The attenuation of a radar signal is a serious problem facing meteorologists and hydrologists. In heavy rain, reflectivity information can be completely lost from large portions of a radar scan. The problem is particularly acute for X-band radars. Current methods of correcting for attenuation face many difficulties, mainly because the actual amount of attenuation at any given time is unknown. In this paper a backward-iterative attenuation-correction algorithm is presented that uses the attenuation measured by a microwave link with its receiver collocated with an X-band weather radar in Essen, Germany. Data are also available from a network of rain gauges located in the vicinity of the link path. This network provides a measure of “ground truth” rainfall against which radar estimates can be compared. The results show that the algorithm can recover much of the reflectivity information that is lost due to attenuation of the radar beam. The method is seen to be particularly effective in convective conditions where heavy rainfall can cause severe attenuation.
., 2014. Comparing quantitative precipitation forecast methods for prediction of sewer flows in a small urban area. Hydrological Sciences Journal, 59 (7), 1418-1436. http://dx.doi.org/ 10. 1080/02626667.2014.920505 Abstract Due to the relatively small spatial scale, as well as rapid response, of urban drainage systems, the use of quantitative rainfall forecasts for providing quantitative flow and depth predictions is a challenging task. Such predictions are important when consideration is given to urban pluvial flooding and receiving water quality, and it is worthwhile to investigate the potential for improved forecasting. In this study, three quantitative precipitation forecast methods of increasing complexity were compared and used to create quantitative forecasts of sewer flows 0-3 h ahead in the centre of a small town in the north of England. The HyRaTrac radar nowcast model was employed, as well as two different versions of the more complex STEPS model. The STEPS model was used as a deterministic nowcasting system, and was also blended with the Numerical Weather Prediction (NWP) model MM5 to investigate the potential of increasing forecast lead-times (LTs) using high-resolution NWP. Predictive LTs between 15 and 90 min gave acceptable results, but were a function of the event type. It was concluded that higher resolution rainfall estimation as well as nowcasts are needed for prediction of both local pluvial flooding and combined sewer overflow spill events.Key words rainfall runoff; radar nowcasting; numerical weather prediction; flow forecasting; urban drainageComparaison des méthodes quantitatives de prévision des précipitations pour la prévision du débit des égouts dans une petite zone urbaine Résumé En raison de l'échelle spatiale relativement petite ainsi que de la réponse rapide des systèmes de drainage urbain, l'utilisation des prévisions quantitatives des précipitations pour fournir des prévisions quantitatives de débits et de hauteurs d'eau est une tâche difficile. Ces prévisions sont importantes lorsque l'on s'intéresse aux inondations pluviales urbaines et à la qualité des eaux réceptrices, et il vaut la peine de chercher les possibilités d'améliorer les prévisions. Dans cette étude, nous avons utilisé et comparé trois méthodes de prévision quantitative des précipitations, de complexité croissante, réalisant des prévisions quantitatives du débit des égouts de 0 à 3 h à l'avance dans le centre d'une petite ville du Nord de l'Angleterre. Nous avons utilisé le modèle de prévision immédiate radar HyRaTrac, ainsi que deux versions différentes du modèle plus complexe STEPS. Le modèle STEPS a été utilisé comme système de prévision immédiate déterministe, et a également été couplé avec le modèle de prévision météorologique numérique (PMN) MM5 pour étudier la possibilité d'augmenter l'horizon de prévision grâce à l'utilisation de PMN à haute résolution. Des résultats acceptables ont été obtenus pour des horizons de prévision compris entre 15 et 90 min, mais ils dépendaient du type d'événement météorolo...
The Bright Band (BB) is a region of enhanced reflectivity in weather radar scans associated with frozen hydrometeors forming a liquid coating as they fall through the melting layer. This enhancement can cause the radar to overestimate precipitation quantities at the surface. The main objective of this study is to develop a hydrometeor classification algorithm that can use dual-polarisation measurements as the only input to classify the BB area. An effort has been made to replicate the current UK Met Office operational method for BB classification. This involves the use of Numerical Weather Prediction outputs of freezing level heights with an assumption of a constant BB thickness. Vertical Profiles of Reflectivity (VPR) can then be used to correct for the reflectivity enhancement. A mean apparent VPR computed from reflectivity measurements at multiple elevation angles is compared to two idealised VPR methods. For validation the corrected 1.5º elevation scans are compared to surface rain gauge observations and lower elevation scans over the course of 7 events. The hydrometeor classification methods showed the greatest error reductions, with the freezing level forecast method performing well when the BB thickness was within 700 m, but poorly when there was more variation. Overall the idealised VPRs allowed for the greatest BB corrections in comparison to the mean profile.
This paper describes a new methodology to process C-band radar data for direct use as rainfall input to hydrologic and hydrodynamic models and in real time control of urban drainage systems. In contrast to the adjustment of radar data with the help of rain gauges, the new approach accounts for the microphysical properties of current rainfall. In a first step radar data are corrected for attenuation. This phenomenon has been identified as the main cause for the general underestimation of radar rainfall. Systematic variation of the attenuation coefficients within predefined bounds allows robust reflectivity profiling. Secondly, event specific R-Z relations are applied to the corrected radar reflectivity data in order to generate quantitative reliable radar rainfall estimates. The results of the methodology are validated by a network of 37 rain gauges located in the Emscher and Lippe river basins. Finally, the relevance of the correction methodology for radar rainfall forecasts is demonstrated. It has become clearly obvious, that the new methodology significantly improves the radar rainfall estimation and rainfall forecasts. The algorithms are applicable in real time.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.