Study on Multi-Scale Blending Initial Condition Perturbations for a Regional Ensemble Prediction System

An initial conditions （ICs） perturbation method was developed with the aim to improve an operational regional ensemble prediction system （REPS）. Three issues were identified and investigated： （1） the impacts of perturbation scale on the ensemble spread and forecast skill of the REPS; （2） the scale characteristic of the IC perturbations of the REPS; and （3） whether the REPS＇s skill could be improved by adding large-scale information to the IC perturbations. Numerical experiments were conducted to reveal the impact of perturbation scale on the ensemble spread and forecast skill. The scales of IC perturbations from the REPS and an operational global ensemble prediction system （GEPS） were analyzed. A ＂multi-scale blending＂ （MSB） IC perturbation scheme was developed, and the main findings can be summarized as follows： The growth rates of the ensemble spread of the REPS are sensitive to the scale of the IC perturbations; the ensemble forecast skills can benefit from large-scale perturbations; the global ensemble IC perturbations exhibit more power at larger scales, while the regional ensemble IC perturbations contain more power at smaller scales; the MSB method can generate IC perturbations by combining the small-scale component from the REPS and the large-scale component from the GEPS; the energy norm growth of the MSB-generated perturbations can be appropriate at all forecast lead times; and the MSB-based REPS shows higher skill than the original system, as determined by ensemble forecast verification.

Development of regional prediction information system of coal and gas outburst

Coal and gas outburst information system is based on-Geographic Information System(GIS), with which the relation among mine geological structure, coal features, stress field and coal and gas outburst were researched, and also the relation between gas distributed condition and dangerous degrees. Various prediction method, index and technique were applied to realize the data visualization; the accuracy of region prediction was increased. The system has successfully applied in Huainan minging area and Pingdingshan minging area.

Possible Sources of Forecast Errors Generated by the Global/Regional Assimilation and Prediction System for Landfalling Tropical Cyclones.PartⅠ:Initial Uncertainties

This paper investigates the possible sources of errors associated with tropical cyclone （TC） tracks forecasted using the Global/Regional Assimilation and Prediction System （GRAPES）. The GRAPES forecasts were made for 16 landfaIling TCs in the western North Pacific basin during the 2008 and 2009 seasons, with a forecast length of 72 hours, and using the default initial conditions （＂initials＂, hereafter）, which are from the NCEP-FNL dataset, as well as ECMWF initials. The forecasts are compared with ECMWF forecasts. The results show that in most TCs, the GRAPES forecasts are improved when using the ECMWF initials compared with the default initials. Compared with the ECMWF initials, the default initials produce lower intensity TCs and a lower intensity subtropical high, but a higher intensity South Asia high and monsoon trough, as well as a higher temperature but lower specific humidity at the TC center. Replacement of the geopotential height and wind fields with the ECMWF initials in and around the TC center at the initial time was found to be the most efficient way to improve the forecasts. In addition, TCs that showed the greatest improvement in forecast accuracy usually had the largest initial uncertainties in TC intensity and were usually in the intensifying phase. The results demonstrate the importance of the initial intensity for TC track forecasts made using GRAPES, and indicate the model is better in describing the intensifying phase than the decaying phase of TCs. Finally, the limit of the improvement indicates that the model error associated with GRAPES forecasts may be the main cause of poor forecasts of landfalling TCs. Thus, further examinations of the model errors are required.

IMPROVEMENT OF REGIONAL PREDICTION OF SEA FOG ON GUANGDONG COASTLAND USING THE FACTOR OF TEMPERATURE DIFFERENCE IN THE NEAR-SURFACE LAYER

The relationship between the factor of temperature difference of the near-surface layer(T_(1000 hPa)-T_(2m))and sea fog is analyzed using the NCEP reanalysis with a horizontal resolution of l°xl°(2000 to 2011) and the station observations(2010 to 2011).The element is treated as the prediction variable factor in the GRAPES model and used to improve the regional prediction of sea fog on Guangdong coastland.(1) The relationship between this factor and the occurrence of sea fog is explicit:When the sea fog happens,the value of this factor is always large in some specific periods,and the negative value of this factor decreases significantly or turns positive,suggesting the enhancement of warm and moist advection of air flow near the surface,which favors the development of sea fog.(2) The transportation of warm and moist advection over Guangdong coastland is featured by some stages and the jumping among these states.It also gets stronger over time.Meanwhile,the northward propagation of warm and moist advection is quite consistent with the northward advancing of sea fog from south to north along the coastland of China.(3) The GRAPES model can well simulate and realize the factor of near-surface temperature difference.Besides,the accuracy of regional prediction of marine fog,the relevant threat score and Heidke skill score are all improved when the factor is involved.

Control of Facies and Potential on Jurassic Hydrocarbon Accumulation and Prediction of Favorable Targets in the Hinterland Region of the Junggar Basin

Exploration practices show that the Jurassic System in the hinterland region of the Junggar Basin has a low degree of exploration but huge potential, however the oil/gas accumulation rule is very complicated, and it is difficult to predict hydrocarbon-bearing properties. The research indicates that the oil and gas is controlled by structure facies belt and sedimentary system distribution macroscopically, and hydrocarbon-bearing properties of sand bodies are controlled by lithofacies and petrophysical facies microscopically. Controlled by ancient and current tectonic frameworks, most of the discovered oil and gas are distributed in the delta front sedimentary system of a palaeo-tectonic belt and an ancient slope belt. Subaqueous branch channels and estuary dams mainly with medium and fine sandstone are the main reservoirs and oil production layers, and sand bodies of high porosity and high permeability have good hydrocarbon-bearing properties; the facies controlling effect shows a reservoir controlling geologic model of relatively high porosity and permeability. The hydrocarbon distribution is also controlled by relatively low potential energy at the high points of local structure macroscopically, while most of the successful wells are distributed at the high points of local structure, and the hydrocarbon-bearing property is good at the place of relatively low potential energy; the hydrocarbon distribution is in close connection with faults, and the reservoirs near the fault in the region of relatively low pressure have good oil and gas shows; the distribution of lithologic reservoirs at the depression slope is controlled by the distribution of sand bodies at positions of relatively high porosity and permeability. The formation of the reservoir of the Jurassic in the Junggar Basin shows characteristics of favorable facies and low-potential coupling control, and among the currenffy discovered reservoirs and industrial hydrocarbon production wells, more than 90% are developed within the scope of facies- potential index FPI〉0.5, while the FPI and oil saturation of the discovered reservoir and unascertained traps have relatively good linear correlation. By establishing the relation model between hydrocarbon- bearing properties of traps and FPI, totally 43 favorable targets are predicted in four main target series of strata and mainly distributed in the Badaowan Formation and the Sangonghe Formation, and the most favorable targets include the north and east of the Shinan Sag, the middle and south of the Mobei Uplift, Cai-35 well area of the Cainan Oilfield, and North-74 well area of the Zhangbei fault-fold zone.

Study of perturbing method in regional BGM ensemble prediction system

Based on an Ensemble Prediction System with the BGM method on the regional numerical prediction model AREM, Static State Perturbation （SSP, including Initial Random Perturbation and Perturbation Restriction） and Dynamic State Perturbation （DSP） are designed. The impacts of both perturbations on precipitation prediction are studied by analyzing a slrong precipitation process oc- curring during July 20-21, 2008. The results show that both SSP and DSP play a positive role in prediction of mesoscale precipita- tion, such as lowering the （missing） rate of precipitation prediction. SSP is mainly helpful for the 24-hour prediction, while DSP can improve both 24-hour and 48-hour prediction. DSP is better than the two SSPs in the hit rate of regional precipitation prediction. However, the former also has a little higher false alarm rate than the latter. DSP enlarges in some extent the dispersion of EPS, which is good for EPS.

The Two-dimensional Time Coordinate System and Time Prediction Research of M≥6.7 Strong Earthquakes in the Sichuan-Yunnan Region

Since the 20 thcentury,the time intervals of M ≥6.7 strong earthquakes in the SichuanYunnan region show obvious regularity.Using the years of the strong events,a twodimensional time coordinate system is generated,based on which,the time prediction model is constructed for strong earthquakes in the Sichuan-Yunnan region.Prediction analysis shows that there is risk of generating four earthquakes with M ≥ 6.7 in the Sichuan-Yunnan region in the future 16 years,and there are strong signals for M ≥6.7earthquakes for periods 2012-2021 and 2025-2029.The strong earthquakes may occur around 2014-2015,2019 and 2027.

On Spatiotemporal Series Analysis and Its Application to Predict the Regional Short Term Climate Process

Based on the theory of reconstructing state space, a technique for spatiotemporal series prediction is presented. By means of this technique and NCEP/NCAR data of the monthly mean geopotential height anomaly of the 500-hPa isobaric surface in the Northern Hemisphere, a regional prediction experiment is also carried out. If using the correlation coefficient R between the observed field and the prediction field to measure the prediction accuracy, the averaged R given by 48 prediction samples reaches 21%, which corresponds to the current prediction level for the short range climate process.

Application of the predictable model ofregional time-magnitude to North and Southwest China region

In this paper, the method which can combine different seismic data with the different precision and completeness, even the palaeo-earthquake data, has been applied to estimate the yearly seismic moment rate in the seismic region. Based on this, the predictable model of regional time-magnitude has been used in North China and Southwest China. The normal correlation between the time interval of the events and the magnitude of the last strong earthquake shows that the model is suitable. The value of the parameter c is less than the average value of 0.33 that is obtained from the events occurred in the plate boundary in the world. It is explained that the correlativity between the recurrence interval of the earthquake and the magnitude of the last strong event is not obvious. It is shown that the continental earthquakes in China are different from that occurred in the plate boundary and the recurrence model for the continental events are different from the one for the plate boundary events. Finally the seismic risk analysis based on this model for North China and Southwest China is given in this paper.

Systematic Errors of Zonal-Mean Flow in Dynamical Monthly Prediction and Its Improvement

An analysis of a large number of cases of 500 hPa height monthly prediction shows that systematic errors exist in the zonal mean components which account for a large portion of the total forecast errors, and such errors are commonly seen in other prediction models. To overcome the difficulties of the numerical model, the authors attempt a 'hybrid' approach to improving the dynamical extended-range (monthly) prediction. The monthly pentad-mean nonlinear dynamical regional prediction model of the zonal-mean geopotential height (wave number 0) based on a large amount of data is constituted by employing the reconstruction of phase-space theory and the spatio-temporal series predictive method. The dynamical prediction of the numerical model is then combined with that of the nonlinear model, i.e., the pentadmean zonal-mean height produced by the nonlinear model is transformed to its counterpart in the numerical model by nudging during the time integration. The forecast experiment results show that the above hybrid approach not only reduces the systematic error in zonal mean height by the numerical model, but also makes an improvement in the non-axisymmetric components due to the wave-flow interaction.

Scale-dependent Regional Climate Predictability over North America Inferred from CMIP3 and CMIP5 Ensemble Simulations

Through the analysis of ensembles of coupled model simulations and projections collected from CMIP3 and CMIP5, we demonstrate that a fundamental spatial scale limit might exist below which useful additional refinement of climate model predictions and projections may not be possible. That limit varies among climate variables and from region to region. We show that the uncertainty（noise） in surface temperature predictions（represented by the spread among an ensemble of global climate model simulations） generally exceeds the ensemble mean（signal） at horizontal scales below 1000 km throughout North America, implying poor predictability at those scales. More limited skill is shown for the predictability of regional precipitation. The ensemble spread in this case tends to exceed or equal the ensemble mean for scales below 2000 km. These findings highlight the challenges in predicting regionally specific future climate anomalies, especially for hydroclimatic impacts such as drought and wetness.

Image Emotion Classification Network Based on Multilayer Attentional Interaction,Adaptive Feature Aggregation

The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the image.Studies have shown that certain local regions are more likely to inspire an emotional response than the whole image.However,existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the dataset.Therefore,this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature aggregation.To perform more accurate emotional region prediction,this study designs a multilayer attentional interaction module.The module calculates spatial attention maps for higher-layer semantic features and fusion features through amultilayer shuffle attention module.Through layer-by-layer up-sampling and gating operations,the higher-layer features guide the lower-layer features to learn,eventually achieving sentiment region prediction at the optimal scale.To complement the important information lost by layer-by-layer fusion,this study not only adds an intra-layer fusion to the multilayer attention interaction module but also designs an adaptive feature aggregation module.The module uses global average pooling to compress spatial information and connect channel information from all layers.Then,the module adaptively generates a set of aggregated weights through two fully connected layers to augment the original features of each layer.Eventually,the semantics and details of the different layers are aggregated through gating operations and residual connectivity to complement the lost information.To reduce overfitting on small datasets,the network is pre-trained on the FI dataset,and further weight fine-tuning is performed on the small dataset.The experimental results on the FI,Twitter I and Emotion ROI(Region of Interest)datasets show that the proposed network exceeds existing image emotion classification methods,with accuracies of 90.27%,84.66%and 84.96%.

The multi-factor recombination and processes superimposition model for hydrocarbon accumulation:application to the Silurian in the Tarim Basin

The multi-factor recombination and processes superimposition model for hydrocarbon accumulation is put forward in view of the hydrocarbon geological characteristics of multiple episodes of structural evolution, multiple sets of source-reservoir-seal assemblage, multiple cycles of hydrocarbon accumulation and multiple episodes of readjustment and reconstruction in the complex superimposed basins in China. It is a system including theories and methods that can help to predict favorable exploration regions. According to this model, the basic discipline for hydrocarbon generation, evolution and distribution in the superimposed basins can be summarized in multi-factor recombination, processes superimposition, multiple stages of oil filling and latest stage preservation. With the Silurian of the Tarim basin as an example, based on the reconstruction of the evolution history of the four factors （paleo-anticline, source rock, regional cap rock and kinematic equilibrium belt） controlling hydrocarbon accumulation, this model was adopted to predict favorable hydrocarbon accumulation areas and favorable exploration regions following structural destruction in three stages of oil filling, to provide guidance for further exploration ofoil and gas in the Silurian of the Tarim basin.

Simulation of Quasi-Linear Mesoscale Convective Systems in Northern China：Lightning Activities and Storm Structure

Two intense quasi-linear mesoscale convective systems（QLMCSs） in northern China were simulated using the WRF（Weather Research and Forecasting） model and the 3D-Var（three-dimensional variational） analysis system of the ARPS（Advanced Regional Prediction System） model.A new method in which the lightning density is calculated using both the precipitation and non-precipitation ice mass was developed to reveal the relationship between the lightning activities and QLMCS structures.Results indicate that,compared with calculating the results using two previous methods,the lightning density calculated using the new method presented in this study is in better accordance with observations.Based on the calculated lightning densities using the new method,it was found that most lightning activity was initiated on the right side and at the front of the QLMCSs,where the surface wind field converged intensely.The CAPE was much stronger ahead of the southeastward progressing QLMCS than to the back it,and their lightning events mainly occurred in regions with a large gradient of CAPE.Comparisons between lightning and non-lightning regions indicated that lightning regions featured more intense ascending motion than non-lightning regions;the vertical ranges of maximum reflectivity between lightning and non-lightning regions were very different;and the ice mixing ratio featured no significant differences between the lightning and non-lightning regions.

Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

MetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

Monthly Extended Predicting Experiments with Nonlinear Regional Prediction.PartⅠ:Prediction of Zonal Mean Flow

Systematic errors have recently been founded to be distinct in the zonal mean component forecasts, which account for a large portion of the total monthly-mean forecast errors. To overcome the difficulty of numerical model, the monthly pentad-mean nonlinear dynamic regional prediction models of the zonal mean geopotential height at 200, 300, 500, and 700 hPa based on a large number of historical data （NCEP/NCAR reanalysis data） were constituted by employing the local approximation of the phase space reconstruction theory and nonlinear spatio-temporal series prediction method. The 12-month forecast experiments of 1996 indicated that the results of the nonlinear model are better than those of the persistent, climatic prediction, and T42L9 model either over the high- and mid-latitude areas of the Northern and Southern Hemispheres or the tropical area. The root-mean-square of the monthly-mean height of T42L9 model was considerably decreased with a change of 30.4%, 26.6%, 82.6%, and 39.4%, respectively, over the high- and mid-latitudes of the Northern Hemisphere, over the high- and mid-latitudes of the Southern Hemisphere, over the tropics and over the globe, and also the corresponding anomaly correlation coefficients over the four areas were respectively increased by 0.306-0.312, 0.304-0.429, 0.739-0.746, and 0.360-0.400 （averagely a relative change of 11.0% over the globe） by nonlinear correction after integration, implying that the forecasts given by nonlinear model include more useful information than those of T42L9 model.

Monthly Extended Predicting Experiments with Nonlinear Regional Prediction.PartⅡ:Improvement of Wave Component Prediction

Based on Chen et al. （2006）, the scheme of the combination of the pentad-mean zonal height departure nonlinear prediction with the T42L9 model prediction was designed, in which the pentad zonal heights at all the 12-initial-value-input isobar levels from 50 hPa to 1000 hPa except 200, 300, 500, and 700 hPa were derived from nonlinear forecasts of the four levels by means of a good correlation between neighboring levels. Then the above pentad zonal heights at 12 isobar-levels were transformed to the spectrum coefficients of the temperature at each integration step of T42L9 model. At last, the nudging was made. On account of a variety of error accumulation, the pentad zonal components of the monthly height at isobar levels output by T42L9 model were replaced by the corresponding nonlinear results once more when integration was over. Multiple case experiments showed that such combination of two kinds of prediction made an improvement in the wave component as a result of wave-flow nonlinear interaction while reducing the systematical forecast errors. Namely the monthly-mean height anomaly correlation coefficients over the high- and mid-latitudes of the Northern Hemisphere, over the Southern Hemisphere and over the globe increased respectively from 0.249 to 0.347, from 0.286 to 0.387, and from 0.343 to 0.414 （relative changes of 31.5%, 41.0%, and 18.3%）. The monthly-mean root-mean-square error （RMSE） of T42L9 model over the three areas was considerably decreased, the relative change over the globe reached 44.2%. The monthly-mean anomaly correlation coeffi- cients of wave 4-9 over the areas were up to 0.392, 0.200, and 0.295, with the relative change of 53.8%, 94.1%, and 61.2%, and correspondingly their RMSEs were decreased respectively with the rate of 8.5%, 6.3%, and 8.1%. At the same time the monthly-mean pattern of parts of cases were presented better.

Regional seismic-damage prediction of buildings under mainshock-aftershock sequence

Strong aftershocks generally occur following a significant earthquake.Aftershocks further damage buildings weakened by mainshocks.Thus,the accurate and efficient prediction of aftershock-induced damage to buildings on a regional scale is crucial for decision making for post-earthquake rescue and emergency response.A framework to predict regional seismic damage of buildings under a mainshock-aftershock(MS-AS)sequence is proposed in this study based on city-scale nonlinear time-history analysis(THA).Specifically,an MS-AS sequence-generation method is proposed to generate a potential MS-AS sequence that can account for the amplification,spectrum,duration,magnitude,and site condition of a target area.Moreover,city-scale nonlinear THA is adopted to predict building seismic damage subjected to MS-AS sequences.The accuracy and reliability of city-scale nonlinear THA for an MS-AS sequence are validated by as-recorded seismic responses of buildings and simulation results in published literature.The town of Longtoushan,which was damaged during the Ludian earthquake,is used as a case study to illustrate the detailed procedure and advantages of the proposed framework.The primary conclusions are as follows.(1)Regional seismic damage of buildings under an MS-AS sequence can be predicted reasonably and accurately by city-scale nonlinear THA.(2)An MS-AS sequence can be generated reasonably by the proposed MS-AS sequencegeneration method.(3)Regional seismic damage of buildings under different MS-AS scenarios can be provided efficiently by the proposed framework,which in turn can provide a useful reference for earthquake emergency response and scientific decision making for earthquake disaster relief.

Helmholtz Solving and Performance Optimization in Global/Regional Assimilation and Prediction System

Despite efficient parallelism in the solution of physical parameterization in the Global/Regional Assimilation and Prediction System(GRAPES),the Helmholtz equation in the dynamic core,with the increase of resolution,can hardly achieve sufficient parallelism in the solving process due to a large amount of communication and irregular access.In this paper,optimizing the Helmholtz equation solution for better performance and higher efficiency has been an urgent task.An optimization scheme for the parallel solution of the Helmholtz equation is proposed in this paper.Specifically,the geometrical multigrid optimization strategy is designed by taking advantage of the data anisotropy of grid points near the pole and the isotropy of those near memory equator in the Helmholtz equation,and the Incomplete LU(ILU)decomposition preconditioner is adopted to speed up the convergence of the improved Generalized Conjugate Residual(GCR),which effectively reduces the number of iterations and the computation time.The overall solving performance of the Helmholtz equation is improved by thread-level parallelism,vectorization,and reuse of data in the cache.The experimental results show that the proposed optimization scheme can effectively eliminate the bottleneck of the Helmholtz equation as regards the solving speed.Considering the test results on a 10-node two-way server,the solution of the Helmholtz equation,compared with the original serial version,is accelerated by 100,with one-third of iterations reduced.

Evaluation of the NMC Regional Ensemble Prediction System During the Beijing 2008 Olympic Games

Based on the B08RDP（Beijing 2008 Olympic Games Mesoscale Ensemble Prediction Research and Development Project） that was launched by the World Weather Research Programme（WWRP） in 2004,a regional ensemble prediction system（REPS） at a 15-km horizontal resolution was developed at the National Meteorological Center（NMC） of the China Meteorological Administration（CMA）.Supplementing to the forecasters＇ subjective affirmation on the promising performance of the REPS during the 2008 Beijing Olympic Games（BOG）,this paper focuses on the objective verification of the REPS for precipitation forecasts during the BOG period.By use of a set of advanced probabilistic verification scores,the value of the REPS compared to the quasi-operational global ensemble prediction system（GEPS） is assessed for a 36-day period（21 July-24 August 2008）.The evaluation here involves different aspects of the REPS and GEPS,including their general forecast skills,specific attributes（reliability and resolution）,and related economic values.The results indicate that the REPS generally performs significantly better for the short-range precipitation forecasts than the GEPS,and for light to heavy rainfall events,the REPS provides more skillful forecasts for accumulated 6-and 24-h precipitation.By further identifying the performance of the REPS through the attribute-focused measures,it is found that the advantages of the REPS over the GEPS come from better reliability（smaller biases and better dispersion） and increased resolution.Also,evaluation of a decision-making score reveals that a much larger group of users benefits from using the REPS forecasts than using the single model（the control run） forecasts,especially for the heavy rainfall events.