Peter Golé

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.

Calibration error of L-band sky-looking ground-based radiometers

A method is presented for estimating the calibration error affecting L-band ground-based radiometers, using the sky as a cold source. In a first step, the optimum conditions to perform this calibration are limited by removing sky areas where large radiation contributors (Sun and Moon) are present. In the region thus selected, an accurate computation of the brightness temperature measured by the antenna is performed using available sky background temperature survey charts (radio continuum and hydrogen HI line) and integrating the sky temperature over the directional gain pattern of a representative radiometer L-band antenna. Contributions from rear lobe are not considered. After adding the atmospheric contribution, maps of the noise temperature and its error are produced for the region of interest. The best calibration orientation for a sky-looking radiometer at medium northern latitude would be 0° in azimuth (northward) and an elevation equal to the radiometers latitude. It is found that the computed total sky noise contribution is 6.6 K, with 24 hour variations of ±0.2 K and a maximum bias of ±0.6 K. The results are valid for the whole year, assuming low to moderate solar activity and no rain.

Combining signal processing and machine learning techniques for real time measurement of raindrops

The data acquisition system for a new type of optical disdrometer is presented. As the device must measure sizes and velocities of raindrops as small as 0.1 mm diameter in real time in the presence of high noise and a variable baseline, algorithm design has been a challenge. The combining of standard signal processing techniques and machine learning methods (in this case, a neural network) has been essential to obtaining good performance.

TDNN Approach to Measuring Raindrop Sizes and Velocities

The paper describes a TDNN solution to a signal processing problem in the meteorological and telecommunications domains. Optical disdrometers measure raindrop sizes and velocites by registering changes in photodiode current as the droplets pass through a collimated light beam. In an improved dual-beam device being built at CETP, feature extraction MLPs applied to 20-sample windows of photodiode current provide input to a higher-level network which reconstructs droplet velocities and diameters in real time. In tests on simulated data, measurement precision is quite good for droplets as small as .05 mm radius. The algorithm can be executed either directly on the acquisition PC, or on a neural net coprocessor for additional speedup.