Heilongjiang Province is the granary of China,which plays a key role in ensuring the national food security.The total grain output of Heilongjiang Province has ranked first in China for 12 consecutive years.In the pas...Heilongjiang Province is the granary of China,which plays a key role in ensuring the national food security.The total grain output of Heilongjiang Province has ranked first in China for 12 consecutive years.In the past four years,it has been stable at more than 75 billion kg,a record high.One bowl of rice in every nine bowls in China comes from Heilongjiang.The work of weather modification and disaster prevention and reduction is an important measure to ensure the development of agricultural production,and is the key of meteorological services for agriculture.Based on the actual work of artificial weather modification in Heilongjiang Province,this paper analyzes the current situation of ground operation in Heilongjiang Province,studies and judges the safety production,and puts forward reasonable countermeasures.The purpose is to improve the ground operation ability of artificial weather modification and provide safe and scientific services for agricultural production.展开更多
The airborne two-dimensional stereo(2D-S) optical array probe has been operating for more than 10 yr, accumulating a large amount of cloud particle image data. However, due to the lack of reliable and unbiased classif...The airborne two-dimensional stereo(2D-S) optical array probe has been operating for more than 10 yr, accumulating a large amount of cloud particle image data. However, due to the lack of reliable and unbiased classification tools,our ability to extract meaningful morphological information related to cloud microphysical processes is limited. To solve this issue, we propose a novel classification algorithm for 2D-S cloud particle images based on a convolutional neural network(CNN), named CNN-2DS. A 2D-S cloud particle shape dataset was established by using the 2D-S cloud particle images observed from 13 aircraft detection flights in 6 regions of China(Northeast, Northwest, North,East, Central, and South China). This dataset contains 33,300 cloud particle images with 8 types of cloud particle shape(linear, sphere, dendrite, aggregate, graupel, plate, donut, and irregular). The CNN-2DS model was trained and tested based on the established 2D-S dataset. Experimental results show that the CNN-2DS model can accurately identify cloud particles with an average classification accuracy of 97%. Compared with other common classification models [e.g., Vision Transformer(ViT) and Residual Neural Network(ResNet)], the CNN-2DS model is lightweight(few parameters) and fast in calculations, and has the highest classification accuracy. In a word, the proposed CNN-2DS model is effective and reliable for the classification of cloud particles detected by the 2D-S probe.展开更多
基金Supported by the Project of Heilongjiang Meteorological Bureau(HQZC2018043)。
文摘Heilongjiang Province is the granary of China,which plays a key role in ensuring the national food security.The total grain output of Heilongjiang Province has ranked first in China for 12 consecutive years.In the past four years,it has been stable at more than 75 billion kg,a record high.One bowl of rice in every nine bowls in China comes from Heilongjiang.The work of weather modification and disaster prevention and reduction is an important measure to ensure the development of agricultural production,and is the key of meteorological services for agriculture.Based on the actual work of artificial weather modification in Heilongjiang Province,this paper analyzes the current situation of ground operation in Heilongjiang Province,studies and judges the safety production,and puts forward reasonable countermeasures.The purpose is to improve the ground operation ability of artificial weather modification and provide safe and scientific services for agricultural production.
基金Supported by the National Key Research and Development Program of China (2019YFC1510301)Key Innovation Team Fund of the China Meteorological Administration (CMA2022ZD10)Basic Research Fund of the Chinese Academy of Meteorological Sciences(2021Y010)。
文摘The airborne two-dimensional stereo(2D-S) optical array probe has been operating for more than 10 yr, accumulating a large amount of cloud particle image data. However, due to the lack of reliable and unbiased classification tools,our ability to extract meaningful morphological information related to cloud microphysical processes is limited. To solve this issue, we propose a novel classification algorithm for 2D-S cloud particle images based on a convolutional neural network(CNN), named CNN-2DS. A 2D-S cloud particle shape dataset was established by using the 2D-S cloud particle images observed from 13 aircraft detection flights in 6 regions of China(Northeast, Northwest, North,East, Central, and South China). This dataset contains 33,300 cloud particle images with 8 types of cloud particle shape(linear, sphere, dendrite, aggregate, graupel, plate, donut, and irregular). The CNN-2DS model was trained and tested based on the established 2D-S dataset. Experimental results show that the CNN-2DS model can accurately identify cloud particles with an average classification accuracy of 97%. Compared with other common classification models [e.g., Vision Transformer(ViT) and Residual Neural Network(ResNet)], the CNN-2DS model is lightweight(few parameters) and fast in calculations, and has the highest classification accuracy. In a word, the proposed CNN-2DS model is effective and reliable for the classification of cloud particles detected by the 2D-S probe.
