Wind Speed Retrieval Research Articles

An Inversion Method for Extraction of Wind Speed From High-Frequency Ground-Wave Radar Oceanic Backscatter

A new approach to extracting sea surface wind speed from high-frequency radar Doppler spectra is presented. Based on certain appropriate approximations associated with the Doppler region close to the first-order (Bragg) peaks, the second-order radar cross section equation is differentiated. Once Doppler shifts due to ocean currents are removed from the data, an expression that relates the wind speed to the frequency position of the second-order peak is derived. The method is applied to simulated noisy data as well as to field data obtained from a Seasonde (a product of CODAR Oceans Sensors) in Breezy Point, NY. In the latter case, the retrieved wind speeds are then compared to ground truth data measured by an anemometer from a National Oceanic and Atmospheric Administration weather station located in the vicinity of the illuminated patch of ocean. Subject to certain constraints as detailed in the manuscript, the algorithm shows significant promise.

On the Consequences of Resampling Microwave Radiometer Observations for Use in Retrieval Algorithms

Abstract How to deal with the different spatial resolutions of multifrequency satellite microwave radiometer measurements is a common problem in retrievals of cloud properties and rainfall. Data convolution and deconvolution is a common approach to resampling the measurements to a single resolution. Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) measurements are resampled to the resolution of the 19-GHz field of view for use in a multifrequency optimal estimation retrieval algorithm of cloud liquid water path, total precipitable water, and wind speed. Resampling the TMI measurements is found to have a strong influence on retrievals of cloud liquid water path and a slight influence on wind speed. Beam-filling effects in the resampled brightness temperatures are shown to be responsible for the large differences between the retrievals using the TMI native resolution and resampled brightness temperatures. Synthetic retrievals are performed to test the sensitivity of the retrieved parameters to beam-filling effects in the resampling of each of the different channels. Beam-filling effects due to the convolution of the 85-GHz channels are shown to be the largest contributor to differences in retrieved cloud liquid water path. Differences in retrieved wind speeds are found to be a combination of effects from deconvolving the 10-GHz brightness temperatures and compensation effects due to the lower liquid water path being retrieved by the high-frequency channels. The influence of beam-filling effects on daily and monthly averages of cloud liquid water path is also explored. Results show that space–time averaging of cloud liquid water path cannot fully compensate for the beam-filling effects and should be considered when using cloud liquid water path data for validation or in climate studies.

Comparison of reflected GPS wind speed retrievals with dropsondes in tropical cyclones

In an earlier communication, data were presented that demonstrated that quasi‐specular, L‐Band reflection measurements could be used to infer ocean surface winds. Applying an indirect calibration technique, a mean square slope versus surface wind speed was developed and reported. Retrievals using this calibration showed that the resulting surface wind speeds were comparable with other measurements. This report extends the previous results by presenting direct comparisons between GPS dropwindsonde (dropsonde)‐reported wind speeds and the Bi‐static GPS wind speed retrievals for data sets acquired in 2008. Editing of the Bi‐static GPS data will be discussed that takes into effect overland and inside‐the‐eye winnowing. Data will be presented with a regression line to determine the comparative relationship. It will be shown that good agreement exists between the reflected Bi‐static GPS retrieved winds and those reported by the dropsondes when certain well‐defined types of data are excluded.

Wind-Vector Retrievals Under Rain With Passive Satellite Microwave Radiometers

We have developed algorithms that retrieve ocean-surface wind speed and direction under rain using brightness-temperature (TB) measurements from passive satellite microwave radiometers. For accurate radiometer retrievals of wind speeds in the rain, it is essential to use TB signals at different frequencies, whose spectral signature makes it possible to find channel combinations that are sufficiently sensitive to wind speed but little or not sensitive to rain. The wind-speed retrieval accuracy of an algorithm that utilizes C-band frequencies and is trained for tropical cyclones ranges from 2.0 m/s in light rain to 4.0 m/s in heavy rain. We have also trained and tested global algorithms that are less accurate in tropical storms but can be applied under all conditions. The wind-direction retrieval accuracy degrades from about 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deg</sup> in light rain to 30 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deg</sup> at the onset of heavy rain. We compare the performance of wind-vector retrievals under rain from microwave radiometers with those from scatterometers and discuss advantages and shortcomings of both instruments. We have also analyzed the wind-induced sea-surface emissivity, including its wind-direction dependence for wind speeds up to 45 m/s.

On SAR wind speed ambiguities and related geophysical model functions

Recent investigations show that normalized radar cross sections for C-band microwave sensors decrease under high wind conditions with certain incident angles instead of increase, as is the case for low to moderate wind speeds. This creates the problem of ambiguities in high wind speed retrievals from synthetic aperture radar (SAR). In the present work, four geophysical model functions (GMFs) are studied, namely the high wind C-band model 4 (CMOD4HW), C-band model 5 (CMOD5), the high wind vertical polarized GMF (HWGMF_VV), and the high wind horizontal polarized GMF (HWGMF_HH). Our focus is on model behaviours relative to wind speed ambiguities. We show that, except for CMOD4HW, the other GMFs exhibit the wind speed ambiguity problem. To consider this problem in high wind speed retrievals from SAR, we focus on hurricanes and propose a method to remove the speed ambiguity using the dominant hurricane wind structure.

Coupling a Neural Network-Based forward Model and a Bayesian Inversion Approach to Retrieve Wind Field from Spaceborne Polarimetric Radiometers.

A simulation study to assess the potentiality of sea surface wind vector estimation based on the approximation of the forward model through Neural Networks and on the Bayesian theory of parameter estimation is presented. A polarimetric microwave radiometer has been considered and its observations have been simulated by means of the two scale model. To perform the simulations, the atmospheric and surface parameters have been derived from ECMWF analysis fields. To retrieve wind speed, Minimum Variance (MV) and Maximum Posterior Probability (MAP) criteria have been used while, for wind direction, a Maximum Likelihood (ML) criterion has been exploited. To minimize the cost function of MAP and ML, conventional Gradient Descent method, as well as Simulated Annealing optimization technique, have been employed. Results have shown that the standard deviation of the wind speed retrieval error is approximately 1.1 m/s for the best estimator. As for the wind direction, the standard deviation of the estimation error is less than 13° for wind speeds larger than 6 m/s. For lower wind velocities, the wind direction signal is too weak to ensure reliable retrievals. A method to deal with the non-uniqueness of the wind direction solution has been also developed. A test on a case study has yielded encouraging results.

Sea-Surface Polarization Ratio From Envisat ASAR AP Data

The polarization ratio (VV/HH) of normalized radar cross section over ocean surface is processed from Envisat Advanced Synthetic Aperture Radar (ASAR) alternating polarization (AP) data and compared with existing electromagnetic (EM) scattering models. The processing of the AP data is based on Level0 (raw data) product that enables us to accurately compute the contribution from additive noise separately from each burst. This is important in estimating the correct polarization ratio, particularly at higher incidence angles where the relative contribution from noise becomes severe. A recently published EM scattering model, general curvature method (GCM), is extended here to incorporate scattering from breaking waves. The performance of this model with and without breaking waves is compared with AP data and with other models for the polarization ratio. The extended GCM model is furthermore used to assess the performance of wind speed retrieval in HH polarization. The inclusion of wave breaking in the GCM model reduces the polarization ratio standard deviation from 0.64 to 0.50 and the bias from -0.34 to 0.16, as compared to the measured ASAR AP polarization ratio. The best semiempirical model from literature gives 0.51 and 0.63 for the standard deviation and bias, respectively. The difference in performance is shown to be caused by the wind speed dependence not accounted for in the semiempirical model. Validation of the new polarization ratio model in combination with C-band Model Function (CMOD) for wind speed retrieval at HH polarization shows similar performance as at VV polarization. We conclude that the GCM polarization ratio model incorporating wave-breaking effects is found to reproduce well polarization ratio measurements (ASAR AP) versus wind vector and incidence angle.

An analytical algorithm with a wave age factor for altimeter wind speed retrieval

Based on the specular reflection theory of electromagnetic waves at rough sea surface and the wind wave spectrum model with a wave age factor, the sea surface wind speeds are retrieved from the normalized radar backscatter cross‐section (NRCS) measured by TOPEX/Poseidon (T/P) Ku‐band altimeter using the mean square slope (MSS) calculated from the spectrum models of the wind waves and the gravity‐capillary waves. A relationship between wave age and non‐dimensional wave height is applied to compute the wave age factor using the significant wave height (SWH) and wind speeds obtained from buoy or altimeter simultaneously. The study indicates that the wave age factor has a significant impact on the retrieval of altimeter wind speed. Compared with the operational algorithm for retrieving altimeter wind speed, the wind speed retrieved from the new analytical algorithm based on the wind wave spectrum model with the wave age factor, proposed in this study, can match the buoy measurements better. The effects of the wave age factor on altimeter wind speed retrieval are also shown quantitatively through a series of experiments and measurements. The comparison with the operational algorithm indicates that both the bias and root mean square error (RMSE) between wind speeds retrieved by the proposed analytical algorithm and those observed by the buoy decrease significantly. In the Gulf of Mexico, with the new analytical algorithm, more accurate altimeter wind speeds are retrieved.

Comparison of Synthetic Aperture Radar–Derived Wind Speeds with Buoy Wind Speeds along the Mountainous Alaskan Coast

Abstract Satellite-borne synthetic aperture radar (SAR) offers the potential for remotely sensing surface wind speed both over the open sea and in close proximity to the coast. The resolution improvement of SAR over scatterometers is of particular advantage near coasts. Thus, there is a need to verify the performance of SAR wind speed retrieval in coastal environments adjacent to very complex terrain and subject to strong synoptic forcing. Mountainous coasts present a challenge because the wind direction values required for SAR wind speed retrieval algorithms cannot be obtained from global model analyses with as much accuracy there as over the open ocean or adjacent to gentle coasts where most previous SAR accuracy studies have been conducted. The performance of SAR wind speed retrieval in this challenging environment is tested using a 7-yr dataset from the mountainous coast of the Gulf of Alaska. SAR-derived wind speeds are compared with direct measurements from three U.S. Navy Oceanographic Meteorological Automatic Device (NOMAD) buoys. Both of the commonly used SAR wind speed retrieval models, CMOD4 and CMOD5, were tested, as was the impact of correcting the buoy-derived wind speed profile for surface-layer stability. Both SAR wind speed retrieval models performed well although there was some wind speed–dependent bias. This may be either a SAR wind speed retrieval issue or a buoy issue because buoys can underestimate winds as wind speed and thus sea state increase. The full set of tests is performed twice, once using wind directions from the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) model analyses and once using wind direction observations from the buoys themselves. It is concluded that useful wind speeds can be derived from SAR backscatter and global model wind directions even in proximity to mountainous coastlines.

Applications of AMSR-E measurements for tropical cyclone predictions Part I: Retrieval of Sea Surface Temperature and Wind speed

Existing satellite microwave algorithms for retrieving Sea Surface Temperature (SST) and Wind (SSW) are applicable primarily for non-raining cloudy conditions. With the launch of the Earth Observing System (EOS) Aqua satellite in 2002, the Advanced Microwave Scanning Radiometer (AMSR-E) onboard provides some unique measurements at lower frequencies which are sensitive to ocean surface parameters under adverse weather conditions. In this study, a new algorithm is developed to derive SST and SSW for hurricane predictions such as hurricane vortex analysis from the AMSR-E measurements at 6.925 and 10.65 GHz. In the algorithm, the effects of precipitation emission and scattering on the measurements are properly taken into account. The algorithm performances are evaluated with buoy measurements and aircraft dropsonde data. It is found that the root mean square (RMS) errors for SST and SSW are about 1.8 K and 1.9 m s−1, respectively, when the results are compared with the buoy data over open oceans under precipitating clouds (e.g., its liquid water path is larger than 0.5 mm), while they are 1.1 K for SST and 2.0 m s−1 for SSW, respectively, when the retrievals are validated against the dropsonde measurements over warm oceans. These results indicate that our newly developed algorithm can provide some critical surface information for tropical cycle predictions. Currently, this newly developed algorithm has been implemented into the hybrid variational scheme for the hurricane vortex analysis to provide predictions of SST and SSW fields.

Improvement of Ocean Wind Speed Retrieval Accuracy Taking Into Account a Relative Wind Direction on Observed Brightness Temperature with Microwave Radiometer

Improvement of Ocean Wind Speed Retrieval Accuracy Taking Into Account a Relative Wind Direction on Observed Brightness Temperature with Microwave Radiometer

Hurricane Surface Wind Measurements from an Operational Stepped Frequency Microwave Radiometer

Abstract For the first time, the NOAA/Aircraft Operations Center (AOC) flew stepped frequency microwave radiometers (SFMRs) on both WP-3D research aircraft for operational hurricane surface wind speed measurement in 2005. An unprecedented number of major hurricanes provided ample data to evaluate both instrument performance and surface wind speed retrieval quality up to 70 m s−1 (Saffir–Simpson category 5). To this end, a new microwave emissivity–wind speed model function based on estimates of near-surface winds in hurricanes by global positioning system (GPS) dropwindsondes is proposed. For practical purposes, utilizing this function removes a previously documented high bias in moderate SFMR-measured wind speeds (10–50 m s−1), and additionally corrects an extreme wind speed (&amp;gt;60 m s−1) underestimate. The AOC operational SFMRs yield retrievals that are precise to within ∼2% at 30 m s−1, which is a factor of 2 improvement over the NOAA Hurricane Research Division’s SFMR, and comparable to the precision found here for GPS dropwindsonde near-surface wind speeds. A small (1.6 m s−1), but statistically significant, overall high bias was found for independent SFMR measurements utilizing emissivity data not used for model function development. Across the range of measured wind speeds (10–70 m s−1), SFMR 10-s averaged wind speeds are within 4 m s−1 (rms) of the dropwindsonde near-surface estimate, or 5%–25% depending on speed. However, an analysis of eyewall peak wind speeds indicates an overall 2.6 m s−1 GPS low bias relative to the peak SFMR estimate on the same flight leg, suggesting a real increase in the maximum wind speed estimate due to SFMR’s high-density sampling. Through a series of statistical tests, the SFMR is shown to reduce the overall bias in the peak surface wind speed estimate by ∼50% over the current flight-level wind reduction method and is comparable at extreme wind speeds. The updated model function is demonstrated to behave differently below and above the hurricane wind speed threshold (∼32 m s−1), which may have implications for air–sea momentum and kinetic energy exchange. The change in behavior is at least qualitatively consistent with recent laboratory and field results concerning the drag coefficient in high wind speed conditions, which show a fairly clear “leveling off” of the drag coefficient with increased wind speed above ∼30 m s−1. Finally, a composite analysis of historical data indicates that the earth-relative SFMR peak wind speed is typically located in the hurricane’s right-front quadrant, which is consistent with previous observational and theoretical studies of surface wind structure.

Sensitivity of Airborne 36.5-GHz Polarimetric Radiometer's Wind-Speed Measurement to Incidence Angle

The Helsinki University of Technology's airborne fully polarimetric profiling radiometer at 36.5 GHz has been used for wind-vector measurements over the Gulf of Finland. The results, collected in a series of measurements over a period of two years, are presented in this paper. The Fourier coefficients of the harmonics of the first three modified Stokes parameters (in brightness temperature) have been solved, and their behavior as a function of the measurement incidence angle and the wind speed has been examined, resulting in a linear model in the measurement range. In this paper, we show a clear relationship between the incidence angle and the third modified Stokes parameter (in brightness temperature), which has been used to compensate for aircraft motion during measurements. Furthermore, the sensitivity of the wind-speed measurement to the incidence angle has been studied, and a model for wind-speed retrieval as a function of the harmonic coefficients and incidence angle was developed.

Neural Network Retrieval of Ocean Surface Parameters from SSM/I Data

Abstract A new algorithm based on the multiparameter neural network is proposed to retrieve wind speed (WS), sea surface temperature (SST), sea surface air temperature, and relative humidity (RH) simultaneously over the global oceans from Special Sensor Microwave Imager (SSM/I) observations. The retrieved geophysical parameters are used to estimate the surface latent heat flux and sensible heat flux using a bulk method over the global oceans. The neural network is trained and validated with the matchups of SSM/I overpasses and National Data Buoy Center buoys under both clear and cloudy weather conditions. In addition, the data acquired by the 85.5-GHz channels of SSM/I are used as the input variables of the neural network to improve its performance. The root-mean-square (rms) errors between the estimated WS, SST, sea surface air temperature, and RH from SSM/I observations and the buoy measurements are 1.48 m s−1, 1.54°C, 1.47°C, and 7.85, respectively. The rms errors between the estimated latent and sensible heat fluxes from SSM/I observations and the Xisha Island (in the South China Sea) measurements are 3.21 and 30.54 W m−2, whereas those between the SSM/I estimates and the buoy data are 4.9 and 37.85 W m−2, respectively. Both of these errors (those for WS, SST, and sea surface air temperature, in particular) are smaller than those by previous retrieval algorithms of SSM/I observations over the global oceans. Unlike previous methods, the present algorithm is capable of producing near-real-time estimates of surface latent and sensible heat fluxes for the global oceans from SSM/I data.

Near-surface wind speed retrieval from space-based, multi-angle imaging of ocean sun glint patterns

The relationship between the MISR multi-angle observations of sun glint pattern width over ocean and the near-surface wind speed is explored. With this relationship, we develop an algorithm that can constrain the standard, wind-driven glint/white cap model that defines the ocean–surface boundary condition in MISR aerosol retrievals. The key geometric factor determining wind retrieval quality is the angular distance between the reflection vector and the closest MISR multi-angle observation — the minimum Sun Glint Angle (SGA). MISR-retrieved winds are within 2.4 m/s of buoy-measured values for minimum SGA below about 15° for the 38 cases studied, and usually better than 1.1 m/s, except one for which the wind speed was extremely low. For minimum SGA above about 15°, MISR angular glint pattern reflectance sampling is not adequate to perform wind retrievals. An ambiguity in the retrieval that can occur for minimum SGA greater than 6° or 7° is resolved by the minimum observed absolute reflectance. We also confirm the high accuracy of the Cox–Munk ocean surface reflectance model, used to analyze the glint pattern dependence on wind speed. The wind-retrieval method could be used in an operational multi-angle aerosol retrieval algorithm to dynamically constrain the ocean surface boundary condition when glint pattern angular sampling is adequate.

A new hurricane wind retrieval algorithm for SAR images

A new algorithm for hurricane wind retrieval is developed based on the primary characteristics of hurricane processes. By assuming constant wind speeds and equal‐distant wind directions in three neighboring sub‐image blocks of any specific concentric circle around the hurricane eye, both wind speed and wind direction are resolved without any external information. To evaluate the accuracy of the new algorithm, a Radarsat‐1 synthetic aperture radar image of hurricane Isabel is studied. The retrieved wind speed is compared with QuikSCAT scatterometer winds and in situ winds. Good consistency is shown. Since the new algorithm doesn't rely on any external wind information, fine resolution wind structures can be retrieved without adding errors due to linear interpolation of the externally‐derived coarse‐resolution wind direction.

Assimilation of SSM/I Radiances in the NCEP Global Data Assimilation System

Abstract A technique for the assimilation of Special Sensor Microwave Imager (SSM/I) data in the National Centers for Environmental Prediction (NCEP) global data assimilation and forecast system is described. Because the radiative transfer model used does not yet allow for cloud/rain effects, it is crucial to properly identify and exclude (or correct) cloud/rain-contaminated radiances using quality control (QC) and bias correction procedures. The assimilation technique is unique in that both procedures take into account the effect of the liquid cloud on the difference between observed and simulated brightness temperature for each SSM/I channel. The estimate of the total column cloud liquid water from observed radiances is used in a frequency-dependent cloud detection component of the QC and as a predictor in the bias correction algorithm. Also, a microwave emissivity Jacobian model with respect to wind speed is developed for oceanic radiances. It was found that the surface wind information in the radiance data can be extracted through the emissivity model Jacobian rather than producing and including a separate SSM/I wind speed retrieval. A two-month-long data assimilation experiment from July to August 2004 using NCEP’s Gridpoint Statistical Interpolation analysis system and the NCEP operational forecast model was performed. In general, the assimilation of SSM/I radiance has a significant positive impact on the analyses and forecasts. Moisture is added in the Northern Hemisphere and Tropics and is slightly reduced in the Southern Hemisphere. The moisture added appears to be slightly excessive in the Tropics verified against rawinsonde observations. Nevertheless, the assimilation of SSM/I radiance data reduces model spinup of precipitation and substantially improves the dynamic fields, especially in measures of the vector wind error at 200 hPa in the Tropics. In terms of hurricane tracks, SSM/I radiance assimilation produces more cases with smaller errors and reduces the average error. No disruption of the Hadley circulation is found from the introduction of the SSM/I radiance data.

Calibration of reflected GPS for tropical storm wind speed retrievals

Since 1996 when GPS reflected signals were purposefully acquired, an effort to assess the utility of these signals has been under way. It was early determined that the reflected GPS signal can be related to ocean surface wind dependent slope probability densities. Quantifying that relationship has resulted in considerable data taken at wind speeds below those associated with tropical storms. During the 2005 hurricane season, data were taken in high wind speed conditions that have been used to compare with the U.S. Navy's COAMPS model. The gridded data were used to develop a calibration for high wind speeds which also represents a measurement of apparent ocean surface slopes at L‐Band. This paper presents the results of this GPS surface reflection calibration for winds up to 35 meters per second. In addition a simple function is developed that models the mean square slope variation with surface wind speed and includes winds above 35 meters per second. This model is applied to data from Hurricanes Dennis and Isabel to demonstrate the ability of surface reflected GPS signals to yield good retrieval performance for winds at and above tropical storm strength.

Wind resource assessment from C-band SAR

Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. First, wind speeds retrieved from a series of 91 ERS-2 SAR and Envisat ASAR images, at moderate wind speeds (2–15 m s − 1 ), were validated against in situ measurements from an offshore mast in the North Sea. The wind direction input, necessary for SAR wind speed retrievals, was obtained from the meteorological mast and from a local gradient analysis of wind streaks in the SAR images. A wind speed standard deviation of ∼ 1.1 m s − 1 was found when in situ wind directions were used. The use of local gradient wind directions yielded a standard deviation of ∼ 1.3 m s − 1 . Wind speeds retrieved from three geophysical model functions (CMOD-IFR2, CMOD4, and CMOD5) were compared. The best approximation to the in situ measurements of wind speed was found for CMOD-IFR2, despite a bias on the order of − 0.3 m s − 1 . CMOD4 retrievals also underestimated the wind speed, whereas the bias on CMOD5 retrievals was negligible. Then, wind resource assessments were made from the SAR-based wind observations to show how errors in wind speed from the different SAR wind retrievals were reflected in the wind statistics. The mean wind speed, obtained for all of the 91 SAR scenes, was linked closely to the bias of SAR wind retrievals. Agreement to ± 15% of the in situ measurements was found for all the wind retrieval methods tested. The accuracy of power density estimates for the entire data set was evaluated by the standard deviation of SAR wind retrievals relative to the in situ measurements. SAR wind fields retrieved with CMOD-IFR2, using in situ wind direction inputs, exactly yielded the power density predicted from in situ measurements alone. The SAR-based wind resource assessment also corresponded well to predictions from longer time series of in situ measurements. This indicates that a reliable wind resource assessment may be achieved from a series of randomly selected SAR images. The findings presented here could be useful in future wind resource assessment based on SAR images.

A wind speed retrieval algorithm by combining 6 and 10 GHz data from Advanced Microwave Scanning Radiometer: Wind speed inside hurricanes

A wind speed retrieval algorithm was developed using 6 and 10 GHz h-pol (6H and 10H) data of the Advanced Microwave Scanning Radiometer (AMSR) aboard the Advanced Earth Observation Satellite-II (ADEOS-II) and AMSR-E aboard AQUA, for the purpose of retrieving wind speed inside rainstorms, primarily hurricanes and typhoons. The h-pol was used rather than the v-pol, because the brightness temperature sensitivity to the ocean wind at h-pol is larger than v-pol. The microwave emission change of 6H and 10H corresponding to ocean wind was evaluated in no-rain areas by combining AMSR and SeaWinds data aboard the ADEOS-II (SeaWinds was NASA’s scatterometer), and it was found that the ratio of the two 6H to 10H increments due to ocean wind is 0.9. Assuming that this result also holds with higher wind speeds and under rainy conditions, the brightness temperatures at 6H and 10H were simulated using a microwave radiative transfer model. A parameter W6 (unit; Kelvin) was then defined, representing an increment at 6H due to ocean wind. W6 is applicable to rainy areas, and to all ranges of sea surface temperature. W6 was compared with wind speed reported by the National Hurricanes Center for several hurricanes in the Western Atlantic Ocean during three years (2002 to 2004). W6 averaged around centers of hurricanes was found to exhibit a sensitivity to wind speed, such as increasing from 22 K to 65 K as the wind speed rose from 65 to 140 knots (33 to 72 m/s), and an empirical relationship relating the averaged W6 to wind speed in hurricanes was derived.