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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Coherent Detector for Near-Angle Scattering and Polarization Characterization of Telescope Mirror Coatings

A report discusses the difficulty of measuring scattering properties of coated mirrors extremely close to the specular reflection peak. A prototype Optical Hetero dyne Near-angle Scatterometer (OHNS) was developed. Light from a long-coherence-length (>150 m) 532-nm laser is split into two arms. Acousto-optic modulators frequency shift the sample and reference beams, establishing a fixed beat frequency between the beams. The sample beam is directed at very high f/# onto a mirror sample, and the point spread function (PSF) formed after the mirror sample is scanned with a pinhole. This light is recombined by a non-polarizing beam splitter and measured through heterodyne detection with a spectrum analyzer. Polarizers control the illuminated and analyzed polarization states, allowing the polarization dependent scatter to be measured. The bidirectional reflective or scattering distribution function is normally measured through use of a scattering goniometer instrument. The instrumental beam width (collection angle span) over which the scatterometer responds is typically many degrees. The OHNS enables measurement at angles as small as the first Airy disk diameter.

Macenka, Steven A.↗
NASA NTRS2014

Aquarius Overview and Up Date

Aquarius is an L-band instrument designed to map the surface salinity field of the global oceans. It consists of three L-band (1.41 GHz) radiometers and an L-band (1.26 GHz) scatterometer. The radiometers are the primary instruments for measuring salinity and the scatterometer provides a correction for surface roughness. Aquarius was launched in June 2011 and has been mapping the surface salinity field since it was turned on in August. In addition, Aquarius is now producing maps of radio frequency interference (RFI), Faraday rotation and soil moisture.

microwave remote sensing↗
NASA NTRS2015

A High-Resolution Merged Wind Dataset for DYNAMO: Progress and Future Plans

In order to support research on optimal data assimilation methods for the Cyclone Global Navigation Satellite System (CYGNSS), launching in 2016, work has been ongoing to produce a high‐resolution merged wind dataset for the Dynamics of the Madden Julian Oscillation (DYNAMO) field campaign, which took place during late 2011/early 2012. The winds are produced by assimilating DYNAMO observations into the Weather Research and Forecasting (WRF) three‐dimensional variational (3DVAR) system. Data sources from the DYNAMO campaign include the upper‐air sounding network, radial velocities from the radar network, vector winds from the Advanced Scatterometer (ASCAT) and Oceansat‐2 Scatterometer (OSCAT) satellite instruments, the NOAA High Resolution Doppler Lidar (HRDL), and several others. In order the prep them for 3DVAR, significant additional quality control work is being done for the currently available TOGA and SMART‐R radar datasets, including automatically dealiasing radial velocities and correcting for intermittent TOGA antenna azimuth angle errors. The assimilated winds are being made available as model output fields from WRF on two separate grids with different horizontal resolutions ‐ a 3‐km grid focusing on the main DYNAMO quadrilateral (i.e., Gan Island, the R/V Revelle, the R/V Mirai, and Diego Garcia), and a 1‐km grid focusing on the Revelle. The wind dataset is focused on three separate approximately 2‐week periods during the Madden Julian Oscillation (MJO) onsets that occurred in October, November, and December 2011. Work is ongoing to convert the 10‐m surface winds from these model fields to simulated CYGNSS observations using the CYGNSS End‐To‐End Simulator (E2ES), and these simulated satellite observations are being compared to radar observations of DYNAMO precipitation systems to document the anticipated ability of CYGNSS to provide information on the relationships between surface winds and oceanic precipitation at the mesoscale level. This research will improve our understanding of the future utility of CYGNSS for documenting key MJO processes.

Lang, Timothy J.↗
NASA NTRS2016

Analysis of RFI Statistics for Aquarius RFI Detection and Mitigation Improvements

Aquarius is an L-band active/passive sensor designed to globally map sea surface salinity from space. Two instruments, a radar scatterometer and a radiometer, observe the same surface footprint almost simultaneously. The radiometer is the primary instrument for sensing sea surface salinity (SSS), while the scatterometer is included to provide a correction for sea surface roughness, which is a primary source of error in the salinity retrieval. Although the primary objective is the measurement of SSS, the instrument combination operates continuously, acquiring data over land and sea ice as well. An important feature of the data processing includes detection and mitigation of Radio Frequency Interference (RFI) which is done separately for both active and passive instruments. Correcting for RFI is particularly critical over ocean because of the high accuracy required in the brightness temperature measurements for SSS retrieval. It is also necessary for applications of the Aquarius data over land, where man-made interference is widespread, even though less accuracy is required in this case. This paper will provide an overview of the current status of the Aquarius RFI processing and an update on the ongoing work on the improvement of the RFI detection and mitigation performance.

Microwave remote sensing↗
NASA NTRS2019

Sea Surface Salinity Distribution in the Southern Ocean as Observed from Space

Large-scale spatial and temporal variabilities of sea surface salinity (SSS) in the Southern Ocean from 2011 to 2017 were studied using products derived from microwave sensors on board Aquarius, Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active and Passive (SMAP) satellites. Four products, three from Aquarius and one from SMOS, were evaluated and shown to be generally consistent within 0.3 to 0.6 psu and agree favorably with in situ measurements. However, although the Aquarius products show consistent seasonality of SSS with high values of 34.45 psu in October and low values of 33.40 psu in May, theSMOS and SMAP products lack such seasonal variations. This may be caused by larger uncertainties in the SMOS and SMAP data due in part to the lack of concurrent scatterometer measurements that is used to correct for roughness effects. The four products provide similar spatial distributions of SSS with root mean square difference from 0.25 to 0.58 psu. Differences among Aquarius products are mainly due to varying salinity retrieval algorithms, smoothing, and masking of sea ice, while the SMOS product showed the highest SSS deviation that is likely due to the bias adjustment done on the data set. Our analyses show that SSS in the Southern Ocean region has significant meridional variations with the lowest SSS near the ice edge and highest at lower latitudes. The SSS is also lowest in summer indicating the predominant influence of sea ice and glacial melt, but it stays low near ice edges even during the growth season. Salinity together with temperature is the key parameters that control the state and circulation of the World's oceans. The large‐scale distribution of the sea surface salinity of the Southern Ocean is quantified for the first time using Aquarius, Soil Moisture and Ocean Salinity, and Soil Moisture Active and Passive satellite microwave data. Seasonal and interannual changes in the distribution as affected by the sea ice cover, surface temperature, and precipitation are evaluated. Comparative studies were done, using four different products that are publicly available, in conjunction with in situ observations to gain insights into the true nature of the distribution and how consistently the sea surface salinity is depicted by the different products. There are general consistencies in the products, and discrepancies are attributed to different algorithms, smoothing techniques, and sea ice masking. Aquarius data are shown to have higher accuracy than Soil Moisture and Ocean Salinity and Soil Moisture Active and Passive products in part because of having concurrent scatterometer that provides accurate correction to roughness effects.

Garcia-Eidell, Cynthia↗
NASA NTRS2019

Validation of Satellite-Based Wind Observations During PISTON

PISTON cruises provide a useful and unique suite of measurements relevant to validating satellite-based ocean wind datasets near tropical convection. In this talk, we will focus on opportunities for using PISTON observations to validate two different scatterometer datasets: (1) Cyclone Global Navigation Satellite System (CYGNSS) (2) Advanced Scatterometer (ASCAT).

Lang, Timothy↗
NASA NTRS2000

High Resolution Scatterometry by Simultaneous Range/Doppler Discrimination

Several spaceborne scatterometer missions have been successfully flown in the past decade. In this paper, the application of simultaneous range/Doppler discrimination techniques are proposed as way of improving the single-pass resolution of conically scaning pencil-beam scatterometer systems.

scatterometry↗
NASA NTRS1996

On the Accurate Calibration of the SeaWinds Radar Antenna: A Cylindrical Near-Field Measurement Approach

This paper demonstrates the utility of a cylindrical near-field measurement approach for the SeaWinds radar antenna calibration. Both generalized measurement error models and measured tests on a standard gain horn and NASA Scatterometer instrument antenna have been performed to achieve and verify the desired calibration accuracy. A comparison between far-field measured data and those obtained from cylindrical near-field measurements was found in excellent agreement.

spaceborne↗
NASA NTRS1999

Improving NSCAT Winds for Tropical Cyclones

There has been a question on whether it is possible to measure the surface wind speeds of tropical cyclones with a spaceborne Ku-band scatterometer.

Typhoon Warning Center (JTWC)↗
NASA NTRS1998

The Impact of NSCAT Data on Simulating Ocean Circulation

Wind taken from the National Aeronautics and Space Administration (NASA) scatterometer (NSCAT) is compared with the operational analysis from European Center for Medium-Rnage Forecast (ECMWF) for the entire duration (about 9 months) of the NSCAT mission.

scatterometer (NSCAT) operational analysis Europea↗