Abstract:The ensemble forecast technique is a practical solution to the uncertainty problem of numerical weather prediction.At present,researchers around the world tend to focus on the Regional Ensemble Forecast(REF),which aims at the improvement of regional high impact weather forecast.As various uncertainty resources exist for meso-scale and small-scale weather phenomena,regional model simulation is a very complicated issue,thus how to generate effective perturbations for REF is a hot topic involving many technical difficulties.In the present paper,the progress of REF research is reviewed in terms of initial condition perturbation,model perturbation and lateral boundary condition perturbation,and the trends of methods related to these aspects are also presented.The results presented show the following:for initial condition perturbation,the mainstream methods include dynamical downscaling,using traditional methods developed from Global Ensemble to generate perturbations for REF,as well as some methods specifically designed for REF.All of these methods are characterized by some advantages and some shortcomings,as downscaling a lack of small scale and other components leads to the generation of insufficient large-scale uncertainty information.In addition,research on the REF initial condition perturbation has only begun to explore more effective methods such as blending,which consider not only sufficiently small-scale uncertainty,but also sufficiently large-scale uncertainty.Finally,the inconsistency problem between initial state and lateral boundary can also be ameliorated.Model perturbation is another important aspect for REF.This technique mostly perturbs model physics,such as multi-physics combination and stochastically perturb model physics.It has been reported that the multi-physics combination is quite simple and can effectively improve the ensemble spread of REF,while using the stochastic method to perturb model physics has greater scientific significance,thus this type of perturbation method has become the trend of model physics perturbations.Furthermore,multi-model combination is another practical method of model perturbation.Related studies have been carried out,the results of which show that this method possesses stronger skill than a single model ensemble,especially when a multi-model ensemble is applied to meso-scale severe weather forecast,such as with a Tropical Cyclone.As REF systems are constructed based on the regional models,the uncertainties originating from lateral boundary conditions cannot be ignored.Lateral boundary condition perturbation schemes mainly use large-scale ensembles,such as Global Ensemble Forecast Products,to provide different lateral boundary conditions for REF.Studies have proven that this method can achieve the goal of perturbing the lateral boundary condition of REF,and lateral boundary condition perturbation is found to aid in amplifying the ensemble spread of REF for long-range forecast lead times.In addition,the ensemble forecast skill can also benefit from lateral boundary perturbation.Although the perturbation techniques for REF have already led to some fruitful achievements,much work is still needed,and all of the methods related to initial condition perturbations,model perturbations and lateral boundary condition perturbations are still under development.It can be predicted that,as the REF perturbation methods continue to improve,the REF will become increasingly more effective,and will play a more important role in operational numerical weather prediction centers.

Abstract:An Ensemble Square Root Filter(EnSRF) is a deterministic algorithm without disturbance observations,which was derived from the traditional Ensemble Kalman Filter(EnKF) in order to avoid the sampling errors caused by disturbance observations.An EnSRF uses the flow dependent background error covariance to analyze data,which solves the problem of adjoint models in the variational assimilation.Previous studies have completed the construction of EnSRF systems for storm scale in the Weather Research and Forecasting(WRF) model.However,some problems,such as the sampling error,still exist in the WRF-EnSRF system.Therefore,various other techniques,such as an empirical localization method,should be used to overcome these problems.Since the weight coefficients of the empirical localization method are linear,and are dependent on the local distance radius,they do not reflect the real situation of the state variables and observations.In this study,attempts were made to improve the assimilation effect of a WRF-EnSRF system by utilizing a sampling error correction localization method instead of the empirical localization method.The sampling error correction localization method took into account the biases of regression coefficients,and used less computation.Then,based on the prior distribution information of the correlation coefficient between the state variables and corresponding observations,the method obtained the coefficient factor of the localization through a lookup table,which was related to the ensemble numbers and sample correlation coefficients,and produced by the offline Monte Carlo technique.The sampling error was then corrected,which had resulted from the underestimation of the background error covariance due to the limitation of the selected ensemble numbers.Meanwhile,the weighting coefficient was updated with the assimilation time for each of the observational data assimilations,and reflected the flow dependent feature.This method has been widely used in large-scale models.However,it has also been considered to be applicable,or even more suitable,to small and medium scale weather systems.Therefore,this study attempted to put the method into the WRF-EnSRF,and conducted a series of storm-scale data assimilation tests using Doppler radar observations during storm periods,in order to prove the feasibility of the localization,as well as to explore the technical features and assimilation effects of the sampling error correction localization method in the storm-scale ensemble Kalman filter assimilation.The data in the WRF during a typical super storm which occurred in Del City(central Oklahoma,USA) on May 20,1977,were used in this study.In order to reduce the calculation and avoid the spurious correlation with long distance observations,this study selected reasonable local distance radiuses for the different variables in the assimilation tests.Then,based on the tests with only assimilating radial velocity,it was found that the sampling error correction localization method was able be implemented in the WRF-EnSRF system,and the results achieved the physical analysis field more accurate after adding the assimilation of the radar reflectivity.Since the weighting coefficient of the sampling error correction localization was not dependent on the distance,the assimilation results reduced the sensitivity to the distance.In addition,it was found that there were some differences in the results with different localization methods for the various observed variables and stages of the storm.This is due to the fact that the sampling error correction localization had strong nonlinear characteristics itself,especially for the variables containing water substances.Therefore,the sampling error correction localization achieved better results of the tests in the nonlinear and rapid development stages of the synoptic system or assimilating nonlinear variables,when compared to the empirical localization method.However,in the stable development stage or assimilating linear variables,the empirical method was determined to have more advantages.In summary,according to the results of the tests,it was necessary to reasonably choose the appropriate localization method according to the object of the assimilation.

Abstract:Quality control of observations directly affects the analysis quality of numerical prediction data assimilation.Based on the “Gaussian plus flat” distribution model of observation error and Bayes’ probability theorem,this paper reports the development of a variational quality control scheme for the 3D-Var(three-dimensional variational) assimilation and forecast system in GRAPES(Global/Regional Assimilation and Prediction System).It also discusses the initial startup and key parameters of this scheme,and furthermore analyzes and verifies its applicability and effectiveness.A heavy rainfall event in southern China is selected as a case for assimilating and forecasting the analysis using Global Forecast System(GFS) data as the background field and conventional observation data including TEMP,SYNOP,SHIPS,AIREP,SATOB and COSMIC satellite retrieval data.Also,we calculate the rain score(ETS and Bias) of batch tests with 31 days in August 2013.The results show that the “Gaussian plus flat” distribution model is a better match for the characteristics of real observation error than the Gaussian distribution.At the same time,the variational quality control method is able to correct the observation weight in accordance with the size of the observation departure.This also proves the rationality of the non-Gaussian distribution assumption for real observation error and the correctness of variational quality control theory.The variational quality control method reasonably adjusts every observation weight according to different qualities of observation,and virtually classifies the observations.This is beneficial to identifying the quality of observations so that we can assimilate every observation with different weights,as available data,effective data and damaging data.The variational quality control method significantly adjusts the analysis increment field,which includes height,wind and specific humidity,especially in some areas where the damaging data are recognized.Due to the change of the analysis increment field,a larger improvement for the analysis field is made.In view of ameliorating the analysis field,the quality of the forecast field also improves;in particular,a more positive effect on heavy rainfall areas.The forecast quality has been further improved in the intensity and center position of the precipitation,in particular,the prediction of the heavy rainfall,large rainstorms and other large-scale precipitation.The ETS and Bias scores of the batch tests also demonstrate the applicability and effectiveness of the variational quality control procedure in the operational assimilation system.Therefore,the variational quality control method plays an important role in data assimilating and forecasting of mesoscale and microscale severe weather processes.On the other hand,it can not only help improve the quality of forecast and analysis with the variational quality control method,but also can avoid the damages to forecast and analysis of some outlier data which cannot been resolved by traditional quality control procedures in extreme weather processes,such as rainstorms and typhoon.This means that variational quality control does not have a negative impact for analysis when the observations are good,and has a positive impact when abnormal observations occur.

Abstract:The ultimate purpose of this study is to investigate the contribution of ice nucleation parameterizations to the difference in estimating anthropogenic aerosol indirect forcing.Three physically-based ice nucleation parameterizations,respectively developed by Liu and Penner(2005,hereafter LP),Barahona and Nenes(2009,hereafter BN) and Kärcher et al.(2006,hereafter KL) are analyzed in this paper.The LP parameterization is derived from fitting the simulation results of a cloud parcel with constant updraft velocities.The number of nucleated ice crystals is a function of relative humidity,temperature,aerosol number concentration and updraft velocity.The BN parameterization is derived from an approximation to the analytical solution of air parcel equations.One advantage of BN parameterization is that the heterogeneous nucleation may be described by different nucleation spectra,derived either from the classical nucleation theory,or from observations.In KL parameterization,the competition between different freezing mechanisms is treated by explicitly calculating the evolution of the water vapor saturation ratio within one host-model's time step.It is worth noting that,in this parameterization,the ice crystal number concentration produced via homogeneous freezing is not sensitive to the sulfate aerosol number concentration in most cases,except for the highest(4 m·s^-1) updraft velocities.The offline experiments show that the ice crystal number concentration calculated from KL parameterization was not sensitive to sulfate aerosols number concentration in most cases,whereas the ice crystal number concentration calculated from the LP and BN parameterizations increased with the increasing sulfate aerosols number concentration.Compared to the BN and KL parameterizations,it is relatively easy for heterogeneous ice nucleation to inhibit homogeneous ice nucleation with LP parameterization.However,the ice crystal number concentration calculated from LP parameterization is usually higher than BN and KL parameterizations.Aside from the default LP parameterization in the CAM5 model,the BN and KL parameterizations were implemented in CAM5 for comparison.The occurrence frequency of homogeneous freezing from simulation with LP parameterization is less than BN and KL parameterizations.However,all of the simulations show that the occurrence frequency of homogeneous freezing is less than 0.1,which is in consistent with the observations.Furthermore,there is no remarkable difference among these three parameterizations in the ice crystal number concentration or cloud radiative forcing,and all of the simulation results show generally high agreements with cirrus cloud observations.This suggests that the model's capability to simulate cirrus clouds is not sensitive to physically-based ice nucleation parameterizations.The CAM5 experiment using the KL parameterization predicts a much smaller anthropogenic aerosol long wave indirect forcing(LWCF,0.05 W·m^-2) than that using the LP(0.36 W·m^-2) and BN(0.33 W·m^-2) parameterizations.This indicates that ice nucleation parameterization plays an important role in estimating anthropogenic aerosol indirect forcing.Previous studies showed that the annual global mean change in LWCF from pre-industrial times to present-day estimated from the ECHAM5 model with KL parameterization was 0.05—0.20 W·m^-2,which was much less than the estimate(0.40—0.52 W·m^-2) given by the CAM5 model with LP parameterization.It appears that this difference can be mostly explained by ice nucleation parameterizations.

Abstract:It is well known that the atmosphere is a nonlinear dynamical system with chaotic characteristics,and small differences in the initial value of the numerical model may lead to completely different results.Ensemble prediction is a new generation of stochastic dynamic forecasting technique.It is based on the analysis of the initial value of the assimilation analysis to generate a set of normal distribution of the initial disturbance,thus it can be used to reflect the uncertainty in the assimilation analysis.The method by which to generate the initial set of disturbances is the core of ensemble prediction.The ETKF method is an initial perturbation technique that has been developed over the past 10 years,and has been widely used.Because the number of actual ensemble members is far less than the prediction of the model,the variance of the ensemble prediction model prediction may be underestimated,thus an amplification factor is introduced to adjust the magnitude of the ETKF.Observation mean square error has a major impact on the structure and initial perturbation to the regional Ensemble Prediction System of the China Meteorological Administration Numerical Prediction Center.In this paper we design three different sets of numerical simulations of the sensitivity tests of observed error from August 1 to August 29 2012.We then analyze the impact of the structure and initial perturbation on the initial perturbation field,and assess the difference of the total energy of vertical perturbation and ensemble forecast skill score by means of the GRAPES-MEPS(Global/Regional Assimilation and Prediction System,Mesoscale Ensemble Prediction System) of the China Meteorological Administration Numerical Prediction Center.In addition,we analyze a typical ensemble prediction rainfall in the Yangtze-Huaihe River Basin.The results indicate that with the observation mean square error reduced,the model variable temperature and initial perturbation wind increases,and the ensemble forecasting dispersion grows slightly better.The precipitation area tests show that the ensemble forecasting precipitation is more effective when the observation mean square error is smaller,in which case the ensemble mean total energy has a better growth and its vertical structure is more obvious.The smaller the mean square error of the observation error is,the larger the total energy of the set predicted perturbation generated by the ETKF scheme will be,which in turn affects the increase of the later disturbance energy.It is also found that the total energy of the low-level initial disturbance is slower,due to the non-uniform distribution of the total energy perturbation of the GRAPES regional set.Therefore,we can use the disturbance observation mean square error appropriately to reflect the impact of observation mean square error on the ensemble prediction,thereby improving the techniques of GRAPES-MEPS ensemble prediction.

Abstract:Based on NCEP/NCAR and ERA-40 reanalysis data,interannual and interdecadal variations of the Mascarene high are analyzed,with specific focus on exchanges in the factors affecting the interannual variability of the Mascarene high against different interdecadal backgrounds.In order to investigate the intensity of the Mascarene high on different timescales,a Mascarene high area index(MHI) is defined to represent the intensity of the Mascarene high.Results show that significant changes of the Mascarene high are apparent on different timescales.On the seasonal timescale,the intensity of the Mascarene high is largest in summer,with its position in the west;whereas,the intensity is weakest in winter,with its position in the east.In the long-term trend,the intensity of the Mascarene high in the four seasons presents a significant linear increasing trend,with its trend strongest in spring and weakest in winter.On the interdecadal timescale,the four seasons experienced a marked interdecadal shift around 1976,characterized by a weaker Mascarene high before 1976 and a significantly strengthened one after 1976.The latest research results,both here and abroad,show that there are three possible factors affecting the Mascarene high:the local sea surface temperature(SST) over the southern Indian Ocean,the Antarctic Oscillation(AAO),and the El Niño-Southern Oscillation(ENSO).On the basis of these achievements,we put out two questions:What are the main factors affecting the Mascarene High? And has the relationship between the Mascarene high and its affecting factors changed since 1976? To answer these questions,regression analysis and correlation analysis are adopted.The results show that the factors affecting the interannual variability of the Mascarene high are different during these two periods.The interannual variability of the Mascarene high is closely related with SST over the southern Indian Ocean and the AAO before 1976,whereas it is closely related with ENSO and the AAO after 1976.In the latter period,the correlation of local SST over the southern Indian Ocean with the Mascarene high is weakened because of the weakening of upward net surface heat flux,while that of ENSO is remarkably strengthened,which is possibly because of the strengthening of ENSO's amplitude after 1976.With the warming of the eastern Pacific Ocean SST,anomalous upward movement in the South Pacific and anomalous downward movement in the Indian Ocean are triggered,thus promoting the strengthening of the Mascarene high.In every month of winter before 1976,local SST over the mid-and low-latitude Indian Ocean is closely related with the Mascarene high from June to August,with the strongest forcing in July.There is no significant relationship between ENSO and the Mascarene high from June to August.After 1976,local SST over the low-latitude Indian Ocean shows no closely relationship with the Mascarene high from June to August,while the relationship between ENSO and the Mascarene high is gradually strengthened from June to August.The relationship between the AAO and the Mascarene high is strongest in June and weakest in July,both before and after 1976.Besides,compared with that before 1976,the relationship between the AAO and the Mascarene high is significantly strengthened in every month in winter.The strengthening of the AAO triggers the anomalous easterly wind along 60°S and the anomalous westerly wind along 20°S,and then promotes the intensity of the Mascarene high.

Abstract:Monsoons are one of the most active members of the global climate system,and the land-sea thermal contrast is the main reason for the establishment and continuity of monsoons.At present,the CMIP5 climate coupling model,which has made significant improvements in the physical process,carbon cycle,etc.,is an important tool by which to carry out research regarding climate variability and change.The characteristics of the land-sea thermal contrast in the 20th century and how the CMIP5 modes have simulated it are problems which are worthy of study.In this study,observations from NCEP/NCAR and ERA-40 are used to contrast and analyze the features of summer land-sea thermal contrast over the Asian monsoon region,and to define the land-sea thermal contrast index.The results indicate that the temperature over land decreased,while that over sea increased,and the contrast tended to decrease.On this basis,the model simulations given by the 20 climate models of historical simulations from Phase 5 of the Coupled Model Inter-comparison Project(CMIP5) are used for comparison with observation analysis.Both the best and worst models in the RCP4.5 scenario are selected for forecasting.The main results of the study are as follows:(1)The observation data show that the average temperature of the upper-middle troposphere had a remarkable trend over the Asian monsoon region from 1955 to 2005,namely the temperature over land decreased and that over sea increased.According to the selected area,we define a land index,sea index and land-sea thermal contrast index.These present a large rate of inter-annual change;the land-sea thermal contrast index reduced;and the land-sea thermal contrast index was able to reflect the features of the difference between strong and weak monsoons.The NCEP is consistent with the ERA-40,yet the NCEP has an obvious linear trend.(2)The CMIP5 models have a better simulation performance regarding the climate condition of the zonal-wind field and height field in 500 hPa and 200 hPa,yet a poor simulation performance on the temperature field.The models could not perform satisfactorily in the simulation of the index inter-annual change.The simulations of interdecadal change are that the sea index increased and land-sea thermal contrast index decreased,while the land index increased.Combined with statistical computing,it may be concluded that GFDL-ESM2G,MPI-ESM-LR and MPI-ESM-MR had better simulation performances,while BCC-CSM1-1-m,MRI-CGCM3 and MIROC5 performed poorly.The models’ capability to simulate land-sea thermal contrast can reflect the ability of simulating the summer monsoon.(3)In the situation of RCP4.5,the simulation temperature of the upper-middle troposphere over the Asian monsoon region observed by the CMIP5 models indicates that it will increase throughout the 21th century.The degree of temperature increase over sea is greater than over land in the simulation data,with both the land and sea indexes increasing,and the land-sea thermal contrast index decreasing.The poorer models have slightly higher simulation data for the future temperature field than the stronger models,especially over sea,and in addition the poorer models cannot simulate the decreasing trend of land-sea thermal contrast.

Abstract:Using the newly developed localized multiscale energy and vorticity analysis(MS-EVA) and the MS-EVA-based theory of canonical transfer and hydrodynamic instability, this study conducts a diagnosis on the variability of the boreal summer MJO kinetic energy(MJO KE).MS-EVA is based on a new functional analysis tool,namely multiscale window transform,which splits a signal orthogonally into different parts,each part characteristic of a specific scale range or scale window.In the present study,the atmospheric fields over the Western Pacific are reconstructed on a large-scale window,an MJO scale window,and a synoptic scale window,and the interactions among these windows are investigated.From the results it is found that,in the upper and lower layers of the troposphere,the MJO KE is governed by buoyancy conversion and pressure work.The positive center of the buoyancy conversion is located in the northern part of the convection,which is considered as the source of the MJO kinetic energy.The pressure work redistributes the energy over the region,the negative center of which is located near the convection,and the positive center is located around the convection.A deeper study reveals that,in the middle-lower layers,the MJO kinetic energy mainly originates from the canonical transfer across the scales.The canonical transfer between the mean flow and MJO scale windows,which are located in the convection dues to the barotropic instability over the zonal band 5—15°N,and the canonical transfer between the MJO and synoptic scale windows,located in the convection,performs a pattern of the energy transfer from the MJO scale to synoptic scale,and depends mainly on the MJO-scale velocity gradient and transport of the synoptic wave momentum flux.Specifically,as the MJO convection propagates northwestward across the Western Pacific,the MJO KE undergoes variabilities,with the maximum located to the north of the convection center in both the mid-lower(～700 hPa) and upper(～200 hPa) troposphere.Our MS-EVA diagnosis reveals that the composite KE integrated through all levels increases with time to the north of the convection center.In the composite KE budget,the conversion from the available potential energy(APE) within the same scale and the work done by the pressure gradient dominate.This result is consistent with the Gill model result,in which heating contributes to the MJO development.In addition,geopotential fluxes from extra-tropics should be taken into account.The balance between the energy conversion from APE and pressure work for the composite MJO event is similar to that synoptic-scale disturbances.A large amount of KE dissipates near the tropopause due to the cumulus friction.The other important term in the KE balance is the cross-scale KE canonical transfer among the three scale windows.The KE transfer between the MJO and the synoptic scale windows is a sink of the MJO KE,while that between the MJO and large-scale windows is a major source.That is to say,barotropic instability is the main mechanism which extracts energy from the mean flow for MJO to grow;in particular,it has been shown that during the active phase over the Western Pacific,MJO extracts much more KE from the background.

Abstract:The first rainy season in southern China(from April to June) accounts for approximately 40% to 50% of the total yearly precipitation.Therefore,it is significant to conduct some research regarding the rainfall during the first rainy season.The precipitation is closely related to the changes in the winds,and wind spectrum distribution is considerably connected with the period of the weather system.Many previous studies regarding spectrum analysis using meteorological tower measurements have been carried out.However,due to the absence of wind measurements,there have been found to be several limitations for the spectrum analysis of the boundary layer.By applying Wind Profile Radar in atmospheric soundings,it has been possible to obtain high-altitude continuous wind data.Therefore,research studies regarding the spectrum based on high-reliability wind profile radar measurements at altitudes from 100 to 3 000 m,have been completed.The turbulence spectrum density was calculated by the wind profiling radar data at the 1 000 to 3 000 m level during rainy season(April,2012) at Yichun based on the FFT.This spectral analysis provided a new method for weather forecasting.The Yichun boundary layer wind profile was detected by five wave beams,with sounding levels from 100 to 5 980 m,and the sampling interval was set at six minutes.The wind profile radar provided the horizontal wind-speed data and signal-to-noise ratio(SNR),echo intensity,spectral width,and so on.The data were obtained from the period ranging from April 1st to May 5th,2012,and a quality control was applied.The average wind is usually treated as the mean wind of the data collected during a short period.However,the average wind of data collected during a long period tends to change with time,which is called a trend.The fluctuation of the wind is obtained by using a least-squares curve fitting to calculate the trend,and then this is deducted from the wind data.In this study,a spectral analysis was carried out with the vertical wind distribution and surface precipitation.The following results were revealed:1) A spectrum analysis of the wind profile radar observations at heights from 100 to 3 000 m was completed.The spectrum during the stable weather and rainy days showed different characteristics,which could then be used to analyze and research the weather systems;2) In the spectrum,two synoptic systems in different time scales were discovered.There were two periods of 5 to 7 days and 2 to 3 days,which appeared in the turbulent spectrum.These also were evident in the wind speed sequence chart and precipitation figures.The turbulence spectrum density during the 5- to 7-day period was found to be four times higher than during the 2- to 3-day period,and the wind speed was stronger as well;3) During the stable weather,the spectrum was determined to be smooth,with no peaks,whereas during the rainy days,there were several peaks observed in the spectrum.The peak of the 5- to 7-day weather system was found to be more obvious at 3 000 m,and was obviously reduced at the lower levels,which indicated that the air at approximately 3 000 m was colder,and wind energy was transmitting faster at the lower levels.The peak of the 2- to 3-day weather system was found to be more obvious in the lower levels.This was mainly due to the turbulence activities in the warmer sector.The results confirmed that,in a comparison of the two different time scale synoptic systems,the 5- to 7-day synoptic system was more profound and had stronger wind speeds.4) The positions of rain belts caused the different time scale synoptic systems.The oscillation from south to north of the rain belts resulted in both long period synoptic and short period synoptic systems,as a result of the fluctuation of the rain belts.The spectral features were found to differ with the different time scale analysis of the wind profiling radar data.The long-time data reflected the overall situation and time period of the system.Meanwhile,the short-time data mirrored the precipitation of the synoptic system.Furthermore,more data from different locations are required for a more comprehensive understanding of the first rainy season.

Abstract:Based on daily precipitation data from 1951 to 2014 in Nanjing,NCEP/NCAR daily reanalysis data,and NOAA monthly Extended Reconstructed Sea Surface Temperature data,the pentad evolution characteristics of summer precipitation in Nanjing and its associated anomalous circulation are analyzed using empirical orthogonal function(EOF) decomposition,wavelet analysis,composite analysis,and regression analysis.Results show that summer precipitation in Nanjing is concentrated between the 5th pentad of June and the 3rd pentad of July,and mean precipitation intensity during this period is 8.2 mm·d^-1.Nevertheless,mean precipitation intensity between the 1st and 3rd pentad of June and between the 4th pentad of July and the 6th pentad of August are only 4 mm·d^-1 and 4.5 mm·d^-1,respectively.Precipitation intensity shows a “double-peak” structure in the climatology,with the peak time occurring in the 5th pentad of June and the 1st pentad of July,with the precipitation intensity in these two pentads both reaching 9.4 mm·d^-1.The wavelet analysis results show that the precipitation peak time also has a decadal variation of ～10 years.The pentad evolution characteristics of precipitation shows a conversion between three states—a “single-peak” structure with the peak time occurring in the 5th pentad of June,a “single-peak” structure with the peak time occurring in the 2nd pentad of July,and a “double-peak” structure—in different years.Regression analysis of atmospheric data demonstrates that it is the atmospheric circulation anomalies that lead to the “single-peak” structure of precipitation.When precipitation's “single-peak” occurs in the 2nd pentad of July(5th pentad of June),the western Pacific subtropical high(WPSH) tends to be weak(strong) and eastward(westward),the East Asia trough and Baikal shallow trough tend to be weak(strong),the vapor in the lower troposphere around Nanjing tends to be anomalously divergent(convergent),and the vertical velocity in the middle troposphere tends to be downward(upward);and this kind of anomalous atmospheric circulation pattern is(is not) conducive to the formation of precipitation,such that the precipitation intensity is anomalously weak(strong) in the 5th pentad of June.On the contrary,in the 1st and 2nd pentad of July,the WPSH tends to be strong(weak) and westward(eastward),the East Asia trough and Baikal shallow trough tend to be strong(weak),the vapor in the lower troposphere around Nanjing tends to be abnormally convergent(divergent),and the vertical velocity in the middle troposphere tends to be upward(downward);and this kind of anomalous atmospheric circulation pattern is(is not) conducive to the formation of precipitation,such that the precipitation intensity is anomalously strong(weak) in the 1st and 2nd pentad of July.The preceding winter sea surface temperature(SST) anomalies of(10°—20°N,175°E—150°W) show significant correlation with the variability of the precipitation peak time—the summer precipitation in Nanjing tends to show a “single-peak” structure with the peak time occurring in the 2nd pentad of July(5th pentad of June) when the preceding winter SST of this sea area is anomalously warm(cold)—such that the preceding winter SST anomalies of this sea area can serve as a forecasting factor for summer precipitation peak time anomalies in Nanjing.

Abstract:The Taihang Mountains are located between the Shanxi Plateau and the North China Plain,and extend over 400 km from north to south.Since the range rises steeply from the North China Plain to an elevation of approximately 1 000 to 1 200 m,hail is much more common along the eastward slope of the mountains.It was found that the upper level observations at 00 UTC on May 25th,2011 showed that the 500 hPa synoptic pattern in North China was dominated by a northwesterly flow,with a cold advection behind the pressure trough.A cyclonic wind shear was located at 850 hPa,with a southwesterly warm/moist conveyor belt to the east of the shear line.It was determined that,from 17:00 to 21:00 (BST),convective precipitation occurred in Xingtai,at the eastern foot of the Taihang Mountains,with amounts of up to 29.3 mm observed.Also,hail of up to 5 cm in diameter was observed in the steep eastward-facing slope.A Weather Research and Forecasting(WRF) model was utilized in an attempt to determine if the dynamical and physical impacts of the Taihang Mountains and its surrounding terrain may have contributed to the triggering and development of the severe convection.The impact of the resolution effects of the terrain data of the topographic height on the precipitation simulation in this special scenario were evaluated by a comparison between resolution of the 5-minute and 30-second terrain resolution with the model's horizontal grid space of 1.67 km.It was observed in this study that the finer representation of the complex terrain improved the precipitation simulation.The simulated results indicated that the easterly warm moist air flow on the north side of the Huanghai low was blocked by the Taihang Mountains,and joined the southwesterly warm moist air flow from the southeast side of low level shear line.This led to a high value in the water vapor mixing ratio at the eastern side of the Taihang Mountains.The upward heat flux at the eastern slope of the Taihang Mountains was apparently higher than that of the North China Plain,due to the larger daily shortwave radiation warming on the slope surface.This resulted in the pseudo-equivalent potential temperature gradient reaching 0.2 K·km^-1 at the 850 hPa level between the mountain slope and the air over the plain.Also,the vertical gradient of the pseudo-equivalent potential temperature reached 4 K·km^-1 between the 850 and 600 hPa levels during the afternoon.The high thermal gradients of the pseudo-equivalent potential temperature increased the horizontal and vertical pressure gradient forces in such a way as to drive the wind in an upslope direction.The resulting upslope winds led to a strong ascending velocity(above 1 m·s^-1),and created dynamic conditions for the convection development on the eastern slope of the Taihang Mountains.The dry and cold westerly flow passed over the warm moist air layer on the eastern side of Taihang Mountains,which resulted in a strong static instability.The occurrence and intensification of the local convection on the eastern side of the Taihang Mountains were caused by a combination of the strong instability,high values of the water vapor mixing ratio,and upslope winds.Due to the local small-scale orographic uplift and gravity wave which were generated by the Luliang Mountains,weak convection cells appeared at the Taiyuan Basin.Then,a weak convection cell developed when it moved to the western slope of the Taihang Mountains due to the uplift caused by the blocking.When the eastward moving convection cell passed the Taihang Mountains,it combined with a local strong convection on the eastern side of the Taihang Mountains,which resulted in the intense development of a combined convection.

Abstract:In this study,weather variables(air temperature,pressure,wind speed,relative humidity,ground temperature etc.) collected from an experimental double-set automatic weather station(noted as stations A and B) and the home station(noted as station Z) were comparatively analyzed at Yixian,Huangshan,Anhui Province in February,2011.The data continuity before and after the new double-set automatic weather station was extensively evaluated.The assessment factors were data integrity and difference.The data difference was evaluated by the average,standard deviation,error rate,the gross error rate and concordance rate.The comparative analysis shows the following:(1)The double-set automatic weather station data are of high reliability,the receiving rate of most of the variables reaches up to 100%,but the receiving rate of relative humidity at station B is as low as 83.3%,due to the failure of the relative humidity module of said station.(2)The observation data from the double sets have high concordance rates for many variables,but the concordance rates are relatively low for the ground temperature differences of station-pairs AB and AZ,and for the pressure difference of station-pair AB.Some additional differences also existed.(3)The T-test results show that the data from double-set automatic weather station have no significant difference compared to the historical data series,and the continuity is very good.(4)The double-set of the automatic weather station is demonstrated to fit for weather observation operation,but parallel work with the home station should be performed for a certain time,in order to obtain more reference data for future analysis and calibration.

Abstract:A conditional nonlinear optimal perturbation(CNOP) represents a kind of initial perturbation which has the largest nonlinear evolution at the end of the concerned time window.Physically,a CNOP describes the initial error which satisfies a certain constraint and yields the largest prediction error at the prediction time.Therefore,solving the CNOP is categorized as a constrained optimization problem.In most cases,CNOPs are obtained by using gradient descend algorithms,such as the spectral projected gradient method(SPG) and sequential quadratic programming(SQP),and the required gradient is obtained by backward integrating the associated adjoint model.This optimization method is hereafter referred to as ADJ.However,the adjoint technology can “work” well only when the nonlinearity of the governing equation is not excessively strong,and when the objective function is differentiable with respect to the optimization variables.When the nonlinear model contains discontinuous “on-off” switches,the ability of the ADJ to capture CNOPs will be weakened much more greatly.In addition,not all models have corresponding adjoint models,and writing the adjoint model of a complex model is very tedious and time-consuming.A genetic algorithm is a population-based heuristic search method,and possesses the characteristic of information sharing among its population members.A member in the population of the GA represents a potential solution which is a point in the search space,and each member has a fit value from which one can judge how strong the current potential solution is.Recently,genetic algorithms(GAs) have received much attention for their effectiveness and robustness in solving constrained non-smooth optimal problems.There are three basic genetic operators in a GA,i.e.selection,crossover and mutation operators.The performance of a GA rests with not only optimization problems,but also with the configuration of the genetic operators.In this study,a new constraint GA(GA1) configured proper genetic operator is applied to capture the CNOP of a nonlinear model with discontinuous “on-off” switches.In order to verify the effectiveness of GA1,numerical experiments capturing CNOPs are conducted by using ADJ,GA1 and GA configured operators(GA2).More specifically,in the selection operation,both GA1 and GA2 use a tournament selection operator,and the comparison criteria are as follows:(1) When both comparative individuals are feasible solutions,the one with the larger fit value is preferred;and(2) When there is any infeasible solution among the two comparative individuals,first pull the infeasible solution to the edge of the spherical constraints to let it become feasible,then apply the comparison criteria(1).For the crossover operation,GA1 blends the simulation binary crossover(SBX) with the BLX-α,while GA2 only uses the BLX-α.In mutation operation,GA1 uses the multiply mutation and GA2 uses the non-uniform mutation.The numerical experiment results show that the ability of global optimization based on GA1and GA2 is much stronger than the one based on ADJ in non-smooth cases.Furthermore,similarity degree is used to test the sensitivity of the spatial structure of the CNOP respectively obtained by ADJ,GA1 and GA2 to the first guess value(initial population),and the results of 200 numerical experiments show that the CNOP capturing by GA1 can retain a steady spatial structure.

Abstract:The intraseasonal oscillations(ISOs) were first found in tropical regions.However,the ISOs are not limited in tropical areas,and in the subtropical regions and middle and high latitude regions,the strength of the ISOs is also very strong.There are some obvious differences of ISOs between the tropical and extratropical areas,including the typical oscillation period,three-dimensional structure,and propagation characteristics.The existing study does not involve the winter ISOs in the middle and high latitude in the north of the Pacific.In this paper,the NCEP/DOE Reanalysis data(geopotential height,air temperature,zonal wind,meridional wind,and so on,at pressure levels) are chosen to study the intraseasonal variations and ISOs of the geopotential height in the north of the Pacific in the troposphere,especially in the upper troposphere. The study results show that the strength of the intraseasonal variation of the geopotential height in the north of the Pacific increases with the height in the troposphere,and the strongest center is located at 250 hPa.The dominant intraseasonal period of the geopotential height is about 10—40 days.The EOF decomposition method is used to analyze the distribution of the 10—40 day filtered geopotential height.The first mode of EOF shows that the oscillation center is located in the northern Pacific and Arctic Oceans,and the second mode of EOF presents the anti-phase oscillation between northeast Asia and northwest USA.101 positive oscillation events and 95 negative oscillation events occurred from 1979 to 2012.The composite analysis is used to study the propagation characteristics of the oscillation.Based on the time series of the first EOF mode,the 10—40 day filtered geopotential height moves westward:the negative center first appears in the northern Pacific,then moves to Asia;at the same time,a positive geopotential height moves from the Americas to the west;and the leading-lagging correlation coefficients show that the first EOF mode is related to the second EOF mode,and in fact,they are the results of the different phases of the geopotential height oscillation.Different from the intraseasonal time scale,the synoptic time scale(shorter than 10 days) geopotential height propagates eastward.The intraseasonal oscillation of the geopotential height occurs along with the evolution of the Pacific ridge and East Asian trough.It is worth noting that in the peak(or break) day of the intraseasonal geopotential height,the distribution of the synoptic geopotential height is similar to that of the 10—40 day filtered geopotential height,which strengthens the Pacific ridge and East Asia trough. The mechanism of the geopotential height ISOs is further analyzed.Due to the relationship between the geopotential height and vorticity,the vorticity equation is diagnosed.The results show that the 10—40 day filtered vorticity tendency is strongest at the 250 hPa level.The vorticity tendency is determined by the relative vorticity advection,geostrophic vorticity advection,vertical vorticity advection,tilt item and divergence term.In the intraseasonal time scale,the geostrophic vorticity advection contributes the most greatly,and from the low to upper troposphere the distributions are similar.The divergence term has the same magnitude as the geostrophic vorticity advection,but for the vertical structure the divergence term in the upper troposphere is opposite to that in the low troposphere.The divergence term strengthens the oscillation of the geopotential height in the upper troposphere,and weakens it in the low troposphere.In the peak day of the geopotential height,the vorticity tendency is negative in the west of the key regions,namely positive height tendency,which guides the wave to spread toward the west.Although the relative vorticity advection has a negative contribution to the vorticity tendency,the geostrophic vorticity advection and divergence term are negative in the west of the key regions,and positive in the east of the key regions.Therefore,the geostrophic vorticity advection and divergence term are the main reasons for the westward propagation of the geopotential height ISOs.