This study is aimed at improving multiple adaptive subtraction.We propose a modified pseudomulti-channel matching method based on the Huber norm,to adjust the matching differences on frequency and phase between the pr...This study is aimed at improving multiple adaptive subtraction.We propose a modified pseudomulti-channel matching method based on the Huber norm,to adjust the matching differences on frequency and phase between the predicted multi-ples and original data.The second-order derivative of the predicted multiples is utilized to replace the derivative of its Hilbert transform.Due to the additional frequency term,this method can enhance the high-frequency component.We introduce 180◦phase rotation of the multiple channels,which can decrease phase differences.The Huber norm interpolates between smooth L2 norm treatment of small residuals and robust L1 norm treatment of large residuals.This method can eliminate the restriction of large value conditions from the L2 norm and weaken the condition of orthogonality from the L1 norm.The applications of the Pluto and Delft models shows that compared with pseudomulti-channel matching filter,the main frequency is increased from 36 Hz to 38 Hz,and the primary reflection wave is more concentrated.The practical application of field data verifies the effectiveness of the proposed method.展开更多
High-quality and accurate precipitation estimations can be obtained by integrating precipitation information measures using ground-based and spaceborne radars in the same target area.Estimating the true precipitation ...High-quality and accurate precipitation estimations can be obtained by integrating precipitation information measures using ground-based and spaceborne radars in the same target area.Estimating the true precipitation state is a typical inverse problem for a given set of noisy radar precipitation observations.The regularization method can appropriately constrain the inverse problem to obtain a unique and stable solution.For different types of precipitation with different prior distributions,the L_(1) and L_(2) norms were more effective in constraining stratiform and convective precipitation,respectively.As a combination of L_(1) and L_(2) norms,the Huber norm is more suitable for mixed precipitation types.This study uses different regularization norms to combine precipitation data from the C-band dual-polarization ground radar(CDP)and dual-frequency precipitation radar(DPR)on the Global Precipitation Measurement(GPM)mission core satellite.Compared to single-source radar data,the fused figures contain more information and present a comprehensive precipitation structure encompassing the reflectivity and precipitation fields.In 27 precipitation cases,the fusion results utilizing the Huber norm achieved a structural similarity index measure(SSIM)and a peak signal-to-noise ratio(PSNR)of 0.8378 and 30.9322,respectively,compared with the CDP data.The fusion results showed that the Huber norm effectively amalgamate the features of convective and stratiform precipitation,with a reduction in the mean absolute error(MAE;16.1%and 22.6%,respectively)and root-mean-square error(RMSE;11.7%and 13.6%,respectively)compared to the 1-norm and 2-norm.Moreover,in contrast to the fusion results of scale recursive estimation(SRE),the Huber norm exhibits superior capability in capturing the localized precipitation intensity and reconstructing the detailed features of precipitation.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.300102268212)Postdoctoral Science Foundation(2013M540756,2014T70925)and Shaanxi Natural Science Foundation(2014JQ2-4019).
文摘This study is aimed at improving multiple adaptive subtraction.We propose a modified pseudomulti-channel matching method based on the Huber norm,to adjust the matching differences on frequency and phase between the predicted multi-ples and original data.The second-order derivative of the predicted multiples is utilized to replace the derivative of its Hilbert transform.Due to the additional frequency term,this method can enhance the high-frequency component.We introduce 180◦phase rotation of the multiple channels,which can decrease phase differences.The Huber norm interpolates between smooth L2 norm treatment of small residuals and robust L1 norm treatment of large residuals.This method can eliminate the restriction of large value conditions from the L2 norm and weaken the condition of orthogonality from the L1 norm.The applications of the Pluto and Delft models shows that compared with pseudomulti-channel matching filter,the main frequency is increased from 36 Hz to 38 Hz,and the primary reflection wave is more concentrated.The practical application of field data verifies the effectiveness of the proposed method.
基金Supported by the National Natural Science Foundation of China(General Program)(41975027)National Key Research and Development Program(2021YFC2802502)。
文摘High-quality and accurate precipitation estimations can be obtained by integrating precipitation information measures using ground-based and spaceborne radars in the same target area.Estimating the true precipitation state is a typical inverse problem for a given set of noisy radar precipitation observations.The regularization method can appropriately constrain the inverse problem to obtain a unique and stable solution.For different types of precipitation with different prior distributions,the L_(1) and L_(2) norms were more effective in constraining stratiform and convective precipitation,respectively.As a combination of L_(1) and L_(2) norms,the Huber norm is more suitable for mixed precipitation types.This study uses different regularization norms to combine precipitation data from the C-band dual-polarization ground radar(CDP)and dual-frequency precipitation radar(DPR)on the Global Precipitation Measurement(GPM)mission core satellite.Compared to single-source radar data,the fused figures contain more information and present a comprehensive precipitation structure encompassing the reflectivity and precipitation fields.In 27 precipitation cases,the fusion results utilizing the Huber norm achieved a structural similarity index measure(SSIM)and a peak signal-to-noise ratio(PSNR)of 0.8378 and 30.9322,respectively,compared with the CDP data.The fusion results showed that the Huber norm effectively amalgamate the features of convective and stratiform precipitation,with a reduction in the mean absolute error(MAE;16.1%and 22.6%,respectively)and root-mean-square error(RMSE;11.7%and 13.6%,respectively)compared to the 1-norm and 2-norm.Moreover,in contrast to the fusion results of scale recursive estimation(SRE),the Huber norm exhibits superior capability in capturing the localized precipitation intensity and reconstructing the detailed features of precipitation.
