Franz Teschl

2007 Special Issue: Improving weather radar estimates of rainfall using feed-forward neural networks

In this paper an approach is described to improve weather radar estimates of rainfall based on a neural network technique. Other than rain gauges which measure the rain rate R directly on the ground, the weather radar measures the reflectivity Z aloft and the rain rate has to be determined over a Z-R relationship. Besides the fact that the rain rate has to be estimated from the reflectivity many other sources of possible errors are inherent to the radar system. In other words the radar measurements contain an amount of observation noise which makes it a demanding task to train the network properly. A feed-forward neural network with Z values as input vector was trained to predict the rain rate R on the ground. The results indicate that the model is able to generalize and the determined input-output relationship is also representative for other sites nearby with similar conditions.

Weather Radar Estimates of Rainfall Adjusted to Rain Gauge Measurements Using Neural Networks

Other than rain gauges which measure the rain rate R directly on the ground, the weather radar measures the reflectivity Z aloft and the rain rate has to be determined over a Z-R relationship. Besides the fact that the rain rate has to be calculated from the reflectivity many other sources of possible errors are inherent to the radar system. Worth mentioning are especially errors caused by the vertical profile of reflectivity (VPR). In this paper an approach is described to estimate ground rainfall using radar data based on a neural network technique. The results indicate that the relationship determined by the neural network model between VRP and rain rate measured on the ground, is also representative for sites nearby.

UWB channel measurements for short range radar applications

This paper presents channel measurements for short range radar applications in a frequency range from 5 GHz up to 7.5 GHz. For these measurements antennas with a half power beam width of 80° and circular polarization were provided by Maxim Integrated. The measured scenarios included a 28 mm copper pipe, a 26 cm × 28 cm aluminium plate, a bicycle and a 75 mm polokal plastic pipe. All measurements were conducted on flat concrete and grass. Through comparison with the radar equation and radar cross section calculations for the far field, it could be shown, that the size of the reflecting objects has to be used for the calculation of the near field (Fraunhofer Region) to far field transition. The measurements show that the radar cross section of an object declines the farther it comes into the near field.