Precipitation Forecast Errors Research Articles

Rapid Development of Systematic ENSO‐Related Seasonal Forecast Errors

AbstractClimate models exhibit known systematic errors in their representation of the El Niño‐Southern Oscillation (ENSO). In this study, we show that such simulation errors are largely present in tropical seasonal prediction, even for short lead times. Regressing monthly forecast errors from 11 different operational models upon the observed ENSO state, we find that predicted ENSO‐related sea surface temperature anomalies (of either sign) for winter/spring are significantly extended or shifted to the west and are also too persistent during the ENSO decay phase, both common climate model errors. There are also corresponding precipitation forecast errors, most notably a robust westward shift of the ENSO‐related precipitation dipole that may impact predictions of extratropical teleconnections. These ENSO‐related errors develop within days after initialization regardless of month, including significant errors appearing in anomalous surface trade winds, and saturate so rapidly that they primarily depend upon the seasonal cycle rather than lead time.

Convection-permitting ensemble forecasts of a double-rainbelt event in South China during the pre-summer rainy season

Heavy rainfall occurred at the inland frontal zone and coastal warm sector in South China on 12–13 June 2019. Three convection-permitting ensemble forecast (CPEF) experiments with perturbed initial conditions (ICs) and lateral boundary conditions (BCs) as well as model physical schemes have been conducted to predict the double-rainbelt process. This study evaluates the predictability of different CPEF experiments and analyzes the causes of precipitation forecast errors by identifying the sensitive factors for frontal rainfall (FR) and warm-sector rainfall (WR). In this double-rainbelt event, the forecast skill of FR is significantly better than that of WR. The experiment with perturbed ICs/BCs combining suitable model physical schemes performs best. Based on the optimal ensemble experiment, the ensemble sensitivity analysis has been conducted for the two rainbelts. FR is sensitive to the synoptic forcing and its sensitive area mainly locates near the frontal system, while the coastal boundary layer jet and water vapor from the South China Sea play an important role in the WR process. The ICs and BCs greatly influence the location of FR through varying the movement direction and velocity of the fronts. The physical schemes have a great impact on convection triggering along the coasts, thus affecting the occurrence of the whole WR process, of which the choice of microphysical scheme is critical.

Convective-scale ensemble forecasts of the heavy precipitation of Typhoon Lekima (2019) in Zhejiang Province

Ensemble forecasting plays an important role in studying the precipitation forecast errors of landfalling tropical cyclones. In this study, convective-scale ensemble forecasts based on the WRF model are conducted, focusing on the heavy precipitation of typhoon Lekima (2019) in Zhejiang, China. The perturbations of the initial/boundary conditions (ICs/BCs) and the microphysical schemes are constructed to represent the uncertainties of ensemble forecasts. The sensitivity of different perturbations on ensemble forecasts and the precipitation forecast errors are discussed. The results show the IC/BC perturbations have a greater impact on ensemble precipitation forecasts. An advantaged group and two disadvantaged groups are selected from 63 ensemble members based on the typhoon track errors, and the precipitation forecasts of the advantaged group have the highest forecast skill scores. The forecast errors are highly correlated with the intensity of the subtropical high (SH) and the steering flow of typhoon. The significantly weak SH in the ICs/BCs leads to a weaker SH in the forecasts, resulting in an easterly and slower typhoon motion and more accumulated precipitation. Also, the precipitation forecast errors are highly associated with the simulation errors of typhoon structure in different landfall stages, while the vertical wind shear may cause these errors. In addition, the orographic convergence and uplift promote the precipitation production, but the model tends to overestimate the orographic effects. About the simulation of hydrometeors, large snow content leads to increased precipitation in the Thompson scheme, while in the WDM6 scheme, the strong evaporation of low-level raindrops results in decreased precipitation.

Impacts of FY-4A GIIRS Water Vapor Channels Data Assimilation on the Forecast of “21·7” Extreme Rainstorm in Henan, China with CMA-MESO

A record-breaking extreme rainstorm occurred in Henan Province of China on 20 July 2021. To investigate the impacts of the Geostationary Interferometric Infrared Sounder (GIIRS) data assimilation on model analysis and forecasts of this rainfall event, the high temporal resolution GIIRS water vapor (WV) channel data were assimilated in the high-resolution CMA-MESO (Mesoscale Weather Numerical Forecast System of China Meteorological Administration) in this study. The results showed that the GIIRS WV radiance assimilation could improve the model WV analysis, which in turn adjusted the distributions of hydrometeors (radar composite reflectivities) and wind field, and finally improved the precipitation forecast. Additionally, although barely any GIIRS observations were assimilated over the cloudy area, the precipitation forecast errors of “21·7” extreme rainstorm events could be reduced by improving the structure of atmospheric circulations through the assimilation of neighboring data around Henan, especially over the upstream region. With the GIIRS WV data assimilation, the location error of maximum 24-h accumulated precipitation forecasts decreased from 128.48 km to 28.97 km (improved by 77.45%) for the cold start at 0000 UTC (Universal Time Coordinated) on 19 July 2021, and it was also reduced by about 60.52% for the warm start experiment at 0600 UTC on 19 July 2021. In addition, the GIIRS assimilation experiment showed an extraordinarily heavy rainfall area (above 250 mm/24 h) around Zhengzhou station, which did not appear in the control experiment, and was closer to the observed extreme precipitation. This study demonstrates the potential value of geostationary hyperspectral infrared sounders data assimilation in extreme weather early warning and forecasting.

Preliminary Application of a Multi-Physical Ensemble Transform Kalman Filter in Cloud and Precipitation Forecasts

In this study, based on the retrieval data from the Fengyun geostationary meteorological satellite and the Tropical Rainfall Measuring Mission satellite, a large-scale precipitation case in eastern China is selected to address the systematic deviations of deterministic forecasts for clouds and precipitation. A multi-physical ensemble transform Kalman filter (ETKF) is constructed in this research based on the Weather Research and Forecast model version 3.6, and its forecasting ability in terms of cloud-top height and temperature, hydrometeors, and precipitation is evaluated by quantitatively comparing three microphysical parameterization schemes (Lin, Morrison, and CAM5.1 schemes) and their corresponding multi-physical ensemble mean. The results show that the Lin, Morrison, and CAM5.1 schemes all underestimate the range of cloud systems and have different advantages and disadvantages in forecasting different elements, while the forecasting improvement of the multi-physical ensemble mean is limited. However, the multi-physical ETKF can effectively improve the forecast accuracy of the cloud system range. In addition, the multi-physical ETKF has the advantages of different physical parameterization schemes, which can dramatically improve the forecast accuracy of cloud hydrometeors, reduce precipitation forecast errors, and improve threat scores.

Spatial Bias in Medium-Range Forecasts of Heavy Precipitation in the Sacramento River Basin: Implications for Water Management

AbstractForecasts of heavy precipitation delivered by atmospheric rivers (ARs) are becoming increasingly important for both flood control and water supply management in reservoirs across California. This study examines the hypothesis that medium-range forecasts of heavy precipitation at the basin scale exhibit recurrent spatial biases that are driven by mesoscale and synoptic-scale features of associated AR events. This hypothesis is tested for heavy precipitation events in the Sacramento River basin using 36 years of NCEP medium-range reforecasts from 1984 to 2019. For each event we cluster precipitation forecast error across western North America for lead times ranging from 1 to 15 days. Integrated vapor transport (IVT), 500-hPa geopotential heights, and landfall characteristics of ARs are composited across clusters and lead times to diagnose the causes of precipitation forecast biases. We investigate the temporal evolution of forecast error to characterize its persistence across lead times, and explore the accuracy of forecasted IVT anomalies across different domains of the North American west coast during heavy precipitation events in the Sacramento basin. Our results identify recurrent spatial patterns of precipitation forecast error consistent with errors of forecasted synoptic-scale features, especially at long (5–15 days) leads. Moreover, we find evidence that forecasts of AR landfalls well outside of the latitudinal bounds of the Sacramento basin precede heavy precipitation events within the basin. These results suggest the potential for using medium-range forecasts of large-scale climate features across the Pacific–North American sector, rather than just local forecasts of basin-scale precipitation, when designing forecast-informed reservoir operations.

Robust Model Predictive Control of Irrigation Systems With Active Uncertainty Learning and Data Analytics

We develop a novel data-driven robust model predictive control (DDRMPC) approach for automatic control of irrigation systems. The fundamental idea is to integrate both mechanistic models, which describe dynamics in soil moisture variations, and data-driven models, which characterize uncertainty in forecast errors of evapotranspiration and precipitation, into a holistic systems control framework. To better capture the support of uncertainty distribution, we take a new learning-based approach by constructing uncertainty sets from historical data. For the evapotranspiration forecast error, the support vector clustering-based uncertainty set is adopted, which can be conveniently built from historical data. As for precipitation forecast errors, we analyze the dependence of their distribution on forecast values, and further design a tailored uncertainty set based on the properties of this type of uncertainty. In this way, the overall uncertainty distribution can be elaborately described, which finally contributes to rational and efficient control decisions. To assure the quality of data-driven uncertainty sets, a training-calibration scheme is used to provide theoretical performance guarantees. A generalized affine decision rule is adopted to obtain tractable approximations of optimal control problems, thereby ensuring the practicability of DDRMPC. Case studies using real data show that DDRMPC can reliably maintain soil moisture above the safety level and avoid crop devastation. The proposed DDRMPC approach leads to a 40% reduction of total water consumption compared to the fine-tuned open-loop control strategy. In comparison with the carefully tuned rule-based control and certainty equivalent MPC, the proposed DDRMPC approach can significantly reduce the total water consumption and improve the control performance.

Test and analysis of high resolution regional model precipitation forecast based on wavelet

In order to avoid the double punitive disadvantage of traditional test methods, based on the advantages of wavelet in multi-resolution analysisthe precipitation forecast products of RMAPS (Rapid-refresh Multi-scale Analysis and Prediction System) system are tested objectively. In this study, Haar is used as basis function. The test results show that the hourly precipitation forecast error of the 3km discriminant ratio of the forecast system is less than 15% for the precipitation process with the horizontal scale exceeding 94km. As the wavelet analysis scale decreases, the precipitation error gradually increases.

Possible hydrologic forecasting improvements resulting from advancements in precipitation estimation and forecasting for a real-time flood forecast system in the Ohio River Valley, USA

Errors in hydrometeorological forcings for hydrologic modeling lead to considerable prediction uncertainty of hydrologic variables. Analyses of Quantitative Precipitation Estimate (QPE) and Quantitative Precipitation Forecast (QPF) errors over the Ohio River Valley were made to quantify QPE and QPF errors and identify hydrologic impacts of forcing errors and possible improvements resulting from advancements in precipitation estimation and forecasting. Monthly, seasonal, and annual bias analyses of Ohio River Forecast Center (OHRFC) NEXt-generation RADar (NEXRAD) based Stage III and Multisensor Precipitation Estimator (MPE) precipitation estimates, for the period 1997-2016, were made with respect to Parameter-elevation Regressions on Independent Slopes Model (PRISM) precipitation estimates. Verification of QPF from NWS River Forecast Centers from the NOAA/NWS National Precipitation Verification Unit (NPVU) was compared to QPF verification measures from several numerical weather prediction models and the NOAA/NWS Weather Prediction Center (WPC). Improvements in NEXRAD based QPE over the OHRFC area have been dramatic from 1997 to present. However, from the perspective of meeting hydrologic forecasting needs, QPF shows marginal improvement. A hydrologic simulation experiment illustrates the sensitivity of hydrologic forecasts to QPF errors indicated by QPF Threat Score (TS) values. A monte carlo experiment shows there can be considerable hydrologic forecast error associated with QPF at expected WPC TS levels and, importantly, that higher TS values do not necessarily translate into improved hydrologic forecasts. These results have significant implications for real-time hydrologic forecasting. First, experimental results demonstrate the value gained in terms of improvements in hydrologic modeling accuracy from long-term radar-based precipitation bias reductions. Second, experimental results show that improvements made in terms of QPF have marginal effect on hydrologic prediction and that, even with significant improvement in QPF accuracy, we should expect large hydrologic prediction uncertainty. These issues are discussed more fully as they relate to the future of operational hydrology and ensemble hydrologic forecasting.

A New Shape Matching-Based Verification Approach for QPFs

In this paper, a novel systematic method on the evaluation of quantitative precipitation forecast (QPF) errors from the perspective of rain-area shape verification is proposed. The method aims to improve the accuracy and efficiency of conventional station-based verifications (i.e., standard skill scores), which are insensitive to the biases of station location and rain-area shape and tend to ignore the continuity of precipitation in time and space. The method develops and combines the shape verification indexes, which include the overlap ratio of a forecasted rain-area (Ratio f ), the overlap ratio of the ground rain-area (Ratio t ), the Jaccard similarity coefficient between the shape of the QPF area, the shape of the ground rain-area (Jaccardshape), the critical success index (CSI) for the rain-area shape (CSIshape), the probability of detection (POD) for the rain-area shape (PODshape), and the false alarm ratio (FAR) for the rain-area shape (FARshape). This definition of QPF verification is applied to a rain event from 2016/08/02 00:30 to 2016/08/02 03:24 in the Guangdong Province. The decomposition of QPF errors into station-based errors and shape error components provides powerful insight into the effects of overall forecasting performance. The experimental results of this investigation show that the proposed method provides an opportunity to assess QPFs objectively and further promote advanced forecasting technologies from this perspective.

Precipitation Calibration Based on the Frequency-Matching Method

Abstract A postprocessing technique is employed to correct model bias for precipitation fields in real time based on a comparison of the frequency distributions of observed and forecast precipitation amounts. Essentially, a calibration is made by defining an adjustment to the forecast value in such a way that the adjusted cumulative forecast distribution over a moving time window dynamically matches the corresponding observed distribution accumulated over a domain of interest, for example, the entire conterminous United States (CONUS), or different River Forecast Center (RFC) regions in the cases examined herein. In particular, the Kalman filter method is used to catch the flow dependence and bias information. Calibration is done on a pointwise basis for a specified domain. Using this unique technique, the calibration of precipitation forecasts for the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) was implemented in May 2004. To further satisfy various users, a recent upgrade to the May 2004 implementation has been made for higher resolution with better analyses. From this study, it was found that this method has a positive impact on the intensity-dominated errors but has some common limitations with extreme events and dry bias elimination like other precipitation calibration methods. Overall, the frequency-matching algorithm substantially improves NCEP Global Forecast System (GFS) and GEFS systematic precipitation forecast errors (or biases) over a wide range of forecast amounts and produces more realistic precipitation patterns. Moreover, this approach improves the deterministic forecast skills measured by most verification scores through applying this method to GFS and GEFS ensemble means.

Calibration and evaluation of a flood forecasting system: Utility of numerical weather prediction model, data assimilation and satellite-based rainfall

Summary A fully-distributed, multi-physics, multi-scale hydrologic and hydraulic modeling system, WRF-Hydro, is used to assess the potential for skillful flood forecasting based on precipitation inputs derived from the Weather Research and Forecasting (WRF) model and the EUMETSAT Multi-sensor Precipitation Estimates (MPEs). Similar to past studies it was found that WRF model precipitation forecast errors related to model initial conditions are reduced when the three dimensional atmospheric data assimilation (3DVAR) scheme in the WRF model simulations is used. A comparative evaluation of the impact of MPE versus WRF precipitation estimates, both with and without data assimilation, in driving WRF-Hydro simulated streamflow is then made. The ten rainfall–runoff events that occurred in the Black Sea Region were used for testing and evaluation. With the availability of streamflow data across rainfall–runoff events, the calibration is only performed on the Bartin sub-basin using two events and the calibrated parameters are then transferred to other neighboring three ungauged sub-basins in the study area. The rest of the events from all sub-basins are then used to evaluate the performance of the WRF-Hydro system with the calibrated parameters. Following model calibration, the WRF-Hydro system was capable of skillfully reproducing observed flood hydrographs in terms of the volume of the runoff produced and the overall shape of the hydrograph. Streamflow simulation skill was significantly improved for those WRF model simulations where storm precipitation was accurately depicted with respect to timing, location and amount. Accurate streamflow simulations were more evident in WRF model simulations where the 3DVAR scheme was used compared to when it was not used. Because of substantial dry bias feature of MPE, as compared with surface rain gauges, streamflow derived using this precipitation product is in general very poor. Overall, root mean squared errors for runoff were reduced by 22.2% when hydrological model calibration is performed with WRF precipitation. Errors were reduced by 36.9% (above uncalibrated model performance) when both WRF model data assimilation and hydrological model calibration was utilized. Our results also indicated that when assimilated precipitation and model calibration is performed jointly, the calibrated parameters at the gauged sites could be transferred to ungauged neighboring basins where WRF-Hydro reduced mean root mean squared error from 8.31 m3/s to 6.51 m3/s.

Flood forecast errors and ensemble spread—A case study

Flood forecasts are generally associated with errors, which can be attributed to uncertainties in the meteorological forecasts and the hydrologic simulations, and ensemble spreads are usually considered capable of representing them. To quantify these two components of the total forecast errors and to compare these to ensemble spreads, an extended data set is used. Four years of operational flood forecasts at hourly time step with lead times up to 48 h are evaluated for 43 catchments in Austria and Germany. Catchment sizes range from 70 to 25,600 km2, elevations from 200 to 3800 m, and mean annual precipitation from 700 to 2000 mm. A combination of ECMWF and ALADIN ensemble forecasts are used as input in a semidistributed conceptual water balance model on an hourly time step. The results indicate that, for short lead times, the ratio of hydrological simulation error to precipitation forecast error is 1.2 to 2.7 with increasing catchment size from 100 to 10,000 km2. For long lead times the ratio of hydrological simulation error to precipitation forecast error decreases from 1.1 to 0.9 with increasing catchment size. Clear scaling relationships of the forecast error components with catchment area are found. A similar scaling is also found for ensemble spreads, which are shown to represent quantitatively the total forecast error when forecasting floods.

Seasonal climate hindcasts with Eta model nested in CPTEC coupled ocean–atmosphere general circulation model

This work evaluates the added value of the downscaling technique employed with the Eta model nested in the CPTEC atmospheric general circulation model and in the CPTEC coupled ocean–atmosphere general circulation model (CGCM). The focus is on the austral summer season, December–January–February, with three members each year. Precipitation, latent heat flux, and shortwave radiation flux at the surface hindcast by the models are compared with observational data and model analyses. The global models generally overestimate the precipitation over South America and tropical Atlantic. The CGCM and the nested Eta (Eta + C) both produce a split in the ITCZ precipitation band. The Eta + C produces better precipitation pattern for the studied season. The Eta model reduces the excessive latent heat flux generated by these global models, in particular the Eta + C. Comparison against PIRATA buoys data shows that the Eta + C results in the smallest precipitation and shortwave radiation forecast errors. The Eta + C comparatively best results are though as a consequence of both: the regional model resolution/physics and smaller errors on the lateral boundary conditions provided by the CGCM.

Relationship between Precipitation Forecast Errors and Skill Scores of Dichotomous Forecasts

Abstract In this paper, the sensitivities of the equitable threat score (ETS) and the true skill score (TSS), obtained with a 2 × 2 contingency table, to continuous precipitation forecast errors are investigated. Two idealized error models are adopted to describe the difference between forecasts and perfect observations. The observations consist of a time series generated by a multiplicative cascade model. The forecasts are constructed by adding the modeled errors to the observations. Two examples that are representative of two precipitation regimes are considered. Monte Carlo simulations of the modeled errors are performed to compute the score uncertainties. Monotonic relationships between the precipitation forecast errors and the two skill scores are found. It is shown that the precipitation regime and the event frequency influence these relationships and the score uncertainties. The score uncertainties also depend on the forecast errors. Furthermore, it is shown that a relationship exists between the ETS and TSS when the forecast errors are very large. Results suggest that more information should be provided together with the scores and their uncertainties in order to provide a complete picture of the forecast performance.

Implications of Ensemble Quantitative Precipitation Forecast Errors on Distributed Streamflow Forecasting

Abstract Evaluating the propagation of errors associated with ensemble quantitative precipitation forecasts (QPFs) into the ensemble streamflow response is important to reduce uncertainty in operational flow forecasting. In this paper, a multifractal rainfall downscaling model is coupled with a fully distributed hydrological model to create, under controlled conditions, an extensive set of synthetic hydrometeorological events, assumed as observations. Subsequently, for each event, flood hindcasts are simulated by the hydrological model using three ensembles of QPFs—one reliable and the other two affected by different kinds of precipitation forecast errors—generated by the downscaling model. Two verification tools based on the verification rank histogram and the continuous ranked probability score are then used to evaluate the characteristics of the correspondent three sets of ensemble streamflow forecasts. Analyses indicate that the best forecast accuracy of the ensemble streamflows is obtained when the reliable ensemble QPFs are used. In addition, results underline (i) the importance of hindcasting to create an adequate set of data that span a wide range of hydrometeorological conditions and (ii) the sensitivity of the ensemble streamflow verification to the effects of basin initial conditions and the properties of the ensemble precipitation distributions. This study provides a contribution to the field of operational flow forecasting by highlighting a series of requirements and challenges that should be considered when hydrologic ensemble forecasts are evaluated.

Incorporating Forecasts of Rainfall in Two Hydrologic Models Used for Medium-Range Streamflow Forecasting

This study reports on the performance of two medium-range streamflow forecast models: (1) a multilayer feed-forward artificial neural network; and (2) a distributed hydrologic model. Quantitative precipitation forecasts were used as input to both models. The Furnas Reservoir on the Rio Grande River was selected as a case study, primarily because of the availability of quantitative precipitation forecasts from the Brazilian Center for Weather Prediction and Climate Studies and due to its importance in the Brazilian hydropower generating system. Streamflow forecasts were calculated for a drainage area of about 51,900 km(2), with lead times up to 12 days, at daily intervals. The Nash-Sutcliffe efficiency index, the root-mean-square error, the mean absolute error, and the mean relative error were used to assess the relative performance of the models. Results showed that the performance of streamflow forecasts was strongly dependent on the quality of quantitative precipitation forecasts used. The artificial neural network (ANN) method seemed to be less sensitive to precipitation forecast error relative to the distributed hydrological model. Hence, the latter presented a better skill in flow forecasting when using the more accurate perfect precipitation forecast. The conceptual hydrological model also demonstrates better forecast skill than ANN models for longer lead times, when the representation of the rainfall-runoff process and of the water storage in the watershed becomes more important than the flow routing along the drainage network. In addition, results obtained by incorporating a quantitative precipitation forecast in both models performed better than the current streamflow obtained by the Brazilian national electric system operator using statistical models which do not utilize information on precipitation, whether observed or forecast. (Less)

Regional Ensemble Forecasts Using the Ensemble Transform Technique

AbstractA computationally inexpensive ensemble transform (ET) method for generating high-resolution initial perturbations for regional ensemble forecasts is introduced. The method provides initial perturbations that (i) have an initial variance consistent with the best available estimates of initial condition error variance, (ii) are dynamically conditioned by a process similar to that used in the breeding technique, (iii) add to zero at the initial time, (iv) are quasi-orthogonal and equally likely, and (v) partially respect mesoscale balance constraints by ensuring that each initial perturbation is a linear sum of forecast perturbations from the preceding forecast. The technique is tested using estimates of analysis error variance from the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) and the Navy’s regional Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) over a 3-week period during the summer of 2005. Lateral boundary conditions are provided by a global ET ensemble. The tests show that the ET regional ensemble has a skillful mean and a useful spread–skill relationship in mass, momentum, and precipitation variables. Diagnostics indicate that ensemble variance was close to, but probably a little less than, the forecast error variance for wind and temperature variables, while precipitation ensemble variance was significantly smaller than precipitation forecast error variance.

On the utility of operational precipitation forecasts to served as input for streamflow forecasting

This article studies the utility of quantitative forecast precipitation for the prediction of daily streamflow. Application is made over the Rhone basin, which was included in the Gewex–Rhone program. The precipitation forecasts of the two numerical weather prediction models operationally used in France, ARPEGE and ALADIN, are tested. The riverflow forecast is made using the precipitation forecast as input to the one-way atmosphere–hydrology coupled model SAFRAN–ISBA–MODCOU (SIM). Such a forecast is very sensitive to the initialisation of soil moisture and snow-pack. Therefore, two kinds of streamflow forecast were made: first, a plain forecast, for which the initial conditions are taken from the guess, and second, a re-initialised forecast, for which the initial conditions are set according to a reference run. This reference run is obtained using 1200 daily observed precipitation, interpolated in time and space by SAFRAN. First, the quality of the precipitation forecasts is checked over the Rhone basin for the period August 1997–July 1998 using the SAFRAN analysis as a reference. Then, the SIM system used to forecast riverflow is briefly presented. The predicted riverflows are compared both to the observations at 22 streamgage locations and to the reference run. The results show that the annual average of the average discharge errors at the 22 streamgages can reach 20% in the forecast without re-initialisation, but that this error is reduced significantly when the model is properly initialised. This is due to the fact the re-initialisation of the soil moisture and snow-pack according to the reference run minimizes influences of the previous precipitation forecast error. It is shown that the use of precipitation forecast as input of the SIM system can be of interest to forecast the progress of the long-duration floods of the main stations of the Rhone basin.

On the utility of operational precipitation forecasts to served as input for streamflow forecasting

This article studies the utility of quantitative forecast precipitation for the prediction of daily streamflow. Application is made over the Rhone basin, which was included in the Gewex–Rhone program. The precipitation forecasts of the two numerical weather prediction models operationally used in France, ARPEGE and ALADIN, are tested. The riverflow forecast is made using the precipitation forecast as input to the one-way atmosphere–hydrology coupled model SAFRAN–ISBA–MODCOU (SIM). Such a forecast is very sensitive to the initialisation of soil moisture and snow-pack. Therefore, two kinds of streamflow forecast were made: first, a plain forecast, for which the initial conditions are taken from the guess, and second, a re-initialised forecast, for which the initial conditions are set according to a reference run. This reference run is obtained using 1200 daily observed precipitation, interpolated in time and space by SAFRAN. First, the quality of the precipitation forecasts is checked over the Rhone basin for the period August 1997–July 1998 using the SAFRAN analysis as a reference. Then, the SIM system used to forecast riverflow is briefly presented. The predicted riverflows are compared both to the observations at 22 streamgage locations and to the reference run. The results show that the annual average of the average discharge errors at the 22 streamgages can reach 20% in the forecast without re-initialisation, but that this error is reduced significantly when the model is properly initialised. This is due to the fact the re-initialisation of the soil moisture and snow-pack according to the reference run minimizes influences of the previous precipitation forecast error. It is shown that the use of precipitation forecast as input of the SIM system can be of interest to forecast the progress of the long-duration floods of the main stations of the Rhone basin.