L’estimation quantitative des précipitations à l’échelle locale est une nécessité sociétale, à cause de l’augmentation des dégâts provoqués par des inondations exacerbées par l’urbanisation croissante. Or, des estimations locales sont particulièrement difficiles à réaliser à cause de la forte variabilité des précipitations. De plus, ce genre d’estimation est sollicité par de petits organismes tels qu’une commune, pour lesquels il n’est pas envisageable d’utiliser des instruments à la pointe de la recherche technologique à cause de leur coût prohibitif. Ainsi, il est nécessaire de développer des méthodes d’estimation quantitative des précipitations applicables à un dispositif expérimental de prix abordable. Dans ce but, un dispositif expérimental innovant est utilisé dans cette thèse. Il est constitué d’instruments de mesure directe, au sol, tels que des pluviomètres et des disdromètres, et d’un prototype de radar à balayage horizontal basé sur un radar nautique commercial, associé à un MRR (Micro Rain Radar) à visée verticale qui fournissent une estimation en altitude de la pluie, respectivement sur une surface donnée et le long d’un profil vertical. Le radar à balayage horizontal est un radar en bande X, c’est-à-dire qu’il fonctionne à une longueur d’onde lui procurant une très haute résolution radiale, mais qui est très atténuée par les précipitations. Le MRR permet d’obtenir une description précise de la microphysique des précipitations et sert de relais entre les mesures au sol et les mesures en altitude du radar en bande X. Ces deux radars étant novateurs, une grande partie de cette thèse consiste à valider leurs mesures : étalonnage, filtrage d’échos aberrants, correction de l’atténuation, etc. Une fois les mesures rendues exploitables, cette thèse se focalise sur l’étude de la variabilité des précipitations afin de proposer et développer différentes méthodes de classification, selon leur type ou leur variations locales, et de vérifier leur potentiel pour l’amélioration de l’estimation des précipitations. Les résultats montrent que cet objectif ne peut être atteint que si la qualité des mesures des radars est encore améliorée : moins d’échos parasites pour le radar en bande X et prise en compte du vent vertical pour le MRR. / Due to the increase of damage associated with floods enhanced by expanding urbanisation, the quantitative estimation of precipitation on a local scale is a societal need. However, such estimations are difficult because of the high variability of precipitation. Moreover, these estimations are requested by small organisations such as local authorities which cannot afford top level research instruments. Hence, new methods of estimation applicable to a cheap experimental set are needed. Toward this goal, an innovative experimental set is used in this work. It consists of ground instruments such as raingauges and disdrometers, and two radars, a prototype of a scanning radar based on a modified marine radar and a vertically pointing MRR (Micro Rain Radar), which give estimation of rain aloft, over an area and along a profile, respectively. The scanning radar works at X-band, meaning that it uses a longwave very attenuated by precipitation, but which provides a high range resolution. The MRR yields a detailed description of microphysics of precipitation and fills the gap between ground measurements and X-band radar measurements aloft. As both these radars are innovative, a large part of this PhD thesis was spent on the measurements validation : radar calibration, abnormal echoes filtering, attenuation consideration, etc. Using these corrected measurements, this PhD focus then on the study of the variability of precipitation, and aims to propose and develop several classification methods based on precipitation type or local variability, and to check their potential for the improvement of precipitation estimation. Results show that this goal can be reached only if the radar measurements quality is further improved : less interference echoes for the X-band radar, and consideration of vertical wind for the MRR.

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. / February 2006

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.

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.

L'estimation quantitative des précipitations à l'échelle locale est une nécessité sociétale, à cause de l'augmentation des dégâts provoqués par des inondations exacerbées par l'urbanisation croissante. Or, des estimations locales sont particulièrement difficiles à réaliser à cause de la forte variabilité des précipitations. De plus, ce genre d'estimation est sollicité par de petits organismes tels qu'une commune, pour lesquels il n'est pas envisageable d'utiliser des instruments à la pointe de la recherche technologique à cause de leur coût prohibitif. Ainsi, il est nécessaire de développer des méthodes d'estimation quantitative des précipitations applicables à un dispositif expérimental de prix abordable. Dans ce but, un dispositif expérimental innovant est utilisé dans cette thèse. Il est constitué d'instruments de mesure directe, au sol, tels que des pluviomètres et des disdromètres, et d'un prototype de radar à balayage horizontal basé sur un radar nautique commercial, associé à un MRR (Micro Rain Radar) à visée verticale qui fournissent une estimation en altitude de la pluie, respectivement sur une surface donnée et le long d'un profil vertical. Le radar à balayage horizontal est un radar en bande X, c'est-à-dire qu'il fonctionne à une longueur d'onde lui procurant une très haute résolution radiale, mais qui est très atténuée par les précipitations. Le MRR permet d'obtenir une description précise de la microphysique des précipitations et sert de relais entre les mesures au sol et les mesures en altitude du radar en bande X. Ces deux radars étant novateurs, une grande partie de cette thèse consiste à valider leurs mesures : étalonnage, filtrage d'échos aberrants, correction de l'atténuation, etc. Une fois les mesures rendues exploitables, cette thèse se focalise sur l'étude de la variabilité des précipitations afin de proposer et développer différentes méthodes de classification, selon leur type ou leur variations locales, et de vérifier leur potentiel pour l'amélioration de l'estimation des précipitations. Les résultats montrent que cet objectif ne peut être atteint que si la qualité des mesures des radars est encore améliorée : moins d'échos parasites pour le radar en bande X et prise en compte du vent vertical pour le MRR.

Flooding is one of the deadliest natural hazards in the world. Forecasting floods in advance can significantly reduce the socio-economic impacts. An accurate and reliable flood forecasting system is heavily dependent on the input precipitation data. Real-time, spatially, and temporally continuous Radar Quantitative Precipitation Estimates (QPEs) is useful precipitation information source. This research aims to investigate the efficacy of American and Canadian weather radar QPEs on hydrological model calibration and validation for flood forecasting in urban and semi-urban watersheds in Canada. A comprehensive review was conducted on the weather Radar network and its’ hydrological applications, challenges, and potential future research in Canada. First, radar QPEs were evaluated to verify the reliability and accuracy as precipitation input for hydrometeorological models. Then, the radar-gauge merging techniques were assessed to select the best method for urban flood forecasting applications. After that, merged Radar QPEs were used as precipitation input for the hydrological models to assess the impact of radar QPEs on hydrological model calibration and validation. Finally, a framework was developed by integrating hydrological and hydraulic models to produce flood forecasts and inundation maps in urbanized watersheds. Results indicated that dual-polarized radar QPEs could be effectively used as a source of precipitation input to hydrological models. The radar-gauge merging enhances both the accuracy and reliability of Radar QPEs, and therefore, the accuracy of streamflow simulation is also improved. Since flood forecasting agencies usually use hydrological models calibrated and validated using gauge data, it is recommended to use bias-corrected Radar QPEs to run existing hydrological models to simulate streamflow to produce flood extent maps. The hydrological and hydraulic models could be integrated into one framework using bias-corrected Radar QPEs to develop a successful flood forecasting system. / Thesis / Doctor of Science (PhD) / Floods are common and increasing deadly natural hazards in the world. Predicting floods in advance using Flood Early Warning System (FEWS) can facilitate flood mitigation. Radar Quantitative Precipitation Estimates (QPEs) can provide real-time, spatially, and temporally continuous precipitation data. This research focuses on bias-correcting and evaluating radar QPEs for hydrologic forecasting. The corrected QPE are applied into a framework connecting hydrological and hydraulic models for operational flood forecasting in urban watersheds in Canada. The key contributions include: (1) Dual-polarized radar QPEs is a useful precipitation input to calibrate, validate and run hydrological models; (2) Radar-gauge merging enhance accuracy and reliability of radar QPEs; (3) Floods could be more accurately predicted by integrating hydrological and hydraulic models in one framework using bias-corrected Radar QPEs; and (4) Gauge-calibrated hydrological models can be run effectively using the bias-corrected radar QPEs. This research will benefit future applications of real-time radar QPEs in operational FEWS.