Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “scatterometer ocean surface wind weather prediction”

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.

13 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

NASA Scatterometer Provides Global Ocean-Surface Wind Fields with More Structures than Numerical Weather Prediction

The major differences between monthly-mean ocean-surface wind fields derived from the observations of the National Aeronautics and Space Administration (NASA) Scatterometer (NSCAT) and produced by the operational numerical weather prediction (NWP) model of the European Center for Medium-Range Weather Forecasts are found in coastal and equatorial regions, where the sharp changes are smoothed over in NWP products.

scatterometer ocean surface wind weather predictio↗
NASA NTRS2001

Impact of Quikscat Data on Numerical Weather Prediction

Scatterometer observations of the ocean surface wind speed and direction improve the depiction and prediction of storms at sea. These data are especially valuable where observations are otherwise sparse ---mostly in the Southern Hemisphere and tropics, but also on occasion in the North Atlantic and North Pacific. The SeaWinds scatterometer on the QuikScat satellite was launched in July 1999 and it represents a dramatic departure in design from the other scatterometer instruments launched during the past decade (ERS-1,2 and NSCAT). The NASA Data Assimilation Office (DAO) was the first data assimilation center to assimilate QuikScat SeaWinds data and evaluate their impact on numerical weather prediction. Several data impact experiments have been performed, using systems from both the DAO (GEOS-3) and from NCEP (GDAS). In general, these experiments have shown a modest impact of SeaWinds data on numerical weather prediction, the magnitude of which appears to be comparable to the magnitude of the impact of AMI scatterometer data from the ERS satellites. Some of the main results from these experiments will be presented at the meeting.

Atlas, Robert↗
NASA NTRS2003

Impact of SeaWinds Scatterometer Data on Numerical Weather Prediction

Scatterometer observations of the ocean surface wind speed and direction improve the depiction and prediction of storms at sea. These data are especially valuable where observations are otherwise sparse - mostly in the Southern Hemisphere and tropics, but also on occasion in the North Atlantic and North Pacific. The SeaWinds scatterometer on the QuikScat satellite was launched in June 1999 and it represents a dramatic departure in design from the other scatterometer instruments launched during the past decade (ERS-1,2 and NSCAT). More details on the SeaWinds instrument can be found in Atlas et a1 (2001) and Bloom et al. (1999). At the time of this writing, SeaWinds scatterometer data from the ADEOS 2 satellite are not yet available . Therefore this paper will be limited to results from the SeaWinds scatterometer on Quikscat. This presentation shows the influence of QuikScat data in data assimilation systems both from the NASA Data Assimilation Office (GEOS-3) and from NCEP (GDAS).

Atlas, Robert↗
NASA NTRS2000

Hydrological Balance in Tropical Cyclones with Scatterometer and TRMM Data

Precipitation over oceans can be estimated from the radar and the microwave radiometer of the Tropical Rain Measuring Mission (TRMM). It can also be estimated from the divergence of the vertically integrated water vapor transport, through the conservation principle, assuming evaporation is relatively small. In tropical cyclones, the divergence of vertically integrated water vapor is highly dependent on the vertical transport and, therefore, on the wind divergence. Spaceborne scatterometers provide surface wind velocity and, therefore, surface wind divergence at spatial resolutions that are much higher than products of numerical weather prediction (NWP). In this study, ocean surface winds derived from the observations of space-based scatterometers and surface precipitation measured by TRMM were objectively interpolated to the same time and location during the passage of a tropical cyclone. Surface precipitation distribution was derived from wind and humidity profiles provided by NWP. When the surface level winds of NWP were replaced by the scatterometer winds, the surface precipitation patterns computed with the conservation method were found to be significantly changed and the new patterns are much closer in agreement with the patterns observed by TRMM.

Liu, W. Timothy↗
NASA NTRS2001

The Impact of QuikScat on Weather Analysis and Forecasting

Scatterometer observations of the ocean surface wind speed and direction improve the depiction and prediction of storms at sea. These data are especially valuable where observations are otherwise sparse, mostly in the Southern Hemisphere and tropics, but also on occasion in the North Atlantic and North Pacific The SeaWinds scatterometer on the QuikScat satellite was launched in June 1999 and it represents a dramatic departure in design from the other scatterometer instruments launched during the past decade (ERS-1,2 and NSCAT). More details on the SeaWinds instrument can be found in Atlas et al. (2001) and Bloom et al. (1999). This presentation shows the influence of QuikScat data in data assimilation systems both from the NASA Data Assimilation Office (GEOS-3) and from NCEP (GDAS).

Atlas, Robert↗
NASA NTRS2005

Estimating moisture transport over oceans using space-based observations

The moisture transport integrated over the depth of the atmosphere (0) is estimated over oceans using satellite data. The transport is the product of the precipitable water and an equivalent velocity (ue), which, by definition, is the depth-averaged wind velocity weighted by humidity. An artificial neural network is employed to construct a relation between the surface wind velocity measured by the spaceborne scatterometer and coincident ue derived using humidity and wind profiles measured by rawinsondes and produced by reanalysis of operational numerical weather prediction (NWP). On the basis of this relation, 0 fields are produced over global tropical and subtropical oceans (40_N- 40_S) at 0.25_ latitude-longitude and twice daily resolutions from August 1999 to December 2003 using surface wind vector from QuikSCAT and precipitable water from the Tropical Rain Measuring Mission. The derived ue were found to capture the major temporal variability when compared with radiosonde measurements. The average error over global oceans, when compared with NWP data, was comparable with the instrument accuracy specification of space-based scatterometers. The global distribution exhibits the known characteristics of, and reveals more detailed variability than in, previous data.

Artificial Neural Network↗
NASA NTRS1984

Scatterometer capabilities in remotely sensing geophysical parameters over the ocean: The status and the possibilities

Extensive comparison between surface measurements and satellite Scatt signal and predicted winds show successful wind and weather analysis comparable with conventional weather service analyses. However, in regions often of the most interest, e.g., fronts and local storms, inadequacies in the latter fields leaves an inability to establish the satellite sensor capabilities. Thus, comparisons must be made between wind detecting measurements and other satellite measurements of clouds, moisture, waves or any other parameter which responds to sharp gradients in the wind. At least for the windfields and the derived surface pressure field analysis, occasional surface measurements are required to anchor and monitor the satellite analyses. Their averaging times must be made compatible with the satellite sensor measurement. Careful attention must be paid to the complex fields which contain many scales of turbulence and coherent structures affecting the averaging process. The satellite microwave system is capable of replacing the conventional point observation/numerical analysis for the ocean weather.

Brown, R. A.↗
NASA NTRS2012

The Aquarius Salinity Retrieval Algorithm: Early Results

The Aquarius L-band radiometer/scatterometer system is designed to provide monthly salinity maps at 150 km spatial scale to a 0.2 psu accuracy. The sensor was launched on June 10, 2011, aboard the Argentine CONAE SAC-D spacecraft. The L-band radiometers and the scatterometer have been taking science data observations since August 25, 2011. The first part of this presentation gives an overview over the Aquarius salinity retrieval algorithm. The instrument calibration converts Aquarius radiometer counts into antenna temperatures (TA). The salinity retrieval algorithm converts those TA into brightness temperatures (TB) at a flat ocean surface. As a first step, contributions arising from the intrusion of solar, lunar and galactic radiation are subtracted. The antenna pattern correction (APC) removes the effects of cross-polarization contamination and spillover. The Aquarius radiometer measures the 3rd Stokes parameter in addition to vertical (v) and horizontal (h) polarizations, which allows for an easy removal of ionospheric Faraday rotation. The atmospheric absorption at L-band is almost entirely due to O2, which can be calculated based on auxiliary input fields from numerical weather prediction models and then successively removed from the TB. The final step in the TA to TB conversion is the correction for the roughness of the sea surface due to wind. This is based on the radar backscatter measurements by the scatterometer. The TB of the flat ocean surface can now be matched to a salinity value using a surface emission model that is based on a model for the dielectric constant of sea water and an auxiliary field for the sea surface temperature. In the current processing (as of writing this abstract) only v-pol TB are used for this last process and NCEP winds are used for the roughness correction. Before the salinity algorithm can be operationally implemented and its accuracy assessed by comparing versus in situ measurements, an extensive calibration and validation (cal/val) activity needs to be completed. This is necessary in order to tune the inputs to the algorithm and remove biases that arise due to the instrument calibration, foremost the values of the noise diode injection temperatures and the losses that occur in the feedhorns. This is the subject of the second part of our presentation. The basic tool is to analyze the observed difference between the Aquarius measured TA and an expected TA that is computed from a reference salinity field. It is also necessary to derive a relation between the scatterometer backscatter measurements and the radiometer emissivity that is induced by surface winds. In order to do this we collocate Aquarius radiometer and scatterometer measurements with wind speed retrievals from the WindSat and SSMIS F17 microwave radiometers. Both of these satellites fly in orbits that have the same equatorial ascending crossing time (6 pm) as the Aquarius/SAC-D observatory. Rain retrievals from WindSat and SSMIS F 17 can be used to remove Aquarius observations that are rain contaminated. A byproduct of this analysis is a prediction for the wind-induced sea surface emissivity at L-band.

Meissner, Thomas↗
NASA NTRS2002

Moisture Advection and Fresh Water Flux over Oceans

Moisture transport in the atmosphere is one of the most significant components in the hydrological cycle. Under stationary condition, ocean surface fresh water flux, which is the difference between precipitation (P) and evaporation (E), is balanced by the divergence of column-integrated moisture transport (IMT) in the atmosphere. Characterizing accurately a global picture of IMT from observation is a difficult task. It requires measurements of vertical profiles for wind vector and humidity. More specifically, IMT can be defined as the integration in pressure coordinates the product of specific humidity q and wind vector u, where g is the gravitational acceleration, and p, is the atmospheric pressure at ocean surface.In this study, a statistical relationship is derived between u, and u(sub)s using data from numerical weather prediction model. The relationship is then validated using surface and vertical profile from radiosonde data, before applied to spacebased measurements.

scatterometers↗
NASA NTRS1998

The Impact of NASA Scatterometer Data on GEOS-2 Analyses and Forecasts

Experiments with the evolving GEOS-2 data assimilation system (DAS) delineate the impact of NASA scatterometer (NSCAT) data on ocean surface analysis and numerical weather prediction (NWP). Extensions and refinements of the DAS to account for the characteristics of NSCAT data produced better results than were obtained with the GEOS-1 DAS. The two key extensions are to increase the vertical influence of the surface wind data and to take proper account of the time difference between the observation and the analysis. The results of these experiments show that surface wind analyses are improved by NSCAT data. This is seen in subjective evaluation of synoptic cases and in forecast impacts. NSCAT data have a very significant positive impact on the GEOS-2 forecasts in the southern hemisphere (SH). In the northern hemisphere (NH) the overall statistics show a modest positive impact. However, on a case by case basis, in the NH, the impact is generally neutral or significantly positive. The GEOS-2 results are compared to results obtained using the GEOS-1 DAS and using the 1995 National Center for Environmental Prediction (NCEP95) DAS. The GEOS-2 control forecasts are more accurate than those of GEOS-1. All three impact experiments show a large positive impact in the SH. In the NH, both the GEOS-2 and the NCEP95 NSCAT impacts are positive while the GEOS-1 impact is neutral.

Atlas, R.↗
NASA NTRS1981

Winds over the ocean as measured by the scatterometer on Seasat

An analysis is presented of the relative accuracy of Seasat scatterometer measurements of the wind speeds and directions at 19.5 m altitude as compared to ground truth measurements taken by surface ships and instrumented buoys. Attention is given to the JASIN, QE II, and GOASEX surface data. The validity of 2-30 min averages taken from surface stations spread out over a wide area and serving as a basis for defining wind field averages over the 50 km resolution of SASS is examined. Satisfactory wind speeds were found to be available from SASS readings in the wind speed range 6-14 m/sec. The use of 25 SASS readings around a grid point was determined to reduce scatter to 0.25 m/sec when used in numerical weather prediction modeling. Improvements to the SASS techniques by the Seasat successor, NOSS, are discussed, and inclusion of momentum, heat, and water turbulent fluxes by NOSS is noted.

Pierson, W. J.↗
NASA NTRS2000

Satellite-Derived Dynamics of Southern Ocean Sea Ice

Antarctic ERS-2, RADARSAT Synthetic Aperture Radar and ERS-1/2 Scatterometer images were analyzed with SMMI radiometer image time-series data to investigate seasonal variability in satellite-tracked sea-ice dynamics in the Southern Ocean during 1992. Supporting field data were acquired during 'in-situ' experiments including the winter 1992 Ice Station Weddell and Winter Weddell Gyre studies. A variety of surface measurements were made during these experiments including Argos-buoy deployment and GPS drift measurements. These are used in conjunction with International Program for Antarctic Buoys drift trajectories for ice-motion tracking validation. Comparisons between gridded Special Sensor Microwave Imager (SSMI) ice-motion vectors and European Center for Medium Range Weather Forecasts/National Centers for Environmental Prediction (ECMWF/NCEP) analyses indicate that large-scale drift is forced predominantly by the long-term mean, large-scale synoptic pressure field. Only sub-daily SAR sea-ice tracking can capture high-frequency fluctuations, driven by polar lows or tidal forcing. In these cases, sea-ice drift can respond rapidly to changes in forcing on semi-diurnal time scales depending on the location with respect to the coastline. Seasonality of ice drift, particularly in the Weddell and Ross Seas, is linked to ice extent and compactness, and internal ice stresses transmitted through the pack ice from the coast. Three-monthly seasonal climatologies are presented of austral winter of ice drift in the Southern Ocean. The large Weddell and Ross Sea gyres are clearly resolved along with key seasonal and spatial attributes of their cyclonic circulation. Regional time series of ice dynamics parameters are used to illustrate correlations with meteorological forcing. Persistent divergence such as that occurring in the Ronne-Filchner polynya system results in large fractions of new ice. Similarly, convergence zones produce large fractions of deformed ice and characterize the dynamics of regions where perennial ice is observed. High shear strains also help delineate the axis of the Antarctic divergence in many places in the ice cover. In these regions, the separation between the coastal 'east wind drift'and ACC-dominated drift regimes is characterized by zonally extended regions of intense shear.

Drinkwater, Mark R.↗