Date of Award
2015
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Atmospheric Science
Committee Chair
Lawrence D. Carey
Committee Member
Phillip M. Bitzer
Committee Member
John R. Mecikalski
Subject(s)
Radar meteorology, Rain and rainfall--Mathematical models, Radar meteorological stations
Abstract
Rainfall has spatial and temporal variability due to the microphysical properties of precipitation. Radar-rainfall algorithms are typically based on assumed raindrop and radar scattering models which include fitting a mathematical function such as a gamma model with three parameters (N0, mu, lamda) to the drop size distribution (DSD). Disdrometer observations are typically combined into a radar model to produce radar observables that are then used to derive empirical radar-rain algorithms. If radar and disdrometer-derived radar measurements are to be inter-compared, the appropriate spatial and temporal scales should be considered. For example, groundbased radar spatial resolutions are on the order of 300 meters while Global Precipitation Measurement Dual-Precipitation Radar has a footprint of 5 km which result in different temporal scales. Integration or sampling period is an important assumption in DSD measurement retrieval from disdrometer data. Prior studies have often assumed 1-minute disdrometer DSD samples based largely on trial-and-error empirical experience. No study to date has documented relative bias and standard error for radar-rain algorithms as a result of DSD integration period assumptions. Therefore, a series of experiments are conducted for various algorithms to evaluate and document bias and standard errors by assuming a truth integration period vs. model t integration period. Experimental results show attenuation estimated from specific differential phase at C-band has a -15% bias when 1 min truth integration is compared against 10 min integration modeled. Rain rate estimated from horizontal and differential reflectivity showed 8% bias when 10 min truth integration is compared against 1 min integration modeled. Uncertainty in integration period should be noted when validating satellite rainfall algorithms against ground-based disdrometer observations because of different implied spatial scales.
Recommended Citation
Pabla, Charanjit S., "Sensitivity of c-band polarimetric radar-rain algorithms to disdrometer drop size distribution integration period" (2015). Theses. 107.
https://louis.uah.edu/uah-theses/107