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ensemble forecast for Tropical Cyclone Based on CNOP-P Method:A Case Study of WRF Model and Two Typhoons

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Journal of Tropical Meteorology 2022年第2期28卷 121-138页

作者：袁时金施博赵紫君穆斌周菲凡段晚锁 School of Software Engineering Tongji UniversityShanghai 201804 China Laboratory of Cloud-Precipitation Physics and Severe Storms Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029 China University of Chinese Academy of Sciences Beijing 100029 China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029 China

In this paper,we set out to study the ensemble forecast for tropical cyclones.The case study is based on the Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)method and the WRF model to improve the prediction accuracy for track and intensity,and two different typhoons are selected as cases for analysis.We first select perturbed parameters in the YSU and WSM6 schemes,and then solve CNOP-Ps with simulated annealing algorithm for single parameters as well as the combination of multiple parameters.Finally,perturbations are imposed on default parameter values to generate the ensemble members.The whole proposed procedures are referred to as the PerturbedParameter ensemble(PPE).We also conduct two experiments,which are control forecast and ensemble forecast,termed Ctrl and perturbed-physics ensemble(PPhyE)respectively,to demonstrate the performance for contrast.In the article,we compare the effects of three experiments on tropical cyclones in aspects of track and intensity,respectively.For track,the prediction errors of PPE are smaller.The ensemble mean of PPE filters the unpredictable situation and retains the reasonably predictable components of the ensemble members.As for intensity,ensemble mean values of the central minimum sea-level pressure and the central maximum wind speed are closer to CMA data during most of the simulation time.The predicted values of the PPE ensemble members included the intensity of CMA data when the typhoon made landfall.The PPE also shows uncertainty in the forecast.Moreover,we also analyze the track and intensity from physical variable fields of PPE.Experiment results show PPE outperforms the other two benchmarks in track and intensity prediction.

关键词： ensemble forecast Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P) WRF parameter perturbation ensemble members simulated annealing algorithm

Coupled conditional nonlinear optimal perturbations and their application to ENSO ensemble forecasts

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Science China Earth Sciences 2024年第3期67卷 826-842页

作者： Wansuo DUAN Lei HU Rong FENG LASG Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China

Limitations are existed in current ensemble forecasting initial perturbation methods for describing the interactions among various spheres of the Earth system. In this study, a new method is proposed, namely, the coupled conditional nonlinear optimal perturbation(C-CNOP) method, which incorporates multisphere interactions much appropriately. The El Nino-Southern Oscillation(ENSO) is a typical ocean-atmosphere “coupling”(or “interaction”) phenomenon. The C-CNOP method is applied to ensemble forecasting of ENSO. It is demonstrated that the C-CNOP method can generate coupled initial perturbations(CPs) that appropriately consider initial ocean-atmosphere coupling uncertainty for ENSO ensemble forecasts. Results reveal that the CPs effectively improve the ability of ENSO ensemble-mean forecasts in both temporal variability of Nio3.4 sea surface temperature anomalies(SSTAs) and spatial variability of ENSO mature-phase SSTAs. Notably, despite the weakest ocean-atmosphere coupling strength in the tropical Pacific occurring during the boreal spring and summer, CPs still capture the uncertainties of this weak coupling when ENSO predictions are initialized at these seasons. This performance of CPs significantly suppresses the rapid increase of ENSO prediction errors due to the high ocean-atmosphere coupling instability during these seasons, and thus effectively extends the lead time of skillful ENSO forecasting. Hence, the C-CNOP method is a suitable initial perturbation approach for ENSO ensemble forecast that can describe initial ocean-atmosphere coupling uncertainty. It is expected that the CCNOP method plays a significant role in predictions of other high-impact climate phenomena, and even future Earth system predictions.

关键词： ensemble forecast Multisphere interaction Initial perturbations ENSO

Predictability of the 7·20 extreme rainstorm in Zhengzhou in stochastic kinetic-energy backscatter ensembles

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Science China Earth Sciences 2024年第7期67卷 2226-2241页

作者： Min YANG Peilong YU Lifeng ZHANG Xiaobing PAN Quanjia ZHONG Yunying LI College of Meteorology and Oceanography National University of Defense TechnologyChangsha 410073China Key Laboratory of High Impact Weather(Special) China Meteorological AdministrationChangsha 410073China Collaborative Innovation Center on forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment ChangeNanjing University of Information Science and TechnologyNanjing 210044China Department of Ocean Science Hong Kong University of Science and TechnologyHong Kong 999077China

The scale-dependent predictability of the devastating 7·20 extreme rainstorm in Zhengzhou,China in 2021 was investigated via ensemble experiments,which were perturbed on different scales using the stochastic kinetic-energy backscatter(SKEB)scheme in the WRF model,with the innermost domain having a 3-km grid spacing.The daily rainfall(RAIN24h)and the cloudburst during 1600-1700 LST(RAIN1h)were considered.Results demonstrated that with larger perturbation scales,the ensemble spread for the rainfall maximum widens and rainfall forecasts become closer to the observations.In ensembles with mesoscale or convective-scale perturbations,RAIN1h loses predictability at scales smaller than 20 km and RAIN24h is predictable for all scales.Whereas in ensembles with synoptic-scale perturbations,the largest scale of predictability loss extends to 60 km for both RAIN1h and RAIN24h.Moreover,the average positional error in forecasting the heaviest rainfall for RAIN24h(RAIN1h)was 400 km(50-60)km.The southerly low-level jet near Zhengzhou was assumed to be directly responsible for the forecast uncertainty of RAIN1h.The rapid intensification in low-level cyclonic vorticity,mid-level divergence,and upward motion concomitant with the jet dynamically facilitated the cloudburst.Further analysis of the divergent,rotational and vertical kinetic spectra and the corresponding error spectra showed that the error kinetic energy at smaller scales grows faster than that at larger scales and saturates more quickly in all experiments.Larger-scale perturbations not only boost larger-scale error growth but are also conducive to error growth at all scales through a downscale cascade,which indicates that improving the accuracy of larger-scale flow forecast may discernibly contributes to the forecast of cloudburst intensity and position.

关键词： Stochastic kinetic-energy backscatter(SKEB) Extreme rainfall ensemble forecast Predictability

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 experience.This made providing meteorological services for this event particularly challenging.The China Meteorological Administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical predictions.Several 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 established.Some of these advancements represent the highest level of capabilities currently available in China.The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality.This included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days.Around 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 systems.These accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the CMA.This 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 forecasting activities.This article provides an

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

ensemble forecast: A New Approach to Uncertainty and Predictability

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Advances in Atmospheric Sciences 2005年第6期22卷 781-788页

作者： Yuejian ZHU Environmental Modeling Center NCEP/NWS/NOAA Washington DC USA

ensemble techniques have been used to generate daily numerical weather forecasts since the 1990s in numerical centers around the world due to the increase in computation ability. One of the main purposes of numerical ensemble forecasts is to try to assimilate the initial uncertainty （initial error） and the forecast uncertainty （forecast error） by applying either the initial perturbation method or the multi-model/multiphysics method. In fact, the mean of an ensemble forecast offers a better forecast than a deterministic （or control） forecast after a short lead time （3-5 days） for global modelling applications. There is about a 1-2-day improvement in the forecast skill when using an ensemble mean instead of a single forecast for longer lead-time. The skillful forecast （65% and above of an anomaly correlation） could be extended to 8 days （or longer） by present-day ensemble forecast systems. Furthermore, ensemble forecasts can deliver a probabilistic forecast to the users, which is based on the probability density function （PDF） instead of a single-value forecast from a traditional deterministic system. It has long been recognized that the ensemble forecast not only improves our weather forecast predictability but also offers a remarkable forecast for the future uncertainty, such as the relative measure of predictability （RMOP） and probabilistic quantitative precipitation forecast （PQPF）. Not surprisingly, the success of the ensemble forecast and its wide application greatly increase the confidence of model developers and research communities.

关键词： ensemble forecast predictability uncertainty

Typhoon storm surge ensemble forecast based on GPU technique

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Acta Oceanologica Sinica 2020年第5期39卷 77-86页

作者： Qiuxing Liu Jinrong Jiang Fujiang Yu Changkuan Zhang Jianxi Dong Xiaojiang Song Yuzhu Wang College of Harbor Coastal and Offshore EngineeringHohai UniversityNanjing 210098China National Marine Environmental forecasting Center Beijing 100081China Computer Network Information Center Chinese Academy of SciencesBeijing 100190China School of Information Engineering China University of GeosciencesBeijing 100083China

The accuracy of typhoon forecasts plays an important role in the prediction of storm surges.The uncertainty of a typhoon’s intensity and track means it is necessary to use an ensemble model to predict typhoon storm surges.A hydrodynamic model,which is operational at the National Marine Environmental forecasting Center,is applied to conduct surge simulations for South China coastal areas using the best track data with parametric wind and pressure models.The results agree well with tidal gauge observations.To improve the calculation efficiency,the hydrodynamic model is modified using CUDA Fortran.The calculation results are almost the same as those from the original model,but the calculation time is reduced by more than 99%.A total of 150 typhoon cases are generated by combining 50 typhoon tracks from the European Centre for Medium-Range Weather forecasts with three possible typhoon intensity forecasts.The surge ensembles are computed by the improved hydrodynamic model.Based on the simulated storm surges for the different typhoon cases,ensemble and probability forecast products can be provided.The mean ensemble results and probability forecast products are shown to agree well with the observed storm surge caused by Typhoon Mangkhut.The improved model is highly suitable for ensemble numerical forecasts,providing better forecast products for decision-making,and can be easily implemented to run on regular workstations.

关键词： typhoon storm surge ensemble forecast probability forecast CUDA Fortran

Dust Storm ensemble forecast Experiments in East Asia

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Advances in Atmospheric Sciences 2009年第6期26卷 1053-1070页

作者：朱江林彩燕王自发 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric Physics Chinese Academy of Sciences

The ensemble Kalman filter （EnKF）, as a unified approach to both data assimilation and ensemble forecasting problems, is used to investigate the performance of dust storm ensemble forecasting targeting a dust episode in the East Asia during 23-30 May 2007. The errors in the input wind field, dust emission intensity, and dry deposition velocity are among important model uncertainties and are considered in the model error perturbations. These model errors are not assumed to have zero-means. The model error means representing the model bias are estimated as part of the data assimilation process. Observations from a LIDAR network are assimilated to generate the initial ensembles and correct the model biases. The ensemble forecast skills are evaluated against the observations and a benchmark/control forecast, which is a simple model run without assimilation of any observations. Another ensemble forecast experiment is also performed without the model bias correction in order to examine the impact of the bias correction. Results show that the ensemble-mean, as deterministic forecasts have substantial improvement over the control forecasts and correctly captures the major dust arrival and cessation timing at each observation site. However, the forecast skill decreases as the forecast lead time increases. Bias correction further improved the forecasts in down wind areas. The forecasts within 24 hours are most improved and better than those without the bias correction. The examination of the ensemble forecast skills using the Brier scores and the relative operating characteristic curves and areas indicates that the ensemble forecasting system has useful forecast skills.

关键词： dust storm ensemble forecast data assimilation bias correction

Study on ensemble-Based forecast of Extremely Heavy Rainfalls in China: Experiments for July 2011 Cases

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Acta meteorologica Sinica 2013年第2期27卷 170-185页

作者：刘琳陈静程龙林春泽吴志鹏 Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research Institute of Heavy Rain China Meteorological Administration Chengdu University of Information Technology Numerical Weather Prediction Center

According to the Anderson-Darling principle, a method for forecast of extremely heavy rainfall （abbre- viated as extreme rainfall/precipitation） was developed based on the ensemble forecast data of the T213 global ensemble prediction system （EPS） of the China Meteorological Administration （CMA）. Using the T213 forecast precipitation data during 2007-2010 and the observed rainfall data in June-August of 2001 2010, characteristics of the cumulative distribution functions （CDFs） of the observed and the T213 EPS forecast precipitation were analyzed. Accordingly, in the light of the continuous differences of the CDFs between model climate and EPS forecasts, a mathematical model of Extreme Precipitation forecast Index （EPFI） was established and applied to forecast experiments of several extreme rainfall events in China during 17-31 July 2011. The results show that the EPFI has taken advantage of the tail information of the model climatic CDF and provided agreeable forecasts of extreme rainfalls. The EPFI based on the T213 EPS is useful for issuing early warnings of extreme rainfalls 3 7 days in advance. With extension of the forecast lead time, the EPFI becomes less skillful. The results also demonstrate that the rationality of the model climate CDF was of vital importance to the skill of EPFI.

关键词： extremely heavy precipitation ensemble forecast extreme precipitation forecast index modelclimatic cumulative distribution function

AN ensemble forecast EXPERIMENT OF A LANDING TYPHOON

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Journal of Tropical Meteorology 2012年第3期18卷 314-321页

作者：谭燕梁旭东 Shanghai Typhoon Institute of the China Meteorological Administration(CMA) Laboratory of Typhoon forecast Technique/CMA Institute of Urban Meteorology of CMA

Based on the Global Regional Assimilation and Prediction System-Tropical Cyclone Model(GRAPES-TCM),an ensemble forecast experiment was performed,in which Typhoon Wipha during the period immediately prior to landfall was selected for the study and the breeding of growing mode(BGM) method was used to perturb the initial conditions of the vortex field and the environment field.The results of the experiment indicate that each member had a different initial status in BGM processing and they show a reasonable spread among members along with the forecast phase.Changes in the large-scale field,thermodynamic structure,and spread among members took place when Wipha made landfall.The steering effect of the large-scale field and the interaction between the thermodynamics and the dynamics resulted in different tracks of the members.Meanwhile,the forecast uncertainty increased.In summary,the ensemble mean did not perform as well as the control forecast,but the cluster mean provided some useful information,and performed better than the control in some instances.The position error was 34 km for 24 h forecast,153 km for 48 h forecast,and 191 km for 66 h forecast.The strike probability chart qualitatively described the forecast uncertainty.

关键词： landing typhoon ensemble forecast GRAPES-TCM breeding of growing mode method cluster analysis