The role of the land surface processes in the rainfall generated by a landfall typhoon: A simulation of the typhoon Sepat (2007)

Landfall tropical cyclones are a major kind of severe weather affecting China. The typhoon Sepat, declassified as tropical storm after its landfalling, caused a continuous heavy rainfall event over China mainland from 19th to 25th August, 2007. The storm cyclone resided over the Hunan province for 60 hours, causing observed accumulated precipitation larger than 300 mm in a large area of the Hunan province and leading severe flood events. This event was simulated using the Weather Research and Forecasting (WRF) model coupled with the surface layer scheme UTOPIA. The model was able to reproduce the main characteristics of the event, including the typhoon track and the rainfall field and timing. In addition, three sets of sensitivity experiments have been performed. In the first one, the effects of different land surface schemes (RUC, NOAH and UTOPIA) coupled with WRF on the precipitation, sensible and latent heat flux fields associated with the Typhoon Sepat (2007) were investigated. The second set of sensitivity experiments analyzed the role of the surface fluxes (sensible and latent heat flux) on the typhoon evolution. The third set of sensitivity experiments regarded the initialization of the soil moisture content. These experiments showed that both latent and sensible heat fluxes sustained this landfalling typhoon, maintaining the spiral structure of rain belt. Among the two fluxes, the latent heat one played a major role in determining the intensity, the track and the rainfall distribution of the typhoon. In addition, the correct initialization of the soil moisture content has reveled a fundamental parameter to be initialized in order to correctly evaluate the distribution and intensity of the rain field. The intercomparison between the three different land surface schemes coupled with WRF showed that the WRF-UTOPIA and WRF-NOAH outputs seem comparable between each other and physically most realistic than those of WRF-RUC. These analyses were helpful to understand the evolution and the development of the landfalling typhoon, and demonstrated that WRF-UTOPIA and WRF-NOAH could be considered a good tool for managing the risk evaluation connected with the occurrence of such events at regional scale.     

Détection des transitions lithologiques par l'analyse de la composante fractale des diagraphies par transformée continue en ondelettes

The frequency analysis of many log data permits to verify that their stochastic component show ‘power-law-type’ spectral densities, characteristic of $1/f$ noise. They can be modelled by fractional Brownian motions. Continuous Wavelet Transformation (CWT) provides us with very efficient methods to determine the local spectral exponents of these scaling laws. These new attributes are related to the local fractality of these signals. We first present some theoretical results and an application to a fractional Brownian motion. The second application concerns a dataset recorded in the MAR203 borehole. We show that clustering of these new pseudo-logs leads to a good resolution between different lithofacies. To cite this article: N. Zaourar et al., C. R. Geoscience 338 (2006).     

Fractal dynamics of geomagnetic storms

We explore fluctuations of the horizontal component of the Earth’s magnetic field to identify scaling behaviour of the temporal variability in geomagnetic data recorded by the Intermagnet observatories during the solar cycle 23 (years 1996 to 2005). In this work, we use the remarkable ability of scaling wavelet exponents to highlight the singularities associated with discontinuities present in the magnetograms obtained at two magnetic observatories for six intense magnetic storms, including the sudden storm commencements of 14 July 2000, 29–31 October and 20–21 November 2003. In the active intervals that occurred during geomagnetic storms, we observe a rapid and unidirectional change in the spectral scaling exponent at the time of storm onset. The corresponding fractal features suggest that the dynamics of the whole time series is similar to that of a fractional Brownian motion. Our findings point to an evident relatively sudden change related to the emergence of persistency of the fractal power exponent fluctuations precedes an intense magnetic storm. These first results could be useful in the framework of extreme events prediction studies.     

Impact of the pre-processings on the fractal properties of the airborne gamma ray measurements: a case study from Hoggar (Algeria)

The analysis of gamma ray measurements provides valuable information about the basement’s lithospherical model and the radioactive elements contained in the subsurface. To be exploited, the raw data need pre-processing operations which consist of corrections of the Compton effect, altimetry and the aircraft background, and 2D interpolations. These transformations may modify the data’s stochastic component which reflects heterogeneities of the investigated medium. In this study, a fractal analysis is carried out on airborne gamma ray data measured in the Hoggar’s area (Algeria). First, it is shown that the analyzed data exhibit fractal properties. Due to the dependence of the latter on the spatial location of the measures, the data can be considered as paths of multifractional Brownian motions. Another result worth noting is that the fractal nature of the raw data is not modified by the pre-processing operations. The fractal analysis can be then performed on the raw spectrometric measurements without submitting them to any pre-processing; thus, the stochastic component of the measurements is kept intact.     

Heterogeneities characterization from velocity logs using multifractional Brownian motion

Well logs provide additional information from the borehole that cannot be derived from other subsurface investigations. Their analysis may bring supplementary features about the earth’s heterogeneities. In previous researches, the logs were modeled using fractional Brownian motions indexed by Hurst (or Hölder) exponents quantifying their global regularity degrees. Indeed, these monofractal models, characterized by the same Hurst exponent, do not allow to study the depth-evolution of the local Hurst parameter. In order to overcome this problem, we propose to model the logs using multifractional Brownian motions and suggest an algorithm, developed previously for financial time series, to estimate the local Hurst exponent function. First, the potential of this algorithm is assessed through its application on synthetic sonic log data simulated by the successive random additions method. We note that the estimated Hurst functions (or regularity profiles) are very close to the theoretical Hurst functions. Second, this analysis is extended to sonic logs data from the KTB pilot borehole (Germany). Using the resulted regularity profiles, we carry out a lithological segmentation and fault identification on the geological layers crossed by the well. We derived also a correlation between the Hurst value variation and the lithological changes.