A cloud clustering and classification algorithm is developed for a ground-based Ka-band radar system in the vertically pointing mode. Cloud profiles are grouped based on the combination of a time–height clustering me...A cloud clustering and classification algorithm is developed for a ground-based Ka-band radar system in the vertically pointing mode. Cloud profiles are grouped based on the combination of a time–height clustering method and the k-means clustering method. The cloud classification algorithm, developed using a fuzzy logic method, uses nine physical parameters to classify clouds into nine types: cirrostratus, cirrocumulus, altocumulus, altostratus, stratus, stratocumulus, nimbostratus,cumulus or cumulonimbus. The performance of the clustering and classification algorithm is presented by comparison with all-sky images taken from January to June 2014. Overall, 92% of the cloud profiles are clustered successfully and the agreement in classification between the radar system and the all-sky imager is 87%. The distribution of cloud types in Beijing from January 2014 to December 2017 is studied based on the clustering and classification algorithm. The statistics show that cirrostratus clouds have the highest occurrence frequency(24%) among the nine cloud types. High-level clouds have the maximum occurrence frequency and low-level clouds the minimum occurrence frequency.展开更多
In the complex countermeasure environment,the pulse description words(PDWs)of the same type of multi-function radar emitters are similar in multiple dimensions.Therefore,it is difficult for conventional methods to dei...In the complex countermeasure environment,the pulse description words(PDWs)of the same type of multi-function radar emitters are similar in multiple dimensions.Therefore,it is difficult for conventional methods to deinterleave such emitters.In order to solve this problem,a pulse deinterleaving method based on implicit features is proposed in this paper.The proposed method introduces long short-term memory(LSTM)neural networks and statistical analysis to mine new features from similar PDW features,that is,the variation law(implicit features)of pulse sequences of different radiation sources over time.The multi-function radar emitter is deinterleaved based on the pulse sequence variation law.Statistical results show that the proposed method not only achieves satisfactory performance,but also has good robustness.展开更多
In this study,cloud base height（CBH） and cloud top height（CTH） observed by the Ka-band（33.44 GHz） cloud radar at the Boseong National Center for Intensive Observation of Severe Weather during fall 2013（Septembe...In this study,cloud base height（CBH） and cloud top height（CTH） observed by the Ka-band（33.44 GHz） cloud radar at the Boseong National Center for Intensive Observation of Severe Weather during fall 2013（September-November） were verified and corrected.For comparative verification,CBH and CTH were obtained using a ceilometer（CL51） and the Communication,Ocean and Meteorological Satellite（COMS）.During rainfall,the CBH and CTH observed by the cloud radar were lower than observed by the ceilometer and COMS because of signal attenuation due to raindrops,and this difference increased with rainfall intensity.During dry periods,however,the CBH and CTH observed by the cloud radar,ceilometer,and COMS were similar.Thin and low-density clouds were observed more effectively by the cloud radar compared with the ceilometer and COMS.In cases of rainfall or missing cloud radar data,the ceilometer and COMS data were proven effective in correcting or compensating the cloud radar data.These corrected cloud data were used to classify cloud types,which revealed that low clouds occurred most frequently.展开更多
Based on radar penetrating measurements and analysis of sea ice in the Arctic Ocean, The potential of radar wave to measure sea ice thickness and map the morphology of the underside of sea ice is investigated. The res...Based on radar penetrating measurements and analysis of sea ice in the Arctic Ocean, The potential of radar wave to measure sea ice thickness and map the morphology of the underside of sea ice is investigated. The results indicate that the radar wave can penetrate Arctic summer sea ice of over 6 meters thick; and the propagation velocity of the radar wave in sea ice is in the range of 0.142 m·ns -1 to 0.154 m·ns -1 . The radar images display the roughness and micro-relief variation of sea ice bottom surface. These features are closely related to sea ice types, which show that radar survey may be used to identify and classify ice types. Since radar images can simultaneously display the linear profile features of both the upper surface and the underside of sea ice, we use these images to quantify their actual linear length discrepancy. A new length factor is suggested in relation to the actual linear length discrepancy in linear profiles of sea ice, which may be useful in further study of the area difference between the upper surface and bottom surface of sea ice.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos. 41775032 and 41275040)
文摘A cloud clustering and classification algorithm is developed for a ground-based Ka-band radar system in the vertically pointing mode. Cloud profiles are grouped based on the combination of a time–height clustering method and the k-means clustering method. The cloud classification algorithm, developed using a fuzzy logic method, uses nine physical parameters to classify clouds into nine types: cirrostratus, cirrocumulus, altocumulus, altostratus, stratus, stratocumulus, nimbostratus,cumulus or cumulonimbus. The performance of the clustering and classification algorithm is presented by comparison with all-sky images taken from January to June 2014. Overall, 92% of the cloud profiles are clustered successfully and the agreement in classification between the radar system and the all-sky imager is 87%. The distribution of cloud types in Beijing from January 2014 to December 2017 is studied based on the clustering and classification algorithm. The statistics show that cirrostratus clouds have the highest occurrence frequency(24%) among the nine cloud types. High-level clouds have the maximum occurrence frequency and low-level clouds the minimum occurrence frequency.
基金the National Major Research&Development project of China(2018YFE0206500)the National Natural Science Foundation of China(62071140)+1 种基金the Program of China International Scientific and Technological Cooperation(2015DFR10220)the Technology Foundation for Basic Enhancement Plan(2021-JCJQ-JJ-0301).
文摘In the complex countermeasure environment,the pulse description words(PDWs)of the same type of multi-function radar emitters are similar in multiple dimensions.Therefore,it is difficult for conventional methods to deinterleave such emitters.In order to solve this problem,a pulse deinterleaving method based on implicit features is proposed in this paper.The proposed method introduces long short-term memory(LSTM)neural networks and statistical analysis to mine new features from similar PDW features,that is,the variation law(implicit features)of pulse sequences of different radiation sources over time.The multi-function radar emitter is deinterleaved based on the pulse sequence variation law.Statistical results show that the proposed method not only achieves satisfactory performance,but also has good robustness.
基金supported by the principal project, “Development and application of technology for weather forecasting (NIMR-2012-B-1)” of the National Institute of Meteorological Sciences of the Korea Meteorological Administration
文摘In this study,cloud base height（CBH） and cloud top height（CTH） observed by the Ka-band（33.44 GHz） cloud radar at the Boseong National Center for Intensive Observation of Severe Weather during fall 2013（September-November） were verified and corrected.For comparative verification,CBH and CTH were obtained using a ceilometer（CL51） and the Communication,Ocean and Meteorological Satellite（COMS）.During rainfall,the CBH and CTH observed by the cloud radar were lower than observed by the ceilometer and COMS because of signal attenuation due to raindrops,and this difference increased with rainfall intensity.During dry periods,however,the CBH and CTH observed by the cloud radar,ceilometer,and COMS were similar.Thin and low-density clouds were observed more effectively by the cloud radar compared with the ceilometer and COMS.In cases of rainfall or missing cloud radar data,the ceilometer and COMS data were proven effective in correcting or compensating the cloud radar data.These corrected cloud data were used to classify cloud types,which revealed that low clouds occurred most frequently.
基金This work was supported by the National Natural Science Foundation of China(No.4007 1022,40231013)the Ministry of Science and technology,the People's Republic of China(No.2001DIA50040)Chinese Arctic expedition foundation and Laboratory foundation of Ice Core and Cold Region Environment,Cold and Arid Regions Environmental and Engineering Institute,Chinese Academy of Sciences(No.BX2001-04).
文摘Based on radar penetrating measurements and analysis of sea ice in the Arctic Ocean, The potential of radar wave to measure sea ice thickness and map the morphology of the underside of sea ice is investigated. The results indicate that the radar wave can penetrate Arctic summer sea ice of over 6 meters thick; and the propagation velocity of the radar wave in sea ice is in the range of 0.142 m·ns -1 to 0.154 m·ns -1 . The radar images display the roughness and micro-relief variation of sea ice bottom surface. These features are closely related to sea ice types, which show that radar survey may be used to identify and classify ice types. Since radar images can simultaneously display the linear profile features of both the upper surface and the underside of sea ice, we use these images to quantify their actual linear length discrepancy. A new length factor is suggested in relation to the actual linear length discrepancy in linear profiles of sea ice, which may be useful in further study of the area difference between the upper surface and bottom surface of sea ice.
