Two wind algorithms of ENVISAT advanced synthetic aperture radar （ASAR）, i. e. CMOD4 model from the European Space Agency （ESA） and CMOD_IFR2 model from Quilfen et al., are compared in this paper. The wind directi...Two wind algorithms of ENVISAT advanced synthetic aperture radar （ASAR）, i. e. CMOD4 model from the European Space Agency （ESA） and CMOD_IFR2 model from Quilfen et al., are compared in this paper. The wind direction is estimated from orientation of low and linear signatures in the ASAR imagery. The wind direction has inherently a 180° ambiguity since only a single ASAR image is used. The 180° ambiguity is eliminated by using the buoy data from the NOAA （National Oceanic and Atmospheric Administration） buoys moored in the Pacific. Wind speed is obtained with the two wind algorithms using both estimated wind direction and normalized radar cross section （NRCS）. The retrieved wind results agree well with the data from Quikscat. The root mean square error （RMSE） of wind direction is 2.80°. The RMSEs of wind speed from CMOD4 model and CMOD_IFR2 model are 1.09 m/s and 0.60 m/s, respectively. The results indicate that the CMOD_IFR2 model is slight better than CMOD4 model at high wind.展开更多
The altimeter normalized radar cross section(NRCS) has been used to retrieve the sea surface wind speed for decades, and more than a dozen of wind speed retrieval algorithms have been proposed. Despite the continuing ...The altimeter normalized radar cross section(NRCS) has been used to retrieve the sea surface wind speed for decades, and more than a dozen of wind speed retrieval algorithms have been proposed. Despite the continuing efforts to improve the wind speed measurements, a bias dependence on wave state persists in all wind algorithms. On the basis of recent evidence that short waves are essentially modulated by local winds and much less affected by wave state, we proposed a physics-based approach to retrieve the wind speed from the dual-frequency difference in terms of the mean square slope of short waves. A collocated dataset of coincident altimeter/buoy measurements were used to develop and validate the approach. Validation against buoy measurements indicates that the approach is almost unbiased and has an overall root mean square error of 1.24 m s-1, which is 5.3% lower than the single-parameter algorithm in operational use(Witter and Chelton, 1991) and 2.4% lower than another dual-frequency approach(Chen et al., 2002). Furthermore, the results indicate that the new approach significantly improves the wave-dependent bias compared to the single-parameter algorithm. The capacity of altimeter to retrieve sea surface wind speed appears to be limited for the case of winds below 3 m s-1. The validity of the approach at high winds needs to be further examined in the future study.展开更多
Rain can significantly degrade the wind vector retrieval from Precipitation Radar （PR） by three mechanisms, namely, two-way rain attenuation, rain volume-backscattering, and ocean surface roughening from the rain sp...Rain can significantly degrade the wind vector retrieval from Precipitation Radar （PR） by three mechanisms, namely, two-way rain attenuation, rain volume-backscattering, and ocean surface roughening from the rain splash effect. Here we first derive the radar equation for PR in rainy conditions. Then we use the rain attenuation model for Ku band, volume backscatter model for spherical raindrops and PR-TMI （TRMM Microwave Imager, TMI） matchup datasets from June to August in 2010 to solve the radar equation, and quantitatively analyze the influence of rainfall on PR radar measurement of ocean surface wind speed. Our results show that the significant effect of rain on radar signal is dominated by two-way rain attenuation and rain splash effect, and the effect of rain volume-backscattering is relatively the weakest, which can even be neglected in rain-weak conditions. Moreover, both the two-way rain attenuation and rain splash effect increase with the increasing of integration rain rate and in- cident angle. Last, we combine volume-backscattering effect and splash effect into a simple phenomenological model for rain calibration and select three typhoon cases from June to August in 2012 to verify the accuracy of this model. Before calibration, the mean difference and mean square error （MSE） between PR-observed σ0 and wind-induced σσ are about 2.95 dB and 3.10 dB respectively. However, after calibration, the mean difference and MSE are reduced to 0.64 dB and 1.61 dB respectively. The model yields an accurate calibration for PR near-nadir normalized radar cross section （NRCS） in rainy conditions.展开更多
基金Supported by the High-Tech Research and Developmenl Program of China (863 Program, No. 2001AA633070 2003AA604040)and the National Natural Science Foundation of China (No. 40476015).
文摘Two wind algorithms of ENVISAT advanced synthetic aperture radar （ASAR）, i. e. CMOD4 model from the European Space Agency （ESA） and CMOD_IFR2 model from Quilfen et al., are compared in this paper. The wind direction is estimated from orientation of low and linear signatures in the ASAR imagery. The wind direction has inherently a 180° ambiguity since only a single ASAR image is used. The 180° ambiguity is eliminated by using the buoy data from the NOAA （National Oceanic and Atmospheric Administration） buoys moored in the Pacific. Wind speed is obtained with the two wind algorithms using both estimated wind direction and normalized radar cross section （NRCS）. The retrieved wind results agree well with the data from Quikscat. The root mean square error （RMSE） of wind direction is 2.80°. The RMSEs of wind speed from CMOD4 model and CMOD_IFR2 model are 1.09 m/s and 0.60 m/s, respectively. The results indicate that the CMOD_IFR2 model is slight better than CMOD4 model at high wind.
基金supported by the National High Technology Research and Development Program of China (2013 AA09A505)
文摘The altimeter normalized radar cross section(NRCS) has been used to retrieve the sea surface wind speed for decades, and more than a dozen of wind speed retrieval algorithms have been proposed. Despite the continuing efforts to improve the wind speed measurements, a bias dependence on wave state persists in all wind algorithms. On the basis of recent evidence that short waves are essentially modulated by local winds and much less affected by wave state, we proposed a physics-based approach to retrieve the wind speed from the dual-frequency difference in terms of the mean square slope of short waves. A collocated dataset of coincident altimeter/buoy measurements were used to develop and validate the approach. Validation against buoy measurements indicates that the approach is almost unbiased and has an overall root mean square error of 1.24 m s-1, which is 5.3% lower than the single-parameter algorithm in operational use(Witter and Chelton, 1991) and 2.4% lower than another dual-frequency approach(Chen et al., 2002). Furthermore, the results indicate that the new approach significantly improves the wave-dependent bias compared to the single-parameter algorithm. The capacity of altimeter to retrieve sea surface wind speed appears to be limited for the case of winds below 3 m s-1. The validity of the approach at high winds needs to be further examined in the future study.
基金supported by National Natural Science Foundation of China(Grant No.11101421)State Oceanic Administration(Grant No.Y1H0810034)the Special Foundation for Young Scientists of Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences(Grant No.Y1S01500CX)
文摘Rain can significantly degrade the wind vector retrieval from Precipitation Radar （PR） by three mechanisms, namely, two-way rain attenuation, rain volume-backscattering, and ocean surface roughening from the rain splash effect. Here we first derive the radar equation for PR in rainy conditions. Then we use the rain attenuation model for Ku band, volume backscatter model for spherical raindrops and PR-TMI （TRMM Microwave Imager, TMI） matchup datasets from June to August in 2010 to solve the radar equation, and quantitatively analyze the influence of rainfall on PR radar measurement of ocean surface wind speed. Our results show that the significant effect of rain on radar signal is dominated by two-way rain attenuation and rain splash effect, and the effect of rain volume-backscattering is relatively the weakest, which can even be neglected in rain-weak conditions. Moreover, both the two-way rain attenuation and rain splash effect increase with the increasing of integration rain rate and in- cident angle. Last, we combine volume-backscattering effect and splash effect into a simple phenomenological model for rain calibration and select three typhoon cases from June to August in 2012 to verify the accuracy of this model. Before calibration, the mean difference and mean square error （MSE） between PR-observed σ0 and wind-induced σσ are about 2.95 dB and 3.10 dB respectively. However, after calibration, the mean difference and MSE are reduced to 0.64 dB and 1.61 dB respectively. The model yields an accurate calibration for PR near-nadir normalized radar cross section （NRCS） in rainy conditions.
