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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 Yellow-Huai 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. [ABSTRACT FROM AUTHOR]

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 Perturbed-Parameter 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study presented an evaluation of tropical cyclone (TC) intensity forecasts from five global ensemble prediction systems (EPSs) during 2015-2019 in the western North Pacific region. Notable error features include the underestimation of the TC intensity by ensemble mean forecast and the under-dispersion of the probability forecasts. The root mean square errors (brier scores) of the ensemble mean (probability forecasts) generally decrease consecutively at long lead times during the five years, but fluctuate between certain values at short lead times. Positive forecast skill appeared in the most recent two years (2018-2019) at 120 h or later as compared with the climatology forecasts. However, there is no obvious improvement for the intensity change forecasts during the 5-year period, with abrupt intensity change remaining a big challenge. The probability forecasts show no skill for strong TCs at all the lead times. Among the five EPSs, ECMWF-EPS ranks the best for the intensity forecast, while NCEPGEFS ranks the best for the intensity change forecast, according to the evaluation of ensemble mean and dispersion. As for the other probability forecast evaluation, ECMWF-EPS ranks the best at lead times shorter than 72 h, while NCEP-GEFS ranks the best later on. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 36h. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]