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.

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.

Use of Radar Quantitative Precipitation Estimates (QPEs) for Improved Hydrological Model Calibration and Flood Forecasting

2021 ◽  
Author(s):  
Dayal Wijayarathne ◽  
Paulin Coulibaly ◽  
Sudesh Boodoo ◽  
David Sills
Keyword(s):  
Real Time ◽  
Model Calibration ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Hydrological Models ◽  
Radar Rainfall ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates ◽  
Streamflow Simulation ◽  
Rainfall Estimates

AbstractFlood forecasting is essential to minimize the impacts and costs of floods, especially in urbanized watersheds. Radar rainfall estimates are becoming increasingly popular in flood forecasting because they provide the much-needed real-time spatially distributed precipitation information. The current study evaluates the use of radar Quantitative Precipitation Estimates (QPEs) in hydrological model calibration for streamflow simulation and flood mapping in an urban setting. Firstly, S-band and C-band radar QPEs were integrated into event-based hydrological models to improve the calibration of model parameters. Then, rain gauge and radar precipitation estimates’ performances were compared for hydrological modeling in an urban watershed to assess radar QPE's effects on streamflow simulation accuracy. Finally, flood extent maps were produced using coupled hydrological-hydraulic models integrated within the Hydrologic Engineering Center- Real-Time Simulation (HEC-RTS) framework. It is shown that the bias correction of radar QPEs can enhance the hydrological model calibration. The radar-gauge merging obtained a KGE, MPFC, NSE, and VE improvement of about + 0.42, + 0.12, + 0.78, and − 0.23, respectively for S-band and + 0.64, + 0.36, + 1.12, and − 0.34, respectively for C-band radar QPEs. Merged radar QPEs are also helpful in running hydrological models calibrated using gauge data. The HEC-RTS framework can be used to produce flood forecast maps using the bias-corrected radar QPEs. Therefore, radar rainfall estimates could be efficiently used to forecast floods in urbanized areas for effective flood management and mitigation. Canadian flood forecasting systems could be efficiently updated by integrating bias-corrected radar QPEs to simulate streamflow and produce flood inundation maps.

scholarly journals Hydrological appraisal of operational weather radar rainfall estimates in the context of different modelling structures

2013 ◽  
Vol 10 (8) ◽  
pp. 10495-10534
Author(s):  
D. Zhu ◽  
Y. Xuan ◽  
I. Cluckie
Keyword(s):  
Hydrological Model ◽  
Scale Effects ◽  
Model Performance ◽  
Extreme Rainfall ◽  
Distributed Model ◽  
Catchment Scale ◽  
Radar Rainfall ◽  
Rainfall Events ◽  
The Impact ◽  
Rainfall Estimates

Abstract. Radar rainfall estimates have become increasingly available for hydrological modellers over recent years, especially for flood forecasting and warning over poorly gauged catchments. However, the impact of using radar rainfall as compared with conventional raingauge inputs, with respect to various hydrological model structures, remains unclear and yet to be addressed. In the study presented by this paper, we analysed the flow simulations of the Upper Medway catchment of Southeast England using the UK NIMROD radar rainfall estimates using three hydrological models based upon three very different structures, e.g. a physically based distributed MIKE SHE model, a lumped conceptual model PDM and an event-based unit hydrograph model PRTF. We focused on the sensitivity of simulations in relation to the storm types and various rainfall intensities. The uncertainty in radar-rainfall estimates, scale effects and extreme rainfall were examined in order to quantify the performance of the radar. We found that radar rainfall estimates were lower than raingauge measurements in high rainfall rates; the resolutions of radar rainfall data had insignificant impact at this catchment scale in the case of evenly distributed rainfall events but was obvious otherwise for high-intensity, localised rainfall events with great spatial heterogeneity. As to hydrological model performance, the distributed model had consistent reliable and good performance on peak simulation with all the rainfall types tested in this study.

scholarly journals Statistical analysis of error propagation from radar rainfall to hydrological models

2013 ◽  
Vol 17 (4) ◽  
pp. 1445-1453 ◽  
Author(s):  
D. Zhu ◽  
D. Z. Peng ◽  
I. D. Cluckie
Keyword(s):  
Stream Flow ◽  
Flow Simulation ◽  
Statistical Error ◽  
Generation Model ◽  
Hydrological Models ◽  
Radar Rainfall ◽  
Lumped Model ◽  
Flow Simulations ◽  
Error Generation ◽  
Rainfall Estimates

Abstract. This study attempts to characterise the manner with which inherent error in radar rainfall estimates input influence the character of the stream flow simulation uncertainty in validated hydrological modelling. An artificial statistical error model described by Gaussian distribution was developed to generate realisations of possible combinations of normalised errors and normalised bias to reflect the identified radar error and temporal dependence. These realisations were embedded in the 5 km/15 min UK Nimrod radar rainfall data and used to generate ensembles of stream flow simulations using three different hydrological models with varying degrees of complexity, which consists of a fully distributed physically-based model MIKE SHE, a semi-distributed, lumped model TOPMODEL and the unit hydrograph model PRTF. These models were built for this purpose and applied to the Upper Medway Catchment (220 km2) in South-East England. The results show that the normalised bias of the radar rainfall estimates was enhanced in the simulated stream flow and also the dominate factor that had a significant impact on stream flow simulations. This preliminary radar-error-generation model could be developed more rigorously and comprehensively for the error characteristics of weather radars for quantitative measurement of rainfall.

scholarly journals Correcting Radar Rainfall Estimates Based on Ground Elevation Function

2019 ◽  
Vol 5 (3) ◽  
pp. 300
Author(s):  
Roby Hambali ◽  
Djoko Legono ◽  
Rachmad Jayadi
Keyword(s):  
Correction Method ◽  
Rain Gauge ◽  
Real Time System ◽  
Radar Rainfall ◽  
Rainfall Condition ◽  
Correction Process ◽  
R Ratio ◽  
X Band ◽  
The Impact ◽  
Rainfall Estimates

X-band radar gives several advantages for quantitative rainfall estimation, involving higher spatial and temporal resolution, also the ability to reduce attenuation effects and hardware calibration errors. However, the estimates error due to attenuation in heavy rainfall condition cannot be avoided. In the mountainous region, the impact of topography is considered to contribute to radar rainfall estimates error. To have more reliable estimated radar rainfall to be used in various applications, a rainfall estimates correction needs to be applied. This paper discusses evaluation and correction techniques for radar rainfall estimates based on ground elevation function. The G/R ratio is used as a primary method in the correction process. The novel approach proposed in this study is the use of correction factor derived from the relationship between Log (G/R) parameter and elevation difference between radar and rain gauge stations. A total of 4590 pairs of rainfall data from X-band MP radar and 15 rain gauge stations in the Mt. Merapi region were used in evaluation and correction process. The results show the correction method based on the elevation function is relatively good in correcting radar rainfall depth with values of Log (G/R) decreased up to 81.1%, particularly for light rainfall (≤ 20 mm/hour) condition. Also, the method is simple to apply in a real-time system.

scholarly journals Statistical analysis of error propagation from radar rainfall to hydrological models

2012 ◽  
Vol 9 (9) ◽  
pp. 10277-10302
Author(s):  
D. H. Zhu ◽  
D. Z. Peng ◽  
I. D. Cluckie
Keyword(s):  
Stream Flow ◽  
Flow Simulation ◽  
Statistical Error ◽  
Distributed Model ◽  
Generation Model ◽  
Hydrological Models ◽  
Radar Rainfall ◽  
Lumped Model ◽  
Flow Simulations ◽  
Rainfall Estimates

Abstract. This study attempts to characterize the manner with which inherent error in radar rainfall estimates input influence the character of the stream flow simulation uncertainty in validated hydrological modelling. An artificial statistical error model described by Gaussian distribution was developed to generate realizations of possible combinations of normalized errors and normalized bias to reflect the identified radar error and temporal dependence. These realizations were embedded in the 5 km/15 min UK Nimrod radar rainfall data and used to generate ensembles of stream flow simulations using three different hydrological models with varying degrees of complexity, which consists of a fully distributed physically-based model MIKE SHE, a semi-distributed model TOPMODEL and a lumped model PRTF. These models were built for this purpose and applied to the Upper Medway Catchment (220 km2) in South-East England. The results show that the normalized bias of the radar rainfall estimates was enhanced in the simulated stream flow and also the dominate factor that had a significant impact on stream flow simulations. This preliminary radar-error-generation model could be developed more rigorously and comprehensively for the error characteristics of weather radars for quantitative measurement of rainfall.

scholarly journals A Study of the Error Covariance Matrix of Radar Rainfall Estimates in Stratiform Rain

2008 ◽  
Vol 23 (6) ◽  
pp. 1085-1101 ◽  
Author(s):  
Marc Berenguer ◽  
Isztar Zawadzki
Keyword(s):  
Covariance Matrix ◽  
Drop Size ◽  
Size Distributions ◽  
Radar Rainfall ◽  
Error Covariance Matrix ◽  
Error Covariance ◽  
Drop Size Distributions ◽  
The Impact ◽  
Dependent Error ◽  
Rainfall Estimates

Abstract The contribution of various physical sources of uncertainty affecting radar rainfall estimates at the ground is quantified toward deriving and understanding the error covariance matrix of these estimates. The focus here is on stratiform precipitation at a resolution of 15 km, which is most relevant for data assimilation onto mesoscale numerical models. In the characterization of the error structure, the following contributions are considered: (i) the individual effect of the range-dependent error (associated with beam broadening and increasing height of radar measurements with range), (ii) the error associated with the transformation from reflectivity to rain rate due to the variability of drop size distributions, and (iii) the interaction of the first two, that is, the term resulting from the cross correlation between the effects of the range-dependent error and the uncertainty related to the variability of drop size distributions (DSDs). For this purpose a large database of S-band radar observations at short range (where reflectivity near the ground is measured and the beam is narrow) is used to characterize the range-dependent error within a simulation framework, and disdrometric measurements collocated with the radar data are used to assess the impact of the variability of DSDs. It is noted that these two sources of error are well correlated in the vicinity of the melting layer as result of the physical processes that determine the density of snow (e.g., riming), which affect both the DSD variability and the vertical profile of reflectivity.

scholarly journals Hydrological appraisal of operational weather radar rainfall estimates in the context of different modelling structures

2014 ◽  
Vol 18 (1) ◽  
pp. 257-272 ◽  
Author(s):  
D. Zhu ◽  
Y. Xuan ◽  
I. Cluckie
Keyword(s):  
Hydrological Model ◽  
Scale Effects ◽  
Model Performance ◽  
Extreme Rainfall ◽  
Distributed Model ◽  
Catchment Scale ◽  
Radar Rainfall ◽  
Rainfall Events ◽  
The Impact ◽  
Rainfall Estimates

Abstract. Radar rainfall estimates have become increasingly available for hydrological modellers over recent years, especially for flood forecasting and warning over poorly gauged catchments. However, the impact of using radar rainfall as compared with conventional raingauge inputs, with respect to various hydrological model structures, remains unclear and yet to be addressed. In the study presented by this paper, we analysed the flow simulations of the upper Medway catchment of southeast England using the UK NIMROD radar rainfall estimates, using three hydrological models based upon three very different structures (e.g. a physically based distributed MIKE SHE model, a lumped conceptual model PDM and an event-based unit hydrograph model PRTF). We focused on the sensitivity of simulations in relation to the storm types and various rainfall intensities. The uncertainty in radar rainfall estimates, scale effects and extreme rainfall were examined in order to quantify the performance of the radar. We found that radar rainfall estimates were lower than raingauge measurements in high rainfall rates; the resolutions of radar rainfall data had insignificant impact at this catchment scale in the case of evenly distributed rainfall events but was obvious otherwise for high-intensity, localised rainfall events with great spatial heterogeneity. As to hydrological model performance, the distributed model had consistent reliable and good performance on peak simulation with all the rainfall types tested in this study.

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