Evidence of Kolmogorov like scalings and multifractality in the rainfall events

TL;DR

This study analyzes rainfall data from North-East India, revealing Kolmogorov-like power spectra, multifractality, and heavy-tailed distributions, indicating complex turbulent-like and multifractal behavior in rainfall patterns.

Contribution

It provides the first detailed statistical evidence of multifractality and turbulence-like scaling in regional rainfall data using advanced analysis methods.

Findings

Rainfall distribution follows a multiplicative Log-Normal distribution.

Power spectral density exhibits a power law with an exponent close to -1.5.

Rainfall patterns are multifractal with a Hurst exponent around 0.65.

Abstract

In this paper we present a detailed statistical analysis related to the characterization of the spatial and temporal fluctuations present in the rainfall patterns of North-East region ($26.05^{\circ}N-26.95^{\circ}N$, $88.05^{\circ}E-94.95^{\circ}E$) of India using half hourly rainfall data over the last 20 years for the range 2001-2020. We analyze the nature of the distribution by computing the mean, second moment of the fluctuation, skewness and kurtosis of the temporal rainfall data that indicate the presence of heavy tail in the right skewed distribution a typical feature of the presence of rare events. We find that the temporal distribution of the rainfall data follow the multiplicative Log-Normal probability distribution. Further we compute the spatial and temporal correlation of the rainfall in this region indicate that the rainfall events are correlated in the spatial direction of about 70 Km. The Power spectral density of temporal rainfall shows power law behaviour with frequency with an exponent $\sim -1.5$ close to the Kolmogorov exponent ($-1.67$) exhibited for the turbulent passive scalar driven by the mean flow. Our wavelet analysis reveals the evidence of multiple frequencies in the rainfall pattern which can attributed to different short and long range factors responsible for the rainfall. We have also used the Hilbert Huang transformation to identify the frequencies corresponding fluctuating part of the rainfall time series. Using multifractal detrended fluctuation analysis, finally we establish the multifractal nature of the rainfall pattern with Hurst exponent close to $0.65$ .