The Probability Distribution of Sea Surface Wind Speeds. Part II: Dataset Intercomparison and Seasonal Variability

2006 ◽  
Vol 19 (4) ◽  
pp. 521-534 ◽  
Author(s):  
Adam Hugh Monahan
Keyword(s):  
Standard Deviation ◽  
Wind Speed ◽  
Probability Distribution ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Leading Order ◽  
Statistical Structure ◽  
Wind Speeds ◽  
Sea Surface Wind

Abstract The statistical structure of sea surface wind speeds is considered, both in terms of the leading-order moments (mean, standard deviation, and skewness) and in terms of the parameters of a best-fit Weibull distribution. An intercomparison is made of the statistical structure of sea surface wind speed data from four different datasets: SeaWinds scatterometer observations, a blend of Special Sensor Microwave Imager (SSM/I) satellite observations with ECMWF analyses, and two reanalysis products [NCEP–NCAR and 40-yr ECMWF Re-Analysis (ERA-40)]. It is found that while the details of the statistical structure of sea surface wind speeds differs between the datasets, the leading-order features of the distributions are consistent. In particular, it is found in all datasets that the skewness of the wind speed is a concave upward function of the ratio of the mean wind speed to its standard deviation, such that the skewness is positive where the ratio is relatively small (such as over the extratropical Northern Hemisphere), the skewness is close to zero where the ratio is intermediate (such as the Southern Ocean), and the skewness is negative where the ratio is relatively large (such as the equatorward flank of the subtropical highs). This relationship between moments is also found in buoy observations of sea surface winds. In addition, the seasonal evolution of the probability distribution of sea surface wind speeds is characterized. It is found that the statistical structure on seasonal time scales shares the relationships between moments characteristic of the year-round data. Furthermore, the seasonal data are shown to depart from Weibull behavior in the same fashion as the year-round data, indicating that non-Weibull structure in the year-round data does not arise due to seasonal nonstationarity in the parameters of a strictly Weibull time series.

scholarly journals Sea Surface Wind Speed Retrieval from the First Chinese GNSS-R Mission: Technique and Preliminary Results

2019 ◽  
Vol 11 (24) ◽  
pp. 3013 ◽  
Author(s):  
Cheng Jing ◽  
Xinliang Niu ◽  
Chongdi Duan ◽  
Feng Lu ◽  
Guodong Di ◽  
...  
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Satellite System ◽  
Sea Surface ◽  
Satellite Mission ◽  
Preliminary Results ◽  
Sea Surface Wind Speed ◽  
Wind Speeds ◽  
Sea Surface Wind

Launched on 5 June 2019, the BuFeng-1 A/B twin satellites were part of the first Chinese global navigation satellite system reflectometry (GNSS-R) satellite mission. In this letter, a brief introduction of the BF-1 mission and its preliminary results of sea surface wind retrieval are presented. Empirical fully developed sea (FDS) geophysical model functions (GMFs) relating the normalized bistatic radar cross-section to the sea surface wind speed are proposed for the BF-1 GNSS-R instruments. The FDS GMFs are derived from the collocated BF-1 observations, the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, and the advanced scatterometer (ASCAT) satellite observations. The preliminary tests reveal that the root-mean-square error (RMSE) between the derived wind speed and the reanalysis is 2.63 m/s for wind speeds in the range of 0.5–40.5 m/s. Further comparisons with the ASCAT observations and mooring buoys show that the RMSEs are 2.04 m/s and 1.77 m/s, respectively, at low-to-moderate wind speeds. This study demonstrates the effectiveness of BF-1 and provides a basis for the future GMF development of the BF-1 A/B mission.

Empirical Models of the Probability Distribution of Sea Surface Wind Speeds

2007 ◽  
Vol 20 (23) ◽  
pp. 5798-5814 ◽  
Author(s):  
Adam Hugh Monahan
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Maximum Entropy ◽  
Surface Wind ◽  
Empirical Models ◽  
Sea Surface ◽  
Wind Speeds ◽  
Sea Surface Wind ◽  
Non Gaussian ◽  
Vector Wind

Abstract This study considers the probability distribution of sea surface wind speeds, which have historically been modeled using the Weibull distribution. First, non-Weibull structure in the observed sea surface wind speeds (from SeaWinds observations) is characterized using relative entropy, a natural information theoretic measure of the difference between probability distributions. Second, empirical models of the probability distribution of sea surface wind speeds, parameterized in terms of the parameters of the vector wind probability distribution, are developed. It is shown that Gaussian fluctuations in the vector wind cannot account for the observed features of the sea surface wind speed distribution, even if anisotropy in the fluctuations is accounted for. Four different non-Gaussian models of the vector wind distribution are then considered: the bi-Gaussian, the centered gamma, the Gram–Charlier, and the constrained maximum entropy. It is shown that so long as the relationship between the skewness and kurtosis of the along-mean sea surface wind component characteristic of observations is accounted for in the modeled probability distribution, then all four vector wind distributions are able to simulate the observed mean, standard deviation, and skewness of the sea surface wind speeds with an accuracy much higher than is possible if non-Gaussian structure in the vector winds is neglected. The constrained maximum entropy distribution is found to lead to the best simulation of the wind speed probability distribution. The significance of these results for the parameterization of air/sea fluxes in general circulation models is discussed.

scholarly journals The Probability Distribution of Sea Surface Wind Speeds. Part I: Theory and SeaWinds Observations

2006 ◽  
Vol 19 (4) ◽  
pp. 497-520 ◽  
Author(s):  
Adam Hugh Monahan
Keyword(s):  
Standard Deviation ◽  
Probability Density Function ◽  
Probability Distribution ◽  
Probability Density ◽  
Density Function ◽  
Surface Wind ◽  
Sea Surface ◽  
Wind Speeds ◽  
Sea Surface Wind ◽  
The Mean

Abstract The probability distribution of sea surface wind speeds, w, is considered. Daily SeaWinds scatterometer observations are used for the characterization of the moments of sea surface winds on a global scale. These observations confirm the results of earlier studies, which found that the two-parameter Weibull distribution provides a good (but not perfect) approximation to the probability density function of w. In particular, the observed and Weibull probability distributions share the feature that the skewness of w is a concave upward function of the ratio of the mean of w to its standard deviation. The skewness of w is positive where the ratio is relatively small (such as over the extratropical Northern Hemisphere), the skewness is close to zero where the ratio is intermediate (such as the Southern Ocean), and the skewness is negative where the ratio is relatively large (such as the equatorward flank of the subtropical highs). An analytic expression for the probability density function of w, derived from a simple stochastic model of the atmospheric boundary layer, is shown to be in good qualitative agreement with the observed relationships between the moments of w. Empirical expressions for the probability distribution of w in terms of the mean and standard deviation of the vector wind are derived using Gram–Charlier expansions of the joint distribution of the sea surface wind vector components. The significance of these distributions for improvements to calculations of averaged air–sea fluxes in diagnostic and modeling studies is discussed.

Sea Surface Wind Speed Detection by Using the Data of CALIPSO Lidar

2012 ◽  
Vol 32 (8) ◽  
pp. 0828002
Author(s):  
Wu Dong ◽  
Zhang Xiaoxue ◽  
Yan Fengqi ◽  
Liu Zhaoyan
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Sea Surface Wind Speed ◽  
Sea Surface Wind

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

2016 ◽  
Vol 33 (7) ◽  
pp. 1363-1375 ◽  
Author(s):  
Sungwook Hong ◽  
Hwa-Jeong Seo ◽  
Young-Joo Kwon
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Japan Meteorological Agency ◽  
Retrieval Algorithm ◽  
Sea Surface Wind Speed ◽  
Brightness Temperatures ◽  
Sea Surface Wind ◽  
Medium Range

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.

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