The upper air weather forecast data used in current business and research and digital data of the recently finished upper air meteorological monthly report were comparatively analyzed in complete data and quality cond...The upper air weather forecast data used in current business and research and digital data of the recently finished upper air meteorological monthly report were comparatively analyzed in complete data and quality condition of data, and sounding curve change caused by the difference of complete data was also compared, which evaluated advantages and disadvantages of two types of data.展开更多
A new method of detecting abnormal sounding data based on LS-SVM is presented.The theorem proves that the trend surface filter is the especial result of LS-SVM.In order to depict the relationship of trend surface filt...A new method of detecting abnormal sounding data based on LS-SVM is presented.The theorem proves that the trend surface filter is the especial result of LS-SVM.In order to depict the relationship of trend surface filter and LS-SVM,a contrast is given.The example shows that abnormal sounding data could be detected effectively by LS-SVM when the training samples and kernel function are reasonable.展开更多
Together,the heart and lung sound comprise the thoracic cavity sound,which provides informative details that reflect patient conditions,particularly heart failure(HF)patients.However,due to the limitations of human he...Together,the heart and lung sound comprise the thoracic cavity sound,which provides informative details that reflect patient conditions,particularly heart failure(HF)patients.However,due to the limitations of human hearing,a limited amount of information can be auscultated from thoracic cavity sounds.With the aid of artificial intelligence–machine learning,these features can be analyzed and aid in the care of HF patients.Machine learning of thoracic cavity sound data involves sound data pre-processing by denoising,resampling,segmentation,and normalization.Afterwards,the most crucial step is feature extraction and se-lection where relevant features are selected to train the model.The next step is classification and model performance evaluation.This review summarizes the currently available studies that utilized different machine learning models,different feature extraction and selection methods,and different classifiers to generate the desired output.Most studies have analyzed the heart sound component of thoracic cavity sound to distinguish between normal and HF patients.Additionally,some studies have aimed to classify HF patients based on thoracic cavity sounds in their entirety,while others have focused on risk strati-fication and prognostic evaluation of HF patients using thoracic cavity sounds.Overall,the results from these studies demonstrate a promisingly high level of accuracy.Therefore,future prospective studies should incorporate these machine learning models to expedite their integration into daily clinical practice for managing HF patients.展开更多
Field experimental seismic sounding permitted us to obtain optimal shallow seismic reflection sounding parameters.In process of data processing,we obtained a high-qualitative shallow seismic reflection sounding profil...Field experimental seismic sounding permitted us to obtain optimal shallow seismic reflection sounding parameters.In process of data processing,we obtained a high-qualitative shallow seismic reflection sounding profile by using the techniques such as filtering,edition surgical blanking,prediction deconvolution,fitting static correlation of first arrival time,and velocity analysis.Comprehensive analysis on the information of reflection wave groups along the seismic sounding profile and the stratigraphic and neogeochronological data obtained from many drills near the sounding line reveals that the upper termination of the detected fault zone is located at depth of 75~80 m,in the Middle Pleistocene deposits dated to be about 220 ka BP.The continuity,discontinuity,increasing and decreasing amount of reflection wave groups and change of their configurations,in combination with geological columns of drills,permitted us to know that the width of upper termination of the fault zone is 100 m.It can be inferred from the variation of number of reflection wave groups along the profile that the scarp of hidden fault is 200 m wide and the fault is a synsedimentary active fault in the Early Pleistocene and the early stage of Middle Pleistocene.No tectonic movement,which offset the covering deposits,had occurred since the late stage of Middle Pleistocene.展开更多
[Objective]The research aimed to discuss shallowly the application of L-band sounding seconds data in the artificial precipitation.[Method]The characteristics,getting manner and displaying method of L-band sounding se...[Objective]The research aimed to discuss shallowly the application of L-band sounding seconds data in the artificial precipitation.[Method]The characteristics,getting manner and displaying method of L-band sounding seconds data were introduced briefly.Moreover,its application prospect in the artificial precipitation operation was analyzed initially.We aimed to improve its application rate in the artificial precipitation operation.[Result]L-band sounding seconds data had the great improvement in the time-space resolution and the space positioning accuracy aspects when compared with the previous sounding data,and the precision reached the second level.It could provide the high-precision data basis for the assimilation of artificial precipitation numerical model initial field,and improve the numerical model.Moreover,the sounding product could provide the accurate scientific basis for the selection of artificial precipitation operation tool,the determination of operation height and range,and guide the artificial precipitation operation,and improve the operation efficiency.[Conclusion]The research provided the analysis and reference basis for the command of artificial precipitation operation.展开更多
Continuous thunderstorm occurring at Qingdao Airport in China from August 7 to 13,2022 was analyzed based on sounding data.The weather was divided into thunderstorm gale,thunderstorm and heavy precipitation,and some p...Continuous thunderstorm occurring at Qingdao Airport in China from August 7 to 13,2022 was analyzed based on sounding data.The weather was divided into thunderstorm gale,thunderstorm and heavy precipitation,and some physical quantities and time variables which can effectively identify severe convective weather types were preliminarily obtained.The results show that CAPE was sensitive to different types of weather,but the uncertainty was relatively large.Convective temperature T_(CON),temperature difference between 500 and 850 hPa,and vertical wind shear can distinguish thunderstorm gale,thunderstorm and heavy precipitation weather obviously.Besides,K index,Showalter index,θ_(se) difference between 500 and 850 hPa were also important basis to distinguish thunderstorm and heavy precipitation weather.Thunderstorm gale can be distinguished by the 24-hour variations of K index,and the difference of dew point between 500 and 850 hPa.The 24-hour variations of(T-T_(d))_(500) and vertical wind shear can be used to distinguish between heavy precipitation and thunderstorm weather;the 24-hour variation of stratification stability Δθ_(se) can distinguish the three kinds of weather well.For the wind field,the existence of vertical wind shear was required for strong convective weather,and the 24-hour increment of thunderstorm gale and thunderstorm was larger than that of heavy precipitation.展开更多
A new set of Infrared Atmospheric Sounding Interferometer （IASI） channels was re-selected from 314 EUMETSAT channels. In selecting channels, we calculated the impact of the individually added channel on the improvem...A new set of Infrared Atmospheric Sounding Interferometer （IASI） channels was re-selected from 314 EUMETSAT channels. In selecting channels, we calculated the impact of the individually added channel on the improvement in the analysis outputs from a one-dimensional variational analysis （1D-Var） for the Unified Model （UM） data assimilation system at the Met Office, using the channel score index （CSI） as a figure of merit. Then, 200 channels were selected in order by counting each individual channel＇s CSI contribution. Compared with the operationally used 183 channels for the UM at the Met Office, the new set shares 149 channels, while the other 51 channels are new. Also examined is the selection from the entropy reduction method with the same 1D-Var approach, Results suggest that channel selection can be made in a more objective fashion using the proposed CSI method. This is because the most important channels can be selected across the whole IASI observation spectrum. In the experimental trial runs using the UM global assimilation system, the new channels had an overall neutral impact in terms of improvement in forecasts, as compared with results from the operational channels. However, upper-tropospheric moist biases shown in the control run with operational channels were significantly reduced in the experimental trial with the newly selected channels. The reduction of moist biases was mainly due to the additional water vapor channels, which are sensitive to the upper-tropospheric water vapor.展开更多
As part of“The Earth Summit Mission-2022”during the second Tibetan Plateau Scientific Expedition and Research(STEP)in April and May 2022,we conducted the ozone sounding experiment(an ozonesonde mated to a radiosonde...As part of“The Earth Summit Mission-2022”during the second Tibetan Plateau Scientific Expedition and Research(STEP)in April and May 2022,we conducted the ozone sounding experiment(an ozonesonde mated to a radiosonde)at Mt.Qomolangma Base Camp(MQBC;86.85°E,28.14°N;5200 m),a location at an extremely high altitude.A total of ten sounding profiles were obtained between April 30 and May 06,2022,of which seven profiles were above35 km in altitude,with a maximum detection altitude up to 39.0 km.This study presents the temporal variation and vertical distributions of atmospheric temperature,humidity,and ozone during the MQBC campaign.The averaged ozone concentration was high(68.3 ppbv)at the surface and then increased smoothly until peaking(~110 ppbv)in the middle troposphere(approximately 10 km),and afterward,the ozone concentration increased rapidly from the upper troposphere to a maximum of~10 ppmv at~30 km.The enhanced ozone concentration in the middle troposphere was associated with the blocking high pressure,and transport from the southern flank of the Himalayas occurred during the campaign period.The average total ozone column was 291.9±21.4 DU for the seven profiles exceeding 35km in altitude.The ozonesonde measurements were also compared with the vertical ozone profiles retrieved from the space-borne ozone products from the Microwave Limb Sounder(MLS)onboard the Aura satellite and the Atmospheric Infrared Sounder(AIRS)onboard the Aqua satellite.展开更多
Ocean sound speed profile(SSP) is the key factor affecting acoustic propagation. The acquisition of SSPsin real time with high precision is meaningful for underwater activities. By means of the remote sensing method, ...Ocean sound speed profile(SSP) is the key factor affecting acoustic propagation. The acquisition of SSPsin real time with high precision is meaningful for underwater activities. By means of the remote sensing method, thesea surface data could be obtained in near-real time. Typically, the subsurface fields are correlated with the sea surfaceparameters. Thus, the SSPs could be obtained by means of satellite remote sensing. In this paper, the history as wellas the current research over the reconstruction of subsurface fields by means of sea surface data is introduced. Thentwo methods to reconstruct the SSPs with sea surface data, including the linear regression method using the empiricalorthogonal function, and the self-organizing method based on the big data theory, are described in detail in the paper.展开更多
Environmental sound classification(ESC)involves the process of distinguishing an audio stream associated with numerous environmental sounds.Some common aspects such as the framework difference,overlapping of different...Environmental sound classification(ESC)involves the process of distinguishing an audio stream associated with numerous environmental sounds.Some common aspects such as the framework difference,overlapping of different sound events,and the presence of various sound sources during recording make the ESC task much more complicated and complex.This research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation resources.In this research,the performance of transformer and convolutional neural networks(CNN)are investigated.Seven audio features,chromagram,Mel-spectrogram,tonnetz,Mel-Frequency Cepstral Coefficients(MFCCs),delta MFCCs,delta-delta MFCCs and spectral contrast,are extracted fromtheUrbanSound8K,ESC-50,and ESC-10,databases.Moreover,this research also employed three data enhancement methods,namely,white noise,pitch tuning,and time stretch to reduce the risk of overfitting issue due to the limited audio clips.The evaluation of various experiments demonstrates that the best performance was achieved by the proposed transformer model using seven audio features on enhanced database.For UrbanSound8K,ESC-50,and ESC-10,the highest attained accuracies are 0.98,0.94,and 0.97 respectively.The experimental results reveal that the proposed technique can achieve the best performance for ESC problems.展开更多
China's new generation of polar-orbiting meteorological satellite FY-3A was successfully launched on May 26,2008,carrying microwave sounding devices which had similar performance to ATOVS of NOAA series.In order t...China's new generation of polar-orbiting meteorological satellite FY-3A was successfully launched on May 26,2008,carrying microwave sounding devices which had similar performance to ATOVS of NOAA series.In order to study the application of microwave sounding data in numerical prediction of typhoons and to improve typhoon forecasting,we assimilated data directly for numerical forecasting of the track and intensity of the 2009 typhoon Morakot(0908)based on the WRF-3DVar system.Results showed that the initial fields of the numerical model due to direct assimilation of FY-3A microwave sounding data was improved much more than that due to assimilation of conventional observations alone,and the improvement was especially significant over the ocean,which is always without conventional observations.The model initial fields were more reasonable in reflecting the initial situation of typhoon circulation as well as temperature and humidity conditions,and typhoon central position at sea was also adjusted.Through direct 3DVar assimilation of FY-3A microwave data,the regional mesoscale model improves the forecasting of typhoon track.Therefore,the FY-3A microwave data could efficiently improve the numerical prediction of typhoons.展开更多
基金Supported by National Natural Science Foundation(40705025)~~
文摘The upper air weather forecast data used in current business and research and digital data of the recently finished upper air meteorological monthly report were comparatively analyzed in complete data and quality condition of data, and sounding curve change caused by the difference of complete data was also compared, which evaluated advantages and disadvantages of two types of data.
基金The National High-Tech Research and Development Program of China (863 Program) under contract No.2007AA12Z326the National Natural Science Foundation of China under contract Nos 40974010 and 40971306
文摘A new method of detecting abnormal sounding data based on LS-SVM is presented.The theorem proves that the trend surface filter is the especial result of LS-SVM.In order to depict the relationship of trend surface filter and LS-SVM,a contrast is given.The example shows that abnormal sounding data could be detected effectively by LS-SVM when the training samples and kernel function are reasonable.
文摘Together,the heart and lung sound comprise the thoracic cavity sound,which provides informative details that reflect patient conditions,particularly heart failure(HF)patients.However,due to the limitations of human hearing,a limited amount of information can be auscultated from thoracic cavity sounds.With the aid of artificial intelligence–machine learning,these features can be analyzed and aid in the care of HF patients.Machine learning of thoracic cavity sound data involves sound data pre-processing by denoising,resampling,segmentation,and normalization.Afterwards,the most crucial step is feature extraction and se-lection where relevant features are selected to train the model.The next step is classification and model performance evaluation.This review summarizes the currently available studies that utilized different machine learning models,different feature extraction and selection methods,and different classifiers to generate the desired output.Most studies have analyzed the heart sound component of thoracic cavity sound to distinguish between normal and HF patients.Additionally,some studies have aimed to classify HF patients based on thoracic cavity sounds in their entirety,while others have focused on risk strati-fication and prognostic evaluation of HF patients using thoracic cavity sounds.Overall,the results from these studies demonstrate a promisingly high level of accuracy.Therefore,future prospective studies should incorporate these machine learning models to expedite their integration into daily clinical practice for managing HF patients.
基金A High-new Technique Project by State Development and Planning Commission of China (2001977).
文摘Field experimental seismic sounding permitted us to obtain optimal shallow seismic reflection sounding parameters.In process of data processing,we obtained a high-qualitative shallow seismic reflection sounding profile by using the techniques such as filtering,edition surgical blanking,prediction deconvolution,fitting static correlation of first arrival time,and velocity analysis.Comprehensive analysis on the information of reflection wave groups along the seismic sounding profile and the stratigraphic and neogeochronological data obtained from many drills near the sounding line reveals that the upper termination of the detected fault zone is located at depth of 75~80 m,in the Middle Pleistocene deposits dated to be about 220 ka BP.The continuity,discontinuity,increasing and decreasing amount of reflection wave groups and change of their configurations,in combination with geological columns of drills,permitted us to know that the width of upper termination of the fault zone is 100 m.It can be inferred from the variation of number of reflection wave groups along the profile that the scarp of hidden fault is 200 m wide and the fault is a synsedimentary active fault in the Early Pleistocene and the early stage of Middle Pleistocene.No tectonic movement,which offset the covering deposits,had occurred since the late stage of Middle Pleistocene.
文摘[Objective]The research aimed to discuss shallowly the application of L-band sounding seconds data in the artificial precipitation.[Method]The characteristics,getting manner and displaying method of L-band sounding seconds data were introduced briefly.Moreover,its application prospect in the artificial precipitation operation was analyzed initially.We aimed to improve its application rate in the artificial precipitation operation.[Result]L-band sounding seconds data had the great improvement in the time-space resolution and the space positioning accuracy aspects when compared with the previous sounding data,and the precision reached the second level.It could provide the high-precision data basis for the assimilation of artificial precipitation numerical model initial field,and improve the numerical model.Moreover,the sounding product could provide the accurate scientific basis for the selection of artificial precipitation operation tool,the determination of operation height and range,and guide the artificial precipitation operation,and improve the operation efficiency.[Conclusion]The research provided the analysis and reference basis for the command of artificial precipitation operation.
文摘Continuous thunderstorm occurring at Qingdao Airport in China from August 7 to 13,2022 was analyzed based on sounding data.The weather was divided into thunderstorm gale,thunderstorm and heavy precipitation,and some physical quantities and time variables which can effectively identify severe convective weather types were preliminarily obtained.The results show that CAPE was sensitive to different types of weather,but the uncertainty was relatively large.Convective temperature T_(CON),temperature difference between 500 and 850 hPa,and vertical wind shear can distinguish thunderstorm gale,thunderstorm and heavy precipitation weather obviously.Besides,K index,Showalter index,θ_(se) difference between 500 and 850 hPa were also important basis to distinguish thunderstorm and heavy precipitation weather.Thunderstorm gale can be distinguished by the 24-hour variations of K index,and the difference of dew point between 500 and 850 hPa.The 24-hour variations of(T-T_(d))_(500) and vertical wind shear can be used to distinguish between heavy precipitation and thunderstorm weather;the 24-hour variation of stratification stability Δθ_(se) can distinguish the three kinds of weather well.For the wind field,the existence of vertical wind shear was required for strong convective weather,and the 24-hour increment of thunderstorm gale and thunderstorm was larger than that of heavy precipitation.
基金supported by the KMA Research and Development Program under Grant No.KMIPA 20151060supported by the BK21 Plus Project of the Korean government
文摘A new set of Infrared Atmospheric Sounding Interferometer （IASI） channels was re-selected from 314 EUMETSAT channels. In selecting channels, we calculated the impact of the individually added channel on the improvement in the analysis outputs from a one-dimensional variational analysis （1D-Var） for the Unified Model （UM） data assimilation system at the Met Office, using the channel score index （CSI） as a figure of merit. Then, 200 channels were selected in order by counting each individual channel＇s CSI contribution. Compared with the operationally used 183 channels for the UM at the Met Office, the new set shares 149 channels, while the other 51 channels are new. Also examined is the selection from the entropy reduction method with the same 1D-Var approach, Results suggest that channel selection can be made in a more objective fashion using the proposed CSI method. This is because the most important channels can be selected across the whole IASI observation spectrum. In the experimental trial runs using the UM global assimilation system, the new channels had an overall neutral impact in terms of improvement in forecasts, as compared with results from the operational channels. However, upper-tropospheric moist biases shown in the control run with operational channels were significantly reduced in the experimental trial with the newly selected channels. The reduction of moist biases was mainly due to the additional water vapor channels, which are sensitive to the upper-tropospheric water vapor.
基金supported by the second Tibetan Plateau Scientific Expedition and Research Program(STEP,2019QZKK0606,2019QZKK0604)the National Natural Science Foundation of China(Grant No.41875183)。
文摘As part of“The Earth Summit Mission-2022”during the second Tibetan Plateau Scientific Expedition and Research(STEP)in April and May 2022,we conducted the ozone sounding experiment(an ozonesonde mated to a radiosonde)at Mt.Qomolangma Base Camp(MQBC;86.85°E,28.14°N;5200 m),a location at an extremely high altitude.A total of ten sounding profiles were obtained between April 30 and May 06,2022,of which seven profiles were above35 km in altitude,with a maximum detection altitude up to 39.0 km.This study presents the temporal variation and vertical distributions of atmospheric temperature,humidity,and ozone during the MQBC campaign.The averaged ozone concentration was high(68.3 ppbv)at the surface and then increased smoothly until peaking(~110 ppbv)in the middle troposphere(approximately 10 km),and afterward,the ozone concentration increased rapidly from the upper troposphere to a maximum of~10 ppmv at~30 km.The enhanced ozone concentration in the middle troposphere was associated with the blocking high pressure,and transport from the southern flank of the Himalayas occurred during the campaign period.The average total ozone column was 291.9±21.4 DU for the seven profiles exceeding 35km in altitude.The ozonesonde measurements were also compared with the vertical ozone profiles retrieved from the space-borne ozone products from the Microwave Limb Sounder(MLS)onboard the Aura satellite and the Atmospheric Infrared Sounder(AIRS)onboard the Aqua satellite.
文摘Ocean sound speed profile(SSP) is the key factor affecting acoustic propagation. The acquisition of SSPsin real time with high precision is meaningful for underwater activities. By means of the remote sensing method, thesea surface data could be obtained in near-real time. Typically, the subsurface fields are correlated with the sea surfaceparameters. Thus, the SSPs could be obtained by means of satellite remote sensing. In this paper, the history as wellas the current research over the reconstruction of subsurface fields by means of sea surface data is introduced. Thentwo methods to reconstruct the SSPs with sea surface data, including the linear regression method using the empiricalorthogonal function, and the self-organizing method based on the big data theory, are described in detail in the paper.
基金the Taif University Researchers Supporting Project number(TURSP-2020/36),Taif University,Taif,Saudi Arabia.
文摘Environmental sound classification(ESC)involves the process of distinguishing an audio stream associated with numerous environmental sounds.Some common aspects such as the framework difference,overlapping of different sound events,and the presence of various sound sources during recording make the ESC task much more complicated and complex.This research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation resources.In this research,the performance of transformer and convolutional neural networks(CNN)are investigated.Seven audio features,chromagram,Mel-spectrogram,tonnetz,Mel-Frequency Cepstral Coefficients(MFCCs),delta MFCCs,delta-delta MFCCs and spectral contrast,are extracted fromtheUrbanSound8K,ESC-50,and ESC-10,databases.Moreover,this research also employed three data enhancement methods,namely,white noise,pitch tuning,and time stretch to reduce the risk of overfitting issue due to the limited audio clips.The evaluation of various experiments demonstrates that the best performance was achieved by the proposed transformer model using seven audio features on enhanced database.For UrbanSound8K,ESC-50,and ESC-10,the highest attained accuracies are 0.98,0.94,and 0.97 respectively.The experimental results reveal that the proposed technique can achieve the best performance for ESC problems.
基金EXPO special Project(10dz0581300)Natural Science Fund from Science and Technology Commission of Shanghai Municipality(09ZR1428700)National Department(Meteorology)Public Benefit Research Foundation(GYHY200906002)
文摘China's new generation of polar-orbiting meteorological satellite FY-3A was successfully launched on May 26,2008,carrying microwave sounding devices which had similar performance to ATOVS of NOAA series.In order to study the application of microwave sounding data in numerical prediction of typhoons and to improve typhoon forecasting,we assimilated data directly for numerical forecasting of the track and intensity of the 2009 typhoon Morakot(0908)based on the WRF-3DVar system.Results showed that the initial fields of the numerical model due to direct assimilation of FY-3A microwave sounding data was improved much more than that due to assimilation of conventional observations alone,and the improvement was especially significant over the ocean,which is always without conventional observations.The model initial fields were more reasonable in reflecting the initial situation of typhoon circulation as well as temperature and humidity conditions,and typhoon central position at sea was also adjusted.Through direct 3DVar assimilation of FY-3A microwave data,the regional mesoscale model improves the forecasting of typhoon track.Therefore,the FY-3A microwave data could efficiently improve the numerical prediction of typhoons.
