Characterization of Maximum Radar Reflectivity Height During Stratiform Rain Events

Abstract

The effects of rain on terrestrial and satellite communication systems, especially at frequencies above 10 GHz, have been statistically dealt with at length. It is also well known that rain height plays an important role in signal fading and co-channel interference due to scattering. The rain height is directly related to the 0 C isotherm. At this height hydrometeors change from solid to liquid state in the melting layer, increasing their reflectivity and causing the bright band effect in radar measurements. The bright band is defined by the top, bottom and maximum reflectivity heights. The peak reflectivity height can be obtained from radar volume scans. This paper presents a statistical characterization of the time and space variability of the maximum reflectivity height during stratiform rain events. This paper also focuses on the dependence between simultaneous meteorological parameters derived from weather radar and from surface automatic weather observation stations at local scale in a temperate climate region. The data used in this study was obtained from 2006 to 2011.Index Terms-Bright band height, radio propagation, satellite communications, weather radar.

Estimation of effective path lengths of rain based on cell size distributions from meteorological radar

“…Rain height is computed from an estimation algorithm of the radar bright band height, in accordance with what is stated in [8].…”

“…Rain height may be derived from the 0º C isotherm during stratiform precipitations, identifying the melting layer, where an enhancement of reflectivity known as the bright band is observed. [8] For attenuation calculations purposes in a given slant path, each rain meteorological event may be characterized at a given time by the actual effective physical rain distance and by the reflectivity (related to rainfall intensity) in the beam path.…”

Research on Data Processing Method of High Altitude Meteorological Parameters Based on Neural Network

“…If we want better forecast results and the smaller target error, we need increase the epochs. So we choose Fletcher-Reeves algorithm to train, when changing the hidden layer node, we can adjust the target error to make the training achieve convergence [9,10].…”

Observational study on melting layer characteristics over Palau in Pacific Ocean

“…The attenuation prediction is usually performed with different meteorological parameters like rain rate, drop size distribution and rain height etc. Among these parameters rain height is the most important because it is very close to 0°C isotherm height (Hall, 1979;Ajayi and Ofoche, 1984;Romo et al, 2012). Hence, 0°C is taken into account for the calculation of rain attenuation (International Telecommunication Union Recommendation P 839-3, 2001).…”