2 research outputs found
Constrained Iterative Technique with Embedded Neural-Network for Dual-Polarization Radar Correction of Rain Path Attenuation
A new stable backward iterative technique to correct
for path attenuation and differential attenuation is presented here.
The technique named, neural network iterative polarimetric precipitation
estimator by radar (NIPPER), is based on a polarimetric
model used to train an embedded neural network, constrained by
the measurement of the differential phase along the rain path. Simulations
are used to investigate the efficiency, accuracy, and the robustness
of the proposed technique. The precipitation is characterized
with respect to raindrop size, shape, and orientation distribution.
The performance of NIPPER is evaluated by using simulated
radar volumes scan generated from S-band radar measurements.
A sensitivity analysis is performed in order to evaluate the expected
errors of NIPPER. These evaluations show relatively better performance
and robustness of the attenuation correction process when
compared with currently available techniques