Quick Scatterometer Winds Research Articles

The Effects of Gap-Wind-Induced Vorticity, the Monsoon Trough, and the ITCZ on East Pacific Tropical Cyclogenesis

Abstract Tropical cyclogenesis in the eastern North Pacific (EPAC) basin is related to gap-wind-induced surface relative vorticity, the monsoon trough, and the intertropical convergence zone (ITCZ). There are several gaps in the Central American mountains, on the eastern edge of the EPAC basin, through which wind can be funneled to generate surface wind jets (gap winds). This study focuses on gap winds that occur over the Gulf of Papagayo and Gulf of Tehuantepec. Quick Scatterometer (QuikSCAT) 10-m equivalent neutral winds are used to identify gap wind events that occur during May through November of 2002–08. Dvorak fix locations, Gridded Satellite (GridSat) infrared (IR) data, and National Hurricane Center (NHC) tropical cyclone (TC) reports are used to track the disturbances during the study period. Surface vorticity is tracked using the QuikSCAT winds and the contribution of surface vorticity from the gap winds to the development of each disturbance is categorized as small, medium, or large. Cross-calibrated multiplatform surface wind data are used to verify the tracking of QuikSCAT-computed surface vorticity and to identify when the monsoon trough and the ITCZ are present. It is found that gap winds are present over the Gulf of Papagayo and Gulf of Tehuantepec for about 50% of the QuikSCAT coverage days and that these gap winds appear to contribute to the development of disturbances in the EPAC. Considerably more TCs form when the monsoon trough is present versus the ITCZ and the majority of the contributions from the gap winds also occur when the monsoon trough is present.

Assimilating QuikSCAT Ocean Surface Winds with the Weather Research and Forecasting Model for Surface Wind-Field Simulation over the Chukchi/Beaufort Seas

To achieve a high-quality simulation of the surface wind field in the Chukchi/Beaufort Sea region, quick scatterometer (QuikSCAT) ocean surface winds were assimilated into the mesoscale Weather Research and Forecasting model by using its three-dimensional variational data assimilation system. The SeaWinds instrument on board the polar-orbiting QuikSCAT satellite is a specialized radar that measures ice-free ocean surface wind speed and direction at a horizontal resolution of 12.5 km. A total of eight assimilation case studies over two five-day periods, 1–5 October 2002 and 20–24 September 2004, were performed. The simulation results with and without the assimilation of QuikSCAT winds were then compared with QuikSCAT data available during the subsequent free-forecast period, coastal station observations, and North American Regional Reanalysis data. It was found that QuikSCAT winds are a potentially valuable resource for improving the simulation of ocean near-surface winds in the Chukchi/Beaufort Seas region. Specifically, the assimilation of QuikSCAT winds improved, (1) offshore surface winds as compared to unassimilated QuikSCAT winds, (2) sea-level pressure, planetary boundary-layer height, as well as surface heat fluxes, and (3) low-level wind fields and geopotential height. Verification against QuikSCAT data also demonstrated the temporal consistency and good quality of QuikSCAT observations.

Comparison of oceanic winds measured by space-borne scatterometers and altimeters

Global ocean surface wind speeds from Quick Scatterometer (QuikSCAT) for 5 years (2005–2009) and from Oceansat-2 Scatterometer (OSCAT) for 1 year (2010) were compared with wind speeds estimated from JASON Altimeters for representative months to investigate the consistency in wind speeds between these sensors. The comparison was carried out through statistical analyses. The spatial window used for comparison with JASON was 0.25° for QuikSCAT and 0.5° for OSCAT, while the temporal window for both QuikSCAT and OSCAT was ±30 min. The results of the inter-comparison indicate that OSCAT wind speeds are almost as consistent with JASON as QuikSCAT wind speeds.

An Estimate of Structural Uncertainty in QuikSCAT Wind Vector Retrievals

AbstractDifferences between Quick Scatterometer (QuikSCAT) level-2b wind vectors from the Jet Propulsion Laboratory [JPL; the Direction Interval Retrieval with Thresholded Nudging (DIRTH) product] and from the Remote Sensing Systems Co. (RSS; smoothed versions 3.0 and 4.0) for one sample month are presented. Each dataset is derived from the same observations, but processing methods result in differences between wind vectors. These differences originate from 1) uncertainty in the geophysical model functions that relate backscatter to wind, 2) noise in the backscatter measurements, and 3) spatial filtering. Statistics of wind vector differences from RSS and JPL are used as an indication of structural uncertainty in QuikSCAT wind retrievals. When grouped by 1 m s−1bins, systematic differences are largest beyond 20 m s−1, where wind speeds from version 3.0 (version 4.0) of RSS can be more than 15 (10) m s−1higher (lower) than JPL wind speeds. Below 20 m s−1, systematic differences on the order of tenths of a meter per second are attributed to differences in the retrieval methods, rain and ice contamination, and cross-swath position. Even once the recommended data flags are applied, differences in individual wind speed retrievals exceed 10 m s−1in a few cases but are much smaller in regions of the swath for which the viewing geometry allows more reliable retrievals. In all parts of the swath, the standard deviations of the differences are smaller than 1.0 m s−1. The analyses provide a measure of the structural uncertainty in QuikSCAT wind velocity that is due to the retrieval process, although such comparisons are not able to determine which dataset is closest to the actual wind.

The Global Characteristics of the Wavenumber Spectrum of Ocean Surface Wind

Abstract The wavenumber spectra of wind kinetic energy over the ocean from Quick Scatterometer (QuikSCAT) observations have revealed complex spatial variability in the wavelength range of 1000–3000 km, with spectral slopes varying from −1.6 to −2.9. Here the authors performed a spectral analysis of QuikSCAT winds over the global ocean and found that (i) the spectral slopes become steeper toward the Poles in the Pacific and in the South Atlantic, and the slopes exhibit minimal longitudinal dependence in the South Pacific; (ii) the steepest slopes are in the tropical Indian Ocean and the shallowest slopes are in the tropical Pacific and Atlantic; and (iii) the spectra are steeper in winter than summer in most regions of the midlatitude Northern Hemisphere. The new findings reported in the paper provide a test bed for theoretical studies and atmospheric general circulation models.

Assimilation of the multisatellite data into the WRF model for track and intensity simulation of the Indian Ocean tropical cyclones

Weather Research and Forecasting (WRF-ARW) model and its three-dimensional variational data assimilation (3D-Var) system are used to investigate the impact of the Quick Scatterometer (QuikSCAT) near surface winds, Special Sensor Microwave/Imager (SSM/I)-derived Total Precipitable Water (TPW), and Meteosat-7-derived Atmospheric Motion Vectors (AMVs) on the track and intensity prediction of tropical cyclones over the North Indian Ocean. The case of tropical cyclone, Gonu (June 2007; Arabian Sea), is first tested and the results show significant improvements particularly due to the assimilation of QuikSCAT winds. Three other cases, cyclone Mala (April 2006; Bay of Bengal), Orissa super cyclone (October 1999; Bay of Bengal), and Very Severe Cyclonic storm (October 1999; Bay of Bengal), are then examined. The prediction of cyclone tracks improved significantly with the assimilation of QuikSCAT winds. The track improvement resulted from the relocation of the initial cyclonic vortices after the assimilation of QuikSCAT wind vectors. After the assimilation of QuikSCAT winds, the mean (for four cyclone cases) track errors for first, second, and third day forecasts are reduced to 72, 101, and 166 km, respectively, from 190, 250, and 381 km of control (without QuikSCAT winds) runs. The assimilation of QuikSCAT winds also shows positive impact on the intensity (in terms of maximum surface level winds) prediction particularly for those cyclones, which are at their initial stages of the developments at the time of data assimilation. The assimilation of SSM/I TPW has significant influence (negative and positive) on the cyclone track. In three of the four cases, the assimilation of the SSM/I TPW resulted in drying of lower troposphere over cyclonic region. This decrease of moisture in TPW assimilation experiment resulted in reduction of cyclonic intensity. In three of the four cyclones, the assimilation of Meteosat-7 AMVs shows negative impact on the track prediction.

Impact of assimilation of conventional and satellite meteorological observations on the numerical simulation of a Bay of Bengal Tropical Cyclone of November 2008 near Tamilnadu using WRF model

The objective of this study is to examine the impact of assimilation of conventional and satellite data on the prediction of a severe cyclonic storm that formed in the Bay of Bengal during November 2008 with the four-dimensional data assimilation (FDDA) technique. The Weather Research and Forecasting (WRF ARW) model was used to study the structure, evolution, and intensification of the storm. Five sets of numerical simulations were performed using the WRF. In the first one, called Control run, the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) was used for the initial and boundary conditions. In the remaining experiments available observations were used to obtain an improved analysis and FDDA grid nudging was performed for a pre-forecast period of 24 h. The second simulation (FDDAALL) was performed with all the data of the Quick Scatterometer (QSCAT), Special Sensor Microwave Imager (SSM/I) winds, conventional surface, and upper air meteorological observations. QSCAT wind alone was used in the third simulation (FDDAQSCAT), the SSM/I wind alone in the fourth (FDDASSMI) and the conventional observations alone in the fifth (FDDAAWS). The FDDAALL with assimilation of all observations, produced sea level pressure pattern closely agreeing with the analysis. Examination of various parameters indicated that the Control run over predicted the intensity of the storm with large error in its track and landfall position. The assimilation experiment with QSCAT winds performed marginally better than the one with SSM/I winds due to better representation of surface wind vectors. The FDDAALL outperformed all the simulations for the intensity, movement, and rainfall associated with the storm. Results suggest that the combination of land-based surface, upper air observations along with satellite winds for assimilation produced better prediction than the assimilation with individual data sets.

Comparisons of monthly mean 10 m wind speeds from satellites and NWP products over the global ocean

The accuracy of wind speed at 10 m above the sea surface from two satellite and three numerical weather prediction (NWP) products is investigated over the global ocean. Rain‐free equivalent neutral winds from the Quick Scatterometer (QuikSCAT) are converted to stability‐dependent winds to be consistent with those from NWP products and are taken as truth in comparisons to winds from other products. Quantitative statistical analyses presented at each grid point over the global ocean reveal that monthly winds from NWP products have almost perfect skill relative to those from QuikSCAT winds during the 3‐year common period (September 1999 to August 2002). Exceptions occur in tropical regions and high southern latitudes. Wind speeds adjusted to 10 m at many moored buoys located in different regions of the global ocean further confirm the accuracy of monthly NWP winds, giving RMS difference of 1.0 m s−1 based on 1281 monthlong time series. The satellite‐based QuikSCAT winds agree with buoy winds relatively better than NWP products. While there is good agreement among wind products on monthly timescales, large differences (>3 m s−1 and more) in NWP winds are found in comparison to QuikSCAT winds on shorter time intervals at high latitudes. Daily means of sensible and latent heat fluxes based on NWP winds can therefore differ as much as 100 W m−2 in comparison to those based on QuikSCAT winds. In general, NWP wind‐based sensible and latent heat fluxes are more similar to their QuikSCAT wind‐based counterparts in tropical regions and midlatitudes.

The Operational Use of QuikSCAT Ocean Surface Vector Winds at the National Hurricane Center

Abstract The utility and shortcomings of near-real-time ocean surface vector wind retrievals from the NASA Quick Scatterometer (QuikSCAT) in operational forecast and analysis activities at the National Hurricane Center (NHC) are described. The use of QuikSCAT data in tropical cyclone (TC) analysis and forecasting for center location/identification, intensity (maximum sustained wind) estimation, and analysis of outer wind radii is presented, along with shortcomings of the data due to the effects of rain contamination and wind direction uncertainties. Automated QuikSCAT solutions in TCs often fail to show a closed circulation, and those that do are often biased to the southwest of the NHC best-track position. QuikSCAT winds show the greatest skill in TC intensity estimation in moderate to strong tropical storms. In tropical depressions, a positive bias in QuikSCAT winds is seen due to enhanced backscatter by rain, while in major hurricanes rain attenuation, resolution, and signal saturation result in a large negative bias in QuikSCAT intensity estimates. QuikSCAT wind data help overcome the large surface data void in the analysis and forecast area of NHC’s Tropical Analysis and Forecast Branch (TAFB). These data have resulted in improved analyses of surface features, better definition of high wind areas, and improved forecasts of high-wind events. The development of a climatology of gap wind events in the Gulf of Tehuantepec has been possible due to QuikSCAT wind data in a largely data-void region. The shortcomings of ocean surface vector winds from QuikSCAT in the operational environment at NHC are described, along with requirements for future ocean surface vector wind missions. These include improvements in the timeliness and quality of the data, increasing the wind speed range over which the data are reliable, and decreasing the impact of rain to allow for accurate retrievals in all-weather conditions.

Comment on “Seasonal heat budgets of the Red and Black seas” by Matsoukas et al.

Abstract : Matsoukas et al. [2007] present a monthly analysis of heat fluxes in relation to heat budget in the Red and Black Seas to provide further insight for air-sea exchange processes in the small ocean basins. Components of net surface heat flux are illustrated during 1984-1995. In computing latent and sensible heat fluxes, Matsoukas et al. [2007] apply traditional bulk formulations. A heat balance method that is based on the available energy for evaporation flux is also presented to compare latent heat fluxes with those from the bulk formulations. All near-surface atmospheric variables, including wind speed at 10 m, used in the heat balance method are obtained from reanalysis of a numerical weather product (NWP). Initial input data for radiation flux calculations are at resolutions of 1.0-degrees and 2-degrees, depending on the availability. Monthly means of heat budget components are computed on the basis of monthly means of atmospheric variables during 1984-2000.

The Impact of Variational Assimilation of SSM/I and QuikSCAT Satellite Observations on the Numerical Simulation of Indian Ocean Tropical Cyclones

Abstract In this study, the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with three-dimensional variational data assimilation (3DVAR) is utilized to investigate the influence of Special Sensor Microwave Imager (SSM/I) and Quick Scatterometer (QuikSCAT) observations on the prediction of an Indian Ocean tropical cyclone. The 3DVAR sensitivity runs were conducted separately with QuikSCAT wind vectors, SSM/I wind speeds, and total precipitable water (TPW) to investigate their individual impact on cyclone intensity and track. The Orissa supercyclone over the Bay of Bengal during October 1999 was used for simulation and assimilation experiments. Assimilation of the QuikSCAT wind vector improves the initial position of the cyclone’s center with a position error of 33 km, which was 163 km in the background analysis. Incorporation of QuikSCAT winds was found to increase the air–sea heat fluxes over the cyclonic region, which resulted in the improved simulated intensity when compared with the simulation made without QuikSCAT winds in the initial conditions. The cyclone track improved significantly with assimilation of QuikSCAT wind vectors. The track improvement resulted from relocation of the initial cyclonic vortex after assimilation of QuikSCAT wind vectors. Like QuikSCAT, assimilation of SSM/I wind speeds strengthened the cyclonic circulation in the initial conditions. This increase in the low-level wind speeds enhanced the air–sea exchange processes and improved the simulated intensity of the cyclone. The lack of information about the wind direction from SSM/I prevented it from making much of an impact on track prediction. As compared to the first guess, assimilation of the SSM/I TPW shows a moistening of the lower troposphere over most of the Bay of Bengal except over the central region of the cyclone, where the assimilation of SSM/I TPW reduces the lower-tropospheric moisture. This decrease of moisture in the TPW assimilation experiment resulted in a weak cyclone intensity.

An Evaluation of Scatterometer-Derived Oceanic Surface Pressure Fields

Abstract Oceanic surface pressure fields are derived from the NASA Quick Scatterometer (QuikSCAT) surface wind vector measurements using a two-layer similarity planetary boundary layer model in the midlatitudes and a mixed layer planetary boundary layer model in the tropics. These swath-based surface pressure fields are evaluated using the following three methods: 1) a comparison of bulk pressure gradients with buoy pressure measurements in the North Pacific and North Atlantic Oceans, 2) a least squares difference comparison with the European Centre for Medium-Range Weather Forecasts (ECMWF) surface pressure analyses, and 3) a parallel spectral analysis of the QuikSCAT and ECMWF surface pressure fields. The correlation coefficient squared between scatterometer-derived pressure fields and buoys is found to be R2 = 0.936. The average root-mean-square difference between the scatterometer-derived and the ECMWF pressure fields ranges from 1 to 3 hPa, depending on the latitude and season, and decreases after the assimilation of QuikSCAT winds in the ECMWF numerical weather prediction model. The spectral components of the scatterometer-derived pressure fields are larger than those of ECMWF surface analyses at all scales in the midlatitudes and only at shorter wavelengths in the tropics.

The Impact of Aircraft Dropsonde and Satellite Wind Data on Numerical Simulations of Two Landfalling Tropical Storms during the Tropical Cloud Systems and Processes Experiment

Abstract Dropwindsonde, Geostationary Operational Environmental Satellite-11 (GOES-11) rapid-scan atmospheric motion vectors, and NASA Quick Scatterometer (QuikSCAT) near-surface wind data collected during NASA’s Tropical Cloud Systems and Processes (TCSP) field experiment in July 2005 were assimilated into an advanced research version of the Weather Research and Forecasting (WRF) model using its three-dimensional variational data assimilation (3DVAR) system. The impacts of the mesoscale data assimilation on WRF numerical simulation of Tropical Storms Cindy and Gert (2005) near landfall are examined. Sensitivity of the forecasts to the assimilation of each single data type is investigated. Specifically, different 3DVAR strategies with different analysis update cycles and resolutions are compared in order to identify the better methodology for assimilating the data from research aircraft and satellite for tropical cyclone study. The results presented herein indicate the following. 1) Assimilation of dropwindsonde and satellite wind data into the WRF model improves the forecasts of the two tropical storms up to the landfall time. The QuikSCAT wind information is very important for improving the storm track forecast, whereas the dropwindsonde and GOES-11 wind data are also necessary for improved forecasts of intensity and precipitation. 2) Data assimilation also improves the quantitative precipitation forecasts (QPFs) near landfall of the tropical storms. 3) A 1-h rapid-update analysis cycle at high resolution (9 km) provides more accurate tropical cyclone forecasts than a regular 6-h analysis cycle at coarse (27 km) resolution. The high-resolution rapidly updated 3DVAR analysis cycle might be a practical way to assimilate the data collected from tropical cyclone field experiments.

The Operational Impact of QuikSCAT Winds in Perth, Australia: Examples and Limitations

Abstract The preparation of accurate operational weather forecasts and the timely issuance of severe marine weather and ocean warnings and advisories for major oceanic weather systems impacting both coastal areas and the open ocean are major forecasting problems facing the Australian Bureau of Meteorology’s Regional Forecast Centre (RFC) and its collocated Tropical Cyclone Warning Centre (TCWC) in Perth, Western Australia. The region of responsibility for the Perth RFC is vast, covering a large portion of the southeast Indian and Southern Oceans, both of which are extremely data sparse, especially for near-surface marine wind data. Given that these coastline and open-ocean areas are subject to some of the world’s most intense tropical cyclones, rapidly intensifying midlatitude cyclones, and powerful cold fronts, there is now a heavy reliance upon NASA Quick Scatterometer (QuikSCAT) data for both routine and severe weather warning forecasts. The focus of this note is on the role of QuikSCAT data in the Perth RFC for the accurate and early detection of maritime severe weather systems, both tropical and extratropical. First, the role of QuikSCAT data is described, and then three cases are presented in which the QuikSCAT data were pivotal in providing forecast guidance. The cases are a severe tropical cyclone in its development phase off the northwest coast of Australia, a strong southeast Indian Ocean cold front, and an explosively developing midlatitude Southern Ocean cyclone. In each case, the Perth RFC would have been unable to provide early and high-quality operational forecast and warning guidance without the timely availability of the QuikSCAT surface wind data.

The Impact of Assimilating SSM/I and QuikSCAT Satellite Winds on Hurricane Isidore Simulations

AbstractThree observational datasets of Hurricane Isidore (in 2002) were analyzed and compared: the Special Sensor Microwave Imager (SSM/I), the Quick Scatterometer (QuikSCAT) winds, and dropsonde winds. SSM/I and QuikSCAT winds were on average about 1.9 and 0.3 m s−1 stronger, respectively, than dropsonde winds. With more than 20 000 points of data, SSM/I wind speed was about 2.2 m s−1 stronger than QuikSCAT. Comparison of the wind direction observed by QuikSCAT with those from the dropsondes showed that the quality of QuikSCAT data is good. The effect of assimilating SSM/I wind speeds and/or QuikSCAT wind vectors for the analysis of Hurricane Isidore was assessed using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and its three-dimensional variational data assimilation system. For the Hurricane Isidore case study, it was found that the assimilation of either satellite winds strengthened the cyclonic circulation in the analysis. However, the increment of the QuikSCAT wind analysis is more complicated than that from the SSM/I analysis due to the correction of the storm location, a positive result from the assimilation of wind vectors. The increase in low-level wind speeds enhanced the air–sea interaction processes and improved the simulated intensity for Isidore. In addition, the storm structure was better simulated. Assimilation of QuikSCAT wind vectors clearly improved simulation of the storm track, in particular during the later period of the simulation, but lack of information about the wind direction from SSM/I data prevented it from having much of an effect. Assessing the assimilation of QuikSCAT wind speed versus wind vector data confirmed this hypothesis. The track improvement partially resulted from the relocation of the storm’s initial position after assimilation of the wind vectors. For this case study, it was found that the assimilation of SSM/I or QuikSCAT data had the greatest impact on the Hurricane Isidore simulation during the first 2 days.

Operational Impact of QuikSCAT Winds at the NOAA Ocean Prediction Center

Abstract The NASA Quick Scatterometer (QuikSCAT) has revolutionized the analysis and short-term forecasting of winds over the oceans at the NOAA Ocean Prediction Center (OPC). The success of QuikSCAT in OPC operations is due to the wide 1800-km swath width, large retrievable wind speed range (0 to in excess of 30 m s−1), ability to view QuikSCAT winds in a comprehensive form in operational workstations, and reliable near-real-time delivery of data. Prior to QuikSCAT, marine forecasters at the OPC made warning and forecast decisions over vast ocean areas based on a limited number of conventional observations or on the satellite presentation of a storm system. Today, QuikSCAT winds are a heavily used tool by OPC forecasters. Approximately 10% of all short-term wind warning decisions by the OPC are based on QuikSCAT winds. When QuikSCAT is available, 50%–68% of all weather features on OPC surface analyses are placed using QuikSCAT. QuikSCAT is the first remote sensing instrument that can consistently distinguish extreme hurricane force conditions from less dangerous storm force conditions in extratropical cyclones. During each winter season (October–April) from 2001 to 2004, 15–23 extratropical cyclones reached hurricane force intensity over both the North Atlantic and North Pacific Oceans. Due to QuikSCAT, OPC forecasters are now more likely to anticipate the onset of hurricane force conditions. QuikSCAT has also revealed significant wind speed gradients in the vicinity of strong sea surface temperature (SST) differences near the Gulf Stream and shelfbreak front of the western North Atlantic. These wind speed gradients are most likely due to changes in low-level stability of the boundary layer across the SST gradients. OPC forecasters now use a variety of numerical guidance based tools to help predict boundary layer stability and the resultant near-surface winds.

Dynamics of Biweekly Oscillations in the Equatorial Indian Ocean*

Abstract Variability of the wind field over the equatorial Indian Ocean is spread throughout the intraseasonal (10–60 day) band. In contrast, variability of the near-surface υ field in the eastern, equatorial ocean is concentrated at biweekly frequencies and is largely composed of Yanai waves. The excitation of this biweekly variability is investigated using an oceanic GCM and both analytic and numerical versions of a linear, continuously stratified (LCS) model in which solutions are represented as expansions in baroclinic modes. Solutions are forced by Quick Scatterometer (QuikSCAT) winds (the model control runs) and by idealized winds having the form of a propagating wave with frequency σ and wavenumber kw. The GCM and LCS control runs are remarkably similar in the biweekly band, indicating that the dynamics of biweekly variability are fundamentally linear and wind driven. The biweekly response is composed of local (nonradiating) and remote (Yanai wave) parts, with the former spread roughly uniformly along the equator and the latter strengthening to the east. Test runs to the numerical models separately forced by the τx and τy components of the QuikSCAT winds demonstrate that both forcings contribute to the biweekly signal, the response forced by τy being somewhat stronger. Without mixing, the analytic spectrum for Yanai waves forced by idealized winds has a narrowband (resonant) response for each baroclinic mode: Spectral peaks occur whenever the wavenumber of the Yanai wave for mode n is sufficiently close to kw and they shift from biweekly to lower frequencies with increasing modenumber n. With mixing, the higher-order modes are damped so that the largest ocean response is restricted to Yanai waves in the biweekly band. Thus, in the LCS model, resonance and mixing act together to account for the ocean's favoring the biweekly band. Because of the GCM's complexity, it cannot be confirmed that vertical mixing also damps its higher-order modes; other possible processes are nonlinear interactions with near-surface currents, and the model's low vertical resolution below the thermocline. Test runs to the LCS model show that Yanai waves from several modes superpose to form a beam (wave packet) that carries energy downward as well as eastward. Reflections of such beams from the near-surface pycnocline and bottom act to maintain near-surface energy levels, accounting for the eastward intensification of the near-surface, equatorial υ field in the control runs.

Detection of the MJO Signal from QuikSCAT

AbstractWind data from the SeaWinds instrument on NASA’s Quick Scatterometer (QuikSCAT) satellite are investigated to ascertain how well the surface manifestation of the Madden–Julian oscillation (MJO) can be resolved. The MJO signal is detected in nonfiltered gridded data using extended EOF analysis of the zonal wind field, overshadowed by annual, semiannual, and monsoon-related modes. After bandpass filtering with Lanczos weights, MJO signals are clearly detected in several kinematic quantities, including the zonal wind speed, the zonal pseudostress, and the velocity potential. Extraction of the MJO using QuikSCAT winds compares favorably with extraction using NCEP Reanalysis 2, except that the QuikSCAT signal appears to be more robust.In addition, an alternative bandpass-filtering technique using variable filter weights near time series endpoints is presented. The method uses least squares minimization to match newly created frequency response functions in edge zones as closely as possible to the predetermined frequency response function of interior points. This method stands in contrast to the common practice of simply discarding those endpoints where a convolution cannot be computed.

High‐wind‐speed evaluation in the Southern Ocean

Space‐based scatterometer instruments provide crucial surface wind measurements with high resolution over global oceans. Midlatitude regions in the Southern Ocean are unique places to evaluate scatterometer winds at high‐wind bands because these regions host the strongest wind fields at the ocean surface. The objective of this study is to evaluate high wind speeds observed by Quick Scatterometer (QuikSCAT) wind measurements and produced by simulation models and compare them with weather station data in the Southern Ocean. The occurrence and intensity of high‐wind events in scatterometer measurements are compared with that of reanalysis winds, and the spatial and seasonal variability of high‐wind characteristics is examined. The results show that the speeds of scatterometer winds are similar to model simulations in the monthly mean field but consistently stronger than both European Centre for Medium‐Range Weather Forecasts and National Centers for Environmental Prediction/National Center for Atmospheric Research winds in high‐wind bands. When scatterometer winds are compared with the weather station observations at Macquarie Island, the present study finds no systematic bias at high‐wind bands across all months. However, both weather station and QuikSCAT winds are higher than the model simulations in high‐wind bands most of the time. This suggests that model simulations may underestimate surface wind strength in high‐wind bands. Such underestimation would lead to up to an 80% reduction in energy flux between the atmosphere and ocean. Even though high winds occur only sporadically and the reanalysis underestimation in high wind speed is not in itself of great magnitude, they have a significant impact on global climate.