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| 1 | A Numerical Study of Mesoscale Vortex Formation in the Midlatitudes:The Role of Moist Processes显示文摘In this study, a three-dimensional mesoscale model was used to numerically simulate the well-known '98.7' heavy rainfall event that affected the Yangtze Valley in July 1998. Two experiments were conducted to analyze the impact of moist processes on the development of meso-β scale vortices(MβV) and their triggering by mesoscale wind perturbation(MWP). In the experiment in which the latent heat feedback(LHF) scheme was switched off, a stable low-level col field(i.e., saddle field—a region between two lows and two highs in the isobaric surface) formed, and the MWP triggered a weak MβV. However, when the LHF scheme was switched on as the MWP was introduced into the model, the MβV developed quickly and intense rainfall and a mesoscale low-level jet(mLLJ) were generated. The thickness of the air column and average temperature between 400 and 700 hPa decreased without the feedback of latent heat, whereas they increased quickly when the LHF scheme was switched on, with the air pressure falling at low levels but rising at upper levels. A schematic representation of the positive feedbacks among the mesoscale vortex, rainfall, and mLLJ shows that in the initial stage of the MβV, the MWP triggers light rainfall and the latent heat occurs at low levels, which leads to weak convergence and ageostrophic winds. In the mature stage of the MβV, convection extends to the middle-to-upper levels, resulting in an increase in the average temperature and a stretching of the air column. A low-level cyclonic circulation forms under the effect of Coriolis torque, and the m LLJ forms to the southeast of the MβV. | Yongqiang JIANG Yuan WANG Chaohui CHEN Hongrang HE Hong HUANG | 2019 | Advances in Atmospheric Sciences2019，36，1： | 4 |
| 2 | A Convection-Allowing Ensemble Forecast Based on the Breeding Growth Mode and Associated Optimization of Precipitation Forecast显示文摘A convection-allowing ensemble forecast experiment on a squall line was conducted based on the breeding growth mode(BGM). Meanwhile, the probability matched mean(PMM) and neighborhood ensemble probability(NEP)methods were used to optimize the associated precipitation forecast. The ensemble forecast predicted the precipitation tendency accurately, which was closer to the observation than in the control forecast. For heavy rainfall, the precipitation center produced by the ensemble forecast was also better. The Fractions Skill Score(FSS) results indicated that the ensemble mean was skillful in light rainfall, while the PMM produced better probability distribution of precipitation for heavy rainfall. Preliminary results demonstrated that convection-allowing ensemble forecast could improve precipitation forecast skill through providing valuable probability forecasts. It is necessary to employ new methods, such as the PMM and NEP, to generate precipitation probability forecasts. Nonetheless, the lack of spread and the overprediction of precipitation by the ensemble members are still problems that need to be solved. | xiang li hongrang he chaohui chen ziqing miao shigang bai | 2017 | Journal of Meteorological Research2017，31，5： | 4 |
| 3 | Algorithm based on local breeding of growing modes for convection-allowing ensemble forecasting显示文摘We propose a method based on the local breeding of growing modes(LBGM) considering strong local weather characteristics for convection-allowing ensemble forecasting. The impact radius was introduced in the breeding of growing modes to develop the LBGM method. In the local breeding process, the ratio between the root mean square error(RMSE) of local space forecast at each grid point and that of the initial full-field forecast is computed to rescale perturbations. Preliminary evaluations of the method based on a nature run were performed in terms of three aspects: perturbation structure, spread,and the RMSE of the forecast. The experimental results confirm that the local adaptability of perturbation schemes improves after rescaling by the LBGM method. For perturbation physical variables and some near-surface meteorological elements, the LBGM method could increase the spread and reduce the RMSE of forecast,improving the performance of the ensemble forecast system.In addition, different from those existing methods of global orthogonalization approach, this new initial-condition perturbation method takes into full consideration the local characteristics of the convective-scale weather system, thus making convectionallowing ensemble forecast more accurate. | Chaohui CHEN Xiang LI Hongrang HE Jie XIANG Shenjia MA | 2018 | Science China Earth Sciences2018，61，4： | 3 |
| 4 | A three-dimensional variational data assimilation system for a size-resolved aerosol model: Implementation and application for particulate matter and gaseous pollutant forecasts across China显示文摘A three-dimensional variational(3DVAR)data assimilation(DA)system is presented here based on a size-resolved sectional aerosol model,the Model for Simulating Aerosol Interactions and Chemistry(MOSAIC)within the Weather Research and Forecasting model coupled to Chemistry(WRF-Chem)model.The use of this approach means that both gaseous pollutants such as SO2,NO2,CO,and O3 as well as particulate matter(PM2.5,PM10)observational data can be assimilated simultaneously.Two one-month parallel simulation experiments were conducted,one with the assimilation of surface hourly concentration observations of the above six pollutants released by the China National Environmental Monitoring Centre(CNEMC)and one without assimilation in order to verify the impact of assimilation on initial chemical fields and subsequent forecasts.Results show that,in the first place,use of the DA system can provide a more accurate model initial field.The root-mean-square error of PM2.5,PM10,SO2,NO2,CO,and O3 mass concentrations in analysis field fell by 29.27μg m-3(53.5%),34.5μg m-3(50.9%),30.36μg m-3(64.2%),8.91μg m-3(39.5%),0.46 mg m-3(47.4%),and 15.11μg m-3(51.0%),respectively,compared to a background field without assimilation.At the same time,mean fraction error was reduced by 42.6%,53.1%,45.2%,43.1%,69.9%,and 48.8%,respectively,while the correlation coefficient increased by 0.51,0.55,0.48,0.38,0.47,0.65,respectively.Secondly,the results of this analysis reveal variable benefits from assimilation on different pollutants.DA significantly improves PM2.5,PM10,and CO forecasts leading to positive effects that last more than 48 h.The positive effects of DA on SO2 and O3 forecasts last up to 8 h but that remains relatively poor for NO2 forecasts.Thirdly,the influence of assimilation varies in different areas.It is possible that the positive effects of DA on PM2.5 and PM10 forecasts can last more than 48 h across most regions of China.Indeed,DA significantly improves SO2 forecasts within 48 h over north China,and much longer CO assimilation benefits(48 h)are found in most regions apart from north and east China and across the Sichuan Basin.DA is able to improve O3 forecasts within 48 h across China with the exception of southwest and northwest regions and the O3 DA benefits in southern China are more evident,while from a spatial distribution perspective,NO2 DA benefits remain relatively poor. | Daichun WANG Wei YOU Zengliang ZANG Xiaobin PAN Hongrang HE Yanfei LIANG | 2020 | Science China Earth Sciences2020，63，9： | 1 |
| 5 | Application of Gaussian Weight to Improve Perturbation Features of Convection-Permitting Ensemble Forecast Based on Local Breeding of Growing Modes显示文摘Local breeding of growing modes(LBGM)is a method used to generate initial condition perturbation(ICP)for convection-permitting ensemble forecasts.Equal weights(EWs)are usually presumed in LBGM during the localization of ICP,without considering different contributions of the grid points within the local radius.To address this problem,Gaussian weights(GWs)are proposed in this study,which can accommodate the varied influences of the grids inside the local radius on the central grid through a Gaussian function.Specifically,two convection-permitting ensemble forecast experiments based on LBGM with GWs and EWs are compared and analyzed respectively for two squall line cases.The results showed that the use of the GWs intensified the local characteristics of the ICP and made the distribution of the ICP fields more flow-dependent.Kinetic energy spectrum of the ICP indicated that there could be more large-scale information in the ICP by using the GWs.In addition,mesoscale information also improved slightly.For forecast of nonprecipitation variables,GWs improved the relationship between the root-mean-square error and the spread and contributed to the forecasting accuracy of wind,temperature,geopotential height,and humidity.For the precipitation forecast,GWs simulated the precipitation structure successfully and provided better probability forecasting during the evolution of the two squall line processes than the EWs. | Kun LI Chaohui CHEN Hongrang HE Ru YANG Yi LI Yongqiang JIANG | 2021 | Journal of Meteorological Research2021，35，3： | 1 |
| 6 | Impacts of Stochastic Forcing on Ensemble Prediction Effect显示文摘Based on the dynamic framework of Lorenz 96 model,the ensemble prediction system(EPS)containing stochastic forcing has been developed.In this system,effects of stochastic forcing on the model climate state and ensemble mean prediction have been studied.The results show that the climate mean and standard deviation provided by a new computing paradigm by means of introduction of the proper stochastic forcing into numerical model integration process are closer to that of the true value than that made by the non-stochastic forcing.In other words,numerical model integration process with stochastic forcing has positive effect on the model climate state,and the effect is found to be positive mainly in the long lead time.Meanwhile,with respect to ensemble forecast effect yielded by white noise stochastic forcing,most results are better than those provided by no-stochastic forcing,and improvements pertaining to white noise stochastic forcing vary non-monotonically with the increase of the size of white noise.Moreover,the effects made by the identical white noise stochastic forcing also are different in various non-linear systems.With respect to EPS effect yielded by red noise stochastic forcing,most results are better than those provided by no-stochastic forcing,but only a part of ensemble forecast effect influenced by red noise is superior to that influenced by white noise.Finally,improvements pertaining to red noise stochastic forcing vary non-symmetrically and non-monotonically with the distribution of coefficientΦ.Besides,the selection of correlation coefficientΦis also dependent on non-linear models. | Chen Chaohui Jiang Yongqiang He Hongrang | 2017 | Meteorological and Environmental Research2017，8，1： | 0 |
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