Search NASA⌕ Search

SEARCH · Search NASA

Results for “wind speed estimates”

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.

At least 91 records · Page 5

GPS Remote Sensing Measurements Using Aerosonde UAV

In February 2004, a NASA-Langley GPS Remote Sensor (GPSRS) unit was flown on an Aerosonde unmanned aerial vehicle (UAV) from the Wallops Flight Facility (WFF) in Virginia. Using direct and surface-reflected 1.575 GHz coarse acquisition (C/A) coded GPS signals, remote sensing measurements were obtained over land and portions of open water. The strength of the surface-reflected GPS signal is proportional to the amount of moisture in the surface, and is also influenced by surface roughness. Amplitude and other characteristics of the reflected signal allow an estimate of wind speed over open water. In this paper we provide a synopsis of the instrument accommodation requirements, installation procedures, and preliminary results from what is likely the first-ever flight of a GPS remote sensing instrument on a UAV. The correct operation of the GPSRS unit on this flight indicates that Aerosonde-like UAV's can serve as platforms for future GPS remote sensing science missions.

Grant, Michael S.↗

Advanced Air Data Systems for Commercial Aircraft

It is possible to get a crude estimate of wind speed and direction while driving a car at night in the rain, with the motion of the raindrop reflections in the headlights providing clues about the wind. The clues are difficult to interpret, though, because of the relative motions of ground, car, air, and raindrops. More subtle interpretation is possible if the rain is replaced by fog, because the tiny droplets would follow the swirling currents of air around an illuminated object, like, for example, a walking pedestrian. Microscopic particles in the air (aerosols) are better for helping make assessments of the wind, and reflective air molecules are best of all, providing the most refined measurements. It takes a bright light to penetrate fog, so it is easy to understand how other factors, like replacing the headlights with the intensity of a searchlight, can be advantageous. This is the basic principle behind a lidar system. While a radar system transmits a pulse of radiofrequency energy and interprets the received reflections, a lidar system works in a similar fashion, substituting a near-optical laser pulse. The technique allows the measurement of relative positions and velocities between the transmitter and the air, which allows measurements of relative wind and of air temperature (because temperature is associated with high-frequency random motions on a molecular level). NASA, as well as the National Oceanic and Atmospheric Administration (NOAA), have interests in this advanced lidar technology, as much of their explorative research requires the ability to measure winds and turbulent regions within the atmosphere. Lidar also shows promise for providing warning of turbulent regions within the National Airspace System to allow commercial aircraft to avoid encounters with turbulence and thereby increase the safety of the traveling public. Both agencies currently employ lidar and optical sensing for a variety of weather-related research projects, such as analyzing the water content of snow and forecasting lightning.

Source record↗

Sensitivity of Depth-Integrated Satellite Lidar to Subaqueous Scattering

A method is presented for estimating subaqueous integrated backscatter from the CALIOP lidar. The algorithm takes into account specular reflection of laser light, laser scattering by wind-generated foam as well as sun glint and solar scattering from the foam Analyses show that the estimated subaqueous integrated backscatter is most sensitive to the estimate of transmittance used in the atmospheric correction, and is very insensitive to the estimate of wind speed used. As a case study, CALIOP data over Tampa Bay were compared to MODIS 645 nm remote sensing reflectance, which previously has been shown to be nearly linearly related to turbidity. The results indicate good correlation on nearly all CALIOP clear-free dates during the period 2006 through 2007, particularly those with relatively high atmospheric transmittance. When data are composited over the entire period the correlation is reduced but still statistically significant, an indication of variability in the biogeochemical composition in the water. Overall, the favorable results show promise for the application of satellite lidar integrated backscatter in providing information about subsurface backscatter properties, which can be extracted using appropriate models

Barton, Jonathan S.↗

[Measurement of Speed and Direction of Ocean Surface Winds Using Quik Scat Scatterometer]

The SeaWinds on QuikSCAT scatterometer was developed by NASA JPL to measure the speed and direction of ocean surface winds. Simulations performed to estimate the performance of the instrument prior to its launch have indicated that the mid-swath accuracy is worse than that of the rest of the swath. This behavior is a general characteristic of scanning pencil beam scatterometers. For SeaWinds, the accuracy of the rest of the swath, and the size of the swath are such that the instrument meets its science requirements despite mid-swath shortcomings. However, by understanding the problem at mid-swath, we can improve the performance there as well. We discuss the underlying causes of the problem in detail and propose a new wind retrieval algorithm which improves mid-swath performance. The directional discrimination ability of the instrument varies with cross track distance wind speed, and direction. By estimating the range of likely wind directions for each measurement cell, one can optimally apply information from neighboring cells where necessary in order to reduce random wind direction errors without significantly degrading the resolution of the resultant wind field. In this manner we are able to achieve mid-swath RMS wind direction errors as low as 15 degrees for low winds and 10 degrees for moderate to high winds, while at the same time preserving high resolution structures such as cyclones and fronts.

Stiles, Bryan↗

Weather watch studies by means of an optical technique

A series of articles is presented on a passive, optical, cross beam system. The passive, optical, cross beam system was evaluated as a remote sensor of atmospheric conditions. The system employed the light scattered from natural aerosols to sense atmospheric mean and turbulent motion. A space-time correlation of the output of two optical sensors was used to evaluate the mean convective wind speed. In approximately 20% of the tests, signal-to-noise levels were sufficient to allow estimation of convective wind speeds. The feasibility of employing intersecting beams to evaluate the atmospheric turbulent motion was also investigated. The intersecting beams produced maximums in the space-time correlation curves for other than zero delay. The non-zero time delay maximums indicate the cross beam system was detecting aerosol layers in the atmosphere, rather than information from the common intersecting volume.

Turner, R. E.↗

RADARSAT Underflights: Comparison of C-band VV and HH Backscatter-Wind Retrieval

The primary objective of this grant was to validate the modified CMOD4 wind speed algorithm used to invert RADARSAT data. We proposed to do the following: TASK 1: Participate in at least two underflights of RADARSAT (collecting backscatter measurements with the UMASS C-band and Ku-band scatterometers, CSCAT and KUSCAT. TASK 2: Process data gathered with CSCAT and KUSCAT into one kilometer along-track pixels with each pixel consisting of seventy-two five degree azimuth bins - each bin containing normalized radar cross section (NRCS) values for that particular azimuth angle and for each of the four incidence angles viewed by CSCAT and KUSCAT. Derive wind speed and direction estimates from each NRCS along-track pixel using CMOD4 and NSCAT models. TASK 3: Compare collocated CSCAT vertically polarized (VV) and RADARSAT horizontally polarized (HH) NRCS data and wind estimates, and based on the comparison, evaluate the performance of the modified CMOD4 wind speed algorithm to invert RADARSAT NRCS measurements. The first two tasks were completed. The third task is still on-going since the RADARSAT data for the underflights has not been released to the University of Massachusetts. We expect that the RADARSAT data will become available in the next few months, and we will complete the third task in a promptly manner once the data has been received. Below the missions flown and the data collected are summarized. Two missions, based out of the NASA Wallops Flight Facility, were flown. The first flight was on 21 September 1997 and the second on 24 September 1997. The time for each mission was approximately 2 hours. Figure 1 plots the flight tracks and marks the location of the NOAA buoys that were in the vicinity.

Source record↗

Gulf of Mexico Offshore Wind Energy Hurricane Risk Assessment

NREL's feasibility assessment of offshore wind in the Gulf of Mexico concluded that hurricane risk was one of the major challenges that would need to be overcome for a mature offshore wind industry to develop in the Gulf of Mexico. To ensure the robust design of wind turbines in the Gulf of Mexico, it is critical to understand the added risk posed by the threat of major hurricanes, as those affecting the Gulf of Mexico region have a significant potential to exceed design limits prescribed by the International Electrotechnical Commission (IEC) wind design standards. To satisfy this charge, this project defines the wind hazard for the Gulf of Mexico Offshore Wind Energy area using the hurricane hazard model develop by Applied Research Associates and published extensively in the open literature. In doing so, the return periods associated with the IEC Class 1A and Typhoon Class limit-state hurricanes are estimated on a grid with nominal resolution of 10 km to determine where hurricane risk results in the exceedance of the IEC design criteria. On the same grid, wind speeds hazard contours associated with return periods varying from 50 to 1,000 years are also estimated. An additional challenge in assessing hurricane wind speed risk in the Gulf of Mexico arises from inconsistent terminology across the Saffir-Simpson hurricane scale and the IEC design criteria. Saffir-Simpson definitions are based on 1-minute sustained wind speeds estimated at 10-m height over marine terrain, while the IEC uses a different averaging period (3-second versus 1-minute) and reference height (assumed herein a hub height of 150 m versus 10 m). Employing the latest research on turbulence characteristics of the hurricane boundary layer, conversions between various durations (e.g., 3-seconds, 1-minute, 10-minutes, 1-hour) and between elevations near the surface (10 m) to near hub height (assumed herein 150 m) are developed. IEC Class 1A and Typhoon Class limit states are also provided in terms of an equivalent Saffir-Simpson hurricane wind speed category.

17 WIND ENERGY↗

Determining Wind Speed and Direction with Ocean Reflected GPS Signals

The power received by a GPS receiver located on an airplane flying over the ocean and collecting the GPS signal reflected off the surface is estimated for varying wind speed and wind direction, platform altitude, satellite elevation angle, and receiver integration time.

GPS Remote Sensing Scatterometry Doppler Filtering↗

Laboratory Heat Flux Estimates of Seawater Foam for Low Wind Speeds

Laboratory experiments were conducted to measure the heat flux from seafoam continuously generated in natural seawater. Using a control volume technique, heat flux was calculated from foam and foam-free surfaces as a function of ambient humidity (ranged from 40% to 78%), air–water temperature difference (ranged from -9 °C to 0 °C), and wind speed (variable up to 3 m s-1). Water-surface skin temperature was imaged with a calibrated thermal infrared camera, and near-surface temperature profiles in the air, water, and foam were recorded. Net heat flux from foam surfaces increased with increasing wind speed and was shown to be up to four times greater than a foam-free surface. The fraction of the total heat flux due to the latent heat flux was observed for foam to be 0.75, with this value being relatively constant with wind speed. In contrast, for a foam-free surface the fraction of the total heat flux due to the latent heat flux decreased at higher wind speeds. Temperature profiles through foam are linear and have larger gradients, which increased with wind speed, while foam free surfaces show the expected logarithmic profile and show no variation with temperature. The radiometric surface temperatures show that foam is cooler and more variable than a foam-free surface, and bubble-resolving thermal images show that radiometrically transparent bubble caps and burst bubbles reveal warm foam below the cool surface layer, contributing to the enhanced variability.

54 ENVIRONMENTAL SCIENCES↗

The minimum free-stream wind speed for initiating motion of surface material on Mars

Estimates of the minimum free-stream wind speed that is required for initiating the motion of surficial material on Mars have ranged from 30 to about 200 meters per second. Thus the best value for this quantity is not well established. Graphical comparison of much of the pertinent data taken in the laboratory and in the field on Earth provides a minimum value for the Bagnold coefficient of 0.08 and this in turn provides a minimum value for the threshold friction velocity of 1.3 meters per second for initiating motion of particulate matter on Mars at low elevations where the pressure is 7 millibars. The most appropriate value of the ratio of friction velocity to free-stream velocity for putative unstable condition appears to be 0.026. Thus the minimum free-stream wind speed for initiating motion is obtained as 50 meters per second. If the surface material on Mars, however, is less cohesive than that on earth, the minimum value may be smaller.

Wood, G. P.↗

The wind-speed measurement capability of spaceborne radar altimeters

This study represents an attempt to quantitatively assess the capability of a spaceborne radar altimeter to infer ocean surface wind speeds from a measurement of the backscattered power at vertical incidence. The study uses data acquired during 184 near overflights of NOAA data buoys with the GEOS-3 satellite radar altimeter and encompasses a wind-speed range from less than 1 to 18 m/s. An algorithm is derived from the data comparison for converting measurements of the normalized scattering cross section of the ocean surface at 13.9 GHz into estimates of the surface wind speed at the standard anemometer height of 10 m. The algorithm is straightforward and potentially useful for on-board processing of raw altimeter data for the purpose of providing real-time estimates of surface wind speed. For winds in the range of 1 to 18 m/s, the mean difference between the altimeter-inferred winds and the buoy measurements is negligible while the standard deviation of the difference is 1.74 m/s.

Brown, G. S.↗

Winds aloft statistical analysis in support of day of launch Shuttle systems evaluation

In connection with the development of the Meteorological Interactive Data Display System (MIDDS) for utilization by the Launch Systems Evaluation Advisory Team (LSEAT), requirements have been established to expand the pre-launch analysis of winds aloft for the Space Shuttle. Statistical analyses developed for the system include: comparison of pre-launch wind component profiles to wind component extremes at each altitude calculated from launch site historical data; conditional probability ellipses for wind vectors at a future time given the wind vector at an initial time; comparison of observed extreme wind shear and associated wind speed with launch site historical data utilizing the bivariate extreme value (Gumbel) distribution; estimation of extremes of wind speed or wind shear at a future time given the extremes of either variable at an initial time, utilizing the conditional extreme value distribution; power spectrum analysis for tracking wind perturbation energy in sequential pre-launch Jimsphere wind profiles.

Adelfang, S. I.↗

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↗

Solar wind Acceleration from the Upper Chromosphere to the Corona in Coronal Hole Regions

The dynamic behavior of the plasma in the chromosphere/transition region /inner corona is vital for the acceleration of the solar wind. With new theoretical descriptions of the solar atmosphere and corona, and the increased observational possibilities provided by the SOHO spacecraft, it is possible to conduct an integrated study of the solar atmosphere and corona using observational and theoretical approaches. Over the past few years a series of observational techniques have been used to estimate the solar wind densities, temperatures and flow speed in the inner corona. These estimates suggest that the solar wind has higher outflow speeds in the inner corona and lower densities than previously assumed. A comparison with densities derived from atmospheric models support these lower densities.

Esser, Ruth↗

Behavior and mechanisms of Doppler wind lidar error in varying stability regimes

Abstract. Wind lidars are widespread and important tools in atmospheric observations. An intrinsic part of lidar measurement error is due to atmospheric variability in the remote-sensing scan volume. This study describes and quantifies the distribution of measurement error due to turbulence in varying atmospheric stability. While the lidar error model is general, we demonstrate the approach using large ensembles of virtual WindCube V2 lidar performing a profiling Doppler-beam-swinging scan in quasi-stationary large-eddy simulations (LESs) of convective and stable boundary layers. Error trends vary with the stability regime, time averaging of results, and observation height. A systematic analysis of the observation error explains dominant mechanisms and supports the findings of the empirical results. Treating the error under a random variable framework allows for informed predictions about the effect of different configurations or conditions on lidar performance. Convective conditions are most prone to large errors (up to 1.5 m s−1 in 1 Hz wind speed in strong convection), driven by the large vertical velocity variances in convective conditions and the high elevation angle of the scanning beams (62∘). Range-gate weighting induces a negative bias into the horizontal wind speeds near the surface shear layer (−0.2 m s−1 in the stable test case). Errors in the horizontal wind speed and direction computed from the wind components are sensitive to the background wind speed but have negligible dependence on the relative orientation of the instrument. Especially during low winds and in the presence of large errors in the horizontal velocity estimates, the reported wind speed is subject to a systematic positive bias (up to 0.4 m s−1 in 1 Hz measurements in strong convection). Vector time-averaged measurements can improve the behavior of the error distributions (reducing the 10 min wind speed error standard deviation to <0.3 m s−1 and the bias to <0.1 m s−1 in strong convection) with a predictable effectiveness related to the number of decorrelated samples in the time window. Hybrid schemes weighting the 10 min scalar- and vector-averaged lidar measurements are shown to be effective at reducing the wind speed biases compared to cup measurements in most of the simulated conditions, with time averages longer than 10 min recommended for best use in some unstable conditions. The approach in decomposing the error mechanisms with the help of the LES flow field could be extended to more complex measurement scenarios and scans.

17 WIND ENERGY↗

Tropical cyclone outer surface winds derived from satellite microwave sounder data

Upper tropospheric temperature anomalies are detected in brightness temperature data from the Nimbus 6 Scanning Microwave Spectrometer (SCAMS). Brightness temperature anomalies are related to surface pressure anomalies through the radiative transfer and hydrostatic equation. Surface wind speeds at outer radii are then estimated using the gradient wind equation and a shearing parameter. The method is first tested using simulated satellite data constructed from temperature, pressure and height data recorded by aircraft reconnaissance of four hurricanes. Wind speeds in the 80-95 kPa region are estimated with 2-3 m/sec accuracy. Next, 55.45 GHz SCAMS data over eight typhoons during 1975 are used to estimate the radii of 15.4 m/sec (30 kt) and 27.5 m/sec (50 kt) winds. Accuracies of about + or - 80 and + or - 70 km, respectively, are found. It is suggested that the technique be further tested using data from the Microwave Sounding Unit on board the TIROS-N and NOAA 6 satellites.

Kidder, S. Q.↗