Use this URL to cite or link to this record in EThOS:
Title: Quantitative weather radar and the effects of the vertical reflectivity profile
Author: Rico-Ramirez, Miguel Angel
ISNI:       0000 0001 2433 9381
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2004
Availability of Full Text:
Access from EThOS:
Access from Institution:
The variation of the vertical reflectivity profile (VRP) of rain is one of the major problems for quantitative precipitation estimation using weather radars. In particular, during stratiform rainfall a region of enhanced reflectivity associated with echoes from melting snowflakes causes overestimation of precipitation. This work is focused on the study of this region commonly known as the bright band. A new algorithm to detect the boundaries of the bright band from single-polarisation VRP has been developed. This algorithm has enabled the analysis of 1835 hours of vertically pointing X-band radar data and 1354 S-band RHI scans from the Chilbolton radar in order to study the characteristics of the bright band such as intensity, depth, height and variability at both frequencies. In addition, the differential reflectivity and the linear depolarisation ratio in the bright band are also included in the analysis. Using the results obtained, the Membership Functions (MF) of a Fuzzy Logic System (FLS) to classify hydrometeors have been proposed. The FLS receives as input parameters the conventional reflectivity factor, the differential reflectivity, the linear depolarisation ratio and the height of the hydrometeors and retrieves three types of hydrometeors: rain, snow and melting snow. The classification of rain and snow presents a high degree of uncertainty because of the large overlapping regions between the MF of both hydrometeors. The FLS is shown to perform a primary classification of melting snow because the depolarisation characteristics are distinct. By establishing the mean height of melting snow it is possible to modify the MF of the height of the hydrometeors in a more constrained way. A secondary classification is then performed with the new MF providing a much improved classification. The hydrometeor classification is followed by an algorithm to estimate the expected rain reflectivity from bright band contaminated reflectivity data. This correction is based on an idealised VRP typical of stratiform precipitation and obtained from the extensive analysis of the VRP at S-band frequencies.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available