Hydro-NEXRAD is a prototype software system that provides hydrology and water resource communities with ready access to the vast data archives of the U.S. weather radar network known as NEXRAD (Next Generation Weather Radar). This paper describes radar-rainfall estimation algorithms and their modular components used in the Hydro-NEXRAD system to generate rainfall products to be delivered to users. A variety of customized modules implemented in Hydro-NEXRAD perform radar-reflectivity data processing, produce radar-rainfall maps with user-requested space and time resolution, and combine multiple radar data for basins covered by multiple radars. System users can select rainfall estimation algorithms that range from simple (‘Quick Look’) to complex and computing-intensive (‘Hi-Fi’). The ‘Pseudo NWS PPS’ option allows close comparison with the algorithm used operationally by the US National Weather Service. The ‘Custom’ algorithm enables expert users to specify values for many of the parameters in the algorithm modules according to their experience and expectations. The Hydro-NEXRAD system, with its rainfall-estimation algorithms, can be used by both novice and expert users who need rainfall estimates as references or as input to their hydrologic modelling and forecasting applications
Radar-rainfall estimation algorithms of Hydro-NEXRAD
Bong-Chul Seo, Witold F. Krajewski, Anton Kruger, Piotr Domaszczynski, James A. Smith, Matthias Steiner; Radar-rainfall estimation algorithms of Hydro-NEXRAD. Journal of Hydroinformatics 1 March 2011; 13 (2): 277–291. doi: https://doi.org/10.2166/hydro.2010.003
Download citation file:
Close
Bong-Chul Seo, Witold F. Krajewski, Anton Kruger, Piotr Domaszczynski, James A. Smith, Matthias Steiner; Radar-rainfall estimation algorithms of Hydro-NEXRAD. Journal of Hydroinformatics 1 March 2011; 13 (2): 277–291. doi: https://doi.org/10.2166/hydro.2010.003
Download citation file:
Close
Impact Factor 1.728
CiteScore 3.5 • Q2
Cited by
Subscribe to Open
This paper is Open Access via a Subscribe to Open model. Individuals can help sustain this model by contributing the cost of what would have been author fees. Find out more here.