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Classification analysis of prediction skill among ensemble members in MJO subseasonal predictions——based on the results of the CAMS-CSM subseasonal prediction system

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Atmospheric and Oceanic Science Letters 2024年第4期17卷 8-14页

作者： Yihao Peng Xiaolei Liu Jingzhi Su Xinli Liu State key laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijingChina Center for earth system modeling and prediction of CMA(CEMC) BeijingChina key laboratory of earth system modeling and prediction china meteorological administration BeijingChina

由于模式误差和初始误差所致,次季节-季节预报技巧整体偏低.国际上多数模式都采用集合预报的方式来提高次季节预报的准确率.热带大气季节内振荡(MJO)作为次季节尺度可预报性的重要来源,其预测水平取决于模式性能和MJO事件本身的物理特性... 详细信息

由于模式误差和初始误差所致,次季节-季节预报技巧整体偏低.国际上多数模式都采用集合预报的方式来提高次季节预报的准确率.热带大气季节内振荡(MJO)作为次季节尺度可预报性的重要来源,其预测水平取决于模式性能和MJO事件本身的物理特性.根据中国气象科学研究院气候系统模式次季节预测系统的回报结果,结合不同类型MJO事件的特征,对模式集合成员间的预报技巧进行了分类和比较.在集合成员预报技巧普遍较高的一类MJO事件中,对流异常信号持续时间较长,强度较大,强对流异常中心主要位于印度洋区域,并逐渐东传至西太平洋.在集合成员预报技巧多数较差的MJO事件中,对流异常信号的强度最弱,维持时间最短.在集合成员预报技巧优劣参半的类别中,MJO往往持续时间较短,强度较低,在后续传播过程中,对流异常中心多停驻在海洋性大陆区域.

关键词：次季节-季节预测预报技巧热带大气季节内振荡

An Initial Perturbation Method for the Multiscale Singular Vector in Global Ensemble prediction

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Advances in Atmospheric Sciences 2024年第3期41卷 545-563页

作者： Xin LIU Jing CHEN Yongzhu LIU Zhenhua HUO Zhizhen XU Fajing CHEN Jing WANG Yanan MA Yumeng HAN School of Atmospheric Sciences Nanjing University of Information Science&TechnologyNanjing 210044China Chinese Academy of meteorological Sciences Beijing 100081China CMA earth system modeling and prediction Centre(CEMC) Beijing 100081China key laboratory of earth system modeling and prediction of china meteorological administration Beijing 100081China Tianjin meteorological Observatory Tianjin 300074China china meteorological administration Xiong’an Atmospheric Boundary Layer key laboratory Hebei 071799China

Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the china meteorological administration Global Ensemble prediction system(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.

关键词： multiscale uncertainty singular vector initial perturbation global ensemble prediction system

Vector Radiative Transfer in a Vertically Inhomogeneous Scattering and Emitting ***Ⅰ:A New Discrete Ordinate Method

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Journal of meteorological Research 2024年第2期38卷 209-224页

作者： Ziqiang ZHU Fuzhong WENG Yang HAN Chinese Academy of meteorological Sciences China Meteorological AdministrationBeijing 100081 University of Chinese Academy of Sciences Beijing 100049 CMA earth system modeling and prediction Centre China Meteorological Administration(CMA)Beijing 100081 State key laboratory of Severe Weather Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081

The original vector discrete ordinate radiative transfer(VDISORT)model takes into account Stokes radiance vector but derives its solution assuming azimuthal symmetric surface reflective matrix and atmospheric scattering phase matrix,such as the phase matrix derived from spherical particles or randomly oriented non-spherical *** this study,a new VDISORT is developed for general atmospheric scattering and boundary *** vector is decomposed into both sinusoidal and cosinusoidal harmonic modes,and the radiance at arbitrary viewing geometry is solved directly by adding two zero-weighted points in the Gaussian quadrature *** complex eigenvalues in homogeneous solutions are also taken into full *** accuracy of VDISORT model is comprehensively validated by four cases:Rayleigh scattering case,the spherical particle scattering case with the Legendre expansion coefficients of 0th-13th orders of the phase matrix(hereinafter L13),L13 with a polarized source,and the randomoriented oblate particle scattering case with the Legendre expansion coefficients of 0th-11th orders of the phase matrix(hereinafter L11).In all cases,the simulated radiances agree well with the benchmarks,with absolute biases less than 0.0065,0.0006,and 0.0008 for Rayleigh,unpolarized L13,and L11,*** a polarized bidirectional reflection distribution function(pBRDF)matrix is used as the lower boundary condition,VDISORT is now able to handle fully coupled atmospheric and surface polarimetric radiative transfer processes.

关键词： radiative transfer discrete ordinate polarization Stokes vector vector discrete ordinate radiative transfer(VDISORT)

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

Impact of Atmospheric Transmittance and NLTE Correction on Simulation of High Spectral Infrared Atmospheric Sounder onboard FY-3E

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Journal of meteorological Research 2024年第2期38卷 225-234页

作者： Chenggege FANG Peiming DONG Yang HAN Wanlin KAN Collaborative Innovation Center on Forecast and Evaluation of meteorological Disasters Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological AdministrationNanjing University of Information Science&TechnologyNanjing 210044 Donghai laboratory Zhoushan 316021 State key laboratory of Severe Weather Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081 CMA earth system modeling and prediction Centre China Meteorological Administration(CMA)Beijing 100081 key laboratory of Transportation Meteorology of china meteorological administration Nanjing Joint Institute for Atmospheric SciencesNanjing 210041

With the launch of the first civilian early-morning orbit satellite Fengyun-3E(FY-3E),higher demands are placed on the accuracy of radiative transfer simulations for hyperspectral infrared ***,several key issues are investigated in the ***,the accuracy of the fast atmospheric transmittance model implemented in the Advanced Research and modeling system(ARMS)has been evaluated with both the line-by-line radiative transfer model(LBLRTM)and the actual satellite *** results indicate that the biases are generally less than 0.25 K when compared to the LBLRTM,while below 1.0 K for the majority of the channels when compared to the ***,during both comparisons,significant biases are observed in certain *** accuracy of Hyperspectral Infrared Atmospheric Sounder-II(HIRAS-II)onboard FY-3E is comparable to,and even superior to that of the Cross-track Infrared Sounder(CrIS)onboard ***,apodization is a crucial step in the processing of hyperspectral data in that the apodization function is utilized as the instrument channel spectral response function to produce the satellite channel-averaged *** further explore the difference between the apodized and unapodized simulations,Sinc function is adopted in the fast transmittance *** is found that the use of Sinc function can make the simulations fit the original satellite observations *** simulating with apodized observations,the use of Sinc function exhibits larger deviations compared to the Hamming ***,a correction module is applied to minimize the impact of Non-Local Thermodynamic Equilibrium(NLTE)in the shortwave infrared *** is verified that the implementation of the NLTE correction model leads to a significant reduction in the bias between the simulation and observation for this band.

关键词： high spectral infrared sounding atmospheric transmittance apodization Non-Local Thermodynamic Equilibrium Fengyun-3E(FY-3E)

A Long-Time-Step-Permitting Tracer Transport Model on the Regular Latitude–Longitude Grid

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Advances in Atmospheric Sciences 2024年第3期41卷 493-508页

作者： Jianghao LI Li DONG key laboratory of earth system modeling and prediction China Meteorological AdministrationBeijing 100081China State key laboratory of Numerical modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG) Institute of Atmospheric Physics(IAP)Chinese Academy of SciencesBeijing 100029China State key laboratory of Severe Weather(LaSW) Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081China College of earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China

If an explicit time scheme is used in a numerical model, the size of the integration time step is typically limited by the spatial resolution. This study develops a regular latitude–longitude grid-based global three-dimensional tracer transport model that is computationally stable at large time-step sizes. The tracer model employs a finite-volume flux-form semiLagrangian transport scheme in the horizontal and an adaptively implicit algorithm in the vertical. The horizontal and vertical solvers are coupled via a straightforward operator-splitting technique. Both the finite-volume scheme's onedimensional slope-limiter and the adaptively implicit vertical solver's first-order upwind scheme enforce monotonicity. The tracer model permits a large time-step size and is inherently conservative and monotonic. Idealized advection test cases demonstrate that the three-dimensional transport model performs very well in terms of accuracy, stability, and efficiency. It is possible to use this robust transport model in a global atmospheric dynamical core.

关键词： tracer transport numerical stability latitude–longitude grid

Comparison of tropical cyclone thermal structures derived from ATMS and synthetic AMSU-A/MHS

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Atmospheric and Oceanic Science Letters 2024年第4期17卷 29-34页

作者： Wenyu Li Fuzhong Weng Chinese Academy of meteorological Sciences BeijingChina Center for earth system modeling and prediction of CMA BeijingChina

热带气旋(TC)的暖心反映了其强度和变化.微波探测数据被广泛用于探测TC暖心,但观测到的TC暖心强度可能因仪器而异.本研究利用先进技术微波探测仪(ATMS)的过采样数据重采样至与先进微波探测仪(AMSU-A)一致的亮温,称为类AMSU数据.通过对... 详细信息

热带气旋(TC)的暖心反映了其强度和变化.微波探测数据被广泛用于探测TC暖心,但观测到的TC暖心强度可能因仪器而异.本研究利用先进技术微波探测仪(ATMS)的过采样数据重采样至与先进微波探测仪(AMSU-A)一致的亮温,称为类AMSU数据.通过对比发现,ATMS的观测更加细致,较好地刻画了TC眼区和云带.使用ATMS和类-AMSU数据反演多个TC的暖心发现,在250 hPa,使用ATMS数据反演的暖心强度比类-AMSU高约1-2K,并且其暖心结构更详细.

关键词：热带气旋暖心 ATMS AMSU-A 微波探测数据

A Neural-network-based Alternative Scheme to Include Nonhydrostatic Processes in an Atmospheric Dynamical Core

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Advances in Atmospheric Sciences 2024年第6期41卷 1083-1099,I0002,I0003页

作者： Yang XIA Bin WANG Lijuan LI Li LIU Jianghao LI Li DONG Shiming XU Yiyuan LI Wenwen XIA Wenyu HUANG Juanjuan LIU Yong WANG Hongbo LIU Ye PU Yujun HE Kun XIA Ministry of Education key laboratory for earth system modeling and Department of Earth System ScienceTsinghua UniversityBeijing 100084China State key laboratory of Numerical modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Innovation Group 311020008 Southern Marine Science and Engineering Guangdong LaboratoryZhuhai 519000China Shanghai Ecological Forecasting and Remote Sensing Center Shanghai 200030China key laboratory of earth system modeling and prediction China Meteorological AdministrationBeijing 100081China College of Ocean Sciences University of Chinese Academy of SciencesBeijing 100049China

Here,a nonhydrostatic alternative scheme(NAS)is proposed for the grey zone where the nonhydrostatic impact on the atmosphere is evident but not large enough to justify the necessity to include an implicit nonhydrostatic solver in an atmospheric dynamical *** NAS is designed to replace this solver,which can be incorporated into any hydrostatic models so that existing well-developed hydrostatic models can effectively serve for a longer *** advances in machine learning(ML)provide a potential tool for capturing the main complicated nonlinear-nonhydrostatic *** this study,an ML approach called a neural network(NN)was adopted to select leading input features and develop the *** NNs were trained and evaluated with 12-day simulation results of dry baroclinic-wave tests by the Weather Research and Forecasting(WRF)*** forward time difference of the nonhydrostatic tendency was used as the target variable,and the five selected features were the nonhydrostatic tendency at the last time step,and four hydrostatic variables at the current step including geopotential height,pressure in two different forms,and potential temperature,***,a practical NAS was developed with these features and trained layer by layer at a 20-km horizontal resolution,which can accurately reproduce the temporal variation and vertical distribution of the nonhydrostatic *** by the NN-based NAS,the improved hydrostatic solver at different horizontal resolutions can run stably for at least one month and effectively reduce most of the nonhydrostatic errors in terms of system bias,anomaly root-mean-square error,and the error of the wave spatial pattern,which proves the feasibility and superiority of this scheme.

关键词： neural network nonhydrostatic alternative scheme atmospheric model dynamical core

Improving the Forecasts of Coastal Wind Speeds in Tianjin,china Based on the WRF Model with Machine Learning Algorithms

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Journal of meteorological Research 2024年第3期38卷 570-585页

作者： Weihang ZHANG Meng TIAN Shangfei HAI Fei WANG Xiadong AN Wanju LI Xiaodong LI Lifang SHENG College of Oceanic and Atmospheric Sciences Ocean University of ChinaQingdao266100China Tianjin key laboratory for Oceanic Meteorology Tianjin Institute of Meteorological ScienceTianjin300074China CMA earth system modeling and prediction Centre China Meteorological Administration(CMA)Beijing100081China Qingdao meteorological Bureau Qingdao266003China

Characterized by sudden changes in strength,complex influencing factors,and significant impacts,the wind speed in the circum-Bohai Sea area is relatively challenging to *** the western side of Bohai Bay,as the economic center of the circum-Bohai Sea,Tianjin exhibits a high demand for accurate wind *** this study,three machine learning algorithms were employed and compared as post-processing methods to correct wind speed forecasts by the Weather Research and Forecast(WRF)model for *** results showed that the random forest(RF)achieved better performance in improving the forecasts because it substantially reduced the model bias at a lower computing cost,while the support vector machine(SVM)performed slightly worse(especially for stronger winds),but it required an approximately 15 times longer computing *** back propagation(BP)neural network produced an average forecast significantly closer to the observed forecast but insufficiently reduced the *** regard to wind speed frequency forecasting,the RF method commendably corrected the forecasts of the frequency of moderate(force 3)wind speeds,while the BP method showed a desirable capability for correcting the forecasts of stronger(force>6)*** addition,the 10-m u and v components of wind(u_(10)and v_(10)),2-m relative humidity(RH_(2))and temperature(T_(2)),925-hPa u(u925),sea level pressure(SLP),and 500-hPa temperature(T_(500))were identified as the main factors leading to bias in wind speed forecasting by the WRF model in Tianjin,indicating the importance of local dynamical/thermodynamic processes in regulating the wind *** study demonstrates that the combination of numerical models and machine learning techniques has important implications for refined local wind forecasting.

关键词： machine learning Weather Research and Forecast(WRF)model wind speed forecasting coastal region