SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Explore the source record for details and available documents.
This paper presents a characterization of performance of 32-GHz Ka-band link in an operational environment. The data come from tracking of Kepler spacecraft by the NASA Deep Space Network (DSN) in the past few years. Ka-band link offers a significant signal-to-noise advantage compared to the more common X- or S-band; however, it is subject to more signal fluctuation caused by the weather. Kepler is the first mission supported by the DSN that solely rely on the higher deep space Ka-band communications link to return its high-rate science data. A well characterization of the operational performance of Kepler would benefit future Ka-band missions, especially for those operating with smaller link margin. The study examines how weather conditions at the DSN facilities (e.g., winds, clouds, rains) affect the received signal, particularly on telemetry data. It addresses questions such as how often the weather affects the link and how much degradation the link could suffer. Among the 22 Ka-band passes in 2012, heavy clouds affected one pass and two passes were impacted by high winds. The adverse weather caused at time as much as 3-dB instantaneous change in the signal to noise ratio (SNR). Estimates of degradation to the SNR as a function of wind speeds are captured based on observations. This study also quantifies the probability distribution of the variation of received signal power. With such information, future missions can better plan their link design. An optimal link design aims for just having enough margin to realize the data return with a targeted probability, with neither having too much margin that it reduces the downlink data rate nor insufficient reserve that causes frequent data outages. The study tries to provide some answers to the questions such as how much does SNR vary from one tracking pass to the next, and what is the cumulative distribution of signal fluctuation. Regarding the signal variation over many tracking passes, we found that the averaged symbol SNR (SSNR) for each pass changed by as much as 4 dB. Some variations were due to geometry such as the changing distance between spacecraft and Earth and different antenna pointing elevations. Other variations seem to be random in nature, reflecting the randomness of operational environments. Some passes were found to be quite stable, with a standard deviation of symbol SNR around 0.25 dB while others had greater variation, up to 1.4 dB. Overall, the cumulative distribution of the symbol SNR reflects that 50% of the fluctuations was less than 0.6 dB, 90% of fluctuation was within 1.6 dB and 95% within 2.2 dB. Some of the challenges in data processing, validation and modeling were captured in this paper. We observed some inconsistent measurements where operational data significantly deviate from a normal expectation. These inconsistent measurements made it hard to develop an accurate and consistent performance model, such as the degradation of signal SNR as a function of high wind. The difficulty was compounded by the fact that there were only a few Ka-band passes observed in high winds.
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. This new data product is known as the Ocean Derived Column Optical Depth product (ODCOD). These estimates use CALIOP’s ocean surface lidar return to estimate the total column optical depth overhead. This new retrieval will be 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. This poster will present a collection of HSRL and HSRL-2 CALIPSO under flights and compare the HSRL measured aerosol optical depth with the estimated total column optical depth of ODCOD. The ODCOD algorithm uses CALIOP’s ocean surface lidar return to estimate the effective total column optical depth overhead; i.e., the single scattering optical depth reduced by the contributions of multiple scattering to the ocean surface signal. Because the retrieval generates a single estimate for the entire column based solely on the surface backscatter, there is currently no way to correct for multiple scattering effects from different kinds of features within the column. This new retrieval will be provided for the entire mission, day and night, at 532nm, and is reported at single shot, 1km, and 5km resolutions for all profiles in which a valid lidar ocean surface return is detected. This poster presents a collection of HSRL and HSRL-2 CALIPSO under-flight case studies that compare the HSRL measured aerosol optical depth with the estimated total column optical depth derived by ODCOD.
AGCMs w/ Specified SSTs (AMIPs) GEOS-5, ERA-20CM Ensembles Incorporate best historical estimates of SST, sea ice, radiative forcing Atmospheric "weather noise" is inconsistent with specified SST. Instantaneous Sfc fluxes can be wrong sign (e.g. Indian Ocean Monsoon, high latitude oceans). Averaging over ensemble members helps isolate SST-forced signal. Reduced Observational Reanalyses: NOAA 20CR V2C, ERA-20C, JRA-55C Incorporate observed Sfc Press (20CR), Marine Winds (ERA-20C) and rawinsondes (JRA-55C) to recover much of true synoptic or weather w/o shock of new sat obs. Comprehensive Reanalyses (MERRA-2) Full suite of observational constraints- both conventional and remote sensing. But... substantial uncertainties owing to evolving satellite observing system. Multi-source Statistically Blended OAFlux, LargeYeager Blend reanalysis, satellite, and ocean buoy information. While climatological biases are removed, non-physical trends or variations in components remain. Satellite Retrievals GSSTF3, SeaFlux, HOAPS3... Global coverage. Retrieved near sfc wind speed, & humidity used with SST to drive accurate bulk aerodynamic flux estimates. Satellite inter-calibration, spacecraft pointing variations crucial. Short record ( late 1987-present). In situ Measurements ICOADS, IVAD, Res Cruises VOS and buoys offer direct measurements. Sparse data coverage (esp south of 30S. Changes in measurement techniques (e.g. shipboard anemometer height).
This work was carried out as part of the Heliostat Consortium (HelioCon) Field Deployment subtask with the aim to develop a reduced order model framework for correlating wind speed and pointing deviation of a heliostat facet. There are only sparse field measurements of heliostat pointing deviations and accompanying wind conditions published in the literature. Heliostat test standards, such as IEC 62862-4-3, propose a suite of tests including laser pointing repeatability at wind speeds below 4 m/s, and provide technical requirements for heliostat slope and tracking deviations in coarse average wind speed bins of 4 m/s, 6 m/s, and 8 m/s. In addressing the gap of the variation of heliostat pointing deviation with wind speed, field measurements of laser pointing on a grid target and wind conditions were analyzed in this study at the Third-Party Metrology Platform at the National Laboratory of the Rockies (NLR) Flatirons Campus. Horizontal pointing deviations were found to follow a logarithmic relationship with peak wind speed, whereas vertical pointing deviations follow an exponential relationship with peak wind speed. Both horizontal and vertical pointing deviations also follow a second order polynomial relationship, as expected from the proportionality of elastic loads and deformations with the square of wind speed. The results indicate that heliostat facet pointing deviations in the vertical direction increase at a faster rate than in the horizontal direction with increasing wind speed over the tested range, however these are dependent on the heliostat structural design. Next steps are recommended for additional field measurements to confirm a linear relationship of pointing deviation with applied moment on a heliostat facet, and to distinguish between gravity-induced and wind-induced pointing deviations at different elevation angles. The derived correlations in the preliminary analysis in this report serve as a case study for heliostat developers and plant operators to estimate the wind-induced pointing deviations and their variation with peak gust wind speed. Next steps in future work would recommend higher resolution and longer duration datasets for different elevation angles and wind directions to reduce uncertainties and variance of collected laser beam spot data and their correlations with bin-averaged wind speed.
Site-specific fatigue estimation is an essential part of wind turbine lifetime extension, with various methods depending on data availability. The present study compares probabilistic lifetime extension assessment results for rotor blades with and without load measurements. It also addresses two key questions in such assessments: the applicability of the Frandsen model for estimating waked turbulence under complex and mixed wake conditions and the extrapolation of mid-term data over longer time periods. The case study wind turbine is SWT-2.3-93, located at the edge of the Lillgrund wind farm, situated in the Øresund Strait between Denmark and Sweden. The turbine is extensively instrumented, with 5 years of data available from its supervisory control and data acquisition (SCADA) system. Although the Frandsen turbulence estimates deviate in a different manner from measurements at below- and above-rated mean wind speeds, the model remains a conservative approach for fatigue load prediction and reliability. In the current case study, the site-specific assessment using strain gauge measurements yields a 33 % higher annual fatigue reliability index after 35 years compared to a scenario based on the Frandsen estimation combined with ambient environmental data and a generic aeroelastic model. The results also demonstrate that the sensitivity of fatigue reliability to load uncertainty is negligible when load measurements are used directly but relatively high when relying on the Frandsen model in combination with a generic aeroelastic model. Overall, the high variability of the lifetime extension in different scenarios of data availability and accuracy shows the importance and added value of high-quality measurements combined with wind-farm-level SCADA and a model updated in real time (digital twins).
WINDPROF provides 10-minute wind and turbulence profiles, integrating Doppler lidars and anemometers during the AWAKEN campaign. Key data include wind speed, direction, vertical velocity, and turbulence parameters, with standardized quality control (e.g., instrument-specific thresholds and inter-instrument validation). Profiles are interpolated to a height grid (20 m spacing below 100 m; 30 m above) and include uncertainty estimates, offering reproducible methods for atmospheric research, model validation, and wind energy studies.
WINDPROF provides 10-minute wind and turbulence profiles, integrating Doppler lidars and anemometers during the AWAKEN campaign. Key data include wind speed, direction, vertical velocity, and turbulence parameters, with standardized quality control (e.g., instrument-specific thresholds and inter-instrument validation). Profiles are interpolated to a height grid (20 m spacing below 100 m; 30 m above) and include uncertainty estimates, offering reproducible methods for atmospheric research, model validation, and wind energy studies.
WINDPROF provides 10-minute wind and turbulence profiles, integrating Doppler lidars and anemometers during the AWAKEN campaign. Key data include wind speed, direction, vertical velocity, and turbulence parameters, with standardized quality control (e.g., instrument-specific thresholds and inter-instrument validation). Profiles are interpolated to a height grid (20 m spacing below 100 m; 30 m above) and include uncertainty estimates, offering reproducible methods for atmospheric research, model validation, and wind energy studies.
A model is developed for the estimation of the surface fluxes of momentum, heat, and moisture of the cloud topped marine atmospheric boundary layer by use of satellite remotely sensed parameters. The parameters chosen for the problem are the integrated liquid water content, q sub li, the integrated water vapor content, q sub vi, the cloud top temperature, and either a measure of the 10 meter neutral wind speed or the friction velocity at the surface. Under the assumption of a horizontally homogeneous, well-mixed boundary layer, the model calculates the equivalent potential temperature and total water profiles of the boundary layer along with the boundary layer height from inputs of q sub li, q sub vi, and cloud top temperature. These values, along with the 10m neutral wind speed or friction velocity and the sea surface temperature are then used to estimate the surface fluxes. The development of a scheme to parameterize the integrated water vapor outside of the boundary layer for the cases of cold air outbreak and California coastal stratus is presented.
The Aquarius end-of-mission (Version 5) salinity data set was released in December 2017. This article gives a comprehensive overview of the main steps of the Level 2 salinity retrieval algorithm. In particular, we will discuss the corrections for wind induced surface roughness, atmospheric oxygen absorption, reflected galactic radiation and side-lobe intrusion from land surfaces. Most of these corrections have undergone major updates from previous versions, which has helped mitigating temporal and zonal biases. Our article also discusses the ocean target calibration for Aquarius Version 5. We show how formal error estimates for the Aquarius retrievals can be obtained by perturbing the input to the algorithm. The performance of the Aquarius Version 5 salinity retrievals is evaluated against salinity measurements from the ARGO network and the HYCOM model. When stratified as function of sea surface temperature or sea surface wind speed, the difference between Aquarius Version 5 and ARGO is within +/-0.1 psu. The estimated global RMS uncertainty for monthly 100 km averages is 0.128 psu for the Aquarius Version 5 retrievals. Finally, we show how the Aquarius Version 5 salinity retrieval algorithm is adapted to retrieve salinity from the Soil-Moisture Active Passion (SMAP) mission.
The Aquarius end-of-mission (Version 5) salinity data set was released in December 2017. This article gives a comprehensive overview of the main steps of the Level 2 salinity retrieval algorithm. In particular, we will discuss the corrections for wind induced surface roughness, atmospheric oxygen absorption, reflected galactic radiation and side-lobe intrusion from land surfaces. Most of these corrections have undergone major updates from previous versions, which has helped mitigating temporal and zonal biases. Our article also discusses the ocean target calibration for Aquarius Version 5. We show how formal error estimates for the Aquarius retrievals can be obtained by perturbing the input to the algorithm. The performance of the Aquarius Version 5 salinity retrievals is evaluated against salinity measurements from the ARGO network and the HYCOM model. When stratified as function of sea surface temperature or sea surface wind speed, the difference between Aquarius Version 5 and ARGO is within +-0.1 psu. The estimated global RMS uncertainty for monthly 100 km averages is 0.128 psu for the Aquarius Version 5 retrievals. Finally, we show how the Aquarius Version 5 salinity retrieval algorithm is adapted to retrieve salinity from the Soil-Moisture Active Passion (SMAP) mission.
Five days of clear sky observations of Kansas and Nebraska are used to examine the statistical relationship between soil moisture and infrared surface temperature observations taken from a geosynchronous satellite. Linear regression is used to relate soil moisture to surface temperature and other variables that represent wind speed, vegetation cover, and low-level temperature advection. Results show good agreement between estimated and observed soil moisture features on each of the 5 days. The average coefficient of determination for five pseudoindependent tests in which the test day is held out of the regression is 0.71. It is shown that a depletion coefficient of 0.92, when used to compute antecedent precipitation index (API), produces the best correlation between API and soil moisture as inferred from GOES thermal infrared data. By averaging daily predicted values over the 5-day rain-free case study period, 92 percent of the variance of the morning surface temperature change is explained by a simple multiple linear regression with all independent variables, or, alternatively, 85 percent of the observed variance in API is explained. It is concluded that this approach can distinguish at least four classes of soil wetness, but the necessity for measurement of surface advection may limit its usefulness in remote areas.
A study of estimated full-scale noise levels based on measured levels from the Advanced Ducted Propeller (ADP) sub-scale model is presented. Testing of this model was performed in the NASA Lewis Low Speed Anechoic Wind Tunnel at a simulated takeoff condition of Mach 0.2. Effective Perceived Noise Level (EPNL) estimates for the baseline configuration are documented, and also used as the control case in a study of the potential benefits of two categories of noise control. The effect of active noise control is evaluated by artificially removing various rotor-stator interaction tones. Passive noise control is simulated by applying a notch filter to the wind tunnel data. Cases with both techniques are included to evaluate hybrid active-passive noise control. The results for EPNL values are approximate because the original source data was limited in bandwidth and in sideline angular coverage. The main emphasis is on comparisons between the baseline and configurations with simulated noise control measures.
Abstract Horizontal axis wind turbines (HAWTs) experience three‐dimensional rotational and unsteady aerodynamic phenomena at the rotor blades sections. These highly unsteady three‐dimensional effects have a dramatic impact on the aerodynamic load distributions on the blades, in particular, when they occur at high angles of attack due to stall delay and dynamic stall. Unfortunately, there is no complete understanding of the flow physics yet at these unsteady 3D flow conditions, and hence, the existing published theoretical models are often incapable of modelling the impact on the turbine response realistically. The purpose of this paper is to provide an insight on the combined influence of the stall delay and dynamic stall on the blade load history of wind turbines in controlled and uncontrolled conditions. New dynamic stall vortex and nonlinear tangential force coefficient modules, which integrally take into account the three dimensional rotational effect, are also proposed in this paper. This module along with the unsteady influence of turbulent wind speed and tower shadow is implemented in a blade element momentum (BEM) model to estimate the aerodynamic loads on a rotating blade more accurately. This work presents an important step to help modelling the combined influence of the stall delay and dynamic stall on the load history of the rotating wind turbine blades which is vital to have lighter turbine blades and improved wind turbine design systems.
Initial results of studies concerning Seasat measurements in and around tropical and severe midlatitude cyclones over the open ocean are presented, together with an assessment of their accuracy and usefulness. Complementary measurements of surface wind speed and direction, rainfall rate, and the sea surface temperature obtained with the Seasat-A Satellite Scatterometer (SASS), the Scanning Multichannel Microwave Radiometer (SMMR), and the Seasat SAR are analyzed. The Seasat data for the Hurrricanes Fico, Ella, and Greta and the QE II storm are compared with data obtained from aircraft, buoys, and ships. It is shown that the SASS-derived wind speeds are accurate to within 10 percent, and the directions are accurate to within 20 percent. In general, the SASS estimates tend to measure light winds too high and intense winds too low. The errors of the SMMR-derived measurements of the winds in hurricanes tend to be higher than those of the SASS-derived measurements.
Three years of radar altimeter data from GEOS-3 for the South Atlantic Bight were processed. Mean monthly topographic maps were produced which estimate geostrophic flow in the region. Statistical distribution of the surface wind speed and significant wave height as a function of both space and time are presented.
Wind-tunnel tests were performed on a scale model of a cab-over-engine tractor-trailer vehicle and several modifications of the model. Results from two of the model configurations were compared with full-scale drag data obtained from similar configurations during coast-down tests. Reductions in fuel consumption derived from these tests are presented in terms of fuel quantity and dollar savings per vehicle year, based on an annual driving distance of 160,900 km (100,000 mi.). The projected savings varied from 13,001 (3435) to 25,848 (6829) liters (gallons) per year which translated to economic savings from $3435 to about $6829 per vehicle year for an operating speed of 88.5 km/h (55 mph) and wind speeds near the national average of 15.3 km/h (9.5 mph). The estimated cumulative fuel savings for the entire U.S. fleet of cab-over-engine tractor, van-type trailer combinations ranged from 4.18 million kl (26.3 million bbl) per year for a low-drag configuration to approximately twice that amount for a more advanced configuration.