Coupling meteorological forecasts with hydrologic and hydraulic models: The Itaipu Dam ensemble inflow forecasting system

2021 ◽  
Author(s):  
José Quevedo ◽  
Daniel Firmo Kazay ◽  
Mariana Maria Werlang ◽  
Giovanni Gomes ◽  
Roberto Takahashi ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Precipitation Forecast ◽  
Distributed Model ◽  
Hydrological Models ◽  
Forecast Horizon ◽  
Generation Scheduling ◽  
Lumped Models ◽  
Forecasting System ◽  
Itaipu Dam ◽  
The Difference

<p>This work presents the development of the inflow ensemble forecasting system for Itaipu Dam. The system is based on combination of twelve Quantitative Precipitation Forecast (QPF) with three hydrological models and one hydrodynamic one-dimensional model. The QPF are provided by different meteorological institutions based in the results of the global Numerical Weather Prediction (NWP) models GFS and ECMWF, as well as of the regional NWP models WRF and COSMO executed by the Brazilian and Paraguay meteorological services (SIMEPAR, INMET and DINAC). The semi-distributed model MGB – Large Basin Model, the lumped models SMAP and HEC-HMS are considered as the hydrological models. Furthermore, the computed flows are propagated in a HEC-RAS scheme designed with extensive field data from bathymetry of Parana River and tributaries. The daily results are presented as fifteen inflow scenarios that are considered for the definition of a unique flow forecast. Finally, that forecast is used for the electric generation scheduling of the power plant. Each of these fifteen methods performance was evaluated as well as the suitability of the system for it purposes. For a short-term forecast horizon (less than 4 days), the performances of the hydrological models forced by the different rain forecast are quite similar. However, it is remarkable the difference between the results of the three hydrological models for the same horizon. On the other hand, for medium-term horizon (more than 4 days) both hydrological and meteorological models have diverse behavior and contribute for a wide representation of the possible scenarios. Overall, it has been showed that the simulations are complementary and provides to the forecaster a general overview of the hydrologic situation. Nevertheless, at this moment, further analysis of accuracy and reliability of the prediction have not been realized, so the forecaster needs to appeal to its own expertise to assure the consistence of the scenarios for decision-making.</p>

scholarly journals Development of a Seamless Forecast for Solar Radiation Using ANAKLIM++

2020 ◽  
Vol 12 (21) ◽  
pp. 3672
Author(s):  
Isabel Urbich ◽  
Jörg Bendix ◽  
Richard Müller
Keyword(s):  
Solar Radiation ◽  
Weather Prediction ◽  
Software Tool ◽  
Absolute Error ◽  
Shortwave Radiation ◽  
Data Sets ◽  
Climate Data ◽  
Forecast Horizon ◽  
Novel Approach ◽  
Forecasting System

A novel approach for a blending between nowcasting and numerical weather prediction (NWP) for the surface incoming shortwave radiation (SIS) for a forecast horizon of 1–5 h is presented in this study. The blending is performed with a software tool called ANAKLIM++ (Adjustment of Assimilation Software for the Reanalysis of Climate Data) which was originally designed for the efficient assimilation of two-dimensional data sets using a variational approach. A nowcasting for SIS was already presented and validated in earlier publications as seamless solar radiation forecast (SESORA). For our blending, two NWP models, namely the ICON (Icosahedral Non-hydrostatic model) from the German weather Service (DWD) and the IFS (Integrated Forecasting System) from the European Centre for Medium-Range Weather Forecasts (ECMWF), were used. The weights for the input data for ANAKLIM++ vary for every single forecast time and pixel, depending on the error growth of the nowcasting. The results look promising, since the root mean square error (RMSE) and mean absolute error (MAE) of the blending are smaller than the error measures of the nowcasting or NWP models, respectively.

scholarly journals Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment

2018 ◽  
Vol 22 (3) ◽  
pp. 1957-1969 ◽  
Author(s):  
Sanjeev K. Jha ◽  
Durga L. Shrestha ◽  
Tricia A. Stadnyk ◽  
Paulin Coulibaly
Keyword(s):  
Weather Prediction ◽  
Ensemble Forecasting ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Forecast System ◽  
Forecast Performance ◽  
Forecasting System ◽  
Environmental Prediction ◽  
Hydrologic Forecast

Abstract. Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effects over a diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall post-processing (RPP), developed in Australia (Robertson et al., 2013; Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global Ensemble Forecasting System (GEFS) Reforecast 2 project, from the National Centers for Environmental Prediction, and Global Deterministic Forecast System (GDPS), from Environment and Climate Change Canada, are used in this study. The study period from January 2013 to December 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias and reduce the errors of both GEFS and GDPS forecasts. Ensembles generated from the RPP reliably quantify the forecast uncertainty.

scholarly journals A meteo-hydrological prediction system based on a multi-model approach for precipitation forecasting

2008 ◽  
Vol 8 (1) ◽  
pp. 143-159 ◽  
Author(s):  
S. Davolio ◽  
M. M. Miglietta ◽  
T. Diomede ◽  
C. Marsigli ◽  
A. Morgillo ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Weather Prediction ◽  
Coupled System ◽  
Numerical Weather Prediction Model ◽  
Precipitation Forecast ◽  
Precipitation Forecasting ◽  
Quantitative Precipitation Forecast ◽  
Forecasting System ◽  
Hydrological Prediction ◽  
Model Approach

Abstract. The precipitation forecasted by a numerical weather prediction model, even at high resolution, suffers from errors which can be considerable at the scales of interest for hydrological purposes. In the present study, a fraction of the uncertainty related to meteorological prediction is taken into account by implementing a multi-model forecasting approach, aimed at providing multiple precipitation scenarios driving the same hydrological model. Therefore, the estimation of that uncertainty associated with the quantitative precipitation forecast (QPF), conveyed by the multi-model ensemble, can be exploited by the hydrological model, propagating the error into the hydrological forecast. The proposed meteo-hydrological forecasting system is implemented and tested in a real-time configuration for several episodes of intense precipitation affecting the Reno river basin, a medium-sized basin located in northern Italy (Apennines). These episodes are associated with flood events of different intensity and are representative of different meteorological configurations responsible for severe weather affecting northern Apennines. The simulation results show that the coupled system is promising in the prediction of discharge peaks (both in terms of amount and timing) for warning purposes. The ensemble hydrological forecasts provide a range of possible flood scenarios that proved to be useful for the support of civil protection authorities in their decision.

scholarly journals Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2201 ◽  
Author(s):  
Awol ◽  
Coulibaly ◽  
Tsanis ◽  
Unduche
Keyword(s):  
Land Surface ◽  
Distributed Model ◽  
Lead Times ◽  
Hydrological Models ◽  
Infiltration Capacity ◽  
Canadian Prairie ◽  
Forecast Performance ◽  
Large Complex ◽  
Lumped Models ◽  
Reservoir Inflow

Accurate and reliable flow forecasting in complex Canadian prairie watersheds has been one of the major challenges faced by hydrologists. In an attempt to improve the accuracy and reliability of a reservoir inflow forecast, this study investigates structurally different hydrological models along with ensemble precipitation forecasts to identify the most skillful and reliable model. The key goal is to assess whether short- and medium-range ensemble flood forecasting in large complex basins can be accurately achieved by simple conceptual lumped models (e.g., SACSMA with SNOW17 and MACHBV with SNOW17) or it requires a medium level distributed model (e.g., WATFLOOD) or an advanced macroscale land-surface based model (VIC coupled with routing module (RVIC)). Eleven (11)-member precipitation forecasts from second-generation Global Ensemble Forecast System reforecast (GEFSv2) were used as inputs. Each of the ensemble members was bias-corrected by Empirical Quantile Mapping method using the Canadian Precipitation Analysis (CaPA) as a training/verification dataset. Forecast evaluation is performed for 1-day up to 8-days forecast lead times in a 6-month hindcast period. Results indicate that bias-correcting precipitation forecasts using verifying datasets (such as CaPA) for a training period of at least two years before the forecast time, produces skillful ensemble hydrological forecasts. A comparison of models in forecast mode shows that the two lumped models (SACSMA and MACHBV) can provide better overall forecast performance than the benchmark WATFLOOD and the macroscale Variable Infiltration Capacity (VIC) model. However, for shorter lead-times, particularly up to day 3, the benchmark distributed model provides competitive reliability, as compared to the lumped models. In general, the SACSMA model provided better forecast quality, reliability and differentiation skill than other considered models at all lead times.

scholarly journals Precipitation Forecast Contribution Assessment in the Coupled Meteo-Hydrological Models

Atmosphere ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 34 ◽  
Author(s):  
Aida Jabbari ◽  
Jae-Min So ◽  
Deg-Hyo Bae
Keyword(s):  
Lead Time ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Model Performance ◽  
Precipitation Forecast ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Point Scale ◽  
Meteorological Model ◽  
Temporal And Spatial

A numerical weather prediction and a rainfall-runoff model employed to evaluate precipitation and flood forecast for the Imjin River (South and North Korea). The real-time precipitation at point and catchment scales evaluated to select proper hydrological model to couple with atmospheric model. As a major limitation of previous studies, temporal and spatial resolutions of hydrological model are smaller than those of meteorological model. Here, through high resolution of temporal (10 min) and spatial (1 km × 1 km), the optimal resolution determined. The results showed Weather Research and Forecasting (WRF) model underestimated precipitation in point and catchment assessment and its skill was relatively higher for catchment than point scale, as illustrated by the lower Root Mean Square Error (RMSE) of 59.67, 160.48, 68.49 for the catchment and 84.49, 212.80 and 91.53 for the point scale in the events 2002, 2007 and 2011, respectively. The findings led to choose the semi-distributed hydrological model. The variations in temporal and spatial resolutions illustrated accuracy decrease; additionally, the optimal spatial resolution obtained at 8 km and temporal resolution did not affect the inherent inaccuracy of the results. Lead-time variation demonstrated that lead-time dependency was almost negligible below 36 h. With reference to this study, comparisons of model performance provided quantitative knowledge for understanding credibility and restrictions of meteo-hydrological models.

scholarly journals Evaluation of ensemble precipitation forecasts generated through postprocessing in a Canadian catchment

2017 ◽  
Author(s):  
Sanjeev K. Jha ◽  
Durga Lal Shrestha ◽  
Tricia Stadnyk ◽  
Paulin Coulibaly
Keyword(s):  
Weather Prediction ◽  
Ensemble Forecasting ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Forecast System ◽  
Forecast Performance ◽  
Continuous Ranked Probability Score ◽  
Forecasting System ◽  
Hydrologic Forecast

Abstract. Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effect over diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall-post processing (RPP), developed in Australia (Robertson et al., 2013; Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global ensemble forecasting system (GEFS) Reforecast 2 project from National Centers for Environmental Protection (NCEP), and Global deterministic forecast system (GDPS) from Environment and Climate Change Canada (ECCC) are used in this study. The study period from Jan 2013 to Dec 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias, and reduce the continuous ranked probability score of both GEFS and GDPS forecasts. Ensembles generated from the RPP better depict the forecast uncertainty.

CSO Modelling Considering Moving Storms and Tipping Bucket Gauge Failures

2007 ◽  
Vol 2 (1) ◽  
Author(s):  
M. Hochedlinger ◽  
W. Sprung ◽  
H. Kainz ◽  
K. König
Keyword(s):  
Hydrological Models ◽  
Rain Intensity ◽  
Hydrodynamic Models ◽  
Data Intensive ◽  
Storm Flow ◽  
Cell Velocity ◽  
Combined Sewer ◽  
Moving Direction ◽  
The Difference

The simulation of combined sewer overflow volumes and loads is important for the assessment of the overflow and overflow load to the receiving water to predict the hydraulic or the pollution impact. Hydrodynamic models are very data-intensive and time-consuming for long-term quality modelling. Hence, for long-term modelling, hydrological models are used to predict the storm flow in a fast way. However, in most cases, a constant rain intensity is used as load for the simulation, but in practice even for small catchments rain occurs in rain cells, which are not constant over the whole catchment area. This paper presents the results of quality modelling considering moving storms depending on the rain cell velocity and its moving direction. Additionally, tipping bucket gauge failures and different corrections are also taken into account. The results evidence the importance of these considerations for precipitation due the effects on overflow load and show the difference up to 28% of corrected and uncorrected data and of moving rain cells instead of constant raining intensities.

scholarly journals Toward a Weather-Based Forecasting System for Fire Blight and Downy Mildew

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 484 ◽  
Author(s):  
Ana Firanj Sremac ◽  
Branislava Lalić ◽  
Milena Marčić ◽  
Ljiljana Dekić
Keyword(s):  
Downy Mildew ◽  
Fire Blight ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Atmospheric Model ◽  
Plasmopara Viticola ◽  
Forecasting System ◽  
Nwp Model ◽  
The Impact

The aim of this research is to present a weather-based forecasting system for apple fire blight (Erwinia amylovora) and downy mildew of grapevine (Plasmopara viticola) under Serbian agroecological conditions and test its efficacy. The weather-based forecasting system contains Numerical Weather Prediction (NWP) model outputs and a disease occurrence model. The weather forecast used is a product of the high-resolution forecast (HRES) atmospheric model by the European Centre for Medium-Range Weather Forecasts (ECMWF). For disease modelling, we selected a biometeorological system for messages on the occurrence of diseases in fruits and vines (BAHUS) because it contains both diseases with well-known and tested algorithms. Several comparisons were made: (1) forecasted variables for the fifth day are compared against measurements from the agrometeorological network at seven locations for three months (March, April, and May) in the period 2012–2018 to determine forecast efficacy; (2) BAHUS runs driven with observed and forecast meteorology were compared to test the impact of forecasted meteorological data; and (3) BAHUS runs were compared with field disease observations to estimate system efficacy in plant disease forecasts. The BAHUS runs with forecasted and observed meteorology were in good agreement. The results obtained encourage further development, with the goal of fully utilizing this weather-based forecasting system.

scholarly journals Using Percentiles to Communicate Snowfall Uncertainty

2014 ◽  
Vol 29 (5) ◽  
pp. 1259-1265 ◽  
Author(s):  
David R. Novak ◽  
Keith F. Brill ◽  
Wallace A. Hogsett
Keyword(s):  
Probability Distribution ◽  
False Alarm ◽  
Weather Prediction ◽  
Winter Precipitation ◽  
Risk Tolerance ◽  
Distribution Functions ◽  
Precipitation Forecast ◽  
Ensemble Forecasts ◽  
False Alarm Ratio ◽  
Probability Distribution Functions

Abstract An objective technique to determine forecast snowfall ranges consistent with the risk tolerance of users is demonstrated. The forecast snowfall ranges are based on percentiles from probability distribution functions that are assumed to be perfectly calibrated. A key feature of the technique is that the snowfall range varies dynamically, with the resultant ranges varying based on the spread of ensemble forecasts at a given forecast projection, for a particular case, for a particular location. Furthermore, this technique allows users to choose their risk tolerance, quantified in terms of the expected false alarm ratio for forecasts of snowfall range. The technique is applied to the 4–7 March 2013 snowstorm at two different locations (Chicago, Illinois, and Washington, D.C.) to illustrate its use in different locations with different forecast uncertainties. The snowfall range derived from the Weather Prediction Center Probabilistic Winter Precipitation Forecast suite is found to be statistically reliable for the day 1 forecast during the 2013/14 season, providing confidence in the practical applicability of the technique.

Urban Finescale Forecasting Reveals Weather Conditions with Unprecedented Detail

2017 ◽  
Vol 98 (12) ◽  
pp. 2675-2688 ◽  
Author(s):  
R. J. Ronda ◽  
G. J. Steeneveld ◽  
B. G. Heusinkveld ◽  
J. J. Attema ◽  
A. A. M. Holtslag
Keyword(s):  
Local Governments ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Morphological Characteristics ◽  
Weather Conditions ◽  
Human Thermal Comfort ◽  
Forecasting System ◽  
The Impact ◽  
Urban Weather ◽  
Neighborhood Level

Abstract Urban landscapes impact the lives of urban dwellers by influencing local weather conditions. However, weather forecasting down to the street and neighborhood scale has been beyond the capabilities of numerical weather prediction (NWP) despite the fact that observational systems are now able to monitor urban climate at these scales. In this study, weather forecasts at intra-urban scales were achieved by exploiting recent advances in topographic element mapping and aerial photography as well as looking at detailed mappings of soil characteristics and urban morphological properties, which were subsequently incorporated into a specifically adapted Weather Research and Forecasting (WRF) Model. The urban weather forecasting system (UFS) was applied to the Amsterdam, Netherlands, metropolitan area during the summer of 2015, where it produced forecasts for the city down to the neighborhood level (a few hundred meters). Comparing these forecasts to the dense network of urban weather station observations within the Amsterdam metropolitan region showed that the forecasting system successfully determined the impact of urban morphological characteristics and urban spatial structure on local temperatures, including the cooling effect of large water bodies on local urban temperatures. The forecasting system has important practical applications for end users such as public health agencies, local governments, and energy companies. It appears that the forecasting system enables forecasts of events on a neighborhood level where human thermal comfort indices exceeded risk thresholds during warm weather episodes. These results prove that worldwide urban weather forecasting is within reach of NWP, provided that appropriate data and computing resources become available to ensure timely and efficient forecasts.

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