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Cross-satellite calibration of high-energy electron fluxes measured by FengYun-4A based on Arase observations

Earth and Planetary Physics 2023年第5期7卷 565-575页

作者： XiaoYu Wang Xing Cao Xin Ma XiaoXin Zhang AnQin Chen JunHu Dong BinBin Ni XianKang Dou Department of Space Physics School of Electronic InformationWuhan UniversityWuhan 430072China Key Laboratory of Space Weather National Satellite Meteorological Center(National Center for Space Weather)China Meteorological AdministrationBeijing 100081China Innovation center for FengYun meteorological Satellite(FYSIC) National Satellite Meteorological CenterChina Meteorological AdministrationBeijing 100081China

We use the High-energy Electron Experiments(HEP)instrument onboard Arase(ERG)to conduct an energy-dependent cross-satellite calibration of electron fluxes measured by the High Energy Particle Detector(HEPD)onboard FengYun-4A(FY-4A)spanning from April 1,2017,to September 30,2019.By tracing the two-dimensional magnetic positions(L,magnetic local time[MLT])of FY-4A at each time,we compare the datasets of the conjugate electron fluxes over the range of 245–894 keV in 6 energy channels for the satellite pair within different sets of L×MLT.The variations in the electron fluxes observed by FY-4A generally agree with the Arase measurements,and the percentages of the ratios of electron flux conjunctions within a factor of 2 are larger than 50%.Compared with Arase,FY-4A systematically overestimates electron fluxes at all 6 energy channels,with the corresponding calibration factors ranging from 0.67 to 0.81.After the cross-satellite calibration,the electron flux conjunctions between FY-4A and Arase show better agreement,with much smaller normalized root mean square errors.Our results provide a valuable reference for the application of FY-4A high-energy electron datasets to in-depth investigations of the Earth’s radiation belt electron dynamics.

关键词： cross-satellite calibration high-energy electron flux FengYun-4A Arase

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Development of a Global Historic Monthly Mean Precipitation Dataset

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Journal of meteorological Research 2016年第2期30卷 217-231页

作者：杨溯徐文慧许艳李庆祥 National Meteorological Information Center China Meteorological Administration

Global historic precipitation dataset is the base for climate and water cycle research.There have been several global historic land surface precipitation datasets developed by international data centers such as the US national Climatic Data center(NCDC),European Climate Assessment & Dataset project team,Met Office,etc.,but so far there are no such datasets developed by any research institute in China.In addition,each dataset has its own focus of study region,and the existing global precipitation datasets only contain sparse observational stations over China,which may result in uncertainties in East Asian precipitation studies.In order to take into account comprehensive historic information,users might need to employ two or more datasets.However,the non-uniform data formats,data units,station IDs,and so on add extra difficulties for users to exploit these datasets.For this reason,a complete historic precipitation dataset that takes advantages of various datasets has been developed and produced in the National Meteorological Information Center of China.Precipitation observations from 12 sources are aggregated,and the data formats,data units,and station IDs are unified.Duplicated stations with the same ID are identified,with duplicated observations removed.Consistency test,correlation coefficient test,significance t-test at the95%confidence level,and significance F-test at the 95%confidence level are conducted first to ensure the data reliability.Only those datasets that satisfy all the above four criteria are integrated to produce the China meteorological Administration global precipitation(CGP) historic precipitation dataset version 1.0.It contains observations at 31 thousand stations with 1.87 x 107 data records,among which 4152 time series of precipitation are longer than 100 yr.This dataset plays a critical role in climate research due to its advantages in large data volume and high density of station network,compared to other datasets.Using the Penalized Maximal t-test method,significant inhomogeneity has been

关键词： global precipitation observation historic data

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A Comparison of Atmospheric Temperature over China between Radiosonde Observations and Multiple Reanalysis Datasets

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Journal of meteorological Research 2016年第2期30卷 242-257页

作者：郭艳君张思齐颜京辉陈哲阮新 national Climate center China Meteorological Administration National Meteorological Information Center China Meteorological Administration

The quality controlled（RAW） and homogenized（ADJ） radiosonde temperatures within 850-30 hPa collected at 118 stations in China are compared,on a monthly mean basis,with the temperatures extracted from 8 reanalysis datasets（REA） including NCEP-1,NCEP-2,ERA-40（ECMWF 45-yr Reanalysis）,ERAInterim,JRA-55（Japanese 55-yr Reanalysis）,20CR（20th Century Reanalysis）,MERRA（Modern Era Retrospective-Analysis）,and CFSR（Climate Forecast System Reanalysis）.Average differences,correlations,standard deviations,and linear trends among RAW,ADJ,and REA for the period 1981-2010 are analyzed.The results reveal significant inhomogeneity in the time series of RAW radiosonde temperature in China;an overall negative adjustment was thus employed to obtain the ADJ temperatures,and the effect of the negative adjustment is the most significant within 200-100 hPa.Such a homogenization process has removed the system errors in RAW,possibly caused by radiosonde instrument changes and observation system upgrades.Hence,the correlation is higher between ADJ and REA than that between RAW and REA.The mean difference between ADJ and REA is about 1℃ during 1981-2010,while REA are mostly cooler in the troposphere and warmer in the stratosphere than ADJ;nonetheless,they have a significant high and positive correlation and their annual variability is notably consistent.Furthermore,the linear trends in REA and ADJ both demonstrate warming in the lower-mid troposphere and cooling in the mid stratosphere,with large uncertainties found in the upper troposphere and lower stratosphere.In general,ERA-Interim,JRA-55,and MERRA are more consistent with ADJ than other reanalysis datasets.

关键词： radiosonde air temperature reanalysis data China raw versus homogenized data

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Bias Adjustment and Analysis of Chinese Daily Historical Radiosonde Temperature Data

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Journal of meteorological Research 2021年第1期35卷 17-31页

作者： Zhe CHEN Zijiang ZHOU Zhiquan LIU Qinglei LI Xiaoling ZHANG National Meteorological Information Center China Meteorological AdministrationBeijing 100081China national center for Atmospheric Research BoulderColorado 80301USA Beijing meteorological information center Beijing Meteorological ServiceBeijing 100089China

The discontinuities in historical Chinese radiosonde datasets are attributed to artificial errors. In order to reflect more realistically basic conditions of the atmosphere over China and provide more reasonable radiosonde data as input to climate change analysis and to atmospheric reanalysis data assimilation systems, this paper proposes a scheme to identify breakpoints and adjust biases in daily radiosonde observations. The ongoing ECMWF Re Analysis-Interim(ERA-Interim) 12-h forecasts are used as reference series in the scheme, complemented by the ECMWF Twentieth Century Reanalysis(ERA-20 C). A series of breakpoint identification schemes are developed and combined with metadata to detect breakpoints. The Quantile-Matching(QM) method is applied to test and adjust daily radiosonde data on 12 mandatory pressure levels collected at 80 sounding stations during 1979–2013. The adjusted temperatures on mandatory levels are interpolated to significant levels for temperature adjustment on these levels. The adjustment scheme not only solves the data discontinuity problem caused by changes in observational instruments and bias correction methods, but also solves the discontinuity problem in the 1200 minus 0000 UTC temperature time series on mandatory levels at individual sounding stations. Before the adjustment, obvious discontinuities can be found in the deviation field between the raw radiosonde data and ERA-Interim reanalysis with relatively large deviations before 2001. The deviation discontinuity is mainly attributed to the nationwide upgrade of the radiosonde system in China around 2001. After the adjustment, the time series of deviations becomes more continuous. In addition, compared with the adjusted temperature data on mandatory levels over 80 radiosonde stations in China contained in the Radiosonde Observation Correction Using Reanalyses(RAOBCORE) 1.5, the dataset adjusted by the method proposed in the present study exhibits higher quality than RAOBCORE 1.5, while discontinuities still exist in the time series of

关键词： daily radiosonde temperature data mandatory levels significant levels bias adjustment

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CLDASSD:Reconstructing Fine Textures of the Temperature Field Using Super-Resolution Technology

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Advances in Atmospheric Sciences 2022年第1期39卷 117-130页

作者： Ruian TIE Chunxiang SHI Gang WAN Xingjie HU Lihua KANG Lingling GE Institute of Aerospace information Space Engineering UniversityBeijing 101400China National Meteorological Information Center China Meteorological AdministrationBeijing 100081China

Before 2008,the number of surface observation stations in China was small.Thus,the surface observation data were too sparse to effectively support the High-resolution China meteorological Administration’s Land Assimilation System(HRCLDAS)which ultimately inhibited the output of high-resolution and high-quality gridded products.This paper proposes a statistical downscaling model based on a deep learning algorithm in super-resolution to research the above problem.Specifically,we take temperature as an example.The model is used to downscale the 0.0625°×0.0625°,2-m temperature data from the China meteorological Administration’s Land Data Assimilation System(CLDAS)to 0.01°×0.01°,named CLDASSD.We performed quality control on the paired data from CLDAS and HRCLDAS,using data from 2018 and 2019.CLDASSD was trained on the data from 31 March 2018 to 28 February 2019,and then tested with the remaining data.Finally,extensive experiments were conducted in the Beijing-Tianjin-Hebei region which features complex and diverse geomorphology.Taking the HRCLDAS product and surface observation data as the"true values"and comparing them with the results of bilinear interpolation,especially in complex terrain such as mountains,the root mean square error(RMSE)of the CLDASSD output can be reduced by approximately 0.1℃,and its structural similarity(SSIM)was approximately 0.2 higher.CLDASSD can estimate detailed textures,in terms of spatial distribution,with greater accuracy than bilinear interpolation and other sub-models and can perform the expected downscaling tasks.

关键词： statistical downscaling deep learning temperature field high-resolution reconstruction

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QpefBD:A Benchmark Dataset Applied to Machine Learning for Minute-Scale Quantitative Precipitation Estimation and Forecasting

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Journal of meteorological Research 2022年第1期36卷 93-106页

作者： Anyuan XIONG Na LIU Yujia LIU Shulin ZHI Linlin WU Yongjian XIN Yan SHI Yunjian ZHAN National Meteorological Information Center China Meteorological AdministrationBeijing 100081 Jiangxi meteorological Observatory Nanchang 330096 Anhui Weather Modification Office Hefei 230061

Nowcasts of strong convective precipitation and radar-based quantitative precipitation estimations have always been hot yet challenging issues in meteorological sciences.Data-driven machine learning,especially deep learning,provides a new technical approach for the quantitative estimation and forecasting of precipitation.A high-quality,large-sample,and labeled training dataset is critical for the successful application of machine-learning technology to a specific field.The present study develops a benchmark dataset that can be applied to machine learning for minutescale quantitative precipitation estimation and forecasting(QpefBD),containing 231,978 samples of 3185 heavy precipitation events that occurred in 6 provinces of central and eastern China from April to October 2016-2018.Each individual sample consists of 8 products of weather radars at 6-min intervals within the time window of the corresponding event and products of 27 physical quantities at hourly intervals that describe the atmospheric dynamic and thermodynamic conditions.Two data labels,i.e.,ground precipitation intensity and areal coverage of heavy precipitation at 6-min intervals,are also included.The present study describes the basic components of the dataset and data processing and provides metrics for the evaluation of model performance on precipitation estimation and forecasting.Based on these evaluation metrics,some simple and commonly used methods are applied to evaluate precipitation estimates and forecasts.The results can serve as the benchmark reference for the performance evaluation of machine learning models using this dataset.This paper also gives some suggestions and scenarios of the QpefBD application.We believe that the application of this benchmark dataset will promote interdisciplinary collaboration between meteorological sciences and artificial intelligence sciences,providing a new way for the identification and forecast of heavy precipitation.

关键词： machine learning benchmark dataset quantitative precipitation estimation precipitation forecast

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Integration, Quality Assurance, and Usage of Global Aircraft Observations in CRA

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Journal of meteorological Research 2021年第1期35卷 1-16页

作者： Jie LIAO Huiying WANG Zijiang ZHOU Zhiquan LIU Lipeng JIANG Fang YUAN National Meteorological Information Center China Meteorological AdministrationBeijing 100081China national center for Atmospheric Research CO 80301USA

This paper presents a detailed description of integration, quality assurance procedure, and usage of global aircraft observations for China's first generation global atmospheric reanalysis(CRA) product(1979–2018). An integration method named "classified integration" is developed. Aircraft observations from nine different sources are integrated into the Integrated Global meteorological Observation Archive from Aircraft(IGMOAA), a new dataset from the National Meteorological Information Center(NMIC) of the China meteorological Administration(CMA). IGMOAA consists of global aircraft temperature, wind, and humidity data from the surface to 100 h Pa, extending from 1973 to the present. Compared with observations assimilated in the Climate Forecast System Reanalysis(CFSR) of NCEP,the observation number of IGMOAA increased by 12.9% between 2010 and 2014, mainly as a result of adding more Chinese Aircraft meteorological Data Relay(AMDAR) data. Complex quality control procedures for aircraft observations of NCEP are applied to detect data errors. Observations are compared with ERA-Interim reanalysis from 1979 to 2018 to investigate data quality of different types and aircraft, and subsequently to develop the blacklists for CRA. IGMOAA data have been assimilated in CRA in 2018 and are real-time updated at the CMA Data-as-a-Service(CMADaa S) platform. For CRA, the fits to observations improve over time. From 1994 to 2018, root-meansquare error(RMSE) of observations relative to CRA background decreases from 1.8 to 1.0℃ for temperature above 300 h Pa, and from 4.5 to 3 m s^(-1) for zonal wind. The RMSE for humidity appears to exhibit an apparent seasonal variation with larger errors in summer and smaller ones in winter.

关键词： aircraft observation integration quality assurance blacklist China's global atmospheric reanalysis(CRA)product

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Bias Analysis and Correction of Ground Surface Temperature Observations across China

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Journal of meteorological Research 2020年第6期34卷 1324-1334页

作者： Yan CUI Wenhui XU Zijiang ZHOU Chunyu ZHAO Yihui DING Xue AO Xiaoyu ZHOU Shenyang Regional Climate center Liaoning Meteorological BureauShenyang 110166 National Meteorological Information Center China Meteorological AdministrationBeijing 100081 national Climate center China Meteorological AdministrationBeijing 100081

Based on the ground surface temperature(GST)and snow surface temperature(SST)measurements during the period of adjustment from manual to automatic observation systems in China,the influence of observation methods on GST and its relationship with snow cover is analyzed.GST is corrected by SST,and the correction effect is evaluated.The results show that,during the parallel observation period,the winter GSTs from automatic observations are generally higher than those from manual observations,with the automatically observed national daily GST 1.18°C higher.The adjustment has a greater impact on GSTs at 0200,0800,and 2000 Beijing Time(BT)than at 1400 BT,and it has the greatest impact in Northeast and Northwest China,with deviations of 5.24 and 2.09°C,respectively.The GST deviation is closely related to the snow depth and annual snow totals.The average daily GST deviation increases at the rate of 0.66°C per 1-cm increase of snow depth when it is<15 cm,while it tends to be stable at around 10°C for snow depth over 15 cm.The GST deviation at a station is affected by its winter snow totals in Northeast and Northwest China,where the largest deviations are found where snow totals are all above 1000 cm.After the correction with SST,the mean deviation between the automatic and manual observations as well as the false trend can be effectively reduced.Following the correction,the mean deviation of daily GST decreases by 5.8°C,and its trend decreases from 1.87 to 0.65°C decade-1.

关键词： ground surface temperature(GST)in winter snow cover correction China

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未来情境下中国高温的人口暴露度变化及影响因素研究（英文）

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Journal of Geographical Sciences 2018年第10期28卷 1371-1384页

作者：黄大鹏张蕾高歌孙劭 national Climate center China Meteorological Administration Collaborative Innovation center on Forecast and Evaluation of meteorological Disasters Nanjing University of Information Science & Technology national meteorological center China Meteorological Administration

Overall population exposure is measured by multiplying the annual average number of extremely hot days by the number of people exposed to the resultant heat. Extreme heat is also subdivided into high temperature（HT） and extremely high temperature（EHT） in cases where daily maximum temperature exceeds 35℃ and 40℃, respectively. Chinese population exposure to HT and EHT over four periods in the future（i.e., 2021–2040, 2041–2060, 2060–2081 and 2081–2100） were projected at the grid cell level in this study using daily maximum temperature based on an ensemble mean of 21 global climate models under the RCP8.5 scenario and with a population projection based on the A2 r socio-economic scenario. The relative importance of population and climate as drivers of population exposure was evaluated at different spatial scales including national and meteorological geographical divisions. Results show that, compared with population exposure seen during 1981–2010, the base period, exposure to HT in China is likely to increase by 1.3, 2.0, 3.6, and 5.9 times, respectively, over the four periods, while concomitant exposure to EHT is likely to increase by 2.0, 8.3, 24.2, and 82.7 times, respectively. Data show that population exposure to HT is likely to increase significantly in Jianghuai region, Southwest China and Jianghan region, in particular in North China, Huanghuai region, South China and Jiangnan region. Population exposure to EHT is also likely to increase significantly in Southwest China and Jianghan region, especially in North China, Huanghuai, Jiangnan, and Jianghuai regions. Results reveal that climate is the most important factor driving the level of population exposure in Huanghuai, Jianghuai, Jianghan, and Jiangnan regions, as well as in South and Southwest China, followed by the interactive effect between population and climate. Data show that the climatic factor is also most significant at the national level, followed by the interactive effect between population and climate. The rate of contribution of climate to nati

关键词： RCP8.5 scenario population exposure high temperature extremely high temperature driving forces

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A Brief of Cold Spell Warning Actions of the national meteorological center of China meteorological Administration

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China CDC weekly 2024年第5期6卷 88-91页

作者： Taiyuan Zhang Mengxue Zhang Qiutong Li Yu Lan Huafeng meteorological Media Group of China meteorological Administration(CMA) BeijingChina national Institute of Environmental Health Chinese Center for Disease Control and PreventionBeijingChina The national meteorological center of China meteorological Administration(CMA) BeijingChina

The“Outline for High-Quality Development of Meteorology(2022-2035)”was published by the State Council of the People’s Republic of China in April 2022(1).This publication highlights the essential role of meteorological disaster warnings,particularly cold spell warnings,in preventing and mitigating disasters.The outline also sets forth stricter expectations for enhancing the overall capacity of society in terms of meteorological disaster preparedness and response.

关键词： disaster meteorological warning

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