Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.574418
Title: Advances in numerical analysis of precipitation reomote sensing with polarimetric radar
Author: Islam, Tanvir
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2012
Availability of Full Text:
Access from EThOS:
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 spectrums in both horizontal (H) and vertical (V) polarization states, thus providing additional information of the target precipitation particles by measuring polarimetric signatures, the reflectivity factor at H polarization (ZH) , differential reflectivity (ZDR) , differential propagation phase (ϕDP) , 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 scrutinized 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 a:ssociated with total differential propagation phase constraint (ΔϕDP) 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), μ(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 proven not only to improve data quality and precipitation estimation, but also characterizing the precipitation particles, thus has a great potential on fostering the precipitation remote sensing research and applications. Keywords: polarimetric radar; dual polarization 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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.574418  DOI: Not available
Share: