Advances in numerical analysis of precipitation remote sensing with polarimetric radar

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@PhdThesis{Islam:thesis,

• author = "Tanvir Islam",
• title = "Advances in numerical analysis of precipitation remote sensing with polarimetric radar",
• school = "Civil Engineering, University of Bristol",
• year = "2012",
• address = "UK",
• note = "University Prize for Best Thesis in Faculty of Engineering in 2012-13",
• keywords = "genetic algorithms, genetic programming, polarimetric radar, dual polarisation radar, microphysics of precipitation, drop size distribution (DSD), clutter and anomalous propagation identification, attenuation correction, rainfall estimators, microphysical DSD retrievals, melting layer and bright band detection, hydrometeor classification",
• broken = "http://www.bristol.ac.uk/engineering/graduate/commendations/",
• URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.574418",
• abstract = "Since the early use of ground radar for precipitation detection in post-world war II, the radar has evolved on its own in precipitation remote sensing research and applications. The recent advances in radar remote sensing is, the development of polarimetric radar, also known as dual polarization radar, which has the capability of transmitting electromagnetic spectra in both horizontal (H) and vertical (V) polarisation states, thus providing additional information of the target precipitation particles by measuring polarimetric signatures, the reflectivity factor at H polarisation (ZH) , differential reflectivity (ZDR) , differential propagation phase (Delta Phi DP) , specific differential phase (KDP) , cross-correlation coefficient (PHV) and linear depolarization ratio (LDR). In commensurate with new era in precipitation remote sensing, this thesis explores the potential of polarimetric radar on the improvements in precipitation remote sensing in the UK context. All major area of the improvements aided by the polarimetry and polarimetric signatures are addressed. These include the clutter and anomalous propagation identification, attenuation signal correction, polarimetric rainfall estimators, drop size distribution retrievals, bright band/melting layer recognition and hydrometeor classification. Several novel approaches and investigations dealing with the polarimetric improvements are scrutinised and proposed in terms of numerical analysis, while some of them employ artificial intelligence (AI) techniques. Key original contributions in synergy with polarimetric radar signatures on precipitation remote sensing are: 1) long-term disdrometer DSD analysis to support the development of polarimetry based algorithms and models, 2) the use of several AI techniques such as support vector machine, artificial neural network, decision tree, and nearest neighbour system for clutter identification, 3) the sensitivity associated with total differential propagation phase constraint (delta phi DP) on ZH correction for attenuation, 4) the exploration of polarimetric rainfall estimators [R(ZH, ZDR, Knp)] for rainfall estimation, 5) a genetic programming approach for drop size distribution retrievals [Do(ZH, ZDR) , Nw(ZH, ZDR, Do), mu(ZH, ZDR, Do)], and its use for convective/stratiform rain indexing, and 6) a fuzzy logic based system for automatic melting layer/bright band recognition and hydrometeor classification as well as appraisal with a numerical weather prediction (NWP) model and radio soundings observations. In fact, the radar polarimetry has been proved not only to improve data quality and precipitation estimation, but also characterising the precipitation particles, thus has a great potential on fostering the precipitation remote sensing research and applications.",
• notes = "EThOS Persistent ID: uk.bl.ethos.574418",

}

Genetic Programming entries for Tanvir Islam

Citations