In this paper,we utilized the deep convolutional neural network D-LinkNet,a model for semantic segmentation,to analyze the Himawari-8 satellite data captured from 16 channels at a spatial resolution of 0.5 km,with a f...In this paper,we utilized the deep convolutional neural network D-LinkNet,a model for semantic segmentation,to analyze the Himawari-8 satellite data captured from 16 channels at a spatial resolution of 0.5 km,with a focus on the area over the Yellow Sea and the Bohai Sea(32°-42°N,117°-127°E).The objective was to develop an algorithm for fusing and segmenting multi-channel images from geostationary meteorological satellites,specifically for monitoring sea fog in this region.Firstly,the extreme gradient boosting algorithm was adopted to evaluate the data from the 16 channels of the Himawari-8 satellite for sea fog detection,and we found that the top three channels in order of importance were channels 3,4,and 14,which were fused into false color daytime images,while channels 7,13,and 15 were fused into false color nighttime images.Secondly,the simple linear iterative super-pixel clustering algorithm was used for the pixel-level segmentation of false color images,and based on super-pixel blocks,manual sea-fog annotation was performed to obtain fine-grained annotation labels.The deep convolutional neural network D-LinkNet was built on the ResNet backbone and the dilated convolutional layers with direct connections were added in the central part to form a string-and-combine structure with five branches having different depths and receptive fields.Results show that the accuracy rate of fog area(proportion of detected real fog to detected fog)was 66.5%,the recognition rate of fog zone(proportion of detected real fog to real fog or cloud cover)was 51.9%,and the detection accuracy rate(proportion of samples detected correctly to total samples)was 93.2%.展开更多
Based on prior investigation,this work defined a new thermodynamic shear advection parameter,which combines the vertical component of convective vorticity vector,horizontal divergence,and vertical gradient of generali...Based on prior investigation,this work defined a new thermodynamic shear advection parameter,which combines the vertical component of convective vorticity vector,horizontal divergence,and vertical gradient of generalized potential temperature.The interaction between waves and fundamental states was computed for the heavyrainfall event generated by landfalling typhoon“Morakot”.The analysis data was produced by ADAS[ARPS(Advanced Regional Prediction System)Data Analysis System]combined with the NCEP/NCAR final analysis data(1°×1°,26 vertical pressure levels and 6-hour interval)with the routine observations of surface and sounding.Because it may describe the typical vertical structure of dynamical and thermodynamic fields,the result indicates that the parameter is intimately related to precipitation systems.The parameter’s positive high-value area closely matches the reported 6-hour accumulated surface rainfall.And the statistical analysis reveals a certain correspondence between the thermodynamic shear advection parameter and the observed 6-hour accumulated surface rainfall in the summer of 2009.This implies that the parameter can predict and indicate the rainfall area,as well as the initiation and evolution of precipitation systems.展开更多
基金National Key R&D Program of China(2021YFC3000905)Open Research Program of the State Key Laboratory of Severe Weather(2022LASW-B09)National Natural Science Foundation of China(42375010)。
文摘In this paper,we utilized the deep convolutional neural network D-LinkNet,a model for semantic segmentation,to analyze the Himawari-8 satellite data captured from 16 channels at a spatial resolution of 0.5 km,with a focus on the area over the Yellow Sea and the Bohai Sea(32°-42°N,117°-127°E).The objective was to develop an algorithm for fusing and segmenting multi-channel images from geostationary meteorological satellites,specifically for monitoring sea fog in this region.Firstly,the extreme gradient boosting algorithm was adopted to evaluate the data from the 16 channels of the Himawari-8 satellite for sea fog detection,and we found that the top three channels in order of importance were channels 3,4,and 14,which were fused into false color daytime images,while channels 7,13,and 15 were fused into false color nighttime images.Secondly,the simple linear iterative super-pixel clustering algorithm was used for the pixel-level segmentation of false color images,and based on super-pixel blocks,manual sea-fog annotation was performed to obtain fine-grained annotation labels.The deep convolutional neural network D-LinkNet was built on the ResNet backbone and the dilated convolutional layers with direct connections were added in the central part to form a string-and-combine structure with five branches having different depths and receptive fields.Results show that the accuracy rate of fog area(proportion of detected real fog to detected fog)was 66.5%,the recognition rate of fog zone(proportion of detected real fog to real fog or cloud cover)was 51.9%,and the detection accuracy rate(proportion of samples detected correctly to total samples)was 93.2%.
基金National Key R&D Program of China(2017YFC1501604)Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province(SCSF202101)+1 种基金Open Grants of the State Key Laboratory of Severe Weather(2022LASW-B09)National Natural Science Foundation of China(41405049)。
文摘Based on prior investigation,this work defined a new thermodynamic shear advection parameter,which combines the vertical component of convective vorticity vector,horizontal divergence,and vertical gradient of generalized potential temperature.The interaction between waves and fundamental states was computed for the heavyrainfall event generated by landfalling typhoon“Morakot”.The analysis data was produced by ADAS[ARPS(Advanced Regional Prediction System)Data Analysis System]combined with the NCEP/NCAR final analysis data(1°×1°,26 vertical pressure levels and 6-hour interval)with the routine observations of surface and sounding.Because it may describe the typical vertical structure of dynamical and thermodynamic fields,the result indicates that the parameter is intimately related to precipitation systems.The parameter’s positive high-value area closely matches the reported 6-hour accumulated surface rainfall.And the statistical analysis reveals a certain correspondence between the thermodynamic shear advection parameter and the observed 6-hour accumulated surface rainfall in the summer of 2009.This implies that the parameter can predict and indicate the rainfall area,as well as the initiation and evolution of precipitation systems.
