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.
Microwave remote sensing is discussed in terms of using GEOS-3 data to infer surface wind speed. Topics covered include: determining surface wind speed from backscattering, estimating surface winds from near normal incidence scattering, and response of the centimeter wavelength components of the ocean surface height spectrum to the wind.
Airborne and Seasat-1 SAR measurements of over-ocean winds through the use of L-band frequencies is described. A consistent dependence has been found between the wind speeds and the microwave backscattering coefficient. Measurements were obtained for cells a few kilometers across and at an incidence angle of 20 deg from nadir. Surface measurements were included in the analyses whenever possible, including data for surface temperatures and current variations, such as in the Gulf Stream. Higher radar cross-sections have been observed from the Gulf Stream than in nearby continental shelf waters. The eye-wall of Hurricane Gloria in Sept. 1976 displayed the largest backscatter of the storm. Wind speed backscatter exponents of 0.5 for winds below 18 m/sec, and 0.58 for winds above 20 m/sec have been determined for the Seasat L-band backscatter. The same wind speeds hold true for 0.05 and 0.50, respectively, for the wind direction component.
A new technique combining active and passive remote sensing instruments for the estimation of surface latent heat flux over the ocean is presented. This synergistic method utilizes aerosol lidar backscatter data, multi-channel infrared radiometer data and microwave scatterometer data acquired onboard the NASA P-3B research aircraft during an extended field campaign over the Atlantic ocean in support of the Lidar In-space Technology Experiment (LITE) in September of 1994. The 10 meter wind speed derived from the scatterometers and the lidar-radiometer inferred near-surface moisture are used to obtain an estimate of the surface flux of moisture via bulk aerodynamic formulae. The results are compared with the Special Sensor Microwave Imager (SSM/I) daily average latent heat flux and show reasonable agreement. However, the SSM/I values are biased high by about 30 W/sq m. In addition, the MABL height, entrainment zone thickness and integrated lidar backscatter intensity are computed from the lidar data and compared with the magnitude of the surface fluxes. The results show that the surface latent heat flux is most strongly correlated with entrainment zone top, bottom and the integrated MABL lidar backscatter, with corresponding correlation coefficients of 0.62, 0.67 and 0.61, respectively.
A new technique combining active and passive remote sensing instruments for the estimation of surface latent heat flux over the ocean is presented. This synergistic method uses aerosol lidar backscatter data, multi-channel infrared radiometer data and microwave scatterometer data acquired onboard the NASA P-3B research aircraft during an extended field campaign over the Atlantic ocean in support of the Lidar In-space Technology Experiment (LITE) in September of 1994. The 10 meter wind speed derived from the scatterometers and the lidar-radiometer inferred near-surface moisture are used to obtain an estimate of the surface flux of moisture via bulk aerodynamic formulae. The results are compared with the Special Sensor Microwave Imager (SSM/I) daily average latent heat flux and show reasonable agreement with an rms error and bias of about 50 and 25 W per square meters, respectively. In addition, the MABL height, entrainment zone thickness and integrated lidar backscatter intensity are computed from the lidar data and compared with the magnitude of the surface fluxes. The results show that the surface latent heat flux is most strongly correlated with entrainment zone top, bottom and the integrated MABL lidar backscatter, with corresponding correlation coefficients of 0.62, 0.67 and 0.61, respectively.
A new technique combining active and passive remote sensing instruments for the estimation of surface latent heat flux over the ocean is presented. This synergistic method utilizes aerosol lidar backscatter data, multi-channel infrared radiometer data, and microwave scatterometer data acquired onboard the NASA P-313 research aircraft during an extended field campaign over the Atlantic ocean in support of the Lidar In-space Technology Experiment (LITE) in September of 1994. The 10 meter wind speed derived from scatterometers and lidar-radiometer inferred near-surface moisture are used to obtain an estimate of the surface flux of moisture via a bulk aerodynamic formula. The results are compared with the Special Sensor Microwave Imager (SSM/I) daily average latent heat flux and show reasonable agreement. However, the SSM/I values are biased low by about 15 W/sq m. In addition, the Marine Atmospheric Boundary Layer (MABL) height, entrainment zone thickness and integrated lidar backscatter intensity are computed from the lidar data and compared with the magnitude of the surface fluxes. The results show that the surface latent heat flux is most strongly correlated with entrainment zone depth, MABL height and the integrated MABL lidar backscatter, with corresponding correlation coefficients of 0.39, 0.43 and 0.71, respectively.
Coastal environments such as islands have unique opportunities for renewable energy resources. This work explores the wave and offshore wind energy potential for the U.S. Pacific Ocean Minor Outlying Islands, including Baker Island, Howland Island, Jarvis Island, Johnston Atoll, Kingman Reef, Palmyra Atoll, and Wake Island. A numerical wave model based on WAVEWATCH III and SWAN (both are NWS / NOAA models) was developed, validated, and executed to generate a 32-year hindcast dataset suitable for resource characterization for each island. A complementary offshore wind resource characterization is provided for the same time period based on the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5). Each island has five netcdf files: 1. wave_[island]_annual_J_map.nc: the data used to generate the map of annual averaged omnidirectional wave power (J [kW/m]) 2. wave_[island]_100m_IEC: six IEC parameters at 100m isobath from 1979/01/01 to 2020/12/31, output hourly 3. wave_[island]_5km_IEC: six IEC parameters at 5km from the shoreline 1979/01/01 to 2020/12/31, output hourly 4. wind_island_100mASL_windspeed_timeseries.nc: wind speed timeseries from ERA5 at 100 m above sea level within a 60 km radius of the island (m/s) 1979/01/01 to 2010/12/31, output hourly 5. wind_island_100mASL_winddirection_timeseries.nc: wind direction timeseries from ERA5 at 100 m above sea level within a 60 km radius of the island (degrees) 1979/01/01 to 2010/12/31, output hourly
Satellite altimetric data routinely map sea surface topography by measuring the ocean return signal. One source of altimeter measurement contamination occurs when the radar ocean backscatter becomes unusually large, a situation termed a Sigma-0 bloom. Past research suggests Sigma-0 blooms are associated with weak wind and natural surface slick conditions where capillary waves at the air–sea interface are suppressed. To date, no explicit connection between these conditions and Sigma-0 bloom presence has been provided. Using a series of simplified equations, our reduced model determines capillary wave heights from estimates of planktonic carbon concentrations and regional wind speed. Our results suggest that the radar signal reflection increases as capillary wave height decreases. This relationship depends on surfactant concentration, surfactant composition, and wind speed. Model sensitivity analysis indicates that the interface reflectivity depends on biological activity and wind speed. Our proposed simplified model provides a method to identify potential Sigma-0 bloom regions. We conclude that because of the demonstrated impact of biological surfactants on ocean roughness, it is necessary to consider the biological activity, i.e., phytoplankton bloom events, when interpreting signals from radar altimetry and when developing ocean hydrology models.
Ratios R of the monthly averaged wind speeds to the magnitudes of the monthly averaged wind vectors are presented over a 41 x 41 grid covering the southern Ocean and the Antarctic continent. The ratio is found to vary from 1 to over 1000, with an average value of 1.86. These ratios R are relevant for converting from sensible and latent heats calculated with mean monthly data to those calculated with 12 hourly data. The corresponding ratios alpha for wind stress, along with the angle deviations involved, are also presented over the same 41 x 41 grid. The values of alpha generally exceed those for R and average 2.66. Regions in zones of variable wind directions have larger R and alpha ratios, over the ice-covered portions of the southern Ocean averaging 2.74 and 4.35 for R and alpha respectively. Thus adjustments to compensate for the use of mean monthly wind velocities should be stronger for wind stress than for turbulent heats and stronger over ice covered regions than over regions with more persistent wind directions, e.g., those in the belt of mid-latitude westerlies.
The 30th Operational Support Squadron Weather Flight (30 OSSWF) provides comprehensive weather services to the space program at Vandenberg Air Force Base (VAFB) in California. One of their responsibilities is to monitor upper-level winds to ensure safe launch operations of the Minuteman Ill ballistic missile. The 30 OSSWF tasked the Applied Meteorology Unit (AMU) to analyze VAFB sounding data with the goal of determining the probability of violating (PoV) their upper-level thresholds for wind speed and shear constraints specific to this launch vehicle, and to develop a tool that will calculate the PoV of each constraint on the day of launch. In order to calculate the probability of exceeding each constraint, the AMU collected and analyzed historical data from VAFB. The historical sounding data were retrieved from the National Oceanic and Atmospheric Administration Earth System Research Laboratory archive for the years 1994-2011 and then stratified into four sub-seasons: January-March, April-June, July-September, and October-December. The AMU determined the theoretical distributions that best fit the maximum wind speed and maximum wind shear datasets and applied this information when calculating the averages and standard deviations needed for the historical and real-time PoV calculations. In addition, the AMU included forecast sounding data from the Rapid Refresh model. This information provides further insight for the launch weather officers (LWOs) when determining if a wind constraint violation will occur over the next few hours on the day of launch. The AMU developed an interactive graphical user interface (GUI) in Microsoft Excel using Visual Basic for Applications. The GUI displays the critical sounding data easily and quickly for LWOs on day of launch. This tool will replace the existing one used by the 30 OSSWF, assist the LWOs in determining the probability of exceeding specific wind threshold values, and help to improve the overall upper winds forecast for the launch customer. This presentation will describe how the AMU calculated the historical and real-time PoV values for the specific upper-level wind launch constraints and outline the development of the interactive GUI display.
Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinel lidar buoys for the U.S. Department of Energy’s Wind Energy Technologies. The purpose of these buoys is to collect hub-height winds and supporting meteorological and oceanographic information to facilitate the development of offshore wind energy in the U.S. In general, each buoy is deployed for a year or more at a given location in order to capture at least a full annual cycle of weather conditions. The initial deployment for one buoy was off the coast of Virginia beginning in 2015, and the other buoy was first deployed off the coast of New Jersey beginning in 2016. Over the last two years, PNNL has had an opportunity to analyze the data collected during these first two deployments. This report describes a substantial analysis of data collected by the two lidar buoys operated off the coasts of Virginia and New Jersey. The centerpiece instrument for each buoy as deployed off Virginia and New Jersey is a lidar system, which is designed to measure the horizontal wind vector from approximately 40 m to 200 m above the sea surface with vertical resolution of 40 m. Since the initial deployments, the original lidars have been replaced with more powerful Leosphere 866 v2 systems. The analyses in this report will apply to the original Vindicator systems. In addition to the wind profiles from the lidars, the buoys collect near-surface measurements of wind speed and direction, air temperature, relative humidity, barometric pressure, and solar irradiance. Oceanographic variables measured include the two-dimensional wave spectrum, water temperature and conductivity, and ocean current vectors to a depth of 90 m. An assessment of overall data recovery and a basic analysis of the data collected was provided in a previous report This report substantially extends that analysis. The various sections describe the development and application of an inertial measurement unit (IMU) data recovery scheme for the New Jersey deployment; a climatological analysis of winds at hub height and at the surface together with thermodynamic variables measured at the surface for the full deployment periods; an analysis of oceanographic observations describe sea state; the development of a refinement for NOAA’s WaveWatch III model to allow its application to near-shore areas; a basic climatology of ocean currents observed from the buoys; an analysis of observed winds in the framework of Monin-Obukhov Similarity Theory; and the development and evaluation of techniques to extract turbulence intensity and turbulence kinetic energy from the lidars. The analyses contained in this report provide a great deal of new information about offshore conditions on the U.S. East Coast. In addition, the experience gained will inform both configurations and analysis of data from future deployments of these lidar buoy systems.
The purpose of these two systems is to determine the winds over the ocean. The passive system may also provide data on precipitation over the ocean and sea surface temperature through clouds. They complement each other. The radar scatterometer will sense those sea surface properties that define the winds over the ocean through clouds so thick and so wet that the passive microwave system will have detected only the cloud properties. Both wind speed and direction can be found from the combined data to be obtained.
A technique is developed and discussed which utilizes only significant wave height and wind speed measurements available from the GEOS-3 radar altimeter in real time to locate swell dominated regions of the earth's oceans. It is shown that reasonable agreement exists between the GEOS-3 map of the North Atlantic for 24 February 1976 and a map produced from hindcasted values of sea and swell obtained from the Special Projects Branch of the National Weather Service. Only data presently available from existing satellite radar altimeters are needed to map those areas of the oceans dominated by swell. It is concluded that the example given indicates that the decision boundary developed in this study to distinguish swell-dominated seas is a realistic choice.
It is possible to estimate long term monthly mean latent and sensible heat fluxes over the ocean to within or approximately 20% relative accuracy of the bulk aerodynamic formulas, by using observations of the monthly mean surface wind speed and the monthly mean sea air temperature and humidity differences. It is possible to make an estimate of the fluxes on a month to month basis from monthly averaged surface data.
A new method for the physical retrieval of rain rates from satellite microwave radiometers is presented and compared to two other rainfall climatologies derived from satellites. The method is part of a unified ocean parameter retrieval algorithm that is based on the fundamental principles of radiative transfer. The algorithm simultaneously finds near-surface wind speed W, columnar water vapor V, columnar cloud liquid water L, rain rate R, and effective radiating temperature T(sub U) for the upwelling radiation. The performance of the algorithm in the absence of rain is discussed in Wentz, and this paper focuses on the rain component of the algorithm. A particular strength of the unified algorithm is its ability to 'orthogonalize' the retrievals so that there is minimum cross-talk between the retrieved parameters. For example, comparisons of the retrieved water vapor with radiosonde observations show that there is very little correlation between the water vapor retrieval error and rain rate. For rain rates from 1 to 15 mm/h, the rms difference between the retrieved water vapor and the radiosonde value is 5 mm. A novel feature of the rain retrieval method is a beamfilling correction that is based upon the ratio of the retrieved liquid water absorption coefficients at 37 GHz and 19.35 GHz. This ratio decreases by about 40% when heavy and light rain co-exist within the SSM/I footprint as compared to the case of uniform rain. This correction has the effect of increasing the rain rate when the spectral ratio of the absorption coefficients is small. Even with this beamfilling correction, tropical rainfall is still unrealistically low when the freezing level in the tropics (approx. 5 km) is used to specify the rain layer thickness. We restore realism by reducing the assumed averaged tropical rain layer thickness to 3 km, thereby accounting for the existence of warm rain processes in which the rain layer does not extend to the freezing level. Global rain rates are produced for the 1991 through 1994 period from observations taken by microwave radiometers (SSM/I) that are aboard two polar-orbiting satellites. We find that approximately 6% of the SSM/I observations detect measurable rain rates (R greater than 0.2 mm/h). Zonal averages of the rain rates show the peak at the intertropical convergence zone (ITCZ) is quite narrow in meridional extent and varies from about 7 mm/day in the winter to a maximum 11 mm/day in the summer. Very low precipitation rates (less than 0.3 mm/day) are observed in those areas of subsidence influenced by the large semipermanent anticyclones. In general, these features are similar to those reported in previously published rain climatologies. However, significant differences do exists between our rain rates and those produced by Spencer. These differences seem to be related to non-precipitating cloud water.
Supermicron sea spray aerosols (SSA) play a crucial role in stratocumulus cloud microphysics and drizzle formation. However, our understanding of SSA's spatiotemporal distribution, production mechanisms, and fluxes in the marine boundary layer remains limited. This study introduces a novel approach to determine supermicron SSA production flux and number concentration at various heights above the ocean surface. Our method leverages Doppler lidar data, specifically attenuated backscatter and vertical velocity, collected at a 105 m range gate during the Eastern Pacific Clouds and Aerosols for Precipitation Experiment (EPCAPE) at Scripps Pier, La Jolla, California. To focus on nascent SSA, we analyzed data from periods with wind directions between 225° and 315° and wind speeds exceeding 4 m s −1 . Cloud and precipitation events were excluded using relative humidity, backscatter thresholds, and 2D Video Disdrometer observations. Results show that calculated supermicron SSA number concentrations at 105 m ranged from 0.26 to 2.93 cm −3 for surface wind speeds between 4 and 6 ms −1 . When exceeding the method's Limit of Detection, estimated production fluxes ranged from 0.20 to 1.53 cm −2 s −1 . This methodology has potential for application in various oceanic locations, likely enhancing our understanding of SSA's role in atmospheric processes and climate interactions.
A future microwave spectroradiometer operating in several frequency bands will have the capability to step or sweep frequencies on an adaptable or programmable basis. The on-board adaptable frequency shifting can make the systems immune from radio interference. Programmable frequency sweeping with on-board data inversion by high speed computers would provide for instantaneous synoptic measurements or sea surface temperature and salinity, water surface and volume pollution, ice thickness, ocean surface winds, snow depth, and soil moisture. Large structure satellites will allow an order of magnitude improvement in the present radiometric measurement spacial resolution.
Air-sea transfers of sensible heat, latent heat and momentum are computed from 25 years of middle-latitude and subtropical ocean weather ship data in the North Atlantic and North Pacific using the bulk aerodynamic method. The results show that monthly averaged wind speeds, temperatures and humidities can be used to estimate the monthly averaged sensible and latent heat fluxes from the bulk aerodynamic equations to within a relative error of approximately 10%. The estimates of monthly averaged wind stress under the assumption of neutral stability are shown to be within approximately 5% of the monthly averaged nonneutral values.
A method for simulating an ocean synthetic-aperture radar (SAR) image is illustrated for a simple internal wave current pattern. The method calculates both the amplitude image and the radar signal history. The simulation model used consists of six stages. In the first stage, the full wave spectrum in two spatial coordinates is calculated from the wind speed and direction using the action spectral density equation. In the second stage, the pixel size is selected and used as the basis for dividing the spectrum into large and small scale motions. Realizations for the large-scale ocean surface height and velocity are then calculated. In the third stage, the sensor wavelength and geometry are used to calculate the small scale statistics (radar cross section, coherence time, root-mean square (RMS), radial velocity and RMS slope). In the fourth stage, an autoregressive method is used to generate a realization of the surface reflectivity history that is consistent with the radial velocity and the radial velocity variance. In the fifth stage, the signal history is generated by summing the reflectivities at the proper times with the appropriate antenna weighting function for the SAR. As a consequence of this process, speckle is automatically included in the signal. In the final stage, the SAR image can be created by using any of the traditional ways to process the signal history, including variable focusing and multilook processing.