Cécile Mallet

The effect of rain-no rain intermittency on the estimation of the universal multifractals model parameters.

Abstract The multifractal properties of rain are investigated within the framework of universal multifractals. The database used in this study includes measurements performed over several months in different locations by means of a disdrometer, the dual-beam spectropluviometer (DBS). An assessment of the effect of the rain–no rain intermittency shows that the analysis of rain-rate time series may lead to a spurious break in the scaling and to erroneous parameters. The estimation of rain multifractal parameters is, therefore, performed on an event-by-event basis, and they are found to be significantly different from those proposed in scientific literature. In particular, the parameter H, which has often been estimated to be 0, is more likely to be 0.53, thus meaning that rain is a fractionally integrated flux (FIF). Finally, a new model is proposed that simulates high-resolution rain-rate time series based on these new parameters and on a simple threshold.

Estimation of Gamma Raindrop Size Distribution Parameters: Statistical Fluctuations and Estimation Errors

Abstract The gamma distribution is often used to characterize raindrop size distributions (DSDs). However, the estimation of measured raindrop distributions suffers from the shortcomings of statistical sampling errors, which become increasingly significant when the collecting surface of the measuring instrument and the integration time are small. Different estimators of the three parameters (N0*, μ, and Dm) that characterize a normalized gamma distribution have been computed from simulated DSD. A database has been established, containing 22 950 simulated DSDs, corresponding to a wide set of various rainfall situations. Moment, least squares, and maximum likelihood estimators have been evaluated. Error measurement considerations are discussed, in particular the difficulty encountered in measuring small drops (diameter <0.5 mm) with a disdrometer. Modified estimation approaches are proposed to compensate for the lack of small drops accounted for by real measurements. For each of the different methods, syste...

Investigation of the fractal dimension of rainfall occurrence in a semi-arid Mediterranean climate

Abstract The scale invariance of rainfall series in the Tunis area, Tunisia (semi-arid Mediterranean climate) is studied in a mono-fractal framework by applying the box counting method to four series of observations, each about 2.5 years in length, based on a time resolution of 5 min. In addition, a single series of daily rainfall records for the period 1873–2009 was analysed. Three self-similar structures were identified: micro-scale (5 min to 2 d) with fractal dimension 0.44, meso-scale (2 d to one week) and synoptic-scale (one week to eight months) with fractal dimension 0.9. Interpretation of these findings suggests that only the micro-scale and transition to saturation are consistent, while the high fractal dimension relating to the synoptic scale might be affected by the tendency to saturation. A sensitivity analysis of the estimated fractal dimension was performed using daily rainfall data by varying the series length, as well as the intensity threshold for the detection of rain. Editor Z.W. Kundzewicz; Associate editor S. Grimaldi Citation Ghanmi, H., Bargaoui, Z., and Mallet, C., 2013. Investigation of the fractal dimension of rainfall occurrence in a semi-arid Mediterranean climate. Hydrological Sciences Journal, 58 (3), 483–497.

Estimation of Finescale Rainfall Fields Using Broadcast TV Satellite Links and a 4DVAR Assimilation Method

AbstractThis study proposes a method based on the use of a set of commercial satellite-to-Earth microwave links to rebuild finescale rainfall fields. Such microwave links exist all over the world and can be used to estimate the integrated rain attenuation over the links’ first 5–7 km with a very high temporal resolution (10 s in the present case). The retrieval algorithm makes use of a four-dimensional variational data assimilation (4DVAR) method involving a numerical advection scheme. The advection velocity is recovered from the observations or from radar rainfall fields at successive time steps.This technique has been successively applied to simulated 2D rain maps and to real data recorded in the autumn of 2013 during the Hydrological Cycle in the Mediterranean Experiment (HyMeX), with one sensor receiving microwave signals from four different satellites. The performance of this system is assessed and is compared to an operational Meteo-France radar and a network of 10 rain gauges. Because of the limita...

Simulation of yearly rainfall time series at microscale resolution with actual properties: Intermittency, scale invariance, and rainfall distribution

Rainfall is a physical phenomenon resulting from the combination of numerous physical processes involving a wide range of scales, from microphysical processes to the general circulation of the atmosphere. Moreover, unlike other geophysical variables such as water vapor concentration, rainfall is characterized by a relaxation behavior that leads to an alternation of wet and dry periods. It follows that rainfall is a complex process which is highly variable both in time and space. Precipitation is thus characterized by the following features: rain/no-rain intermittency, multiple scaling regimes, and extreme events. All these properties are difficult to model simultaneously, especially when a large time and/or space scale domain is required. The aim of this paper is to develop a simulator capable of generating high-resolution rain-rate time series (15 s), the main statistical properties of which are close to an observed rain-rate time series. We also attempt to develop a model having consistent properties even when the fine-resolution-simulated time series are aggregated to a coarser resolution. In order to break the simulation problem down into subcomponents, the authors have focused their attention on several key properties of rainfall. The simulator is based on a sequential approach in which, first, the simulation of rain/no-rain durations permits the retrieval of fractal properties of the rain support. Then, the generation of rain rates through the use of a multifractal, Fractionally Integrated Flux (FIF), model enables the restitution of the rainfalls multifractal properties. This second step includes a denormalization process that was added in order to generate realistic rain-rate distributions.

Effect of microphysical characteristics of rain on frequency scaling in microwave band

Frequency scaling concerns the variation of propagation effects with respect to frequency. The objective is to find the relationship between attenuation at a given frequency from the attenuation measured at another frequency, generally lower. Two different kinds of frequency scaling model, corresponding to different interests, can be considered: Long term frequency scaling, describes the relationship between attenuation for the same probability level. It allows studying the design of system operating at high frequency bands (Ka or V band) from the performances of existing systems operating at lower frequency band (Ku-band). Short term frequency scaling or instantaneous frequency scaling (IFS), describes the relationship between simultaneous attenuation at different frequencies. It allows performing uplink power control, where the attenuation on the uplink is estimated from the attenuation measured on the downlink. The different contributions: rains, gas, clouds, which contribute to the total attenuation, depend on frequency in different ways, thats why this technique is most satisfactory when one cause predominates. The present study focus on IFS of rain, the aim is to deduce the attenuation due to rain for one frequency (higher than 40 GHz) from the measurements at another lowers frequencies (Ka Band).

Estimating Confidence Intervals around Relative Humidity Profiles from Satellite Observations: Application to the SAPHIR Sounder

A novel scheme for the estimation of layer-averaged relative humidity (RH) profiles from space-borne observations in the 183.31GHz line is presented. Named ARPIA for Atmospheric Relative humidity Profiles Including Analysis of confidence intervals, it provides for each vector of observations the parameters of the distribution of the RH instead of its expectation as usually done by the current methods. The profiles are composed of 6 layers distributed between 100 and 950hPa. The approach combines the 6 channels of the SAPHIR instrument onboard the Megha-Tropiques satellite and the Generalized Additive Model for Location, Scale and Shape (GAMLSS) to infer the parametric distributions, assuming that they follow a Gaussian law. The knowledge of the conditional uncertainty is an asset in the evaluation using radiosounding profiles of RH with a dedicated bayesian method. Taking the uncertainties into account in both the ARPIA estimates and the in situ measurements yields to have biases, root-mean-square and correlation coefficients in the range -0.56% - 9.79%, 1.58% - 13.32% and 0.55 - 0.98 respectively, the largest biases being obtained over the continent, in the mid-tropospheric layers.

Validation of a neural network model for the separation of atmospheric effects on attenuation

In high frequency bands, between 10 and 50 GHz. atmospheric attenuation is caused by several types of atmospheric component: gases (oxygen and water vapour), clouds and rain. Each of these components behaves quite differently, when considered in terms of its temporal and spatial variability. Separation of the different atmospheric contributions (also called separation effects) is an essential step for the improvement of propagation model. Our aim in this study is to develop and valid an artificial neural network (ANN) able to separate out the contribution of different atmospheric component. A wide simulated database, corresponding to different sets of meteorological conditions is used to train the ANN. The selection of input variables among following quantities: attenuation at one, two or three frequencies, humidity, pressure, and temperature at ground level, is performed in computing their relative contribution to output. The best ANN obtained is thus validated with actual measured attenuations performed during Olympus experiment. The validation of separation effects is performed by the comparison of rain attenuation statistics.

A neural network model for the separation of atmospheric effects on attenuation: Application to frequency scaling: ATMOSPHERIC EFFECTS SEPARATION

Attenuation due to the propagation of radio waves through the Earths atmosphere plays a major role in satellite link attenuation at frequencies beyond 20 GHz. This paper presents the development of an artificial neural network (ANN) to separate out the respective roles played by the three types of contributor, namely, gases (oxygen and water vapor), clouds, and rain, to the overall attenuation of radio waves. Whereas the inputs to the ANN are the total attenuation measured at either one, two, or three frequencies, the ANN outputs provide the three atmospheric attenuation components at a single frequency. Several neural networks were trained by using a simulated statistically significant data set, derived from absorption and diffusion models applied to atmospheric profiles. Good overall performance was observed, and a particularly good fit was achieved in the case where attenuation inputs were provided at two frequencies. From the estimated values of atmospheric attenuation for the three contributors, corresponding frequency scaling models were applied on each to estimate the three contributions at a new frequency. Total atmospheric attenuation at this new frequency can then be estimated. The method works using measured data at either one, two, or three frequencies and allows the total attenuation to be predicted at any other frequency in the range 20–50 GHz. Validation was successfully performed on real data.