scholarly journals Enhancement of radar rainfall estimates for urban hydrology through optical flow temporal interpolation and Bayesian gauge-based adjustment

2015 ◽  
Vol 531 ◽  
pp. 408-426 ◽  
Author(s):  
Li-Pen Wang ◽  
Susana Ochoa-Rodríguez ◽  
Johan Van Assel ◽  
Rui Daniel Pina ◽  
Mieke Pessemier ◽  
...  
Keyword(s):  
Optical Flow ◽  
Urban Hydrology ◽  
Radar Rainfall ◽  
Temporal Interpolation ◽  
Rainfall Estimates

Radar–raingauge data combination techniques: a revision and analysis of their suitability for urban hydrology

2013 ◽  
Vol 68 (4) ◽  
pp. 737-747 ◽  
Author(s):  
Li-Pen Wang ◽  
Susana Ochoa-Rodríguez ◽  
Nuno Eduardo Simões ◽  
Christian Onof ◽  
Čedo Maksimović
Keyword(s):  
Error Variance ◽  
Bias Reduction ◽  
Spatiotemporal Variability ◽  
Urban Hydrology ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Radar Rainfall ◽  
North East ◽  
Raingauge Data ◽  
Rainfall Estimates

The applicability of the operational radar and raingauge networks for urban hydrology is insufficient. Radar rainfall estimates provide a good description of the spatiotemporal variability of rainfall; however, their accuracy is in general insufficient. It is therefore necessary to adjust radar measurements using raingauge data, which provide accurate point rainfall information. Several gauge-based radar rainfall adjustment techniques have been developed and mainly applied at coarser spatial and temporal scales; however, their suitability for small-scale urban hydrology is seldom explored. In this paper a review of gauge-based adjustment techniques is first provided. After that, two techniques, respectively based upon the ideas of mean bias reduction and error variance minimisation, were selected and tested using as case study an urban catchment (∼8.65 km2) in North-East London. The radar rainfall estimates of four historical events (2010–2012) were adjusted using in situ raingauge estimates and the adjusted rainfall fields were applied to the hydraulic model of the study area. The results show that both techniques can effectively reduce mean bias; however, the technique based upon error variance minimisation can in general better reproduce the spatial and temporal variability of rainfall, which proved to have a significant impact on the subsequent hydraulic outputs. This suggests that error variance minimisation based methods may be more appropriate for urban-scale hydrological applications.

Estimation of Rainfall Based on the Results of Polarimetric Echo Classification

2008 ◽  
Vol 47 (9) ◽  
pp. 2445-2462 ◽  
Author(s):  
Scott E. Giangrande ◽  
Alexander V. Ryzhkov
Keyword(s):  
Type A ◽  
New Method ◽  
Polarimetric Radar ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Different Types ◽  
Rms Error ◽  
Radar Resolution ◽  
Rainfall Estimates

Abstract The quality of polarimetric radar rainfall estimation is investigated for a broad range of distances from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D). The results of polarimetric echo classification have been integrated into the study to investigate the performance of radar rainfall estimation contingent on hydrometeor type. A new method for rainfall estimation that capitalizes on the results of polarimetric echo classification (EC method) is suggested. According to the EC method, polarimetric rainfall relations are utilized if the radar resolution volume is filled with rain (or rain and hail), and multiple R(Z) relations are used for different types of frozen hydrometeors. The intercept parameters in the R(Z) relations for each class are determined empirically from comparisons with gauges. It is shown that the EC method exhibits better performance than the conventional WSR-88D algorithm with a reduction by a factor of 1.5–2 in the rms error of 1-h rainfall estimates up to distances of 150 km from the radar.

Quantifying the potential of AQPI gap-filling radar network for streamflow simulation through a WRF-Hydro experiment

2021 ◽  
Author(s):  
Yingzhao Ma ◽  
V. Chandrasekar ◽  
Haonan Chen ◽  
Robert Cifelli
Keyword(s):  
Water Model ◽  
Urban Region ◽  
Gap Filling ◽  
Radar Rainfall ◽  
Bay Area ◽  
Radar Network ◽  
X Band ◽  
The Impact ◽  
Streamflow Simulation ◽  
Rainfall Estimates

AbstractIt remains a challenge to provide accurate and timely flood warnings in many parts of the western United States. As part of the Advanced Quantitative Precipitation Information (AQPI) project, this study explores the potential of using the AQPI gap-filling radar network for streamflow simulation of selected storm events in the San Francisco Bay Area under a WRF-Hydro modeling system. Two types of watersheds including natural and human-affected among the most flood-prone region of the Bay Area are investigated. Based on the high-resolution AQPI X-band radar rainfall estimates, three basic routing configurations, including Grid, Reach, and National Water Model (NWM), are used to quantify the impact of different model physics options on the simulated streamflow. It is found that the NWM performs better in terms of reproducing streamflow volumes and hydrograph shapes than the other routing configurations when reservoirs exist in the watershed. Additionally, the AQPI X-band radar rainfall estimates (without gauge correction) provide reasonable streamflow simulations, and they show better performance in reproducing the hydrograph peaks compared with the gauge-corrected rainfall estimates based on the operational S-band Next Generation Weather Radar network. Also, sensitivity test reveals that surficial conditions have a significant influence on the streamflow simulation during the storm: the discharge increases to a higher level as the infiltration factor (REFKDT) decreases, and its peak goes down and lags as surface roughness coefficient (Mann) increases. The time delay analysis of precipitation input on the streamflow at the two outfalls of the surveyed watersheds further demonstrates the link between AQPI gap-filling radar observations and streamflow changes in this urban region.

scholarly journals Introducing uncertainty of radar-rainfall estimates to the verification of mesoscale model precipitation forecasts

2008 ◽  
Vol 8 (3) ◽  
pp. 445-460 ◽  
Author(s):  
M. P. Mittermaier
Keyword(s):  
Weather Prediction ◽  
Forecast Skill ◽  
Forecast Errors ◽  
Intensity Scale ◽  
Radar Rainfall ◽  
Scale Method ◽  
Skill Scores ◽  
Forecast Lead Time ◽  
The Uk ◽  
Rainfall Estimates

Abstract. A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP) verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS) and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radar-composite were used. The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.

A preliminary appraisal of Thurnham dual polarisation radar in the context of hydrological modelling structure

2012 ◽  
Vol 43 (5) ◽  
pp. 736-752 ◽  
Author(s):  
D. Zhu ◽  
I. D. Cluckie
Keyword(s):  
Direct Consequence ◽  
Hydrological Models ◽  
Short Term ◽  
Radar Rainfall ◽  
Mathematical Structures ◽  
Extreme Flood ◽  
Clutter Removal ◽  
The Impact ◽  
Further Development ◽  
Rainfall Estimates

The Thurnham radar is a prototype of a potential operational C-Band dual-polarisation weather radar designed specifically for the measurement of rainfall. It is also designed to increase the radar coverage over London when operating as a conventional C-Band radar as a direct consequence of the Lewes floods of October 2000. Dual-polarisation processing is expected to provide improved estimation of rainfall rates, especially at higher intensities, in terms of clutter removal, attenuation correction and rainfall estimation. In this study, three hydrological models with different mathematical structures were selected to evaluate the impact that dual-polarisation technology could have on operational hydrology and recommendations provided on the further development of the dual-polarisation algorithms in the short term. The preliminary appraisal was focused on the Upper Medway Catchment (south of London, UK) using different precipitation inputs, including raingauge measurements, radar rainfall estimates from single-polarised algorithms (cartesian format) and five different dual-polarisation algorithms (polar format). The influence of the different rainfall inputs on the various hydrological models were compared using a extreme flood event to provide an initial evaluation of the performance of the Thurnham radar. Recommendations for applying dual-polarisation radar to real-time flood forecasting are discussed in detail.

Uncertainty analysis of radar rainfall estimates over two different climates in Iran

2017 ◽  
Vol 38 (18) ◽  
pp. 5106-5126 ◽  
Author(s):  
Esmail Ghaemi ◽  
Mohammadreza Kavianpour ◽  
Saber Moazami ◽  
Yang Hong ◽  
Hooman Ayat
Keyword(s):  
Uncertainty Analysis ◽  
Radar Rainfall ◽  
Different Climates ◽  
Rainfall Estimates

scholarly journals Debris-flow forecasting at regional scale by combining susceptibility mapping and radar rainfall

2015 ◽  
Vol 15 (3) ◽  
pp. 587-602 ◽  
Author(s):  
M. Berenguer ◽  
D. Sempere-Torres ◽  
M. Hürlimann
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Regional Scale ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Susceptibility Assessment ◽  
Radar Rainfall ◽  
Eastern Pyrenees ◽  
Rainfall Map ◽  
Rainfall Estimates

Abstract. This work presents a technique for debris-flow (DF) forecasting able to be used in the framework of DF early warning systems at regional scale. The developed system is applied at subbasin scale and is based on the concepts of fuzzy logic to combine two ingredients: (i) DF subbasin susceptibility assessment based on geomorphological variables and (ii) the magnitude of the rainfall situation as depicted from radar rainfall estimates. The output of the developed technique is a three-class warning ("low", "moderate" or "high") in each subbasin when a new radar rainfall map is available. The developed technique has been applied in a domain in the eastern Pyrenees (Spain) from May to October 2010. The warning level stayed "low" during the entire period in 20% of the subbasins, while in the most susceptible subbasins the warning level was at least "moderate" for up to 10 days. Quantitative evaluation of the warning level was possible in a subbasin where debris flows were monitored during the analysis period. The technique was able to identify the three events observed in the catchment (one debris flow and two hyperconcentrated flow events) and produced no false alarm.

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