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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Sensitivity of Global Sea-Air CO2 Flux to Gas Transfer Algorithms, Climatological Wind Speeds, and Variability of Sea Surface Temperature and Salinity

Sensitivity analyses of sea-air CO2 flux to gas transfer algorithms, climatological wind speeds, sea surface temperatures (SST) and salinity (SSS) were conducted for the global oceans and selected regional domains. Large uncertainties in the global sea-air flux estimates are identified due to different gas transfer algorithms, global climatological wind speeds, and seasonal SST and SSS data. The global sea-air flux ranges from -0.57 to -2.27 Gt/yr, depending on the combination of gas transfer algorithms and global climatological wind speeds used. Different combinations of SST and SSS global fields resulted in changes as large as 35% on the oceans global sea-air flux. An error as small as plus or minus 0.2 in SSS translates into a plus or minus 43% deviation on the mean global CO2 flux. This result emphasizes the need for highly accurate satellite SSS observations for the development of remote sensing sea-air flux algorithms.

McClain, Charles R.↗
NASA NTRS1985

Observations of the polar regions from satellites using active and passive microwave techniques

Observations of the ocean ice, ice sheets, ice edges, and ocean waves in the Antarctic, performed by the Seasat and Nimbus-7 satellites are described. Specific features of the Seasat altimeter, the SAR, the Seasat-A Scatterometer System, and the Scanning Multichannel Microwave Radiometer (SMMR) are discussed, together with the advantages and limitations of each instrument as applied to the study of ice dynamics. The Nimbus-7 SMMR algorithm for calculating sea-ice concentrations is presented. In addition, the Seasat altimeter-derived data on wind speed and wave heights in the Southern Ocean, and on the ice sheets of the Antarctica and Greenland were evaluated.

Swift, C. T.↗
NASA NTRS1972

Mission 119 passive microwave results

Passive microwave measurements of the sea surface were made for determining surface wind speeds from the NP3A aircraft (NASA-927). Observations were made at frequencies of 1.4, 10.6, and 31.4 GHz during NASA mission 119, undertaken off Bermuda in the vicinity of Argus Island sea tower during January 1970. Passive microwave observations from Argus Island ocean showed that the surface roughness effect, dependent on wind speed, is also dependent on observational frequency, increasing with increasing frequency. The roughness effect appears to be dominant for wind speeds less than 30 to 40 knots (2).

Hollinger, J. P.↗
NASA NTRS2024

Machine Learning Based Aerosol and Ocean Color Joint Retrieval Algorithm for Multiangle Polarimeters over Coastal Waters

NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, recently launched in February 2024, carries two multiangle polarimeters (MAPs): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and SRON Spectropolarimeter for Planetary Exploration One (SPEXone). Measurements from these MAPs will greatly advance ocean ecosystem and aerosol studies as their measurements contain rich information on microphysical properties of aerosols and hydrosols. The Multi-Angular Polarimetric Ocean coLor (MAPOL) joint retrieval algorithm has been developed to retrieve aerosol and ocean color information, which uses a vector radiative transfer (RT) model as the forward model. The RT model is computationally expensive, which makes processing a large amount of data challenging. FastMAPOL was developed to expedite retrieval using neural networks to replace the RT forward models. As a prototype study, FastMAPOL was initially limited to open ocean applications where the ocean Inherent Optical Properties (IOPs) were parameterized in terms of one parameter: chlorophyll-a concentration (Chla). In this study we further expand the FastMAPOL joint retrieval algorithm to incorporate NN based forward models for coastal waters, which use multi-parameter bio-optical models. In addition, aerosols are represented by six components, i.e., fine mode non absorbing insoluble (FNAI), brown carbon (BrC), black carbon (BC), fine mode non absorbing soluble (FNAS), sea salt (SS) and non-spherical dust (Dust). Sea salt and dust are coarse mode aerosols, while the other components are in the fine mode. The sizes and spectral refractive indices are fixed for each aerosol component, while their abundances are retrievable. The multi-parameter bio-optical model and aerosol components are chosen to represent the coastal marine environment. The retrieval algorithm is applied to synthetic measurements in three different configurations ofMAPs in the PACE mission: HARP2 observations only, SPEXone observations only and combined HARP2 and SPEXone observations. The retrieval results from synthetic measurements show that for aerosol retrieval the SPEXone-only configuration works equally well with the HAPR2-only configuration. On the other hand, for ocean color retrieval the SPEXone instrument provides better information due to its larger spectral coverage. For the surface parameters (wind speed), HARP2 measurements provide better information due to its wide field of view. Combined measurement configuration HARP2+SPEXone performed the best to retrieve all aerosol, ocean color and surface parameters. We also studied the impact of sun glint to aerosol and ocean color retrievals. The retrieval test revealed that wind speed and absorbing aerosol retrieval improves significantly when including measurements at glint geometries. Furthermore, the retrieval algorithm is equipped with modules for atmospheric correction and bidirectional reflectance distribution (BRDF) correction to obtain the remote sensing reflectance, which enables ocean biogeochemistry studies using the PACE polarimeter data.

PACE↗
NASA NTRS2012

The Operational MODIS Cloud Optical and Microphysical Property Product: Overview of the Collection 6 Algorithm and Preliminary Results

Operational Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals of cloud optical and microphysical properties (part of the archived products MOD06 and MYD06, for MODIS Terra and Aqua, respectively) are currently being reprocessed along with other MODIS Atmosphere Team products. The latest "Collection 6" processing stream, which is expected to begin production by summer 2012, includes updates to the previous cloud retrieval algorithm along with new capabilities. The 1 km retrievals, based on well-known solar reflectance techniques, include cloud optical thickness, effective particle radius, and water path, as well as thermodynamic phase derived from a combination of solar and infrared tests. Being both global and of high spatial resolution requires an algorithm that is computationally efficient and can perform over all surface types. Collection 6 additions and enhancements include: (i) absolute effective particle radius retrievals derived separately from the 1.6 and 3.7 !-lm bands (instead of differences relative to the standard 2.1 !-lm retrieval), (ii) comprehensive look-up tables for cloud reflectance and emissivity (no asymptotic theory) with a wind-speed interpolated Cox-Munk BRDF for ocean surfaces, (iii) retrievals for both liquid water and ice phases for each pixel, and a subsequent determination of the phase based, in part, on effective radius retrieval outcomes for the two phases, (iv) new ice cloud radiative models using roughened particles with a specified habit, (v) updated spatially-complete global spectral surface albedo maps derived from MODIS Collection 5, (vi) enhanced pixel-level uncertainty calculations incorporating additional radiative error sources including the MODIS L1 B uncertainty index for assessing band and scene-dependent radiometric uncertainties, (v) and use of a new 1 km cloud top pressure/temperature algorithm (also part of MOD06) for atmospheric corrections and low cloud non-unity emissivity temperature adjustments.

Platnick, Steven↗
NASA NTRS1981

Seasat SAR cross-section modulation by surface winds - GOASEX observations

Seasat radar observations of the Gulf of Alaska by the Synthetic Aperture Radar (SAR) and the Wind Speed Scatterometer (SASS) indicate that L-band backscatter is modulated by wind speed and possibly by wind direction. It is noted that the L-band backscatter is approximately proportional to the square root of surface winds and that upwind/crosswind differences in backscatter can be as large as 10% in the range of these wind speeds. It is suggested that Seasat SAR images, in conjunction with Seasat Scatterometer data may provide fine-scale mapping of ocean winds and directions. The need for further Seasat SAR-SASS comparisons in order to extend the range of wind speeds is stressed.

Thompson, T. W.↗
NASA NTRS1992

The effect of small-wave modulation on the electromagnetic bias

The effect of the modulation of small ocean waves by large waves on the physical mechanism of the EM bias is examined by conducting a numerical scattering experiment which does not assume the applicability of geometric optics. The modulation effect of the large waves on the small waves is modeled using the principle of conservation of wave action and includes the modulation of gravity-capillary waves. The frequency dependence and magnitude of the EM bias is examined for a simplified ocean spectral model as a function of wind speed. These calculations make it possible to assess the validity of previous assumptions made in the theory of the EM bias, with respect to both scattering and hydrodynamic effects. It is found that the geometric optics approximation is inadequate for predictions of the EM bias at typical radar altimeter frequencies, while the improved scattering calculations provide a frequency dependence of the EM bias which is in qualitative agreement with observation. For typical wind speeds, the EM bias contribution due to small-wave modulation is of the same order as that due to modulation by the nonlinearities of the large-scale waves.

Rodriguez, Ernesto↗
NASA NTRS2016

Applications of SMAP Data to Retrieval of Ocean Surface Wind and Salinity

We have examined the L-band radiometer and radar data from NASA’s Soil Moisture Active Passive (SMAP) mission for ocean research and applications. We find that the SMAP data are in excellent agreement with the geophysical model function (GMF) derived from the Aquarius data up to a wind speed of 20 ms-1. For severe wind conditions, the higher resolution data from SMAP allowed us to assess the sensitivity of L-band radiometer signals to hurricane force winds. We applied the L-band GMF to the retrieval of ocean surface wind and SSS from the SMAP data. Comparison with the European Center for Medium-Range Weather Forecasting, WindSat and RapidSCAT wind speeds suggests that SMAP’s radiometer wind speed reaches an excellent accuracy of about 1.1-1.7 ms-1 below a wind speed of 20 ms-1. We have also found that the maximum wind speed derived from the SMAP radiometer data can reach 140 knots for severe storms and are generally in good agreement with the hurricane track analysis and operational aircraft Stepped Frequency Microwave Radiometer wind speeds. The spatial patterns of the SMAP SSS agree well with climatological distributions, but exhibit several unique spatial and temporal features.

ocean surface wind↗
NASA NTRSDate not supplied

Using CALIPSO's New Ocean Derived Column Optical Depths

CALIPSO’s Version 4.51 Level 2 data release introduces an all-new group of science data sets containing estimates of total column two-way transmittances and effective optical depths derived from CALIOP ocean surface backscatter measurements and MERRA-2 reanalysis wind speed data. These estimates use data from the standard CALIOP lidar signal but in a passive sensor-like way, thus creating a unique link to passive instrument measurements. These new retrievals are provided for the entire mission, day and night, at 532nm, and are reported at single shot, 1km, and 5km resolutions for all profiles in which a valid lidar ocean surface return is detected. The addition of a total column optical depth constraint on subsequent retrievals of cloud and aerosol optical properties opens the door for many exciting new ways to leverage the already rich and versatile CALIPSO dataset. Following a brief review of the retrieval technique, this talk will focus on quality assurance assessments, estimated uncertainties, and data usage scenarios. We will conclude with examples highlighting some of the exciting work already being done using this new addition to CALIPSO’s already rich data record.

R Ryan↗
DOE OSTI2020

Geophysical and Planetary Acoustics on Balloons

Natural events and human activity often generate acoustic waves capable of traveling tens to tens of thousands of kilometers across the globe. Ground-based acoustic sensors are limited to dry land and often suffer from wind noise. In contrast, balloon borne acoustic sensors can cross oceans, polar ice caps, and other inhospitable areas, greatly expanding sensor coverage. Since they move with the mean wind speed, their background noise levels are exceptionally low. In the last six years, such sensors have recorded sounds from colliding ocean waves, surface and buried chemical explosions, thunder, wind/mountain interactions, wind turbines, aircraft, and possibly meteors and the aurora. These results have led to new insights on acoustic heating of the upper atmosphere, the detectability of underground explosions, and directional sound fields generated by ocean waves.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Comparative Evaluation of the Ability of the MYNN‐EDMF PBL Scheme in WRF Model to Reproduce Near Surface Wind Speed Over Different Topographical Types

Abstract This study systematically evaluates the performance of the Mellor‐Yamada‐Nakanishi‐Niino‐Eddy‐Diffusion‐Mass‐Flux planetary boundary layer (PBL) scheme within the Weather Research and Forecasting (WRF) model in simulating near‐surface wind speeds across various topographies in New York State (NYS). Simulated wind speeds are compared with in‐situ measurements from 22 surface sites, grouped into six topographic categories: continental plain (CT), lakeside (LS), river valley (RV), Long Island (LI), Block Island (BI), and offshore ocean (OO). A quantitative evaluation based on Relative Euclidean Distance shows that wind speeds at the OO site are the most accurately reproduced, followed by those at LI sites, while the model performs less accurately for the remaining topographic groups. Wind speeds over CT sites tend to be overestimated by approximately 1 m/s, although their diurnal variability (DV) is well captured. In contrast, the model underestimates wind DV at LS, RV, LI, and BI sites, with the largest biases occurring at LI and BI, resulting in underestimated daytime wind speed and/or overestimated nighttime wind speed. The OO winds exhibit minimal diurnal variation, accurately captured by our WRF model. The surface wind diurnal variation is closely linked to PBL development. Among the indicators of PBL development, surface potential temperature biases most strongly correlate with wind speed biases. Our WRF model faces challenges in capturing the distinctions between winds influenced by local circulations and those over continental plains, and the significantly stronger winds at OO compared to BI. Potential causes for these biases are discussed, offering pathways for improving surface wind simulations in future.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

Sensitivity of Wide Bandwidth Radiometer for Remote Sensing of Ocean Salinity

Passive microwave remote sensing of sea surface salinity from space is currently being done with measurements in the 27 MHz wide spectral window at 1.413 GHz (L-band) protected for passive use. Modern L-band instruments, such as the radiometers on SMOS and Aquarius, have demonstrated the feasibility of monitoring surface salinity from space, and they have also demonstrated the need for better accuracy, especially in cold water. Proposals to improve accuracy have largely involved adding measurement at more frequencies. For example, adding lower frequencies to improve the sensitivity to salinity in cold water and adding higher frequencies to enable simultaneous retrieval of sea surface temperature which is needed in the retrieval of salinity. These strategies involve trade-offs, some obvious such as the effects of interference from anthropogenic sources of radio frequency radiation (RFI) when operating outside the protected band and the loss of spatial resolution at lower frequencies. But, some are more subtle and arise because of the dependence of the retrieval of salinity on other parameters of the ocean surface, in particular, water temperature and roughness (wind speed). The effect of these interdependencies on the potential accuracy of salinity remote sensing in the frequency range 0.3 – 3.0 GHz is examined here to gain insight into the potential for future wide bandwidth instruments for remote sensing of salinity and the optimization of their design. There is benefit including the low frequencies, especially for cold water, but a danger of increased error including frequencies above 1.5 - 2.0 GHz depending on temperature.

Microwave Remote Sensing↗
NASA NTRS1985

NASA scatterometer oceanic wind measurement system

Satellite scatterometers are active microwave radars designed to yield measurements of near surface horizontal wind velocity over the ocean. Scatterometers are unique in that they are the only existing microwave remote sensing instruments that allow measurements of both wind speed and wind direction. NASA will fly a scatterometer, NSCAT, aboard the Navy Ocean Remote Sensing System (N-ROSS) mission starting in late 1990. N-ROSS is a spaceborne ocean remote sensing system with a planned mission life of three years. In addition to the NSCAT, N-ROSS will have three other microwave instruments mounted on a single satellite flying in a near polar orbit: an altimeter; a special sensor microwave/imager (SSM/I); and a low frequency microwave radiometer. The NSCAT to be flown on N-ROSS is described.

Freilich, M.↗
NASA NTRS1992

The Split Window Microwave Radiometer (SWMR) for hurricane wind speed measurement from space

The monitoring of hurricanes demands considerable resources each year by the National Oceanic and Atmospheric Administration. Even with the extensive use of satellite and airborne probing of those storms, there is still much uncertainty involved in predicting landfall for timely evacuation of people subject to the threat. The concept of the Split Window Microwave Radiometer (SWMR) is to add an additional capability of remotely measuring surface winds to hopefully improve prediction capabilities or at least define the severity of the storm while it is far from land. Some of the present science and observational needs are addressed in this report as are remote sensing limitations which impact the design of a minimal system which can be launched into low earth orbit by a low cost launch system. This study has concluded that wind speed and rain rate maps of hurricanes can be generated with an X-Band radiometer system with an antenna whose aperture is 2 m on a side.

Swift, Calvin T.↗
DOE OSTI2020

US East Coast synthetic aperture radar wind atlas for offshore wind energy

We present the first synthetic aperture radar (SAR) offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from RADARSAT-1, Envisat, and Sentinel-1A/B are processed to wind maps using the geophysical model function (GMF) CMOD5.N. Extensive comparisons with 6008 collocated buoy observations of the wind speed reveal that biases of the individual systems range from -0.8 to 0.6 m s -1 . Unbiased wind retrievals are crucial for producing an accurate wind atlas, and intercalibration of the SAR observations is therefore applied. Wind retrievals from the intercalibrated SAR observations show biases in the range of to -0.2 to 0.0 m s -1 , while at the same time improving the root-mean-squared error from 1.67 to 1.46 m s -1 . The intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas at the height 10 m a.s.l. (above sea level). The SAR wind atlas is used as a reference to study wind resources derived from the Wind Integration National Dataset Toolkit (WTK), which is based on 7 years of modelling output from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation in wind resources and show that model outputs lead to lower coastal wind speed gradients than those derived from SAR. Areas designated for offshore wind development by the Bureau of Ocean Energy Management are investigated in more detail; the wind resources in terms of the mean wind speed show spatial variations within each designated area between 0.3 and 0.5 m s -1 for SAR and less than 0.2m s -1 for the WTK. Our findings indicate that wind speed gradients and variations might be underestimated in mesoscale model outputs along the US East Coast.

17 WIND ENERGY↗
NASA NTRS1997

A Well-Calibrated Ocean Algorithm for Special Sensor Microwave/Imager

I describe an algorithm for retrieving geophysical parameters over the ocean from special sensor microwave/imager (SSM/I) observations. This algorithm is based on a model for the brightness temperature T(sub B) of the ocean and intervening atmosphere. The retrieved parameters are the near-surface wind speed W, the columnar water vapor V, the columnar cloud liquid water L, and the line-of-sight wind W(sub LS). I restrict my analysis to ocean scenes free of rain, and when the algorithm detects rain, the retrievals are discarded. The model and algorithm are precisely calibrated using a very large in situ database containing 37,650 SSM/I overpasses of buoys and 35,108 overpasses of radiosonde sites. A detailed error analysis indicates that the T(sub B) model rms accuracy is between 0.5 and 1 K and that the rms retrieval accuracies for wind, vapor, and cloud are 0.9 m/s, 1.2 mm, and 0.025 mm, respectively. The error in specifying the cloud temperature will introduce an additional 10% error in the cloud water retrieval. The spatial resolution for these accuracies is 50 km. The systematic errors in the retrievals are smaller than the rms errors, being about 0.3 m/s, 0.6 mm, and 0.005 mm for W, V, and L, respectively. The one exception is the systematic error in wind speed of -1.0 m/s that occurs for observations within +/-20 deg of upwind. The inclusion of the line-of-sight wind W(sub LS) in the retrieval significantly reduces the error in wind speed due to wind direction variations. The wind error for upwind observations is reduced from -3.0 to -1.0 m/s. Finally, I find a small signal in the 19-GHz, horizontal polarization (h(sub pol) T(sub B) residual DeltaT(sub BH) that is related to the effective air pressure of the water vapor profile. This information may be of some use in specifying the vertical distribution of water vapor.

Wentz, Frank J.↗