A Numerical Study of Mesoscale Vortex Formation in the Midlatitudes:The Role of Moist Processes

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.

Numerical Simulation and Moist Potential Vorticity Analysis of Torrential Rain in Jiangxi Province during June 2010

Based on the conventional ground observational data,a numerical simulation and moist potential vorticity( MPV) analysis has been carried on heavy rainfall event over Jiangxi province from 19 June to 20 June 2010,with a meso-scale rainstorm model. The results show that this rare rainstorm is a typical heavy rainfall over Meiyu front. The cold air flow behind North China vortex joined up the southwestern flow located in the northwest part of the strong and stable subtropical high,thus the cold air and warm air converged and maintained over the northern part of Hunan and Jiangxi province. The simulated precipitation of the high resolution model is very similar to the observational rainfall. The model has a good predictive skill for the location,intensity and center of heavy rainfall. By moist potential vorticity analysis,it is found that the distribution characteristic of MPV which heavy rainfall happens ahead has an obvious indication for precipitation forecast. The vertical overlapping of the positive and negative MPV1 areas is favorable to the generation and development of rainstorm. This zone is also the conjoint area of convective instability and baroclinic instability.

A Convection-Allowing Ensemble Forecast Based on the Breeding Growth Mode and Associated Optimization of Precipitation Forecast

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 precipita- tion tendency accurately, which was closer to the observation than in the control forecast. For heavy rainfall, the pre- cipitation 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 pre- cipitation for heavy rainfall. Preliminary results demonstrated that convection-allowing ensemble forecast could im- prove 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.

Application of Gaussian Weight to Improve Perturbation Features of Convection-Permitting Ensemble Forecast Based on Local Breeding of Growing Modes

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.

Algorithm based on local breeding of growing modes for convection-allowing ensemble forecasting

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.

A dual-amplification system for colorimetric DNA detection based on the assembly of biomolecules

A dual-amplification system is reported to apply in DNA sensing via the assembly of DNA and protein. In this process, the biotinylatedcapature DNA bounded with streptavidin（SA） through the biotinstreptavidin reaction, and then the assembly of DNA and protein was triggered by the linker DNA after the target hybridized with biotinylatedcapature DNA. Sequentially, the 3,3＇,5,5＇-tetramethylbenzidine（TMB）was oxidized by H_2O_2 under the catalysis of horseradish peroxidase. Based on the variation of the color and the UV–vis absorbance intensities, qualitative and quantitative DNA analyses were realized. This proposed method could detect the target DNA as low as 1.75 pmol/L and discriminate perfectly matched target DNA from the mismatch DNA. What＇s more, it can be expanded to detect other molecules with a reasonable design of the corresponding DNA sequences.