Assimilation of Doppler Weather Radar Data in WRF Model for Simulation of Tropical Cyclone Aila

For the accurate and effective forecasting of a cyclone, it is critical to have accurate initial structure of the cyclone in numerical models. In this study, Kolkata Doppler weather radar (DWR) data were assimilated for the numerical simulation of a land-falling Tropical Cyclone Aila (2009) in the Bay of Bengal. To study the impact of radar data on very short-range forecasting of a cyclone's path, intensity and precipitation, both reflectivity and radial velocity were assimilated into the weather research and forecasting (WRF) model through the ARPS data assimilation system (ADAS) and cloud analysis procedure. Numerical experiment results indicated that radar data assimilation significantly improved the simulated structure of Cyclone Aila. Strong influences on hydrometeor structures of the initial vortex and precipitation pattern were observed when radar reflectivity data was assimilated, but a relatively small impact was observed on the wind fields at all height levels. The assimilation of radar wind data significantly improved the prediction of divergence/convergence conditions over the cyclone's inner-core area, as well as its wind field in the low-to-middle troposphere (600–900 hPa), but relatively less impact was observed on analyzed moisture field. Maximum surface wind speed produced from DWR–Vr and DWR–ZVr data assimilation experiments were very close to real-time values. The impact of radar data, after final analysis, on minimum sea level pressure was relatively less because the ADAS system does not adjust for pressure due to the lack of pressure observations, and from not using a 3DVAR balance condition that includes pressure. The greatest impact of radar data on forecasting was realized when both reflectivity and wind data (DWR–ZVr and DWR–ZVr00 experiment) were assimilated. It is concluded that after final analysis, the center of the cyclone was relocated very close to the observed position, and simulated cyclone maintained its intensity for a longer duration. Using this analysis, different stages of the cyclone are better captured, and cyclone structure, intensification, direction of movement, speed and location are significantly improved when both radar reflectivity and wind data are assimilated. As compared to other experiments, the maximum reduction in track error was noticed in the DWR–ZVr and DWR–ZVr00 experiments, and the predicted track in these experiments was very close to the observed track. In the DWR–ZVr and DWR–ZVr00 experiments, rainfall pattern and amount of rainfall forecasts were remarkably improved and were similar to the observation over West Bengal, Orissa and Jharkhand; however, the rainfall over Meghalaya and Bangladesh was missed in all the experiments. The influence of radar data reduces beyond a 12-h forecast, due to the dominance of the flow from large-scale, global forecast system models. This study also demonstrates successful coupling of the data assimilation package ADAS with the WRF model for Indian DWR data.     

Hydrological control of flooding: Tuscany,October 1992

In the study of flash-flood occurrence in small catchments the lack of flow measurements is often one of the main limiting factors. Prior to estimating the forecasting potentialities and techniques for such events, an accurate reconstruction of past event flood dynamics is first required. This issue is here addressed by analyzing, with the use of a distributed hydrological model, the hydrometeorological conditions in which a severe flash-flood occurred, on October 1992, on a 48 square kilometers catchment in the Arno basin. Such an event was caused by the persistence of intense convective clusters on the background of widespread rain bands of frontal origin. The distributed hydrological model here adopted is devoted to simulate the evolution and the variability of the primary processes involved in the runoff cycle. Together with the hydrological model structure, other particular aspects of the event reconstruction procedure are discussed: the managing and processing of the information coming from different sensors, with different temporal and spatial resolutions; the identification of local precipitation dynamics (frontal or convective) within small areas of integrated radar and rain gauges data fields; the interpolation of rain gauge data on the basis of the radar-estimated spatial correlation. The results of the distributed modeling, concerning the estimate of the flood wave at various sites, are compared with analogous results obtained with simpler lumped models.     

Assimilation of Doppler Weather Radar Radial Velocity and Reflectivity Observations in WRF-3DVAR System for Short-Range Forecasting of Convective Storms

In this paper the impact of Doppler weather radar (DWR) reflectivity and radial velocity observations for the short range forecasting of a tropical storm and associated rainfall event have been examined. Doppler radar observations of a tropical storm case that occurred during 29–30 October 2006 from SHARDWR (13.6° N, 80.2° E) are assimilated in the WRF 3DVAR system. The observation operator for radar reflectivity and radial velocity is included within latest version of WRF 3DVAR system. Keeping all model physics the same, three experiments were conducted at a horizontal resolution of 30?km. In the control experiment (CTRL), NCEP Final Analysis (FNL) interpolated to the model grid was used as the initial condition for 48-h free forecast. In the second experiment (NODWR), 6-h assimilation cycles have been carried out using all conventional (radiosonde and surface data) and non-conventional (satellite) observations from the Global Telecommunication System (GTS). The third experiment (DWR) is the same as the second, except Doppler radar radial velocity and reflectivity observations are also used in the assimilation cycle. Continuous 6-h assimilation cycle employed in the WRF-3DVAR system shows positive impact on the rainfall forecast. Assimilation of DWR data creates several small scale features near the storm centre. Additional sensitivity experiments were conducted to study the individual impact of reflectivity and radial velocity in the assimilation cycle. Radar data assimilation with reflectivity alone produced large analysis response on both thermodynamical and dynamical fields. However, radial velocity assimilation impacted only on dynamical fields. Analysis increments with radar reflectivity and radial velocity produce adjustments in both dynamical and thermodynamical fields. Verification of QPF skill shows that radar data assimilation has a considerable impact on the short range precipitation forecast. Improvement of the QPF skill with radar data assimilation is more clearly seen in the heavy rainfall (for thresholds >7?mm) event than light rainfall (for thresholds of 1 and 3?mm). The spatial pattern of rainfall is well simulated by the DWR experiment and is comparable to TRMM observations.     

Correcting surface winds by assimilating high-frequency radar surface currents in the German Bight

Surface winds are crucial for accurately modeling the surface circulation in the coastal ocean. In the present work, high-frequency radar surface currents are assimilated using an ensemble scheme which aims to obtain improved surface winds taking into account European Centre for Medium-Range Weather Forecasts winds as a first guess and surface current measurements. The objective of this study is to show that wind forcing can be improved using an approach similar to parameter estimation in ensemble data assimilation. Like variational assimilation schemes, the method provides an improved wind field based on surface current measurements. However, the technique does not require an adjoint, and it is thus easier to implement. In addition, it does not rely on a linearization of the model dynamics. The method is validated directly by comparing the analyzed wind speed to independent in situ measurements and indirectly by assessing the impact of the corrected winds on model sea surface temperature (SST) relative to satellite SST.     

Accounting for soil parameter uncertainty in a physically based and distributed approach for rainfall‐triggered landslides

In this study we propose a probabilistic approach for coupled distributed hydrological‐hillslope stability models that accounts for soil parameters uncertainty at basin scale. The geotechnical and soil retention curve parameters are treated as random variables across the basin and theoretical probability distributions of the Factor of Safety (FS) are estimated. The derived distributions are used to obtain the spatio‐temporal dynamics of probability of failure, in terms of parameters uncertainty, conditioned to soil moisture dynamics. The framework has been implemented in the tRIBS‐VEGGIE (Triangulated Irregular Network (TIN)‐based Real‐time Integrated Basin Simulator‐VEGetation Generator for Interactive Evolution)‐Landslide model and applied to a basin in the Luquillo Experimental Forest (Puerto Rico) where shallow landslides are common. In particular, the methodology was used to evaluate how the spatial and temporal patterns of precipitation, whose variability is significant over the basin, affect the distribution of probability of failure, through event scale analyses. Results indicate that hyetographs where heavy precipitation is near the end of the event lead to the most critical conditions in terms of probability of failure. Copyright © 2015 John Wiley & Sons, Ltd.     

Probabilistic forecasting of drought: a hidden Markov model aggregated with the RCP 8.5 precipitation projection

The creeping characteristics of drought make it possible to mitigate drought’s effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, we proposed a new probabilistic scheme to forecast droughts that used a discrete-time finite state-space hidden Markov model (HMM) aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The standardized precipitation index (SPI) with a 3-month time scale was employed to represent the drought status over the selected stations in South Korea. The new scheme used a reversible jump Markov chain Monte Carlo algorithm for inference on the model parameters and performed an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to perform a probabilistic forecast of SPI at the 3-month time scale that considered uncertainties. The point forecasts which were derived as the HMM-RCP forecast mean values, as measured by forecasting skill scores, were much more accurate than those from conventional models and a climatology reference model at various lead times. We also used probabilistic forecast verification and found that the HMM-RCP provided a probabilistic forecast with satisfactory evaluation for different drought categories, even at long lead times. In a drought event analysis, the HMM-RCP accurately predicted about 71.19 % of drought events during the validation period and forecasted the mean duration with an error of less than 1.8 months and a mean severity error of <0.57. The results showed that the HMM-RCP had good potential in probabilistic drought forecasting.     

Handling non-linearity in radar data assimilation using the non-linear least squares enhanced POD-4DVar

The Proper Orthogonal Decomposition(POD)-based ensemble four-dimensional variational(4DVar) assimilation method(POD4DEnVar) was proposed to combine the strengths of EnKF(i.e.,the ensemble Kalman filter) and 4DVar assimilation methods.Recently,a POD4DEnVar-based radar data assimilation scheme(PRAS) was built and its effectiveness was demonstrated.POD4 DEnVar is based on the assumption of a linear relationship between the model perturbations(MPs)and the observation perturbations(OPs);however,this assumption is likely to be destroyed by the highly non-linear forecast model or observation operator.To address this issue,using the Gauss-Newton iterative method,the nonlinear least squares enhanced POD4 DEnVar algorithm(referred to as NLS-4DVar) was proposed.Naturally,the PRAS was upgraded to form the NLS-4DVar-based radar data assimilation scheme(NRAS).To evaluate the performance of NRAS against PRAS,observing system simulation experiments(OSSEs) were conducted to assimilate reflectivity and radial velocity individually,with one,two,and three iterations.The results demonstrated that the NRAS outperformed PRAS in improving the initial condition and the forecasting of model variables and rainfall.The NRAS,with a smaller number of iterations,can yield a convergent result.In contrast to the situation when assimilating radial velocity,the advantages of NRAS over PRAS were more obvious when assimilating reflectivity.     

A multi‐source precipitation estimation approach to fill gaps over a radar precipitation field: a case study in the Colorado River Basin

The present work develops an approach to seamlessly blend satellite, available radar, climatological and gauge precipitation products to fill gaps in ground‐based radar precipitation field. To mix different precipitation products, the error of any of the products relative to each other should be removed. For bias correction, the study uses an ensemble‐based method that aims to estimate spatially varying multiplicative biases in SPEs using a radar precipitation product. A weighted successive correction method (SCM) is used to make the merging between error corrected satellite and radar precipitation estimates. In addition to SCM, we use a combination of SCM and Bayesian spatial model for merging the rain gauges (RGs) and climatological precipitation sources with radar and SPEs. We demonstrated the method using a satellite‐based hydro‐estimator; a radar‐based, stage‐II; a climatological product, Parameter‐elevation Regressions on Independent Slopes Model and a RG dataset for several rain events from 2006 to 2008 over an artificial gap in Oklahoma and a real radar gap in the Colorado River basin. Results show that: the SCM method in combination with the Bayesian spatial model produced a precipitation product in good agreement with independent measurements. The study implies that using the available radar pixels surrounding the gap area, RG, Parameter‐elevation Regressions on Independent Slopes Model and satellite products, a radar‐like product is achievable over radar gap areas that benefit the operational meteorology and hydrology community. Copyright © 2013 John Wiley & Sons, Ltd.     

Improving NWP through radar rainfall-driven land surface parameters: A case study on convective precipitation forecasting

In this study we investigate the effect of forcing the land surface scheme of an atmospheric mesoscale model with radar rainfall data instead of the model-generated rainfall fields. The goal is to provide improved surface conditions for the atmospheric model in order to achieve accurate simulations of the mesoscale circulations that can significantly affect the timing, distribution and intensity of convective precipitation. The performance of the approach is evaluated in a set of numerical experiments on the basis of a 2-day-long mesoscale convective system that occurred over the US Great Plains in July 2004. The experimental design includes multiple runs covering a variety of forcing periods. Continuous data integration was initially used to investigate the sensitivity of the model’s performance in varying soil state conditions, while shorter time windows prior to the storm event were utilized to assess the effectiveness of the procedure for improving convective precipitation forecasting. Results indicate that continuous integration of radar rainfall data brings the simulated precipitation fields closer to the observed ones, as compared to the control simulation. The precipitation forecasts (up to 48 h) appear improved also in the cases of shorter integration periods (24 and 36 h), making this technique potentially useful for operational settings of weather forecasting systems. A physical interpretation of the results is provided on the basis of surface moisture and energy exchange.     

Impact of Doppler Radar Wind in Simulating the Intensity and Propagation of Rainbands Associated with Mesoscale Convective Complexes Using MM5-3DVAR System

Pre-monsoon rainfall around Kolkata (northeastern part of India) is mostly of convective origin as 80% of the seasonal rainfall is produced by Mesoscale Convective Systems (MCS). Accurate prediction of the intensity and structure of these convective cloud clusters becomes challenging, mostly because the convective clouds within these clusters are short lived and the inaccuracy in the models initial state to represent the mesoscale details of the true atmospheric state. Besides the role in observing the internal structure of the precipitating systems, Doppler Weather Radar (DWR) provides an important data source for mesoscale and microscale weather analysis and forecasting. An attempt has been made to initialize the storm-scale numerical model using retrieved wind fields from single Doppler radar. In the present study, Doppler wind velocities from the Kolkata Doppler weather radar are assimilated into a mesoscale model, MM5 model using the three-dimensional variational data assimilation (3DVAR) system for the prediction of intense convective events that occurred during 0600 UTC on 5 May and 0000 UTC on 7 May, 2005. In order to evaluate the impact of the DWR wind data in simulating these severe storms, three experiments were carried out. The results show that assimilation of Doppler radar wind data has a positive impact on the prediction of intensity, organization and propagation of rain bands associated with these mesoscale convective systems. The assimilation system has to be modified further to incorporate the radar reflectivity data so that simulation of the microphysical and thermodynamic structure of these convective storms can be improved.     

Virtual California: Fault Model, Frictional Parameters, Applications

Virtual California is a topologically realistic simulation of the interacting earthquake faults in California. Inputs to the model arise from field data, and typically include realistic fault system topologies, realistic long-term slip rates, and realistic frictional parameters. Outputs from the simulations include synthetic earthquake sequences and space-time patterns together with associated surface deformation and strain patterns that are similar to those seen in nature. Here we describe details of the data assimilation procedure we use to construct the fault model and to assign frictional properties. In addition, by analyzing the statistical physics of the simulations, we can show that that the frictional failure physics, which includes a simple representation of a dynamic stress intensity factor, leads to self-organization of the statistical dynamics, and produces empirical statistical distributions (probability density functions: PDFs) that characterize the activity. One type of distribution that can be constructed from empirical measurements of simulation data are PDFs for recurrence intervals on selected faults. Inputs to simulation dynamics are based on the use of time-averaged event-frequency data, and outputs include PDFs representing measurements of dynamical variability arising from fault interactions and space-time correlations. As a first step for productively using model-based methods for earthquake forecasting, we propose that simulations be used to generate the PDFs for recurrence intervals instead of the usual practice of basing the PDFs on standard forms (Gaussian, Log-Normal, Pareto, Brownian Passage Time, and so forth). Subsequent development of simulation-based methods should include model enhancement, data assimilation and data mining methods, and analysis techniques based on statistical physics.     

Evolutionary assimilation of streamflow in distributed hydrologic modeling using in-situ soil moisture data

This study has applied evolutionary algorithm to address the data assimilation problem in a distributed hydrological model. The evolutionary data assimilation (EDA) method uses multi-objective evolutionary strategy to continuously evolve ensemble of model states and parameter sets where it adaptively determines the model error and the penalty function for different assimilation time steps. The assimilation was determined by applying the penalty function to merge background information (i.e., model forecast) with perturbed observation data. The assimilation was based on updated estimates of the model state and its parameterizations, and was complemented by a continuous evolution of competitive solutions.The EDA was illustrated in an integrated assimilation approach to estimate model state using soil moisture, which in turn was incorporated into the soil and water assessment tool (SWAT) to assimilate streamflow. Soil moisture was independently assimilated to allow estimation of its model error, where the estimated model state was integrated into SWAT to determine background streamflow information before they are merged with perturbed observation data. Application of the EDA in Spencer Creek watershed in southern Ontario, Canada generates a time series of soil moisture and streamflow. Evaluation of soil moisture and streamflow assimilation results demonstrates the capability of the EDA to simultaneously estimate model state and parameterizations for real-time forecasting operations. The results show improvement in both streamflow and soil moisture estimates when compared to open-loop simulation, and a close matching between the background and the assimilation illustrates the forecasting performance of the EDA approach.     

Ensemble rainfall forecasting with numerical weather prediction and radar‐based nowcasting models

The overall objective of this study is to improve the forecasting accuracy of the precipitation in the Singapore region by means of both rainfall forecasting and nowcasting. Numerical Weather Predication (NWP) and radar‐based rainfall nowcasting are two important sources for quantitative precipitation forecast. In this paper, an attempt to combine rainfall prediction from a high‐resolution mesoscale weather model and a radar‐based rainfall model was performed. Two rainfall forecasting methods were selected and examined: (i) the weather research and forecasting model (WRF); and (ii) a translation model (TM). The WRF model, at a high spatial resolution, was run over the domain of interest using the Global Forecast System data as initializing fields. Some heavy rainfall events were selected from data record and used to test the forecast capability of WRF and TM. Results obtained from TM and WRF were then combined together to form an ensemble rainfall forecasting model, by assigning weights of 0.7 and 0.3 weights to TM and WRF, respectively. This paper presented results from WRF and TM, and the resulting ensemble rainfall forecasting; comparisons with station data were conducted as well. It was shown that results from WRF are very useful as advisory of anticipated heavy rainfall events, whereas those from TM, which used information of rain cells already appearing on the radar screen, were more accurate for rainfall nowcasting as expected. The ensemble rainfall forecasting compares reasonably well with the station observation data. Copyright © 2012 John Wiley & Sons, Ltd.     

Quantitative assessment of short‐term rainfall forecasts from radar nowcasts and MM5 forecasts

Short‐term Quantitative Precipitation Forecasts (QPFs) can be achieved from numerical weather prediction (NWP) models or radar nowcasting, that is the extrapolation of the precipitation at a future time from consecutive radar scans. Hybrid forecasts obtained by merging rainfall forecasts from radar nowcasting and NWP models are potentially more skilful than either radar nowcasts or NWP rainfall forecasts alone. This paper provides an assessment of deterministic and probabilistic high‐resolution QPFs achieved by implementing the Short‐term Ensemble Prediction System developed by the UK Met Office. Both radar nowcasts and hybrid forecasts have been performed. The results show that the performance of both deterministic nowcasts and deterministic hybrid forecasts decreases with increasing rainfall intensity and spatial resolution. The results also show that the blending with the NWP forecasts improves the performance of the forecasting system. Probabilistic hybrid forecasts have been obtained through the modelling of a stochastic noise component to produce a number of equally likely ensemble members, and the comparative assessment of deterministic and probabilistic hybrid forecasts shows that the probabilistic forecasting system is characterised by a higher discrimination accuracy than the deterministic one. Copyright © 2011 John Wiley & Sons, Ltd.     

A comparison of gauge and radar precipitation data for simulating an extreme hydrological event in the Severn Uplands,UK

This paper provides a comparison of gauge and radar precipitation data sources during an extreme hydrological event. November–December 2006 was selected as a time period of intense rainfall and large river flows for the Severn Uplands, an upland catchment in the United Kingdom. A comparison between gauge and radar precipitation time‐series records for the event indicated discrepancies between data sources, particularly in areas of higher elevation. The HEC‐HMS rainfall‐runoff model was selected to assess the accuracy of the precipitation to simulate river flows for the extreme event. Gauge, radar and gauge‐corrected radar rainfall were used as model inputs. Universal cokriging was used to geostatistically interpolate gauge data with radar and elevation data as covariates. This interpolated layer was used to calculate the mean‐field bias and correct the radar composites. Results indicated that gauge‐ and gauge‐corrected radar‐driven models replicated flows adequately for the extreme event. Gauge‐corrected flow predictions produced an increase in flow prediction accuracy when compared with the raw radar, yet predictions were comparative in accuracy to those using the interpolated gauge network. Subsequent investigations suggested this was due to an adequate spatial and temporal resolution of the precipitation gauge network within the Severn Uplands. Results suggested that the six rain gauges could adequately represent precipitation variability of the Severn Uplands to predict flows at an approximately equal accuracy to that obtained by radar. Temporally, radar produced an increase in flow prediction accuracy in mountainous reaches once the gauge time step was in excessive of an hourly interval. Copyright © 2010 John Wiley & Sons, Ltd.     

Estimation of daily space–time precipitation series for macroscale hydrological modelling

An important problem in modelling macroscale basins, especially for sparsely observed regions, is the lack of precipitation information. Alternatives to using straightforward interpolated surface observations include the utilization of more advanced interpolation techniques and the use of additional precipitation information from atmospheric models. Conventional and geostatistical methods are applied for optimal interpolation and assimilation of observed and model precipitation. Various time-series of daily areal precipitation distributions are produced and compared using not only an internal precipitation validation, but also an objective verification based on stream flow simulations. The Mackenzie River Basin in north-western Canada is used as the study area and hydrological simulations are carried out with the model SLURP. It was found that better interpolation techniques and the use of combined precipitation data can improve the hydrological simulations and that the enhancements are related to the relative size of the simulation units used. © 1998 John Wiley & Sons, Ltd.     

Probabilistic forecasting of hydrological events using geostatistical analysis / Prévision probabiliste d'événements hydrologiques par analyse géostatistique

Abstract

A method is introduced for probabilistic forecasting of hydrological events based on geostatistical analysis. In this method, the predictors of a hydrological variable define a virtual field such that, in this field, the observed dependent variables are considered as measurement points. Variography of the measurement points enables the use of the system of kriging equations to estimate the value of the variable at non-measured locations of the field. Non-measured points are the forecasts associated with specific predictors. Calculation of the estimation variance facilitates probabilistic analysis of the forecast variables. The method is applied to case studies of the Red River in Manitoba, Canada and Karoon River in Khoozestan, Iran. The study analyses the advantages and limitations of the proposed method in comparison with a K-nearest neighbour approach and linear and nonlinear multiple regression. The utility of the proposed method for forecasting hydrological variables with a conditional probability distribution is demonstrated.     

Probabilistic radar rainfall nowcasts using empirical and theoretical uncertainty models

Weather radar has a potential to provide accurate short‐term (0–3 h) forecasts of rainfall (i.e. radar nowcasts), which are of great importance in warnings and risk management for hydro‐meteorological events. However, radar nowcasts are affected by large uncertainties, which are not only linked to limitations in the forecast method but also because of errors in the radar rainfall measurement. The probabilistic quantitative precipitation nowcasting approach attempts to quantify these uncertainties by delivering the forecasts in a probabilistic form. This study implements two forms of probabilistic quantitative precipitation nowcasting for a hilly area in the south of Manchester, namely, the theoretically based scheme [ensemble rainfall forecasts (ERF)‐TN] and the empirically based scheme (ERF‐EM), and explores which one exhibits higher predictive skill. The ERF‐TN scheme generates ensemble forecasts of rainfall in which each ensemble member is determined by the stochastic realisation of a theoretical noise component. The so‐called ERF‐EM scheme proposed and applied for the first time in this study, aims to use an empirically based error model to measure and quantify the combined effect of all the error sources in the radar rainfall forecasts. The essence of the error model is formulated into an empirical relation between the radar rainfall forecasts and the corresponding ‘ground truth’ represented by the rainfall field from rain gauges measurements. The ensemble members generated by the two schemes have been compared with the rain gauge rainfall. The hit rate and the false alarm rate statistics have been computed and combined into relative operating characteristic curves. The comparison of the performance scores for the two schemes shows that the ERF‐EM achieves better performance than the ERF‐TN at 1‐h lead time. The predictive skills of both schemes are almost identical when the lead time increases to 2 h. In addition, the relation between uncertainty in the radar rainfall forecasts and lead time is also investigated by computing the dispersion of the generated ensemble members. Copyright © 2013 John Wiley & Sons, Ltd.     

Coupling between weather radar rainfall data and a distributed hydrological model for real-time flood forecasting / Couplage de données pluviométriques issues de radars météorologiques et d’un modèle hydrologique distribué pour la prévision de crue en temps réel

Abstract

Abstract After the destructive flood in 1998, the Chinese government planned to build national weather radar networks and to use radar data for real-time flood forecasting. Hence, coupling of weather radar rainfall data and a hydrological (Xinanjiang) model became an important issue. The present study reports on experience in such coupling at the Shiguanhe watershed. After having corrected the radar reflectivity and the attenuation data, the weather radar rainfall was estimated and then corrected in real time using a Kalman filter. In general, the precipitation estimated from weather radar is reasonably accurate in most of the catchment investigated, after corrections as above. Compared to the results simulated by raingauge data, the simulations based on the weather radar data are of similar accuracy. Present research results show that rainfall estimated from the weather radar, the radar data correction method, the method of coupling, and the Xinanjiang model lend themselves well to application in operational real-time flood forecasting.