The ice-phase microphysical characteristics of a stratiform cloud system over the Qilian Mountains in northwestern China on 15 September 2022 were analyzed via aircraft data.The stratiform cloud system developed under...The ice-phase microphysical characteristics of a stratiform cloud system over the Qilian Mountains in northwestern China on 15 September 2022 were analyzed via aircraft data.The stratiform cloud system developed under southwesterly flows at 500 hPa and was affected locally by topography.Synoptic features and aircraft observations revealed strengthened cloud development on the leeward slope.The ice particle habits and microphysical processes at heights of 6-8 km were investigated.The cloud system was characterized by extremely low supercooled liquid water content at temperatures between−4℃ and−17℃.The ice particle concentrations ranged predominantly from 10 to 30 L^(−1),corresponding to ice water content ranging from 0.01 to 0.05 g m^(−3).Active ice aggregation was observed at temperatures colder than−10°C.The windward side of the cloud system exhibited weaker development and two distinct cloud layers.Intense orographic uplift on the leeward slope enhanced ice particle aggregation.The clouds on the leeside presented lower ice particle concentrations but larger sizes than those on the windward side.The influence of aggregation on the ice particle size distribution was reflected in two main aspects.One aspect was the bimodal spectra at−16℃,with the first peak at 125μm and subpeak at 400-500μm;the other was the broadened size spectra at−13℃ due to significant aggregation of dendrites.展开更多
The extraordinary Super Typhoon(STY)Muifa(2022)made landfall four times and had a significant impact on the coastal regions from south to north of China.Although previous studies have demonstrated the‘pumping effect&...The extraordinary Super Typhoon(STY)Muifa(2022)made landfall four times and had a significant impact on the coastal regions from south to north of China.Although previous studies have demonstrated the‘pumping effect'of typhoons on the enhancement of reactive nitrogen(Nr)wet deposition over the ocean,it is uncertain how Nr deposition is influenced by typhoons thatmake prolonged mechanism due tomultiple landfalls.In this study,theNr wet deposition induced by STYMuifawas investigated fromthe perspective of in-and below-cloud processes based on the Nested Air Quality Prediction Modeling System with an online tracer-tagging module.High volume of Nr wet deposition caused by Muifa migrated from south to north,passing over half of China's coastal cities.Compared to the typhoon generated vicinity,both mean values of the oxidized and reduced nitrogen wet deposition over the Typhoon affected regions were increased about 20.4 and 66.1 times after landfall even with the similar rainfall.Emissions from the four landfall areas of China contributed to the majority of Nr wet deposition with significantly enhanced proportion of in-cloud deposition.The strong pumping effect of typhoon to the Nr deposition along the coastal areas and the risk of ecosystem effects requires further researches and higher demands on the control of nitrogen emissions of National Industrial Park,which usually located in China's coastal cities.展开更多
To evaluate the ability of the Predicted Particle Properties(P3)scheme in the Weather Research and Forecasting(WRF)model,we simulated a stratiform rainfall event over northern China on 22 May 2017.WRF simulations with...To evaluate the ability of the Predicted Particle Properties(P3)scheme in the Weather Research and Forecasting(WRF)model,we simulated a stratiform rainfall event over northern China on 22 May 2017.WRF simulations with two P3 versions,P3-nc and P3-2ice,were evaluated against rain gauge,radar,and aircraft observations.A series of sensitivity experiments were conducted with different collection efficiencies between ice and cloud droplets.The comparison of the precipitation evolution between P3-nc and P3-2ice suggested that both P3 versions overpredicted surface precipitation along the Taihang Mountains but underpredicted precipitation in the localized region on the leeward side.P3-2ice had slightly lower peak precipitation rates and smaller total precipitation amounts than P3-nc,which were closer to the observations.P3-2ice also more realistically reproduced the overall reflectivity structures than P3-nc.A comparison of ice concentrations with observations indicated that P3-nc underestimated aggregation,whereas P3-2ice produced more active aggregation from the self-collection of ice and ice-ice collisions between categories.Efficient aggregation in P3-2ice resulted in lower ice concentrations at heights between 4 and 6 km,which was closer to the observations.In this case,the total precipitation and precipitation pattern were not sensitive to riming.Riming was important in reproducing the location and strength of the embedded convective region through its impact on ice mass flux above the melting level.展开更多
Cloud effective radius(CER)is a fundamental microphysical property of clouds,critical for understanding cloud formation and radiative effects.Satellite spectral imagers,widely utilized in passive remote sensing,facili...Cloud effective radius(CER)is a fundamental microphysical property of clouds,critical for understanding cloud formation and radiative effects.Satellite spectral imagers,widely utilized in passive remote sensing,facilitate the monitoring of cloud characteristics,including CER,over extended time periods and spatial scales.Various observational methods have been employed to evaluate satellite cloud property products;however,in situ measurement evaluations of CER remain limited,particularly for products over China.This study utilized aircraft observations provided by the China Meteorological Administration Weather Modification Centre to evaluate the CER retrieved by Fengyun-4A Advanced Geosynchronous Radiation Imager(AGRI)and Himawari-8 Advanced Himawari Imager(AHI).Three flights were selected from the aircraft dataset for evaluation,involving flights through non-precipitating stratiform clouds with stable,high-quality measurements.Rigorous data selection and collocation procedures were employed to ensure a comprehensive comparison.Satellite retrievals from heterogeneous cloud fields were excluded,and representative in-cloud aircraft measurements were identified through multi-parameter filtering.The flight trajectory was adjusted to account for horizontal cloud movement corresponding to time differences between observations from different platforms.Additionally,in situ measurements from different vertical layers were adjusted to a comparable position near the cloud top.Results indicate that CER retrieved from satellites is generally overestimated compared to in situ measurements.For AGRI,the average difference(AD)is 2.90μm,with a root mean square difference(RMSD)of 3.53μm.For AHI,the AD is 2.92μm,and the RMSD is 3.59μm.To enhance future validation and evaluation of remote sensing results,factors such as instrument calibration,flight patterns,and cloud conditions will be carefully considered.Increasing the number of cases should further reduce errors associated with individual instances,enabling more precise assessments.展开更多
In this study,we employed a three-dimensional mesoscale cold-cloud seeding model to simulate the microphysical impacts of artificial ice crystals used as cloud seeding catalysts.Our objective was to elucidate the mech...In this study,we employed a three-dimensional mesoscale cold-cloud seeding model to simulate the microphysical impacts of artificial ice crystals used as cloud seeding catalysts.Our objective was to elucidate the mechanism of snowfall enhancement in stratiform clouds in the Bayanbulak test area of Xinjiang,China.The results indicated that the optimal seeding time was the early stages of weather system development.In this case,the optimal seeding zone was identified as the northwest of the test area,especially near the cloud top(altitudes between 3500 and 4000 m,temperatures range−11 to−15℃),and the ideal concentration of catalyst was with ice crystal density of 1.0×10^(7)kg^(−1)within the target area.Under such conditions,the total precipitation rate in the seeding-affected area increased to 50.1 mm h^(−1).The results also showed that the favorable seeding region was featured by high content of supercooled water and low population of natural ice crystals,where artificial ice crystals could substantially increase the snowfall.This augmentation typically appeared in a unimodal pattern,with the peak formed within 2–3 h after seeding.Seeding in the ice–water mixed zone of a supercooled cloud facilitated rapid ice crystal growth to snow-flake pieces via the Bergeron process,which in turn consumed more supercooled water via collision–coalescence with cloud water droplets.Simultaneously,the intensive consumption of supercooled water impeded the riming process and reduced the formation of graupel particles within the cloud.The dispersion of artificial ice crystals extended over tens of kilometers horizontally;however,in the vertical direction most particles remained approximately 1 km below the seeding layer,due to limited vertical ascent rate in the stratiform clouds restricting upward movement of artificial ice crystals.The above results help better understand the snowfall enhancement mechanism in stratiform clouds and facilitate related weather modification practice.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 National Natural Science Foundation of China(Grant Nos.42475100 and 42405091)supported by the CMA Key Innovation Team(Grant No.CMA2022ZD10)+1 种基金the CMA Weather Modification Centre Innovation Team(Grant No.WMC2023IT02)the National Key R&D Program of China(Grant No.2019YFC1510305).
文摘The ice-phase microphysical characteristics of a stratiform cloud system over the Qilian Mountains in northwestern China on 15 September 2022 were analyzed via aircraft data.The stratiform cloud system developed under southwesterly flows at 500 hPa and was affected locally by topography.Synoptic features and aircraft observations revealed strengthened cloud development on the leeward slope.The ice particle habits and microphysical processes at heights of 6-8 km were investigated.The cloud system was characterized by extremely low supercooled liquid water content at temperatures between−4℃ and−17℃.The ice particle concentrations ranged predominantly from 10 to 30 L^(−1),corresponding to ice water content ranging from 0.01 to 0.05 g m^(−3).Active ice aggregation was observed at temperatures colder than−10°C.The windward side of the cloud system exhibited weaker development and two distinct cloud layers.Intense orographic uplift on the leeward slope enhanced ice particle aggregation.The clouds on the leeside presented lower ice particle concentrations but larger sizes than those on the windward side.The influence of aggregation on the ice particle size distribution was reflected in two main aspects.One aspect was the bimodal spectra at−16℃,with the first peak at 125μm and subpeak at 400-500μm;the other was the broadened size spectra at−13℃ due to significant aggregation of dendrites.
基金supported by the National Natural Science Foundation of China(Nos.42122049 and 42377104)the Basic Strengthening Research Program(No.2021-JCJQ-JJ-1058)+1 种基金the Strategy Priority Research Programof Chinese Academy of Sciences(No.XDB0760403)the National Key Scientific and Technological Infrastructure project"Earth System Science Numerical Simulator Facility"(EarthLab)the Innovation Foundation of CPML/CMA(No.2024CPML-C027).
文摘The extraordinary Super Typhoon(STY)Muifa(2022)made landfall four times and had a significant impact on the coastal regions from south to north of China.Although previous studies have demonstrated the‘pumping effect'of typhoons on the enhancement of reactive nitrogen(Nr)wet deposition over the ocean,it is uncertain how Nr deposition is influenced by typhoons thatmake prolonged mechanism due tomultiple landfalls.In this study,theNr wet deposition induced by STYMuifawas investigated fromthe perspective of in-and below-cloud processes based on the Nested Air Quality Prediction Modeling System with an online tracer-tagging module.High volume of Nr wet deposition caused by Muifa migrated from south to north,passing over half of China's coastal cities.Compared to the typhoon generated vicinity,both mean values of the oxidized and reduced nitrogen wet deposition over the Typhoon affected regions were increased about 20.4 and 66.1 times after landfall even with the similar rainfall.Emissions from the four landfall areas of China contributed to the majority of Nr wet deposition with significantly enhanced proportion of in-cloud deposition.The strong pumping effect of typhoon to the Nr deposition along the coastal areas and the risk of ecosystem effects requires further researches and higher demands on the control of nitrogen emissions of National Industrial Park,which usually located in China's coastal cities.
基金supported by the National Key R&D Program of China(2019YFC1510305)the National Natural Science Foundation of China(Grant Nos.41705119 and 41575131)+2 种基金Baojun CHEN also acknowledges support from the CMA Key Innovation Team(CMA2022ZD10)Qiujuan FENG was supported by the General Project of Natural Science Research in Shanxi Province(20210302123358)the Key Projects of Shanxi Meteorological Bureau(SXKZDDW20217104).
文摘To evaluate the ability of the Predicted Particle Properties(P3)scheme in the Weather Research and Forecasting(WRF)model,we simulated a stratiform rainfall event over northern China on 22 May 2017.WRF simulations with two P3 versions,P3-nc and P3-2ice,were evaluated against rain gauge,radar,and aircraft observations.A series of sensitivity experiments were conducted with different collection efficiencies between ice and cloud droplets.The comparison of the precipitation evolution between P3-nc and P3-2ice suggested that both P3 versions overpredicted surface precipitation along the Taihang Mountains but underpredicted precipitation in the localized region on the leeward side.P3-2ice had slightly lower peak precipitation rates and smaller total precipitation amounts than P3-nc,which were closer to the observations.P3-2ice also more realistically reproduced the overall reflectivity structures than P3-nc.A comparison of ice concentrations with observations indicated that P3-nc underestimated aggregation,whereas P3-2ice produced more active aggregation from the self-collection of ice and ice-ice collisions between categories.Efficient aggregation in P3-2ice resulted in lower ice concentrations at heights between 4 and 6 km,which was closer to the observations.In this case,the total precipitation and precipitation pattern were not sensitive to riming.Riming was important in reproducing the location and strength of the embedded convective region through its impact on ice mass flux above the melting level.
基金Supported by the National Natural Science Foundation of China(42122038 and 42230604)。
文摘Cloud effective radius(CER)is a fundamental microphysical property of clouds,critical for understanding cloud formation and radiative effects.Satellite spectral imagers,widely utilized in passive remote sensing,facilitate the monitoring of cloud characteristics,including CER,over extended time periods and spatial scales.Various observational methods have been employed to evaluate satellite cloud property products;however,in situ measurement evaluations of CER remain limited,particularly for products over China.This study utilized aircraft observations provided by the China Meteorological Administration Weather Modification Centre to evaluate the CER retrieved by Fengyun-4A Advanced Geosynchronous Radiation Imager(AGRI)and Himawari-8 Advanced Himawari Imager(AHI).Three flights were selected from the aircraft dataset for evaluation,involving flights through non-precipitating stratiform clouds with stable,high-quality measurements.Rigorous data selection and collocation procedures were employed to ensure a comprehensive comparison.Satellite retrievals from heterogeneous cloud fields were excluded,and representative in-cloud aircraft measurements were identified through multi-parameter filtering.The flight trajectory was adjusted to account for horizontal cloud movement corresponding to time differences between observations from different platforms.Additionally,in situ measurements from different vertical layers were adjusted to a comparable position near the cloud top.Results indicate that CER retrieved from satellites is generally overestimated compared to in situ measurements.For AGRI,the average difference(AD)is 2.90μm,with a root mean square difference(RMSD)of 3.53μm.For AHI,the AD is 2.92μm,and the RMSD is 3.59μm.To enhance future validation and evaluation of remote sensing results,factors such as instrument calibration,flight patterns,and cloud conditions will be carefully considered.Increasing the number of cases should further reduce errors associated with individual instances,enabling more precise assessments.
基金Supported by the Scientific Research Project of the Bayingol Mongolian Autonomous Prefecture in Xinjiang(202318)China Meteorological Administration(CMA)Weather Modification Centre Innovation Team Project(WMC2023IT01)CMA Key Innovation Team Project(CMA2022ZD10).
文摘In this study,we employed a three-dimensional mesoscale cold-cloud seeding model to simulate the microphysical impacts of artificial ice crystals used as cloud seeding catalysts.Our objective was to elucidate the mechanism of snowfall enhancement in stratiform clouds in the Bayanbulak test area of Xinjiang,China.The results indicated that the optimal seeding time was the early stages of weather system development.In this case,the optimal seeding zone was identified as the northwest of the test area,especially near the cloud top(altitudes between 3500 and 4000 m,temperatures range−11 to−15℃),and the ideal concentration of catalyst was with ice crystal density of 1.0×10^(7)kg^(−1)within the target area.Under such conditions,the total precipitation rate in the seeding-affected area increased to 50.1 mm h^(−1).The results also showed that the favorable seeding region was featured by high content of supercooled water and low population of natural ice crystals,where artificial ice crystals could substantially increase the snowfall.This augmentation typically appeared in a unimodal pattern,with the peak formed within 2–3 h after seeding.Seeding in the ice–water mixed zone of a supercooled cloud facilitated rapid ice crystal growth to snow-flake pieces via the Bergeron process,which in turn consumed more supercooled water via collision–coalescence with cloud water droplets.Simultaneously,the intensive consumption of supercooled water impeded the riming process and reduced the formation of graupel particles within the cloud.The dispersion of artificial ice crystals extended over tens of kilometers horizontally;however,in the vertical direction most particles remained approximately 1 km below the seeding layer,due to limited vertical ascent rate in the stratiform clouds restricting upward movement of artificial ice crystals.The above results help better understand the snowfall enhancement mechanism in stratiform clouds and facilitate related weather modification practice.
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.
