A statistical dynamics model of the marine ecosystem and its application in Jiaozhou Bay

Models of marine ecosystem dynamics play an important role in revealing the evolution mechanisms of marine ecosystems and in forecasting their future changes. Most traditional ecological dynamics models are established based on basic physical and biological laws, and have obvious dynamic characteristics and ecological significance. However, they are not flexible enough for the variability of environment conditions and ecological processes found in offshore marine areas, where it is often difficult to obtain parameters for the model, and the precision of the model is often low. In this paper, a new modeling method is introduced, which aims to establish an evolution model of marine ecosystems by coupling statistics with differential dynamics. Firstly, we outline the basic concept and method of inverse modeling of marine ecosystems. Then we set up a statistical dynamics model of marine ecosystems evolution according to annual ecological observation data from Jiaozhou Bay. This was done under the forcing conditions of sea surface temperature and surface irradiance and considering the state variables of phytoplankton, zooplankton and nutrients. This model is dynamic, makes the best of field observation data, and the average predicted precision can reach 90% or higher. A simpler model can be easily obtained through eliminating the terms with smaller contributions according to the weight coefficients of model differential items. The method proposed in this paper avoids the difficulties of obtaining and optimizing parameters, which exist in traditional research, and it provides a new path for research of marine ecological dynamics.

A Medium/Long-Range Forecast of Pacific Subtropical High Based on Dynamic Statistic Model Reconstruction

Based on the 500-hPa geopotential height field series of T106 numerical forecast products, by empirical orthogonal function （EOF） time-space separation, and on the hypotheses of EOF space-models being stable, the EOF time coefficient series were taken as dynamical statistic model variables. The dynamic system reconstruction idea and genetic algorithm were introduced to make the dynamical model parameters optimized, and a nonlinear dynamic statistic model of EOF separating time coefficient series was established. By the model time integral and EOF time-space reconstruction, a medium/long-range forecast of subtropical high was carried out. The results show that the dynamical model forecast and T106 numerical forecast were approximately similar in the short-range forecast （≤5 days）, but in the medium/long-range forecast （≥5 days）, the forecast results of dynamical model was superior to that of T106 numerical products. A new method and idea were presented for diagnosing and forecasting complicated weathers such as subtropical high, and showed a better application outlook.

ON THE MECHANISM OF TURBULENT COHERENT STRUCTURE (III)──A STATISTICAL AND DYNAMICALMODEL OF COHERENT STRUCTURE AND ITSHEAT TRANSFER MECHANISM

Following Tsai & Ma[1] and Tsai & Liu[2], a statistical and dynamical near-wall turbulent coherent structural model with separate consideration of two different portions:locally generated and upstream-transported large eddies has been established.With this model, heat transfer in a fully developed open channel in the absence of pressure gradient is numerically simulated. Database of fluctuations of velocity and temperature has also been set. Numerical analysis shows the existence of high-low temperature streak caused by near-wall coherent structure and its swing in the lateral direction.Numerical results are in accordance with the computations and experimental results of other researchers.

A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model

An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).

Robust description and recognition of various viewpoint dynamic textures

The problem of recognizing natural scenes, such as water, smoke, fire, wind-blown vegetation and a flock of flying birds, is considered. These scenes exhibit the characteristic dynamic pattern, but have stochastic extent. They are referred to as dynamic texture（DT）. In reality, the diversity of DTs on different viewpoints and scales are very common, which also bring great difficulty to recognize DTs. In the previous studies, due to no considering of the deformable and transient nature of elements in DT, the motion estimation method is based on brightness constancy assumption,which seem inappropriate for aggregate and complex motions. A novel motion model based on relative motion in the neighborhood of two-dimensional motion fields is proposed. The estimation of non-rigid motion of DTs is based on the continuity equation, and then the local vector difference（LVD） is proposed to characterize DT local relative motion. Spatiotemporal statistics of the LVDs is used as the representation of DT sequences. Excellent performances of classifying all DTs in UCLA database demonstrate the capability of the proposed method in describing DT.

Verification and Improvement of the Ability of CFSv2 to Predict the Antarctic Oscillation in Boreal Spring

The boreal spring Antarctic Oscillation(AAO)has a significant impact on the spring and summer climate in China.This study evaluates the capability of the NCEP's Climate Forecast System,version 2(CFSv2),in predicting the boreal spring AAO for the period 1983-2015.The results indicate that CFSv2 has poor skill in predicting the spring AAO,failing to predict the zonally symmetric spatial pattern of the AAO,with an insignificant correlation of 0.02 between the predicted and observed AAO Index(AAOI).Considering the interannual increment approach can amplify the prediction signals,we firstly establish a dynamical-statistical model to improve the interannual increment of the AAOI(DY AAOI),with two predictors of CFSv2-forecasted concurrent spring sea surface temperatures and observed preceding autumn sea ice.This dynamical-statistical model demonstrates good capability in predicting DY AAOI,with a significant correlation coeffcient of 0.58 between the observation and prediction during 1983-2015 in the two-year-out cross-validation.Then,we obtain an improved AAOI by adding the improved DY AAOI to the preceding observed AAOI.The improved AAOI shows a significant correlation coeffcient of 0.45 with the observed AAOI during 1983-2015.Moreover,the unrealistic atmospheric response to March-April-May sea ice in CFSv2 may be the possible cause for the failure of CFSv2 to predict the AAO.This study gives new clues regarding AAO prediction and short-term climate prediction.

A Hybrid Dynamical-Statistical Approach for Predicting Winter Precipitation over Eastern China

Correlation analysis revealed that winter precipitation in six regions of eastern China is closely related not only to preceding climate signals but also to synchronous atmospheric general circulation fields. It is therefore necessary to use a method that combines both dynamical and statistical predictions of winter precipitation over eastern China （hereinafter called the hybrid approach）, in this connection, seasonal real-time prediction models for winter precipitation were established for the six regions. The models use both the preceding observations and synchronous numerical predictions through a multivariate linear regression analysis. To improve the prediction accuracy, the systematic error between the original regression model result and the corresponding observation was corrected. Cross-validation analysis and real-time prediction experiments indicate that the prediction models using the hybrid approach can reliably predict the trend, sign, and interannual variation of regionally averaged winter precipitation in the six regions of concern. Averaged over the six target regions, the anomaly correlation coefficient and the rate with the same sign of anomaly between the cross-validation analysis and observation during 1982-2008 are 0.69 and 78%, respectively. This indicates that the hybrid prediction approach adopted in this study is applicable in operational practice.

Analogue Correction Method of Errors by Combining Statistical and Dynamical Methods

Based on the atmospheric analogy principle, the inverse problem that the information of historical analogue data is utilized to estimate model errors is put forward and a method of analogue correction of errors （ACE） of model is developed in this paper. The ACE can combine effectively statistical and dynamical methods, and need not change the current numerical prediction models. The new method not only adequately utilizes dynamical achievements but also can reasonably absorb the information of a great many analogues in historical data in order to reduce model errors and improve forecast skill. Purthermore, the ACE may identify specific historical data for the solution of the inverse problem in terms of the particularity of current forecast. The qualitative analyses show that the ACE is theoretically equivalent to the principle of the previous analogue-dynamical model, but need not rebuild the complicated analogue-deviation model, so has better feasibility and operational foreground. Moreover, under the ideal situations, when numerical models or historical analogues are perfect, the forecast of the ACE would transform into the forecast of dynamical or statistical method, respectively.

Analysis of High Codimensional Bifurcation and Chaos for the Quad Bundle Conductor＇s Galloping

A two-degree-of-freedom model of iced, electrical quad bundle conductor is developed to comprehensively describe the different galloping behaviors observed. By applying centre manifold and invertible linear transformation, the co-dimension-2 bifurcation is analyzed. The relationships of parameters between this system and the original system are obtained to analyze and to control the galloping of the quad iced bundle conductor. The space trajectory, Lyapunov exponent and Lyapunov dimension are investigated via numerical simulation to present a rigorous proof of existence of chaos.

Research on fault detection method for heat pump air conditioning system under cold weather

Building energy consumption accounts for nearly 40% of global energy consumption, HVAC （Heating, Ventilating, and Air Conditioning） systems are the major building energy consumers, and as one type of HVAC systems, the heat pump air conditioning system, which is more energy-efficient compared to the traditional air conditioning system, is being more widely used to save energy. However, in northern China, extreme climatic conditions increase the cooling and heating load of the heat pump air conditioning system and accelerate the aging of the equipment, and the sensor may detect drifted parameters owing to climate change. This non-linear drifted parameter increases the false alarm rate of the fault detection and the need for unnecessary troubleshooting. In order to overcome the impact of the device aging and the drifted parameter, a Kalman filter and SPC （statistical process control） fault detection method are introduced in this paper. In this method, the model parameter and its standard variance can he estimated by Kalman filter based on the gray model and the real-time data of the air conditioning system. Further, by using SPC to construct the dynamic control limits, false alarm rate is reduced. And this paper mainly focuses on the cold machine failure in the component failure and its soft fault detection. This approach has been tested on a simulation model of the ＂Sino-German Energy Conservation Demonstration Center＂ building heat pump air-conditioning system in Shenyang, China, and the results show that the Kalman filter and SPC fault detection method is simple and highly efficient with a low false alarm rate, and it can deal with the difficulties caused by the extreme environment and the non-linear influence of the parameters, and what＇s more, it provides a good foundation for dynamic fault diagnosis and fault prediction analysis.

Attack Vulnerability of Network with Duplication-Divergence Mechanism

We study the attack vulnerability of network with duplication-divergence mechanism. Numerical results have shown that the duplication-divergence network with larger retention probability a is more robust against target attack relatively. Furthermore, duplication-divergence network is broken down more quickly than its counterpart BA network under target attack. Such result is consistent with the fact of WWW and Internet networks under target attack. So duplication-divergence model is a more realistic one for us to investigate the characteristics of the world wide web in future. We also observe that the exponent γ of degree distribution and average degree are important parameters of networks, reflecting the performance of networks under target attack. Our results are helpful to the research on the security of network.

Relaxation Oscillation of Thermal Convection in Rotating Cylindrical Annulus

Chaotic thermal convection in a rapidly rotating cylindrical annulus is investigated numerically and the relaxation oscillation state is obtained under the no-slip boundary condition. The dominant frequency of the oscillation is inherited directly from a vacillating mode, whose nonlinear interaction with another high-frequency vacillating mode leads to the chaotic state at high Rayleigh numbers through an RTN-type route. Furthermore, the effects of Coriolis parameter and Rayleigh number on the quasi-periodic burst of kinetic energy are discussed as well.

Generalized Lorenz Equation Derived from Thermal Convection of Viscoelastic Fluids in a Loop

A new generalized Lorenz system is presented based on the thermal convection of Oldroyd-B fluids in a circular loop. Two non-dimensional parameters De1 （a measure of the fluid relaxation） and De2 （a measure of the fluid retardation） appear in the equation. Then we study this generalized Lorenz equation numerically and find that the values of De1 and De2 can greatly influence the behavior of the solution. The fluid relaxation De1 is found to precipitate the onset of periodic solution （limit cycle） in the system and impedes the onset of chaos while the fluid retardation （De2） tends to delay the onset of the periodic solution and precipitate the onset of chaos in the system.

Impact of Climate Change on Maize Potential Productivity and the Potential Productivity Gap in Southwest China

The impact of climate change on maize potential productivity and the potential productivity gap in Southwest China（SWC） are investigated in this paper.We analyze the impact of climate change on the photosynthetic,light-temperature,and climatic potential productivity of maize and their gaps in SWC,by using a crop growth dynamics statistical method.During the maize growing season from 1961 to 2010,minimum temperature increased by 0.20℃ per decade（p 〈 0.01） across SWC.The largest increases in average and minimum temperatures were observed mostly in areas of Yunnan Province.Growing season average sunshine hours decreased by 0.2 h day^（-1） per decade（p 〈 0.01） and total precipitation showed an insignificant decreasing trend across SWC.Photosynthetic potential productivity decreased by 298 kg ha^（-1）per decade（p 〈 0.05）.Both light-temperature and climatic potential productivity decreased（p 〈 0.05） in the northeast of SWC,whereas they increased（p 〈 0.05） in the southwest of SWC.The gap between lighttemperature and climatic potential productivity varied from 12 to 2729 kg ha^（-1）,with the high value areas centered in northern and southwestern SWC.Climatic productivity of these areas reached only 10%-24%of the light-temperature potential productivity,suggesting that there is great potential to increase the maize potential yield by improving water management in these areas.In particular,the gap has become larger in the most recent 10 years.Sensitivity analysis shows that the climatic potential productivity of maize is most sensitive to changes in temperature in SWC.The findings of this study are helpful for quantification of irrigation water requirements so as to achieve maximum yield potentials in SWC.