scholarly journals Evaluating the accuracy of C- and X-band weather radars and their application for stream flow simulation

2012 ◽  
Vol 15 (4) ◽  
pp. 1121-1136 ◽  
Author(s):  
N. K. Shrestha ◽  
T. Goormans ◽  
P. Willems
Keyword(s):  
Conceptual Model ◽  
Stream Flow ◽  
Flow Simulation ◽  
Winter Storms ◽  
Weather Radars ◽  
X Band ◽  
Rainfall Estimates

This paper investigates the accuracy of rainfall estimates from C- and X-band weather radars and their application for stream flow simulation. Different adjustment procedures are applied to raw radar estimates using gauge readings from a network of 12 raingauges. The stream flow is simulated for the 48.17 km2 Molenbeek/Parkbeek catchment located in the Flemish region of Belgium based on a lumped conceptual model. Results showed that raw radar estimates can be greatly improved using adjustment procedures. The gauge-radar residuals however, remain large even after adjustments. The adjusted X-band radar estimates are observed to be better estimates than corresponding C-band estimates. Their application for stream flow simulation showed that raingauges and radars can simulate spatially more uniform winter storms with almost the same accuracy, whereas differences are more evident on summer events.

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 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 Use of Specific Attenuation for Rainfall Measurement at X-Band Radar Wavelengths. Part II: Rainfall Estimates and Comparison with Rain Gauges

2015 ◽  
Vol 16 (2) ◽  
pp. 503-516 ◽  
Author(s):  
Malte Diederich ◽  
Alexander Ryzhkov ◽  
Clemens Simmer ◽  
Pengfei Zhang ◽  
Silke Trömel
Keyword(s):  
Phase Shift ◽  
Rain Gauge ◽  
Differential Phase Shift ◽  
Rain Gauges ◽  
Specific Attenuation ◽  
Weather Radars ◽  
Ground Clutter ◽  
X Band ◽  
Rain Rates ◽  
Rainfall Estimates

Abstract In a series of two papers, rain-rate retrievals based on specific attenuation A at radar X-band wavelength using the R(A) method presented by Ryzhkov et al. are thoroughly investigated. Continuous time series of overlapping measurements from two polarimetric X-band weather radars in Germany during the summers of 2011–13 are used to analyze various aspects of the method, like miscalibration correction, ground clutter contamination, partial beam blockage (PBB), sensitivity to precipitation characteristics, and sensitivity to temperature assumptions in the retrievals. In Part I of the series, the relations inherent to the R(A) method were used to calculate radar reflectivity Z from specific attenuation and it was compared with measured reflectivity to estimate PBB and calibration errors for both radars. In this paper, R(A) rain estimates are compared to R(Z) and R(KDP) retrievals using specific phase shift KDP. PBB and calibration corrections derived in Part I made the R(Z) rainfall estimates almost perfectly consistent. Accumulated over five summer months, rainfall maps showed strong effects of clutter contamination if R(KDP) is used and weaker impact on R(A). These effects could be reduced by processing the phase shift measurements with more resilience toward ground clutter contamination and by substituting problematic R(KDP) or R(A) estimates with R(Z). Hourly and daily accumulations from rain estimators are compared with rain gauge measurements; the results show that R(A) complemented by R(Z) in segments with low total differential phase shift correlates best with gauges and has the lowest bias and RMSE, followed by R(KDP) substituted with R(Z) at rain rates below 8 mm h−1.

A Review on SWAT Model for Stream Flow Simulation

2013 ◽  
Vol 726-731 ◽  
pp. 3792-3798
Author(s):  
Wen Ju Zhao ◽  
Wei Sun ◽  
Zong Li Li ◽  
Yan Wei Fan ◽  
Jian Shu Song ◽  
...  
Keyword(s):  
Spatial Data ◽  
Stream Flow ◽  
Assessment Tool ◽  
Swat Model ◽  
Flow Simulation ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Rs And Gis ◽  
Model Research ◽  
Gis And Rs

SWAT (Soil and Water Assessment Tool) model is one of distributed hydrological model, based on spatial data offered by GIS and RS. This article mainly introduces the SWAT model principle, structure, and it is the application of stream flow simulation in China and other countries, then points out the deficiency existing in the process of model research. In order to service in water resources management work better, experts and scholars further research the rate constant and uncertainty of the simplification of the model parameters, and the combination of RS and GIS to use, and hydrological scale problems.

scholarly journals Over-parameterisation, a major obstacle to the use of artificial neural networks in hydrology?

2003 ◽  
Vol 7 (5) ◽  
pp. 693-706 ◽  
Author(s):  
E. Gaume ◽  
R. Gosset
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Conceptual Model ◽  
Linear Model ◽  
Case Studies ◽  
Stream Flow ◽  
River Flow ◽  
Prediction Models ◽  
Rainfall Runoff ◽  
Artificial Neural

Abstract. Recently Feed-Forward Artificial Neural Networks (FNN) have been gaining popularity for stream flow forecasting. However, despite the promising results presented in recent papers, their use is questionable. In theory, their “universal approximator‿ property guarantees that, if a sufficient number of neurons is selected, good performance of the models for interpolation purposes can be achieved. But the choice of a more complex model does not ensure a better prediction. Models with many parameters have a high capacity to fit the noise and the particularities of the calibration dataset, at the cost of diminishing their generalisation capacity. In support of the principle of model parsimony, a model selection method based on the validation performance of the models, "traditionally" used in the context of conceptual rainfall-runoff modelling, was adapted to the choice of a FFN structure. This method was applied to two different case studies: river flow prediction based on knowledge of upstream flows, and rainfall-runoff modelling. The predictive powers of the neural networks selected are compared to the results obtained with a linear model and a conceptual model (GR4j). In both case studies, the method leads to the selection of neural network structures with a limited number of neurons in the hidden layer (two or three). Moreover, the validation results of the selected FNN and of the linear model are very close. The conceptual model, specifically dedicated to rainfall-runoff modelling, appears to outperform the other two approaches. These conclusions, drawn on specific case studies using a particular evaluation method, add to the debate on the usefulness of Artificial Neural Networks in hydrology. Keywords: forecasting; stream-flow; rainfall-runoff; Artificial Neural Networks

scholarly journals Combined wind profiler-weather radar observations of orographic rainband around Kyushu, Japan in the Baiu season

2004 ◽  
Vol 22 (11) ◽  
pp. 3971-3982 ◽  
Author(s):  
Y. Umemoto ◽  
M. Teshiba ◽  
Y. Shibagaki ◽  
H. Hashiguchi ◽  
M. D. Yamanaka ◽  
...  
Keyword(s):  
Weather Radar ◽  
Horizontal Wind ◽  
Wind Component ◽  
Wind Profiler ◽  
Strong Wind ◽  
Clockwise Rotation ◽  
Weather Radars ◽  
X Band ◽  
Western Japan ◽  
Lifting Condensation Level

Abstract. A special observation campaign (X-BAIU), using various instruments (wind profilers, C-band weather radars, X-band Doppler radars, rawinsondes, etc.), was carried out in Kyushu (western Japan) during the Baiu season, from 1998 to 2002. In the X-BAIU-99 and -02 observations, a line-shaped orographic rainband extending northeastward from the Koshikijima Islands appeared in the low-level strong wind with warm-moist airs. The weather radar observation indicated that the rainband was maintained for 11h. The maximum length and width of the rainband observed in 1999 was ~200km and ~20km, respectively. The rainband observed in 2002 was not so developed compared with the case in 1999. The Froude number averaged from sea level to the top of the Koshikijima Islands (~600m) was large (>1), and the lifting condensation level was below the tops of the Koshikijima Islands. Thus, it is suggested that the clouds organizing the rainband are formed by the triggering of the mountains on the airflow passing over them. The vertical profile of horizontal wind in/around the rainband was investigated in the wind profiler observations. In the downdraft region 60km from the Koshikijima Islands, strong wind and its clockwise rotation with increasing height was observed below 3km altitude. In addition, a strong wind component perpendicular to the rainband was observed when the rainband was well developed. These wind behaviors were related to the evolution of the rainband.

scholarly journals Adaptive and High-Resolution Estimation of Specific Differential Phase for Polarimetric X-Band Weather Radars

2018 ◽  
Vol 35 (3) ◽  
pp. 555-573 ◽  
Author(s):  
Ricardo Reinoso-Rondinel ◽  
Christine Unal ◽  
Herman Russchenberg
Keyword(s):  
Standard Deviation ◽  
Spatial Variability ◽  
Heavy Rain ◽  
Storm Events ◽  
Band Frequency ◽  
Adaptive Approach ◽  
Range Resolution ◽  
Differential Phase ◽  
Weather Radars ◽  
X Band

ABSTRACTOne of the most beneficial polarimetric variables may be the specific differential phase KDP because of its independence from power attenuation and radar miscalibration. However, conventional KDP estimation requires a substantial amount of range smoothing as a result of the noisy characteristic of the measured differential phase ΨDP. In addition, the backscatter differential phase δhv component of ΨDP, significant at C- and X-band frequency, may lead to inaccurate KDP estimates. In this work, an adaptive approach is proposed to obtain accurate KDP estimates in rain from noisy ΨDP, whose δhv is of significance, at range resolution scales. This approach uses existing relations between polarimetric variables in rain to filter δhv from ΨDP while maintaining its spatial variability. In addition, the standard deviation of the proposed KDP estimator is mathematically formulated for quality control. The adaptive approach is assessed using four storm events, associated with light and heavy rain, observed by a polarimetric X-band weather radar in the Netherlands. It is shown that this approach is able to retain the spatial variability of the storms at scales of the range resolution. Moreover, the performance of the proposed approach is compared with two different methods. The results of this comparison show that the proposed approach outperforms the other two methods in terms of the correlation between KDP and reflectivity, and KDP standard deviation reduction.

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.

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