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The importance of data assimilation components for initial conditions and subsequent error growth

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Science China Earth Sciences 2024年第1期67卷 105-116页

作者： Zhongrui WANG Haohao SUN Lili LEI Zhe-Min TAN Yi ZHANG Key lab.ratory of mesoscale severe weather Ministry of Educationand School of Atmospheric SciencesNanjing UniversityNanjing 210023China Frontiers Science Center for Critical Earth Material Cycling Nanjing UniversityNanjing 210023China

Despite a specific data assimilation method,data assimilation(DA)in general can be decomposed into components of the prior information,observation forward operator that is given by the observation type,observation error covariances,and background error *** a classic Lorenz model,the influences of the DA components on the initial conditions(ICs)and subsequent forecasts are systematically investigated,which could provide a theoretical basis for the design of DA for different scales of *** forecast errors undergo three typical stages:a slow growth stage from 0 h to 5 d,a fast growth stage from 5 d to around 15 d with significantly different error growth rates for ensemble and deterministic forecasts,and a saturation stage after 15 *** strategies that provide more accurate ICs can improve the *** assimilation is superior to offline assimilation,and a flow-dependent background error covariance matrix(Pf)provides better analyses than a static background error covariance matrix(B)for instantaneous observations and frequent time-averaged observations;but the opposite is true for infrequent time-averaged observations,since cycling simulation cannot construct informative priors when the model lacks predictive skills and the flow-dependent Pf cannot effectively extract information from low-informative observations as the static *** observations contain more information than time-averaged observations,thus the former is preferred,especially for infrequent observing ***,ensemble forecasts have advantages over deterministic forecasts,and the advantages are enlarged with less informative observations and lower predictive-skill model priors.

关键词： Data assimilation Atmospheric predictability Background error covariances Ensemble forecasts

A hybrid deep learning and data assimilation method for model error estimation

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Science China Earth Sciences 2024年

作者： Ziyi PENG Lili LEI Zhe-Min TAN Key lab.ratory of mesoscale severe weather Ministry of Education and School of Atmospheric Sciences Nanjing University Department of Atmospheric and Oceanic Sciences Fudan University

Forecast errors of numerical weather prediction consist of model errors and the growth of initial condition errors,while the initial condition is often optimized based on short-term forecasts. Thus it is difficult to untangle the initial condition error and model error, but it is essential to infer model errors not just for prediction but also for data assimilation (DA). A hybrid deep learning (DL) and DA method is proposed here, aiming to correct model errors. It uses a convolutional neural network(CNN) to extract characteristics of initial conditions and forecast errors, and then provides estimations for model errors. The CNN-based model error estimation method can consider the model error resulted from inaccurate model parameters, or simultaneously consider the model error and initial condition error. Based on the Lorenz05 model, offline and online experiments demonstrate that the CNN-based model error estimation method can effectively correct model errors resulted from inaccurate model parameters, including the forcing F, coupling coefficient c, and relative scale b. For both online and offline model error estimations, simultaneously considering model errors and initial condition errors are beneficial to infer the model errors,compared to considering model errors only. Moreover, using the observations to verify the forecasts has advantages over using the analyses, to estimate the model errors. Using observations can also achieve a faster convergence of model error estimation with online DA than using analyses.

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Track-Pattern-Based Characteristics of Extratropical Transitioning Tropical Cyclones in the Western North Pacific

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Advances in Atmospheric Sciences 2024年第6期41卷 1251-1263页

作者： Hong HUANG Dan WU Yuan WANG Zhen WANG Yu LIU College of Meteorology and Oceanography National University of Defense TechnologyChangsha 410000China No.31631 Army of PLA Huizhou 516000China School of Atmospheric Sciences and Key lab.ratory of mesoscale severe weather/Ministry of Education Nanjing 210023China No.93721 Army of PLA Shuozhou 038300China

Based on the Regional Specialized Meteorological Center(RSMC)Tokyo-Typhoon Center best-track data and the NCEP-NCAR reanalysis dataset,extratropical transitioning(ET)tropical cyclones(ETCs)over the western North Pacific(WNP)during 1951–2021 are classified into six clusters using the fuzzy c-means clustering method(FCM)according to their track *** characteristics of the six hard-clustered ETCs with the highest membership coefficient are *** tropical cyclones(TCs)that were assigned to clusters C2,C5,and C6 made landfall over eastern Asian countries,which severely threatened these *** landfalling TCs,93.2%completed their ET after landfall,whereas 39.8%of ETCs completed their transition within one *** frequency of ETCs over the WNP has decreased in the past four decades,wherein cluster C5 demonstrated a significant decrease on both interannual and interdecadal timescales with the expansion and intensification of the western Pacific subtropical high(WPSH).This large-scale circulation pattern is favorable for C2 and causes it to become the dominant track pattern,owning to it containing the largest number of intensifying ETCs among the six clusters,a number that has increased insignificantly over the past four *** surface roughness variation and three-dimensional background circulation led to C5 containing the maximum number of landfalling TCs and a minimum number of intensifying *** results will facilitate a better understanding of the spatiotemporal distributions of ET events and associated environment background fields,which will benefit the effective monitoring of these events over the WNP.

关键词： Western North Pacific tropical cyclone extratropical transition fuzzy c-means clustering method

Ensemble projections of climate and streamflow in a typical basin of semi-arid steppes in Mongolian Plateau of 2021-2100

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Advances in Climate Change Research 2024年第2期15卷 230-243页

作者： Hang PAN Jian-Ping TANG Liang CHENG Man-Chun LI Jiangsu Provincial Key lab.ratory of Geographic Information Science and Technology Nanjing UniversityNanjing 210023China School of Geography and Ocean Science Nanjing UniversityNanjing 210023China Key lab.ratory of mesoscale severe weather/Ministry of Education Nanjing UniversityNanjing 210023China School of Atmospheric Sciences Nanjing UniversityNanjing 210023China

The Kherlen River is the main water source for Hulun Lake,the largest lake in northern *** to reduced inflow from the Kherlen River,Hulun Lake experienced rapid shrinkage at the beginning of the 21st century,posing a serious threat to the ecological security of northern ***,there is still a significant lack of projections regarding future climate change and its hydrological response in the Kherlen River *** study analyzed the projected climate and streamflow changes in the Kherlen River basin,a vital yet vulnerable international semi-arid steppes type basin.A combination of multi-model ensemble projection techniques,and the soil and water assessment tool(SWAT)model was employed to examine the spatio-temporal changes in precipitation,temperature,streamflow,and the associated uncertainties in the *** temperature(an increase of 1.84-6.42℃)and the precipitation(an increase of 15.0-46.0 mm)of Kherlen River basin are projected to increase by 2100,leading to a rise in streamflow(1.08-4.78 m^(3) s^(-1)).The upstream of the Kherlen River exhibits remarkable increasing trends in precipitation,which has a dominant influence on streamflow of Kherlen *** increases in streamflow are observed in April,August,September,and October compared to the reference period(1971-2000).These findings suggest a partial alleviation of water scarcity in the Kherlen River,but also an increased likelihood of hydrological extreme *** projected temperature increase in the Kherlen River basin exhibits the smallest uncertainty,while more pronounced uncertainties are found in precipitation and *** spread among the results of CMIP6 models is greater than that of CMIP5 models,with lower signal-to-noise ratio(SNR)values for temperature,precipitation,and streamflow.

关键词： Climate change Flow projection Uncertainty SWAT Mongolian Plateau

A Physics-informed Deep-learning Intensity Prediction Scheme for Tropical Cyclones over the Western North Pacific

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Advances in Atmospheric Sciences 2024年第7期41卷 1391-1402页

作者： Yitian ZHOU Ruifen ZHAN Yuqing WANG Peiyan CHEN Zhemin TAN Zhipeng XIE Xiuwen NIE Department of Atmospheric and Oceanic Sciences&Institute of Atmospheric Sciences Fudan UniversityShanghai 200438China Shanghai Typhoon Institute of China Meteorological Administration Shanghai 200030China Department of Atmospheric Sciences and International Pacific Research Center University of Hawaii at ManoaHonolulu 96822USA Key lab.ratory of mesoscale severe weather/MOE and School of the Atmospheric SciencesNanjing UniversityNanjing 210023China School of Computer Science Fudan UniversityShanghai 200438China

Accurate prediction of tropical cyclone(TC)intensity is challenging due to the complex physical processes ***,we introduce a new TC intensity prediction scheme for the western North Pacific(WNP)based on a time-dependent theory of TC intensification,termed the energetically based dynamical system(EBDS)model,together with the use of a long short-term memory(LSTM)neural *** time-dependent theory,TC intensity change is controlled by both the internal dynamics of the TC system and various environmental factors,expressed as environmental dynamical *** LSTM neural network is used to predict the environmental dynamical efficiency in the EBDS model trained using besttrack TC data and global reanalysis data during 1982–*** transfer learning and ensemble methods are used to retrain the scheme using the environmental factors predicted by the Global Forecast System(GFS)of the National Centers for Environmental Prediction during 2017–*** predicted environmental dynamical efficiency is finally iterated into the EBDS equations to predict TC *** new scheme is evaluated for TC intensity prediction using both reanalysis data and the GFS prediction *** intensity prediction by the new scheme shows better skill than the official prediction from the China Meteorological Administration(CMA)and those by other state-of-art statistical and dynamical forecast systems,except for the 72-h *** at the longer lead times of 96 h and 120 h,the new scheme has smaller forecast errors,with a more than 30%improvement over the official forecasts.

关键词： tropical cyclones western North Pacific intensity prediction EBDS LSTM

Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning

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Advances in Atmospheric Sciences 2024年第6期41卷 1147-1160页

作者： Jiang HUANGFU Zhiqun HU Jiafeng ZHENG Lirong WANG Yongjie ZHU State Key lab.of severe weather Chinese Academy of Meteorological SciencesBeijing 100081China Meteorological observatory of Jiangxi Province Nanchang 330096China Hebei Meteorological Disaster Prevention and Environmental Meteorological Center Shijiazhuang 050021China School of Atmospheric Sciences Chengdu University of Information TechnologyChengdu 610225China

Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and *** this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are ***,a self-defined loss function(SLF)is proposed during *** dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North *** that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)***,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,*** results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based *** mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis ***,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.

关键词： polarimetric radar quantitative precipitation estimation deep learning single-parameter network multi-parameter network

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 lab.ratory of severe weather China Meteorological AdministrationBeijing 100081China Key lab.ratory 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 lab.ratory 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 availab.e 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

Understanding Simulated Causes of Damaging Surface Winds in a Derecho-Producing mesoscale Convective System near the East China Coast Based on Convection-Permitting Simulations

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Advances in Atmospheric Sciences 2024年第11期41卷 2112-2130页

作者： Liping LUO Ming XUE Xin XU Lijuan LI Qiang ZHANG Ziqi FAN College of Civil Aviation Nanjing University of Aeronautics and AstronauticsNanjing 211106China State Key lab.ratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Key lab.ratory of mesoscale severe weather/Ministry of Education and School of Atmospheric Sciences Nanjing UniversityNanjing 210023China Center for Analysis and Prediction of Storms and School of Meteorology University of OklahomaNormanOklahoma 73072USA Civil Aviation North China Air Traffic Management Bureau Meteorological Center Beijing 100621China

A mesoscale convective system(MCS) occurred over the East China coastal provinces and the East China Sea on 30April 2021, producing damaging surface winds near the coastal city Nantong with observed speeds reaching 45 m s^(–1). A simulation using the weather Research and Forecasting model with a 1.5-km grid spacing generally reproduces the development and subsequent organization of this convective system into an MCS, with an eastward protruding bow segment over the sea. In the simulation, an east-west-oriented high wind swath is generated behind the gust front of the MCS. Descending dry rear-to-front inflows behind the bow and trailing gust front are found to feed the downdrafts in the main precipitation regions. The inflows help to establish spreading cold outflows and enhance the downdrafts through evaporative cooling. Meanwhile, front-to-rear inflows from the south are present, associated with severely rearward-tilted updrafts initially forming over the gust front. Such inflows descend behind(north of) the gust front, significantly enhancing downdrafts and near-surface winds within the cold pool. Consistently, calculated trajectories show that these parcels that contribute to the derecho originate primarily from the region ahead(south) of the east-west-oriented gust front, and dry southwesterly flows in the low-to-middle levels contribute to strong downdrafts within the MCS. Moreover, momentum budget analyses reveal that a large westward-directed horizontal pressure gradient force within the simulated cold pool produced rapid flow acceleration towards Nantong. The analyses enrich the understanding of damaging wind characteristics over coastal East China and will prove helpful to operational forecasters.

关键词： damaging surface winds convection-permitting simulations mesoscale convective system gust front cold pool

Direct assimilation of simulated radar re fl ectivity for typhoon In-fa using EnKF: Issue with state variables updating

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TROPICAL CYCLONE RESEARCH AND REVIEW 2024年第1期13卷 24-32页

作者： Wang, Ziyue Luo, Jingyao Li, Hong Zhu, Yijie Chinese Acad Meteorol Sci State Key Lab Severe Weather Key Lab Atmospher Chem CMA Beijing Peoples R China China Meteorol Adm Shanghai Typhoon Inst Shanghai 200030 Peoples R China China Meteorol Adm Key Lab Numer Modeling Trop Cyclones Shanghai 200030 Peoples R China

Using a convective scale WRF-GSI system and a re fl ectivity observation operator based on the double-moment microphysics (Thompson) scheme, simulated radar re fl ectivity data are produced and then directly assimilated with EnKF through Observing System Simulation Experiments (OSSEs) for the case of typhoon In-Fa (2021). We examined the ability of the EnKF to simultaneously estimate state variables and conducted sensitivity tests to evaluate the impact of updating different state variables. The results show that updating a full set of analysis variables can help obtain highly precise initial fi elds in the model and improve typhoon forecast skills. Excluding the horizontal wind update will affect the adjustment of the temperature fi eld and the sea level pressure fi eld during the cyclic assimilation process. Updating the variables directly related to the re fl ectivity operator alone could adjust hydrometers well, but the positive impact arising from the assimilation quickly vanishes during the forecast. In addition, this study also includes a quantitative RMSE analysis for each variable during the assimilation cycle and compares the effect of each schemes on different variables. (c) 2024 The Shanghai Typhoon Institute of China Meteorological Administration. Publishing services by Elsevier B.V. on behalf of KeAi Communication Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Radar re fl ectivity data EnKF Direct assimilation State variables ENSEMBLE KALMAN FILTER MULTICASE COMPARATIVE-ASSESSMENT LEVEL-II DATA PART II TORNADIC THUNDERSTORMS CLOUD ANALYSIS FORT-WORTH MICROPHYSICS REFLECTIVITY PREDICTION