The spatial propagation of meso-and small-scale errors in a Meiyu frontal heavy rainfall event,which occurred in eastern China during 4-6 July 2003,is investigated by using the mesoscale numerical model MM5.In general,the spatial propagation of simulated errors depends on their horizontal scales.Small-scale（L 〈 100 km） initial error may spread rapidly as an isotropic circle through the sound wave.Then,many scattered convection-scale errors are triggered in moist convection zone that will spread abroad through the isotropic,round-shaped sound wave further more.Corresponding to the evolution of the rainfall system,several new convection-scale errors may be generated continuously by moist convection within the propagated round-shaped errors.Through the above circular process,the small-scale error increases in amplitude and grows in scale rapidly.Mesoscale（100 km 〈 L 〈 1000 km） initial error propagates up-and down-stream wavelike through the gravity wave,meanwhile migrating down-stream slowly along with the rainfall system by the mean flow.The up-stream propagation of the mesoscale error is very important to the error growth because it can accumulate error energy locally at a place where there is no moist convection and far upstream from the initial perturbation source.Although moist convection plays an important role in the rapid growth of errors,it has no impact on the propagation of meso-and small-scale errors.The diabatic heating could trigger,strengthen,and promote upscaling of small-scale errors successively,and provide ＂error source＂ to error growth and propagation.The rapid growth of simulated errors results from both intense moist convection and appropriate spatial propagation of the errors.
In this study, an approach combining dynamical initialization and large-scale spectral nudging is proposed to achieve improved numerical simulations of tropical cyclones （TCs）, including track, structure, intensity, and their changes, based on the Advanced Weather Research and Forecasting （ARW-WRF） model. The effectiveness of the approach has been demonstrated with a case study of Typhoon Megi （2010）. The ARW-WRF model with the proposed approach realistically reproduced many aspects of Typhoon Megi in a 7-day-long simulation. In particular, the model simulated quite well not only the storm track and intensity changes but also the structure changes before, during, and after its landfall over the Luzon Island in the northern Philippines, as well as after it reentered the ocean over the South China Sea （SCS）. The results from several sensitivity experiments demonstrate that the proposed approach is quite effective and ideal for achieving realistic simulations of real TCs, and thus is useful for understanding the TC inner-core dynamics, and structure and intensity changes.
In the previous study, the influences of introducing larger- and smaller-scale errors on the background error covariances estimated at the given scales were investigated, respectively. This study used the eovariances obtained in the previous study in the data assimilation and model forecast system based on three-dimensional variational method and the Weather Research and Forecasting model. In this study, analyses and forecasts from this system with different covariances for a period of one month were compared, and the causes for differing results were presented. The varia- tions of analysis increments with different-scale errors are consistent with those of variances and correlations of back- ground errors that were reported in the previous paper. In particular, the introduction of smaller-scale errors leads to greater amplitudes in analysis increments for medium-scale wind at the heights of both high- and low-level jets. Tem- perature and humidity analysis increments are greater at the corresponding scales at the middle- and upper-levels. These analysis increments could improve the intensity of the jet-convection system that includes jets at different levels and the coupling between them that is associated with latent heat release. These changes in analyses will contribute to more ac- curate wind and temperature forecasts in the corresponding areas. When smaller-scale errors are included, humidity analysis increments are significantly enhanced at large scales and lower levels, to moisten southern analyses. Thus, dry bias can be corrected, which will improve humidity forecasts. Moreover, the inclusion of larger- （smaller-） scale errors will be beneficial for the accuracy of forecasts of heavy （light） precipitation at large （small） scales because of the ampli- fication （diminution） of the intensity and area in precipitation forecasts.
The atmospheric and oceanic conditions are examined during different stages of the lifecycle of western North Pacific tropical cyclones （TCs）, with the intention to understand how the environment affects the intensity change of TCs in this area. It is found that the intensification usually occurs when the underlying sea surface temperature （SST） is higher than 26℃. TCs usually experience a rapid intensification when the SST is higher than 27.5℃ while lower than 29.5℃. However, TCs decay or only maintain its intensity when the SST is lower than 26℃. The intensifying TCs usually experience a low-to-moderate vertical wind shear （2-10 m s-l）. The larger the vertical wind shear, the slower the TCs strengthen. In addition, the convective available potential energy （CAPE） is much smaller in the developing stage than in the formation stage of TCs. For the rapidly intensifying TCs, the changes of SST, CAPE, and vertical wind shear are usually small, indicating that the rapid intensification of TCs occurs when the evolution of the environment is relatively slow.
The initiation of convective cells in the late morning of 24 June 2010 along the eastward extending ridge of the Dabie Mountains in the Anhui region, China, is studied through numerical simulations that include local data assimilation. A primary convergence line is found over the ridge of the Dabie Mountains, and along the ridge line several locally enhanced convergence centers preferentially initiate convection. Three processes responsible for creating the overall convergence pattern are identified. First, thermally-driven upslope winds induce convergence zones over the main mountain peaks along the ridge, which are shifted slightly downwind in location by the moderate low-level easterly flow found on the north side of a Mei-yu front. Second, flows around the main mountain peaks along the ridge create further convergence on the lee side of the peaks. Third, upslope winds develop along the roughly north-south oriented valleys on both sides of the ridge due to thermal and dynamic channeling effects, and create additional convergence between the peaks along the ridge. The superposition of the above convergence features creates the primary convergence line along the ridge line of the Dabie Mountains. Locally enhanced convergence centers on the primary line cause the initiation of the first convection cells along the ridge. These conclusions are supported by two sensitivity experiments in which the environmental wind （dynamic forcing） or radiative and land surface thermal forcing are removed, respectively. Overall, the thermal forcing effects are stronger than dynamic forcing given the relatively weak environmental flow.
The large-scale and small-scale errors could affect background error covariances for a regional numerical model with the specified grid resolution.Based on the different background error covariances influenced by different scale errors,this study tries to construct a so-called"optimal background error covariances"to consider the interactions among different scale errors.For this purpose,a linear combination of the forecast differences influenced by information of errors at different scales is used to construct the new forecast differences for estimating optimal background error covariances.By adjusting the relative weight of the forecast differences influenced by information of smaller-scale errors,the relative influence of different scale errors on optimal background error covariances can be changed.For a heavy rainfall case,the corresponding optimal background error covariances can be estimated through choosing proper weighting factor for forecast differences influenced by information of smaller-scale errors.The data assimilation and forecast with these optimal covariances show that,the corresponding analyses and forecasts can lead to superior quality,compared with those using covariances that just introduce influences of larger-or smallerscale errors.Due to the interactions among different scale errors included in optimal background error covariances,relevant analysis increments can properly describe weather systems(processes)at different scales,such as dynamic lifting,thermodynamic instability and advection of moisture at large scale,high-level and low-level jet at synoptic scale,and convective systems at mesoscale and small scale,as well as their interactions.As a result,the corresponding forecasts can be improved.
