Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2405.03463

Evidence of Kolmogorov like scalings and multifractality in the rainfall events

Joya GhoshDastider·D. Pal·Pankaj K. Mishra
View original source ↗Read source document ↗Export metadata ↓

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 $0.65$ .

Explore related subjects

physics.ao-ph

Keep this discovery

BibTeXRIS

Joya GhoshDastider, D. Pal, Pankaj K. Mishra. 2024-05-06. Evidence of Kolmogorov like scalings and multifractality in the rainfall events. https://doi.org/10.1088/1742-5468/adc242

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph↗
arXiv2026

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph↗