A radar rainfall forecasting method designed for hydrological purposes

1990 ◽  
Vol 114 (3-4) ◽  
pp. 229-244 ◽  
Author(s):  
Thomas Einfalt ◽  
Thierry Denoeux ◽  
Guy Jacquet
Keyword(s):  
Rainfall Forecasting ◽  
Radar Rainfall ◽  
Forecasting Method

scholarly journals A study on prediction error in radar rainfall forecasting and its application to on-line operation of a detention storage

1996 ◽  
Vol 40 ◽  
pp. 309-315
Author(s):  
Takeharu ETOH ◽  
Yoshiyuki KAMIBAYASHI ◽  
Masanori NAKANISHI ◽  
Masaki YOSHIDA
Keyword(s):  
Prediction Error ◽  
Rainfall Forecasting ◽  
Radar Rainfall ◽  
On Line

Evaluating the effects of image filtering in short-term radar rainfall forecasting for hydrological applications

2006 ◽  
Vol 13 (03) ◽  
pp. 289 ◽  
Author(s):  
Matthew P. Van Horne ◽  
Enrique R. Vivoni ◽  
Dara Entekhabi ◽  
Ross N. Hoffman ◽  
Christopher Grassotti
Keyword(s):  
Image Filtering ◽  
Rainfall Forecasting ◽  
Short Term ◽  
Radar Rainfall ◽  
Hydrological Applications

scholarly journals Ensemble Radar-Based Rainfall Forecasts for Urban Hydrological Applications

Geosciences ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 297 ◽  
Author(s):  
Mayra Codo ◽  
Miguel A. Rico-Ramirez
Keyword(s):  
Urban Areas ◽  
Ground Truth ◽  
Roc Curves ◽  
Rain Gauge ◽  
Rainfall Forecasting ◽  
Relative Operating Characteristic ◽  
Radar Rainfall ◽  
Flood Warning ◽  
The North ◽  
Rainfall Forecast

Radar rainfall forecasting is of major importance to predict flows in the sewer system to enhance early flood warning systems in urban areas. In this context, reducing radar rainfall estimation uncertainties can improve rainfall forecasts. This study utilises an ensemble generator that assesses radar rainfall uncertainties based on historical rain gauge data as ground truth. The ensemble generator is used to produce probabilistic radar rainfall forecasts (radar ensembles). The radar rainfall forecast ensembles are compared against a stochastic ensemble generator. The rainfall forecasts are used to predict sewer flows in a small urban area in the north of England using an Infoworks CS model. Uncertainties in radar rainfall forecasts are assessed using relative operating characteristic (ROC) curves, and the results showed that the radar ensembles overperform the stochastic ensemble generator in the first hour of the forecasts. The forecast predictability is however rapidly lost after 30 min lead-time. This implies that knowledge of the statistical properties of the radar rainfall errors can help to produce more meaningful radar rainfall forecast ensembles.

State-space adjustment of radar rainfall and skill score evaluation of stochastic volume forecasts in urban drainage systems

2013 ◽  
Vol 68 (3) ◽  
pp. 584-590 ◽  
Author(s):  
Roland Löwe ◽  
Peter Steen Mikkelsen ◽  
Michael R. Rasmussen ◽  
Henrik Madsen
Keyword(s):  
State Space ◽  
Radar Data ◽  
Skill Score ◽  
Rain Gauge ◽  
Real Time Control ◽  
Rainfall Forecasting ◽  
Radar Rainfall ◽  
Data Merging ◽  
Time Control ◽  
Point Forecast

Merging of radar rainfall data with rain gauge measurements is a common approach to overcome problems in deriving rain intensities from radar measurements. We extend an existing approach for adjustment of C-band radar data using state-space models and use the resulting rainfall intensities as input for forecasting outflow from two catchments in the Copenhagen area. Stochastic grey-box models are applied to create the runoff forecasts, providing us with not only a point forecast but also a quantification of the forecast uncertainty. Evaluating the results, we can show that using the adjusted radar data improves runoff forecasts compared with using the original radar data and that rain gauge measurements as forecast input are also outperformed. Combining the data merging approach with short-term rainfall forecasting algorithms may result in further improved runoff forecasts that can be used in real time control.

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