Seasonal Characteristics and Interannual Variability of Monthly Scale Low-Frequency Oscillation in a Low-Order Global Spectral Model

Analysis is done of five-year low-pass filtered data by a five-layer low-order global spectral model, indicating that although any non-seasonal external forcing is not considered in the model atmosphere,monthly-scale anomaly takes place which is of remarkable seasonality and interannual variability.Analysis also shows that for the same seasonal external forcing the model atmosphere can exhibit two climatic states,similar in the departure pattern but opposite in sign, indicating that the anomaly is but the manifestation of the adverse states, which supports the theory of multi-equilibria proposed by Charney and Devore(1979) once again.Finally, the source for the low-frequency oscillation of the global atmosphere is found to be the convective heat source / sink inside the tropical atmosphere as discussed before in our study.Therefore, the key approach to the exploration of atmospheric steady low-frequency oscillation and the associated climatic effect lies in the examination of the distribution of convective heat sources / sinks and the variation in the tropical atmosphere.

Scale invariant features extraction for stereo vision

Stable local feature detection is a fundamental component of many stereo vision problems such as 3-D reconstruction, object localization, and object tracking. A robust method for extracting scale-invariant feature points is presented. First, the Harris corners in three-level pyramid are extracted. Then, the points detected at the highest level of the pyramid are correctly propagated to the lower level by pyramid based scale invariant （PBSI） method. The corners detected repeatedly in different levels are chosen as final feature points. Finally, the characteristic scale is obtained based on maximum entropy method. The experimental results show that the algorithm has low computation cost, strong antinoise capability, and excellent performance in the presence of significant scale changes.

Parameterization of ocean wave-induced mixing processes for finite water depth

Three dimensional wave-induced mixing plays an important role in shallow water area. A quite direct approach through the Reynolds average upon characteristic length scale is proposed to parameterize the horizontal and vertical shallow water mixing. Comparison of finite depth case with infinite depth results indicates that the difference of the wave-induced mixing strength is evident. In the shallow water condition, the infinite water depth approximation overestimates the mixing strength in the lower layers. The nonzero horizontal wave-induced mixing presents anisotropic property near the shore. The Prandtl＇s mixing length theory underestimated the wave-induced mixing in the previous studies.

STUDY ON THE STABILITY STRUCTURE OF SYNOPTIC SCALE SYSTEMS,THE BIFURCATION OF PHASE.PATH CHARACTERISTICS AND THE INFLUENCE OF THE TIME-OSCILLATION OF HEAT SOURCE

This paper discusses the stability structure and the bifurcation of phase path characteristics of synoptic scale system.The analytic results show that the catastrophe of the synoptic scale disturbance may be caused by the nonlinear effects of barotropic and baroclinic instability and advection of ambient large-scale flow.Also, foregoing nonlinear effects on the speed of development and decay of the system are presented in the processes deviating from or approaching to equilibrium state.It has been found that there is a resonance phenomenon between the time-oscillation of heat source and the atmospheric disturbance.

Extraction of lacunarity variation index for revealing the slope pattern in the Loess Plateau of China

Lacunarity analysis is frequently used in multiscale and spatial pattern studies.However,the explanation for the lacunarity analysis results is limited mainly at a qualitative description level.In other words,this approach can be used to judge whether the spatial pattern of the objective is regular,random or aggregated in space.The lacunarity analysis,however,cannot afford many quantitative information.Therefore,this study proposed the lacunarity variation index(LVI)to reflect the rates of variation of lacunarity with the resolution.In comparison with lacunarity analysis,the simulated experiments show that the LVI analysis can distinguish the basic spatial pattern of the geography objects more clearly and detect the scale of aggregated data.The experiment showed that different slope types in the Loess Plateau display aggregated patterns,and the characteristic scales of these patterns were detected using the slope pattern in the Loess Plateau as the research data.This study can improve the spatial pattern analysis and scale detecting methods,as well as provide a new method for landscape and vegetation community pattern analyses.Lacunarity analysis is frequently used in multiscale and spatial pattern studies.However,the explanation for the lacunarity analysis results is limited mainly at a qualitative description level.In other words,this approach can be used to judge whether the spatial pattern of the objective is regular,random or aggregated in space.The lacunarity analysis,however,cannot afford many quantitative information.Therefore,this study proposed the lacunarity variation index(LVI)to reflect the rates of variation of lacunarity with the resolution.In comparison with lacunarity analysis,the simulated experiments show that the LVI analysis can distinguish the basic spatial pattern of the geography objects more clearly and detect the scale of aggregated data.The experiment showed that different slope types in the Loess Plateau display aggregated patterns,and the characteristic scales of these patterns were detected using the slope pattern in the Loess Plateau as the research data.This study can improve the spatial pattern analysis and scale detecting methods,as well as provide a new method for landscape and vegetation community pattern analyses.