The long-termtrend of the sea surface wind speed and the wave height (wind wave, swell, mixed wave) in global ocean during the last 44 a

2013 ◽  
Vol 32 (10) ◽  
pp. 1-4 ◽  
Author(s):  
Chongwei Zheng ◽  
Lin Zhou ◽  
Chaofan Huang ◽  
Yinglong Shi ◽  
Jiaxun Li ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
Wind Wave ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Global Ocean ◽  
Sea Surface Wind Speed ◽  
Sea Surface Wind ◽  
Mixed Wave

The seasonal variations in the significant wave height and sea surface wind speed of the China’s seas

2015 ◽  
Vol 34 (9) ◽  
pp. 58-64 ◽  
Author(s):  
Chongwei Zheng ◽  
Jing Pan ◽  
Yanke Tan ◽  
Zhansheng Gao ◽  
Zhenfeng Rui ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
Seasonal Variations ◽  
Significant Wave Height ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Significant Wave ◽  
Sea Surface Wind Speed ◽  
Sea Surface Wind

scholarly journals Assessment of Sea Surface Wind from NWP Reanalyses and Satellites in the Southern Ocean

2013 ◽  
Vol 30 (8) ◽  
pp. 1842-1853 ◽  
Author(s):  
Ming Li ◽  
Jiping Liu ◽  
Zhenzhan Wang ◽  
Hui Wang ◽  
Zhanhai Zhang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Southern Ocean ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Global Ocean ◽  
Sea Surface Wind Speed ◽  
Sea Surface Wind ◽  
Wind Regimes ◽  
The Antarctic

Abstract Reanalysis projects and satellite data analysis have provided surface wind over the global ocean. To assess how well one can reconstruct the variations of surface wind in the data-sparse Southern Ocean, sea surface wind speed data from 1) the National Centers for Environmental Prediction–Department of Energy reanalysis (NCEP–DOE), 2) the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim), 3) National Climate Data Center (NCDC) blended sea winds, and 4) cross-calibrated multiplatform (CCMP) ocean surface velocity are evaluated. First, the accuracy of sea surface wind speed is validated with quality-controlled in situ measurements from research vessels. The results show that the CCMP value is closer to the ship observations than other products, whereas the NCEP–DOE value has the largest systematic positive bias. All four products show large positive biases under weak wind regimes, good agreement with the ship observations under moderate wind regimes, and large negative biases under high wind regimes. Second, the consistency and discrepancy of sea surface wind speed across different products is examined. The intercomparisons suggest that these products show encouraging agreement in the spatial distribution of the annual-mean sea surface wind speed. The largest across-data scatter is found in the central Indian sector of the Antarctic Circumpolar Current, which is comparable to its respective interannual variability. The monthly-mean correlations between pairs of products are high. However, differing from the decadal trends of NCEP–DOE, NCDC, and CCMP that show an increase of sea surface wind speed in the Antarctic Circumpolar region, ERA-Interim has an opposite sign there.

GPS Reflections for Sea Surface Wind Speed Measurement

2008 ◽  
Vol 5 (4) ◽  
pp. 569-572 ◽  
Author(s):  
D. K. Yang ◽  
Y. Q. Zhang ◽  
Y. Lu ◽  
Q. S. Zhang
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Sea Surface Wind Speed ◽  
Sea Surface Wind ◽  
Speed Measurement ◽  
Wind Speed Measurement

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.

scholarly journals Characteristics and Applications of the Ground-Based X Band Low Elevation Angle Brightness Temperatures under Low Sea State Based on Measured Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1736
Author(s):  
Zhongqing Cao ◽  
Lixin Guo ◽  
Shifeng Kang ◽  
Xianhai Cheng ◽  
Qingliang Li ◽  
...  
Keyword(s):  
Wind Speed ◽  
Meteorological Data ◽  
Surface Wind ◽  
Elevation Angle ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Sea State ◽  
Sea Surface Wind Speed ◽  
Sea Surface Wind ◽  
X Band

In ground-based microwave radiometer remote sensing, low-elevation-angle (−3°~3°) radiation data are often discarded because they are considered to be of little value and are often difficult to model due to the complicated mechanism. Based on the observed X-band horizontal polarization low elevation angle microwave radiation data and the meteorological data at the same time, this study investigated the generation mechanism of low elevation angle brightness temperature (LEATB) and its relationship with meteorological data, i.e., temperature, humidity, and wind speed, under low sea state. As a result, one could find that the LEATB was sensitive to the atmosphere at the elevation angle between 1° to 3°, and a diurnal variation of the LEATB reached up to 10 K. This study also found a linear relationship between the LEATB and sea surface wind speed under low sea state at an elevation range from −3° to 0°, i.e., the brightness temperature decreased as the wind speed increased, which was inconsistent with the observations at the elevation angle from −10° to −5°. The variation of the LEATB difference according to the change in the over-the-horizon detection capability (OTHDC) of the shipborne microwave radar was examined to identify the reason for this phenomenon theoretically. The results showed that the LEATB difference was significantly influenced by a change in the OTHDC. Further, this study examined a remote sensing method to extract the sea surface wind speed data from experimental LEATB data under low sea state. The results demonstrated that the X-band horizontal polarization LEATBs were useful to retrieve the sea surface wind speed data at a reasonable accuracy—the root mean square error of 0.02408 m/s. Overall, this study proved the promising potential of the LEATB data for retrieving temperature profiles, humidity profiles, sea surface winds, and the OTHDC.

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