Search NASA⌕ Search

SEARCH · Search NASA

Results for “Wind Speed”

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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Relationship between wind speed and gas exchange over the ocean

A quadratic dependence of gas exchange on wind speed is employed to analyze the relationship between gas transfer and wind speed with particular emphasizing variable and/or low wind speeds. The quadratic dependence is fit through gas-transfer velocities over the ocean determined by methods based on the natural C-14 disequilibrium and the bomb C-14 inventory. The variation in the CO2 levels is related to these mechanisms, but the results show that other causes play significant roles. A weaker dependence of gas transfer on wind is suggested for steady winds, and long-term averaged winds demonstrate a stronger dependence in the present model. The chemical enhancement of CO2 exchange is also shown to play a role by increasing CO2 fluxes at low wind speeds.

Wanninkhof, Rik↗
DOE OSTI2020

How accurate is a machine learning-based wind speed extrapolation under a round-robin approach?

As the size of commercial wind turbines keeps increasing, having accurate ways to vertically extrapolate wind speed is essential to obtain a precise characterization of the wind resource for wind energy production. Recently, machine learning has been proposed and applied to extrapolate wind speed to hub heights. However, previous studies trained and tested the machine learning methods at the same site, giving them an unfair advantage over the conventional extrapolation techniques, which are instead more universal. Here, we use data from four sites in Oklahoma to test a round-robin validation approach for machine learning, under which we train a random forest at a site, and test it at a different site, where the model has no prior knowledge of the wind resource. We quantify how the accuracy of this technique varies with distance from the training site, and we find that it outperforms conventional techniques for wind extrapolation at all the considered spatial separations. We then assess how the accuracy of the machine-learning based approach varies when it is used to predict wind speed in a wind farm far wake. Finally, we explore as case study the performance of the random forest in extrapolating winds during a low-level jet event.

17 WIND ENERGY↗
NASA NTRS1985

11- and 22-year variations of the cosmic ray density and of the solar wind speed

Cosmic ray density variations for 17-21 solar activity cycles and the solar wind speed for 20-21 events are investigated. The 22-year solar wind speed recurrence was found in even and odd cycles. The 22-year variations of cosmic ray density were found to be opposite that of solar wind speed and solar activity. The account of solar wind speed in 11-year variations significantly decreases the modulation region of cosmic rays when E = 10-20 GeV.

Chirkov, N. P.↗
NASA NTRS1990

Solar wind speed and coronal flux-tube expansion

The hypothesis that the solar wind speed at 1 AU and the rate of magnetic flux-tube expansion in the corona are inversely correlated is shown to be consistent with observations extending over the last 22 years. This empirical relationship allows the daily wind speeds at earth to be predicted from a current-free extrapolation of the observed photospheric field into the corona. The narrow boundaries of high-speed wind streams are attributed to steep gradients in the flux-tube expansion rates at the edges of coronal holes. When a heliospheric current sheet is included in the model, it is found that the flux tubes near the hole axis, although diverging more slowly than those near the hole boundary in the corona, have undergone the greatest net expansion at 1 AU, an effect consistent with the low densities within high-speed streams.

Wang, Y.-M.↗
NASA NTRS2016

Observed Trend in Surface Wind Speed Over the Conterminous USA and CMIP5 Simulations

There has been no spatial surface wind map even over the conterminous USA due to the difficulty of spatial interpolation of wind field. As a result, the reanalysis data were often used to analyze the statistics of spatial pattern in surface wind speed. Unfortunately, no consistent trend in wind field was found among the available reanalysis data, and that obstructed the further analysis or projection of spatial pattern of wind speed. In this study, we developed the methodology to interpolate the observed wind speed data at weather stations using random forest algorithm. We produced the 1-km daily climate variables over the conterminous USA from 1979 to 2015. The validation using Ameriflux daily data showed that R2 is 0.59. Existing studies have found the negative trend over the Eastern US, and our study also showed same results. However, our new datasets also revealed the significant increasing trend over the southwest US especially from April to June. The trend in the southwestern US represented change or seasonal shift in North American Monsoon. Global analysis of CMIP5 data projected the decrease trend in mid-latitude, while increase trend in tropical region over the land. Most likely because of the low resolution in GCM, CMIP5 data failed to simulate the increase trend in the southwest US, even though it was qualitatively predicted that pole ward shift of anticyclone help the North American Monsoon.

simulations↗
DOE OSTI2021

Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error

Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting. Many machine learning methods utilize exogenous variables as input features, but there remains the question of which atmospheric variables are most beneficial for forecasting, especially in handling non-linearities that lead to forecasting error. This question is addressed via creation of a hybrid model that utilizes an autoregressive integrated moving-average (ARIMA) model to make an initial wind speed forecast followed by a random forest model that attempts to predict the ARIMA forecasting error using knowledge of exogenous atmospheric variables. Variables conveying information about atmospheric stability and turbulence as well as inertial forcing are found to be useful in dealing with non-linear error prediction. Streamwise wind speed, time of day, turbulence intensity, turbulent heat flux, vertical velocity, and wind direction are found to be particularly useful when used in unison for hourly and 3 h timescales. The prediction accuracy of the developed ARIMA–random forest hybrid model is compared to that of the persistence and bias-corrected ARIMA models. The ARIMA–random forest model is shown to improve upon the latter commonly employed modeling methods, reducing hourly forecasting error by up to 5 % below that of the bias-corrected ARIMA model and achieving an R 2 value of 0.84 with true wind speed.

17 WIND ENERGY↗
NASA NTRS1994

A new approach for determining fully empirical altimeter wind speed model functions

A statistical technique is developed for determining fully empirical model functions relating altimeter backscatter (sigma(sub 0)) measurements to near-surface neutral stability wind speed. By assuming that sigma(sub 0) varies monotonically and uniquely with wind speed, the method requires knowledge only of the separate, rather than joint distribution functions of sigma(sub 0) and wind speed. Analytic simplifications result from using a Weibull distribution to approximate the global ocean wind speed distribution; several different wind data sets are used to demonstrate the validity of the Weibull approximation. The technique has been applied to 1 year of Geosat data. Validation of the new and historical model functions using an independent buoy data set demonstrates that the present model function not only has small overall bias and root mean square (RMS) errors, but yields smaller systematic error trends with wind speed and pseudowave age than previously published models. The present analysis suggests that generally accuracte altimeter model functions can be derived without the use of colocated measurements, nor is additional significant wave height information measured by the altimeter necessary.

Freilich, M. H.↗
NASA NTRS2024

Meteorology Modulates the Impact of GCM Horizontal Resolution on Underestimation of Midlatitude Ocean Wind Speeds

We utilize ocean 10-m wind speed (U 10m ) from the microwave Multi-sensor Advanced Climatology data set to examine the coupling between convective cloud and precipitation processes, synoptic state, and U 10m and to evaluate the representation of U 10m in global climate models (GCMs). We find that midlatitude U 10m is underestimated by GCMs relative to observations. We examine two potential mechanisms to explain this model behavior: cold pool formation in cold air outbreaks (CAOs) associated with downdrafts that enhance U 10m and sea surface temperature (SST) gradients affecting U 10m through thermally forced surface winds at regional scales. When the effects of the CAO index (M) and SST gradients on U 10m are accounted for, a relationship between GCM horizontal resolution and U 10m appears. The strongest correlation between resolution and U 10m is over the western boundary currents characterized by frequent CAOs atop strong SST gradients which drives the strongest surface fluxes on Earth.

surface wind speed↗
NASA NTRS1983

Linear retrieval and global measurements of wind speed from the Seasat SMMR

Retrievals of wind speed (WS) from Seasat Scanning Multichannel Microwave Radiometer (SMMR) were performed using a two-step statistical technique. Nine subsets of two to five SMMR channels were examined for wind speed retrieval. These subsets were derived by using a leaps and bound procedure based on the coefficient of determination selection criteria to a statistical data base of brightness temperatures and geophysical parameters. Analysis of Monsoon Experiment and ocean station PAPA data showed a strong correlation between sea surface temperature and water vapor. This relation was used in generating the statistical data base. Global maps of WS were produced for one and three month periods.

Pandey, P. C.↗
NASA NTRS2012

Effect of Wind Speed on Aerosol Optical Depth over Remote Oceans, Based on Data from the Maritime Aerosol Network

The Maritime Aerosol Network (MAN) has been collecting data over the oceans since November 2006. The MAN archive provides a valuable resource for aerosol studies in maritime environments. In the current paper we investigate correlations between ship-borne aerosol optical depth (AOD) and near-surface wind speed, either measured (onboard or from satellite) or modeled (NCEP). According to our analysis, wind speed influences columnar aerosol optical depth, although the slope of the linear regression between AOD and wind speed is not steep (approx. 0.004 - 0.005), even for strong winds over 10m/s. The relationships show significant scatter (correlation coefficients typically in the range 0.3 - 0.5); the majority of this scatter can be explained by the uncertainty on the input data. The various wind speed sources considered yield similar patterns. Results are in good agreement with the majority of previously published relationships between surface wind speed and ship-based or satellite-based AOD measurements. The basic relationships are similar for all the wind speed sources considered; however, the gradient of the relationship varies by around a factor of two depending on the wind data used

Smirnov, A.↗
NASA NTRS1991

The dependence of wind stress on wave height and wind speed

Three near-neutral boundary layer data sets were investigated with the aim of finding a dependence of wind stress on both wind speed and significant wave height. The data set most representative of open-ocean wave height, wind speed, and momentum flux conditions, was selected and analyzed by means of the least-squares method to produce a new parameterization for the wind stress as a function of both wind speed and significant wave height. This study shows that the wind stress, and consequently the drag coefficient, decreases with increasing wave height for a fixed wind speed. The study also shows that the curvature of the wind profile decreases with increasing wave height and that the C(DN) = A + BU-bar(10) form for the drag coefficient parameterization is inadequate. A drag coefficient that applies to both smooth and rough flows is proposed. These results are more applicable for open-ocean deep-water conditions and less applicable for sheltered, closed, shallow water sites.

Blake, Reginald A.↗
NASA NTRS1991

An atlas of monthly mean distributions of GEOSAT sea surface height, SSMI surface wind speed, AVHRR/2 sea surface temperature, and ECMWF surface wind components during 1988

Monthly mean global distributions for 1988 are presented with a common color scale and geographical map. Distributions are included for sea surface height variation estimated from GEOSAT; surface wind speed estimated from the Special Sensor Microwave Imager on the Defense Meteorological Satellite Program spacecraft; sea surface temperature estimated from the Advanced Very High Resolution Radiometer on NOAA spacecrafts; and the Cartesian components of the 10m height wind vector computed by the European Center for Medium Range Weather Forecasting. Charts of monthly mean value, sampling distribution, and standard deviation value are displayed. Annual mean distributions are displayed.

Halpern, D.↗
DOE OSTI2020

Decreasing wind speed extrapolation error via domain-specific feature extraction and selection

Abstract. Model uncertainty is a significant challenge in the wind energy industry and can lead to mischaracterization of millions of dollars' worth of wind resources. Machine learning methods, notably deep artificial neural networks (ANNs), are capable of modeling turbulent and chaotic systems and offer a promising tool to produce high-accuracy wind speed forecasts and extrapolations. This paper uses data collected by profiling Doppler lidars over three field campaigns to investigate the efficacy of using ANNs for wind speed vertical extrapolation in a variety of terrains, and it quantifies the role of domain knowledge in ANN extrapolation accuracy. A series of 11 meteorological parameters (features) are used as ANN inputs, and the resulting output accuracy is compared with that of both standard log-law and power-law extrapolations. It is found that extracted nondimensional inputs, namely turbulence intensity, current wind speed, and previous wind speed, are the features that most reliably improve the ANN's accuracy, providing up to a 65 % and 52 % increase in extrapolation accuracy over log-law and power-law predictions, respectively. The volume of input data is also deemed important for achieving robust results. One test case is analyzed in depth using dimensional and nondimensional features, showing that the feature nondimensionalization drastically improves network accuracy and robustness for sparsely sampled atmospheric cases.

17 WIND ENERGY↗
NASA NTRS1981

Wind speed statistics for Goldstone, California, anemometer sites

An exploratory wind survey at an antenna complex was summarized statistically for application to future windmill designs. Data were collected at six locations from a total of 10 anemometers. Statistics include means, standard deviations, cubes, pattern factors, correlation coefficients, and exponents for power law profile of wind speed. Curves presented include: mean monthly wind speeds, moving averages, and diurnal variation patterns. It is concluded that three of the locations have sufficiently strong winds to justify consideration for windmill sites.

Berg, M.↗
NASA NTRS2018

Hurricane Imaging Radiometer (HIRAD) Wind Speed Retrievals and Assessment Using Dropsondes

The Hurricane Imaging Radiometer (HIRAD) is an experimental C-band passive microwave radiometer designed to map the horizontal structure of surface wind speed fields in hurricanes. New data processing and customized retrieval approaches were developed after the 2015 Tropical Cyclone Intensity (TCI) experiment, which featured flights over Hurricanes Patricia, Joaquin, Marty, and the remnants of Tropical Storm Erika. These new approaches produced maps of surface wind speed that looked more realistic than those from previous campaigns. Dropsondes from the High Definition Sounding System (HDSS) that was flown with HIRAD on a WB-57 high altitude aircraft in TCI were used to assess the quality of the HIRAD wind speed retrievals. The root mean square difference between HIRAD-retrieved surface wind speeds and dropsonde-estimated surface wind speeds was 6.0 meters per second. The largest differences between HIRAD and dropsonde winds were from data points where storm motion during dropsonde descent compromised the validity of the comparisons. Accounting for this and for uncertainty in the dropsonde measurements themselves, we estimate the root mean square error for the HIRAD retrievals as around 4.7 meters per second. Prior to the 2015 TCI experiment, HIRAD had previously flown on the WB-57 for missions across Hurricanes Gonzalo (2014), Earl (2010), and Karl (2010). Configuration of the instrument was not identical to the 2015 flights, but the methods devised after the 2015 flights may be applied to that previous data in an attempt to improve retrievals from those cases.

wind retrieval↗
NASA NTRS1983

Evaluation of Seasat SMMR wind speed measurements

The Seasat scanning multichannel microwave radiometer (SMMR) is able to measure the wind speed at the ocean surface through the change in ocean surface microwave emissivity caused by the wind. In this paper the SMMR-derived wind speeds are compared to the wind speeds derived from an active microwave scatterometer also aboard the Seasat, the Seasat A scanning scatterometer (SASS). Four orbits that passed over the severe storm that damaged the Queen Elizabeth II are examined in detail. These orbits and five others were used to investigate effects which degrade the SMMR wind retrievals. When the data are filtered for such effects, it is found that the SMMR winds agree with the SASS winds with a scatter (1 sigma) of less than 2 m/s about a bias of 1 to 2 m/s.

Cardone, V.↗
NASA NTRS1995

Source region of low-speed wind

We have been carrying out the interplanetary scintillation observations at a frequency of 327 MHz. The IPS measurements at this frequency can probe the distance range of 0.1-1 AU. We will report on source regions of the low-speed winds which were observed within 0.3 AU by the IPS method. The source regions of low-speed winds have been studied. In 1991, two spacecraft of Sakigake and IMP observed two low-speed streams in one solar rotation, which originated from a magnetic neutral line on the source surface. However speeds are slightly different from each other: one is 300 km/s while the other one is 400 km/s. Similar speed difference was also observed by the IPS method. We examined differences of these source regions in the soft X-ray images observed by the Yohkoh satellite. At the source region of the lower speed wind, sun spots were found under the neutral line, while nothing except the neutral line was found for the higher speed wind. We made a synoptic chart of the solar wind speeds which were observed within 0.3 AU. In this chart, compact regions of very low speed can be found clearly, and the amplitude of a low-speed belt is smaller than that of a magnetic neutral line. Distribution of the low-speed belt is rather suited above active regions than on a neutral line calculated by the potential field model.

Watanabe, H.↗
NASA NTRS2005

Nature and Variability of Coronal Streamers and their Relationship to the Slow Speed Wind

NASA Grant NAG5-12781 is a study on the "Nature and Variability of Coronal Streamers and their Relationship to the Slow Speed Wind." The two main goals of this study are to identify: 1) Where in the streamer structure does the solar wind originate, and 2) What coronal conditions are responsible for the variability of the slow speed wind. To answer the first question, we examined the mostly closed magnetic field regions in streamer cores to search for evidence of outflow. Preliminary results from the OVI Doppler dimming ratios indicates that most of the flow originates from the edges of coronal streamers but this idea should be confirmed by a comparison of the coronal plasma properties with in situ solar wind data. To answer the second question, the work performed thus far suggests that solar minimum streamers have larger perpendicular velocity distributions than do solar maximum streamers. If it can be shown that solar minimum streamers also produce higher solar wind speeds then this would suggest that streamers and coronal holes have similar solar wind acceleration mechanisms. The key to both questions lie in the analysis of the in situ solar wind data sets. This work was not able to be completed during the period of performance and therefore the grant was formally extended for an additional year at no cost to NASA. We hope to have final results and a publication by the end of the calendar year 2004. The SAO personnel involved in the research are Leonard Strachan (PI), Mari Paz Miralles, Alexander Panasyuk, and a Southern University student Michael Baham.

Strachan, Leonard↗