Growth and Interactions of Multi-Source Perturbations in Convection-Allowing Ensemble Forecasts

This study investigated the growth of forecast errors stemming from initial conditions(ICs),lateral boundary conditions(LBCs),and model(MO)perturbations,as well as their interactions,by conducting seven 36 h convectionallowing ensemble forecast(CAEF)experiments.Two cases,one with strong-forcing(SF)and the other with weak-forcing(WF),occurred over the Yangtze-Huai River basin(YHRB)in East China,were selected to examine the sources of uncertainties associated with perturbation growth under varying forcing backgrounds and the influence of these backgrounds on growth.The perturbations exhibited distinct characteristics in terms of temporal evolution,spatial propagation,and vertical distribution under different forcing backgrounds,indicating a dependence between perturbation growth and forcing background.A comparison of the perturbation growth in different precipitation areas revealed that IC and LBC perturbations were significantly influenced by the location of precipitation in the SF case,while MO perturbations were more responsive to convection triggering and dominated in the WF case.The vertical distribution of perturbations showed that the sources of uncertainties and the performance of perturbations varied between SF and WF cases,with LBC perturbations displaying notable case dependence.Furthermore,the interactions between perturbations were considered by exploring the added values of different source perturbations.For the SF case,the added values of IC,LBC,and MO perturbations were reflected in different forecast periods and different source uncertainties,suggesting that the combination of multi-source perturbations can yield positive interactions.In the WF case,MO perturbations provided a more accurate estimation of uncertainties downstream of the Dabie Mountain and need to be prioritized in the research on perturbation development.

Bias-Corrected Short-Range Ensemble Forecasts for Near-Surface Variables during the Summer Season of 2010 in Northern China

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.

Ensemble Forecast: A New Approach to Uncertainty and Predictability

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.

A Regional Ensemble Forecast System for Stratiform Precipitation Events in the Northern China Region.Part Ⅱ:Seasonal Evaluation for Summer 2010

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.

A Regional Ensemble Forecast System for Stratiform Precipitation Events in Northern China.Part I:A Case Study

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.

AN ENSEMBLE FORECAST EXPERIMENT OF A LANDING TYPHOON

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.

Ensemble Forecasting of Tropical Cyclone Motion Using a Baroclinic Model

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.

A STUDY ON THE ENSEMBLE FORECAST REAL-TIME CORRECTION METHOD

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.

Evaluation of TIGGE Ensemble Forecasts of Precipitation in Distinct Climate Regions in Iran

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.

Evaluation of WRF-based Convection-Permitting Multi-Physics Ensemble Forecasts over China for an Extreme Rainfall Event on 21 July 2012 in Beijing

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.

Dust Storm Ensemble Forecast Experiments in East Asia

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.

The Simulation of Five Tropical Cyclones by Sample Optimization of Ensemble Forecasting Based on the Observed Track and Intensity

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.

Sensitivity Analysis of the Super Heavy Rainfall Event in Henan on 20 July(2021)Using ECMWF Ensemble Forecasts

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.

Typhoon storm surge ensemble forecast based on GPU technique

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.

SAMPLE OPTIMIZATION OF ENSEMBLE FORECAST TO SIMULATE TROPICAL STORMS(MERBOK, MAWAR, AND GUCHOL) USING THE OBSERVED TRACK

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.

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

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.

Key Deviation Analysis of Initial Fields on Ensemble Forecast in South China During the Rainfall Event on May 21,2020

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.

Impact of Soil Moisture Uncertainty on Summertime Short-range Ensemble Forecasts

To investigate the impact of soil moisture uncertainty on summertime short-range ensemble forecasts（SREFs）, a fivemember SREF experiment with perturbed initial soil moisture（ISM） was performed over a northern China domain in summertime from July to August 2014. Five soil moisture analyses from three different operational/research centers were used as the ISM for the ensemble. The ISM perturbation produced notable ensemble spread in near-surface variables and atmospheric variables below 800 h Pa, and produced skillful ensemble-mean 24-h accumulated precipitation（APCP24） forecasts that outperformed any single ensemble member. Compared with a second SREF experiment with mixed microphysics parameterization options, the ISM-perturbed ensemble produced comparable ensemble spread in APCP24 forecasts, and had better Brier scores and resolution in probabilistic APCP24 forecasts for 10-mm, 25-mm and 50-mm thresholds. The ISM-perturbed ensemble produced obviously larger ensemble spread in near-surface variables. It was, however, still under-dispersed, indicating that perturbing ISM alone may not be adequate in representing all the uncertainty at the near-surface level, indicating further SREF studies are needed to better represent the uncertainties in land surface processes and their coupling with the atmosphere.

Dynamic Analogue Initialization for Ensemble Forecasting

This paper introduces a new approach for the initialization of ensemble numerical forecasting： Dynamic Analogue Initialization （DAI）. DAI assumes that the best model state trajectories for the past provide the initial conditions for the best forecasts in the future. As such, DAI performs the ensemble forecast using the best analogues from a full size ensemble. As a pilot study, the Lorenz63 and Lorenz96 models were used to test DAI＇s effectiveness independently. Results showed that DAI can improve the forecast significantly. Especially in lower-dimensional systems, DAI can reduce the forecast RMSE by ~50% compared to the Monte Carlo forecast （MC）. This improvement is because DAI is able to recognize the direction of the analysis error through the embedding process and therefore selects those good trajectories with reduced initial error. Meanwhile, a potential improvement of DAI is also proposed, and that is to find the optimal range of embedding time based on the error＇s growing speed.

Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data

Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC （National Meteorological Center） method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated.