Diurnally driven scaling properties of Amazonian rainfall fields: Fourier spectra and order‐q statistical moments

Abstract

[1] The influence of the diurnal cycle on spatial scaling properties of Amazonian rainfall fields is investigated using data gathered during the January-February 1999 Wet Season Atmospheric Meso-scale Campaign in the state of Rondonia (Brazil, SW Amazonia). Most intense precipitation events with large spatial coverage occur during early afternoon. Amplitudes of average and maximum intensity diurnal cycles are higher during the easterly than during the westerly atmospheric regime. The diurnal cycle of average rainfall occupancy exhibits a significantly larger amplitude during the westerly regime. Storms exhibit power law Fourier spectra, E(k) = ck Àb , with two scaling regimes characterized by different scaling exponents (b 1 and b 2 ), separated at a critical distance, which depends on the spatial extent of rainfall organization. Inversely correlated diurnal cycles for b 1 and b 2 reflect rainfall organization patterns at different spatial scales through the 24-h period. The break occurs at smaller (larger) spatial scales during the morning (afternoon-evening). Average values of c and b exhibit inversely related diurnal cycles, and different behavior during either atmospheric regime. Order-q statistical moments indicate multiscaling of rainfall fields. Departures from simple scaling are also driven by the diurnal cycle, reflecting differences in convective activity and the spatial organization of rainfall throughout the 24-h cycle. Departures from simple scaling are dependent on the moment order q. Clear-cut differences between the estimated order-q statistical moments appear during both atmospheric regimes. These results shed light toward linking physical processes with statistics in Amazonian storms.Citation: Morales, J. E., and G. Poveda (2009), Diurnally driven scaling properties of Amazonian rainfall fields: Fourier spectra and order-q statistical moments,

Testing the Beta-Lognormal Model in Amazonian Rainfall Fields Using the Generalized Space q-Entropy

“…According to data from the NCEP-NCAR Reanalysis, as well as from the Fazenda Nossa Senhora radiosonde, the Easterly (negative values) and Westerly (positive) regimes are clearly differentiated in both data sets [19,31]. Furthermore, radar observations from southwestern Amazonia during TRMM-LBA suggest that there was relatively little difference in the daily mean rainfall totals between Easterly and Westerly regimes [57].…”

“…In particular, Ref. [19] found that 2-D rainfall fields over Amazonia exhibit multiscaling properties in space, which means that the relationship M r (λ) ∼ λ −τ(r) exhibits a non-linear behavior, where λ is the spatial scale, r the order of the statistical moment and τ(r) is the r-th moment scaling exponent. Additionally, they show that both the diurnal cycle and the predominant atmospheric regime of Amazonian rainfall (Easterly or Westerly) exert a strong control on the scaling properties of Amazonian storms, thus shedding light towards understanding the linkages between scaling statistics and physical features of Amazonian rainfall fields.…”

“…This model provides a framework to carry out numerical experiments controlling features of rainfall (e.g., σ and β), but also to link diverse statistical and physical characteristics across spatial scales (e.g., Refs. [19,31] and Section 1.5, respectively). In addition, the model provides a tool to investigate the robustness and sensitivity of statistical metrics to poor sampling, data sparsity and intermittency of high-resolution 2-D rainfall fields.…”

“…The strong variability and intermittence of convective tropical rainfall constitute an adequate setting to study the scaling characteristics of rainfall in a wide range of spatio-temporal scales [19][20][21][22][23][24][25][26][27][28][29][30][31]. In particular, Ref.…”

“…Statistical scaling has provided a rich framework to understand and model the spatiotemporal dynamics and the complexity and intermitency of rainfall fields, including (multi-)fractal, multiscaling, and random cascade models [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”