Wind Speed Retrieval Research Articles

Retrieving Hurricane Wind Speed From Dominant Wave Parameters

One of the most difficult issues in wind measurement using microwave radar is the decreased or loss of sensitivity of the return signal in high winds. Recent analyses of wind speed, wave height, and wave period data from hurricane hunter missions show that surface waves inside hurricanes adhere to the nature of fetch- and duration-limited wind wave generation. Making use of this property, the hurricane wind speed is retrievable from the dominant wave parameter (significant wave height or dominant wave period) using the fetch- or duration-limited wave growth function. An algorithm based on such consideration is developed and applied to two hurricanes of different strengths (categories 2 and 4). The retrieved wind speeds are in good agreement with the reference wind speeds from hurricane hunter measurements. For example, combining the two hurricanes the regression statistics of the bias, slope of linear fitting, root mean squares difference, and correlation coefficient of wind retrieval from significant wave height are 0.50 m/s, 1.01, 4.03 m/s, 0.87 using the fetch-limited wind wave growth function. The range of wind speeds in the combined data is from 22.4 to 65.4 m/s, and there is no indication of saturation problem in the wind retrieval using the dominant wave parameters.

Observation of Wind Direction Change on the Sea Surface Temperature Front Using High-Resolution Full Polarimetric SAR Data

In this study, we derive high-resolution wind speeds and directions from full-polarization synthetic aperture radar (SAR) data. Previous wind retrieval result from conventional single-polarization SAR data has a limitation to resolve small-scale structures in the surface wind because external wind direction data with coarser spatial resolution than those of SARs have been commonly used as an input. Using fully polarimetric SAR data, however, both wind speed and direction can be derived with high resolution from the image itself without any ancillary data. We derive wind field off the southern coast of Korea from the Radarsat-2 quad-polarization data and investigate the spatial variation. The retrieved wind field from the Radarsat-2 image presents a detailed structure including small-scale variations which is unobtainable from conventional wind observations. Comparison of the derived wind directions with in-situ buoy wind measurements shows a small difference of 8° which is regarded as sufficient to analyze small-scale wind vector changes. The retrieved wind field off the southern coast of Korea demonstrates the distinct patterns of direction changes. While blowing over the sea surface temperature (SST) frontal zone, the veering angles of wind vectors decrease and then are restored. The analysis of SAR-derived wind vectors with coinciding temperature distributions confirms that the variation in SAR-derived wind vectors on the SST fronts is mainly induced by the stability effect. This study also addresses the important role of precise wind direction retrieval on the accuracy of retrieved wind speed.

Bayesian Wind Speed Estimation Conditioned on Significant Wave Height for GNSS-R Ocean Observations

AbstractSpaceborne Global Navigation Satellite System reflectometry observations of the ocean surface are found to respond to components of roughness forced by local winds and to a longer wave swell that is only partially correlated with the local wind. This dual sensitivity is largest at low wind speeds. If left uncorrected, the error in wind speeds retrieved from the observations is strongly correlated with the significant wave height (SWH) of the ocean. A Bayesian wind speed estimator is developed to correct for the long-wave sensitivity at low wind speeds. The approach requires a characterization of the joint probability of occurrence of wind speed and SWH, which is derived from archival reanalysis sea-state records. The Bayesian estimator is applied to spaceborne data collected by the Technology Demonstration Satellite-1 (TechDemoSat-1) and is found to provide significant improvement in wind speed retrieval at low winds, relative to a conventional retrieval that does not account for SWH. At higher wind speeds, the wind speed and SWH are more highly correlated and there is much less need for the correction.

Sea State Impacts on Wind Speed Retrievals From C-Band Radars

Scatterometers, a proven technology, provide ocean wind speeds and directions that are essential in operational forecasts, monitoring of the climate, and scientific applications. While the missions and geophysical model functions are performing well, challenges remain. We analyze data from advanced scatterometer (ASCAT) aboard MetOp-A and the advanced synthetic aperture radar (ASAR) aboard Envisat, both of which operate in the C-band, against the in situ buoy wind speeds. We observe large variability in the wind speed residuals. Through analysis of these residuals, we find that they are related to sea state effects and atmospheric stability. The sea state dependence created by low-frequency swells is more pronounced for the lower incidence angles in ASCAT. In ASAR with a fixed angle of $23^{\circ }$ , the sea state dominates the wind speed errors and these trends increase with the significant wave height. We observe that wind speeds from ASAR and ASCAT have a close resemblance, which helps us to extrapolate our findings. The synergy between the two technologies can be further exploited to improve wind speed retrievals. Future scatterometer missions, such as the next MetOp, will operate with the wider range of incidence angles (including lower angles) to increase their coverage together, have higher spatial resolution, and obtain measurements closer to the coasts. In these cases, high-resolution SAR data can aide in the understanding of the radar response.

Estimate of Ocean Wind Vectors Inside Tropical Cyclones From Polarimetric Radiometer

Microwave polarimetry provides a distinctive opportunity to retrieve wind speed (WS) and wind direction (WD) inside tropical cyclones (TCs) due to the high atmospheric transmissivity through clouds and under rain conditions. A WS retrieval algorithm for WS above 20 m/s in TCs using brightness temperature at 6.8- and 10.7-GHz has been developed and a new set of parameters has been optimized from WindSat TB and the HWind analysis matches. This algorithm is estimated to have an encouraging degree of accuracy for retrieving WS in TCs. For WS above 20 m/s, the mean (std) of the differences between retrieved WS and HWind analysis for 17 TCs from 2003 to 2009 is 0.2 m/s (3.1 m/s). WD signals in the third ( T 3) and fourth Stokes ( T 4) parameters at 10.7-, 18.7- and 37-GHz for ocean surfaces in TCs under rain are presented. T 3 observations from the WindSat 10.7-, 18.7-, and 37-GHz channels are collocated with the ocean-surface winds from the HWind analysis. The collocated data are binned as a function of WS and WD. The 10.7 GHz T 3 data show clear 4-K peak-to-peak directional signals at 30–40 m/s WS after correction for the atmospheric attenuation. The data are fitted by harmonics of the relative azimuth angles between the HWind analysis and radiometer look directions. The new coefficients of WD harmonics are used to retrieve WD in TCs under rain using WindSat T 3 and T 4 channels. The rms difference between retrieved WD and HWind WD is 24.2.

Data processing error analysis based on Doppler asymmetric spatial heterodyne measurement.

The Doppler asymmetric spatial heterodyne (DASH) interferometer provides the capability to retrieve wind speed in the upper atmosphere. The data processing leads to a significant retrieving error with the development of wind precision. The influence of window parameters on the isolated interferogram is analyzed theoretically. Based on the derivation, the system is established, and the interferogram with a small shift is sampled. The phase and wind speed are calculated under various window types and window width values. We conclude that, by choosing the Nuttall window with a small width, the wind retrieving error can be minimized.

Fast tracking of wind speed with a differential absorption LiDAR system: first results of an experimental campaign at Stromboli volcano

Carbon dioxide ( CO 2 ) is considered a precursor gas of volcanic eruptions by volcanologists. Monitoring the anomalous release of this parameter, we can retrieve useful information for the mitigation of volcanic hazards, such as for air traffic security. From a dataset collected during the Stromboli volcano field campaign, an assessment of the wind speed, in both horizontal and vertical paths, performing a fast tracking of this parameter was retrieved. This was determined with a newly designed shot-per-shot differential absorption LiDAR system operated in the near-infrared spectral region due to the simultaneous reconstruction of CO 2 concentrations and wind speeds, using the same sample of LiDAR returns. A correlation method was used for the wind speed retrieval in which the transport of the spatial inhomogeneities of the aerosol backscattering coefficient, along the optical path of the system, was analyzed.

A Hurricane Morphology and Sea Surface Wind Vector Estimation Model Based on C-Band Cross-Polarization SAR Imagery

Over the last decades, data from spaceborne synthetic aperture radar (SAR) have been used in hurricane research. However, some issues remain. When wind is at hurricane strength, the wind speed retrievals from single-polarization SAR may have errors, because the backscatter signal may experience saturation and become double valued. By comparison, wind direction retrievals from cross-polarization SAR are not possible until now. In this paper, we develop a 2-D model, the symmetric hurricane estimates for wind (SHEW) model, and combine it with the modified inflow angle model to detect hurricane morphology and estimate the wind vector field imaged by cross-polarization SAR. By fitting SHEW to the SAR derived hurricane wind speed, we find the initial closest elliptical-symmetrical wind speed fields, hurricane center location, major and minor axes, the azimuthal (orientation) angle relative to the reference ellipse, and maximum wind speed. This set of hurricane morphology parameters, along with the speed of hurricane motion, are input to the inflow angle model, modified with an ellipse-shaped eye, to derive the hurricane wind direction. A total of 14 RADARSAT-2 ScanSAR images are employed to tune the combined model. Two SAR images acquired over Hurricane Arthur (2014) and Hurricane Earl (2010) are used to validate this model. Comparisons between the modeled surface wind vector and measurements from airborne stepped-frequency microwave radiometer and dropwindsondes show excellent agreement. The proposed method works well in areas with significant radar attenuation by precipitation.

Texture-Analysis-Incorporated Wind Parameters Extraction from Rain-Contaminated X-Band Nautical Radar Images

In this paper, a method for extracting wind parameters from rain-contaminated X-band nautical radar images is presented. The texture of the radar image is first generated based on spatial variability analysis. Through this process, the rain clutter in an image can be removed while the wave echoes are retained. The number of rain-contaminated pixels in each azimuthal direction of the texture is estimated, and this is used to determine the azimuthal directions in which the rain-contamination is negligible. Then, the original image data in these directions are selected for wind direction and speed retrieval using the modified intensity-level-selection-based wind algorithm. The proposed method is applied to shipborne radar data collected from the east Coast of Canada. The comparison of the radar results with anemometer data shows that the standard deviations of wind direction and speed using the rain mitigation technique can be reduced by about 14.5° and 1.3 m/s, respectively.

The Research on Ocean Surface Wind Speed Retrieval by Neural Network Algorithm for HY2 Altimeter

The Research on Ocean Surface Wind Speed Retrieval by Neural Network Algorithm for HY2 Altimeter

Recent improvements in Hurricane Imaging Radiometer’s brightness temperature image reconstruction

Abstract NASA MSFCs airborne Hurricane Imaging Radiometer (HIRAD) uses interferometric aperture synthesis to produce high resolution wide swath images of scene brightness temperature (Tb) distribution at four discrete C-band microwave frequencies (4.0, 5.0, 6.0 and 6.6 GHz). Images of ocean surface wind speed under heavy precipitation such as in tropical cyclones, is inferred from these measurements. The baseline HIRAD Tb reconstruction algorithm had produced prominent along-track streaks in the Tb images. Particularly the 4.0 GHz channel had been so dominated by the streaks as to be unusable.The loss of a frequency channel had compromised the final wind speed retrievals. During 2016, the HIRAD team made substantial progress in developing a quality controlled signal processing technique for the HIRAD data collected in 2015’s Tropical Cyclone Intensity (TCI) experiment and reduced the effect of streaks in all channels including 4.0 GHz.

Influence of the type of sea waves on the backscattered radar cross section at medium incidence angles

We consider the influence of the sea surface state on the backscattered radar cross section and the accuracy of the wind speed retrieval from the scatterometer data. We used a joint set of radars and buoys to determine the type of sea waves. Three types of sea waves were distinguished: developing wind waves, fully developed wind waves, and mixed sea. It is shown that the retrieval error of the near surface wind speed using a one-parameter algorithm is minimal in the case of fully developed wind waves. We compared these data with the results of radio-altimeter data analysis and showed that in both cases underestimation of the retrieval wind speed exists for developing wind waves and overestimation occurs for mixed sea. A variety of swell parameters (length of the dominating wave, swell height, swell age) significantly influence the backscattered radar cross section, leading to a growth in the mean square error of the retrieved wind speed during vertical sounding (radio-altimeter data), and only slightly influence the mean square error of the scatterometer data (medium incidence angles). It is necessary to include the information about the parameters of sea waves in the algorithms and take into account the regional wave properties to increase the accuracy of wind speed retrieval.

Sea Surface Wind Speed Inversion Using the Low Incident NRCS Measured by TRMM Precipitation Radar

As the launch of radars that observe the ocean at low incident angles, such as the Precipitation Radar (PR) on Tropical Rainfall Measuring Mission (TRMM) satellite and the Surface Wave Investigation and Monitoring (SWIM) on China France Oceanography SATellite, more and more normalized radar cross sections (NRCS) σ 0 of ocean surface in low incident angles are obtained. In this paper, the ocean surface NRCS of PR, when there are no rain, are used for wind speed retrieve. The sea surface wind speeds are retrieved by the Maximum Likelihood Estimate. First, the data preprocessing and wind speed inversion method of PR 2A21 data are introduced. In order to improve the inversion accuracy, the empirical GMF at low incident angle is established, using the PR NRCS and QuikScat wind speed. The accuracy of retrieved wind speed is analyzed by comparing it with the buoy, Advanced Scatterometer (ASCAT), and QuikScat. The retrieved wind speeds have a standard deviation of about 1.5 m/s when compared with the buoy, and a standard deviation of 1.1 m/s when compared with ASCAT. The year averaged global wind speed maps of PR as also calculated and compared with that of QuikScat, ASCAT, and TRMM Microwave Imager. Their spatial structures of wind speed are very consistent and correlation coefficient is greater than 0.99. At last, the standard deviation and bias of retrieved wind speeds versus incident angles and wind speeds are also analyzed.

Sea-State Observation Using Reflected BeiDou GEO Signals in Frequency Domain

This letter focuses on exploiting parameters, including peak power spectral density (PSD), integrated power, mean frequency, and spectrum width of reflected BeiDou signals in the frequency domain, to retrieve wind speed. The PSD of reflected signals is estimated after choosing proper parameters of the Welch method. Then, the features of PSD are illustrated, and a method is proposed based on fitting estimated PSD with a Gaussian function to evaluate the aforementioned PSD parameters. The estimated parameters of collected BeiDou medium earth orbit (MEO)/inclined geosynchronous satellite orbit (IGSO) and geostationary orbit (GEO) data are fitted with in situ wind speed using polynomial. Fitting results show that the peak PSD, mean frequency, and spectrum width of reflected signals from GEO have more evident dependence on wind speed, compared with MEO/IGSO. To obtain the most accurate results, the impact of delay used in lagging direct replica to align reflected signals is analyzed. Results show that regardless of delay locating within the interval of $[\tau_{0}-\tau_{c},\tau_{0}+\tau_{c}]$ , the better root-mean-square error (rmse) of less than 1.7 m/s and the larger coefficient of determination over 0.8 can be obtained by retrieving from the spectrum width, compared with peak PSD and mean frequency, whose optimal results, with rmse values of 2.03 and 1.70 m/s and coefficient of determination values of 0.73 and 0.81, are obtained as delay is $\tau_{0}-0.85\tau_{c}$ and $\tau_{0}+0.9\tau_{c}$ , respectively, where $\tau_{0}$ is the delay of specular reflection, and $\tau_{c}$ is the length of the B1 code.

Initial Results of Typhoon Wind Speed Observation Using Coastal GNSS-R of BeiDou GEO Satellite

Sea surface-reflected signals of global navigation satellite system (GNSS) were collected during a coastal experiment to evaluate the potential use of these signals on typhoon investigation. This work focuses on processing the signals from BeiDou geostationary Earth orbit (GEO) satellites and assessing the sensitivities of waveform observables to the wind speed evolution during the typhoons. After the processing of the raw samples, both the delay- and spectral-related observables are obtained from the complex waveforms and then compared with in situ wind measurement collected during two tropical cyclones. Results from the data analysis are presented, confirming that the proposed observables are well correlated with the wind speed evolution and suitable for coastal wind speed retrieval.

Retrieval of sea surface winds under hurricane conditions from GNSS-R observations

Reflected signals from global navigation satellite systems (GNSSs) have been widely acknowledged as an important remote sensing tool for retrieving sea surface wind speeds. The power of GNSS reflectometry (GNSS-R) signals can be mapped in delay chips and Doppler frequency space to generate delay Doppler power maps (DDMs), whose characteristics are related to sea surface roughness and can be used to retrieve wind speeds. However, the bistatic radar cross section (BRCS), which is strongly related to the sea surface roughness, is extensively used in radar. Therefore, a bistatic radar cross section (BRCS) map with a modified BRCS equation in a GNSS-R application is introduced. On the BRCS map, three observables are proposed to represent the sea surface roughness to establish a relationship with the sea surface wind speed. Airborne Hurricane Dennis (2005) GNSS-R data are then used. More than 16 000 BRCS maps are generated to establish GMFs of the three observables. Finally, the proposed model and classic one-dimensional delay waveform (DW) matching methods are compared, and the proposed model demonstrates a better performance for the high wind speed retrievals.

Hurricane Wind Speed Estimation Using WindSat 6 and 10 GHz Brightness Temperatures

The realistic and accurate estimation of hurricane intensity is highly desired in many scientific and operational applications. With the advance of passive microwave polarimetry, an alternative opportunity for retrieving wind speed in hurricanes has become available. A wind speed retrieval algorithm for wind speeds above 20 m/s in hurricanes has been developed by using the 6.8 and 10.7 GHz vertically and horizontally polarized brightness temperatures of WindSat. The WindSat measurements for 15 category 4 and category 5 hurricanes from 2003 to 2010 and the corresponding H*wind analysis data are used to develop and validate the retrieval model. In addition, the retrieved wind speeds are also compared to the Remote Sensing Systems (RSS) global all-weather product and stepped-frequency microwave radiometer (SFMR) measurements. The statistical results show that the mean bias and the overall root-mean-square (RMS) difference of the retrieved wind speeds with respect to the H*wind analysis data are 0.04 and 2.75 m/s, respectively, which provides an encouraging result for retrieving hurricane wind speeds over the ocean surface. The retrieved wind speeds show good agreement with the SFMR measurements. Two case studies demonstrate that the mean bias and RMS difference are 0.79 m/s and 1.79 m/s for hurricane Rita-1 and 0.63 m/s and 2.38 m/s for hurricane Rita-2, respectively. In general, the wind speed retrieval accuracy of the new model in hurricanes ranges from 2.0 m/s in light rain to 3.9 m/s in heavy rain.

Application of AMSR-E and AMSR2 Low-Frequency Channel Brightness Temperature Data for Hurricane Wind Retrievals

We present a method to retrieve wind speeds in hurricanes from spaceborne passive microwave radiometer data. Brightness temperature $(T_{B})$ observations acquired at the 6.9-GHz horizontal polarization channel by the AMSR-E and AMSR2 onboard the Earth Observing System Aqua and Global Change Observation Mission-Water 1 satellites are selected for wind retrieval due to the fact that the signal at this frequency is sensitive to high wind speeds but less sensitive to rain scatter than those acquired at other higher frequency channels. The AMSR-E and AMSR2 observations of 53 hurricanes between 2002 and 2014 are collected and collocated with stepped-frequency microwave radiometer (SFMR) measurements. Based on the small slope approximation/small perturbation method model and an ocean surface roughness spectrum, the wind speeds are retrieved from the $T_{B}$ data and validated against the SFMR measurements. The statistical comparison of the entire data set shows that the bias and root-mean-square error (RMSE) of the retrieved wind speeds are 1.11 and 4.34 m/s, respectively, which suggests that the proposed method can obtain high wind speeds under hurricane conditions. Two case studies show that the wind speed retrieval bias and RMSE are 1.08 and 3.93 m/s for Hurricane Earl and 0.09 and 3.23 m/s for Hurricane Edouard, respectively. The retrieved wind speeds from the AMSR-E and AMSR2 continuous three-day observations clearly show the process of hurricane intensification and weakening.

Wind Speed Retrieval Algorithm for the Cyclone Global Navigation Satellite System (CYGNSS) Mission

A retrieval algorithm is presented for the Level 2 ocean surface wind speed data product of the Cyclone Global Navigation Satellite System (CYGNSS) mission. The algorithm is based on the approach described by Clarizia et al ., 2014. The approach is applied to the specific orbital measurement geometry, antenna, and receiver hardware characteristics of the CYGNSS mission. Several additional processing steps have also been added to improve the performance. A best weighted estimator is used to optimally combine two different partially correlated estimates of the winds by taking their weighted average. The optimal weighting dynamically adjusts for variations in the signal-to-noise ratio of the observations that result from changes in the measurement geometry. Variations in the incidence angle of the measurements are accounted for by the use of a 2-D geophysical model function that depends on both wind speed and incidence angle. Variations in the propagation time and signal Doppler shift at different measurement geometries affect the instantaneous spatial resolution of the measurements, and these effects are compensated by a variable temporal integration of the data. In addition to a detailed description of the algorithm itself, the root-mean-square wind speed retrieval error is characterized as a function of the measurement geometry and the wind speed using a detailed mission end-to-end simulator.

A Unique Satellite-Based Sea Surface Wind Speed Algorithm and Its Application in Tropical Cyclone Intensity Analysis

AbstractThis study proposes a sea surface wind speed retrieval algorithm (the Hong wind speed algorithm) for use in rainy and rain-free conditions. It uses a combination of satellite-observed microwave brightness temperatures, sea surface temperatures, and horizontally polarized surface reflectivities from the fast Radiative Transfer for TOVS (RTTOV), and surface and atmospheric profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF). Regression relationships between satellite-observed brightness temperature and satellite-simulated brightness temperatures, satellite-simulated brightness temperatures, rough surface reflectivities, and between sea surface roughness and sea surface wind speed are derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2). Validation results of sea surface wind speed between the proposed algorithm and the Tropical Atmosphere Ocean (TAO) data show that the estimated bias and RMSE for AMSR-2 6.925- and 10.65-GHz bands are 0.09 and 1.13 m s−1, and −0.52 and 1.21 m s−1, respectively. Typhoon intensities such as the current intensity (CI) number, maximum wind speed, and minimum pressure level based on the proposed technique (the Hong technique) are compared with best-track data from the Japan Meteorological Agency (JMA), the Joint Typhoon Warning Center (JTWC), and the Cooperative Institute for Mesoscale Meteorological Studies (CIMSS) for 13 typhoons that occurred in the northeastern Pacific Ocean throughout 2012. Although the results show good agreement for low- and medium-range typhoon intensities, the discrepancy increases with typhoon intensity. Consequently, this study provides a useful retrieval algorithm for estimating sea surface wind speed, even during rainy conditions, and for analyzing characteristics of tropical cyclones.