NASA Scatterometer Winds Research Articles

An Intercomparison of TOPEX, NSCAT, and ECMWF Wind Speeds: Illustrating and Understanding Systematic Discrepancies

Abstract The availability of multiple satellite missions with wind measuring capacity has made it more desirable than ever before to integrate wind data from various sources in order to achieve an improved accuracy, resolution, and duration. A clear understanding of the error characteristics associated with each type of data is needed for a meaningful merging or combination. The two kinds of errors—namely, random error and systematic error—are expected to evolve differently with increasing volume of available data. In this study, a collocated ocean Topography Experiment (TOPEX)–NASA Scatterometer (NSCAT)–ECMWF dataset, which covers 66°S–66°N and spans the entire 10-month lifetime of NSCAT, is compiled to investigate the systematic discrepancies among the three kinds of wind estimates, yielding a number of interesting results. First, the satellite-derived wind speeds are found to have a larger overall bias but a much smaller overall root-mean-square (rms) error compared to ECMWF winds, implying that they a...

Impact of NSCAT Winds on Tropical Cyclones in the ECMWF 4DVAR Assimilation System

The impact of NASA Scatterometer (NSCAT) data on tropical cyclone forecasting on the European Centre for Medium-Range Weather Forecasts four-dimensional variational (4DVAR) data assimilation system is examined. Parallel runs with and without NSCAT data were conducted. The 4DVAR can use single-level data, such as scatterometer winds, to good advantage. The 4DVAR system uses data at appropriate times and has the potential to accurately resolve the ambiguity inherent in scatterometer data, by using a two-ambiguity cost function at each NSCAT location. Scatterometer data are shown to improve the depiction of the surface wind field in both tropical cyclones and extratropical lows, and can provide early detection of these features. Case studies of Hurricane Lili, and of Typhoons Yates and Zane (all in 1996), show major positive impacts of NSCAT data on forecasts of tropical cyclone intensity and position.

A case study of sea surface temperature–cooling dynamics near the Indian tip during May 1997

The sea surface temperature (SST) off the southwest coast of India is important to the Indian summer monsoon onset, which generally occurs in late May off this coast. It is known that both locally and remotely forced coastal processes contribute to the SST cooling. In this paper we focus on the latter by studying the SST‐cooling dynamics near the Indian tip (IT) during 1–15 May, using advanced very high resolution radiometer (AVHRR) SST, NASA scatterometer (NSCAT) winds, National Centers for Environmental Prediction/National Center for Atmospheric Research surface meteorological variables, and the Princeton Ocean Model. High spatial resolution (9 km) daily mean AVHRR SST maps indicate that the SST cooling near the IT is a recurrent phenomenon, wherein the cold SST water occupies the southwest India shelf and extends southward of the IT. During the 15 day period the NSCAT‐observed winds increased from near zero, the net surface heat flux into the ocean changed by ∼62 W/m2, and the SST decreased by 1.8°C near the IT. Under these conditions the model integration is carried out as an initial value problem with realistic bottom topography, climatological density stratification, and observed NSCAT winds. The results show that the Ekman dynamics along the west India shelf promote upwelling and initiate SST cooling. Shallow and spatially variable shelf topography near the IT enhance wind mixing, while sharp gradients in the isobaths at the shelf break and stratification enhance upwelling/downwelling intensity through cross‐isobath flow. It is inferred that the SST cooling near the IT is a locally forced process that is dictated by shelf topography.

Cyclone surface pressure fields and frontogenesis from NASA scatterometer (NSCAT) winds

Two extratropical marine cyclones and their associated frontal features are examined by computing surface pressure fields from NASA scatterometer (NSCAT) winds. A variational method solves for a new surface pressure field by blending high‐resolution (25 km) relative vorticity computed along the satellite track with an initial geostrophic vorticity field. Employing this method with each successive pass of the satellite over the study area allows this surface pressure field to evolve as dictated by the relative vorticity patterns computed from NSCAT winds. The result is a high‐resolution surface pressure field that captures features such as fronts and low‐pressure centers in more detail than National Centers for Environmental Prediction (NCEP) reanalyses. While using the actual relative vorticity to adjust the geostrophic vorticity ignores the ageostrophy of surface winds, which can be significant in the vicinity of fronts and jet streaks, it is a necessary approximation given that the technique uses only surface data. The NSCAT surface pressure fields prove to be nearly as accurate as NCEP reanalyses when compared to ship and buoy observations, which is an encouraging result given that NCEP reanalyses incorporate a myriad of data sources and the NSCAT fields rely primarily on one source. In addition, the high‐resolution relative vorticity fields computed from NSCAT winds reveal the location of surface fronts in great detail. These fronts are verified using NCEP analyses, in situ data, and satellite imagery.

Correlation-Based Interpolation of NSCAT Wind Data

Abstract Daily NASA Scatterometer (NSCAT) wind estimates cover about 75% of the globe. The remaining data gaps, which require interpolation, are regularly distributed in space and time. The development of this interpolation algorithm was guided by a balance between the smoothness of the end product and its fidelity to the original data. Three-dimensional matrices of autocorrelation coefficients incorporate information about the dominant propagation pattern into the interpolation program. These coefficients are continuously updated in space and time and are used as weights to interpolate each point in a regular space–time grid. For the first step, European Centre for Medium-Range Weather Forecasts (ECMWF) wind data are used to simulate the NSCAT data distribution and interpolated using two different methods: one uses a single set of coefficients from a prescribed function based on the average decorrelation scales, and the other uses the locally estimated autocorrelation coefficients. The comparison of thes...

Wintertime Wind Forcing and Sea Surface Cooling Near the South India Tip Observed Using NSCAT and AVHRR

This report addresses a case of topographic air–sea interaction in the Gulf of Mannar, near the Indian tip, for the winter monsoon of 1996–1997. Using high spatial resolution NASA-Scatterometer (NSCAT) wind data, a 1°×1° region of strong wind is identified in the Gulf during the peak of the winter monsoon. The characteristic topography of South India and Sri Lanka and their orientation to the monsoon wind tend to channel this strong wind. Air–sea heat flux analyses, using the NSCAT wind and the European Center for Medium-Range Weather Forecast objective analyses surface data, reveal that the strong winter monsoon bursts cause large latent heat loss (180 W/m 2) from a wind-fetch region centered on 7.5°N, 77.5°E during January. Weak air–sea temperature gradients result in weak sensible heat loss (<15 W/m 2) from the ocean. The ocean response to this forcing is examined using weekly and monthly mean satellite-derived sea surface temperature (SST) maps; these indicate a sea surface cooling of about 1°C along the axis of the wind flow. A by-product of this cooling is an emergence of a SST front along the periphery of the strong wind-stress region. Time-series analyses of the surface meteorology reveal that this forcing has a periodicity of about 15 days, with a peak during the last week of December. Wind stress curl derived from the NSCAT wind data exhibits high negative values (anticyclonic Ekman pumping) on a 2-week time scale, concomitant with strong wind bursts. The features observed in the Gulf of Mannar are similar to those reported from some other locations.

On the assimilation of Ku-band scatterometer winds for weather analysis and forecasting

Following the successful assimilation of European remote sensing satellite (ERS) scatterometer winds for weather analysis and forecasting, the authors further develop this methodology for the assimilation of the NASA scatterometer (NSCAT) and QuikSCAT Ku-band scatterometer data. Besides retrieval problems in cases of a confused sea state, the quality control (QC) developed identifies cases with rain on a wind vector cell (WVC) by WVC basis. The elimination of such geophysical conditions is a prerequisite to arrive at a successful assimilation of Ku-band scatterometer data. Moreover, the authors propose to assimilate ambiguous winds rather than radar backscatter measurements, as is being done at most meteorological centers assimilating ERS scatterometer data. After their quality assessment, NSCAT winds still have more difficult ambiguity removal properties than ERS winds. A further testing of the data assimilation method proposed is being carried out at the European Center for Medium-range Weather Forecasts in NSCAT impact experiments. A normalized wind inversion residual is used for QC. In order to determine a threshold for the rejection of poor quality wind solutions, the inversion residual and the wind vector departure from the ECMWF model are correlated. They end up rejecting around 7.4% of wind vector solutions and 4.2% of the NSCAT WVCs. In order to perform a qualitative assessment of this rejection, comparisons to collocated SSM/I rain and ECMWF winds are used. Confused sea state and presence of rain seem to be the most likely causes for the rejection of WVCs.

Evaluation of vector Winds Observed by NSCAT in the Seas around Japan

In order to validate wind vectors derived from the NASA Scatterometer (NSCAT), two NSCAT wind products of different spatial resolutions are compared with observations by buoys and research vessels in the seas around Japan. In general, the NSCAT winds agree well with the wind data from the buoys and vessels. It is shown that the root-mean-square (rms) difference between NSCAT-derived wind speeds and the buoy observations is 1.7 ms−1, which satisfies the mission requirement of accuracy, 2 ms−1. However, the rms difference of wind directions is slightly larger than the mission requirement, 20°. This result does not agree with those of previous studies on validation of the NSCAT-derived wind vectors using buoy observations, and is considered to be due to differences in the buoy observation systems. It is also shown that there are no significant systematic trends of the NSCAT wind speed and direction depending on the wind speed and incidence angle. Comparison with ship winds shows that the NSCAT wind speeds are lower than those observed by the research vessels by about 0.7 ms−1 and this bias is twice as large for data observed by moving ships than by stationary ships. This result suggests that the ship winds may be influenced by errors caused by ship's motion, such as pitching and rolling.

Baroclinic modification of midlatitude marine surface wind vectors observed by the NASA scatterometer

Baroclinic modification of surface wind vectors determined from satellite microwave radar data over the Northern Hemisphere midlatitude (30°–60°N) oceans is presented. The analysis uses data from the NASA scatterometer (NSCAT) collocated with operational surface analyses from the European Centre for Medium‐Range Weather Forecasts (ECMWF) at synoptic times. A total of 71,769 NSCAT wind vector observations from the Northwest Pacific and Northeast Atlantic Oceans were used in this study. A statistically significant baroclinic modification of the surface wind is detectable in the NSCAT winds that accounts for 5–35% of the variance in wind speed and 5–20% of the variance in wind direction depending primarily on the thermal wind strength. The amplitude of the baroclinic wind speed modification can be much larger than the effects of stratification in the surface layer. For the maximum thermal wind strength considered here (7×10−3 s−1), the explained variances due to baroclinic effects were 59% for speed and 35% in direction. Because of data uncertainties these results probably underestimate the actual contribution of baroclinity to the surface wind variance. The NSCAT results are compared to ECMWF and to a theoretical model. The baroclinic modification of the NSCAT and ECMWF surface wind speed agree quite closely, but larger differences are seen in the cross‐isobar angles. Reasons for these differences are discussed. These observations show that in addition to the primary surface wind data product, the scatterometer radar backscatter contains potentially useful information about the boundary layer temperature field.

Quasi‐stationary wave variability in NSCAT winds

Planetary and interseasonal properties of the NASA scatterometer (NSCAT) winds are explored in the context of the quasi‐stationary waves (QSWs) of the southern hemisphere. The QSWs are examined by means of zonal asymmetries of 3‐month running averages of the meridional velocity derived from NSCAT. The study period spans the entire NSCAT record from September 15, 1996, through June 29, 1997. Meridional winds from the European Space Agency ERS 2 scatterometer are used to augment 3‐month averages centered on August 1996 through May 1997. The time period corresponds to the transition from year −1 to 0 of the 1997 warm event in the Southern Oscillation. Comparisons are made with QSW signals in geopotential height anomalies from the National Centers for Environmental Prediction/National Center for Atmospheric Research Climate Data Assimilation System. The zonal anomalies of meridional wind from NSCAT are shown to be in approximate geostrophic balance with zonal gradients in the zonal anomalies of geopotential height at 500 and 1000 hPa.

Noninertial flow in NSCAT observations of Tehuantepec winds

NASA scatterometer (NSCAT) observations provide high temporal and spatial resolution wind fields, which are used to examine gap flow through the Chivela Pass and the influence of Hurricane Marco. For approximately 1 week in November 1996, Hurricane Marco caused the flow through the gap to change from its usual flow pattern: the winds turned to the left rather than the right. Previous studies of this gap flow have used monthly averages of sparse ship observations, European Centre for Medium‐Range Weather Forecasts fields, mesoscale models, or proxies such as cloud motion or sea surface temperatures (SSTs). NSCAT provided unprecedented accuracy (rms differences less than 1.5 m s−1 and 15°) and resolution (daily and 1° × 1° in this study). The observations show that the gap flow often turns to the right (dominated by the Coriolis force); however, unusual events can cause highly noninertial flow for several days. The SSTs respond slowly to changes in the wind pattern, and they are a poor proxy for short‐term wind patterns. The NSCAT winds, gridded with a new methodology presented herein, have the spatial and temporal resolution required to show the evolution of the gap outflow in the presence of a hurricane. The wind fields are used to generate parcel pseudotrajectories, which show that Marco caused Tehuantepec winds to turn to the left and pass through the Gulf of Papagayo into the Caribbean Sea. Gridding techniques are required to fill gaps in fields of NSCAT observations. NSCAT coverage of the (ice free) global oceans is approximately 77% in 1 day and 90% in 2 days. The new gridding technique temporally averages the winds in a manner that leaves very little evidence of the pattern of satellite tracks in the wind fields and little evidence of wind field curl and divergence. Daily fields of 1° × 1° resolution are generated. The temporal sampling characteristics of the wind fields are shown to be nonhomogeneous, with the distribution of characteristic sampling times peaking at 1 day and usually within the range of 0.75 to 1.75 days.

Geophysical validation of NSCAT winds using atmospheric data and analyses

A detailed geophysical evaluation of the initial NASA scatterometer (NSCAT) wind data sets was performed in order to determine the error characteristics of these data and their applicability to ocean surface analysis and numerical prediction. The first component of this evaluation consisted of collocations of NSCAT data to ship and buoy wind reports, special sensor microwave imager wind observations, and National Centers for Environmental Prediction and Goddard Earth Observing System (GEOS) model wind analyses. This was followed by data assimilation experiments to determine the impact of NSCAT data on analysis and forecasting. The collocation comparisons showed the NSCAT wind velocity data to be of higher accuracy than operational ERS 2 wind data. The impact experiments showed that NSCAT has the ability to correct major errors in analyses over the oceans and also to improve numerical weather prediction. NSCAT data typically show the precise locations of both synoptic‐scale and smaller‐scale cyclones and fronts over the oceans. This often results in significant improvements to analyses. Forecast experiments using the GEOS model show approximately a 1‐day extension of useful forecast skill in the southern hemisphere, in good agreement with the results of Observing System Simulation Experiments conducted prior to launch.

The accuracy of the NSCAT 1 vector winds: Comparisons with National Data Buoy Center buoys

The overall accuracies of vector wind measurements from the NASA scatterometer (NSCAT) are quantified by comparisons with collocated data from operational U.S. National Data Buoy Center ocean buoys. A vector correlation statistic is used to examine the geographical distribution of full‐mission NSCAT‐buoy differences. The dependences of the vector correlation on collocation radius and scatterometer errors such as wind speed offset and random component errors are quantified. For purposes of NSCAT validation, 30 ocean moored buoys having high full‐mission sample correlations are considered. When applied to this set of ocean buoys, a nonlinear analysis shows that the 25‐km resolution NSCAT wind speeds have unity gain, an offset of −0.3 m s−1, an rms error of 1.3 m s−1, and component standard deviations of ∼1.3 m s−1. For wind speeds greater than 6 m s−1, fewer than 3% of all collocated solutions have significant ambiguity removal errors. The rms directional difference for the remaining vectors is less than 17°. At lower wind speeds the frequency of ambiguity removal errors increases and the directional accuracy of the “correct” vectors decreases with decreasing wind speed, consistent with the assumptions of the random error model underlying the nonlinear wind speed regression approach. While the primary emphasis of this paper is on determining the quantitative accuracy of the NSCAT wind velocity measurements, the analysis techniques and interpretations have more general application to validation studies involving vector quantities.

A model function for the ocean‐normalized radar cross section at 14 GHz derived from NSCAT observations

A model for the ocean surface normalized radar cross section σo is derived from 3 months of NASA scatterometer (NSCAT) observations (September 15 to December 18, 1996). The model expresses σo as a function of wind speed, relative wind direction, incidence angle, and polarization. The dependence of σo on wind speed is based on collocated special sensor microwave/imager (SSM/I) satellite wind retrievals and European Centre for Medium‐Range Weather Forecasts (ECMWF) model winds. We find that at low winds (&lt;5 ms−1), the SSM/I winds are more reliable than ECWMF, probably owing to small location errors in the ECMWF wind features. The primary wind direction dependence of σo (i.e., the second harmonic) is found from histograms of the σo difference between the forward and aft antennas. The σo versus wind speed relationship is adjusted for cross‐swath incidence angle differences and is then incorporated into the NSCAT 1 model used to process the 10‐month (September 15, 1996, to June 29, 1997) NSCAT data set. The resulting NSCAT 1 wind vectors are compared to ECMWF wind fields and buoys. The mean and standard deviation of the NSCAT minus ECMWF (buoy) wind speed difference are 0.05 and 1.78 ms−1 (−0.29 and 1.26 ms−1), respectively. The wind direction mean and standard deviation differences are 0.8° and 18.5° (7.9° and 15.7°), respectively. The difference between the NSCAT and the ECMWF (buoy) direction exceeds 90° only 1.1% (1.2%) of the time. We have no explanation for why the buoy wind directions are biased 8° relative to both NSCAT and ECMWF.

NSCAT tropical wind stress maps: Implications for improving ocean modeling

Using wind vectors from the NASA scatterometer (NSCAT), daily maps of pseudostress have been constructed for the tropical Pacific Ocean and compared with pseudostress maps derived from the European Centre for Medium‐Range Weather Forecasts (ECMWF) 10‐m wind product. The map resolution for the NSCAT pseudostress maps was selected using both a statistical measure of the expected mapping errors and tests on realistic wind fields. The selected map resolution is 5 days and 2°, which minimizes residual effects from the NSCAT sampling pattern, while maximizing temporal and spatial resolution. Comparisons with the ECMWF maps showed significant differences in most regions, corresponding to mean wind speeds of 2–3 m s−1, particularly in the Intertropical Convergence Zone (ITCZ) and at 20°S and 20°N. A canonical correlation analysis between NSCAT and ECMWF fields showed a high degree of correlation of temporal variations and systematic differences in spatial structure. In the NSCAT fields the ITCZ is narrower, stronger, and is located 1–2° latitude farther south than in the ECMWF fields. The high degree of correlation between the two fields suggests that “hybrid” fields can be generated. The dynamical implications of the differences in wind forcing are illustrated using estimates of the Sverdrup stream function and the Ekman pumping. A simple reduced‐gravity, linear vorticity model, forced by both the ECMWF and hybrid winds to examine predicted differences in ocean response, showed higher skill for the NSCAT winds.

Formation mechanism of Japan Sea Proper Water in the flux center off Vladivostok

It is known that wintertime air‐sea interaction in the Japan Sea, enhanced by outbreaks of dry and cold air masses from the Eurasian continent, generates a characteristic water mass called Japan Sea Proper Water (JSPW) through deep convection. Using NASA scatterometer (NSCAT) wind vectors with high spatial resolution of 25 km, the role of the wind field over the Japan Sea in JSPW formation was investigated during the period of winter monsoon. It is revealed by NSCAT observations that the wintertime surface winds over the Japan Sea are strongly influenced by the upstream topography of the coastal region of the Eurasian continent. A strong wind area appears off Vladivostok, both in the snapshot and the monthly mean wind fields. The dynamics behind this strong wind flow may be attributed to the highly stratified surface wind being blocked by the coastal mountains and exiting through the narrow valley near Vladivostok into the Japan Sea. The NSCAT winds are coupled with European Centre for Medium‐Range Weather Forecasts air temperature and humidity and Japan Meteorological Agency sea surface temperature (SST) data for turbulent flux estimation. Monthly mean wind speed, momentum flux, sensible heat flux, latent heat flux, and evaporation have peak values exceeding 9 m s−1, 0.275 N m−2, 170 W m−2, 130 W m−2, and 120 mm, respectively, in the strong wind area, which has a diameter of about 150 km. Multichannel sea surface temperature (MCSST) images of the Japan Sea for late January 1997 show that the cold SST (∼0°C) area extends from the coastal region to the outer sea off Vladivostok. The MCSSTs decreased by 1°C for January in this region. The cold SST region coincides with the strong wind area, and both locations agree well with the JSPW formation region, which, according to Sudo [1986], is north of 41°N between 132° and 134°E. The spatial agreement strongly suggests that the wind, enhanced by the topographic effect around Vladivostok, causes the large turbulent heat flux and evaporation in this area, which generates the coldest SST and dense water mass, i.e., JSPW. This may be a process of coastal topography‐air‐sea interaction that leads to deep‐sea water formation in the Japan Sea. Because of the concentrated feature of turbulent fluxes and the inferred relation to the center of deep convection phenomenon, the strong wind area is called the “flux center” in this study.

Wind observations from new satellite and research vessels agree

An instrument designed to observe wind speeds and directions over the ocean surface has realized the promise suggested by the first spaceborne scatterometer on the SeaSAT satellite nearly 20 years ago. In 1996, NSCAT (NASA Scatterometer) rode into orbit on the Japanese satellite ADEOS and gathered 8.5 months of valuable wind data. NSCAT's unprecedented ability to determine wind speed and direction over 90% of the icefree global water surface with a 25 km resolution in 2 days should have profound impacts on oceanographic and meteorological applications. Prior to these applications, however, the uncertainty of NSCAT data must be determined from calibration and validation with in‐situ observations. Comparison of NSCAT wind speeds and direction to those observed from research vessels shows an extremely good match. The analysis suggests that NSCAT winds appear to be sufficiently accurate for use in forcing ocean models.

The use of satellite scatterometer winds to drive a primitive equation model of the Indian Ocean: The impact of bandlike sampling

The aim of this study is to evaluate the impact of the bandlike sampling of spaceborne scatterometers on the ability of scatterometer winds to successfully force the mean flow and seasonal cycle of an ocean model in the context of equatorial and tropical dynamics. The equatorial ocean is simulated with a four‐layer, primitive equation, reduced gravity model of the Indian Ocean. The variable wind stress used in this study is derived from one year (1988) of 6‐hour analyses of the 10‐m wind vector over the Indian Ocean performed at the European Centre for Medium‐Range Weather Forecasts (ECMWF). It is applied as a forcing at every grid point of the model to drive a reference circulation. Scatterometer winds are simulated from ECMWF winds, using the nominal configurations and orbital parameters of the European Remote Sensing 1 (ERS‐1) and NASA Scatterometer (NSCAT) missions. The model is forced in real time under swaths with the raw scatterometer winds of ERS‐1 and NSCAT, with a persistence condition (i.e., the wind is kept constant until the next passage of the satellite provides a new value). The circulation obtained for each of the scatterometer experiments is compared with the reference circulation. The seasonal circulation of the Indian Ocean with NSCAT winds is very similar to the reference. The perturbations introduced by the bandlike sampling and the persistence condition have an impact similar to that of a small uncorrelated noise added to the reference forcing. The persistence condition for ERS‐1 does not give results which are as good as those obtained for NSCAT.

Spaceborne radar measurement of wind velocity over the ocean-an overview of the NSCAT scatterometer system

Scatterometry and scatterometer design issues are reviewed. The design of the NASA Scatterometer (NSCAT) to be flown on the Japanese ADEOS mission is presented. Building on Seasat experience, the NSCAT system includes several enhancements, such as three antenna azimuths in each of two swaths, and an onboard digital Doppler processor to allow backscatter measurements to be colocated everywhere within the orbit. These enhancements will greatly increase the quality of the NSCAT wind data. The ground processing of data is discussed, and scatterometers of the next decade are briefly described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>