation, Comparison, and Integration of GOCI, AHI, MODIS, MISR, and VIIRS Aerosol Optical Depth Over East Asia During the 2016 KORUS-AQ Campaign

Recently launched multichannel geostationary Earth orbit (GEO) satellite sensors, such as the Geostationary Ocean Color Imager (GOCI) and the Advanced Himawari Imager (AHI), provide aerosol products over East Asia with high accuracy, which enables the monitoring of rapid diurnal variations and the transboundary transport of aerosols. Most aerosol studies to date have used low Earth orbit (LEO) satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging Spectroradiometer (MISR), with a maximum of one or two overpass daylight times per day from midlatitudes to low latitudes. Thus, the demand for new GEO observations with high temporal resolution and improved accuracy has been significant. In this study the latest versions of aerosol optical depth (AOD) products from three LEO sensors – MODIS (Dark Target, Deep Blue, and MAIAC), MISR, and the Visible/Infrared Imager Radiometer Suite (VIIRS), along with two GEO sensors (GOCI and AHI), are validated, compared, and integrated for a period during the Korea–United States Air Quality Study (KORUS-AQ) field campaign from 1 May to 12 June 2016 over East Asia. The AOD products analyzed here generally have high accuracy with high R (0.84–0.93) and low RMSE (0.12–0.17), but their error characteristics differ according to the use of several different surface-reflectance estimation methods. High-accuracy near-real-time GOCI and AHI measurements facilitate the detection of rapid AOD changes, such as smoke aerosol transport from Russia to Japan on 18–21 May 2016, heavy pollution transport from China to the Korean Peninsula on 25 May 2016, and local emission transport from the Seoul Metropolitan Area to the Yellow Sea in South Korea on 5 June 2016. These high-temporal-resolution GEO measurements result in more representative daily AOD values and make a greater contribution to a combined daily AOD product assembled by median value selection with a 0.5∘×0.5∘ grid resolution. The combined AOD is spatially continuous and has a greater number of pixels with high accuracy (fraction within expected error range of 0.61) than individual products. This study characterizes aerosol measurements from LEO and GEO satellites currently in operation over East Asia, and the results presented here can be used to evaluate satellite measurement bias and air quality models.

Myungje Choi↗
NASA NTRSDate not supplied

Evaluating and Constraining Models’ Stratocumulus and Cumulus Cloud Feedbacks in the Tropics using Satellite Observations to Reduce Uncertainties in Future Climate Projections

Low-cloud feedbacks are thought to be the greatest source of uncertainty in climate projections on the centennial scale. Past research has mainly focused on the global low-cloud feedback as a whole whereas only a few studies have examined separated contributions from the two main low cloud categories: stratocumulus (Sc) and shallow cumulus (Cu). Here we aim to evaluate and constrain Sc and Cu cloud feedbacks in models using satellite observations to ultimately reduce their contribution to the spread in equilibrium climate sensitivity. We utilize the Cumulus and Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD) recently developed by Cesana et al. (2019), which discriminates Sc from Cu clouds. This algorithm – based on cloud morphological characteristics at the orbital level – outperforms previous cloud-type classifications stemming from passive-sensor measurements. We investigate the observed relationship between the Sc and Cu cloud cover in tropical subsidence oceanic regions and their link to the primary low-cloud controlling factors (sea surface temperature, estimated inversion strength). To gain further insight into the Sc-Cu cloud partitioning, we explore the Sc-Cu relationship during the four meteorological seasons (DJF, MAM, JJA, SON) as well as on monthly timescale. We find a substantial negative correlation between the Sc and Cu cloud cover on annual, seasonal and monthly timescales. We finally evaluate how well climate models of the Coupled Model Intercomparison Project phase 5 (CMIP5) and phase 6 (CMIP6) agree with the observations. These results establish a framework to advance model parameterizations of the underlying physical processes driving the stratocumulus and shallow cumulus clouds (turbulence and convection).

Cloud feedbacks↗
NASA NTRSDate not supplied

GeoNEX-ML: A Machine Learning System for Geostationary Satellite Imagery

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. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), Himawari-8/9 (JAXA), and GK-2A (Korea), we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools 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.

Geostationary satellites↗