Improved quantitative estimation of rainfall by radar
| dc.contributor.author | Islam, Md Rashedul | |
| dc.contributor.examiningcommittee | Dr. Shalaby, Ahmed (Civil Engineering) Dr. Hanesiak, John (Environment and Geography) | en |
| dc.contributor.supervisor | Dr. Rasmussen, Peter (Civil Engineering) | en |
| dc.date.accessioned | 2006-01-06T20:47:27Z | |
| dc.date.available | 2006-01-06T20:47:27Z | |
| dc.date.issued | 2006-01-06T20:47:27Z | |
| dc.degree.discipline | Civil Engineering | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | Although higher correlation between gauge and radar at hourly or daily accumulations are reported, it is rarely observed at higher time resolution (e.g. 10 -minute). This study investigates six major rainfall events in year 2000 in the greater Winnipeg area with durations varying from four to nine hours. The correlation between gauge and radar measurements of precipitation is found to be only 0.3 at 10-minute resolution and 0.55 at hourly resolution using Marshall-Palmer’s Z-R relationship (Z=200R1.6). The rainfalls are classified into convective and stratiform regions using Steiner et al. (1995)’s algorithm and two different Z-R relationships are tested to minimize the error associated with the variability of drop-size-distribution, however no improvement is observed. The performance of the artificial neural network is explored as a reflectivity-rainfall mapping function. Three different types of neural networks are explored: the back propagation network, the radial basis function network, and the generalized regression neural network. It is observed that the neural network’s performance is better than the Z-R relationship to estimate the rainfall events which was used for training and validation (correlation 0.67). When this network is tested on a new rainfall its performance is found quite similar to that obtained from the Z-R relationship (correlation 0.33). Based on this observation neural network may be recommended as a post-processing tool but may not be very useful for operational purposes - at least as used in this study. Variability in weather and precipitation scenarios affects the radar measurements which apparently makes it impossible for the neural network or the Z-R relationship to show consistent performance at every rainfall event. To account for variability in weather and rainfall scenarios conventional correction schemes for attenuation and hail contamination are applied and a trajectory model is developed to account for rainfall advection due to wind drift. The trajectory model uses velocity obtained from the single-doppler observation. A space-time interpolation technique is applied to generate reflectivity maps at one-minute resolution based on the direction obtained from the correlation based tracking algorithm. The trajectory model uses the generated reflectivity maps having one-minute resolution which help to account for the travel time by the rainfall mass to reach to the ground. It was found that the attenuation correction algorithm adversely increases the reflectivity. This study assumes that the higher reflectivity caused by hail contaminated regions is one reason for the overestimation in the attenuation correction process. It was observed that the hail capping method applied prior to the attenuation correction algorithm helps to improve the situation. A statistical expression to account for radome attenuation is also developed. It is observed that the correlation between the gauge and the radar measurement is 0.81 after applying the various algorithms. Although Marshall-Palmer’s relationship is recommended for stratiform precipitation only, this study found it suitable for both convective and stratiform precipitation when attenuation is properly taken into account. The precipitation processing model developed in this study generates more accurate rainfall estimates at the surface from radar observations and may be a better choice for rainfall-runoff modellers. | en |
| dc.description.note | February 2006 | en |
| dc.format.extent | 4062968 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1993/190 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | radar | en |
| dc.subject | rainfall | en |
| dc.subject | neural-network | en |
| dc.subject | wind-drift | en |
| dc.subject | Z-R | en |
| dc.subject | attenuation | en |
| dc.subject | hail | en |
| dc.subject | tracking | en |
| dc.title | Improved quantitative estimation of rainfall by radar | en |
| dc.type | master thesis | en_US |