This study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hour...This study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hourly rainfall of 118.8 mm.The performance of two sets of convection-permitting ensemble forecast systems(CEFSs),each with 30 members and a 3-km horizontal grid spacing,is evaluated.The CEFS_ICBCs,using multiple initial and boundary conditions(ICs and BCs),and the CEFS_ICBCs Phys,which incorporates both multi-physics schemes and ICs/BCs,are compared to the CMA-REPS(China Meteorological Administration-Regional Ensemble Prediction System)with a coarser 10-km grid spacing.The two CEFSs demonstrate more uniform rank histograms and lower Brier scores(with higher resolution),improving precipitation intensity predictions and providing more reliable probability forecasts,although they overestimate precipitation over Mt.Dabie.It is challenging for the CEFSs to capture the evolution of mesoscale rainstorms that are known to be related to the errors in predicting the southwesterly low-level winds.Sensitivity experiments reveal that the microphysics and radiation schemes introduce considerable uncertainty in predicting the intensity and location of heavy rainfall in and near Nanjing and Mt.Dabie.In particular,the Asymmetric Convection Model 2(ACM2)planetary boundary layer scheme combined with the Pleim-Xiu surface layer scheme tends to produce a biased northeastward extension of the boundary-layer jet,contributing to the northeastward bias of heavy precipitation around Nanjing in the CEFS_ICBCs.展开更多
The impacts of lateral boundary conditions(LBCs)provided by numerical models and data-driven networks on convective-scale ensemble forecasts are investigated in this study.Four experiments are conducted on the Hangzho...The impacts of lateral boundary conditions(LBCs)provided by numerical models and data-driven networks on convective-scale ensemble forecasts are investigated in this study.Four experiments are conducted on the Hangzhou RDP(19th Hangzhou Asian Games Research Development Project on Convective-scale Ensemble Prediction and Application)testbed,with the LBCs respectively sourced from National Centers for Environmental Prediction(NCEP)Global Forecast System(GFS)forecasts with 33 vertical levels(Exp_GFS),Pangu forecasts with 13 vertical levels(Exp_Pangu),Fuxi forecasts with 13 vertical levels(Exp_Fuxi),and NCEP GFS forecasts with the vertical levels reduced to 13(the same as those of Exp_Pangu and Exp_Fuxi)(Exp_GFSRDV).In general,Exp_Pangu performs comparably to Exp_GFS,while Exp_Fuxi shows slightly inferior performance compared to Exp_Pangu,possibly due to its less accurate large-scale predictions.Therefore,the ability of using data-driven networks to efficiently provide LBCs for convective-scale ensemble forecasts has been demonstrated.Moreover,Exp_GFSRDV has the worst convective-scale forecasts among the four experiments,which indicates the potential improvement of using data-driven networks for LBCs by increasing the vertical levels of the networks.However,the ensemble spread of the four experiments barely increases with lead time.Thus,each experiment has insufficient ensemble spread to present realistic forecast uncertainties,which will be investigated in a future study.展开更多
Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly effi...Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly efficient calculations for O-CNOPs are still challenging in the field of ensemble forecasting.In this study,we combine a gradient-based iterative idea with the Gram‒Schmidt orthogonalization,and propose an iterative optimization method to compute O-CNOPs.This method is different from the original sequential optimization method,and allows parallel computations of O-CNOPs,thus saving a large amount of computational time.We evaluate this method by using the Lorenz-96 model on the basis of the ensemble forecasting ability achieved and on the time consumed for computing O-CNOPs.The results demonstrate that the parallel iterative method causes O-CNOPs to yield reliable ensemble members and to achieve ensemble forecasting skills similar to or even slightly higher than those produced by the sequential method.Moreover,the parallel method significantly reduces the computational time for O-CNOPs.Therefore,the parallel iterative method provides a highly effective and efficient approach for calculating O-CNOPs for ensemble forecasts.Expectedly,it can play an important role in the application of the O-CNOPs to realistic ensemble forecasts for high-impact weather and climate events.展开更多
A running mean bias （RMB） correction ap- proach was applied to the forecasts of near-surface variables in a seasonal short-range ensemble forecasting experiment with 57 consecutive cases during summer 2010 in the no...A running mean bias （RMB） correction ap- proach was applied to the forecasts of near-surface variables in a seasonal short-range ensemble forecasting experiment with 57 consecutive cases during summer 2010 in the northern China region. To determine a proper training window length for calculating RMB, window lengths from 2 to 20 days were evaluated, and 16 days was taken as an optimal window length, since it receives most of the benefit from extending the window length. The raw and 16-day RMB corrected ensembles were then evaluated for their ensemble mean forecast skills. The results show that the raw ensemble has obvious bias in all near-surface variables. The RMB correction can remove the bias reasonably well, and generate an unbiased ensemble. The bias correction not only reduces the ensemble mean forecast error, but also results in a better spreaderror relationship. Moreover, two methods for computing calibrated probabilistic forecast （PF） were also evaluated through the 57 case dates： 1） using the relative frequency from the RMB-eorrected ensemble; 2） computing the forecasting probabilities based on a historical rank histogram. The first method outperforms the second one, as it can improve both the reliability and the resolution of the PFs, while the second method only has a small effect on the reliability, indicating the necessity and importance of removing the systematic errors from the ensemble.展开更多
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 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.展开更多
In this study, the Institute of Atmospheric Physics, Chinese Academy of Sciences - regional ensemble forecast system （IAP-REFS） described in Part I was further validated through a 65-day experiment using the summer ...In this study, the Institute of Atmospheric Physics, Chinese Academy of Sciences - regional ensemble forecast system （IAP-REFS） described in Part I was further validated through a 65-day experiment using the summer season of 2010. The verification results show that IAP-REFS is skillful for quantitative precipitation forecasts （QPF） and probabilistic QPF, but it has a systematic bias in forecasting near-surface variables. Applying a 7-day running mean bias correction to the forecasts of near-surface variables remarkably improved the reliability of the forecasts. In this study, the perturbation extraction and inflation method （proposed with the single case study in Part I） was further applied to the full season with different inflation factors. This method increased the ensemble spread and improved the accuracy of forecasts of precipitation and near-surface variables. The seasonal mean profiles of the IAP-REFS ensemble indicate good spread among ensemble members and some model biases at certain vertical levels.展开更多
A single-model, short-range, ensemble forecasting system （Institute of Atmospheric Physics, Regional Ensemble Forecast System, IAP REFS） with 15-km grid spacing, configured with multiple initial conditions, multiple...A single-model, short-range, ensemble forecasting system （Institute of Atmospheric Physics, Regional Ensemble Forecast System, IAP REFS） with 15-km grid spacing, configured with multiple initial conditions, multiple lateral boundary conditions, and multiple physics parameterizations with 11 ensemble members, was developed using the Weather and Research Forecasting Model Advanced Research modeling system for prediction of stratiform precipitation events in northern China. This is the first part of a broader research project to develop a novel cloud-seeding operational system in a probabilistic framework. The ensemble perturbations were extracted from selected members of the National Center for Environmental Prediction Global Ensemble Forecasting System （NCEP GEFS） forecasts, and an inflation factor of two was applied to compensate for the lack of spread in the GEFS forecasts over the research region. Experiments on an actual stratiform precipitation case that occurred on 5-7 June 2009 in northern China were conducted to validate the ensemble system. The IAP REFS system had reasonably good performance in predicting the observed stratiform precipitation system. The perturbation inflation enlarged the ensemble spread and alleviated the underdispersion caused by parent forecasts. Centering the extracted perturbations on higher-resolution NCEP Global Forecast System forecasts resulted in less ensemble mean root-mean-square error and better accuracy in probabilistic quantitative precipitation forecasts （PQPF）. However, the perturbation inflation and recentering had less effect on near-surface-level variables compared to the mid-level variables, and its influence on PQPF resolution was limited as well.展开更多
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 w...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.展开更多
The purpose of this study is to investigate the effectiveness of two different ensemble forecasting （EF） techniques-the lagged-averaged forecast （LAF） and the breeding of growing modes （BGM）. In the BGM experime...The purpose of this study is to investigate the effectiveness of two different ensemble forecasting （EF） techniques-the lagged-averaged forecast （LAF） and the breeding of growing modes （BGM）. In the BGM experiments, the vortex and the environment are perturbed separately （named BGMV and BGME）. Tropical cyclone （TC） motions in two difficult situations are studied： a large vortex interacting with its environment, and an apparent binary interaction. The former is Typhoon Yancy and the latter involves Typhoon Ed and super Typhoon Flo, all occurring during the Tropical Cyclone Motion Experiment TCM- 90. The model used is the baroclinic model of the University of New South Wales. The lateral boundary tendencies are computed from atmospheric analysis data. Only the relative skill of the ensemble forecast mean over the control run is used to evaluate the effectiveness of the EF methods, although the EF technique is also usecl to quantify forecast uncertainty in some studies. In the case of Yancy, the ensemble mean forecasts of each of the three methodologies are better than that of the control, with LAF being the best. The mean track of the LAF is close to the best track, and it predicts landfall over Taiwan. The improvements in LAF and the full BGM where both the environment and vortex are perturbed suggest the importance of combining the perturbation of the vortex and environment when the interaction between the two is appreciable. In the binary interaction case of Ed and Flo, the forecasts of Ed appear to be insensitive to perturbations of the environment and/or the vortex, which apparently results from erroneous forecasts by the model of the interaction between the subtropical ridge and Ed, as well as from the interaction between the two typhoons, thus reducing the effectiveness of the EF technique. This conclusion is reached through sensitivity experiments on the domain of the model and by adding or eliminating certain features in the model atmosphere. Nevertheless, the forecast tracks in some of the cases are improved over that of the control. On the other hand, the EF technique has little impact on the forecasts of Flo because the control forecast is already very close to the best track. The study provides a basis for the. future development of the EF technique. The limitations of this study are also addressed. For example, the above results are based on a small sample, and the study is actually a simulation, which is different than operational forecasting. Further tests of these EF techniques are proposed.展开更多
Using real-time correction technology for typhoons, this paper discusses real-time correction for forecasting the track of four typhoons during 2009 and 2010 in Japan, Beijing, Guangzhou, and Shanghai. It was determin...Using real-time correction technology for typhoons, this paper discusses real-time correction for forecasting the track of four typhoons during 2009 and 2010 in Japan, Beijing, Guangzhou, and Shanghai. It was determined that the short-time forecast effect was better than the original objective mode. By selecting four types of integration schemes after multiple mode path integration for those four objective modes, the forecast effect of the multi-mode path integration is better, on average, than any single model. Moreover, multi-mode ensemble forecasting has obvious advantages during the initial 36 h.展开更多
On 21 July 2012,an extreme rainfall event that recorded a maximum rainfall amount over 24 hours of 460 mm,occurred in Beijing,China. Most operational models failed to predict such an extreme amount. In this study,a co...On 21 July 2012,an extreme rainfall event that recorded a maximum rainfall amount over 24 hours of 460 mm,occurred in Beijing,China. Most operational models failed to predict such an extreme amount. In this study,a convective-permitting ensemble forecast system（CEFS）,at 4-km grid spacing,covering the entire mainland of China,is applied to this extreme rainfall case. CEFS consists of 22 members and uses multiple physics parameterizations. For the event,the predicted maximum is 415 mm d^-1 in the probability-matched ensemble mean. The predicted high-probability heavy rain region is located in southwest Beijing,as was observed. Ensemble-based verification scores are then investigated. For a small verification domain covering Beijing and its surrounding areas,the precipitation rank histogram of CEFS is much flatter than that of a reference global ensemble. CEFS has a lower（higher） Brier score and a higher resolution than the global ensemble for precipitation,indicating more reliable probabilistic forecasting by CEFS. Additionally,forecasts of different ensemble members are compared and discussed. Most of the extreme rainfall comes from convection in the warm sector east of an approaching cold front. A few members of CEFS successfully reproduce such precipitation,and orographic lift of highly moist low-level flows with a significantly southeasterly component is suggested to have played important roles in producing the initial convection. Comparisons between good and bad forecast members indicate a strong sensitivity of the extreme rainfall to the mesoscale environmental conditions,and,to less of an extent,the model physics.展开更多
The application of numerical weather prediction （NWP） products is increasing dramatically. Existing reports indicate that ensemble predictions have better skill than deterministic forecasts. In this study, numerical...The application of numerical weather prediction （NWP） products is increasing dramatically. Existing reports indicate that ensemble predictions have better skill than deterministic forecasts. In this study, numerical ensemble precipitation forecasts in the TIGGE database were evaluated using deterministic, dichotomous （yes/no）, and probabilistic techniques over Iran for the period 2008-16. Thirteen rain gauges spread over eight homogeneous precipitation regimes were selected for evaluation. The Inverse Distance Weighting and Kriging methods were adopted for interpolation of the prediction values, downscaled to the stations at lead times of one to three days. To enhance the forecast quality, NWP values were post-processed via Bayesian Model Averaging. The results showed that ECMWF had better scores than other products. However, products of all centers underestimated precipitation in high precipitation regions while overestimating precipitation in other regions. This points to a systematic bias in forecasts and demands application of bias correction techniques. Based on dichotomous evaluation, NCEP did better at most stations, although all centers overpredicted the number of precipitation events. Compared to those of ECMWF and NCER UKMO yielded higher scores in mountainous regions, but performed poorly at other selected stations. Furthermore, the evaluations showed that all centers had better skill in wet than in dry seasons. The quality of post-processed predictions was better than those of the raw predictions. In conclusion, the accuracy of the NWP predictions made by the selected centers could be classified as medium over Iran, while post-processing of predictions is recommended to improve the quality.展开更多
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 episod...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.展开更多
The quality of ensemble forecasting is seriously affected by sample quality.In this study,the distributions of ensemble members based on the observed track and intensity of tropical cyclones(TCs)were optimized and the...The quality of ensemble forecasting is seriously affected by sample quality.In this study,the distributions of ensemble members based on the observed track and intensity of tropical cyclones(TCs)were optimized and their influence on the simulation results was analyzed.Simulated and observed tracks and intensities of TCs were compared and these two indicators were combined and weighted to score the sample.Samples with higher scores were retained and samples with lower scores were eliminated to improve the overall quality of the ensemble forecast.For each sample,the track score and intensity score were added as the final score of the sample with weight proportions of 10 to 0,9 to 1,8 to 2,7 to 3,6 to 4,5 to 5.These were named as“tr”,“91”,“82”,“73”,“64”,and“55”,respectively.The WRF model was used to simulate five tropical cyclones in the northwestern Pacific to test the ability of this scheme to improve the forecast track and intensity of these cyclones.The results show that the sample optimization effectively reduced the track and intensity error,“55”usually had better performance on the short-term intensity prediction,and“tr”had better performance in short-term track prediction.From the overall performance of the track and intensity simulation,“91”was the best and most stable among all sample optimization schemes.These results may provide some guidance for optimizing operational ensemble forecasting of TCs.展开更多
An unprecedented heavy rainfall event occurred in Henan Province,China,during the period of 1200 UTC 19-1200 UTC 20 July 2021 with a record of 522 mm accumulated rainfall.Zhengzhou,the capital city of Henan,received 2...An unprecedented heavy rainfall event occurred in Henan Province,China,during the period of 1200 UTC 19-1200 UTC 20 July 2021 with a record of 522 mm accumulated rainfall.Zhengzhou,the capital city of Henan,received 201.9 mm of rainfall in just one hour on the day.In the present study,the sensitivity of this event to atmospheric variables is investigated using the ECMWF ensemble forecasts.The sensitivity analysis first indicates that a local YellowHuai River low vortex(YHV)in the southern part of Henan played a crucial role in this extreme event.Meanwhile,the western Pacific subtropical high(WPSH)was stronger than the long-term average and to the west of its climatological position.Moreover,the existence of a tropical cyclone(TC)In-Fa pushed into the peripheral of the WPSH and brought an enhanced easterly flow between the TC and WPSH channeling abundant moisture to inland China and feeding into the YHV.Members of the ECMWF ensemble are selected and grouped into the GOOD and the POOR groups based on their predicted maximum rainfall accumulations during the event.Some good members of ECMWF ensemble Prediction System(ECMWF-EPS)are able to capture good spatial distribution of the heavy rainfall,but still underpredict its extremity.The better prediction ability of these members comes from the better prediction of the evolution characteristics(i.e.,intensity and location)of the YHV and TC In-Fa.When the YHV was moving westward to the south of Henan,a relatively strong southerly wind in the southwestern part of Henan converged with the easterly flow from the channel wind between In-Fa and WPSH.The convergence and accompanying ascending motion induced heavy precipitation.展开更多
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 s...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.展开更多
The prediction of the particle number concentration and liquid/ice water content of cloud is significant for many aspects of atmospheric science.However,given the uncertainties in the initial and boundary conditions a...The prediction of the particle number concentration and liquid/ice water content of cloud is significant for many aspects of atmospheric science.However,given the uncertainties in the initial and boundary conditions and imperfections of microphysical schemes,the accurate prediction of these microphysical properties of cloud is still a big challenge.The ensemble approach may be a viable way to reduce forecast uncertainties.In this paper,a large-scale stratiform cloud precipitation process is studied by comparing results of a 10-member ensemble forecast model with aircraft observation data.By means of the ensemble average,the prediction of bulk parameters such as liquid water content and ice water content can be improved in comparison with the control member,but the particle number concentrations are still one to two orders of magnitude less than those from observations.Intercomparison of raindrop size spectra reveals a big distinction between observations and predictions for particles with a diameter less than 1000μm.展开更多
Nowadays,ensemble forecasting is popular in numerical weather prediction(NWP).However,an ensemble may not produce a perfect Gaussian probability distribution due to limited members and the fact that some members signi...Nowadays,ensemble forecasting is popular in numerical weather prediction(NWP).However,an ensemble may not produce a perfect Gaussian probability distribution due to limited members and the fact that some members significantly deviate from the true atmospheric state.Therefore,event samples with small probabilities may downgrade the accuracy of an ensemble forecast.In this study,the evolution of tropical storms(weak typhoon)was investigated and an observed tropical storm track was used to limit the probability distribution of samples.The ensemble forecast method used pure observation data instead of assimilated data.In addition,the prediction results for three tropical storm systems,Merbok,Mawar,and Guchol,showed that track and intensity errors could be reduced through sample optimization.In the research,the vertical structures of these tropical storms were compared,and the existence of different thermal structures was discovered.One possible reason for structural differences is sample optimization,and it may affect storm intensity and track.展开更多
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 t...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.展开更多
Extreme rainfall is common from May to October in south China.This study investigates the key deviation of initial fields on ensemble forecast of a persistent heavy rainfall event from May 20 to 22,2020 in Guangdong P...Extreme rainfall is common from May to October in south China.This study investigates the key deviation of initial fields on ensemble forecast of a persistent heavy rainfall event from May 20 to 22,2020 in Guangdong Province,south China by comparing ensemble members with different performances.Based on the rainfall distribution and pattern,two types are selected for analysis compared with the observed precipitation.Through the comparison of the thermal and dynamic fields in the middle and lower layers,it can be found that the thermal difference between the middle and lower layers was an important factor which led to the deviation of precipitation distribution.The dynamic factors also have some effects on the precipitation area although they were not as important as the thermal factors in this case.Correlating accumulated precipitation with atmospheric state variables further corroborates the above conclusion.This study suggests that the uncertainty of the thermal and dynamic factors in the numerical model can have a strong impact on the quantitative skills of heavy rainfall forecasts.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42030610 and 42205006)the Startup Foundation for Introducing Talent of NUIST(2023r121)。
文摘This study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hourly rainfall of 118.8 mm.The performance of two sets of convection-permitting ensemble forecast systems(CEFSs),each with 30 members and a 3-km horizontal grid spacing,is evaluated.The CEFS_ICBCs,using multiple initial and boundary conditions(ICs and BCs),and the CEFS_ICBCs Phys,which incorporates both multi-physics schemes and ICs/BCs,are compared to the CMA-REPS(China Meteorological Administration-Regional Ensemble Prediction System)with a coarser 10-km grid spacing.The two CEFSs demonstrate more uniform rank histograms and lower Brier scores(with higher resolution),improving precipitation intensity predictions and providing more reliable probability forecasts,although they overestimate precipitation over Mt.Dabie.It is challenging for the CEFSs to capture the evolution of mesoscale rainstorms that are known to be related to the errors in predicting the southwesterly low-level winds.Sensitivity experiments reveal that the microphysics and radiation schemes introduce considerable uncertainty in predicting the intensity and location of heavy rainfall in and near Nanjing and Mt.Dabie.In particular,the Asymmetric Convection Model 2(ACM2)planetary boundary layer scheme combined with the Pleim-Xiu surface layer scheme tends to produce a biased northeastward extension of the boundary-layer jet,contributing to the northeastward bias of heavy precipitation around Nanjing in the CEFS_ICBCs.
基金supported by the Strategic Research and Consulting Project of the Chinese Academy of Engineering[grant number 2024-XBZD-14]the National Natural Science Foundation of China[grant numbers 42192553 and 41922036]the Fundamental Research Funds for the Central Universities–Cemac“GeoX”Interdisciplinary Program[grant number 020714380207]。
文摘The impacts of lateral boundary conditions(LBCs)provided by numerical models and data-driven networks on convective-scale ensemble forecasts are investigated in this study.Four experiments are conducted on the Hangzhou RDP(19th Hangzhou Asian Games Research Development Project on Convective-scale Ensemble Prediction and Application)testbed,with the LBCs respectively sourced from National Centers for Environmental Prediction(NCEP)Global Forecast System(GFS)forecasts with 33 vertical levels(Exp_GFS),Pangu forecasts with 13 vertical levels(Exp_Pangu),Fuxi forecasts with 13 vertical levels(Exp_Fuxi),and NCEP GFS forecasts with the vertical levels reduced to 13(the same as those of Exp_Pangu and Exp_Fuxi)(Exp_GFSRDV).In general,Exp_Pangu performs comparably to Exp_GFS,while Exp_Fuxi shows slightly inferior performance compared to Exp_Pangu,possibly due to its less accurate large-scale predictions.Therefore,the ability of using data-driven networks to efficiently provide LBCs for convective-scale ensemble forecasts has been demonstrated.Moreover,Exp_GFSRDV has the worst convective-scale forecasts among the four experiments,which indicates the potential improvement of using data-driven networks for LBCs by increasing the vertical levels of the networks.However,the ensemble spread of the four experiments barely increases with lead time.Thus,each experiment has insufficient ensemble spread to present realistic forecast uncertainties,which will be investigated in a future study.
基金sponsored by the National Natural Science Foundation of China(Grant Nos.41930971,42330111,and 42405061)the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(Earth Lab).
文摘Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly efficient calculations for O-CNOPs are still challenging in the field of ensemble forecasting.In this study,we combine a gradient-based iterative idea with the Gram‒Schmidt orthogonalization,and propose an iterative optimization method to compute O-CNOPs.This method is different from the original sequential optimization method,and allows parallel computations of O-CNOPs,thus saving a large amount of computational time.We evaluate this method by using the Lorenz-96 model on the basis of the ensemble forecasting ability achieved and on the time consumed for computing O-CNOPs.The results demonstrate that the parallel iterative method causes O-CNOPs to yield reliable ensemble members and to achieve ensemble forecasting skills similar to or even slightly higher than those produced by the sequential method.Moreover,the parallel method significantly reduces the computational time for O-CNOPs.Therefore,the parallel iterative method provides a highly effective and efficient approach for calculating O-CNOPs for ensemble forecasts.Expectedly,it can play an important role in the application of the O-CNOPs to realistic ensemble forecasts for high-impact weather and climate events.
基金supported by a project of the National Natural Science Foundation of China (Grant No. 41305099)
文摘A running mean bias （RMB） correction ap- proach was applied to the forecasts of near-surface variables in a seasonal short-range ensemble forecasting experiment with 57 consecutive cases during summer 2010 in the northern China region. To determine a proper training window length for calculating RMB, window lengths from 2 to 20 days were evaluated, and 16 days was taken as an optimal window length, since it receives most of the benefit from extending the window length. The raw and 16-day RMB corrected ensembles were then evaluated for their ensemble mean forecast skills. The results show that the raw ensemble has obvious bias in all near-surface variables. The RMB correction can remove the bias reasonably well, and generate an unbiased ensemble. The bias correction not only reduces the ensemble mean forecast error, but also results in a better spreaderror relationship. Moreover, two methods for computing calibrated probabilistic forecast （PF） were also evaluated through the 57 case dates： 1） using the relative frequency from the RMB-eorrected ensemble; 2） computing the forecasting probabilities based on a historical rank histogram. The first method outperforms the second one, as it can improve both the reliability and the resolution of the PFs, while the second method only has a small effect on the reliability, indicating the necessity and importance of removing the systematic errors from the ensemble.
文摘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.
基金supported by a project of the National Natural Science Foundation of China (Grant No. 40875079)
文摘In this study, the Institute of Atmospheric Physics, Chinese Academy of Sciences - regional ensemble forecast system （IAP-REFS） described in Part I was further validated through a 65-day experiment using the summer season of 2010. The verification results show that IAP-REFS is skillful for quantitative precipitation forecasts （QPF） and probabilistic QPF, but it has a systematic bias in forecasting near-surface variables. Applying a 7-day running mean bias correction to the forecasts of near-surface variables remarkably improved the reliability of the forecasts. In this study, the perturbation extraction and inflation method （proposed with the single case study in Part I） was further applied to the full season with different inflation factors. This method increased the ensemble spread and improved the accuracy of forecasts of precipitation and near-surface variables. The seasonal mean profiles of the IAP-REFS ensemble indicate good spread among ensemble members and some model biases at certain vertical levels.
基金supported by the project of the NSFC (Grants No. 40875079)
文摘A single-model, short-range, ensemble forecasting system （Institute of Atmospheric Physics, Regional Ensemble Forecast System, IAP REFS） with 15-km grid spacing, configured with multiple initial conditions, multiple lateral boundary conditions, and multiple physics parameterizations with 11 ensemble members, was developed using the Weather and Research Forecasting Model Advanced Research modeling system for prediction of stratiform precipitation events in northern China. This is the first part of a broader research project to develop a novel cloud-seeding operational system in a probabilistic framework. The ensemble perturbations were extracted from selected members of the National Center for Environmental Prediction Global Ensemble Forecasting System （NCEP GEFS） forecasts, and an inflation factor of two was applied to compensate for the lack of spread in the GEFS forecasts over the research region. Experiments on an actual stratiform precipitation case that occurred on 5-7 June 2009 in northern China were conducted to validate the ensemble system. The IAP REFS system had reasonably good performance in predicting the observed stratiform precipitation system. The perturbation inflation enlarged the ensemble spread and alleviated the underdispersion caused by parent forecasts. Centering the extracted perturbations on higher-resolution NCEP Global Forecast System forecasts resulted in less ensemble mean root-mean-square error and better accuracy in probabilistic quantitative precipitation forecasts （PQPF）. However, the perturbation inflation and recentering had less effect on near-surface-level variables compared to the mid-level variables, and its influence on PQPF resolution was limited as well.
基金National Basic Research Program of China (2009CB421500)Shanghai Science and Technology Program (10231203700)National Natural Science Foundation of China (40921160381)
文摘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.
文摘The purpose of this study is to investigate the effectiveness of two different ensemble forecasting （EF） techniques-the lagged-averaged forecast （LAF） and the breeding of growing modes （BGM）. In the BGM experiments, the vortex and the environment are perturbed separately （named BGMV and BGME）. Tropical cyclone （TC） motions in two difficult situations are studied： a large vortex interacting with its environment, and an apparent binary interaction. The former is Typhoon Yancy and the latter involves Typhoon Ed and super Typhoon Flo, all occurring during the Tropical Cyclone Motion Experiment TCM- 90. The model used is the baroclinic model of the University of New South Wales. The lateral boundary tendencies are computed from atmospheric analysis data. Only the relative skill of the ensemble forecast mean over the control run is used to evaluate the effectiveness of the EF methods, although the EF technique is also usecl to quantify forecast uncertainty in some studies. In the case of Yancy, the ensemble mean forecasts of each of the three methodologies are better than that of the control, with LAF being the best. The mean track of the LAF is close to the best track, and it predicts landfall over Taiwan. The improvements in LAF and the full BGM where both the environment and vortex are perturbed suggest the importance of combining the perturbation of the vortex and environment when the interaction between the two is appreciable. In the binary interaction case of Ed and Flo, the forecasts of Ed appear to be insensitive to perturbations of the environment and/or the vortex, which apparently results from erroneous forecasts by the model of the interaction between the subtropical ridge and Ed, as well as from the interaction between the two typhoons, thus reducing the effectiveness of the EF technique. This conclusion is reached through sensitivity experiments on the domain of the model and by adding or eliminating certain features in the model atmosphere. Nevertheless, the forecast tracks in some of the cases are improved over that of the control. On the other hand, the EF technique has little impact on the forecasts of Flo because the control forecast is already very close to the best track. The study provides a basis for the. future development of the EF technique. The limitations of this study are also addressed. For example, the above results are based on a small sample, and the study is actually a simulation, which is different than operational forecasting. Further tests of these EF techniques are proposed.
基金National Natural Science Foundation of China(41475060,41275067,41405060)
文摘Using real-time correction technology for typhoons, this paper discusses real-time correction for forecasting the track of four typhoons during 2009 and 2010 in Japan, Beijing, Guangzhou, and Shanghai. It was determined that the short-time forecast effect was better than the original objective mode. By selecting four types of integration schemes after multiple mode path integration for those four objective modes, the forecast effect of the multi-mode path integration is better, on average, than any single model. Moreover, multi-mode ensemble forecasting has obvious advantages during the initial 36 h.
基金supported by the National Fundamental Research (973) Program of China (Grant No. 2013CB430103)the Special Foundation of the China Meteorological Administration (Grant No. GYHY201506006)supported by the National Science Foundation of China (Grant No. 41405100)
文摘On 21 July 2012,an extreme rainfall event that recorded a maximum rainfall amount over 24 hours of 460 mm,occurred in Beijing,China. Most operational models failed to predict such an extreme amount. In this study,a convective-permitting ensemble forecast system（CEFS）,at 4-km grid spacing,covering the entire mainland of China,is applied to this extreme rainfall case. CEFS consists of 22 members and uses multiple physics parameterizations. For the event,the predicted maximum is 415 mm d^-1 in the probability-matched ensemble mean. The predicted high-probability heavy rain region is located in southwest Beijing,as was observed. Ensemble-based verification scores are then investigated. For a small verification domain covering Beijing and its surrounding areas,the precipitation rank histogram of CEFS is much flatter than that of a reference global ensemble. CEFS has a lower（higher） Brier score and a higher resolution than the global ensemble for precipitation,indicating more reliable probabilistic forecasting by CEFS. Additionally,forecasts of different ensemble members are compared and discussed. Most of the extreme rainfall comes from convection in the warm sector east of an approaching cold front. A few members of CEFS successfully reproduce such precipitation,and orographic lift of highly moist low-level flows with a significantly southeasterly component is suggested to have played important roles in producing the initial convection. Comparisons between good and bad forecast members indicate a strong sensitivity of the extreme rainfall to the mesoscale environmental conditions,and,to less of an extent,the model physics.
文摘The application of numerical weather prediction （NWP） products is increasing dramatically. Existing reports indicate that ensemble predictions have better skill than deterministic forecasts. In this study, numerical ensemble precipitation forecasts in the TIGGE database were evaluated using deterministic, dichotomous （yes/no）, and probabilistic techniques over Iran for the period 2008-16. Thirteen rain gauges spread over eight homogeneous precipitation regimes were selected for evaluation. The Inverse Distance Weighting and Kriging methods were adopted for interpolation of the prediction values, downscaled to the stations at lead times of one to three days. To enhance the forecast quality, NWP values were post-processed via Bayesian Model Averaging. The results showed that ECMWF had better scores than other products. However, products of all centers underestimated precipitation in high precipitation regions while overestimating precipitation in other regions. This points to a systematic bias in forecasts and demands application of bias correction techniques. Based on dichotomous evaluation, NCEP did better at most stations, although all centers overpredicted the number of precipitation events. Compared to those of ECMWF and NCER UKMO yielded higher scores in mountainous regions, but performed poorly at other selected stations. Furthermore, the evaluations showed that all centers had better skill in wet than in dry seasons. The quality of post-processed predictions was better than those of the raw predictions. In conclusion, the accuracy of the NWP predictions made by the selected centers could be classified as medium over Iran, while post-processing of predictions is recommended to improve the quality.
基金supported by the Chinese Academy of Sciences (Grant Nos.KZCX2-YW-202, KZCX2-YW-205)the National Basic Research Program of China (2006CB403600)the National Natural Science Foundation of China (Grant No.40221503)
文摘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.
基金This work was supported by the National Key R&D Program of China(Grant No.2018YFC1507602,2017YFC1501603)the National Natural Science Foundation of China(Grant No.41975136)+1 种基金the Guangdong Basic and Applied Basic Research Foundation(Grant No.2019A1515011118)Sci-entific research project of Shanghai Science and Technology Com-mission(19dz1200101).
文摘The quality of ensemble forecasting is seriously affected by sample quality.In this study,the distributions of ensemble members based on the observed track and intensity of tropical cyclones(TCs)were optimized and their influence on the simulation results was analyzed.Simulated and observed tracks and intensities of TCs were compared and these two indicators were combined and weighted to score the sample.Samples with higher scores were retained and samples with lower scores were eliminated to improve the overall quality of the ensemble forecast.For each sample,the track score and intensity score were added as the final score of the sample with weight proportions of 10 to 0,9 to 1,8 to 2,7 to 3,6 to 4,5 to 5.These were named as“tr”,“91”,“82”,“73”,“64”,and“55”,respectively.The WRF model was used to simulate five tropical cyclones in the northwestern Pacific to test the ability of this scheme to improve the forecast track and intensity of these cyclones.The results show that the sample optimization effectively reduced the track and intensity error,“55”usually had better performance on the short-term intensity prediction,and“tr”had better performance in short-term track prediction.From the overall performance of the track and intensity simulation,“91”was the best and most stable among all sample optimization schemes.These results may provide some guidance for optimizing operational ensemble forecasting of TCs.
基金National Natural Science Foundation of China(42175003,42088101)Graduate Research and Innovation Projects of Jiangsu Province(KYCX22_1134)。
文摘An unprecedented heavy rainfall event occurred in Henan Province,China,during the period of 1200 UTC 19-1200 UTC 20 July 2021 with a record of 522 mm accumulated rainfall.Zhengzhou,the capital city of Henan,received 201.9 mm of rainfall in just one hour on the day.In the present study,the sensitivity of this event to atmospheric variables is investigated using the ECMWF ensemble forecasts.The sensitivity analysis first indicates that a local YellowHuai River low vortex(YHV)in the southern part of Henan played a crucial role in this extreme event.Meanwhile,the western Pacific subtropical high(WPSH)was stronger than the long-term average and to the west of its climatological position.Moreover,the existence of a tropical cyclone(TC)In-Fa pushed into the peripheral of the WPSH and brought an enhanced easterly flow between the TC and WPSH channeling abundant moisture to inland China and feeding into the YHV.Members of the ECMWF ensemble are selected and grouped into the GOOD and the POOR groups based on their predicted maximum rainfall accumulations during the event.Some good members of ECMWF ensemble Prediction System(ECMWF-EPS)are able to capture good spatial distribution of the heavy rainfall,but still underpredict its extremity.The better prediction ability of these members comes from the better prediction of the evolution characteristics(i.e.,intensity and location)of the YHV and TC In-Fa.When the YHV was moving westward to the south of Henan,a relatively strong southerly wind in the southwestern part of Henan converged with the easterly flow from the channel wind between In-Fa and WPSH.The convergence and accompanying ascending motion induced heavy precipitation.
基金The National Key Research and Development Program of China under contract Nos 2016YFC14015032018YFC140066 and 2018YFC1407001。
文摘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.
基金supported by the National Key R&D Program of China grant number 2018YFC1507900the Demonstration Project of Artificial Precipitation Enhancement and Hail Suppression Operation Technology at the Eastern Side of the Taihang Mountains grant number hbrywcsy-2017-2sponsored by the National Natural Science Foundation of China grant numbers 41530427 and 41875172。
文摘The prediction of the particle number concentration and liquid/ice water content of cloud is significant for many aspects of atmospheric science.However,given the uncertainties in the initial and boundary conditions and imperfections of microphysical schemes,the accurate prediction of these microphysical properties of cloud is still a big challenge.The ensemble approach may be a viable way to reduce forecast uncertainties.In this paper,a large-scale stratiform cloud precipitation process is studied by comparing results of a 10-member ensemble forecast model with aircraft observation data.By means of the ensemble average,the prediction of bulk parameters such as liquid water content and ice water content can be improved in comparison with the control member,but the particle number concentrations are still one to two orders of magnitude less than those from observations.Intercomparison of raindrop size spectra reveals a big distinction between observations and predictions for particles with a diameter less than 1000μm.
基金Science and Technology Planning Project of Guangdong Province(2017B020244002,2018B020208004,2017B030314140)Natural Science Foundation of Guangdong Province(2019A1515011118)+1 种基金National Natural Science Fund(41705089)Science and Technology Project of Guangdong Meteorological Service(GRMC2017Q01)
文摘Nowadays,ensemble forecasting is popular in numerical weather prediction(NWP).However,an ensemble may not produce a perfect Gaussian probability distribution due to limited members and the fact that some members significantly deviate from the true atmospheric state.Therefore,event samples with small probabilities may downgrade the accuracy of an ensemble forecast.In this study,the evolution of tropical storms(weak typhoon)was investigated and an observed tropical storm track was used to limit the probability distribution of samples.The ensemble forecast method used pure observation data instead of assimilated data.In addition,the prediction results for three tropical storm systems,Merbok,Mawar,and Guchol,showed that track and intensity errors could be reduced through sample optimization.In the research,the vertical structures of these tropical storms were compared,and the existence of different thermal structures was discovered.One possible reason for structural differences is sample optimization,and it may affect storm intensity and track.
基金National Key Research and Development Program of China(2020YFA0608002)Key Project Fund of Shanghai 2020“Science and Technology Innovation Action Plan”for Social Development(20dz1200702)+2 种基金National Natural Science Foundation of China(42075141)Meteorological Joint Funds of the National Natural Science Foundation of China(U2142211)Fundamental Research Funds for the Central Universities(13502150039/003)。
文摘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.
基金National Key R&D Program of China(2018YFC1507602)National Natural Science Foundation of China(41975136)+1 种基金Guangdong Basic and Applied Basic Research Foundation(2019A1515011118)Science and Technology Planning Project of Guangdong Province(2017B020244002,2018B020208004)。
文摘Extreme rainfall is common from May to October in south China.This study investigates the key deviation of initial fields on ensemble forecast of a persistent heavy rainfall event from May 20 to 22,2020 in Guangdong Province,south China by comparing ensemble members with different performances.Based on the rainfall distribution and pattern,two types are selected for analysis compared with the observed precipitation.Through the comparison of the thermal and dynamic fields in the middle and lower layers,it can be found that the thermal difference between the middle and lower layers was an important factor which led to the deviation of precipitation distribution.The dynamic factors also have some effects on the precipitation area although they were not as important as the thermal factors in this case.Correlating accumulated precipitation with atmospheric state variables further corroborates the above conclusion.This study suggests that the uncertainty of the thermal and dynamic factors in the numerical model can have a strong impact on the quantitative skills of heavy rainfall forecasts.
