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Effects of nuclear chimney on climate

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Journal of Environmental sciences 1990年第4期2卷 111-116页

作者： gao suhua, ding yihui, pan yaru and Zhang Qingchenacademy of meteorological science state meteorological administration,beijing 100081,china

In this paper, according to the data from several single nuclear explosion experiments of an equivalent to magnitude of million tons, and combined with meteorological data, their effects on the regional climate nave b... 详细信息

关键词： nuclear chimney nuclear explosion temperature change.

Monitoring Sea Fog over the Yellow Sea and Bohai Bay Based on Deep Convolutional Neural Network

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Journal of Tropical Meteorology 2024年第3期30卷 223-229页

作者： HUANG Bin gao Shi-bo YU Run-ling zhao Wei ZHOU Guan-bo National meteorological Center Beijing 100081 China state Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing 100081 China Key Laboratory of South china Sea meteorological Disaster Prevention and Mitigation of Hainan Province Haikou 570203 China Agronomy College Shenyang Agricultural UniversityShenyang 110866 China Shanghai Typhoon Institute China Meteorological AdministrationShanghai 200030 China Key Laboratory of Numerical Modeling for Tropical Cyclones China Meteorological AdministrationShanghai 200030 China

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 ***,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 ***,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 *** 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 *** 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%.

关键词： deep convolutional neural network satellite images sea fog detection multi-channel image fusion

A nowcasting model for the prediction of typhoon tracks based on a long short term memory neural network

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Acta Oceanologica Sinica 2018年第5期37卷 8-12页

作者： gao Song zhao Peng pan Bin LI yaru ZHOU Min XU Jiangling ZHONG Shan SHI Zhenwei North china Sea Marine Forecasting Center of state Oceanic administration State Oceanic Administration Qingdao 266061 China Shandong Provincial Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation Qingdao 266061 China Image Processing Center School of Astronautics Beihang University Beijing 100191 China

It is of vital importance to reduce injuries and economic losses by accurate forecasts of typhoon tracks. A huge amount of typhoon observations have been accumulated by the meteorological department, however, they are yet to be adequately utilized. It is an effective method to employ machine learning to perform forecasts. A long short term memory（LSTM） neural network is trained based on the typhoon observations during 1949–2011 in china＇s Mainland, combined with big data and data mining technologies, and a forecast model based on machine learning for the prediction of typhoon tracks is developed. The results show that the employed algorithm produces desirable 6–24 h nowcasting of typhoon tracks with an improved precision.

关键词： typhoon tracks machine learning LSTM big data

Scientific Advances and Weather Services of the china meteorological administration’s National Forecasting Systems during the beijing 2022 Winter Olympics

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Advances in Atmospheric sciences 2024年第5期41卷 767-776页

作者： Guo DENG Xueshun SHEN Jun DU Jiandong GONG Hua TONG Liantang DENG Zhifang XU Jing CHEN Jian SUN Yong WANG Jiangkai HU Jianjie WANG Mingxuan CHEN Huiling YUAN Yutao ZHANG Hongqi LI Yuanzhe WANG Li gao Li SHENG Da LI Li LI Hao WANG Ying zhao Yinglin LI Zhili LIU Wenhua GUO china meteorological administration Earth System Modeling and Prediction Centre Beijing 100081China state Key Laboratory of Severe Weather China Meteorological AdministrationBeijing 100081China Key Laboratory of Earth System Modeling and Prediction China Meteorological AdministrationBeijing 100081China National meteorological Center China Meteorological AdministrationBeijing 100081China Environmental Modeling Center/NCEP/NWS/NOAA College ParkMD 20740USA Nanjing University of Information science and Technology Nanjing 210044China Institute of Urban Meteorology China Meteorological AdministrationBeijing 100089China School of Atmospheric sciences and Key Laboratory of Mesoscale Severe Weather/Ministry of Education Nanjing UniversityNanjing 210023China Tsinghua University Beijing 100084China

Since the beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational techniques,and *** made providing meteorological services for this event particularly *** china meteorological administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical *** key technologies crucial for precise winter weather services during the Winter Olympics were developed.A comprehensive framework,known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics,was *** of these advancements represent the highest level of capabilities currently available in *** meteorological service provided to the beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and *** included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 *** 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed,and many of these techniques have since been integrated into the CMA’s operational national forecasting *** accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the *** program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project(SMART2022),and continues to play an important role in their Regional Association(RA)II Research Development Project(Hangzhou RDP).Therefore,the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming highimpact weather

关键词： beijing Winter Olympic Games CMA national forecasting system data assimilation ensemble forecast bias correction and downscaling machine learning-based fusion methods

The Extra-Area Effect in 71 Cloud Seeding Operations during Winters of 2008–14 over Jiangxi Province, East china

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Journal of meteorological Research 2019年第3期33卷 528-539页

作者： Weijian WANG Zhanyu YAO Jianping GUO Chao TAN Shuo JIA Wenhui zhao Pei ZHANG Liangshu gao Key Laboratory for Cloud Physics of china meteorological administration Chinese Academy of Meteorological Sciences China Meteorological Administration Beijing 100081 state Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences China Meteorological Administration Beijing 100081

Effects of weather modification operations on precipitation in target areas have been widely reported, but little is specifically known about the downwind (extra-area) effects in china. We estimated the extra-area effect of an operational winter (November-February) aircraft cloud-seeding project in northern Jiangxi Province in eastern china by using a revised historical target/control regression analysis method based on the precipitation data in winter. The results showed that the overall seasonal average rainfall at the downwind stations increased by 21.67%(p=0.0013). This enhancement effect was detected as far as 120 km away from the target area. Physical testing was used to compare the cloud characteristics before and after seeding on 29 November 2014. A posteriori analysis with respect to the characteristics of cloud units derived from operational weather radar data in Jiangxi was performed by tracking cloud units. Radar features in the target unit were enhanced relative to the control unit for more than two hours after the operational cloud seeding, which is indicative of the extra-area seeding effect. The findings could be used to help relieve water shortages in china.

关键词： cloud seeding extra-area effects radar derived rainfall characteristics

Evolution of boundary layer ozone in Shijiazhuang,a suburban site on the North china Plain

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Journal of Environmental sciences 2019年第9期31卷 152-160页

作者： Wei zhao Guiqian Tang Huan Yu Yang Yang Yinghong Wang Lili Wang Junlin An Wenkang gao Bo Hu Mengtian Cheng Xingqin An Xin Li Yuesi Wang Nanjing University of Information science and Technology Nanjing 210044 China state Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry fLAPC) Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China Key Laboratory of Atmospheric Chemistry China Meteorological Administration Beijing 100081 China Department of Atmospheric science School of Environmental Studies China University of Geosciences Wuhan 430074 China Weather Modification Office of Hebei Province Shijiazhuang 050021 China Key Laboratory of meteorological Disaster Ministry ofEducation (KLME) Joint International Research Laboratory of Climate and Environment Change (ILCEC) Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Key Laboratory for Aerosol-Cloud- Precipitation of China Meteorological Administration Nanjing University of Information Science and Technology Nanjing 210044 China Center for Excellence in Regional Atmospheric Environment Institute of Urban Environment Chinese Academy of Sciences Xiamen 361021 China University of Chinese academy of sciences Beijing 100049 China

The structure of the boundary layer affects the evolution of ozone(O3), and research into this structure will provide important insights for understanding photochemical *** this study, we conducted a one-month observation(from June 15 to July 14, 2016) of the boundary layer meteorological factors as well as O3 and its precursors in Luancheng County,Shijiazhuang(37°53′N, 114°38′E). Our research showed that photochemical pollution in Shijiazhuang is serious, and the mean hourly maximum and mean 8-hr maximum O3 concentrations are 97.9 ± 26.1 and 84.4 ± 22.4 ppbV, respectively. meteorological factors play a significant role in the formation of O3. High temperatures and southeasterly winds lead to elevated O3 values, and at moderate relative humidity(40%–50%) and medium boundary layer heights(1200–1500 m), O3 production sensitivity occurred in the transitional region between volatile organic compounds(VOC) and nitrogen oxides(NOx) limitations,and the O3 concentration was the highest. The vertical profiles of O3 were also measured by a tethered balloon. The results showed that a large amount of O3 was stored in the residual layer, and the concentration was positively correlated with the O3 concentration measured the previous day. During the daytime of the following day, the contribution of O3 stored in the residual layer to the boundary layer reached 27%± 7% on average.

关键词： Ozone Boundary layer Residual layer Vertical evolution Sensitivity

High Spatial Resolution and High Temporal Frequency(30-m/15-day) Fractional Vegetation Cover Estimation over china Using Multiple Remote Sensing Datasets:Method Development and Validation

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Journal of meteorological Research 2021年第1期35卷 128-147页

作者： Xihan MU Tian zhao Gaiyan RUAN Jinling SONG Jindi WANG Guangjian YAN Tim RMCVICAR Kai YAN Zhan gao Yaokai LIU Yuanyuan WANG state Key Laboratory of Remote Sensing science Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China beijing Engineering Research Center for Global Land Remote Sensing Products Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China CSIRO Land and Water ACT2601Australia Department of Earth and Environment Boston UniversityBostonMA 02215USA Jiangsu Institute of Urban Planning and Design Nanjing 210036China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China National Satellite meteorological Center China Meteorological AdministrationBeijing 100081China

High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over china by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite(HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer(MODIS). The algorithm was implemented for each main vegetation class and each land cover type over china. First, the high spatial resolution and high temporal frequency normalized difference vegetation index(NDVI) was acquired by using the continuous correction(CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product(GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors(MEs) of forest, cropland, and grassland were-0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809,root-mean-square deviation(RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial–temporal consistency and similar magnitude(RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.

关键词： fractional vegetation cover(FVC) high spatial resolution and high temporal frequency data fusion normalized difference vegetation index(NDVI) pixel unmixing model multiple remote sensing datasets

Seamless Prediction in china: A Review

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Advances in Atmospheric sciences 2023年第8期40卷 1501-1520页

作者： Hong-Li REN Qing BAO Chenguang ZHOU Jie WU Li gao Lin WANG Jieru MA Yao TANG Yangke LIU Yujun WANG Zuosen zhao state Key Laboratory of Severe Weather and Institute of Tibetan Plateau MeteorologyChinese Academy of Meteorological SciencesBeijing 100081China Collaborative Innovation Center on Forecast and Evaluation of meteorological Disasters(CIC-FEMD) Nanjing University of Information Science and TechnologyNanjing 210044China state Key Laboratory of Numerical Modeling for Atmospheric sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Department of Atmospheric science School of Environmental StudiesChina University of GeosciencesWuhan 430074China china meteorological administration Key Laboratory for Climate Prediction Studies National Climate CenterChina Meteorological AdministrationBeijing 100081China Ensemble Forecasting Division CMA Earth System Modeling and Prediction Center(CEMC)Beijing 100081China School of Atmospheric sciences Chengdu University of Information TechnologyChengdu 610225China

Seamless prediction is a weather-climate integrated prediction covering multiple time scales that include days,weeks,months,seasons,years,and *** prediction can provide different industries with information such as weather conditions and climate variations from the next few days to years,which have important impacts on economic and social development and important reference value for short-,medium-and long-term decision-making and planning of the ***,seamless prediction has received widespread attention from the international scientific community *** Chinese scientists have also carried out relevant research,this paper reviews the research in china on developments and applications of seamless prediction methods and prediction systems in recent *** them,the main progress of seamless prediction methods studies is reviewed from four aspects:short-and medium-range weather forecasting,subseasonal-to-seasonal,seasonal-to-interannual,and decadal climate *** terms of development and application of seamless prediction systems,the main achievements made by meteorological operational departments,scientific institutes,and universities in china in recent years are ***,some of the issues in seamless prediction that need further study are discussed.

关键词： seamless prediction weather-climate integrated prediction prediction system

京津风沙源治理工程区植被对沙尘天气的时空影响

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农业工程学报 2018年第12期34卷 171-179,310页

作者：崔晓赵媛媛丁国栋信忠保高广磊于明含北京林业大学水土保持学院水土保持国家林业局重点实验室北京100083 宁夏盐池毛乌素沙地生态系统国家定位观测研究站盐池751500

为了理解京津风沙源治理工程区植被对沙尘天气的影响,该文采用沙尘天气气象数据和归一化植被指数NDVI(normalized difference vegetation index)时间序列,分析了京津风沙源治理工程区(二期)2000-2013年沙尘天气发生日数和植被NDVI的时... 详细信息

为了理解京津风沙源治理工程区植被对沙尘天气的影响,该文采用沙尘天气气象数据和归一化植被指数NDVI(normalized difference vegetation index)时间序列,分析了京津风沙源治理工程区(二期)2000-2013年沙尘天气发生日数和植被NDVI的时空格局,探究了沙尘天气与当年春季、前一年植被生长季NDVI之间的关系,揭示了植被对沙尘天气影响的时间格局和空间异质性特征。结果表明,2000-2013年,京津风沙源治理工程区(二期)沙尘天气发生日数呈显著下降趋势,同时区域植被NDVI显著增加;沙尘天气发生日数与当年5月、前一年8月的NDVI的相关性最强;约60.99%区域沙尘天气发生日数与当年5月NDVI显著相关,主要分布在典型草原中区、浑善达克沙地和农牧交错区;约19.59%的地区植被NDVI对沙尘天气的影响具有一定的滞后性,主要分布在鄂尔多斯高原东部、典型草原西区、燕山丘陵山地水源区北部。

关键词：植被沙尘 NDVI 京津风沙源区时空格局