Evidence of dynamical transition and maximum predictability of air temperature, relative humidity and dew point temperature

Monitoring and predicting the climatic phenomenon are the major global concern because of its devasting effects on people's lives and their environments. As a result of this, there is a need to understand the natural processes that control the dynamic evolution of the climatic phenomenon. Air temperature and relative humidity data collected from Nsukka station by the Centre for Atmospheric Research (CAR), measured in 5 minutes time steps from 1st January till 31st December, 2012 have been analysed. Dew point temperature was calculated from the actual readings of air temperature and relative humidity using appropriate empirical relation. In this paper, Average Mutual Information (AMI), False Nearest Neighbour (FNN) and Lyapunov Exponent methods were used to study changes and transitions in the dynamics of these meteorological parameters or temporal deviations from their overall dynamical regimes. The results show that the dynamic model needed to describe the data has 4-5 dimensions for air temperature, 4-6 for relative humidity and 4-5 for dew point temperature. Positive and negative Lyapunov exponents were observed in the air temperature, relative humidity and dew point temperature time series. This indicates that there exists periodicity inherent in the chaotic behaviour of these meteorological time series, causing a transition from chaoticity (positive Lyapunov exponent) to periodicity (negative Lyapunov exponent) and thereafter to chaoticity (positive Lyapunov exponent). The results, therefore, provide additional information about the climate transitions, maximum predictability and also, for formulating a weather prediction model.