An approach to improving the dynamical extended-range (monthly) prediction

Chen, Bomin

doi:10.1360/03tb9149

Cited by 4 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, based on some climate observations, the concept of a "climate hierarchy" that implied climate is a cascade phenomenon originating from different hierarchies was proposed [6][7][8][9]. Techniques termed "compound reconstruction" and "segregated prediction" were applied to predict non-stationary time series; technically, though, these approximated the non-stationary contributions of the signals into stationary components.…”

mentioning

confidence: 99%

A novel approach in predicting non-stationary time series by combining external forces

et al. 2011

View full text Add to dashboard Cite

In this paper, we investigate a novel technique that reconstructs the observed time series and incorporates driving forces. Furthermore, to illustrate and test the technique, we consider a couple of predictive experiments using ideal time series provided by the logistic and Lorenz systems with specific driving forces. The preliminary results show this approach can improve prediction proficiency to some extent, and the external forces play a similar role to that of state variables. Short-term prediction is one of the difficult issues in climate change studies. Because of the complexity of short-term climate processes and lack of general knowledge underpinning its mechanisms, proficiency in prediction remains at a stand-still [1,2]. As for climate change in the 21st century, global warming is the most important factor, with many studies devoted to the topic. The possible causes range from change in natural forces (such as solar activity and volcanic emission) to human activities (such as green-house gases, sulfate emission in aerosols, and land use). In addition, the internal variability of climate systems should not be ignored either. However, no matter the cause of climate change, global warming in fact gives notice that climate processes are non-stationary. So far many climate predictive theories formulated in statistics and in nonlinear science are based on the hypothesis that the process is stationary, thus ergodicity theory can be used under such conditions to reconstruct the dynamics from an observed time series and establish predictive equations constructed from it.This reconstruction is at variance with the basic behavior of climate process. As is well-known for any weather or climate system, it is impossible to state the initial conditions incontrovertible. In fact, except for global warming, recent work has addressed the proper characterization of non-stationarity using weather and climate data. For instance, Tsonis [3] analyzed low-frequency (decadal to multi-decadal) variation in global precipitation over the past century and found that fluctuations about the global mean have increased significantly, implying that the second-order moment of the precipitation has changed on those scales, or that global precipitation process is non-stationary in the past century. In another case, Trenberth [4] found that the observed winter Pacific mean sea-level pressure underwent an abrupt change towards the end of the 70s; such a change in regime highlights the decline in the stationary behavior of the system. They noted that the behavior of these quantities is non-stationary. Most real world time series have some degree of non-stationarity due to external perturbations of the observed system [5].However, there is as yet no general theory about nonstationary processes. What scientists can do for the moment

show abstract

mentioning

confidence: 99%

A novel approach in predicting non-stationary time series by combining external forces

et al. 2011

View full text Add to dashboard Cite

show abstract

“…This idea was first applied to estimate the dimensionality of a climate attractor and achieved some success (Essex et al, 1987;Keppenne and Nicolis, 1989;Yang and Brasseur, 1994). Then, it was also extended to research of spatio-temporal phenomena and prediction of the climate component field (Abarbanel et al, 1993;Yang et al, 2000;Chen et al, 2003). For ease of comprehension, we will address this procedure by using a simple case below.…”

Section: Reconstruction For Spatio-temporal Systemmentioning

confidence: 99%

A compound reconstructed prediction model for nonstationary climate processes

Wang

Yang

2005

Int. J. Climatol.

View full text Add to dashboard Cite

Based on the idea of climate hierarchy and the theory of state space reconstruction, a local approximation prediction model with the compound structure is built for predicting some nonstationary climate process. By means of this model and the data sets consisting of north Indian Ocean sea-surface temperature, Asian zonal circulation index and monthly mean precipitation anomaly from 37 observation stations in the Inner Mongolia area of China (IMC), a regional prediction experiment for the winter precipitation of IMC is also carried out. When using the same sign ratio R between the prediction field and the actual field to measure the prediction accuracy, an averaged R of 63% given by 10 predictions samples is reached.

show abstract

“…Therefore it is necessary to further investigate the dynamical systems of regional precipitation in China from the nonlinear angle [3][4][5][6][7]. But up to now, there isn't a method generally recognized to determine which points in how large area belong to a same dynamical system [8], and furthermore the essence and formation mechanisms of those dynamical system regions and their effects on the summer precipitation in China are also not clear. This paper aims to identify and distinguish the summer precipitation dynamical system in China monsoon area using a new physical quantity Q exponent, which is able to discern the dynamical similarities and differences of two shorter time series [9].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamical Structure of precipitation in China and its Regional Climatic Characters

Feng

Wan

Zhi

et al. 2009

2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery

View full text Add to dashboard Cite

The statistical data show that weather (precipitation) in the five dynamic climate characteristic regions simultaneously becomes dry, wet and normal, that is to say, each region possesses a common spatial-temporal characteristic structure. Further analysis of physical mechanisms suggests that the Jianghuai pattern and South China pattern are close related with ENSO, which may results from the effect of the India-East Asia (IEA) teleconnection pattern; among the other three patterns North and Northeast China patterns are positively related with the ridge line position of subtropical high, but the ENSO shows no obvious influence on the three patterns, which may be associated with that the regions are mainly out of the weak impact area of ENSO; furthermore the influence of the ridge line of subtropical high on precipitation in North China is most remarkable. This has instructive significance for the development of regional climate models, and the analysis of precipitation characters and precipitation forecast.

show abstract

An approach to improving the dynamical extended-range (monthly) prediction

Cited by 4 publications

References 0 publications

A novel approach in predicting non-stationary time series by combining external forces

A novel approach in predicting non-stationary time series by combining external forces

A compound reconstructed prediction model for nonstationary climate processes

Nonlinear Dynamical Structure of precipitation in China and its Regional Climatic Characters

Contact Info

Product

Resources

About