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- Author or Editor: Ian Young x
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Calibration and Cross Validation of Global Ocean Wind Speed Based on Scatterometer ObservationsAbstract
Global ocean wind speed observed from seven different scatterometers, namely, ERS-1, ERS-2, QuikSCAT, MetOp-A, OceanSat-2, MetOp-B, and Rapid Scatterometer (RapidScat) were calibrated against National Data Buoy Center (NDBC) data to form a consistent long-term database of wind speed and direction. Each scatterometer was calibrated independently against NDBC buoy data and then cross validation between scatterometers was performed. The total duration of all scatterometer data is approximately 27 years, from 1992 until 2018. For calibration purposes, only buoys that are greater than 50 km offshore were used. Moreover, only scatterometer data within 50 km of the buoy and for which the overpass occurred within 30 min of the buoy recording data were considered as a “matchup.” To carry out the calibration, reduced major axis (RMA) regression has been applied where the regression minimizes the size of the triangle formed by the vertical and horizontal offsets of the data point from the regression line and the line itself. Differences between scatterometer and buoy data as a function of time were investigated for long-term stability. In addition, cross validation between scatterometers and independent altimeters was also performed for consistency. The performance of the scatterometers at high wind speeds was examined against buoy and platform measurements using quantile–quantile (Q–Q) plots. Where necessary, corrections were applied to ensure scatterometer data agreed with the in situ wind speed for high wind speeds. The resulting combined dataset is believed to be unique, representing the first long-duration multimission scatterometer dataset consistently calibrated, validated and quality controlled.
Abstract
Global ocean wind speed observed from seven different scatterometers, namely, ERS-1, ERS-2, QuikSCAT, MetOp-A, OceanSat-2, MetOp-B, and Rapid Scatterometer (RapidScat) were calibrated against National Data Buoy Center (NDBC) data to form a consistent long-term database of wind speed and direction. Each scatterometer was calibrated independently against NDBC buoy data and then cross validation between scatterometers was performed. The total duration of all scatterometer data is approximately 27 years, from 1992 until 2018. For calibration purposes, only buoys that are greater than 50 km offshore were used. Moreover, only scatterometer data within 50 km of the buoy and for which the overpass occurred within 30 min of the buoy recording data were considered as a “matchup.” To carry out the calibration, reduced major axis (RMA) regression has been applied where the regression minimizes the size of the triangle formed by the vertical and horizontal offsets of the data point from the regression line and the line itself. Differences between scatterometer and buoy data as a function of time were investigated for long-term stability. In addition, cross validation between scatterometers and independent altimeters was also performed for consistency. The performance of the scatterometers at high wind speeds was examined against buoy and platform measurements using quantile–quantile (Q–Q) plots. Where necessary, corrections were applied to ensure scatterometer data agreed with the in situ wind speed for high wind speeds. The resulting combined dataset is believed to be unique, representing the first long-duration multimission scatterometer dataset consistently calibrated, validated and quality controlled.
Calibration of Scatterometer Wind Speed under Hurricane ConditionsAbstract
Four scatterometers, namely, MetOp-A, MetOp-B, ERS-2, and OceanSat-2 were recalibrated against combined National Data Buoy Center (NDBC) data and aircraft Stepped Frequency Microwave Radiometer (SFMR) data from hurricanes. As a result, continuous calibration relations over the wind speed range from 0 to 45 m s−1 were developed. The calibration process uses matchup criteria of 50 km and 30 min for the buoy data. However, due to the strong spatiotemporal wind speed gradients in hurricanes, a method that considers both scatterometer and SFMR data in a storm-centered translating frame of reference is adopted. The results show that although the scatterometer radar cross section is degraded at high wind speeds, it is still possible to recover wind speed data using the recalibration process. Data validation between the scatterometers shows that the calibration relations produce consistent results across all scatterometers and reduce the bias and root-mean-square error compared to previous calibrations. In addition, the results extend the useful range of scatterometer measurements to as high as 45 m s−1.
Abstract
Four scatterometers, namely, MetOp-A, MetOp-B, ERS-2, and OceanSat-2 were recalibrated against combined National Data Buoy Center (NDBC) data and aircraft Stepped Frequency Microwave Radiometer (SFMR) data from hurricanes. As a result, continuous calibration relations over the wind speed range from 0 to 45 m s−1 were developed. The calibration process uses matchup criteria of 50 km and 30 min for the buoy data. However, due to the strong spatiotemporal wind speed gradients in hurricanes, a method that considers both scatterometer and SFMR data in a storm-centered translating frame of reference is adopted. The results show that although the scatterometer radar cross section is degraded at high wind speeds, it is still possible to recover wind speed data using the recalibration process. Data validation between the scatterometers shows that the calibration relations produce consistent results across all scatterometers and reduce the bias and root-mean-square error compared to previous calibrations. In addition, the results extend the useful range of scatterometer measurements to as high as 45 m s−1.
Abstract
One of the main limitations to current wave data assimilation systems is the lack of an accurate representation of the structure of the background errors. One method that may be used to determine background errors is the observational method of Hollingsworth and Lönnberg. The observational method considers correlations of the differences between observations and the background. For the case of significant wave height (SWH), potential observations come from satellite altimeters. In this work, the effect of the irregular sampling pattern of the satellite on estimates of background errors is examined. This is achieved by using anomalies from a 3-month mean as a proxy for model errors. A set of anomaly correlations is constructed from modeled wave fields. The isotropic length scales of the anomaly correlations are found to vary considerably over the globe. In addition, the anomaly correlations are found to be significantly anisotropic. The modeled wave fields are then sampled at simulated altimeter observation locations, and the anomaly correlations are recalculated from the simulated altimeter data. The results are compared to the original anomaly correlations. It is found that, in general, the simulated altimeter data can capture most of the geographic and seasonal variability in the isotropic anomaly correlation length scale. The best estimates of the isotropic length scales come from a method in which correlations are calculated between pairs of observations from prior and subsequent ground tracks, in addition to along-track pairs of observations. This method was found to underestimate the isotropic anomaly correlation length scale by approximately 10%. The simulated altimeter data were not so successful in producing realistic anisotropic correlation functions. This is because of the lack of information in the zonal direction in the simulated altimeter data. However, examination of correlations along ascending and descending ground tracks separately can provide some indication of the areas on the globe for which the anomaly correlations are more anisotropic than others.
Abstract
One of the main limitations to current wave data assimilation systems is the lack of an accurate representation of the structure of the background errors. One method that may be used to determine background errors is the observational method of Hollingsworth and Lönnberg. The observational method considers correlations of the differences between observations and the background. For the case of significant wave height (SWH), potential observations come from satellite altimeters. In this work, the effect of the irregular sampling pattern of the satellite on estimates of background errors is examined. This is achieved by using anomalies from a 3-month mean as a proxy for model errors. A set of anomaly correlations is constructed from modeled wave fields. The isotropic length scales of the anomaly correlations are found to vary considerably over the globe. In addition, the anomaly correlations are found to be significantly anisotropic. The modeled wave fields are then sampled at simulated altimeter observation locations, and the anomaly correlations are recalculated from the simulated altimeter data. The results are compared to the original anomaly correlations. It is found that, in general, the simulated altimeter data can capture most of the geographic and seasonal variability in the isotropic anomaly correlation length scale. The best estimates of the isotropic length scales come from a method in which correlations are calculated between pairs of observations from prior and subsequent ground tracks, in addition to along-track pairs of observations. This method was found to underestimate the isotropic anomaly correlation length scale by approximately 10%. The simulated altimeter data were not so successful in producing realistic anisotropic correlation functions. This is because of the lack of information in the zonal direction in the simulated altimeter data. However, examination of correlations along ascending and descending ground tracks separately can provide some indication of the areas on the globe for which the anomaly correlations are more anisotropic than others.
Abstract
An experimental study of wind energy and momentum input into finite-depth wind waves was undertaken at Lake George, New South Wales, Australia. To measure microscale oscillations of induced pressure above surface waves, a high-precision wave-follower system was developed at the University of Miami, Florida. The principal sensing hardware included Elliott pressure probes, hot-film anemometers, and Pitot tubes. The wave-follower recordings were supplemented by a complete set of relevant measurements in the atmospheric boundary layer, on the surface, and in the water body. This paper is dedicated to technical aspects of the measurement procedure and data analysis. The precision of the feedback wave-following mechanism did not impose any restrictions on the measurement accuracy in the range of wave heights and frequencies relevant to the problem. Thorough calibrations of the pressure transducers and moving Elliott probes were conducted. It is shown that the response of the air column in the connecting tubes provides a frequency-dependent phase shift, which must be accounted for to recover the low-level induced pressure signal. In the finite-depth environment of Lake George, breaking waves play an important role in the momentum exchange between wind and waves, as will be shown in a subsequent paper.
Abstract
An experimental study of wind energy and momentum input into finite-depth wind waves was undertaken at Lake George, New South Wales, Australia. To measure microscale oscillations of induced pressure above surface waves, a high-precision wave-follower system was developed at the University of Miami, Florida. The principal sensing hardware included Elliott pressure probes, hot-film anemometers, and Pitot tubes. The wave-follower recordings were supplemented by a complete set of relevant measurements in the atmospheric boundary layer, on the surface, and in the water body. This paper is dedicated to technical aspects of the measurement procedure and data analysis. The precision of the feedback wave-following mechanism did not impose any restrictions on the measurement accuracy in the range of wave heights and frequencies relevant to the problem. Thorough calibrations of the pressure transducers and moving Elliott probes were conducted. It is shown that the response of the air column in the connecting tubes provides a frequency-dependent phase shift, which must be accounted for to recover the low-level induced pressure signal. In the finite-depth environment of Lake George, breaking waves play an important role in the momentum exchange between wind and waves, as will be shown in a subsequent paper.
Abstract
A field experiment to study the spectral balance of the source terms for wind-generated waves in finite water depth was carried out in Lake George, Australia. The measurements were made from a shore-connected platform at varying water depths from 1.2 m down to 20 cm. Wind conditions and the geometry of the lake were such that fetch-limited conditions with fetches ranging from approximately 10 km down to 1 km prevailed. The resulting waves were intermediate-depth wind waves with inverse wave ages in the range 1 < U 10/Cp < 8. The atmospheric input, bottom friction, and whitecap dissipation were measured directly and synchronously by an integrated measurement system, described in the paper. In addition, simultaneous data defining the directional wave spectrum, atmospheric boundary layer profile, and atmospheric turbulence were available. The contribution to the spectral evolution due to nonlinear interactions of various orders is investigated by a combination of bispectral analysis of the data and numerical modeling. The relatively small scale of the lake enabled experimental conditions such as the wind field and bathymetry to be well defined. The observations were conducted over a 3-yr period, from September 1997 to August 2000, with a designated intensive measurement period [the Australian Shallow Water Experiment (AUSWEX)] carried out in August–September 1999. High data return was achieved.
Abstract
A field experiment to study the spectral balance of the source terms for wind-generated waves in finite water depth was carried out in Lake George, Australia. The measurements were made from a shore-connected platform at varying water depths from 1.2 m down to 20 cm. Wind conditions and the geometry of the lake were such that fetch-limited conditions with fetches ranging from approximately 10 km down to 1 km prevailed. The resulting waves were intermediate-depth wind waves with inverse wave ages in the range 1 < U 10/Cp < 8. The atmospheric input, bottom friction, and whitecap dissipation were measured directly and synchronously by an integrated measurement system, described in the paper. In addition, simultaneous data defining the directional wave spectrum, atmospheric boundary layer profile, and atmospheric turbulence were available. The contribution to the spectral evolution due to nonlinear interactions of various orders is investigated by a combination of bispectral analysis of the data and numerical modeling. The relatively small scale of the lake enabled experimental conditions such as the wind field and bathymetry to be well defined. The observations were conducted over a 3-yr period, from September 1997 to August 2000, with a designated intensive measurement period [the Australian Shallow Water Experiment (AUSWEX)] carried out in August–September 1999. High data return was achieved.
