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Sample records for ensemble streamflow forecasts

  1. A streamflow assimilation system for ensemble streamflow forecast over France

    NASA Astrophysics Data System (ADS)

    Thirel, G.; Martin, E.; Mahfouf, J. F.; Massart, S.; Ricci, S.; Habets, F.

    2009-04-01

    SAFRAN-ISBA-MODCOU (SIM) is a hydro-meteorological model used at Météo-France to predict soil water content and river streamflows. In order to produce a better initial state for the Ensemble Streamflow forecasts, an assimilation system is developed at Météo-France. This system uses past streamflow measurements in order to assess the best initial state of soil water content of the model for streamflow prediction. The data assimilation system is developed with a modular software (PALM, from the Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique), and is based on the Best Linear Unbiased Estimator method. Data from a maximum of 186 gauge stations are assimilated over France. This first study focuses on the selection of the best model variables for the assimilation process : root zone layer only or root and sub root layers taken together or apart. Two versions of SIM, including or not an exponential profile of hydraulic conductivity in the soil, are tested, and a set of classical hydrologic scores will be performed in order to describe the performances of the experiments. The impact of this improvement of the initial state of the model on ensemble streamflow forecasts scores will be assessed in a subsequent work.

  2. Short-term ensemble streamflow forecasting using operationally-produced single-valued streamflow forecasts

    NASA Astrophysics Data System (ADS)

    Regonda, Satish; Seo, Dong-Jun; Lawrence, Bill

    2010-05-01

    We present a statistical procedure that generates short-term streamflow ensemble forecasts from single-valued, or deterministic, forecasts operationally produced by the National Weather Service (NWS) River Forecast Centers (RFC). The resulting ensemble forecast provides an estimate of the uncertainty in the single-valued forecast to aid risk-based decision making by the emergency managers and by the users of the forecast products and services. The single-valued forecasts are produced at a 6-hr time step for 5 days into the future, and reflect single-valued short-term quantitative precipitation and temperature forecasts (QPF, QTF) and various run-time modifications (MOD), or manual data assimilation, by human forecasters to reduce various sources of error in the end-to-end forecast process. The proposed procedure generates 5 day-ahead ensemble traces of streamflow from a very parsimonious approximation of the conditional multivariate probability distribution of future streamflow given the single-valued streamflow forecasts, QPF and recent streamflow observations. For parameter estimation and evaluation, we used a 10-year archive of the single-valued river stage forecasts for six forecast points in Oklahoma produced operationally by the Arkansas-Red River Basin River Forecast Center (ABRFC). To evaluate the procedure, we carried out dependent and leave-one-year-out cross validation. The resulting ensemble hindcasts are then verified using the Ensemble Verification System (EVS) developed at the NWS Office of Hydrologic Development (OHD).

  3. A past discharge assimilation system for ensemble streamflow forecasts over France - Part 2: Impact on the ensemble streamflow forecasts

    NASA Astrophysics Data System (ADS)

    Thirel, G.; Martin, E.; Mahfouf, J.-F.; Massart, S.; Ricci, S.; Regimbeau, F.; Habets, F.

    2010-08-01

    The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc

  4. A past discharge assimilation system for ensemble streamflow forecasts over France - Part 2: Impact on the ensemble streamflow forecasts

    NASA Astrophysics Data System (ADS)

    Thirel, G.; Martin, E.; Mahfouf, J.-F.; Massart, S.; Ricci, S.; Regimbeau, F.; Habets, F.

    2010-04-01

    The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Such systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first

  5. Towards reliable seasonal ensemble streamflow forecasts for ephemeral rivers

    NASA Astrophysics Data System (ADS)

    Bennett, James; Wang, Qj; Li, Ming; Robertson, David

    2016-04-01

    Despite their inherently variable nature, ephemeral rivers are an important water resource in many dry regions. Water managers are likely benefit considerably from even mildly skilful ensemble forecasts of streamflow in ephemeral rivers. As with any ensemble forecast, forecast uncertainty - i.e., the spread of the ensemble - must be reliably quantified to allow users of the forecasts to make well-founded decisions. Correctly quantifying uncertainty in ephemeral rivers is particularly challenging because of the high incidence of zero flows, which are difficult to handle with conventional statistical techniques. Here we apply a seasonal streamflow forecasting system, the model for generating Forecast Guided Stochastic Scenarios (FoGSS), to 26 Australian ephemeral rivers. FoGSS uses post-processed ensemble rainfall forecasts from a coupled ocean-atmosphere prediction system to force an initialised monthly rainfall runoff model, and then applies a staged hydrological error model to describe and propagate hydrological uncertainty in the forecast. FoGSS produces 12-month streamflow forecasts; as forecast skill declines with lead time, the forecasts are designed to transit seamlessly to stochastic scenarios. The ensemble rainfall forecasts used in FoGSS are known to be unbiased and reliable, and we concentrate here on the hydrological error model. The FoGSS error model has several features that make it well suited to forecasting ephemeral rivers. First, FoGSS models the error after data is transformed with a log-sinh transformation. The log-sinh transformation is able to normalise even highly skewed data and homogenise its variance, allowing us to assume that errors are Gaussian. Second, FoGSS handles zero values using data censoring. Data censoring allows streamflow in ephemeral rivers to be treated as a continuous variable, rather than having to model the occurrence of non-zero values and the distribution of non-zero values separately. This greatly simplifies parameter

  6. Post Processing Numerical Weather Prediction Model Rainfall Forecasts for Use in Ensemble Streamflow Forecasting in Australia

    NASA Astrophysics Data System (ADS)

    Shrestha, D. L.; Robertson, D.; Bennett, J.; Ward, P.; Wang, Q. J.

    2012-12-01

    Through the water information research and development alliance (WIRADA) project, CSIRO is conducting research to improve flood and short-term streamflow forecasting services delivered by the Australian Bureau of Meteorology. WIRADA aims to build and test systems to generate ensemble flood and short-term streamflow forecasts with lead times of up to 10 days by integrating rainfall forecasts from Numerical Weather Prediction (NWP) models and hydrological modelling. Here we present an overview of the latest progress towards developing this system. Rainfall during the forecast period is a major source of uncertainty in streamflow forecasting. Ensemble rainfall forecasts are used in streamflow forecasting to characterise the rainfall uncertainty. In Australia, NWP models provide forecasts of rainfall and other weather conditions for lead times of up to 10 days. However, rainfall forecasts from Australian NWP models are deterministic and often contain systematic errors. We use a simplified Bayesian joint probability (BJP) method to post-process rainfall forecasts from the latest generation of Australian NWP models. The BJP method generates reliable and skilful ensemble rainfall forecasts. The post-processed rainfall ensembles are then used to force a semi-distributed conceptual rainfall runoff model to produce ensemble streamflow forecasts. The performance of the ensemble streamflow forecasts is evaluated on a number of Australian catchments and the benefits of using post processed rainfall forecasts are demonstrated.

  7. Toward an Ensemble Streamflow Forecast Over the Entire France

    NASA Astrophysics Data System (ADS)

    Rousset, F.; Habets, F.; Noilhan, J.; Morel, S.; Le Moigne, P.

    2004-12-01

    Since the year 2003, the French National Weather Service (Meteo-France) uses an operationnal real-time system that provides a daily monitoring of the water budget, streamflows and aquifer levels over the entire France : the SAFRAN-ISBA-MODCOU (SIM) system. This coupled model is composed of the ISBA surface scheme and of the distributed hydrological model MODCOU. The system is used in a forced mode, with the atmospheric forcing derived from observations through the use of the SAFRAN analysis system. Such a system has been validated over 3 large french basins~: the Rhone, the Adour-Garonne and the Seine basins. It was shown that the system satisfactorily reproduces the water and energy budgets, as well as the observed streamflows, aquifer levels and snow-packs. In particular, the main long-duration floods of the Seine are well simulated. The SIM system is also used for streamflow forecasting. As a first step, experiments of determinist forecasts have been performed over the Rhone basin, using 2- and 3-day quantitive precipitation forecast. The encouraging results showed the potential of SIM for flood forecasting. As a next step, an ensemble streamflow prediction system is now being built. The forecasts from the Ensemble Prediction System of the ECMWF are used to force the system. The initial conditions of soil moisture, aquifer levels, etc. are given by the operationnal run of SIM, and the results are analysed for each forecast day. This system is expected to give 10-day forecasts of the streamflow of the main french rivers with a measure of the associated confidence, which is greatly valuable for flood warning and water management.

  8. Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 2. Streamflow verification

    NASA Astrophysics Data System (ADS)

    Brown, James D.; He, Minxue; Regonda, Satish; Wu, Limin; Lee, Haksu; Seo, Dong-Jun

    2014-11-01

    Retrospective forecasts of precipitation, temperature, and streamflow were generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) for a 20-year period between 1979 and 1999. The hindcasts were produced for two basins in each of four River Forecast Centers (RFCs), namely the Arkansas-Red Basin RFC, the Colorado Basin RFC, the California-Nevada RFC and the Middle Atlantic RFC. In a companion paper, temperature and precipitation hindcasts were produced with the Meteorological Ensemble Forecast Processor (MEFP) and verified against observed temperature and precipitation, respectively. Inputs to the MEFP comprised raw precipitation and temperature forecasts from the frozen (circa 1997) version of the NWS Global Forecast System (MEFP-GFS) and a conditional or "resampled" climatology (MEFP-CLIM). For this paper, streamflow hindcasts were produced with the Community Hydrologic Prediction System and were bias-corrected with the Ensemble Post-processor (EnsPost). In order to separate the meteorological and hydrologic uncertainties, the raw streamflow forecasts were verified against simulated streamflows, as well as observed flows. Also, when verifying the bias-corrected streamflow forecasts, the total skill was decomposed into contributions from the MEFP-GFS and the EnsPost. In general, the streamflow forecasts are substantially more skillful when using the MEFP-GFS together with the EnsPost than using the MEFP with resampled climatology alone. However, both the raw and bias-corrected streamflow forecasts have lower biases, stronger correlations and are more skillful in CB- and CN-RFCs than AB- and MA-RFCs. In addition, there are strong variations in forecast quality with streamflow amount, forecast lead time, season and aggregation period. The relative importance of the meteorological and hydrologic uncertainties also varies between basins and is modulated by the same controls on forecast quality. For example, the MEFP

  9. Ensemble Streamflow Forecast Improvements in NYC's Operations Support Tool

    NASA Astrophysics Data System (ADS)

    Wang, L.; Weiss, W. J.; Porter, J.; Schaake, J. C.; Day, G. N.; Sheer, D. P.

    2013-12-01

    Like most other water supply utilities, New York City's Department of Environmental Protection (DEP) has operational challenges associated with drought and wet weather events. During drought conditions, DEP must maintain water supply reliability to 9 million customers as well as meet environmental release requirements downstream of its reservoirs. During and after wet weather events, DEP must maintain turbidity compliance in its unfiltered Catskill and Delaware reservoir systems and minimize spills to mitigate downstream flooding. Proactive reservoir management - such as release restrictions to prepare for a drought or preventative drawdown in advance of a large storm - can alleviate negative impacts associated with extreme events. It is important for water managers to understand the risks associated with proactive operations so unintended consequences such as endangering water supply reliability with excessive drawdown prior to a storm event are minimized. Probabilistic hydrologic forecasts are a critical tool in quantifying these risks and allow water managers to make more informed operational decisions. DEP has recently completed development of an Operations Support Tool (OST) that integrates ensemble streamflow forecasts, real-time observations, and a reservoir system operations model into a user-friendly graphical interface that allows its water managers to take robust and defensible proactive measures in the face of challenging system conditions. Since initial development of OST was first presented at the 2011 AGU Fall Meeting, significant improvements have been made to the forecast system. First, the monthly AR1 forecasts ('Hirsch method') were upgraded with a generalized linear model (GLM) utilizing historical daily correlations ('Extended Hirsch method' or 'eHirsch'). The development of eHirsch forecasts improved predictive skill over the Hirsch method in the first week to a month from the forecast date and produced more realistic hydrographs on the tail

  10. Generation of ensemble streamflow forecasts using an enhanced version of the snowmelt runoff model

    USDA-ARS?s Scientific Manuscript database

    As water demand increases in the western United States, so does the need for accurate streamflow forecasts. We describe a method for generating ensemble streamflow forecasts (1-15 days) using an enhanced version of the snowmelt runoff model (SRM). Forecasts are produced for three snowmelt-dominated ...

  11. Ensemble forecasting of sub-seasonal to seasonal streamflow by a Bayesian joint probability modelling approach

    NASA Astrophysics Data System (ADS)

    Zhao, Tongtiegang; Schepen, Andrew; Wang, Q. J.

    2016-10-01

    The Bayesian joint probability (BJP) modelling approach is used operationally to produce seasonal (three-month-total) ensemble streamflow forecasts in Australia. However, water resource managers are calling for more informative sub-seasonal forecasts. Taking advantage of BJP's capability of handling multiple predictands, ensemble forecasting of sub-seasonal to seasonal streamflows is investigated for 23 catchments around Australia. Using antecedent streamflow and climate indices as predictors, monthly forecasts are developed for the three-month period ahead. Forecast reliability and skill are evaluated for the period 1982-2011 using a rigorous leave-five-years-out cross validation strategy. BJP ensemble forecasts of monthly streamflow volumes are generally reliable in ensemble spread. Forecast skill, relative to climatology, is positive in 74% of cases in the first month, decreasing to 57% and 46% respectively for streamflow forecasts for the final two months of the season. As forecast skill diminishes with increasing lead time, the monthly forecasts approach climatology. Seasonal forecasts accumulated from monthly forecasts are found to be similarly skilful to forecasts from BJP models based on seasonal totals directly. The BJP modelling approach is demonstrated to be a viable option for producing ensemble time-series sub-seasonal to seasonal streamflow forecasts.

  12. Ensemble Data Assimilation for Streamflow Forecasting: Experiments with Ensemble Kalman Filter and Particle Filter

    NASA Astrophysics Data System (ADS)

    Hirpa, F. A.; Gebremichael, M.; Hopson, T. M.; Wojick, R.

    2011-12-01

    We present results of data assimilation of ground discharge observation and remotely sensed soil moisture observations into Sacramento Soil Moisture Accounting (SACSMA) model in a small watershed (1593 km2) in Minnesota, the Unites States. Specifically, we perform assimilation experiments with Ensemble Kalman Filter (EnKF) and Particle Filter (PF) in order to improve streamflow forecast accuracy at six hourly time step. The EnKF updates the soil moisture states in the SACSMA from the relative errors of the model and observations, while the PF adjust the weights of the state ensemble members based on the likelihood of the forecast. Results of the improvements of each filter over the reference model (without data assimilation) will be presented. Finally, the EnKF and PF are coupled together to further improve the streamflow forecast accuracy.

  13. Constraining the Ensemble Kalman Filter for improved streamflow forecasting

    NASA Astrophysics Data System (ADS)

    Maxwell, Deborah; Jackson, Bethanna; McGregor, James

    2016-04-01

    Data assimilation techniques such as the Kalman Filter and its variants are often applied to hydrological models with minimal state volume/capacity constraints. Flux constraints are rarely, if ever, applied. Consequently, model states can be adjusted beyond physically reasonable limits, compromising the integrity of model output. In this presentation, we investigate the effect of constraining the Ensemble Kalman Filter (EnKF) on forecast performance. An EnKF implementation with no constraints is compared to model output with no assimilation, followed by a 'typical' hydrological implementation (in which mass constraints are enforced to ensure non-negativity and capacity thresholds of model states are not exceeded), and then a more tightly constrained implementation where flux as well as mass constraints are imposed to limit the rate of water movement within a state. A three year period (2008-2010) with no significant data gaps and representative of the range of flows observed over the fuller 1976-2010 record was selected for analysis. Over this period, the standard implementation of the EnKF (no constraints) contained eight hydrological events where (multiple) physically inconsistent state adjustments were made. All were selected for analysis. Overall, neither the unconstrained nor the "typically" mass-constrained forecasts were significantly better than the non-filtered forecasts; in fact several were significantly degraded. Flux constraints (in conjunction with mass constraints) significantly improved the forecast performance of six events relative to all other implementations, while the remaining two events showed no significant difference in performance. We conclude that placing flux as well as mass constraints on the data assimilation framework encourages physically consistent state updating and results in more accurate and reliable forward predictions of streamflow for robust decision-making. We also experiment with the observation error, and find that this

  14. Towards an Australian ensemble streamflow forecasting system for flood prediction and water management

    NASA Astrophysics Data System (ADS)

    Bennett, J.; David, R. E.; Wang, Q.; Li, M.; Shrestha, D. L.

    2016-12-01

    Flood forecasting in Australia has historically relied on deterministic forecasting models run only when floods are imminent, with considerable forecaster input and interpretation. These now co-existed with a continually available 7-day streamflow forecasting service (also deterministic) aimed at operational water management applications such as environmental flow releases. The 7-day service is not optimised for flood prediction. We describe progress on developing a system for ensemble streamflow forecasting that is suitable for both flood prediction and water management applications. Precipitation uncertainty is handled through post-processing of Numerical Weather Prediction (NWP) output with a Bayesian rainfall post-processor (RPP). The RPP corrects biases, downscales NWP output, and produces reliable ensemble spread. Ensemble precipitation forecasts are used to force a semi-distributed conceptual rainfall-runoff model. Uncertainty in precipitation forecasts is insufficient to reliably describe streamflow forecast uncertainty, particularly at shorter lead-times. We characterise hydrological prediction uncertainty separately with a 4-stage error model. The error model relies on data transformation to ensure residuals are homoscedastic and symmetrically distributed. To ensure streamflow forecasts are accurate and reliable, the residuals are modelled using a mixture-Gaussian distribution with distinct parameters for the rising and falling limbs of the forecast hydrograph. In a case study of the Murray River in south-eastern Australia, we show ensemble predictions of floods generally have lower errors than deterministic forecasting methods. We also discuss some of the challenges in operationalising short-term ensemble streamflow forecasts in Australia, including meeting the needs for accurate predictions across all flow ranges and comparing forecasts generated by event and continuous hydrological models.

  15. Multimodel Ensembles of Streamflow Forecasts: Role of Predictor State in Developing Optimal Combinations

    NASA Astrophysics Data System (ADS)

    Devineni, N.; Arumugam, S.

    2007-12-01

    Seasonal streamflow forecasts based on climate information are essential for short-term planning and for setting up contingency measures during years of extreme climatic conditions. Recent research shows that operational climate forecasts obtained by combining different General Circulation Models (GCM) have improved predictability/skill in comparison to the predictability that could be obtained from single GCM. In this study, we present a new approach for developing multi-model forecasts that combines streamflow forecasts from various models by evaluating their skill from the predictor state space. Based on this, we show that any systematic errors in model prediction with reference to a particular predictor conditions could be reduced by combining forecasts from multiple models along with climatological ensembles. The methodology is demonstrated through development of multi-model ensembles of streamflow forecasts for the Falls Lake reservoir in Neuse river basin, NC by combining probabilistic streamflow forecasts from two low dimensional statistical models that uses SST conditions in Tropical Pacific, North Atlantic and North Carolina Coast as predictors. Using Rank Probability Score (RPS) for evaluating the predictability of seasonal (July- August-September) streamflow forecasts available each year from the two candidate low dimensional models, the methodology proportionately gives higher representation by drawing increased ensembles for a model that has better predictability under similar predictor conditions. The performance of the multi-model forecasts are compared with the individual model's performance using various performance evaluation measures such as correlation coefficient, root mean square error (RMSE), average Rank Probability Skill Score, average Rank Probability Skill Score (RPSS) and reliability diagrams. By developing multi-model ensembles for leave-one out cross validated forecasts and adaptive forecasts based on the proposed methodology, the

  16. Developing Climate-Informed Ensemble Streamflow Forecasts over the Colorado River Basin

    NASA Astrophysics Data System (ADS)

    Miller, W. P.; Lhotak, J.; Werner, K.; Stokes, M.

    2014-12-01

    As climate change is realized, the assumption of hydrometeorologic stationarity embedded within many hydrologic models is no longer valid over the Colorado River Basin. As such, resource managers have begun to request more information to support decisions, specifically with regards to the incorporation of climate change information and operational risk. To this end, ensemble methodologies have become increasingly popular among the scientific and forecasting communities, and resource managers have begun to incorporate this information into decision support tools and operational models. Over the Colorado River Basin, reservoir operations are determined, in large part, by forecasts issued by the Colorado Basin River Forecast Center (CBRFC). The CBRFC produces both single value and ensemble forecasts for use by resource managers in their operational decision-making process. These ensemble forecasts are currently driven by a combination of daily updating model states used as initial conditions and weather forecasts plus historical meteorological information used to generate forecasts with the assumption that past hydroclimatological conditions are representative of future hydroclimatology. Recent efforts have produced updated bias-corrected and spatially downscaled projections of future climate over the Colorado River Basin. In this study, the historical climatology used as input to the CBRFC forecast model is adjusted to represent future projections of climate based on data developed by the updated projections of future climate data. Ensemble streamflow forecasts reflecting the impacts of climate change are then developed. These forecasts are subsequently compared to non-informed ensemble streamflow forecasts to evaluate the changing range of streamflow forecasts and risk over the Colorado River Basin. Ensemble forecasts may be compared through the use of a reservoir operations planning model, providing resource managers with ensemble information regarding changing

  17. Using ENSO Indices to Enhance Long-Range Ensemble Streamflow Forecasts in The Blue Nile River

    NASA Astrophysics Data System (ADS)

    Habib, M. A.; Bradley, A.

    2012-12-01

    River forecasting centers around the globe have been using hydrologic ensemble forecast systems operationally. Better-enhanced forecasting systems are the ultimate need of the decision maker. This study proposes an enhancement technique for Long-Range Ensemble Streamflow Forecasts in The Blue Nile River. Many studies showed the link between summer rainfall and the large scale climate oscillations. Strong correlation is found between the El-Nino Southern Oscillations (ENSO) and annual flows in the Nile River at Diem on the Sudanese-Ethiopian boarders. Long-range streamflow forecasts produced by The Nile Forecast System at Ministry of Public Works and Irrigation in Egypt are used. Traditionally, baseline forecast weights each trace of the ensemble equally. In this study, we add an enhancement process to the forecasts by changing their weights depending on the correlation between the Streamflow and the ENSO indices (e.g. SST3.4, SOI). Weighting techniques using different distributions of the ENSO indices (e.g. Kernel, Gaussian) are examined. Diagnostic verification measures are used to evaluate this enhancement process.

  18. Improving medium-range ensemble streamflow forecasts through statistical post-processing

    NASA Astrophysics Data System (ADS)

    Mendoza, Pablo; Wood, Andy; Clark, Elizabeth; Nijssen, Bart; Clark, Martyn; Ramos, Maria-Helena; Nowak, Kenneth; Arnold, Jeffrey

    2017-04-01

    Probabilistic hydrologic forecasts are a powerful source of information for decision-making in water resources operations. A common approach is the hydrologic model-based generation of streamflow forecast ensembles, which can be implemented to account for different sources of uncertainties - e.g., from initial hydrologic conditions (IHCs), weather forecasts, and hydrologic model structure and parameters. In practice, hydrologic ensemble forecasts typically have biases and spread errors stemming from errors in the aforementioned elements, resulting in a degradation of probabilistic properties. In this work, we compare several statistical post-processing techniques applied to medium-range ensemble streamflow forecasts obtained with the System for Hydromet Applications, Research and Prediction (SHARP). SHARP is a fully automated prediction system for the assessment and demonstration of short-term to seasonal streamflow forecasting applications, developed by the National Center for Atmospheric Research, University of Washington, U.S. Army Corps of Engineers, and U.S. Bureau of Reclamation. The suite of post-processing techniques includes linear blending, quantile mapping, extended logistic regression, quantile regression, ensemble analogs, and the generalized linear model post-processor (GLMPP). We assess and compare these techniques using multi-year hindcasts in several river basins in the western US. This presentation discusses preliminary findings about the effectiveness of the techniques for improving probabilistic skill, reliability, discrimination, sharpness and resolution.

  19. Improving medium-range ensemble streamflow forecasts through statistical post-processing

    NASA Astrophysics Data System (ADS)

    Mendoza, P. A.; Wood, A.; Clark, E.; Nijssen, B.; Clark, M. P.; Ramos, M. H.; Voisin, N.

    2016-12-01

    Probabilistic hydrologic forecasts are a powerful source of information for decision-making in water resources operations. A common approach is the hydrologic model-based generation of streamflow forecast ensembles, which can be implemented to account for different sources of uncertainties - e.g., from initial hydrologic conditions (IHCs), weather forecasts, and hydrologic model structure and parameters. In practice, hydrologic ensemble forecasts typically have biases and spread errors stemming from errors in the aforementioned elements, resulting in a degradation of probabilistic properties. In this work, we compare several statistical post-processing techniques applied to medium-range ensemble streamflow forecasts obtained with the System for Hydrometeorological Applications, Research and Prediction (SHARP). SHARP is a fully automated prediction system for the assessment and demonstration of short-term to seasonal streamflow forecasting applications, developed by the National Center for Atmospheric Research, University of Washington, U.S. Army Corps of Engineers, and U.S. Bureau of Reclamation. The suite of post-processing techniques includes linear blending, quantile mapping, extended logistic regression, quantile regression, ensemble analogs, and the generalized linear model post-processor (GLMPP). We assess and compare these techniques using multi-year hindcasts in several river basins in the western US. This presentation discusses preliminary findings about the effectiveness of the techniques for improving probabilistic skill, reliability, discrimination, sharpness and resolution.

  20. Hindcast experiments of ensemble streamflow forecasting for the Paraopeba river (Brazil)

    NASA Astrophysics Data System (ADS)

    Collischonn, W.; Meller, A.; Dias, P. L. S.; Moreira, D. S.

    2012-04-01

    Streamflow forecasts are routinely produced and used in Brazil to predict inflow to major hydropower reservoirs . In this field of application quantitative precipitation forecasts are becoming increasingly used to extend the range and increase the skill of streamflow forecasts. Forecasting systems designed to provide flood alert, on the other side, are relatively rare in Brazil, and are often based on simplified river routing models. However, a number of recent floods with significant loss of lives and economical impact is now motivating the creation of a new governmental institution dedicated to natural disaster and flood forecasting. This will further motivate the incorporation of numerical weather predictions (NWP) as input data to hydrological flood forecasting models, with the aim of increasing forecast lead time. In this context ensemble meteorological forecasts will be increasingly useful, since it is expected that ensembles can give some idea of the confidence level of the forecasts, and that extremes can be better captured by a high number of NWP runs with different initial conditions, or with different meteorological models. Silva Dias and Moreira (2006) organized a grand ensemble including several different models and model members for South America. We used forecasts of individual models of this grand ensemble to run a series of streamflow hindcast experiments (in forecast mode), using the MGB-IPH hydrological model. These tests were conducted in the Paraopeba river basin, which is a tributary of the São Francisco river, located in Minas Gerais State, in a Tropical region in the range from 21 S to 19 S. Results of 72 hour streamflow forecasts were compared to hourly observed discharge at Porto Mesquita gauging station, were the drainage area is 10280 square kilometers, during the Austral Summer of 2011. Results were assessed by visual inspection of hydrographs and by the analysis of a number of summary statistics. These preliminary results suggest that

  1. Multi-model Ensembling based on Predictor State Space: Seasonal Streamflow Forecasts and Causal Relations

    NASA Astrophysics Data System (ADS)

    Arumugam, S.; Devineni, N.; Ghosh, S.

    2006-12-01

    Seasonal streamflow forecasts contingent on climate information are essential for short-term planning and for setting up contingency measures during extreme years. Recent research show that operational climate forecasts obtained from multiple General Circulation Models (GCM) have improved predictability than climate forecasts from single GCMs. In this study, we present a new approach for multi-model ensembling by evaluating model performance from the predictor state space. By analyzing the model performance using retrospective forecasts, we show that any systematic errors in model prediction with reference to the predictor state could be reduced by combining forecasts from multiple models as well as with climatology. The methodology is demonstrated for obtaining seasonal streamflow forecasts for the Neuse river basin from two different GCMs and from two statistical models. We employ Rank Probability Score (RPS) as the basis for performing developing multi-model ensembles. The performance of the multi-model forecasts are compared with the individual model's performance using various forecast verification measures including reliability diagrams and likelihood ratio. By developing both retrospective and adaptive forecasts using this methodology, we show that evaluating the model performance from predictor state space is a good alternative in developing multi-model ensembles instead of climatology (long-term predictability) based model performance evaluation.

  2. Evaluation of ECMWF System 4 product for ensemble streamflow forecast in Upper Hanjiang River Basin

    NASA Astrophysics Data System (ADS)

    Li, Yilu; Tian, Fuqiang

    2017-04-01

    This study attempts to investigate the application of ECMWF System 4 forecast dataset for long term streamflow forecasts with the lead time of 0-2 months in China. The case study is Upper Hanjiang River Basin (UHRB), where forecast results are essential for the central route of South to North Water Diversion Project (SNWDP) in China. A semi-distributed hydrological model (THREW) was applied to simulate the rainfall-runoff processes over the UHRB during the period of 2001-2008. The accuracy of streamflow prediction decreases with lead time, while it is no significant relationship with the drainage areas. All the stations become more reliable as lead time increases, but the Yangxian station shows less reliable than others. The forecast uncertainty is effectively estimated by applying the ECMWF System 4 forecast dataset for the ensemble streamflow forecasts. Significant differences in the performance of ECMWF system 4 are found in seasonal predictions. The forecast is more skillful in Post-dry season than otherwise in term of accuracy and reliability. This study will broaden the application field of ECMWF System 4 dataset to long term streamflow forecast for similar climate region. The results would provide effective guidelines for reservoir operation and be helpful for potential users to employ ECMWF System 4 dataset in other basins over China.

  3. Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations

    PubMed Central

    Devineni, Naresh; Sankarasubramanian, A.; Ghosh, Sujit

    2008-01-01

    A new approach for developing multimodel streamflow forecasts is presented. The methodology combines streamflow forecasts from individual models by evaluating their skill, represented by rank probability score (RPS), contingent on the predictor state. Using average RPS estimated over the chosen neighbors in the predictor state space, the methodology assigns higher weights for a model that has better predictability under similar predictor conditions. We assess the performance of the proposed algorithm by developing multimodel streamflow forecasts for Falls Lake Reservoir in the Neuse River Basin, North Carolina (NC), by combining streamflow forecasts developed from two low-dimensional statistical models that use sea-surface temperature conditions as underlying predictors. To evaluate the proposed scheme thoroughly, we consider a total of seven multimodels that include existing multimodel combination techniques such as combining based on long-term predictability of individual models and by simple pooling of ensembles. Detailed nonparametric hypothesis tests comparing the performance of seven multimodels with two individual models show that the reduced RPS from multimodel forecasts developed using the proposed algorithm is statistically significant from the RPSs of individual models and from the RPSs of existing multimodel techniques. The study also shows that adding climatological ensembles improves the multimodel performance resulting in reduced average RPS. Contingency analyses on categorical (tercile) forecasts show that the proposed multimodel combination technique reduces average Brier score and total number of false alarms, resulting in improved reliability of forecasts. However, adding multiple models with climatology also increases the number of missed targets (in comparison to individual models' forecasts) which primarily results from the reduction of increased resolution that is exhibited in the individual models' forecasts under various forecast

  4. Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations

    NASA Astrophysics Data System (ADS)

    Devineni, Naresh; Sankarasubramanian, A.; Ghosh, Sujit

    2008-09-01

    A new approach for developing multimodel streamflow forecasts is presented. The methodology combines streamflow forecasts from individual models by evaluating their skill, represented by rank probability score (RPS), contingent on the predictor state. Using average RPS estimated over the chosen neighbors in the predictor state space, the methodology assigns higher weights for a model that has better predictability under similar predictor conditions. We assess the performance of the proposed algorithm by developing multimodel streamflow forecasts for Falls Lake Reservoir in the Neuse River Basin, North Carolina (NC), by combining streamflow forecasts developed from two low-dimensional statistical models that use sea-surface temperature conditions as underlying predictors. To evaluate the proposed scheme thoroughly, we consider a total of seven multimodels that include existing multimodel combination techniques such as combining based on long-term predictability of individual models and by simple pooling of ensembles. Detailed nonparametric hypothesis tests comparing the performance of seven multimodels with two individual models show that the reduced RPS from multimodel forecasts developed using the proposed algorithm is statistically significant from the RPSs of individual models and from the RPSs of existing multimodel techniques. The study also shows that adding climatological ensembles improves the multimodel performance resulting in reduced average RPS. Contingency analyses on categorical (tercile) forecasts show that the proposed multimodel combination technique reduces average Brier score and total number of false alarms, resulting in improved reliability of forecasts. However, adding multiple models with climatology also increases the number of missed targets (in comparison to individual models' forecasts) which primarily results from the reduction of increased resolution that is exhibited in the individual models' forecasts under various forecast

  5. Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations.

    PubMed

    Devineni, Naresh; Sankarasubramanian, A; Ghosh, Sujit

    2008-09-01

    A new approach for developing multimodel streamflow forecasts is presented. The methodology combines streamflow forecasts from individual models by evaluating their skill, represented by rank probability score (RPS), contingent on the predictor state. Using average RPS estimated over the chosen neighbors in the predictor state space, the methodology assigns higher weights for a model that has better predictability under similar predictor conditions. We assess the performance of the proposed algorithm by developing multimodel streamflow forecasts for Falls Lake Reservoir in the Neuse River Basin, North Carolina (NC), by combining streamflow forecasts developed from two low-dimensional statistical models that use sea-surface temperature conditions as underlying predictors. To evaluate the proposed scheme thoroughly, we consider a total of seven multimodels that include existing multimodel combination techniques such as combining based on long-term predictability of individual models and by simple pooling of ensembles. Detailed nonparametric hypothesis tests comparing the performance of seven multimodels with two individual models show that the reduced RPS from multimodel forecasts developed using the proposed algorithm is statistically significant from the RPSs of individual models and from the RPSs of existing multimodel techniques. The study also shows that adding climatological ensembles improves the multimodel performance resulting in reduced average RPS. Contingency analyses on categorical (tercile) forecasts show that the proposed multimodel combination technique reduces average Brier score and total number of false alarms, resulting in improved reliability of forecasts. However, adding multiple models with climatology also increases the number of missed targets (in comparison to individual models' forecasts) which primarily results from the reduction of increased resolution that is exhibited in the individual models' forecasts under various forecast

  6. Ensemble Forecasts with Useful Skill-Spread Relationships for African meningitis and Asia Streamflow Forecasting

    NASA Astrophysics Data System (ADS)

    Hopson, T. M.

    2014-12-01

    One potential benefit of an ensemble prediction system (EPS) is its capacity to forecast its own forecast error through the ensemble spread-error relationship. In practice, an EPS is often quite limited in its ability to represent the variable expectation of forecast error through the variable dispersion of the ensemble, and perhaps more fundamentally, in its ability to provide enough variability in the ensembles dispersion to make the skill-spread relationship even potentially useful (irrespective of whether the EPS is well-calibrated or not). In this paper we examine the ensemble skill-spread relationship of an ensemble constructed from the TIGGE (THORPEX Interactive Grand Global Ensemble) dataset of global forecasts and a combination of multi-model and post-processing approaches. Both of the multi-model and post-processing techniques are based on quantile regression (QR) under a step-wise forward selection framework leading to ensemble forecasts with both good reliability and sharpness. The methodology utilizes the ensemble's ability to self-diagnose forecast instability to produce calibrated forecasts with informative skill-spread relationships. A context for these concepts is provided by assessing the constructed ensemble in forecasting district-level humidity impacting the incidence of meningitis in the meningitis belt of Africa, and in forecasting flooding events in the Brahmaputra and Ganges basins of South Asia.

  7. A past discharges assimilation system for ensemble streamflow forecasts over France

    NASA Astrophysics Data System (ADS)

    Thirel, Guillaume; Martin, E.; Regimbeau, F.; Mahfouf, J.-F.; Massart, S.; Ricci, S.; Habets, F.

    2010-05-01

    The coupled physically-based hydro-meteorological model SAFRAN-ISBA-MODCOU (SIM) is developed at Météo-France for many years. This fully distributed catchment model is used in a pre-operational mode since 2005 for producing mid-range ensemble streamflow forecasts based on the 51-member 10-day ECMWF EPS. A past discharges assimilation system has been implemented in order to improve the initial states of these ensemble streamflow forecasts. The daily observed discharges of a selection of 186 gauging stations distributed over France were used over a 19-month period. The analysis operator is the Best Linear Unbiased Operator (BLUE), and 3 configurations of the assimilation system were tested, each one adjusting the soil moisture in a different way. An optional improvement of the physics of the model (the exponential profile of the hydraulic conductivity in the soil) was tested. The performance of the system was assessed for a selection of 148 assimilated stations, as well as for a selection of 49 totally independent stations for each configuration. A global improvement of the simulated streamflows was found, and the modifications imposed by the BLUE remained low. Finally, the impact of the assimilation system on the ensemble streamflow forecasts, and the impact of the improved physics were assessed separately in comparison with the operational streamflow forecasts. The results show a significant improvement of the forecasts, and the best configuration demonstrate the benefit of the method along the 10-day range, even for very high flows and for stations where assimilation was not directly performed.

  8. Skill of a global forecasting system in seasonal ensemble streamflow prediction

    NASA Astrophysics Data System (ADS)

    Candogan Yossef, Naze; van Beek, Rens; Weerts, Albrecht; Winsemius, Hessel; Bierkens, Marc F. P.

    2017-08-01

    In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system Flood Early Warning System (FEWS)-World, which has been set up within the European Commission 7th Framework Programme Project Global Water Scarcity Information Service (GLOWASIS). FEWS-World incorporates the distributed global hydrological model PCR-GLOBWB (PCRaster Global Water Balance). We produce ensemble forecasts of monthly discharges for 20 large rivers of the world, with lead times of up to 6 months, forcing the system with bias-corrected seasonal meteorological forecast ensembles from the European Centre for Medium-range Weather Forecasts (ECMWF) and with probabilistic meteorological ensembles obtained following the ESP procedure. Here, the ESP ensembles, which contain no actual information on weather, serve as a benchmark to assess the additional skill that may be obtained using ECMWF seasonal forecasts. We use the Brier skill score (BSS) to quantify the skill of the system in forecasting high and low flows, defined as discharges higher than the 75th and lower than the 25th percentiles for a given month, respectively. We determine the theoretical skill by comparing the results against model simulations and the actual skill in comparison to discharge observations. We calculate the ratios of actual-to-theoretical skill in order to quantify the percentage of the potential skill that is achieved. The results suggest that the performance of ECMWF S3 forecasts is close to that of the ESP forecasts. While better meteorological forecasts could potentially lead to an improvement in hydrological forecasts, this cannot be achieved yet using the ECMWF S3 dataset.

  9. A multi-model hydrologic ensemble for seasonal streamflow forecasting in the western U.S.

    NASA Astrophysics Data System (ADS)

    Bohn, T. J.; Wood, A. W.; Akanda, A.; Lettenmaier, D. P.

    2005-12-01

    Since 2003, the Variable Infiltration Capacity (VIC) macroscale hydrology model has been applied in real time over the western U.S. for experimental ensemble hydrologic prediction at lead times of six months to a year. VIC hydrologic initial conditions are produced from gridded station observations during a two-year runup period prior to the forecast date; and hydrologic forecast ensembles are driven by climate forecasts from several sources, including NCEP and NASA climate model outputs, CPC official seasonal outlooks and, as a baseline forecast, Extended Streamflow Prediction (ESP). We are now in the process of expanding this approach to include forecasts made from a Bayesian combination of the results from a suite of land surface models. Our initial set of LSMs includes VIC, the NWS grid-based Sacramento model (HL-RMS) and the NCEP NOAH model. All three LSMs are implemented on the 1/8 degree grid used by the North American Land Data Assimilation System (N-LDAS). Here we present preliminary results from several river basins in the Western US, focusing on both retrospective deterministic simulations and retrospective ESP-based ensemble forecasts and forecast error properties. We compare linear regression and Bayesian methods of combining model results, and investigate seasonal and geographic variations in forecast skill. Our data set includes 20+ years of 1-year, ESP-based, 25-member ensemble forecasts for each model, using both April 1 and October 1 as starting dates, from several basins including the Salmon River, ID, the Feather River, CA, and the San Juan River, UT.

  10. An Integrated Risk Approach for Assessing the Use of Ensemble Streamflow Forecasts in Hydroelectric Reservoir Operations

    NASA Astrophysics Data System (ADS)

    Lowry, T. S.; Wigmosta, M.; Barco, J.; Voisin, N.; Bier, A.; Coleman, A.; Skaggs, R.

    2012-12-01

    This paper presents an integrated risk approach using ensemble streamflow forecasts for optimizing hydro-electric power generation. Uncertainty in the streamflow forecasts are translated into integrated risk by calculating the deviation of an optimized release schedule that simultaneously maximizes power generation and environmental performance from release schedules that maximize the two objectives individually. The deviations from each target are multiplied by the probability of occurrence and then summed across all probabilities to get the integrated risk. The integrated risk is used to determine which operational scheme exposes the operator to the least amount of risk or conversely, what are the consequences of basing future operations on a particular prediction. Decisions can be made with regards to the tradeoff between power generation, environmental performance, and exposure to risk. The Hydropower Seasonal Concurrent Optimization for Power and Environment (HydroSCOPE) model developed at Sandia National Laboratories (SNL) is used to model the flow, temperature, and power generation and is coupled with the DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) optimization package to identify the maximum potential power generation, the maximum environmental performance, and the optimal operational scheme that maximizes both for each instance of the ensemble forecasts. The ensemble forecasts were developed in a collaborative effort between the Pacific Northwest National Laboratory (PNNL) and the University of Washington to develop an Enhanced Hydrologic Forecasting System (EHFS) that incorporates advanced ensemble forecasting approaches and algorithms, spatiotemporal datasets, and automated data acquisition and processing. Both the HydroSCOPE model and the EHFS forecast tool are being developed as part of a larger, multi-laboratory water-use optimization project funded through the US Department of Energy. The simulations were based on the

  11. Potential application of wavelet neural network ensemble to forecast streamflow for flood management

    NASA Astrophysics Data System (ADS)

    Kasiviswanathan, K. S.; He, Jianxun; Sudheer, K. P.; Tay, Joo-Hwa

    2016-05-01

    Streamflow forecasting, especially the long lead-time forecasting, is still a very challenging task in hydrologic modeling. This could be due to the fact that the forecast accuracy measured in terms of both the amplitude and phase or temporal errors and the forecast precision/reliability quantified in terms of the uncertainty significantly deteriorate with the increase of the lead-time. In the model performance evaluation, the conventional error metrics, which primarily quantify the amplitude error and do not explicitly account for the phase error, have been commonly adopted. For the long lead-time forecasting, the wavelet based neural network (WNN) among a variety of advanced soft computing methods has been shown to be promising in the literature. This paper presented and compared WNN and artificial neural network (ANN), both of which were combined with the ensemble method using block bootstrap sampling (BB), in terms of the forecast accuracy and precision at various lead-times on the Bow River, Alberta, Canada. Apart from conventional model performance metrics, a new index, called percent volumetric error, was proposed, especially for quantifying the phase error. The uncertainty metrics including percentage of coverage and average width were used to evaluate the precision of the modeling approaches. The results obtained demonstrate that the WNN-BB consistently outperforms the ANN-BB in both the categories of the forecast accuracy and precision, especially in the long lead-time forecasting. The findings strongly suggest that the WNN-BB is a robust modeling approach for streamflow forecasting and thus would aid in flood management.

  12. Statistical-dynamical long-range seasonal forecasting of streamflow with the North-American Multi Model Ensemble (NMME)

    NASA Astrophysics Data System (ADS)

    Slater, Louise; Villarini, Gabriele

    2017-04-01

    There are two main approaches to long-range (monthly to seasonal) streamflow forecasting: statistical approaches that typically relate climate precursors directly to streamflow, and dynamical physically-based approaches in which spatially distributed models are forced with downscaled meteorological forecasts. While the former approach is potentially limited by a lack of physical causality, the latter tends to be complex and time-consuming to implement. In contrast, hybrid statistical-dynamical techniques that use global climate model (GCM) ensemble forecasts as inputs to statistical models are both physically-based and rapid to run, but are a relatively new field of research. Here, we conduct the first systematic multimodel statistical-dynamical forecasting of streamflow using NMME climate forecasts from eight GCMs (CCSM3, CCSM4, CanCM3, CanCM4, GFDL2.1, FLORb01, GEOS5, and CFSv2) across a broad region. At several hundred U.S. Midwest stream gauges with long (50+ continuous years) streamflow records, we fit probabilistic statistical models for seasonal streamflow percentiles ranging from minimum to maximum flows. As predictors, we use basin-averaged values of precipitation, antecedent wetness, temperature, agricultural row crop acreage, and population density. Using the observed data, we select the best-fitting probabilistic model for every site, season, and streamflow percentile (ranging from low to high flows). The best-fitting models are then used to obtain streamflow predictions by incorporating the NMME climate forecasts and the extrapolated agricultural and population time series as predictors. The forecasting skill of our models is assessed using both deterministic and probabilistic verification measures. The influence of the different predictors is evaluated for all streamflow percentiles and across the full range of lead times. Our findings reveal that statistical-dynamical streamflow forecasting produces promising results, which may enable water managers

  13. Operational value of ensemble streamflow forecasts for hydropower production: A Canadian case study

    NASA Astrophysics Data System (ADS)

    Boucher, Marie-Amélie; Tremblay, Denis; Luc, Perreault; François, Anctil

    2010-05-01

    increased hydropower production. The ensemble precipitation forecasts extend from March 1st of 2002 to December 31st of 2003. They were obtained using two atmospheric models, SEF (8 members plus the control deterministic forecast) and GEM (8 members). The corresponding deterministic precipitation forecast issued by SEF model is also used within HYDROTEL in order to compare ensemble streamflow forecasts with their deterministic counterparts. Although this study does not incorporate all the sources of uncertainty, precipitation is certainly the most important input for hydrological modeling and conveys a great portion of the total uncertainty. References: Fortin, J.P., Moussa, R., Bocquillon, C. and Villeneuve, J.P. 1995: HYDROTEL, un modèle hydrologique distribué pouvant bénéficier des données fournies par la télédétection et les systèmes d'information géographique, Revue des Sciences de l'Eau, 8(1), 94-124. Jaun, S., Ahrens, B., Walser, A., Ewen, T. and Schaer, C. 2008: A probabilistic view on the August 2005 floods in the upper Rhine catchment, Natural Hazards and Earth System Sciences, 8 (2), 281-291. Krzysztofowicz, R. 2001: The case for probabilistic forecasting in hydrology, Journal of Hydrology, 249, 2-9. Murphy, A.H. 1994: Assessing the economic value of weather forecasts: An overview of methods, results and issues, Meteorological Applications, 1, 69-73. Mylne, K.R. 2002: Decision-Making from probability forecasts based on forecast value, Meteorological Applications, 9, 307-315. Laio, F. and Tamea, S. 2007: Verification tools for probabilistic forecasts of continuous hydrological variables, Hydrology and Earth System Sciences, 11, 1267-1277. Roulin, E. 2007: Skill and relative economic value of medium-range hydrological ensemble predictions, Hydrology and Earth System Sciences, 11, 725-737. Velazquez, J.-A., Petit, T., Lavoie, A., Boucher, M.-A., Turcotte, R., Fortin, V. and Anctil, F. 2009: An evaluation of the Canadian global meteorological ensemble

  14. A real-time evaluation and demonstration of strategies for 'Over-The-Loop' ensemble streamflow forecasting in US watersheds

    NASA Astrophysics Data System (ADS)

    Wood, Andy; Clark, Elizabeth; Mendoza, Pablo; Nijssen, Bart; Newman, Andy; Clark, Martyn; Nowak, Kenneth; Arnold, Jeffrey

    2017-04-01

    Many if not most national operational streamflow prediction systems rely on a forecaster-in-the-loop approach that require the hands-on-effort of an experienced human forecaster. This approach evolved from the need to correct for long-standing deficiencies in the models and datasets used in forecasting, and the practice often leads to skillful flow predictions despite the use of relatively simple, conceptual models. Yet the 'in-the-loop' forecast process is not reproducible, which limits opportunities to assess and incorporate new techniques systematically, and the effort required to make forecasts in this way is an obstacle to expanding forecast services - e.g., though adding new forecast locations or more frequent forecast updates, running more complex models, or producing forecast and hindcasts that can support verification. In the last decade, the hydrologic forecasting community has begun develop more centralized, 'over-the-loop' systems. The quality of these new forecast products will depend on their ability to leverage research in areas including earth system modeling, parameter estimation, data assimilation, statistical post-processing, weather and climate prediction, verification, and uncertainty estimation through the use of ensembles. Currently, many national operational streamflow forecasting and water management communities have little experience with the strengths and weaknesses of over-the-loop approaches, even as such systems are beginning to be deployed operationally in centers such as ECMWF. There is thus a need both to evaluate these forecasting advances and to demonstrate their potential in a public arena, raising awareness in forecast user communities and development programs alike. To address this need, the US National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the US Army Corps of Engineers, using the NCAR 'System for Hydromet Analysis Research and Prediction Applications

  15. Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model

    NASA Astrophysics Data System (ADS)

    Bennett, James C.; Wang, Q. J.; Li, Ming; Robertson, David E.; Schepen, Andrew

    2016-10-01

    We present a new streamflow forecasting system called forecast guided stochastic scenarios (FoGSS). FoGSS makes use of ensemble seasonal precipitation forecasts from a coupled ocean-atmosphere general circulation model (CGCM). The CGCM forecasts are post-processed with the method of calibration, bridging and merging (CBaM) to produce ensemble precipitation forecasts over river catchments. CBaM corrects biases and removes noise from the CGCM forecasts, and produces highly reliable ensemble precipitation forecasts. The post-processed CGCM forecasts are used to force the Wapaba monthly rainfall-runoff model. Uncertainty in the hydrological modeling is accounted for with a three-stage error model. Stage 1 applies the log-sinh transformation to normalize residuals and homogenize their variance; Stage 2 applies a conditional bias-correction to correct biases and help remove negative forecast skill; Stage 3 applies an autoregressive model to improve forecast accuracy at short lead-times and propagate uncertainty through the forecast. FoGSS generates ensemble forecasts in the form of time series for the coming 12 months. In a case study of two catchments, FoGSS produces reliable forecasts at all lead-times. Forecast skill with respect to climatology is evident to lead-times of about 3 months. At longer lead-times, forecast skill approximates that of climatology forecasts; that is, forecasts become like stochastic scenarios. Because forecast skill is virtually never negative at long lead-times, forecasts of accumulated volumes can be skillful. Forecasts of accumulated 12 month streamflow volumes are significantly skillful in several instances, and ensembles of accumulated volumes are reliable. We conclude that FoGSS forecasts could be highly useful to water managers.

  16. Integrating Ensemble Forecasts of Precipitation and Streamflow into Decision Support for Reservoir Operations in North Central Texas

    NASA Astrophysics Data System (ADS)

    Kim, S.; Limon, R. A.; Alizadeh, B.; Seo, D. J.; Fincannon, T. J.; Winguth, A. M. E.; Brown, J.; Blaylock, L.; Lampe, M.; Philpott, A.; Bell, F.

    2016-12-01

    North Central Texas relies heavily on surface water for water supply. To meet the growing demand, large raw water suppliers, such as the Tarrant Regional Water District (TRWD), operate systems of reservoirs that are connected by extensive networks of pipelines over long distances. To ensure water supply at all times, while minimizing flooding risks and pumping cost, TRWD utilizes a suite of decision support tools. This research aims to improve the operating efficiency of the water delivery system by providing skillful ensemble precipitation and inflow forecasts that can be used to optimize the water supply operations under uncertain environmental conditions. To assess the value of medium- and long-range forecasts of precipitation and inflow to operation and management of the TRWD's reservoir-pipeline system, a set of hindcasting and verification experiments is being carried out. The hindcasting experiments use weather and climate reforecasts from the Global Ensemble Forecast System and the Climate Forecast System Version 2, and the forecasting and verification tools of the National Weather Service's Hydrologic Ensemble Forecast Service, the Community Hydrologic Prediction System, and the Ensemble Verification System. The value of ensemble forecasts will be demonstrated via the TRWD forecasting model, which uses RiverWare. We also present a multiscale bias correction procedure for post-processing the raw streamflow ensembles.

  17. A Distributed Modeling System for Short-Term to Seasonal Ensemble Streamflow Forecasting in Snowmelt Dominated Basins

    SciTech Connect

    Wigmosta, Mark S.; Gill, Muhammad K.; Coleman, Andre M.; Prasad, Rajiv; Vail, Lance W.

    2007-12-01

    This paper describes a distributed modeling system for short-term to seasonal water supply forecasts with the ability to utilize remotely-sensed snow cover products and real-time streamflow measurements. Spatial variability in basin characteristics and meteorology is represented using a raster-based computational grid. Canopy interception, snow accumulation and melt, and simplified soil water movement are simulated in each computational unit. The model is run at a daily time step with surface runoff and subsurface flow aggregated at the basin scale. This approach allows the model to be updated with spatial snow cover and measured streamflow using an Ensemble Kalman-based data assimilation strategy that accounts for uncertainty in weather forecasts, model parameters, and observations used for updating. Model inflow forecasts for the Dworshak Reservoir in northern Idaho are compared to observations and to April-July volumetric forecasts issued by the Natural Resource Conservation Service (NRCS) for Water Years 2000 – 2006. October 1 volumetric forecasts are superior to those issued by the NRCS, while March 1 forecasts are comparable. The ensemble spread brackets the observed April-July volumetric inflows in all years. Short-term (one and three day) forecasts also show excellent agreement with observations.

  18. Ensemble streamflow forecasting experiments in a tropical basin: The São Francisco river case study

    NASA Astrophysics Data System (ADS)

    Fan, Fernando Mainardi; Collischonn, Walter; Meller, Adalberto; Botelho, Luiz César Mendes

    2014-11-01

    The present study shows experiments of ensemble forecasting applied to a large tropical river basin, where such forecasting methodologies have many potential applications. The case study is the Três Marias hydroelectric power plant basin (Brazil), on the São Francisco river, where forecast results are particularly important for reservoir operation and downstream flood control. Results showed some benefits in the use of ensembles, particularly for the reservoir inflow on flooding events, and in comparison to the deterministic values given by the control member of the ensemble and by the ensemble mean. The study also discusses the improvements that must be tested and implemented in order to achieve better results, what is particularly important for the smaller basins within the study case. Despite the necessary improvements mentioned, the results suggest that benefits can result from the application of ensemble forecasts for hydropower plants with large basins within the Brazilian energy system.

  19. Assessing the viability of `over-the-loop' real-time short-to-medium range ensemble streamflow forecasts

    NASA Astrophysics Data System (ADS)

    Wood, A. W.; Clark, E.; Mendoza, P. A.; Nijssen, B.; Newman, A. J.; Clark, M. P.; Arnold, J.; Nowak, K. C.

    2016-12-01

    Many if not most national operational short-to-medium range streamflow prediction systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow are automated, but others require the hands-on-effort of an experienced human forecaster. This approach evolved out of the need to correct for deficiencies in the models and datasets that were available for forecasting, and often leads to skillful predictions despite the use of relatively simple, conceptual models. On the other hand, the process is not reproducible, which limits opportunities to assess and incorporate process variations, and the effort required to make forecasts in this way is an obstacle to expanding forecast services - e.g., though adding new forecast locations or more frequent forecast updates, running more complex models, or producing forecast ensembles and hindcasts that can support verification. In the last decade, the hydrologic forecasting community has begun to develop more centralized, `over-the-loop' systems. The quality of these new forecast products will depend on their ability to leverage research in areas including earth system modeling, parameter estimation, data assimilation, statistical post-processing, weather and climate prediction, verification, and uncertainty estimation through the use of ensembles. Currently, the operational streamflow forecasting and water management communities have little experience with the strengths and weaknesses of over-the-loop approaches, even as the systems are being rolled out in major operational forecasting centers. There is thus a need both to evaluate these forecasting advances and to demonstrate their potential in a public arena, raising awareness in forecast user communities and development programs alike. To address this need, the National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the US Army Corps of Engineers, using the NCAR 'System for

  20. Lessons learned from four years of actively using River Forecast Center Ensemble Streamflow Predictions to inform reservoir management

    NASA Astrophysics Data System (ADS)

    Polebitski, A.; Palmer, R.; Meaker, B.

    2012-12-01

    The National Weather Service's River Forecast Centers (RFCs), located throughout the US, produce operational streamflow forecasts for short term application and long-term lead forecasts at selected locations. These forecasts are targeted for a variety of users, including water supply management, flood control, hydropower production, navigation, and recreation. This presentation highlights the challenges and successes associated with the use of RFC produced ensemble streamflow predictions (ESP) in generating system operations forecasts over the past four years for Snohomish County Public Utility District #1's (SnoPUD) Henry Jackson hydropower system. This research documents a multiyear collaboration between SnoPUD and academic researchers. The collaboration began with a proof of concept study in 2007 and evolved into a weekly decision support activity that has been ongoing since 2008 ( documented in Alemu et al. 2010). The Alemu et al. paper demonstrates the usefulness of ESP forecasts in hydropower operations decision making. This paper focuses on the value of forecasts and a decision support system (DSS) in improving skills in operating reservoir systems. During the application period, the model provided weekly guidance on meeting operational objectives and a probabilistic approach to quantifying system vulnerability during critical periods such as floods and drought. The ESP forecasts and the DSS were heavily used during periods of uncertainty and less so during periods of high system constraint or low system risk.

  1. Objective Use of Climate Indices to Inform Ensemble Streamflow Forecasts in the Columbia River Basin - An Initial Review

    NASA Astrophysics Data System (ADS)

    Pytlak, E.; McManamon, A.; Hughes, S. P.; Van Der Zweep, R. A.; Butcher, P.; Karafotias, C.; Beckers, J.; Welles, E.

    2016-12-01

    Numerous studies have documented the impacts that large scale weather patterns and climate phenomenon like the El Niño Southern Oscillation (ENSO), Pacific-North American (PNA) Pattern, and others can have on seasonal temperature and precipitation in the Columbia River Basin (CRB). While far from perfect in terms of seasonal predictability in specific locations, these intra-annual weather and climate signal do tilt the odds toward different temperature and precipitation outcomes, which in turn can have impacts on seasonal snowpacks, streamflows and water supply in large river basins like the CRB. We hypothesize that intraseasonal climate signals and long wave jet stream patterns can be objectively incorporated into what it is otherwise a climatology-based set of Ensemble Streamflow Forecasts, and can increase the predictive skill and utility of these forecasts used for mid-range hydropower planning. The Bonneville Power Administration (BPA) and Deltares have developed a subsampling-resampling method to incorporate climate mode information into the Ensemble Streamflow Prediction (ESP) forecasts (Beckers, et al., 2016). Since 2015, BPA and Deltares USA have experimented with this method in pre-operational use, using five objective multivariate climate indices that appear to have the greatest predictive value for seasonal temperature and precipitation in the CRB. The indices are used to objectively select historical weather from about twenty analog years in the 66-year (1949-2015) historical ESP set. These twenty scenarios then serve as the starting point to generate monthly synthetic weather and streamflow time series to return to a set of 66 streamflow traces. Our poster will share initial results from the 2015 and 2016 water years, which included large swings in the Quasi-Biennial Oscillation, persistent blocking jet stream patterns, and the development of a strong El Niño event. While the results are very preliminary and for only two seasons, there may be some

  2. A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

    NASA Astrophysics Data System (ADS)

    Saleh, Firas; Ramaswamy, Venkatsundar; Georgas, Nickitas; Blumberg, Alan F.; Pullen, Julie

    2016-07-01

    This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River basin ( ˜ 36 000 km2) in the United States using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were forced into HEC-HMS to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 h pre-event by utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modeling framework was implemented on the Hudson River basin, it is flexible and applicable in other parts of the world where atmospheric reanalysis products and streamflow data are available.

  3. Description and validation of a streamflow assimilation system for a distributed hydrometeorological model over France. Impacts on the ensemble streamflow forecasts.

    NASA Astrophysics Data System (ADS)

    Thirel, G.; Martin, E.; Mahfouf, J. F.; Massart, S.; Ricci, S.; Regimbeau, F.; Habets, F.

    2009-09-01

    SAFRAN-ISBA-MODCOU (SIM) is a distributed hydro-meteorological model used at Météo-France to predict soil water content and river streamflows. In order to produce a more accurate initial state for the ensemble streamflow forecasts systems based on SIM, an assimilation system is developed at Météo-France. This assimilation system uses past streamflow measurements in order to assess the best initial state of soil water content of the model for ensemble streamflow prediction. The data assimilation system is developed with a modular software (PALM, from the Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique, CERFACS), and is based on the Best Linear Unbiased Estimator (BLUE) method. Data from a maximum of 186 gauge stations are assimilated over France, as well as for single stations, than for embedded stations networks. The first part of the study focuses on the selection of the best model variables for the assimilation process : root zone layer only, root zone and deep layers taken together, and finally taken apart. Two versions of the physics in SIM, including or not an exponential profile of hydraulic conductivity in the soil, are tested for each one of the three configurations. A set of classical hydrological scores are performed on a 18-month period in order to describe the performances of the experiments. This work showed a significant improvement of the Nash criteria and a decrease of the root mean square error for the configuration using the exponential profile of hydraulic conductivity in the soil and with the state variable including the root zone and deep layers taken together. The assimilation system seems more efficient for floods than for low flows. The second part of the work is about the impact of the assimilated initial states of the model on two ensemble streamflow prediction systems (ESPS) based on SIM and the ECMWF EPS and the Météo-France PEARP EPS. The scores are assessed on the same 18-month period, and validated

  4. Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 1. Experimental design and forcing verification

    NASA Astrophysics Data System (ADS)

    Brown, James D.; Wu, Limin; He, Minxue; Regonda, Satish; Lee, Haksu; Seo, Dong-Jun

    2014-11-01

    Retrospective forecasts of precipitation, temperature, and streamflow were generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) for a 20-year period between 1979 and 1999. The hindcasts were produced for two basins in each of four River Forecast Centers (RFCs), namely the Arkansas-Red Basin RFC, the Colorado Basin RFC, the California-Nevada RFC, and the Middle Atlantic RFC. Precipitation and temperature forecasts were produced with the HEFS Meteorological Ensemble Forecast Processor (MEFP). Inputs to the MEFP comprised "raw" precipitation and temperature forecasts from the frozen (circa 1997) version of the NWS Global Forecast System (GFS) and a climatological ensemble, which involved resampling historical observations in a moving window around the forecast valid date ("resampled climatology"). In both cases, the forecast horizon was 1-14 days. This paper outlines the hindcasting and verification strategy, and then focuses on the quality of the temperature and precipitation forecasts from the MEFP. A companion paper focuses on the quality of the streamflow forecasts from the HEFS. In general, the precipitation forecasts are more skillful than resampled climatology during the first week, but comprise little or no skill during the second week. In contrast, the temperature forecasts improve upon resampled climatology at all forecast lead times. However, there are notable differences among RFCs and for different seasons, aggregation periods and magnitudes of the observed and forecast variables, both for precipitation and temperature. For example, the MEFP-GFS precipitation forecasts show the highest correlations and greatest skill in the California Nevada RFC, particularly during the wet season (November-April). While generally reliable, the MEFP forecasts typically underestimate the largest observed precipitation amounts (a Type-II conditional bias). As a statistical technique, the MEFP cannot detect, and thus

  5. Forecast-skill-based simulation of streamflow forecasts

    NASA Astrophysics Data System (ADS)

    Zhao, Tongtiegang; Zhao, Jianshi

    2014-09-01

    Streamflow forecasts are updated periodically in real time, thereby facilitating forecast evolution. This study proposes a forecast-skill-based model of forecast evolution that is able to simulate dynamically updated streamflow forecasts. The proposed model applies stochastic models that deal with streamflow variability to generate streamflow scenarios, which represent cases without forecast skill of future streamflow. The model then employs a coefficient of prediction to determine forecast skill and to quantify the streamflow variability ratio explained by the forecast. By updating the coefficients of prediction periodically, the model efficiently captures the evolution of streamflow forecast. Simulated forecast uncertainty increases with increasing lead time; and simulated uncertainty during a specific future period decreases over time. We combine the statistical model with an optimization model and design a hypothetical case study of reservoir operation. The results indicate the significance of forecast skill in forecast-based reservoir operation. Shortage index reduces as forecast skill increases and ensemble forecast outperforms deterministic forecast at a similar forecast skill level. Moreover, an effective forecast horizon exists beyond which more forecast information does not contribute to reservoir operation and higher forecast skill results in longer effective forecast horizon. The results illustrate that the statistical model is efficient in simulating forecast evolution and facilitates analysis of forecast-based decision making.

  6. SWIFT2: Software for continuous ensemble short-term streamflow forecasting for use in research and operations

    NASA Astrophysics Data System (ADS)

    Perraud, Jean-Michel; Bennett, James C.; Bridgart, Robert; Robertson, David E.

    2016-04-01

    Research undertaken through the Water Information Research and Development Alliance (WIRADA) has laid the foundations for continuous deterministic and ensemble short-term forecasting services. One output of this research is the software Short-term Water Information Forecasting Tools version 2 (SWIFT2). SWIFT2 is developed for use in research on short term streamflow forecasting techniques as well as operational forecasting services at the Australian Bureau of Meteorology. The variety of uses in research and operations requires a modular software system whose components can be arranged in applications that are fit for each particular purpose, without unnecessary software duplication. SWIFT2 modelling structures consist of sub-areas of hydrologic models, nodes and links with in-stream routing and reservoirs. While this modelling structure is customary, SWIFT2 is built from the ground up for computational and data intensive applications such as ensemble forecasts necessary for the estimation of the uncertainty in forecasts. Support for parallel computation on multiple processors or on a compute cluster is a primary use case. A convention is defined to store large multi-dimensional forecasting data and its metadata using the netCDF library. SWIFT2 is written in modern C++ with state of the art software engineering techniques and practices. A salient technical feature is a well-defined application programming interface (API) to facilitate access from different applications and technologies. SWIFT2 is already seamlessly accessible on Windows and Linux via packages in R, Python, Matlab and .NET languages such as C# and F#. Command line or graphical front-end applications are also feasible. This poster gives an overview of the technology stack, and illustrates the resulting features of SWIFT2 for users. Research and operational uses share the same common core C++ modelling shell for consistency, but augmented by different software modules suitable for each context. The

  7. Comparison of streamflow prediction skills from NOAH-MP/RAPID, VIC/RAPID and SWAT toward an ensemble flood forecasting framework over large scales

    NASA Astrophysics Data System (ADS)

    Rajib, M. A.; Tavakoly, A. A.; Du, L.; Merwade, V.; Lin, P.

    2015-12-01

    Considering the differences in how individual models represent physical processes for runoff generation and streamflow routing, use of ensemble output is desirable in an operational streamflow estimation and flood forecasting framework. To enable the use of ensemble streamflow, comparison of multiple hydrologic models at finer spatial resolution over a large domain is yet to be explored. The objective of this work is to compare streamflow prediction skills from three different land surface/hydrologic modeling frameworks: NOAH-MP/RAPID, VIC/RAPID and SWAT, over the Ohio River Basin with a drainage area of 491,000 km2. For a uniform comparison, all the three modeling frameworks share the same setup with common weather inputs, spatial resolution, and gauge stations being employed in the calibration procedure. The runoff output from NOAH-MP and VIC land surface models is routed through a vector-based river routing model named RAPID, that is set up on the high resolution NHDPlus reaches and catchments. SWAT model is used with its default tightly coupled surface-subsurface hydrology and channel routing components to obtain streamflow for each NHDPlus reach. Model simulations are performed in two modes, including: (i) hindcasting/calibration mode in which the models are calibrated against USGS daily streamflow observations at multiple locations, and (ii) validation mode in which the calibrated models are executed at 3-hourly time interval for historical flood events. In order to have a relative assessment on the model-specific nature of biases during storm events as well as dry periods, time-series of surface runoff and baseflow components at the specific USGS gauging locations are extracted from corresponding observed/simulated streamflow data using a recursive digital filter. The multi-model comparison presented here provides insights toward future model improvements and also serves as the first step in implementing an operational ensemble flood forecasting framework

  8. Dynamical forecast vs Ensemble Streamflow Prediction (ESP): how sensitive are monthly and seasonal hydrological forecasts to the quality of rainfall drivers?

    NASA Astrophysics Data System (ADS)

    Tanguy, Maliko; Prudhomme, Christel; Harrigan, Shaun; Smith, Katie

    2017-04-01

    Seasonal forecasting of hydrological extremes is challenging for the hydro-meteorological modelling community, and the performance of hydrological forecasts at lead times over 1 month is still poor especially for catchments with limited hydrological memory. A considerable amount of effort is being invested within the meteorological community to improve dynamic meteorological forecasting which can then be used to drive hydrological models to produce physically-driven hydrological forecasts. However, currently for the UK, these meteorological forecasts are being produced at 1 month or seasonal time-step, whereas hydrological models often require daily or sub-daily time-steps. A simpler way to get seasonal forecasts is to use historical climate data to drive hydrological models using Ensemble Streamflow Prediction (ESP). This gives a range of possible future hydrological status given known initial conditions, but it does not contain any information on the future dynamic of the atmosphere. The error is highly dependent on the type of catchment, but ESP is an improvement compared to simply using climatology of river flows, especially in groundwater dominated catchments. The objective of this study is to find out how accurate the seasonal rainfall forecast has to be (in terms of total rainfall and temporal distribution) for the dynamical seasonal forecast to beat ESP. To this aim, we have looked at the sensitivity of hydrological models to the quality of driving rainfall input, proxy of 'best possible' forecasts. Study catchments representative of the range of UK's hydro-climatic conditions were selected. For these catchments, synthetic rainfall time series derived from observed data were created by increasingly degrading the data. The number of rainy days, their intensity and their sequencing were artificially modified to analyse which of these characteristics is most important to get a better hydrological forecast using a simple lumped hydrological model (GR4J), and

  9. Impact of streamflow data assimilation and length of the verification period on the quality of short-term ensemble hydrologic forecasts

    NASA Astrophysics Data System (ADS)

    Randrianasolo, A.; Thirel, G.; Ramos, M. H.; Martin, E.

    2014-11-01

    Data assimilation has gained wide recognition in hydrologic forecasting due mainly to its capacity to improve the quality of short-term forecasts. In this study, a comparative analysis is conducted to assess the impact of discharge data assimilation on the quality of streamflow forecasts issued by two different modeling conceptualizations of catchment response. The sensitivity of the performance metrics to the length of the verification period is also investigated. The hydrological modeling approaches are: the coupled physically-based hydro-meteorological model SAFRAN-ISBA-MODCOU, a distributed model with a data assimilation procedure that uses streamflow measurements to assess the initial state of soil water content that optimizes discharge simulations, and the lumped soil moisture-accounting type rainfall-runoff model GRP, which assimilates directly the last observed discharge to update the state of the routing store. The models are driven by the weather ensemble prediction system PEARP of Météo-France, which is based on the global spectral ARPEGE model zoomed over France. It runs 11 perturbed members for a forecast range of 60 h. Forecast and observed data are available for 86 catchments over a 17-month period (March 2005-July 2006) for both models and for 82 catchments over a 52-month period (April 2005-July 2009) for the GRP model. The first dataset is used to investigate the impact of streamflow data assimilation on forecast quality, while the second is used to evaluate the impact of the length of the verification period on the assessment of forecast quality. Forecasts are compared to daily observed discharges and scores are computed for lead times 24 h and 48 h. Results indicate an overall good performance of both hydrological models forced by the PEARP ensemble predictions when the models are run with their data assimilation procedures. In general, when data assimilation is performed, the quality of the forecasts increases: median differences between

  10. A past discharges assimilation system for ensemble streamflow forecasts over France - Part 1: Description and validation of the assimilation system

    NASA Astrophysics Data System (ADS)

    Thirel, G.; Martin, E.; Mahfouf, J.-F.; Massart, S.; Ricci, S.; Habets, F.

    2010-04-01

    Two Ensemble Streamflow Prediction Systems (ESPSs) have been set up at Météo-France. They are based on the French SIM distributed hydrometeorological model. A deterministic analysis run of SIM is used to initialize the two ESPSs. In order to obtain a better initial state, a past discharges assimilation system has been implemented into this analysis SIM run, using the Best Linear Unbiased Estimator (BLUE). Its role is to improve the model soil moisture by using observed streamflows in order to better simulate streamflow. The skills of the assimilation system were assessed for a 569-day period on six different configurations, including two different physics schemes of the model (the use of an exponential profile of hydraulic conductivity or not) and, for each one, three different ways of considering the model soil moisture in the BLUE state variables. Respect of the linearity hypothesis of the BLUE was verified by assessing of the impact of iterations of the BLUE. The configuration including the use of the exponential profile of hydraulic conductivity and the combination of the moisture of the two soil layers in the state variable showed a significant improvement of streamflow simulations. It led to a significantly better simulation than the reference one, and the lowest soil moisture corrections. These results were confirmed by the study of the impacts of the past discharge assimilation system on a set of 49 independent stations.

  11. A past discharges assimilation system for ensemble streamflow forecasts over France - Part 1: Description and validation of the assimilation system

    NASA Astrophysics Data System (ADS)

    Thirel, G.; Martin, E.; Mahfouf, J.-F.; Massart, S.; Ricci, S.; Habets, F.

    2010-08-01

    Two Ensemble Streamflow Prediction Systems (ESPSs) have been set up at Météo-France. They are based on the French SIM distributed hydrometeorological model. A deterministic analysis run of SIM is used to initialize the two ESPSs. In order to obtain a better initial state, a past discharges assimilation system has been implemented into this analysis SIM run, using the Best Linear Unbiased Estimator (BLUE). Its role is to improve the model soil moisture by using streamflow observations in order to better simulate streamflow. The skills of the assimilation system were assessed for a 569-day period on six different configurations, including two different physics schemes of the model (the use of an exponential profile of hydraulic conductivity or not) and, for each one, three different ways of considering the model soil moisture in the BLUE state variables. Respect of the linearity hypothesis of the BLUE was verified by assessing of the impact of iterations of the BLUE. The configuration including the use of the exponential profile of hydraulic conductivity and the combination of the moisture of the two soil layers in the state variable showed a significant improvement of streamflow simulations. It led to a significantly better simulation than the reference one, and the lowest soil moisture corrections. These results were confirmed by the study of the impacts of the past discharge assimilation system on a set of 49 independent stations.

  12. Seasonal Streamflow Forecasts for African Basins

    NASA Astrophysics Data System (ADS)

    Serrat-Capdevila, A.; Valdes, J. B.; Wi, S.; Roy, T.; Roberts, J. B.; Robertson, F. R.; Demaria, E. M.

    2015-12-01

    Using high resolution downscaled seasonal meteorological forecasts we present the development and evaluation of seasonal hydrologic forecasts with Stakeholder Agencies for selected African basins. The meteorological forecasts are produced using the Bias Correction and Spatial Disaggregation (BCSD) methodology applied to NMME hindcasts (North American Multi-Model Ensemble prediction system) to generate a bootstrap resampling of plausible weather forecasts from historical observational data. This set of downscaled forecasts is then used to drive hydrologic models to produce a range of forecasts with uncertainty estimates suitable for water resources planning in African pilot basins (i.e. Upper Zambezi, Mara Basin). In an effort to characterize the utility of these forecasts, we will present an evaluation of these forecast ensembles over the pilot basins, and discuss insights as to their operational applicability by regional actors. Further, these forecasts will be contrasted with those from a standard Ensemble Streamflow Prediction (ESP) approach to seasonal forecasting. The case studies presented here have been developed in the setting of the NASA SERVIR Applied Sciences Team and within the broader context of operational seasonal forecasting in Africa. These efforts are part of a dialogue with relevant planning and management agencies and institutions in Africa, which are in turn exploring how to best use uncertain forecasts for decision making.

  13. Seasonal streamflow forecasting by conditioning climatology with precipitation indices

    NASA Astrophysics Data System (ADS)

    Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian; Perrin, Charles

    2017-03-01

    Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

  14. Ensemble global ocean forecasting

    NASA Astrophysics Data System (ADS)

    Brassington, G. B.

    2016-02-01

    A novel time-lagged ensemble system based on multiple independent cycles has been performed in operations at the Australian Bureau of Meteorology for the past 3 years. Despite the use of only four cycles the ensemble mean provided robustly higher skill and the ensemble variance was a reliable predictor of forecast errors. A spectral analysis comparing the ensemble mean with the members demonstrated the gradual increase in power of random errors with wavenumber up to a saturation length scale imposed by the resolution of the observing system. This system has been upgraded to a near-global 0.1 degree system in a new hybrid six-member ensemble system configuration including a new data assimilation system, cycling pattern and initialisation. The hybrid system consists of two ensemble members per day each with a 3 day cycle. We will outline the performance of both the deterministic and ensemble ocean forecast system.

  15. Precipitation and temperature ensemble forecasts from single-value forecasts

    NASA Astrophysics Data System (ADS)

    Schaake, J.; Demargne, J.; Hartman, R.; Mullusky, M.; Welles, E.; Wu, L.; Herr, H.; Fan, X.; Seo, D. J.

    2007-04-01

    A procedure is presented to construct ensemble forecasts from single-value forecasts of precipitation and temperature. This involves dividing the spatial forecast domain and total forecast period into a number of parts that are treated as separate forecast events. The spatial domain is divided into hydrologic sub-basins. The total forecast period is divided into time periods, one for each model time step. For each event archived values of forecasts and corresponding observations are used to model the joint distribution of forecasts and observations. The conditional distribution of observations for a given single-value forecast is used to represent the corresponding probability distribution of events that may occur for that forecast. This conditional forecast distribution subsequently is used to create ensemble members that vary in space and time using the "Schaake Shuffle" (Clark et al, 2004). The resulting ensemble members have the same space-time patterns as historical observations so that space-time joint relationships between events that have a significant effect on hydrological response tend to be preserved. Forecast uncertainty is space and time-scale dependent. For a given lead time to the beginning of the valid period of an event, forecast uncertainty depends on the length of the forecast valid time period and the spatial area to which the forecast applies. Although the "Schaake Shuffle" procedure, when applied to construct ensemble members from a time-series of single value forecasts, may preserve some of this scale dependency, it may not be sufficient without additional constraint. To account more fully for the time-dependent structure of forecast uncertainty, events for additional "aggregate" forecast periods are defined as accumulations of different "base" forecast periods. The generated ensemble members can be ingested by an Ensemble Streamflow Prediction system to produce ensemble forecasts of streamflow and other hydrological variables that reflect

  16. The HEPEX Seasonal Streamflow Forecast Intercomparison Project

    NASA Astrophysics Data System (ADS)

    Wood, A. W.; Schepen, A.; Bennett, J.; Mendoza, P. A.; Ramos, M. H.; Wetterhall, F.; Pechlivanidis, I.

    2016-12-01

    The Hydrologic Ensemble Prediction Experiment (HEPEX; www.hepex.org) has launched an international seasonal streamflow forecasting intercomparison project (SSFIP) with the goal of broadening community knowledge about the strengths and weaknesses of various operational approaches being developed around the world. While some of these approaches have existed for decades (e.g. Ensemble Streamflow Prediction - ESP - in the United States and elsewhere), recent years have seen the proliferation of new operational and experimental streamflow forecasting approaches. These have largely been developed independently in each country, thus it is difficult to assess whether the approaches employed in some centers offer more promise for development than others. This motivates us to establish a forecasting testbed to facilitate a diagnostic evaluation of a range of different streamflow forecasting approaches and their components over a common set of catchments, using a common set of validation methods. Rather than prescribing a set of scientific questions from the outset, we are letting the hindcast results and notable differences in methodologies on a watershed-specific basis motivate more targeted analyses and sub-experiments that may provide useful insights. The initial pilot of the testbed involved two approaches - CSIRO's Bayesian joint probability (BJP) and NCAR's sequential regression - for two catchments, each designated by one of the teams (the Murray River, Australia, and Hungry Horse reservoir drainage area, USA). Additional catchments/approaches are in the process of being added to the testbed. To support this CSIRO and NCAR have developed data and analysis tools, data standards and protocols to formalize the experiment. These include requirements for cross-validation, verification, reference climatologies, and common predictands. This presentation describes the SSFIP experiments, pilot basin results and scientific findings to date.

  17. Monthly hydrometeorological ensemble prediction of streamflow droughts and corresponding drought indices

    NASA Astrophysics Data System (ADS)

    Fundel, F.; Jörg-Hess, S.; Zappa, M.

    2013-01-01

    Streamflow droughts, characterized by low runoff as consequence of a drought event, affect numerous aspects of life. Economic sectors that are impacted by low streamflow are, e.g., power production, agriculture, tourism, water quality management and shipping. Those sectors could potentially benefit from forecasts of streamflow drought events, even of short events on the monthly time scales or below. Numerical hydrometeorological models have increasingly been used to forecast low streamflow and have become the focus of recent research. Here, we consider daily ensemble runoff forecasts for the river Thur, which has its source in the Swiss Alps. We focus on the evaluation of low streamflow and of the derived indices as duration, severity and magnitude, characterizing streamflow droughts up to a lead time of one month. The ECMWF VarEPS 5-member ensemble reforecast, which covers 18 yr, is used as forcing for the hydrological model PREVAH. A thorough verification reveals that, compared to probabilistic peak-flow forecasts, which show skill up to a lead time of two weeks, forecasts of streamflow droughts are skilful over the entire forecast range of one month. For forecasts at the lower end of the runoff regime, the quality of the initial state seems to be crucial to achieve a good forecast quality in the longer range. It is shown that the states used in this study to initialize forecasts satisfy this requirement. The produced forecasts of streamflow drought indices, derived from the ensemble forecasts, could be beneficially included in a decision-making process. This is valid for probabilistic forecasts of streamflow drought events falling below a daily varying threshold, based on a quantile derived from a runoff climatology. Although the forecasts have a tendency to overpredict streamflow droughts, it is shown that the relative economic value of the ensemble forecasts reaches up to 60%, in case a forecast user is able to take preventive action based on the forecast.

  18. Monthly streamflow forecasting in the Rhine basin

    NASA Astrophysics Data System (ADS)

    Schick, Simon; Rössler, Ole; Weingartner, Rolf

    2017-04-01

    Forecasting seasonal streamflow of the Rhine river is of societal relevance as the Rhine is an important water way and water resource in Western Europe. The present study investigates the predictability of monthly mean streamflow at lead times of zero, one, and two months with the focus on potential benefits by the integration of seasonal climate predictions. Specifically, we use seasonal predictions of precipitation and surface air temperature released by the European Centre for Medium-Range Weather Forecasts (ECMWF) for a regression analysis. In order to disentangle forecast uncertainty, the 'Reverse Ensemble Streamflow Prediction' framework is adapted here to the context of regression: By using appropriate subsets of predictors the regression model is constrained to either the initial conditions, the meteorological forcing, or both. An operational application is mimicked by equipping the model with the seasonal climate predictions provided by ECMWF. Finally, to mitigate the spatial aggregation of the meteorological fields the model is also applied at the subcatchment scale, and the resulting predictions are combined afterwards. The hindcast experiment is carried out for the period 1982-2011 in cross validation mode at two gauging stations, namely the Rhine at Lobith and Basel. The results show that monthly forecasts are skillful with respect to climatology only at zero lead time. In addition, at zero lead time the integration of seasonal climate predictions decreases the mean absolute error by 5 to 10 percentage compared to forecasts which are solely based on initial conditions. This reduction most likely is induced by the seasonal prediction of precipitation and not air temperature. The study is completed by bench marking the regression model with runoff simulations from ECMWFs seasonal forecast system. By simply using basin averages followed by a linear bias correction, these runoff simulations translate well to monthly streamflow. Though the regression model

  19. Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

    NASA Astrophysics Data System (ADS)

    Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian

    2016-09-01

    Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias-corrected precipitation and streamflow ensemble forecasts in 16 catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e. ESP method) is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contributes mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping

  20. Using Ensemble Streamflows for Power Marketing at Bonneville Power Administration

    NASA Astrophysics Data System (ADS)

    Barton, S. B.; Koski, P.

    2014-12-01

    Bonneville Power Administration (BPA) is a federal non-profit agency within the Pacific Northwest responsible for marketing the power generated from 31 federal hydro projects throughout the Columbia River Basin. The basin encompasses parts of five states and portions of British Columbia, Canada. BPA works with provincial entities, federal and state agencies, and tribal members to manage the water resources for a variety of purposes including flood risk management, power generation, fisheries, irrigation, recreation, and navigation. This basin is subject to significant hydrologic variability in terms of seasonal volume and runoff shape from year to year which presents new water management challenges each year. The power generation planning group at BPA includes a team of meteorologists and hydrologists responsible for preparing both short-term (up to three weeks) and mid-term (up to 18 months) weather and streamflow forecasts including ensemble streamflow data. Analysts within the mid-term planning group are responsible for running several different hydrologic models used for planning studies. These models rely on these streamflow ensembles as a primary input. The planning studies are run bi-weekly to help determine the amount of energy available, or energy inventory, for forward marketing (selling or purchasing energy up to a year in advance). These studies are run with the objective of meeting the numerous multi-purpose objectives of the basin under the various streamflow conditions within the ensemble set. In addition to ensemble streamflows, an ensemble of seasonal volume forecasts is also provided for the various water conditions in order to set numerous constraints on the system. After meeting all the various requirements of the system, a probabilistic energy inventory is calculated and used for marketing purposes.

  1. Infusion of Ensemble Data Assimilation in the operational NWS-Ensemble Streamflow Prediction (ESP) System

    NASA Astrophysics Data System (ADS)

    Meskele, T. T.; Moradkhani, H.

    2009-12-01

    Operational Streamflow forecasting accuracy is one of the issues to be addressed yet for effective water resources Management. The traditional based linear regression procedure produces a single-valued forecast that lacks treatment of associated uncertainties. The Ensemble Streamflow Prediction (ESP) was developed to improve the quantity and quality of information in the forecasts. The ESP produces multiple estimates of a streamflow variable based on current basin conditions and resampled past meteorological observations. Despite the fact that the ESP forecasting process is designed to account for the uncertain nature of the climate variable during the forecasting period, the procedure is entirely deterministic up to the starting point of forecast. Thus the operation represents forecast uncertainty due to forcing uncertainty only assuming the historical forcing in the past and the initial condition as perfect. However, in addition to the future forcing data uncertainty in the streamflow prediction, uncertainty may arise from the initial conditions (moisture states, and snowpack), model structure and parameters.. Thus in this study we addressed the initial condition, model structure and forcing data uncertainty via the data assimilation procedure, known as the Particle Filter (PF). We show that the ESP based on uncertain initial condition will create higher uncertainty as compared to traditional ESP. We used Sacramento Soil Moisture Accounting Model (SAC-SMA) for our study and tested the procedure over the Leaf River Basin in Mississippi.

  2. Assessment of SWE data assimilation for ensemble streamflow predictions

    NASA Astrophysics Data System (ADS)

    Franz, Kristie J.; Hogue, Terri S.; Barik, Muhammad; He, Minxue

    2014-11-01

    An assessment of data assimilation (DA) for Ensemble Streamflow Prediction (ESP) using seasonal water supply hindcasting in the North Fork of the American River Basin (NFARB) and the National Weather Service (NWS) hydrologic forecast models is undertaken. Two parameter sets, one from the California Nevada River Forecast Center (RFC) and one from the Differential Evolution Adaptive Metropolis (DREAM) algorithm, are tested. For each parameter set, hindcasts are generated using initial conditions derived with and without the inclusion of a DA scheme that integrates snow water equivalent (SWE) observations. The DREAM-DA scenario uses an Integrated Uncertainty and Ensemble-based data Assimilation (ICEA) framework that also considers model and parameter uncertainty. Hindcasts are evaluated using deterministic and probabilistic forecast verification metrics. In general, the impact of DA on the skill of the seasonal water supply predictions is mixed. For deterministic (ensemble mean) predictions, the Percent Bias (PBias) is improved with integration of the DA. DREAM-DA and the RFC-DA have the lowest biases and the RFC-DA has the lowest Root Mean Squared Error (RMSE). However, the RFC and DREAM-DA have similar RMSE scores. For the probabilistic predictions, the RFC and DREAM have the highest Continuous Ranked Probability Skill Scores (CRPSS) and the RFC has the best discrimination for low flows. Reliability results are similar between the non-DA and DA tests and the DREAM and DREAM-DA have better reliability than the RFC and RFC-DA for forecast dates February 1 and later. Despite producing improved streamflow simulations in previous studies, the hindcast analysis suggests that the DA method tested may not result in obvious improvements in streamflow forecasts. We advocate that integration of hindcasting and probabilistic metrics provides more rigorous insight on model performance for forecasting applications, such as in this study.

  3. Streamflow forecasting using functional regression

    NASA Astrophysics Data System (ADS)

    Masselot, Pierre; Dabo-Niang, Sophie; Chebana, Fateh; Ouarda, Taha B. M. J.

    2016-07-01

    Streamflow, as a natural phenomenon, is continuous in time and so are the meteorological variables which influence its variability. In practice, it can be of interest to forecast the whole flow curve instead of points (daily or hourly). To this end, this paper introduces the functional linear models and adapts it to hydrological forecasting. More precisely, functional linear models are regression models based on curves instead of single values. They allow to consider the whole process instead of a limited number of time points or features. We apply these models to analyse the flow volume and the whole streamflow curve during a given period by using precipitations curves. The functional model is shown to lead to encouraging results. The potential of functional linear models to detect special features that would have been hard to see otherwise is pointed out. The functional model is also compared to the artificial neural network approach and the advantages and disadvantages of both models are discussed. Finally, future research directions involving the functional model in hydrology are presented.

  4. Benchmarking Ensemble Streamflow Prediction Skill in the UK

    NASA Astrophysics Data System (ADS)

    Harrigan, Shaun; Smith, Katie; Parry, Simon; Tanguy, Maliko; Prudhomme, Christel

    2017-04-01

    Skilful hydrological forecasts at weekly to seasonal lead times would be extremely beneficial for decision-making in operational water management, especially during drought conditions. Hydro-meteorological ensemble forecasting systems are an attractive approach as they use two sources of streamflow predictability: (i) initial hydrologic conditions (IHCs), where soil moisture, groundwater and snow storage states can provide an estimate of future streamflow situations, and (ii) atmospheric predictability, where skilful forecasts of weather and climate variables can be used to force hydrological models. In the UK, prediction of rainfall at long lead times and for summer months in particular is notoriously difficult given the large degree of natural climate variability in ocean influenced mid-latitude regions, but recent research has uncovered exciting prospects for improved rainfall skill at seasonal lead times due to improved prediction of the North Atlantic Oscillation. However, before we fully understand what this improved atmospheric predictability might mean in terms of improved hydrological forecasts, we must first evaluate how much skill can be gained from IHCs alone. Ensemble Streamflow Prediction (ESP) is a well-established method for generating an ensemble of streamflow forecasts in the absence of skilful future meteorological predictions. The aim of this study is therefore to benchmark when (lead time/forecast initialisation month) and where (spatial pattern/catchment characteristics) ESP is skilful across a diverse set of catchments in the UK. Forecast skill was evaluated seamlessly from lead times of 1-day to 12-months and forecasts were initialised at the first of each month over the 1965-2015 hindcast period. This ESP output also provides a robust benchmark against which to assess how much improvement in skill can be achieved when meteorological forecasts are incorporated (next steps). To provide a 'tough to beat' benchmark, several variants of ESP with

  5. Effect of Streamflow Forecast Uncertainty on Real-Time Reservoir Operation

    NASA Astrophysics Data System (ADS)

    Zhao, T.; Cai, X.; Yang, D.

    2010-12-01

    Various hydrological forecast products have been applied to real-time reservoir operation, including deterministic streamflow forecast (DSF), DSF-based probabilistic streamflow forecast (DPSF), and ensemble streamflow forecast (ESF), which represent forecast uncertainty in the form of deterministic forecast error, deterministic forecast error-based uncertainty distribution, and ensemble forecast errors, respectively. Compared to previous studies that treat these forecast products as ad hoc inputs for reservoir operation models, this paper attempts to model the uncertainties involved in the various forecast products and explores their effect on real-time reservoir operation decisions. In hydrology, there are various indices reflecting the magnitude of streamflow forecast uncertainty; meanwhile, few models illustrate the forecast uncertainty evolution process. This research introduces Martingale Model of Forecast Evolution (MMFE) from supply chain management and justifies its assumptions for quantifying the evolution of uncertainty in streamflow forecast as time progresses. Based on MMFE, this research simulates the evolution of forecast uncertainty in DSF, DPSF, and ESF, and applies the reservoir operation models (dynamic programming, DP; stochastic dynamic programming, SDP; and standard operation policy, SOP) to assess the effect of different forms of forecast uncertainty on real-time reservoir operation. Through a hypothetical single-objective real-time reservoir operation model, the results illustrate that forecast uncertainty exerts significant effects. Reservoir operation efficiency, as measured by a utility function, decreases as the forecast uncertainty increases. Meanwhile, these effects also depend on the type of forecast product being used. In general, the utility of reservoir operation with ESF is nearly as high as the utility obtained with a perfect forecast; the utilities of DSF and DPSF are similar to each other but not as efficient as ESF. Moreover

  6. Advancing Ensemble Streamflow Prediction with Stochastic Meteorological Forcings for Hydrologic Modeling

    NASA Astrophysics Data System (ADS)

    Caraway, N.; Wood, A. W.; Rajagopalan, B.; Zagona, E. A.; Daugherty, L.

    2012-12-01

    River Forecast Centers of National Weather Service (NWS) produce seasonal streamflow forecasts via a method called Ensemble Streamflow Prediction (ESP). NWS ESP forces the temperature index Snow17 and Sacramento Soil Moisture Accounting model (SAC-SMA) models with historical weather sequences for the forecasting period, starting from models' current watershed initial conditions, to produce ensemble streamflow forecasts. There are two major drawbacks of this method: (i) the ensembles are limited to the length of historical, limiting ensemble variability and (ii) incorporating seasonal climate forecasts (e.g., El Nino Southern Oscillation) relies on adjustment or weighting of ESP streamflow sequences. These drawbacks motivate the research presented here, which has two components: (i) a multi-site stochastic weather generator and (ii) generation of ensemble weather forecast inputs to the NWS model to produce ensemble streamflow forecasts. We enhanced the K-nearest neighbor bootstrap based stochastic generator include: (i) clustering the forecast locations into climatologically homogeneous regions to better capture the spatial heterogeneity and, (ii) conditioning the weather forecasts on a probabilistic seasonal climate forecast. This multi-site stochastic weather generator runs in R and the NWS models run within the new Community Hydrologic Prediction System, a forecasting sequence we label WG-ESP. The WG-ESP framework was applied to generate ensemble forecasts of spring season (April-July) streamflow in the San Juan River Basin, one of the major tributaries of the Colorado River, for the period 1981-2010. The hydrologic model requires daily weather sequences at 66 locations in the basin. The enhanced daily weather generator sequences captured the distributional properties and spatial dependence of the climatological ESP, and also generated weather sequences consistent with conditioning on seasonal climate forecasts. Spring season ensemble forecast lead times from

  7. Seasonal hydrological ensemble forecasts over Europe

    NASA Astrophysics Data System (ADS)

    Arnal, Louise; Wetterhall, Fredrik; Pappenberger, Florian

    2015-04-01

    Seasonal forecasts have an important socio-economic value in hydro-meteorological forecasting. The applications are for example hydropower management, spring flood prediction and water resources management. The latter includes prediction of low flows, primordial for navigation, water quality assessment, droughts and agricultural water needs. Traditionally, seasonal hydrological forecasts are done using the observed discharge from previous years, so called Ensemble Streamflow Prediction (ESP). With the recent increasing development of seasonal meteorological forecasts, the incentive for developing and improving seasonal hydrological forecasts is great. In this study, a seasonal hydrological forecast, driven by the ECMWF's System 4 (SEA), was compared with an ESP of modelled discharge using observations. The hydrological model used for both forecasts was the LISFLOOD model, run over a European domain with a spatial resolution of 5 km. The forecasts were produced from 1990 until the present time, with a daily time step. They were issued once a month with a lead time of seven months. The SEA forecasts are constituted of 15 ensemble members, extended to 51 members every three months. The ESP forecasts comprise 20 ensembles and served as a benchmark for this comparative study. The forecast systems were compared using a diverse set of verification metrics, such as continuous ranked probability scores, ROC curves, anomaly correlation coefficients and Nash-Sutcliffe efficiency coefficients. These metrics were computed over several time-scales, ranging from a weekly to a six-months basis, for each season. The evaluation enabled the investigation of several aspects of seasonal forecasting, such as limits of predictability, timing of high and low flows, as well as exceedance of percentiles. The analysis aimed at exploring the spatial distribution and timely evolution of the limits of predictability.

  8. Ensemble flood forecasting: A review

    NASA Astrophysics Data System (ADS)

    Cloke, H. L.; Pappenberger, F.

    2009-09-01

    SummaryOperational medium range flood forecasting systems are increasingly moving towards the adoption of ensembles of numerical weather predictions (NWP), known as ensemble prediction systems (EPS), to drive their predictions. We review the scientific drivers of this shift towards such 'ensemble flood forecasting' and discuss several of the questions surrounding best practice in using EPS in flood forecasting systems. We also review the literature evidence of the 'added value' of flood forecasts based on EPS and point to remaining key challenges in using EPS successfully.

  9. Ensemble flood forecasting: A review

    NASA Astrophysics Data System (ADS)

    Cloke, Hannah; Pappenberger, Florian

    2010-05-01

    Operational medium range flood forecasting systems are increasingly moving towards the adoption of ensembles of numerical weather predictions (NWP), known as ensemble prediction systems (EPS), to drive their predictions. We review the scientific drivers of this shift towards such ‘ensemble flood forecasting' and discuss several of the questions surrounding best practice in using EPS in flood forecasting systems. We also review the literature evidence of the ‘added value' of flood forecasts based on EPS and point to remaining key challenges in using EPS successfully. A continuous review can be found on the website: http://www.floodrisk.net/.

  10. An integrated uncertainty and ensemble-based data assimilation approach for improved operational streamflow predictions

    NASA Astrophysics Data System (ADS)

    He, M.; Hogue, T. S.; Margulis, S. A.; Franz, K. J.

    2012-03-01

    The current study proposes an integrated uncertainty and ensemble-based data assimilation framework (ICEA) and evaluates its viability in providing operational streamflow predictions via assimilating snow water equivalent (SWE) data. This step-wise framework applies a parameter uncertainty analysis algorithm (ISURF) to identify the uncertainty structure of sensitive model parameters, which is subsequently formulated into an Ensemble Kalman Filter (EnKF) to generate updated snow states for streamflow prediction. The framework is coupled to the US National Weather Service (NWS) snow and rainfall-runoff models. Its applicability is demonstrated for an operational basin of a western River Forecast Center (RFC) of the NWS. Performance of the framework is evaluated against existing operational baseline (RFC predictions), the stand-alone ISURF and the stand-alone EnKF. Results indicate that the ensemble-mean prediction of ICEA considerably outperforms predictions from the other three scenarios investigated, particularly in the context of predicting high flows (top 5th percentile). The ICEA streamflow ensemble predictions capture the variability of the observed streamflow well, however the ensemble is not wide enough to consistently contain the range of streamflow observations in the study basin. Our findings indicate that the ICEA has the potential to supplement the current operational (deterministic) forecasting method in terms of providing improved single-valued (e.g., ensemble mean) streamflow predictions as well as meaningful ensemble predictions.

  11. An integrated uncertainty and ensemble-based data assimilation approach for improved operational streamflow predictions

    NASA Astrophysics Data System (ADS)

    He, M.; Hogue, T. S.; Margulis, S. A.; Franz, K. J.

    2011-08-01

    The current study proposes an integrated uncertainty and ensemble-based data assimilation framework (ICEA) and evaluates its viability in providing operational streamflow predictions via assimilating snow water equivalent (SWE) data. This step-wise framework applies a parameter uncertainty analysis algorithm (ISURF) to identify the uncertainty structure of sensitive model parameters, which is subsequently formulated into an Ensemble Kalman Filter (EnKF) to generate updated snow states for streamflow prediction. The framework is coupled to the US National Weather Service (NWS) snow and rainfall-runoff models. Its applicability is demonstrated for an operational basin of a western River Forecast Center (RFC) of the NWS. Performance of the framework is evaluated against existing operational baseline (RFC predictions), the stand-alone ISURF, and the stand-alone EnKF. Results indicate that the ensemble-mean prediction of ICEA considerably outperforms predictions from the other three scenarios investigated, particularly in the context of predicting high flows (top 5th percentile). The ICEA streamflow ensemble predictions capture the variability of the observed streamflow well, however the ensemble is not wide enough to consistently contain the range of streamflow observations in the study basin. Our findings indicate that the ICEA has the potential to supplement the current operational (deterministic) forecasting method in terms of providing improved single-valued (e.g., ensemble mean) streamflow predictions as well as meaningful ensemble predictions.

  12. Configurational entropy theory for streamflow forecasting

    NASA Astrophysics Data System (ADS)

    Cui, Huijuan; Singh, Vijay P.

    2015-02-01

    This study develops configurational entropy theory (CET) for monthly streamflow forecasting. The theory is comprised of three main parts: (1) determination of spectral density (2) determination of parameters by cepstrum analysis, and (3) extension of autocorrelation function. Comparison with the Burg entropy theory (BET) shows that CET yields higher resolution spectral density with more accurate location of spectral peaks. Cepstrum analysis yields more accurate parameters than the Levinson algorithm in the autoregressive (AR) method and the Levinson-Burg algorithm in BET. CET is tested using monthly streamflow data from 19 river basins covering a broad range of physiographic characteristics. Testing shows that CET captures streamflow seasonality and satisfactorily forecasts both high and low flows. High flows are satisfactorily forecasted with the coefficient of determination (r2) higher than 0.92 for one year ahead of time, with r2 higher than 0.85 for two years ahead of time, and up to 60 months ahead with r2 higher than 0.80. However, low flows are forecasted with r2 higher than 0.50 for one year ahead time. When relative drainage area is considered for analyzing streamflow characteristics and spectral patterns, it is found that upstream streamflow is forecasted more accurately (r2 = 0.84) than downstream streamflow (r2 = 0.75). Residuals of forecasted values relative to observed values are found to follow normal distribution.

  13. Conditional Weather Resampling Method for Seasonal Ensemble Streamflow Prediction

    NASA Astrophysics Data System (ADS)

    Beckers, Joost; Weerts, Albrecht; Welles, Edwin

    2014-05-01

    Ensemble Streamflow Prediction (ESP) is a commonly used method for water resources planning on the seasonal time scale. The starting point for the ESP is the current state of the hydrological system, which is generated form a short historical simulation up to the time of forecast. Starting from this initial state, a hydrologic model is run to produce an ensemble of possible realizations of future streamflows, taking meteorological time series from historical years as input. It is assumed that these historical weather time series represent climatology. One disadvantage of the original ESP method is that an expected deviation from average climatology is not accounted for. Here, we propose a variation to the ESP, in which shorter periods from historical time years are resampled and assembled to generate additional possible realizations of future weather. The resampling is done in such a way as to incorporate statistical deviations from the average climate that are linked to climate modes, such as El Niño Southern Oscillation (ENSO) or Pacific Decadal Oscillation (PDO). These climate modes are known to affect the local weather in many regions around the world. The resampling of historical weather periods is conditioned on the climate mode indices, starting with the current climate index value and searching for historical years with similar climate indices. The resampled weather time series are used as input for the hydrological model, similar to the original ESP procedure. The method was implemented in the operational forecasting environment of Bonneville Power Administration (BPA), which based on Delft-FEWS. The method was run for 55 non-operational years of hindcasts (forecasts in retrospect) for the Columbia River in the North-West of the U.S. An increase in forecast skill up to 5% was found relative to the standard ESP for streamflow predictions at three test-locations.

  14. Seasonal hydrological ensemble forecasts over Europe

    NASA Astrophysics Data System (ADS)

    Arnal, Louise; Wetterhall, Fredrik; Stephens, Elisabeth; Cloke, Hannah; Pappenberger, Florian

    2016-04-01

    This study investigates the limits of predictability in dynamical seasonal discharge forecasting, in both space and time, over Europe. Seasonal forecasts have an important socioeconomic value. Applications are numerous and cover hydropower management, spring flood prediction, low flow prediction for navigation and agricultural water demands. Additionally, the constant increase in NWP skill for longer lead times and the predicted increase in the intensity and frequency of hydro-meteorological extremes, have amplified the incentive to promote and further improve hydrological forecasts on sub-seasonal to seasonal timescales. In this study, seasonal hydrological forecasts (SEA), driven by the ECMWF's System 4 in hindcast mode, were analysed against an Ensemble Streamflow Prediction (ESP) benchmark. The ESP was forced with an ensemble of resampled historical meteorological observations and started with perfect initial conditions. Both forecasts were produced by the LISFLOOD model, run on the pan-European scale with a spatial resolution of 5 by 5 km. The forecasts were issued monthly on a daily time step, from 1990 until the current time, up to a lead time of 7 months. The seasonal discharge forecasts were analysed against the ESP on a catchment scale in terms of their accuracy, skill and sharpness, using a diverse set of verification metrics (e.g. KGE, CRPSS and ROC). Additionally, a reverse-ESP was constructed by forcing the LISFLOOD model with a single perfect meteorological set of observations and initiated from an ensemble of resampled historical initial conditions. The comparison of the ESP with the reverse-ESP approach enabled the identification of the respective contribution of meteorological forcings and hydrologic initial conditions errors to seasonal discharge forecasting uncertainties in Europe. These results could help pinpoint target elements of the forecasting chain which, after being improved, could lead to substantial increase in discharge predictability

  15. Ensemble Streamflow Prediction in Korea: Past and Future 5 Years

    NASA Astrophysics Data System (ADS)

    Jeong, D.; Kim, Y.; Lee, J.

    2005-05-01

    The Ensemble Streamflow Prediction (ESP) approach was first introduced in 2000 by the Hydrology Research Group (HRG) at Seoul National University as an alternative probabilistic forecasting technique for improving the 'Water Supply Outlook' That is issued every month by the Ministry of Construction and Transportation in Korea. That study motivated the Korea Water Resources Corporation (KOWACO) to establish their seasonal probabilistic forecasting system for the 5 major river basins using the ESP approach. In cooperation with the HRG, the KOWACO developed monthly optimal multi-reservoir operating systems for the Geum river basin in 2004, which coupled the ESP forecasts with an optimization model using sampling stochastic dynamic programming. The user interfaces for both ESP and SSDP have also been designed for the developed computer systems to become more practical. More projects for developing ESP systems to the other 3 major river basins (i.e. the Nakdong, Han and Seomjin river basins) was also completed by the HRG and KOWACO at the end of December 2004. Therefore, the ESP system has become the most important mid- and long-term streamflow forecast technique in Korea. In addition to the practical aspects, resent research experience on ESP has raised some concerns into ways of improving the accuracy of ESP in Korea. Jeong and Kim (2002) performed an error analysis on its resulting probabilistic forecasts and found that the modeling error is dominant in the dry season, while the meteorological error is dominant in the flood season. To address the first issue, Kim et al. (2004) tested various combinations and/or combining techniques and showed that the ESP probabilistic accuracy could be improved considerably during the dry season when the hydrologic models were combined and/or corrected. In addition, an attempt was also made to improve the ESP accuracy for the flood season using climate forecast information. This ongoing project handles three types of climate

  16. A System for Continuous Hydrological Ensemble Forecasting (SCHEF) to lead times of 9 days

    NASA Astrophysics Data System (ADS)

    Bennett, James C.; Robertson, David E.; Shrestha, Durga Lal; Wang, Q. J.; Enever, David; Hapuarachchi, Prasantha; Tuteja, Narendra K.

    2014-11-01

    This study describes a System for Continuous Hydrological Ensemble Forecasting (SCHEF) designed to forecast streamflows to lead times of 9 days. SCHEF is intended to support optimal management of water resources for consumptive and environmental purposes and ultimately to support the management of impending floods. Deterministic rainfall forecasts from the ACCESS-G numerical weather prediction (NWP) model are post-processed using a Bayesian joint probability model to correct biases and quantify uncertainty. Realistic temporal and spatial characteristics are instilled in the rainfall forecast ensemble with the Schaake shuffle. The ensemble rainfall forecasts are then used as inputs to the GR4H hydrological model to produce streamflow forecasts. A hydrological error correction is applied to ensure forecasts transit smoothly from recent streamflow observations. SCHEF forecasts streamflows skilfully for a range of hydrological and climate conditions. Skill is particularly evident in forecasts of streamflows at lead times of 1-6 days. Forecasts perform best in temperate perennially flowing rivers, while forecasts are poorest in intermittently flowing rivers. The poor streamflow forecasts in intermittent rivers are primarily the result of poor rainfall forecasts, rather than an inadequate representation of hydrological processes. Forecast uncertainty becomes more reliably quantified at longer lead times; however there is considerable scope for improving the reliability of streamflow forecasts at all lead times. Additionally, we show that the choice of forecast time-step can influence forecast accuracy: forecasts generated at a 1-h time-step tend to be more accurate than at longer time-steps (e.g. 1-day). This is largely because at shorter time-steps the hydrological error correction is able to correct streamflow forecasts with more recent information, rather than the ability of GR4H to simulate hydrological processes better at shorter time-steps. SCHEF will form the

  17. Maximum entropy spectral analysis for streamflow forecasting

    NASA Astrophysics Data System (ADS)

    Cui, Huijuan; Singh, Vijay P.

    2016-01-01

    Configurational entropy spectral analysis (CESAS) is developed with spectral power as a random variable for streamflow forecasting. It is found that the CESAS derived by maximizing the configurational entropy yields the same solution as by the Burg entropy spectral analysis (BESA). Comparison of forecasted streamflows by CESAS and BESA shows less than 0.001% difference between the two analyses and thus the two entropy spectral analyses are concluded to be identical. Thus, the Burg entropy spectral analysis and two configurational entropy spectral analyses form the maximum entropy spectral analysis.

  18. Adaptive correction of ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Pelosi, Anna; Battista Chirico, Giovanni; Van den Bergh, Joris; Vannitsem, Stephane

    2017-04-01

    Forecasts from numerical weather prediction (NWP) models often suffer from both systematic and non-systematic errors. These are present in both deterministic and ensemble forecasts, and originate from various sources such as model error and subgrid variability. Statistical post-processing techniques can partly remove such errors, which is particularly important when NWP outputs concerning surface weather variables are employed for site specific applications. Many different post-processing techniques have been developed. For deterministic forecasts, adaptive methods such as the Kalman filter are often used, which sequentially post-process the forecasts by continuously updating the correction parameters as new ground observations become available. These methods are especially valuable when long training data sets do not exist. For ensemble forecasts, well-known techniques are ensemble model output statistics (EMOS), and so-called "member-by-member" approaches (MBM). Here, we introduce a new adaptive post-processing technique for ensemble predictions. The proposed method is a sequential Kalman filtering technique that fully exploits the information content of the ensemble. One correction equation is retrieved and applied to all members, however the parameters of the regression equations are retrieved by exploiting the second order statistics of the forecast ensemble. We compare our new method with two other techniques: a simple method that makes use of a running bias correction of the ensemble mean, and an MBM post-processing approach that rescales the ensemble mean and spread, based on minimization of the Continuous Ranked Probability Score (CRPS). We perform a verification study for the region of Campania in southern Italy. We use two years (2014-2015) of daily meteorological observations of 2-meter temperature and 10-meter wind speed from 18 ground-based automatic weather stations distributed across the region, comparing them with the corresponding COSMO

  19. Bias correcting precipitation forecasts for extended-range skilful seasonal streamflow predictions

    NASA Astrophysics Data System (ADS)

    Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian

    2016-04-01

    Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to also benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France in order to provide insights into the way bias correcting seasonal precipitation forecasts can contribute to maintain skill of seasonal flow predictions at extended lead times. First, we evaluate the skill of raw (i.e., without bias correction) seasonal precipitation ensemble forecasts for streamflow forecasting in sixteen French catchments. A lumped daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. A reference prediction system based on historic observed precipitation and watershed initial conditions at the time of forecast (i.e., ESP method) is used as benchmark. In a second step, we apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the flow forecasts. The approaches were based on the linear scaling and the distribution mapping methods. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy, and overall performance. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, reliability is an attribute that is not significantly improved. Forecast skill is generally improved when applying bias correction. Two bias correction methods showed the best performance for the studied catchments, with, however, each method being more successful in improving specific attributes of forecast quality: the simple linear scaling of monthly values contributed mainly to increase forecast

  20. ENSO-conditioned weather resampling method for seasonal ensemble streamflow prediction

    NASA Astrophysics Data System (ADS)

    Beckers, Joost V. L.; Weerts, Albrecht H.; Tijdeman, Erik; Welles, Edwin

    2016-08-01

    Oceanic-atmospheric climate modes, such as El Niño-Southern Oscillation (ENSO), are known to affect the local streamflow regime in many rivers around the world. A new method is proposed to incorporate climate mode information into the well-known ensemble streamflow prediction (ESP) method for seasonal forecasting. The ESP is conditioned on an ENSO index in two steps. First, a number of original historical ESP traces are selected based on similarity between the index value in the historical year and the index value at the time of forecast. In the second step, additional ensemble traces are generated by a stochastic ENSO-conditioned weather resampler. These resampled traces compensate for the reduction of ensemble size in the first step and prevent degradation of skill at forecasting stations that are less affected by ENSO. The skill of the ENSO-conditioned ESP is evaluated over 50 years of seasonal hindcasts of streamflows at three test stations in the Columbia River basin in the US Pacific Northwest. An improvement in forecast skill of 5 to 10 % is found for two test stations. The streamflows at the third station are less affected by ENSO and no change in forecast skill is found here.

  1. Evaluation Of Ensemble Forecasts By PECA

    NASA Astrophysics Data System (ADS)

    Wei, M.; Toth, Z.

    2002-12-01

    A method called Perturbation vs. Error Correlation Analysis (PECA), which evaluates the ensemble perturbations instead of the forecasts themselves by measuring their ability to explain forecast error variance, is used to evaluate ensemble forecasts from NCEP and ECMWF. Ensemble perturbations from NCEP and ECMWF were found to perform similarly. The error variance explained by either ensemble increases with the number of members and the lead time. The dynamically conditioned NCEP and ECMWF perturbations outperform both randomly chosen perturbations and differences between lagged forecasts ("NMC" method). Thus ensemble forecasts potentially could be used to construct flow dependent short-range forecast error covariance matrices for use in data assimilation schemes.

  2. Application of artificial neural network ensembles in probabilistic hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Araghinejad, Shahab; Azmi, Mohammad; Kholghi, Majid

    2011-09-01

    SummaryEnsemble techniques are used in regression/classification tasks with considerable success. Due to the flexible geometry of artificial neural networks (ANNs), they have been recognized as suitable models for ensemble techniques. The application of an ensemble technique is divided into two steps. The first step is to create individual ensemble members, and the second step is the appropriate combination of outputs of the ensemble members to produce the most appropriate output. This paper deals with the techniques of both generation and combination of ANN ensembles. A new performance function is proposed for generating neural network ensembles. Also a probabilistic method based on the K-nearest neighbor regression is proposed to combine individual networks and to improve the accuracy and precision of hydrological forecasts. The proposed method is applied on the peak discharge forecasting of the floods of Red River in Canada as well as the seasonal streamflow forecasting of Zayandeh-rud River in Iran. The study analyses the advantages of the proposed methods in comparison with the conventional empirical methods such as conventional artificial neural networks, and K-nearest neighbor regression. The utility of the proposed method for forecasting hydrological variables with a conditional probability distribution is demonstrated. The results of this study show that the application of the ensemble ANNs through the proposed method can improve the probabilistic forecast skill for hydrological events.

  3. Short-term Ensemble Flood Forecasting Experiments in Brazil

    NASA Astrophysics Data System (ADS)

    Collischonn, Walter; Meller, Adalberto; Fan, Fernando; Moreira, Demerval; Dias, Pedro; Buarque, Diogo; Bravo, Juan

    2013-04-01

    Flood Forecasting and issuing early warnings to communities under risk can help reduce the impacts of those events. However, to be effective, warnings should be given several hours in advance. The best solution to extend the lead time is possibly the use of rainfall-runoff models with input given by rainfall and streamflow observations and by forecasts of future precipitation derived from numerical weather prediction (NWP) models. Recent studies showed that probabilistic or ensemble flood forecasts produced using ensemble precipitation forecasts as input data outperform deterministic flood forecasts in several cases in Europe and the United States, and ensemble flood forecasting systems are increasingly becoming operational in these regions. In Brazil, on the other hand, operational flood warning systems are rare, and often based on simplified river routing or linear transfer function models. However, a large number of global and regional meteorological models is operationally run covering most of the country, and forecasts of those models are available for recent years. We used this available data to conduct experiments of short term ensemble flood forecasting in the Paraopeba River basin (12 thousand km2), located in Southeastern Brazil. Streamflow forecasts were produced using the MGB-IPH hydrological model, using a simple empirical state updating method and using an ensemble of precipitation forecasts generated by several models, with different initial conditions and parameterizations, from several weather forecasting centers. A single deterministic streamflow forecast, based on a quantitative precipitation forecast derived from the optimal combination of several outputs of NWP models was used as a reference to assess the performance of the ensemble streamflow forecasts. Flood forecasts experiments were performed for three rainy seasons (austral summer) between 2008-2011. The results for predictions of dichotomous events, which mean exceeding or not flood

  4. Assessing the skill of seasonal precipitation and streamflow forecasts in sixteen French catchments

    NASA Astrophysics Data System (ADS)

    Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian

    2015-04-01

    Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful. Streamflow forecasting is one of the many applications than can benefit from these efforts. Seasonal flow forecasts generated using seasonal ensemble precipitation forecasts as input to a hydrological model can help to take anticipatory measures for water supply reservoir operation or drought risk management. The objective of the study is to assess the skill of seasonal precipitation and streamflow forecasts in France. First, we evaluated the skill of ECMWF SYS4 seasonal precipitation forecasts for streamflow forecasting in sixteen French catchments. Daily flow forecasts were produced using raw seasonal precipitation forecasts as input to the GR6J hydrological model. Ensemble forecasts are issued every month with 15 or 51 members according to the month of the year and evaluated for up to 90 days ahead. In a second step, we applied eight variants of bias correction approaches to the precipitation forecasts prior to generating the flow forecasts. The approaches were based on the linear scaling and the distribution mapping methods. The skill of the ensemble forecasts was assessed in accuracy (MAE), reliability (PIT Diagram) and overall performance (CRPS). The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are more skilful in terms of accuracy and overall performance than a reference prediction based on historic observed precipitation and watershed initial conditions at the time of forecast. Reliability is the only attribute that is not significantly improved. The skill of the forecasts is, in general, improved when applying bias correction. Two bias correction methods showed the best performance for the studied catchments: the simple linear scaling of monthly values and the empirical distribution mapping of daily values. L. Crochemore is funded by the Interreg IVB DROP Project (Benefit of governance in DROught adaPtation).

  5. An intercomparison of approaches for improving operational seasonal streamflow forecasts

    NASA Astrophysics Data System (ADS)

    Mendoza, Pablo A.; Wood, Andrew W.; Clark, Elizabeth; Rothwell, Eric; Clark, Martyn P.; Nijssen, Bart; Brekke, Levi D.; Arnold, Jeffrey R.

    2017-07-01

    For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the US Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches - statistical regression against IHCs and model-based ensemble streamflow prediction (ESP) - and then systematically intercompare WSFs across a range of lead times. Additional methods include (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction - HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1) objective approaches supporting methodologically

  6. Impacts of calibration strategies and ensemble methods on ensemble flood forecasting over Lanjiang basin, Southeast China

    NASA Astrophysics Data System (ADS)

    Liu, Li; Xu, Yue-Ping

    2017-04-01

    Ensemble flood forecasting driven by numerical weather prediction products is becoming more commonly used in operational flood forecasting applications.In this study, a hydrological ensemble flood forecasting system based on Variable Infiltration Capacity (VIC) model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated.The hydrological model is optimized by parallel programmed ɛ-NSGAII multi-objective algorithm and two respectively parameterized models are determined to simulate daily flows and peak flows coupled with a modular approach.The results indicatethat the ɛ-NSGAII algorithm permits more efficient optimization and rational determination on parameter setting.It is demonstrated that the multimodel ensemble streamflow mean have better skills than the best singlemodel ensemble mean (ECMWF) and the multimodel ensembles weighted on members and skill scores outperform other multimodel ensembles. For typical flood event, it is proved that the flood can be predicted 3-4 days in advance, but the flows in rising limb can be captured with only 1-2 days ahead due to the flash feature. With respect to peak flows selected by Peaks Over Threshold approach, the ensemble means from either singlemodel or multimodels are generally underestimated as the extreme values are smoothed out by ensemble process.

  7. Model averaging methods to merge statistical and dynamic seasonal streamflow forecasts in Australia

    NASA Astrophysics Data System (ADS)

    Schepen, A.; Wang, Q. J.

    2014-12-01

    The Australian Bureau of Meteorology operates a statistical seasonal streamflow forecasting service. It has also developed a dynamic seasonal streamflow forecasting approach. The two approaches produce similarly reliable forecasts in terms of ensemble spread but can differ in forecast skill depending on catchment and season. Therefore, it may be possible to augment the skill of the existing service by objectively weighting and merging the forecasts. Bayesian model averaging (BMA) is first applied to merge statistical and dynamic forecasts for 12 locations using leave-five-years-out cross-validation. It is seen that the BMA merged forecasts can sometimes be too uncertain, as shown by ensemble spreads that are unrealistically wide and even bi-modal. The BMA method applies averaging to forecast probability densities (and thus cumulative probabilities) for a given forecast variable value. An alternative approach is quantile model averaging (QMA), whereby forecast variable values (quantiles) are averaged for a given cumulative probability (quantile fraction). For the 12 locations, QMA is compared to BMA. BMA and QMA perform similarly in terms of forecast accuracy skill scores and reliability in terms of ensemble spread. Both methods improve forecast skill across catchments and seasons by combining the different strengths of the statistical and dynamic approaches. A major advantage of QMA over BMA is that it always produces reasonably well defined forecast distributions, even in the special cases where BMA does not. Optimally estimated QMA weights and BMA weights are similar; however, BMA weights are more efficiently estimated.

  8. Forecasting daily streamflow using online sequential extreme learning machines

    NASA Astrophysics Data System (ADS)

    Lima, Aranildo R.; Cannon, Alex J.; Hsieh, William W.

    2016-06-01

    While nonlinear machine methods have been widely used in environmental forecasting, in situations where new data arrive continually, the need to make frequent model updates can become cumbersome and computationally costly. To alleviate this problem, an online sequential learning algorithm for single hidden layer feedforward neural networks - the online sequential extreme learning machine (OSELM) - is automatically updated inexpensively as new data arrive (and the new data can then be discarded). OSELM was applied to forecast daily streamflow at two small watersheds in British Columbia, Canada, at lead times of 1-3 days. Predictors used were weather forecast data generated by the NOAA Global Ensemble Forecasting System (GEFS), and local hydro-meteorological observations. OSELM forecasts were tested with daily, monthly or yearly model updates. More frequent updating gave smaller forecast errors, including errors for data above the 90th percentile. Larger datasets used in the initial training of OSELM helped to find better parameters (number of hidden nodes) for the model, yielding better predictions. With the online sequential multiple linear regression (OSMLR) as benchmark, we concluded that OSELM is an attractive approach as it easily outperformed OSMLR in forecast accuracy.

  9. Integrating weather and climate predictions for seamless hydrologic ensemble forecasting: A case study in the Yalong River basin

    NASA Astrophysics Data System (ADS)

    Ye, Aizhong; Deng, Xiaoxue; Ma, Feng; Duan, Qingyun; Zhou, Zheng; Du, Chao

    2017-04-01

    Despite the tremendous improvement made in numerical weather and climate models over the recent years, the forecasts generated by those models still cannot be used directly for hydrological forecasting. A post-processor like the Ensemble Pre-Processor (EPP) developed by U.S. National Weather Service must be used to remove various biases and to extract useful predictive information from those forecasts. In this paper, we investigate how different designs of canonical events in the EPP can help post-process precipitation forecasts from the Global Ensemble Forecast System (GEFS) and Climate Forecast System Version 2 (CFSv2). The use of canonical events allow those products to be linked seamlessly and then the post-processed ensemble precipitation forecasts can be generated using the Schaake Shuffle procedure. We used the post-processed ensemble precipitation forecasts to drive a distributed hydrological model to obtain ensemble streamflow forecasts and evaluated those forecasts against the observed streamflow. We found that the careful design of canonical events can help extract more useful information, especially when up-to-date observed precipitation is used to setup the canonical events. We also found that streamflow forecasts using post-processed precipitation forecasts have longer lead times and higher accuracy than streamflow forecasts made by traditional Extend Streamflow Prediction (ESP) and the forecasts based on original GEFS and CFSv2 precipitation forecasts.

  10. A Robust Multimodel Framework for Ensemble Seasonal Hydroclimatic Forecasts

    NASA Astrophysics Data System (ADS)

    Mendoza, P. A.; Rajagopalan, B.; Clark, M. P.; Cortés, G.; McPhee, J. P.

    2014-12-01

    We provide a framework for careful analysis of the different methodological choices we make when constructing multimodel ensemble seasonal forecasts of hydroclimatic variables. Specifically, we focus on three common modeling decisions: (i) number of models, (ii) multimodel combination approach, and (iii) lead time for prediction. The analysis scheme includes a multimodel ensemble forecasting algorithm based on nonparametric regression, a set of alternatives for the options previously pointed, and a selection of probabilistic verification methods for ensemble forecast evaluation. The usefulness of this framework is tested through an example application aimed to generate spring/summer streamflow forecasts at multiple locations in Central Chile. Results demonstrate the high impact that subjectivity in decision-making may have on the quality of ensemble seasonal hydroclimatic forecasts. In particular, we note that the probabilistic verification criteria may lead to different choices regarding the number of models or the multimodel combination method. We also illustrate how this objective analysis scheme may lead to results that are extremely relevant for the case study presented here, such as skillful seasonal streamflow predictions for very dry conditions.

  11. A robust multimodel framework for ensemble seasonal hydroclimatic forecasts

    NASA Astrophysics Data System (ADS)

    Mendoza, Pablo A.; Rajagopalan, Balaji; Clark, Martyn P.; Cortés, Gonzalo; McPhee, James

    2014-07-01

    We provide a framework for careful analysis of the different methodological choices we make when constructing multimodel ensemble seasonal forecasts of hydroclimatic variables. Specifically, we focus on three common modeling decisions: (i) number of models, (ii) multimodel combination approach, and (iii) lead time for prediction. The analysis scheme includes a multimodel ensemble forecasting algorithm based on nonparametric regression, a set of alternatives for the options previously pointed, and a selection of probabilistic verification methods for ensemble forecast evaluation. The usefulness of this framework is tested through an example application aimed to generate spring/summer streamflow forecasts at multiple locations in Central Chile. Results demonstrate the high impact that subjectivity in decision-making may have on the quality of ensemble seasonal hydroclimatic forecasts. In particular, we note that the probabilistic verification criteria may lead to different choices regarding the number of models or the multimodel combination method. We also illustrate how this objective analysis scheme may lead to results that are extremely relevant for the case study presented here, such as skillful seasonal streamflow predictions for very dry conditions.

  12. Monthly streamflow forecasting using Gaussian Process Regression

    NASA Astrophysics Data System (ADS)

    Sun, Alexander Y.; Wang, Dingbao; Xu, Xianli

    2014-04-01

    Streamflow forecasting plays a critical role in nearly all aspects of water resources planning and management. In this work, Gaussian Process Regression (GPR), an effective kernel-based machine learning algorithm, is applied to probabilistic streamflow forecasting. GPR is built on Gaussian process, which is a stochastic process that generalizes multivariate Gaussian distribution to infinite-dimensional space such that distributions over function values can be defined. The GPR algorithm provides a tractable and flexible hierarchical Bayesian framework for inferring the posterior distribution of streamflows. The prediction skill of the algorithm is tested for one-month-ahead prediction using the MOPEX database, which includes long-term hydrometeorological time series collected from 438 basins across the U.S. from 1948 to 2003. Comparisons with linear regression and artificial neural network models indicate that GPR outperforms both regression methods in most cases. The GPR prediction of MOPEX basins is further examined using the Budyko framework, which helps to reveal the close relationships among water-energy partitions, hydrologic similarity, and predictability. Flow regime modification and the resulting loss of predictability have been a major concern in recent years because of climate change and anthropogenic activities. The persistence of streamflow predictability is thus examined by extending the original MOPEX data records to 2012. Results indicate relatively strong persistence of streamflow predictability in the extended period, although the low-predictability basins tend to show more variations. Because many low-predictability basins are located in regions experiencing fast growth of human activities, the significance of sustainable development and water resources management can be even greater for those regions.

  13. On the impact of bias correcting and conditioning precipitation inputs on seasonal streamflow forecast quality

    NASA Astrophysics Data System (ADS)

    Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian; Perrin, Charles

    2017-04-01

    Skillful seasonal streamflow forecasts are increasingly requested for decision-making in areas such as drought risk assessment or reservoir management. Meteorological forcing can be the major source of uncertainty in seasonal forecasts as early as in the first month of the forecast period. The choice of the hydrological model inputs thus has a major impact on the quality of generated streamflow forecasts. In this study, we assess the impact of two types of precipitation forecast post-treatment: 1) bias correction and 2) conditioning, on streamflow forecast quality. We first evaluated several bias correction approaches and conditioned precipitation scenarios in sixteen catchments in France, with the help of ECMWF System 4 seasonal precipitation forecasts and the GR6J hydrological model. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often sharper than the conventional ESP method. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. The empirical distribution mapping of daily values was successful in improving forecast reliability, but sometimes at the expense of forecast sharpness. We also evaluated several conditioning methods based on ECMWF System 4 precipitation forecasts to generate seasonal streamflow forecasts in the same sixteen catchments. Four precipitation indices based on System 4 precipitation were used to condition historical streamflow or historical precipitations to be used as input to the GR6J model. Our results evaluate how the conditioning impacts the reliability and sharpness of streamflow forecasts, as well as forecasts of drought indices. We show that conditioning past observations based on the three-month Standardized Precipitation Index (SPI3) can improve the sharpness of ensemble forecasts based on historical data, but also often decrease reliability. References: Crochemore, L., Ramos, M.-H., and Pappenberger

  14. A Global Hydrological Ensemble Forecasting System: Uncertainty Quantification and Data Assimilation

    NASA Astrophysics Data System (ADS)

    Hong, Y.; Zhang, Y.; Xue, X.; Wang, X.; Gourley, J. J.; Kirstetter, P.

    2012-12-01

    A Global Hydrological Ensemble Forecasting System (GHEFS) driven by TRMM Multi-satellite Prediction Analysis (TMPA) precipitation ensembles and Global Ensemble Forecast System (GEFS) Quantitative Precipitation Forecast (QPF) ensembles, via the Coupled Routing and Excess STorage (CREST) distributed hydrological model, provides deterministic and probabilistic (e.g. 95% confidence boundaries) simulations of streamflow. The TMPA inputs enable flood monitoring and short-term forecasts while the GEFS ensembles provide for forecasts up to a seven-day lead time. This talk will focus on a quantification of the system's uncertainty and streamflow ensemble prediction generation using the following three techniques: 1) an error model that first quantifies and then perturbs both temporal and spatial variability of the real-time, TMPA precipitation estimates by considering the version-7 research product as the reference rainfall product; 2) in forecast mode, utilization of the Ensemble Transform method to account for the uncertainty of GEFS forecasts from its initial condition errors; 3) a sequential data assimilation approach - the Ensemble Square Root Kalman Filter (EnSRF) applied to update the CREST model's internal states whenever observations (e.g. streamflow, soil moisture, and actual ET etc.) are available. The GHEFS is validated in several basins in the U.S. and other continents in terms of flood detection capability (e.g. CSI, NSCE, Peak, Timing), showing improved prognostic capability by offering more time for responding agencies and yielding unique uncertainty information about the magnitude of the forecast impacts.

  15. Moving beyond the cost-loss ratio: economic assessment of streamflow forecasts for a risk-averse decision maker

    NASA Astrophysics Data System (ADS)

    Matte, Simon; Boucher, Marie-Amélie; Boucher, Vincent; Fortier Filion, Thomas-Charles

    2017-06-01

    A large effort has been made over the past 10 years to promote the operational use of probabilistic or ensemble streamflow forecasts. Numerous studies have shown that ensemble forecasts are of higher quality than deterministic ones. Many studies also conclude that decisions based on ensemble rather than deterministic forecasts lead to better decisions in the context of flood mitigation. Hence, it is believed that ensemble forecasts possess a greater economic and social value for both decision makers and the general population. However, the vast majority of, if not all, existing hydro-economic studies rely on a cost-loss ratio framework that assumes a risk-neutral decision maker. To overcome this important flaw, this study borrows from economics and evaluates the economic value of early warning flood systems using the well-known Constant Absolute Risk Aversion (CARA) utility function, which explicitly accounts for the level of risk aversion of the decision maker. This new framework allows for the full exploitation of the information related to a forecasts' uncertainty, making it especially suited for the economic assessment of ensemble or probabilistic forecasts. Rather than comparing deterministic and ensemble forecasts, this study focuses on comparing different types of ensemble forecasts. There are multiple ways of assessing and representing forecast uncertainty. Consequently, there exist many different means of building an ensemble forecasting system for future streamflow. One such possibility is to dress deterministic forecasts using the statistics of past error forecasts. Such dressing methods are popular among operational agencies because of their simplicity and intuitiveness. Another approach is the use of ensemble meteorological forecasts for precipitation and temperature, which are then provided as inputs to one or many hydrological model(s). In this study, three concurrent ensemble streamflow forecasting systems are compared: simple statistically dressed

  16. Forecast of iceberg ensemble drift

    SciTech Connect

    El-Tahan, M.S.; El-Tahan, H.W.; Venkatesh, S.

    1983-05-01

    The objectives of the study are to gain a better understanding of the characteristics of iceberg motion and the factors controlling iceberg drift, and to develop an iceberg ensemble drift forecast system to be operated by the Canadian Atmospheric Environment Service. An extensive review of field and theoretical studies on iceberg behaviour, and the factors controlling iceberg motion has been carried out. Long term and short term behaviour of icebergs are critically examined. A quantitative assessment of the effects of the factors controlling iceberg motion is presented. The study indicated that wind and currents are the primary driving forces. Coriolis Force and ocean surface slope also have significant effects. As for waves, only the higher waves have a significant effect. Iceberg drift is also affected by iceberg size characteristics. Based on the findings of the study a comprehensive computerized forecast system to predict the drift of iceberg ensembles off Canada's east coast has been designed. The expected accuracy of the forecast system is discussed and recommendations are made for future improvements to the system.

  17. Skill Analysis of Seasonal Hydrologic Streamflow Forecasting for the Upper Zambezi, Africa

    NASA Astrophysics Data System (ADS)

    Valdés-Pineda, R.; Valdes, J. B.; Serrat-Capdevila, A.; Wi, S.; Demaria, E. M.; Roberts, J. B.; Robertson, F. R.

    2016-12-01

    Seasonal Hydrological Streamflow Forecasting (SHSF) is a powerful tool aimed to provide information to water resources managers. SHSF incorporates information provided by coupled atmosphere-ocean-land general circulation models (CGCMs) into hydrologic models to produce seasonal (up to 180 days) daily streamflow forecasts. In this research the forecasting skills of four distributed hydrologic models: the HyMod model, the HBV model; the Variable Infiltration Capacity (VIC) model; and the Sacramento Soil Moisture Accounting (SAC-SMA) are evaluated for the period 2000-2016 in the Upper Zambezi River Basin (UZRB). Seasonal streamflow forecasts for the UZRB are produced using seasonal forecasts of precipitation and temperature obtained from the North American Multi-Model Ensemble (NMME). A stratified sampling of NMME scenarios is used to provide uncertainty levels of the forecasts; and the impact of the initial conditions over the seasonal predictions is also analyzed and discussed. The ensemble of the multi-model satellite based streamflow forecasts will serve as decision support tools for managing water resources in the Zambezi River basin using a website-based platform developed as a collaborative effort between the SERVIR Water Africa-Arizona Team (SWAAT) and the SERVIR Program of NASA and USAID.

  18. Unorganized machines for seasonal streamflow series forecasting.

    PubMed

    Siqueira, Hugo; Boccato, Levy; Attux, Romis; Lyra, Christiano

    2014-05-01

    Modern unorganized machines--extreme learning machines and echo state networks--provide an elegant balance between processing capability and mathematical simplicity, circumventing the difficulties associated with the conventional training approaches of feedforward/recurrent neural networks (FNNs/RNNs). This work performs a detailed investigation of the applicability of unorganized architectures to the problem of seasonal streamflow series forecasting, considering scenarios associated with four Brazilian hydroelectric plants and four distinct prediction horizons. Experimental results indicate the pertinence of these models to the focused task.

  19. Post-processing ECMWF precipitation and temperature ensemble reforecasts for operational hydrologic forecasting at various spatial scales

    NASA Astrophysics Data System (ADS)

    Verkade, J. S.; Brown, J. D.; Reggiani, P.; Weerts, A. H.

    2013-09-01

    The ECMWF temperature and precipitation ensemble reforecasts are evaluated for biases in the mean, spread and forecast probabilities, and how these biases propagate to streamflow ensemble forecasts. The forcing ensembles are subsequently post-processed to reduce bias and increase skill, and to investigate whether this leads to improved streamflow ensemble forecasts. Multiple post-processing techniques are used: quantile-to-quantile transform, linear regression with an assumption of bivariate normality and logistic regression. Both the raw and post-processed ensembles are run through a hydrologic model of the river Rhine to create streamflow ensembles. The results are compared using multiple verification metrics and skill scores: relative mean error, Brier skill score and its decompositions, mean continuous ranked probability skill score and its decomposition, and the ROC score. Verification of the streamflow ensembles is performed at multiple spatial scales: relatively small headwater basins, large tributaries and the Rhine outlet at Lobith. The streamflow ensembles are verified against simulated streamflow, in order to isolate the effects of biases in the forcing ensembles and any improvements therein. The results indicate that the forcing ensembles contain significant biases, and that these cascade to the streamflow ensembles. Some of the bias in the forcing ensembles is unconditional in nature; this was resolved by a simple quantile-to-quantile transform. Improvements in conditional bias and skill of the forcing ensembles vary with forecast lead time, amount, and spatial scale, but are generally moderate. The translation to streamflow forecast skill is further muted, and several explanations are considered, including limitations in the modelling of the space-time covariability of the forcing ensembles and the presence of storages.

  20. One-day-ahead streamflow forecasting via super-ensembles of several neural network architectures based on the Multi-Level Diversity Model

    NASA Astrophysics Data System (ADS)

    Brochero, Darwin; Hajji, Islem; Pina, Jasson; Plana, Queralt; Sylvain, Jean-Daniel; Vergeynst, Jenna; Anctil, Francois

    2015-04-01

    Theories about generalization error with ensembles are mainly based on the diversity concept, which promotes resorting to many members of different properties to support mutually agreeable decisions. Kuncheva (2004) proposed the Multi Level Diversity Model (MLDM) to promote diversity in model ensembles, combining different data subsets, input subsets, models, parameters, and including a combiner level in order to optimize the final ensemble. This work tests the hypothesis about the minimisation of the generalization error with ensembles of Neural Network (NN) structures. We used the MLDM to evaluate two different scenarios: (i) ensembles from a same NN architecture, and (ii) a super-ensemble built by a combination of sub-ensembles of many NN architectures. The time series used correspond to the 12 basins of the MOdel Parameter Estimation eXperiment (MOPEX) project that were used by Duan et al. (2006) and Vos (2013) as benchmark. Six architectures are evaluated: FeedForward NN (FFNN) trained with the Levenberg Marquardt algorithm (Hagan et al., 1996), FFNN trained with SCE (Duan et al., 1993), Recurrent NN trained with a complex method (Weins et al., 2008), Dynamic NARX NN (Leontaritis and Billings, 1985), Echo State Network (ESN), and leak integrator neuron (L-ESN) (Lukosevicius and Jaeger, 2009). Each architecture performs separately an Input Variable Selection (IVS) according to a forward stepwise selection (Anctil et al., 2009) using mean square error as objective function. Post-processing by Predictor Stepwise Selection (PSS) of the super-ensemble has been done following the method proposed by Brochero et al. (2011). IVS results showed that the lagged stream flow, lagged precipitation, and Standardized Precipitation Index (SPI) (McKee et al., 1993) were the most relevant variables. They were respectively selected as one of the firsts three selected variables in 66, 45, and 28 of the 72 scenarios. A relationship between aridity index (Arora, 2002) and NN

  1. Multi-Model Long-Range Ensemble Forecast for Decision Support in Hydroelectric Operations

    NASA Astrophysics Data System (ADS)

    Kunkel, M. L.; Parkinson, S.; Blestrud, D.; Holbrook, V. P.

    2014-12-01

    Idaho Power Company (IPC) is a hydroelectric based utility serving over a million customers in southern Idaho and eastern Oregon. Hydropower makes up ~50% of our power generation and accurate predictions of streamflow and precipitation drive our long-term planning and decision support for operations. We investigate the use of a multi-model ensemble approach for mid and long-range streamflow and precipitation forecasts throughout the Snake River Basin. Forecast are prepared using an Idaho Power developed ensemble forecasting technique for 89 locations throughout the Snake River Basin for periods of 3 to 18 months in advance. A series of multivariable linear regression, multivariable non-linear regression and multivariable Kalman filter techniques are combined in an ensemble forecast based upon two data types, historical data (streamflow, precipitation, climate indices [i.e. PDO, ENSO, AO, etc…]) and single value decomposition derived values based upon atmospheric heights and sea surface temperatures.

  2. Model averaging methods to merge operational statistical and dynamic seasonal streamflow forecasts in Australia

    NASA Astrophysics Data System (ADS)

    Schepen, Andrew; Wang, Q. J.

    2015-03-01

    The Australian Bureau of Meteorology produces statistical and dynamic seasonal streamflow forecasts. The statistical and dynamic forecasts are similarly reliable in ensemble spread; however, skill varies by catchment and season. Therefore, it may be possible to optimize forecasting skill by weighting and merging statistical and dynamic forecasts. Two model averaging methods are evaluated for merging forecasts for 12 locations. The first method, Bayesian model averaging (BMA), applies averaging to forecast probability densities (and thus cumulative probabilities) for a given forecast variable value. The second method, quantile model averaging (QMA), applies averaging to forecast variable values (quantiles) for a given cumulative probability (quantile fraction). BMA and QMA are found to perform similarly in terms of overall skill scores and reliability in ensemble spread. Both methods improve forecast skill across catchments and seasons. However, when both the statistical and dynamical forecasting approaches are skillful but produce, on special occasions, very different event forecasts, the BMA merged forecasts for these events can have unusually wide and bimodal distributions. In contrast, the distributions of the QMA merged forecasts for these events are narrower, unimodal and generally more smoothly shaped, and are potentially more easily communicated to and interpreted by the forecast users. Such special occasions are found to be rare. However, every forecast counts in an operational service, and therefore the occasional contrast in merged forecasts between the two methods may be more significant than the indifference shown by the overall skill and reliability performance.

  3. A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed

    NASA Astrophysics Data System (ADS)

    Slater, Louise J.; Villarini, Gabriele; Bradley, A. Allen; Vecchi, Gabriel A.

    2017-07-01

    The state of Iowa in the US Midwest is regularly affected by major floods and has seen a notable increase in agricultural land cover over the twentieth century. We present a novel statistical-dynamical approach for probabilistic seasonal streamflow forecasting using land cover and General Circulation Model (GCM) precipitation forecasts. Low to high flows are modelled and forecast for the Raccoon River at Van Meter, a 8900 km2 catchment located in central-western Iowa. Statistical model fits for each streamflow quantile (from seasonal minimum to maximum; predictands) are based on observed basin-averaged total seasonal precipitation, annual row crop (corn and soybean) production acreage, and observed precipitation from the month preceding each season (to characterize antecedent wetness conditions) (predictors). Model fits improve when including agricultural land cover and antecedent precipitation as predictors, as opposed to just precipitation. Using the dynamically-updated relationship between predictand and predictors every year, forecasts are computed from 1 to 10 months ahead of every season based on annual row crop acreage from the previous year (persistence forecast) and the monthly precipitation forecasts from eight GCMs of the North American Multi-Model Ensemble (NMME). The skill of our forecast streamflow is assessed in deterministic and probabilistic terms for all initialization months, flow quantiles, and seasons. Overall, the system produces relatively skillful streamflow forecasts from low to high flows, but the skill does not decrease uniformly with initialization time, suggesting that improvements can be gained by using different predictors for specific seasons and flow quantiles.

  4. Seasonal streamflow forecasting with the global hydrological forecasting system FEWS-World

    NASA Astrophysics Data System (ADS)

    Candogan Yossef, N.; Van Beek, L. P.; Winsemius, H.; Bierkens, M. F.

    2011-12-01

    The year-to-year variability of river discharge brings about risks and opportunities in water resources management. Reliable hydrological forecasts and effective communication allow several sectors to make more informed management decisions. In many developing regions of the world, there are no efficient hydrological forecasting systems. For these regions, a global forecasting system which indicates increased probabilities of streamflow excesses or shortages over long lead-times can be of great value. FEWS-World is developed for this purpose. The system incorporates the global hydrological model PCR-GLOBWB and delivers streamflow forecasts on a global scale. This study investigates the skill and value of FEWS-World. Skill is defined as the ability of the system to forecast discharge extremes; and value is its usefulness for possible users and ultimately for affected populations. Skill is assessed in historical simulation mode as well as retroactive forecasting mode. The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts from the European Center for Medium-Range Weather Forecasts (ECMWF). The results will be disseminated on the internet to provide valuable information for users in data and model-poor regions of the world. The preliminary skill assessment of PCR-GLOBWB in reproducing flow extremes is carried out for a selection of 20 large rivers of the world. The model is run for a historical period, with a meteorological forcing data set based on observations from the Climate Research Unit of the University of East Anglia, and the ERA-40 reanalysis of ECMWF. Model skill in reproducing monthly anomalies as well as floods and droughts is assessed by applying verification measures developed for deterministic meteorological forecasts. The results of this preliminary analysis shows that even where the simulated hydrographs are biased, higher skills can be attained in reproducing monthly

  5. Ensemble forecasts of road surface temperatures

    NASA Astrophysics Data System (ADS)

    Sokol, Zbyněk; Bližňák, Vojtěch; Sedlák, Pavel; Zacharov, Petr; Pešice, Petr; Škuthan, Miroslav

    2017-05-01

    This paper describes a new ensemble technique for road surface temperature (RST) forecasting using an energy balance and heat conduction model. Compared to currently used deterministic forecasts, the proposed technique allows the estimation of forecast uncertainty and probabilistic forecasts. The ensemble technique is applied to the METRo-CZ model and stems from error covariance analyses of the forecasted air temperature and humidity 2 m above the ground, wind speed at 10 m and total cloud cover N in octas by the numerical weather prediction (NWP) model. N is used to estimate the shortwave and longwave radiation fluxes. These variables are used to calculate the boundary conditions in the METRo-CZ model. We found that the variable N is crucial for generating the ensembles. Nevertheless, the ensemble spread is too small and underestimates the uncertainty in the RST forecast. One of the reasons is not considering errors in the rain and snow forecast by the NWP model when generating ensembles. Technical issues, such as incorrect sky view factors and the current state of road surface conditions also contribute to errors. Although the ensemble technique underestimates the uncertainty in the RST forecasts, it provides additional information to road authorities who provide winter road maintenance.

  6. Use of medium-range numerical weather prediction model output to produce forecasts of streamflow

    USGS Publications Warehouse

    Clark, M.P.; Hay, L.E.

    2004-01-01

    he accuracy of precipitation forecasts over the northeastern United States, but overall, the accuracy of MOS-based precipitation forecasts is slightly lower than the raw NCEP forecasts. Four basins in the United States were chosen as case studies to evaluate the value of MRF output for predictions of streamflow. Streamflow forecasts using MRF output were generated for one rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango, Colorado: East Fork of the Carson River near Gardnerville, Nevada: and Cle Elum River near Roslyn, Washington). Hydrologic model output forced with measured-station data were used as "truth" to focus attention on the hydrologic effects of errors in the MRF forecasts. Eight-day streamflow forecasts produced using the MOS-corrected MRF output as input (MOS) were compared with those produced using the climatic Ensemble Streamflow Prediction (ESP) technique. MOS-based streamflow forecasts showed increased skill in the snowmelt-dominated river basins, where daily variations in streamflow are strongly forced by temperature. In contrast, the skill of MOS forecasts in the rainfall-dominated basin (the Alapaha River) were equivalent to the skill of the ESP forecasts. Further improvements in streamflow forecasts require more accurate local-scale forecasts of precipitation and temperature, more accurate specification of basin initial conditions, and more accurate model simulations of streamflow. ?? 2004 American Meteorological Society.

  7. Layered Ensemble Architecture for Time Series Forecasting.

    PubMed

    Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin

    2016-01-01

    Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.

  8. A platform for probabilistic Multimodel and Multiproduct Streamflow Forecasting

    NASA Astrophysics Data System (ADS)

    Roy, Tirthankar; Serrat-Capdevila, Aleix; Gupta, Hoshin; Valdes, Juan

    2017-01-01

    We develop and test a probabilistic real-time streamflow-forecasting platform, Multimodel and Multiproduct Streamflow Forecasting (MMSF), that uses information provided by a suite of hydrologic models and satellite precipitation products (SPPs). The SPPs are bias-corrected before being used as inputs to the hydrologic models, and model calibration is carried out independently for each of the model-product combinations (MPCs). Forecasts generated from the calibrated models are further bias-corrected to compensate for the deficiencies within the models, and then probabilistically merged using a variety of model averaging techniques. Use of bias-corrected SPPs in streamflow forecasting applications can overcome several issues associated with sparsely gauged basins and enable robust forecasting capabilities. Bias correction of streamflow significantly improves the forecasts in terms of accuracy and precision for all different cases considered. Results show that the merging of individual forecasts from different MPCs provides additional improvements. All the merging techniques applied in this study produce similar results, however, the Inverse Weighted Averaging (IVA) proves to be slightly superior in most cases. We demonstrate the implementation of the MMSF platform for real-time streamflow monitoring and forecasting in the Mara River basin of Africa (Kenya & Tanzania) in order to provide improved monitoring and forecasting tools to inform water management decisions.

  9. Reducing streamflow forecast uncertainty: Application and qualitative assessment of the upper klamath river Basin, Oregon

    USGS Publications Warehouse

    Hay, L.E.; McCabe, G.J.; Clark, M.P.; Risley, J.C.

    2009-01-01

    The accuracy of streamflow forecasts depends on the uncertainty associated with future weather and the accuracy of the hydrologic model that is used to produce the forecasts. We present a method for streamflow forecasting where hydrologic model parameters are selected based on the climate state. Parameter sets for a hydrologic model are conditioned on an atmospheric pressure index defined using mean November through February (NDJF) 700-hectoPascal geopotential heights over northwestern North America [Pressure Index from Geopotential heights (PIG)]. The hydrologic model is applied in the Sprague River basin (SRB), a snowmelt-dominated basin located in the Upper Klamath basin in Oregon. In the SRB, the majority of streamflow occurs during March through May (MAM). Water years (WYs) 1980-2004 were divided into three groups based on their respective PIG values (high, medium, and low PIG). Low (high) PIG years tend to have higher (lower) than average MAM streamflow. Four parameter sets were calibrated for the SRB, each using a different set of WYs. The initial set used WYs 1995-2004 and the remaining three used WYs defined as high-, medium-, and low-PIG years. Two sets of March, April, and May streamflow volume forecasts were made using Ensemble Streamflow Prediction (ESP). The first set of ESP simulations used the initial parameter set. Because the PIG is defined using NDJF pressure heights, forecasts starting in March can be made using the PIG parameter set that corresponds with the year being forecasted. The second set of ESP simulations used the parameter set associated with the given PIG year. Comparison of the ESP sets indicates that more accuracy and less variability in volume forecasts may be possible when the ESP is conditioned using the PIG. This is especially true during the high-PIG years (low-flow years). ?? 2009 American Water Resources Association.

  10. Improving statistical forecasts of seasonal streamflows using hydrological model output

    NASA Astrophysics Data System (ADS)

    Robertson, D. E.; Pokhrel, P.; Wang, Q. J.

    2013-02-01

    Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.

  11. Improving statistical forecasts of seasonal streamflows using hydrological model output

    NASA Astrophysics Data System (ADS)

    Robertson, D. E.; Pokhrel, P.; Wang, Q. J.

    2012-07-01

    Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.

  12. Streamflow Simulations for the Mississippi River Basin Based on Ensemble Regional Climate Model Simulations

    NASA Astrophysics Data System (ADS)

    Arritt, R. W.; Jha, M.; Takle, E. S.; Gu, R.

    2004-12-01

    Ensemble simulations provide a useful tool for studying uncertainties in climate projections and for deriving probabilistic information from deterministic forecasts. Although a number of studies have examined variability within climate models, fewer have quantified the extent to which variability and uncertainty in climate simulations then propagates through impacts models. Here we evaluate the variability in simulated streamflow that result from taking the streamflow model's inputs from different members of an ensemble of simulations by a decadal-scale nested regional climate model. The regional climate model, RegCM3, simulated a domain covering the continental U.S. and most of Mexico for the period 1986-2003 using initial and lateral boundary conditions from the NCEP-DOE Reanalysis 2. Three RegCM3 realizations were created, each initialized one month apart but otherwise identical in configuration so that their collective behavior provides a measure of internal variability of the climate model. RegCM3 output for daily precipitation, temperature, and radiation were then used as input to the Soil and Water Assessment Tool (SWAT) over the upper Mississippi River basin. Seasonal and interannual variability of SWAT-predicted streamflow indicate that the internal variability of the RegCM3 climate model carries through to produce spread in simulated streamflow from SWAT.

  13. Role of Hydrological Ensemble Forecasts in Operational Decision Making

    NASA Astrophysics Data System (ADS)

    Ramaswamy, V.; Saleh, F.; Georgas, N.; Blumberg, A. F.

    2016-12-01

    Considerable importance has been placed on addressing uncertainties in hydrologic forecasts, particularly in regards to operational decision making. This work investigates the utility of short term hydrological ensemble forecasts for operational decision making using meteorological inputs from more than 100 ensemble members from different numerical weather prediction (NWP) models. To this end, an advanced automated hydrologic framework comprising of a regional scale hydrologic model, GIS datasets and the meteorological ensemble predictions from different weather prediction facilities was implemented over the Hudson and Raritan River basins, USA. The uncertainties associated with ensemble streamflow forecasts was analysed for three different flood events classified as minor, moderate and major. This was done by visually and statistically comparing the spread, magnitude and timing of the peak of the hydrologic outputs. Results from this work demonstrate the effectiveness of different NWP models for different operational scenarios, thus providing a better understanding of the uncertainties and risks associated with decision making. In addition to gaining insights into the risks associated with issuing flood alerts, this work also offers useful perspectives on the operationally managing water resources.

  14. A Simple Bayesian Climate Index Weighting Method for Seasonal Ensemble Forecasting

    NASA Astrophysics Data System (ADS)

    Bradley, A.; Habib, M. A.; Schwartz, S. S.

    2014-12-01

    Climate information — in the form of a measure of climate state or a climate forecast — can be an important predictor of future hydrologic conditions. For instance, streamflow variability for many locations around the globe is related to large-scale atmospheric oscillations, like the El Nino Southern Oscillation (ENSO) or the Pacific/Decadal Oscillation (PDO). Furthermore, climate forecast models are growing more skillful in their predictions of future climate variables on seasonal time scales. Finding effective ways to translate this climate information into improved hydrometeorological predictions is an area of ongoing research. In ensemble streamflow forecasting, where historical weather inputs or streamflow observations are used to generate the ensemble, climate index weighting is one way to represent the influence of current climate information. Using a climate index, each forecast variable member of the ensemble is selectively weighted to reflect climate conditions at the time of the forecast. A simple Bayesian climate index weighting of ensemble forecasts is presented. The original hydrologic ensemble members define a sample of the prior distribution; the relationship between the climate index and the ensemble member forecast variable is used to estimate a likelihood function. Given an observation of the climate index at the time of the forecast, the estimated likelihood function is then used to assign weights to each ensemble member. The weighted ensemble forecast is then used to estimate the posterior distribution of the forecast variable conditioned on the climate index. The proposed approach has several advantages over traditional climate index weighting methods. The weights assigned to the ensemble members accomplish the updating of the (prior) ensemble forecast distribution based on Bayes' Theorem, so the method is theoretically sound. The method also automatically adapts to the strength of the relationship between the climate index and the

  15. Multimodel hydrological ensemble forecasts for the Baskatong catchment in Canada using the TIGGE database.

    NASA Astrophysics Data System (ADS)

    Tito Arandia Martinez, Fabian

    2014-05-01

    Adequate uncertainty assessment is an important issue in hydrological modelling. An important issue for hydropower producers is to obtain ensemble forecasts which truly grasp the uncertainty linked to upcoming streamflows. If properly assessed, this uncertainty can lead to optimal reservoir management and energy production (ex. [1]). The meteorological inputs to the hydrological model accounts for an important part of the total uncertainty in streamflow forecasting. Since the creation of the THORPEX initiative and the TIGGE database, access to meteorological ensemble forecasts from nine agencies throughout the world have been made available. This allows for hydrological ensemble forecasts based on multiple meteorological ensemble forecasts. Consequently, both the uncertainty linked to the architecture of the meteorological model and the uncertainty linked to the initial condition of the atmosphere can be accounted for. The main objective of this work is to show that a weighted combination of meteorological ensemble forecasts based on different atmospheric models can lead to improved hydrological ensemble forecasts, for horizons from one to ten days. This experiment is performed for the Baskatong watershed, a head subcatchment of the Gatineau watershed in the province of Quebec, in Canada. Baskatong watershed is of great importance for hydro-power production, as it comprises the main reservoir for the Gatineau watershed, on which there are six hydropower plants managed by Hydro-Québec. Since the 70's, they have been using pseudo ensemble forecast based on deterministic meteorological forecasts to which variability derived from past forecasting errors is added. We use a combination of meteorological ensemble forecasts from different models (precipitation and temperature) as the main inputs for hydrological model HSAMI ([2]). The meteorological ensembles from eight of the nine agencies available through TIGGE are weighted according to their individual performance and

  16. Incorporation of a Stochastic Weather Generator to Further Inform Ensemble Streamflow Prediction in the Colorado River Basin

    NASA Astrophysics Data System (ADS)

    Miller, W. P.; Verdin, A.; Hakala, J.

    2016-12-01

    The National Oceanic and Atmospheric Administration's (NOAA's) Colorado Basin River Forecast Center (CBRFC) provides forecasts of water supply conditions to resource managers throughout the Colorado River Basin and Eastern Great Basin regions using Ensemble Streamflow Prediction (ESP) methods that are largely driven by historical observations of temperature and precipitation. Currently, the CBRFC does not incorporate climatic information, such as teleconnections (e.g., the El Niño Southern Oscillation [ENSO]), into the development of its streamflow ensembles; further, as the impacts of climate change are realized, the past may no longer be representative of future conditions. To address these issues, the CBRFC is investigating the incorporation of a Stochastic Weather Generator (SWG) developed by the University of Colorado. Additionally, numerous agencies issue climate outlooks that describe the probability that a region with experience warmer, normal, or cooler conditions with regards to temperature and wetter, normal, or drier conditions with regards to precipitation. Research published by the University of Colorado has shown that a SWG weighted by probabilities related to the ENSO may improve forecast skill in the San Juan River Basin, as well as other regions outside of the Colorado River Basin. In this study, the SWG is applied within the Colorado River Basin and weighted using climate probabilities developed by the National Weather Service's Climate Prediction Center. The ensemble of streamflow events developed using the SWG is then compared to historical ensembles used in the forecast of water supply over the region.

  17. Seasonal streamflow forecasting: experiences with precipitation bias correction and SPI conditioning to improve performance for hydrological events

    NASA Astrophysics Data System (ADS)

    Ramos, M. H.; Crochemore, L.; Pappenberger, F.; Perrin, C.

    2016-12-01

    Many fields such as drought risk assessment or reservoir management can benefit from seasonal streamflow forecasts. This study presents the results of two analyses aiming to: 1) assess the skill of seasonal precipitation forecasts in France and provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times, and 2) evaluate how the conditioning of historical data based on the Standardized Precipitation Index (SPI) from bias-corrected GCM precipitation forecasts can be useful to select traces within the historical data and further improve the forecast of droughts. We evaluated several bias correction approaches and conditioned precipitation scenarios in sixteen catchments in France, with the help of ECMWF System 4 seasonal precipitation forecasts and the GR6J hydrological model. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often sharper than the conventional ESP method. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. The simple linear scaling of monthly values contributed mainly to increase forecast sharpness and accuracy, while the empirical distribution mapping of daily values was successful in improving forecast reliability. Our results also show that conditioning past observations based on the three-month Standardized Precipitation Index (SPI3) can improve the sharpness of ensemble forecasts based on historical data, while maintaining good reliability. An evaluation of forecast ensembles for low-flow forecasting showed that the SPI3-conditioned ensembles provided reliable forecasts of low-flow duration and deficit volume based on the 80th exceedance percentile. Drought risk forecasting is illustrated for the 2003 drought event.

  18. Ensemble predictions of future streamflow drought in Europe

    NASA Astrophysics Data System (ADS)

    Forzieri, Giovanni; Feyen, Luc; Rojas, Rodrigo

    2013-04-01

    Recent developments in climate modeling suggest that global warming and growing human water use are likely to favor conditions for the development of streamflow droughts in several parts of Europe by the end of this century. In this study, we quantify how future drought hazard in Europe may develop in view of these drivers by comparing low-flow predictions of the LISFLOOD hydrological model coupled to a water consumption module and driven by an ensemble of climate projections. This ensemble consists of 12 bias-corrected climate simulations conducted within the ENSEMBLES project, forced by the A1B emission scenario for the period 1961-2100. For time slices of 30 years, low-flow characteristics - quantified in terms of minimum flows, environmental flows and deficits - are derived from the simulated streamflow series and further analyzed using extreme value theory. Changes in extreme river conditions are then analyzed with respect to the 1961-1990 control period. Two main domains with opposite signal of change in drought characteristics can be identified in Europe, as well as a transition zone between them. Southern parts of Europe - from the Iberian to Balkan Peninsula- but also France, Belgium and British Isles are expected to be more prone to severe and persistent low-flow conditions. In contrast, the Scandinavia Peninsula and Northeast Europe show a robust decrease in future drought hazard. In a transition zone between these two regions, climate-induced changes are projected to be marginal. Water use under an A1B-consistent scenario will further aggravate drought conditions in the south as well as in the transition zone. In the regions with a clear pattern of change in streamflow drought, indices derived from the hydrological simulations for different climate experiments are highly consistent, whereas in the transition zone between North and South Europe the consistency in changes amongst the ensemble members is lower.

  19. Total probabilities of ensemble runoff forecasts

    NASA Astrophysics Data System (ADS)

    Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

    2017-04-01

    Ensemble forecasting has a long history from meteorological modelling, as an indication of the uncertainty of the forecasts. However, it is necessary to calibrate and post-process the ensembles as the they often exhibit both bias and dispersion errors. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters varying in space and time, while giving a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, which makes it unsuitable for our purpose. Our post-processing method of the ensembles is developed in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu), where we are making forecasts for whole Europe, and based on observations from around 700 catchments. As the target is flood forecasting, we are also more interested in improving the forecast skill for high-flows rather than in a good prediction of the entire flow regime. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different meteorological forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to estimate the total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but we are adding a spatial penalty in the calibration process to force a spatial correlation of the parameters. The penalty takes

  20. Evaluating Snow Data Assimilation Framework for Streamflow Forecasting Applications Using Hindcast Verification

    NASA Astrophysics Data System (ADS)

    Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

    2012-12-01

    Snow water equivalent (SWE) estimation is a key factor in producing reliable streamflow simulations and forecasts in snow dominated areas. However, measuring or predicting SWE has significant uncertainty. Sequential data assimilation, which updates states using both observed and modeled data based on error estimation, has been shown to reduce streamflow simulation errors but has had limited testing for forecasting applications. In the current study, a snow data assimilation framework integrated with the National Weather System River Forecasting System (NWSRFS) is evaluated for use in ensemble streamflow prediction (ESP). Seasonal water supply ESP hindcasts are generated for the North Fork of the American River Basin (NFARB) in northern California. Parameter sets from the California Nevada River Forecast Center (CNRFC), the Differential Evolution Adaptive Metropolis (DREAM) algorithm and the Multistep Automated Calibration Scheme (MACS) are tested both with and without sequential data assimilation. The traditional ESP method considers uncertainty in future climate conditions using historical temperature and precipitation time series to generate future streamflow scenarios conditioned on the current basin state. We include data uncertainty analysis in the forecasting framework through the DREAM-based parameter set which is part of a recently developed Integrated Uncertainty and Ensemble-based data Assimilation framework (ICEA). Extensive verification of all tested approaches is undertaken using traditional forecast verification measures, including root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), volumetric bias, joint distribution, rank probability score (RPS), and discrimination and reliability plots. In comparison to the RFC parameters, the DREAM and MACS sets show significant improvement in volumetric bias in flow. Use of assimilation improves hindcasts of higher flows but does not significantly improve performance in the mid flow and

  1. Evaluating a coupled discrete wavelet transform and support vector regression for daily and monthly streamflow forecasting

    NASA Astrophysics Data System (ADS)

    Liu, Zhiyong; Zhou, Ping; Chen, Gang; Guo, Ledong

    2014-11-01

    This study investigated the performance and potential of a hybrid model that combined the discrete wavelet transform and support vector regression (the DWT-SVR model) for daily and monthly streamflow forecasting. Three key factors of the wavelet decomposition phase (mother wavelet, decomposition level, and edge effect) were proposed to consider for improving the accuracy of the DWT-SVR model. The performance of DWT-SVR models with different combinations of these three factors was compared with the regular SVR model. The effectiveness of these models was evaluated using the root-mean-squared error (RMSE) and Nash-Sutcliffe model efficiency coefficient (NSE). Daily and monthly streamflow data observed at two stations in Indiana, United States, were used to test the forecasting skill of these models. The results demonstrated that the different hybrid models did not always outperform the SVR model for 1-day and 1-month lead time streamflow forecasting. This suggests that it is crucial to consider and compare the three key factors when using the DWT-SVR model (or other machine learning methods coupled with the wavelet transform), rather than choosing them based on personal preferences. We then combined forecasts from multiple candidate DWT-SVR models using a model averaging technique based upon Akaike's information criterion (AIC). This ensemble prediction was superior to the single best DWT-SVR model and regular SVR model for both 1-day and 1-month ahead predictions. With respect to longer lead times (i.e., 2- and 3-day and 2-month), the ensemble predictions using the AIC averaging technique were consistently better than the best DWT-SVR model and SVR model. Therefore, integrating model averaging techniques with the hybrid DWT-SVR model would be a promising approach for daily and monthly streamflow forecasting. Additionally, we strongly recommend considering these three key factors when using wavelet-based SVR models (or other wavelet-based forecasting models).

  2. Total probabilities of ensemble runoff forecasts

    NASA Astrophysics Data System (ADS)

    Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

    2016-04-01

    Ensemble forecasting has for a long time been used as a method in meteorological modelling to indicate the uncertainty of the forecasts. However, as the ensembles often exhibit both bias and dispersion errors, it is necessary to calibrate and post-process them. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters which are different in space and time, but still can give a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, and cannot directly be regionalized in the way we would like, so we suggest a different path below. The target of our work is to create a mean forecast with uncertainty bounds for a large number of locations in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu) We are therefore more interested in improving the forecast skill for high-flows rather than the forecast skill of lower runoff levels. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to find a total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but assuring that they have some spatial correlation, by adding a spatial penalty in the calibration process. This can in some cases have a slight negative

  3. Ensemble postprocessing for probabilistic quantitative precipitation forecasts

    NASA Astrophysics Data System (ADS)

    Bentzien, S.; Friederichs, P.

    2012-12-01

    Precipitation is one of the most difficult weather variables to predict in hydrometeorological applications. In order to assess the uncertainty inherent in deterministic numerical weather prediction (NWP), meteorological services around the globe develop ensemble prediction systems (EPS) based on high-resolution NWP systems. With non-hydrostatic model dynamics and without parameterization of deep moist convection, high-resolution NWP models are able to describe convective processes in more detail and provide more realistic mesoscale structures. However, precipitation forecasts are still affected by displacement errors, systematic biases and fast error growth on small scales. Probabilistic guidance can be achieved from an ensemble setup which accounts for model error and uncertainty of initial and boundary conditions. The German Meteorological Service (Deutscher Wetterdienst, DWD) provides such an ensemble system based on the German-focused limited-area model COSMO-DE. With a horizontal grid-spacing of 2.8 km, COSMO-DE is the convection-permitting high-resolution part of the operational model chain at DWD. The COSMO-DE-EPS consists of 20 realizations of COSMO-DE, driven by initial and boundary conditions derived from 4 global models and 5 perturbations of model physics. Ensemble systems like COSMO-DE-EPS are often limited with respect to ensemble size due to the immense computational costs. As a consequence, they can be biased and exhibit insufficient ensemble spread, and probabilistic forecasts may be not well calibrated. In this study, probabilistic quantitative precipitation forecasts are derived from COSMO-DE-EPS and evaluated at more than 1000 rain gauges located all over Germany. COSMO-DE-EPS is a frequently updated ensemble system, initialized 8 times a day. We use the time-lagged approach to inexpensively increase ensemble spread, which results in more reliable forecasts especially for extreme precipitation events. Moreover, we will show that statistical

  4. Ensemble projections of future streamflow drought in Europe

    NASA Astrophysics Data System (ADS)

    Feyen, Luc; Dankers, Rutger

    2010-05-01

    Global warming - with higher temperatures, hence higher evaporative demands, but also with changes in the seasonality of precipitation patterns and an increase in the frequency and intensity of extreme climatic events - is likely to favor conditions for the development of droughts in many regions of Europe. This study evaluates the impact of global warming on streamflow drought in Europe by examining changes in low-flow predictions of a hydrological model driven by a multi-model ensemble of climate projections. The ensemble consists of simulations from two regional climate models (HIRHAM and RCAO), both run with boundary conditions from two global models (HadAM3H and ECHAM4/OPYC3), and for two scenarios (SRES A2 and B2) of greenhouse gas emissions. We employed the methods of block maxima and partial duration series to obtain minimum flows and flow deficits and fitted extreme value distributions by the maximum likelihood method. In order not to mix drought events with different physical causes the analysis was performed separately for the frost and non-frost season. The ensemble analysis shows that in the frost-free season streamflow droughts will become more severe and persistent in most parts of Europe by the end of this century, except in the most northern and northeastern regions. In snow dominated regions winter droughts are projected to be less severe because a lower fraction of precipitation will fall as snow in warmer winters. Regions most prone to an increase in river flow drought are southern and south-eastern Europe. The decrease in summer precipitation over large parts of Europe, as well as the rise in winter temperature and precipitation over northern Europe is well established and fairly consistent between the various regional climate simulations. Therefore, the changes in streamflow drought are less sensitive to the decadal-scale internal variability that is usually present in climate simulations and that may partially or completely obscure the

  5. Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

    SciTech Connect

    Turner, Sean W. D.; Bennett, James C.; Robertson, David E.; Galelli, Stefano

    2017-01-01

    Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

  6. Ensemble Streamflow Predictions in the Três Marias Basin, Brazil

    NASA Astrophysics Data System (ADS)

    Mainardi Fan, Fernando; Schwanenberg, Dirk; Kuwajima, Julio; Assis dos Reis, Alberto; Collischonn, Walter

    2014-05-01

    Hydropower is the main electricity source of Brazil. The related hydropower reservoirs are multi-purpose thus besides efficient and reliable energy production, they are relevant for flood control. In this context, the present study shows results of an Ensemble Streamflow Prediction (ESP) for supporting the operational decision making implemented at Três Marias hydroelectric power project located in the São Francisco River basin in Brazil. It is a large tropical river basin with approximately 55,000km² up to the Três Marias dam. The hydrological model used in the study is the MGB-IPH (Modelo de Grandes Bacias from Instituto de Pesquisas Hidráulicas), a large scale distributed hydrological model. Applied in an operational forecasting mode, it uses an empirical data assimilation method to take into account real time streamflow observations to update its state variables. We present results of a hindcast experiment with observed precipitation and streamflow data from the local energy utility, CEMIG (Companhia Energética de Minas Gerais), and from the Brazilian water agency, ANA (Agencia Nacional de Água),. Probabilistic Numerical Weather Predictions (NWP) from CPTEC (Centro de Previsão de Tempo e Estudos Climáticos), ECMWF (European Centre for Medium-Range Weather Forecast) and NOAA (National Oceanic and Atmospheric Administration) are used to generate the ESP. The data products and the MGB-IPH model are integrated into an open shell forecasting platform based on the software package Delft-FEWS. Inside the forecasting platform a hindcast mode over a forecast lead time of 10-16 days in recent rainfall periods is applied in. The ESP results are compared to deterministic forecasts of the Três Marias reservoir inflow. The results assessment verifies the added value of the ESP in general in comparison to the use of deterministic forecasts by means of different performance indicators. The ESP derived from the ECMWP ensemble shows the best performance. A future

  7. An Assessment of Melting Season Streamflow Forecasts using EPS for a Snow Dominated Basin in Turkey

    NASA Astrophysics Data System (ADS)

    Ertaş, Cansaran; Şensoy, Aynur; Akkol, Bulut; Şorman, Arda; Uysal, Gökçen; Çoşkun, Cihan

    2016-04-01

    In many mountainous regions, snowmelt makes significant contribution to streamflow, particularly during spring and summer months. Therefore, runoff modeling and forecasting during spring and early summer is important in terms of energy and water resources management. In this study, the Upper Euphrates Basin (10,275 km2 area and elevation range of 1125-3500 m) located at the headwater of Euphrates River, one of Turkey's most important rivers, is selected as the application area. In this region, snowmelt runoff constitutes approximately 2/3 in volume of the total yearly runoff. The aim of the study is to make a forward-oriented, medium-range flow forecasting using Ensemble Prediction System (EPS) which is a pioneer study for Turkey. Conceptual hydrological model HBV, which has a common usage in the literature, is chosen to predict streamflows. According to preliminary results, Nash-Sutcliffe model efficiencies are 0.85 for calibration (2001-2008) and 0.71 for validation (2009-2014) respectively. After calibrating/validating the hydrologic model, EPS data including 51 different combinations produced by ECMWF is used as probability based weather forecasts. Melting period during March-June of 2009-2015 is chosen as the forecast period. The probabilistic skill of EPS based hydrological model results are analyzed to verify the ensemble forecasts.

  8. Pre- and post-processing of hydro-meteorological ensembles for the Norwegian flood forecasting system in 145 basins.

    NASA Astrophysics Data System (ADS)

    Jahr Hegdahl, Trine; Steinsland, Ingelin; Merete Tallaksen, Lena; Engeland, Kolbjørn

    2016-04-01

    Probabilistic flood forecasting has an added value for decision making. The Norwegian flood forecasting service is based on a flood forecasting model that run for 145 basins. Covering all of Norway the basins differ in both size and hydrological regime. Currently the flood forecasting is based on deterministic meteorological forecasts, and an auto-regressive procedure is used to achieve probabilistic forecasts. An alternative approach is to use meteorological and hydrological ensemble forecasts to quantify the uncertainty in forecasted streamflow. The hydrological ensembles are based on forcing a hydrological model with meteorological ensemble forecasts of precipitation and temperature. However, the ensembles of precipitation are often biased and the spread is too small, especially for the shortest lead times, i.e. they are not calibrated. These properties will, to some extent, propagate to hydrological ensembles, that most likely will be uncalibrated as well. Pre- and post-processing methods are commonly used to obtain calibrated meteorological and hydrological ensembles respectively. Quantitative studies showing the effect of the combined processing of the meteorological (pre-processing) and the hydrological (post-processing) ensembles are however few. The aim of this study is to evaluate the influence of pre- and post-processing on the skill of streamflow predictions, and we will especially investigate if the forecasting skill depends on lead-time, basin size and hydrological regime. This aim is achieved by applying the 51 medium-range ensemble forecast of precipitation and temperature provided by the European Center of Medium-Range Weather Forecast (ECMWF). These ensembles are used as input to the operational Norwegian flood forecasting model, both raw and pre-processed. Precipitation ensembles are calibrated using a zero-adjusted gamma distribution. Temperature ensembles are calibrated using a Gaussian distribution and altitude corrected by a constant gradient

  9. Comparative evaluation of maximum likelihood ensemble filter and ensemble Kalman filter for real-time assimilation of streamflow data into operational hydrologic models

    NASA Astrophysics Data System (ADS)

    Rafieeinasab, Arezoo; Seo, Dong-Jun; Lee, Haksu; Kim, Sunghee

    2014-11-01

    Various data assimilation (DA) methods have been used and are being explored for use in operational streamflow forecasting. For ensemble forecasting, ensemble Kalman filter (EnKF) is an appealing candidate for familiarity and relative simplicity. EnKF, however, is optimal in the second-order sense, only if the observation equation is linear. As such, without an iterative approach, EnKF may not be appropriate for assimilating streamflow data for updating soil moisture states due to the strong nonlinear relationships between the two. Maximum likelihood ensemble filter (MLEF), on the other hand, is not subject to the above limitation. Being an ensemble extension of variational assimilation (VAR), MLEF also offers a strong connection with the traditional single-valued forecast process through the control, or the maximum likelihood, solution. In this work, we apply MLEF and EnKF as a fixed lag smoother to the Sacramento (SAC) soil moisture accounting model and unit hydrograph (UH) for assimilation of streamflow, mean areal precipitation (MAP) and potential evaporation (MAPE) data for updating soil moisture states. For comparative evaluation, three experiments were carried out. Comparison between homoscedastic vs. heteroscedastic modeling of selected statistical parameters for DA indicates that heteroscedastic modeling does not improve over homoscedastic modeling, and that homoscedastic error modeling with sensitivity analysis may suffice for application of MLEF for soil moisture updating using streamflow data. Comparative evaluation with respect to the model errors associated with soil moisture dynamics, the ensemble size and the number of streamflow observations assimilated per cycle showed that, in general, MLEF outperformed EnKF under varying conditions of observation and model errors, and ensemble size, and that MLEF performed well with an ensemble size as small as 5 while EnKF required a much larger ensemble size to perform closely to MLEF. Also, MLEF was not very

  10. Operational Ensemble River Forecasting in the United States and Australia: Practices and Challenges

    NASA Astrophysics Data System (ADS)

    Pagano, T. C.

    2012-04-01

    Operational river forecasts have been long produced to support water resources management in the United States and Australia. These forecasts cover a range of timescales from flash flooding (e.g. minutes to hours ahead) to seasonal (e.g. months ahead) and are generated by a range of statistical (e.g. regression-based) and dynamical (e.g. rainfall-runoff) model based techniques. Forecast uncertainty is commonly estimated operationally by using an ensemble of future precipitation scenarios and/or a measure of historical model error. Retrospective ensemble forecasting and the use of reforecasts for bias-adjustment and post-processing have become popular research topics and a few successful demonstration projects exist in both countries. Practical methods of post-processing, such as ensemble dressing, have been used to improve the probabilistic reliability of forecasts. The translation of predictions of probability distributions of streamflow into temporally and spatially consistent ensemble hydrographs remains an area for further development. However, probabilistic forecast communication and use remains a stumbling block for many. Furthermore, ensemble generation and post-processing typically require completely automated systems, making it difficult for humans to contribute their expertise to the forecasting process. This talk draws on ten years of experience as an operational forecaster with the US Department of Agriculture and as a developer of short-term flood forecasting systems to support the Australian Bureau of Meteorology.

  11. Uncertainty in dispersion forecasts using meteorological ensembles

    SciTech Connect

    Chin, H N; Leach, M J

    1999-07-12

    The usefulness of dispersion forecasts depends on proper interpretation of results. Understanding the uncertainty in model predictions and the range of possible outcomes is critical for determining the optimal course of action in response to terrorist attacks. One of the objectives for the Modeling and Prediction initiative is creating tools for emergency planning for special events such as the upcoming the Olympics. Meteorological forecasts hours to days in advance are used to estimate the dispersion at the time of the event. However, there is uncertainty in any meteorological forecast, arising from both errors in the data (both initial conditions and boundary conditions) and from errors in the model. We use ensemble forecasts to estimate the uncertainty in the forecasts and the range of possible outcomes.

  12. Comparison of ensemble post-processing approaches, based on empirical and dynamical error modelisation of rainfall-runoff model forecasts

    NASA Astrophysics Data System (ADS)

    Chardon, J.; Mathevet, T.; Le Lay, M.; Gailhard, J.

    2012-04-01

    In the context of a national energy company (EDF : Electricité de France), hydro-meteorological forecasts are necessary to ensure safety and security of installations, meet environmental standards and improve water ressources management and decision making. Hydrological ensemble forecasts allow a better representation of meteorological and hydrological forecasts uncertainties and improve human expertise of hydrological forecasts, which is essential to synthesize available informations, coming from different meteorological and hydrological models and human experience. An operational hydrological ensemble forecasting chain has been developed at EDF since 2008 and is being used since 2010 on more than 30 watersheds in France. This ensemble forecasting chain is characterized ensemble pre-processing (rainfall and temperature) and post-processing (streamflow), where a large human expertise is solicited. The aim of this paper is to compare 2 hydrological ensemble post-processing methods developed at EDF in order improve ensemble forecasts reliability (similar to Monatanari &Brath, 2004; Schaefli et al., 2007). The aim of the post-processing methods is to dress hydrological ensemble forecasts with hydrological model uncertainties, based on perfect forecasts. The first method (called empirical approach) is based on a statistical modelisation of empirical error of perfect forecasts, by streamflow sub-samples of quantile class and lead-time. The second method (called dynamical approach) is based on streamflow sub-samples of quantile class and streamflow variation, and lead-time. On a set of 20 watersheds used for operational forecasts, results show that both approaches are necessary to ensure a good post-processing of hydrological ensemble, allowing a good improvement of reliability, skill and sharpness of ensemble forecasts. The comparison of the empirical and dynamical approaches shows the limits of the empirical approach which is not able to take into account hydrological

  13. Improving Streamflow Forecasts Using Predefined Sea Surface Temperature

    NASA Astrophysics Data System (ADS)

    Kalra, A.; Ahmad, S.

    2011-12-01

    With the increasing evidence of climate variability, water resources managers in the western United States are faced with greater challenges of developing long range streamflow forecast. This is further aggravated by the increases in climate extremes such as floods and drought caused by climate variability. Over the years, climatologists have identified several modes of climatic variability and their relationship with streamflow. These climate modes have the potential of being used as predictor in models for improving the streamflow lead time. With this as the motivation, the current research focuses on increasing the streamflow lead time using predefine climate indices. A data driven model i.e. Support Vector Machine (SVM) based on the statistical learning theory is used to predict annual streamflow volume 3-year in advance. The SVM model is a learning system that uses a hypothesis space of linear functions in a Kernel induced higher dimensional feature space, and is trained with a learning algorithm from the optimization theory. Annual oceanic-atmospheric indices, comprising of Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), El Niño-Southern Oscillations (ENSO), and a new Sea Surface Temperature (SST) data set of "Hondo" Region for a period of 1906-2005 are used to generate annual streamflow volumes. The SVM model is applied to three gages i.e. Cisco, Green River, and Lees Ferry in the Upper Colorado River Basin in the western United States. Based on the performance measures the model shows very good forecasts, and the forecast are in good agreement with measured streamflow volumes. Previous research has identified NAO and ENSO as main drivers for extending streamflow forecast lead-time in the UCRB. Inclusion of "Hondo Region" SST information further improve the model's forecasting ability. The overall results of this study revealed that the annual streamflow of the UCRB is significantly influenced by

  14. A Streamflow Forecast Model for Central Arizona.

    NASA Astrophysics Data System (ADS)

    Young, Kenneth C.; Gall, Robert L.

    1992-05-01

    A spring-runoff forecast model for central Arizona was developed based on multiple discriminant analysis. More than 6500 potential predictor variables were analyzed, including local precipitation and temperature variables, as well as global sea level pressure variables. The forecast model was evaluated on nine years exclusive of the years on which the model was based. Forecasts are provided in the form of a cumulative distribution function (cdf) of the expected runoff, based on analogs. A ranked probability score to evaluate forecast skill for the cdf forecasts was developed. Ranked probability skill scores ranged from 25% to 45%.Local and global forecast models were developed and compared to the combined data source model. The global forecast model was equivalent in skill to the local forecast model. The combined model exhibited a marked improvement in skill over either the local or global models.Three recurrent patterns in the predictor variables used by the forecast model are analyzed in some depth. Above-normal pressure at Raoul Island northeast of New Zealand 14 to 18 months prior to the event forecast was found to be associated with above-normal runoff. A westward shift of the Bermuda high, as evidenced by the pressure change at Charleston, South Carolina, from December to August of the preceding year, was found to be associated with above-normal runoff. Above-normal pressure at Port Moresby, New Guinea coupled with below-normal pressure at San Diego, California, the month prior to the forecast, was found to be associated with above-normal runoff.

  15. Practical implementation of a particle filter data assimilation approach to estimate initial hydrologic conditions and initialize medium-range streamflow forecasts

    NASA Astrophysics Data System (ADS)

    Clark, E.; Wood, A.; Nijssen, B.; Newman, A. J.; Mendoza, P. A.

    2016-12-01

    The System for Hydrometeorological Applications, Research and Prediction (SHARP), developed at the National Center for Atmospheric Research (NCAR), University of Washington, U.S. Army Corps of Engineers, and U.S. Bureau of Reclamation, is a fully automated ensemble prediction system for short-term to seasonal applications. It incorporates uncertainty in initial hydrologic conditions (IHCs) and in hydrometeorological predictions. In this implementation, IHC uncertainty is estimated by propagating an ensemble of 100 plausible temperature and precipitation time series through the Sacramento/Snow-17 model. The forcing ensemble explicitly accounts for measurement and interpolation uncertainties in the development of gridded meteorological forcing time series. The resulting ensemble of derived IHCs exhibits a broad range of possible soil moisture and snow water equivalent (SWE) states. To select the IHCs that are most consistent with the observations, we employ a particle filter (PF) that weights IHC ensemble members based on observations of streamflow and SWE. These particles are then used to initialize ensemble precipitation and temperature forecasts downscaled from the Global Ensemble Forecast System (GEFS), generating a streamflow forecast ensemble. We test this method in two basins in the Pacific Northwest that are important for water resources management: 1) the Green River upstream of Howard Hanson Dam, and 2) the South Fork Flathead River upstream of Hungry Horse Dam. The first of these is characterized by mixed snow and rain, while the second is snow-dominated. The PF-based forecasts are compared to forecasts based on a single IHC (corresponding to median streamflow) paired with the full GEFS ensemble, and 2) the full IHC ensemble, without filtering, paired with the full GEFS ensemble. In addition to assessing improvements in the spread of IHCs, we perform a hindcast experiment to evaluate the utility of PF-based data assimilation on streamflow forecasts at 1

  16. Implementing Verification Procedures on a Calibrated vs non-Calibrated Streamflow for Agricultural Drought Forecast

    NASA Astrophysics Data System (ADS)

    Rico, D.; Corzo, G.; Chacon, J.; Munoz-Arriola, F.

    2016-12-01

    The goal of our research is to verify the improvements in agricultural drought (low soil moisture anomalies) forecast skill though calibration. Forecast verification is necessary to determine the quality of forecasts. The question arises of how accurate are forecasts after calibrating a hydrologic seasonal forecast system, and what is the range of predictability? Our objective is to use forecast verification methods on climate data in an ensemble-based platform post calibration. We hypothesize that the calibrated version of our hydrologic seasonal forecast system outperforms un-calibrated forecasts in its ability to more accurately predict seasonal drought when compared to observational data. We test our hypothesis by adding a verification logic code into the previously developed forecast system coded in C, Java, and Python. Standard Error (SE) and Probability of forecast accuracy percentages are set as verification statistics. This system conducts an ensemble streamflow prediction (ESP, consistent multiple 1-year simulations initialized with a single initial condition) based on the use of the Variable Infiltration Capacity model (VIC) and constantly determine accuracy of output data via direct relationship to observational values to quantify improvements in agricultural drought predictability in the Platte River Basin (PRB). Automatic calibration consisted of a krigging homogenization of the parameter fields, which developed more accurate soil parameter initial conditions. The system will be (1) initialized with wet and dry conditions for an ESP-initial condition and 50, 1-year simulations (1950 to 1999); (2) will run for wet and dry years in the northern high plains; (3) will run with and without VIC's calibration, and (4) each forecast will be matched against observational data to verify forecast skill. Preliminary calibration results have been indicative of an increase in streamflow forecast accuracy when compared against non-calibration and observations. This

  17. Prediction of regional streamflow frequency using model tree ensembles

    NASA Astrophysics Data System (ADS)

    Schnier, Spencer; Cai, Ximing

    2014-09-01

    This study introduces a novel data-driven method called model tree ensembles (MTEs) to predict streamflow frequency statistics based on known drainage area characteristics, which yields insights into the dominant controls of regional streamflow. The database used to induce the models contains both natural and anthropogenic drainage area characteristics for 294 USGS stream gages (164 in Texas and 130 in Illinois). MTEs were used to predict complete flow duration curves (FDCs) of ungaged streams by developing 17 models corresponding to 17 points along the FDC. Model accuracy was evaluated using ten-fold cross-validation and the coefficient of determination (R2). During the validation, the gages withheld from the analysis represent ungaged watersheds. MTEs are shown to outperform global multiple-linear regression models for predictions in ungaged watersheds. The accuracy of models for low flow is enhanced by explicit consideration of variables that capture human interference in watershed hydrology (e.g., population). Human factors (e.g., population and groundwater use) appear in the regionalizations for low flows, while annual and seasonal precipitation and drainage area are important for regionalizations of all flows. The results of this study have important implications for predictions in ungaged watersheds as well as gaged watersheds subject to anthropogenically-driven hydrologic changes.

  18. Effect of initial conditions of a catchment on seasonal streamflow prediction using ensemble streamflow prediction (ESP) technique for the Rangitata and Waitaki River basins on the South Island of New Zealand

    NASA Astrophysics Data System (ADS)

    Singh, Shailesh Kumar; Zammit, Christian; Hreinsson, Einar; Woods, Ross; Clark, Martyn; Hamlet, Alan

    2013-04-01

    Increased access to water is a key pillar of the New Zealand government plan for economic growths. Variable climatic conditions coupled with market drivers and increased demand on water resource result in critical decision made by water managers based on climate and streamflow forecast. Because many of these decisions have serious economic implications, accurate forecast of climate and streamflow are of paramount importance (eg irrigated agriculture and electricity generation). New Zealand currently does not have a centralized, comprehensive, and state-of-the-art system in place for providing operational seasonal to interannual streamflow forecasts to guide water resources management decisions. As a pilot effort, we implement and evaluate an experimental ensemble streamflow forecasting system for the Waitaki and Rangitata River basins on New Zealand's South Island using a hydrologic simulation model (TopNet) and the familiar ensemble streamflow prediction (ESP) paradigm for estimating forecast uncertainty. To provide a comprehensive database for evaluation of the forecasting system, first a set of retrospective model states simulated by the hydrologic model on the first day of each month were archived from 1972-2009. Then, using the hydrologic simulation model, each of these historical model states was paired with the retrospective temperature and precipitation time series from each historical water year to create a database of retrospective hindcasts. Using the resulting database, the relative importance of initial state variables (such as soil moisture and snowpack) as fundamental drivers of uncertainties in forecasts were evaluated for different seasons and lead times. The analysis indicate that the sensitivity of flow forecast to initial condition uncertainty is depend on the hydrological regime and season of forecast. However initial conditions do not have a large impact on seasonal flow uncertainties for snow dominated catchments. Further analysis indicates

  19. A Hybrid Architecture of Neural Networks for Daily Streamflow Forecasting

    NASA Astrophysics Data System (ADS)

    Moradkhani, H.

    2001-12-01

    Streamflow forecasting has always been a challenging task for water resources engineers and managers and the major component of water resources system control. For years numerous techniques have been suggested and employed for streamflow forecasting. Computational Neural Networks (NNs), which are capable of recognizing hidden patterns in data, have recently become popular in many hydrologic applications. In this study, hybrid NN is developed for one step ahead forecasting of daily streamflow. Radial Basis Function (RBF) composed of a group of Gausian functions is used in conjunction with Self-Organizing Feature Map (SOFM) used in data classification. RBF transfers those classified input variables into the desired output estimate. Eight years of daily rainfall, streamflow, and temperature in Salt River basin were used for calibration and validation. Since 60%-80% of the water supply produced by the basin comes in the form of snow, further consideration of the existing time delay of snow melting process in the basin to the watershed outlet is important. Therefore two separated settings were considered in this simulation: the first one only includes several short-term daily rainfall and streamflow in the input sequence; the second setting, on the other hand, includes a longer time period (three-months) of temperature data sequence. Various statistical analyses, such as root mean square error, bias estimate, noise to signal ratio, and correlation coefficients of estimates and observations, were done to evaluate the forecast models. The preliminary results show that the accuracy of the model once considering the long-term effect of the snowmelt is conspicuous with respect to short-term effect. The effectiveness of the proposed and current operational models is evaluated.

  20. Practical implementation of a particle filter data assimilation approach to estimate initial hydrologic conditions and initialize medium-range streamflow forecasts

    NASA Astrophysics Data System (ADS)

    Clark, Elizabeth; Wood, Andy; Nijssen, Bart; Mendoza, Pablo; Newman, Andy; Nowak, Kenneth; Arnold, Jeffrey

    2017-04-01

    In an automated forecast system, hydrologic data assimilation (DA) performs the valuable function of correcting raw simulated watershed model states to better represent external observations, including measurements of streamflow, snow, soil moisture, and the like. Yet the incorporation of automated DA into operational forecasting systems has been a long-standing challenge due to the complexities of the hydrologic system, which include numerous lags between state and output variations. To help demonstrate that such methods can succeed in operational automated implementations, we present results from the real-time application of an ensemble particle filter (PF) for short-range (7 day lead) ensemble flow forecasts in western US river basins. We use the System for Hydromet Applications, Research and Prediction (SHARP), developed by the National Center for Atmospheric Research (NCAR) in collaboration with the University of Washington, U.S. Army Corps of Engineers, and U.S. Bureau of Reclamation. SHARP is a fully automated platform for short-term to seasonal hydrologic forecasting applications, incorporating uncertainty in initial hydrologic conditions (IHCs) and in hydrometeorological predictions through ensemble methods. In this implementation, IHC uncertainty is estimated by propagating an ensemble of 100 temperature and precipitation time series through conceptual and physically-oriented models. The resulting ensemble of derived IHCs exhibits a broad range of possible soil moisture and snow water equivalent (SWE) states. The PF selects and/or weights and resamples the IHCs that are most consistent with external streamflow observations, and uses the particles to initialize a streamflow forecast ensemble driven by ensemble precipitation and temperature forecasts downscaled from the Global Ensemble Forecast System (GEFS). We apply this method in real-time for several basins in the western US that are important for water resources management, and perform a hindcast

  1. Incorporating Multi-model Ensemble Techniques Into a Probabilistic Hydrologic Forecasting System

    NASA Astrophysics Data System (ADS)

    Sonessa, M. Y.; Bohn, T. J.; Lettenmaier, D. P.

    2008-12-01

    Multi-model ensemble techniques have been shown to reduce bias and to aid in quantification of the effects of model uncertainty in hydrologic modeling. However, these techniques are only beginning to be applied in operational hydrologic forecast systems. To investigate the performance of a multi-model ensemble in the context of probabilistic hydrologic forecasting, we have extended the University of Washington's West-wide Seasonal Hydrologic Forecasting System to use an ensemble of three models: the Variable Infiltration Capacity (VIC) model version 4.0.6, the NCEP NOAH model version 2.7.1, and the NWS grid-based Sacramento/Snow-17 model (SAC). The objective of this presentation is to assess the performance of the ensemble of the three models as compared to the performance of the models individually. Three forecast points within the West-wide forecast system domain were used for this research: the Feather River at Oroville, CA, the Salmon River at White horse, ID, and the Colorado River at Grand Junction. The forcing and observed streamflow data are for years 1951-2005 for the Feather and Salmon Rivers; and 1951-2003 for the Colorado. The models were first run for the retrospective period, then bias-corrected, and model weights were then determined using multiple linear regression. We assessed the performance of the ensemble in comparison with the individual models in terms of correlation with observed flows and Root Mean Square Error, and Nash-Sutcliffe. We found that for evaluations of retrospective simulations in comparison with observations, the ensemble performed better overall than any of the models individually even though in few individual months individual models performed slightly better than the ensemble. To test forecast skill, we performed Ensemble Streamflow Prediction (ESP) forecasts for each year of the retrospective period, using forcings from all other years, for individual models and for the multi-model ensemble. To form the ensemble for the ESP

  2. Performance assessment of sequential data assimilation methods on streamflow forecasting using a distributed hydrologic model

    NASA Astrophysics Data System (ADS)

    Noh, S. J.; Tachikawa, Y.; Shiiba, M.

    2012-04-01

    Flood disaster is the main cause of losses from natural hazards in the world and is responsible for a greater number of damaging events than any other type of natural disaster. Effective early-warning system is needed where floods regularly claim victims and rob people of their livelihoods. However, due to uncertainty of simulation model and observation, it is very hard to get accurate flood forecasting results for required lead time. Therefore, the applications of assimilation techniques have been increasing to estimate and reduce uncertainty in the predictions by updating state variables according to observation sequentially. In this study, performance of sequential data assimilation methods such as the ensemble Kalman filters (EnKF) and the particle filters (PF) is assessed for short-term streamflow forecasting with a distributed hydrologic model. For both the EnKF and the PF, sequential data assimilation is performed within a lag time window to take into account of lag and response times among internal hydrologic processes in a hydrologic model. Proposed methods are applied for several catchments in Japan and Korea to assess their performance. Model ensembles, perturbed by noises of states and parameters, are assimilated by streamflow observation. Improvement of forecasting accuracy is found in both the EnKF and the PF when sufficient lag times are provided. The EnKF is sensitive to lag times and shows limited forecasts in short lead times, while the PF shows more stable forecasts for overall lead times. Filtering in a lag time window also presents improved performance with a limited number of ensembles.

  3. Optimizing multiple reliable forward contracts for reservoir allocation using multitime scale streamflow forecasts

    NASA Astrophysics Data System (ADS)

    Lu, Mengqian; Lall, Upmanu; Robertson, Andrew W.; Cook, Edward

    2017-03-01

    Streamflow forecasts at multiple time scales provide a new opportunity for reservoir management to address competing objectives. Market instruments such as forward contracts with specified reliability are considered as a tool that may help address the perceived risk associated with the use of such forecasts in lieu of traditional operation and allocation strategies. A water allocation process that enables multiple contracts for water supply and hydropower production with different durations, while maintaining a prescribed level of flood risk reduction, is presented. The allocation process is supported by an optimization model that considers multitime scale ensemble forecasts of monthly streamflow and flood volume over the upcoming season and year, the desired reliability and pricing of proposed contracts for hydropower and water supply. It solves for the size of contracts at each reliability level that can be allocated for each future period, while meeting target end of period reservoir storage with a prescribed reliability. The contracts may be insurable, given that their reliability is verified through retrospective modeling. The process can allow reservoir operators to overcome their concerns as to the appropriate skill of probabilistic forecasts, while providing water users with short-term and long-term guarantees as to how much water or energy they may be allocated. An application of the optimization model to the Bhakra Dam, India, provides an illustration of the process. The issues of forecast skill and contract performance are examined. A field engagement of the idea is useful to develop a real-world perspective and needs a suitable institutional environment.

  4. Using the TIGGE database for ensemble hydrological forecasting: a study on 74 catchments in France (Invited)

    NASA Astrophysics Data System (ADS)

    Ramos, M.; Zalachori, I.; Mathevet, T.; Loumagne, C.

    2010-12-01

    This study assesses the quality of streamflow forecasts issued by the GRP rainfall-runoff model, driven by ensemble weather predictions from the TIGGE database. The GRP is a lumped soil-moisture-accounting type model developed at Cemagref in France for operational flood forecasting. In this study, the model is run at daily time steps and its assimilation procedure makes use of the last observed discharge at the time of the forecast to update the state of the model routing store. Ensemble predictions from 8 meteorological centres from the TIGGE archive are considered. They are available over a 2-year period, from October 2006 to October 2008. Single ensembles from each centre, with a number of members ranging from 15 to 51 ensembles, are considered in the hydrological model, as well as a combined multi-model ensemble that takes into account all members available at each day of the forecast period. Forecast data were spatially averaged over the studied catchments to obtain the areal forecast precipitation at each lead time. Hydrological forecast evaluation is performed at catchment-based spatial scales and for lead-times up to 15 days. Simulations were carried out over 74 catchments in France, with areas ranging from 1000 km2 to 44,000 km2. Forecasts are compared to observed data and typical skill scores used for forecast verification are computed. Issues related to the quality of the ensemble hydrological forecasts, and the impacts of factors like catchment area and hydrological regime type on the performance of the forecasts are discussed.

  5. Value of long-term streamflow forecast to reservoir operations for water supply in snow-dominated catchments

    SciTech Connect

    Anghileri, Daniela; Voisin, Nathalie; Castelletti, Andrea F.; Pianosi, Francesca; Nijssen, B.; Lettenmaier, Dennis P.

    2016-04-12

    In this study, we develop a forecast-based adaptive control framework for Oroville reservoir, California, to assess the value of seasonal and inter-annual forecasts for reservoir operation.We use an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity hydrology model. The optimal sequence of daily release decisions from the reservoir is then determined by Model Predictive Control, a flexible and adaptive optimization scheme.We assess the forecast value by comparing system performance based on the ESP forecasts with that based on climatology and a perfect forecast. In addition, we evaluate system performance based on a synthetic forecast, which is designed to isolate the contribution of seasonal and inter-annual forecast skill to the overall value of the ESP forecasts.Using the same ESP forecasts, we generalize our results by evaluating forecast value as a function of forecast skill, reservoir features, and demand. Our results show that perfect forecasts are valuable when the water demand is high and the reservoir is sufficiently large to allow for annual carry-over. Conversely, ESP forecast value is highest when the reservoir can shift water on a seasonal basis.On average, for the system evaluated here, the overall ESP value is 35% less than the perfect forecast value. The inter-annual component of the ESP forecast contributes 20-60% of the total forecast value. Improvements in the seasonal component of the ESP forecast would increase the overall ESP forecast value between 15 and 20%.

  6. Probabilistic Flash Flood Forecasting using Stormscale Ensembles

    NASA Astrophysics Data System (ADS)

    Hardy, J.; Gourley, J. J.; Kain, J. S.; Clark, A.; Novak, D.; Hong, Y.

    2013-12-01

    Flash flooding is one of the most costly and deadly natural hazards in the US and across the globe. The loss of life and property from flash floods could be mitigated with better guidance from hydrological models, but these models have limitations. For example, they are commonly initialized using rainfall estimates derived from weather radars, but the time interval between observations of heavy rainfall and a flash flood can be on the order of minutes, particularly for small basins in urban settings. Increasing the lead time for these events is critical for protecting life and property. Therefore, this study advances the use of quantitative precipitation forecasts (QPFs) from a stormscale NWP ensemble system into a distributed hydrological model setting to yield basin-specific, probabilistic flash flood forecasts (PFFFs). Rainfall error characteristics of the individual members are first diagnosed and quantified in terms of structure, amplitude, and location (SAL; Wernli et al., 2008). Amplitude and structure errors are readily correctable due to their diurnal nature, and the fine scales represented by the CAPS QPF members are consistent with radar-observed rainfall, mainly showing larger errors with afternoon convection. To account for the spatial uncertainty of the QPFs, we use an elliptic smoother, as in Marsh et al. (2012), to produce probabilistic QPFs (PQPFs). The elliptic smoother takes into consideration underdispersion, which is notoriously associated with stormscale ensembles, and thus, is good for targeting the approximate regions that may receive heavy rainfall. However, stormscale details contained in individual members are still needed to yield reasonable flash flood simulations. Therefore, on a case study basis, QPFs from individual members are then run through the hydrological model with their predicted structure and corrected amplitudes, but the locations of individual rainfall elements are perturbed within the PQPF elliptical regions using Monte

  7. Diagnostic studies of ensemble forecast "jumps"

    NASA Astrophysics Data System (ADS)

    Magnusson, Linus; Hewson, Tim; Ferranti, Laura; Rodwell, Mark

    2016-04-01

    During 2015 we saw exceptional consistency in successive seasonal forecasts produced at ECMWF, for the winter period 2015/16, right across the globe. This winter was characterised by a well-predicted and unusually strong El Nino, and some have ascribed the consistency to that. For most of December this consistency was mirrored in the (separate) ECMWF monthly forecast system, which correctly predicted anomalously strong (mild) zonal flow, over the North Atlantic and western Eurasia, even in forecasts for weeks 3 and 4. In monthly forecasts in general these weeks are often devoid of strong signals. However in late December and early January strong signals, even in week 2, proved to be incorrect, most notably over the North Atlantic and Eurasian sectors. Indeed on at least two occasions the outcome was beyond the ensemble forecast range over Scandinavia. In one of these conditions flipped from extreme mild to extreme cold as a high latitude block developed. Temperature prediction is very important to many customers, notably those dealing with renewable energy, because cold weather causes increased demand but also tends to coincide with reduced wind power production. So understandably jumps can cause consternation amongst some customer groups, and are very difficult to handle operationally. This presentation will discuss the results of initial diagnostic investigations into what caused the "ensemble jumps", particularly at the week two lead, though reference will also be made to a related shorter range (day 3) jump that was important for flooding over the UK. Initial results suggest that an inability of the ECMWF model to correctly represent convective outbreaks over North America (that for winter-time were quite extreme) played an important role. Significantly, during this period, an unusually large amount of upper air data over North America was rejected or ascribed low weight. These results bear similarities to previous diagnostic studies at ECMWF, wherein major

  8. Ensemble forecasting for a hydrological testbed

    NASA Astrophysics Data System (ADS)

    Jankov, Isidora; Albers, Steve; Wharton, Linda; Tollerud, Ed; Yuan, Huiling; Toth, Zoltan

    2010-05-01

    Significant precipitation events in California during the winter season are often caused by land-falling "atmospheric rivers" associated with extratropical cyclones from the Pacific Ocean. Atmospheric rivers are narrow, elongated plumes of enhanced water vapor transport over the Pacific and Atlantic oceans that can extend from the tropics and subtropics into the extratropics. Large values of integrated water vapor are advected within the warm sector of extratropical cyclones immediately ahead of polar cold fronts, although the source of these vapor plumes can originate in the tropics beyond the cyclone warm sector. When an atmospheric river makes a landfall on the coast of California, the northwest to southeast orientation of the Sierra Mountain chain exerts orographic forcing on the southwesterly low-level flow in the warm sector of approaching extratropical cyclones. As a result, sustained precipitation is typically enhanced and modified by the complex terrain. This has major hydrological consequences. The National Oceanic Atmospheric Administration (NOAA) has established the Hydrometeorological Testbed (HMT) to design and support a series of field and numerical modeling experiments to better understand and forecast precipitation in the Central Valley. The main role of the Forecast Application Branch (NOAA/ESRL/GSD) in HMT has been in supporting the real time numerical forecasts as well as research activities targeting better understanding and improvement of Quantitative Precipitation Forecasts (QPF). For this purpose ensemble modeling system has been developed. The ensemble system consists of mixed dynamic cores, mixed physics and mixed lateral boundary conditions. Performance evaluation results for this system will be presented at the conference.

  9. Evaluation of seasonal ensemble forecasts in Norway

    NASA Astrophysics Data System (ADS)

    Tore Sinnes, Svein; Engeland, Kolbjørn; Langsholt, Elin; Roar Sælthun, Nils

    2017-04-01

    Throughout the winter and spring season, seasonal forecasts are used by the Norwegian Water Resources and Energy Directorate (NVE) in order to assess the probability for sever floods or for low seasonal runoff volumes. The latter is especially important for hydropower production. The seasonal forecasts are generated by a set of 145 lumped, elevation distributed HBV models distributed all over Norway. The observed weather is used to establish the initial snow cover, soil moisture and groundwater levels in the HBV model. Subsequently, scenarios are created by using time series of observed weather the previous 50 years, creating a total of 50 ensembles. The predictability of this seasonal forecasting system depends therefore on the importance of the initial conditions, and in Norway the seasonal snow cover is especially important. The aim of this study is to evaluate the performance of the seasonal forecasts of flood peaks and seasonal runoff volumes and especially to evaluate of the predictability depends on (i) catchment climatology and (ii) issue dates and lead times. For achieving these aims, evaluation criterions assessing reliability and sharpness were used. The results shows that the predictability is the highest for catchments where the spring runoff is dominated by snow melt. The predictability is the highest for the shortest lead times (up to 1 months ahead).The predictive performance is higher for runoff volumes than for the flood peaks.

  10. Flood forecast sensitivity to temperature using ECMWF ensembles for 145 catchments in Norway

    NASA Astrophysics Data System (ADS)

    Jahr Hegdahl, Trine; Engeland, Kolbjørn; Grønbech, Bård Johan; Steinsland, Ingelin; Merete Tallaksen, Lena

    2017-04-01

    The Norwegian flood forecasting service is based on a flood-forecasting model run on 145 basins. The basins are located all across Norway and differ in both size and hydrological regime. Current flood forecasting system is based on deterministic meteorological forecasts, and uses an auto-regressive procedure to achieve probabilistic forecasts. An alternative approach is to use meteorological and hydrological ensemble forecasts to quantify the uncertainty in forecasted streamflow. The aim of our study is to establish and assess the performance of both meteorological and hydrological ensembles for 145 catchments in Norway, which differ in size, elevation and hydrological regime. We identify regional differences and improvements in performance for preprocessed meteorological forecasts. A separate study further investigates the sensitivity to forecasted temperature for specific snowmelt induced floods. In Norway, snowmelt and combined rain and snowmelt floods are frequent. Hence, temperature is important for correct calculations of snowmelt. Temperature and precipitation ensembles are derived from ECMWF covering a period of nearly three years (01.03.2013 to 31.12.2015). To improve the spread and reduce bias we used standard methods provided by the Norwegian Meteorological Institute. Precipitation is corrected applying a zero-adjusted gamma distribution method (correcting the spread), and temperature is bias corrected using a quantile-quantile mapping (using Hirlam (RCM) 5 km temperature grid as a reference). Observed temperature and precipitation data are station data for all of Norway, interpolated to a 1×1 km2 grid (SeNorge.no). Streamflow observations are available from the NVE database. The hydrological model is the flood-forecasting operational HBV model, run with daily catchment average values. The results show that the methods applied to meteorological ensemble data reduce the cold bias present in the ECMWF temperature ensembles. Catchments on the western coast

  11. Monthly streamflow forecasting with auto-regressive integrated moving average

    NASA Astrophysics Data System (ADS)

    Nasir, Najah; Samsudin, Ruhaidah; Shabri, Ani

    2017-09-01

    Forecasting of streamflow is one of the many ways that can contribute to better decision making for water resource management. The auto-regressive integrated moving average (ARIMA) model was selected in this research for monthly streamflow forecasting with enhancement made by pre-processing the data using singular spectrum analysis (SSA). This study also proposed an extension of the SSA technique to include a step where clustering was performed on the eigenvector pairs before reconstruction of the time series. The monthly streamflow data of Sungai Muda at Jeniang, Sungai Muda at Jambatan Syed Omar and Sungai Ketil at Kuala Pegang was gathered from the Department of Irrigation and Drainage Malaysia. A ratio of 9:1 was used to divide the data into training and testing sets. The ARIMA, SSA-ARIMA and Clustered SSA-ARIMA models were all developed in R software. Results from the proposed model are then compared to a conventional auto-regressive integrated moving average model using the root-mean-square error and mean absolute error values. It was found that the proposed model can outperform the conventional model.

  12. Discrete post-processing of total cloud cover ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Hemri, Stephan; Haiden, Thomas; Pappenberger, Florian

    2017-04-01

    This contribution presents an approach to post-process ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical post-processing of ensemble predictions are tested. The first approach is based on multinomial logistic regression, the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on station-wise post-processing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model. Reference Hemri, S., Haiden, T., & Pappenberger, F. (2016). Discrete post-processing of total cloud cover ensemble forecasts. Monthly Weather Review 144, 2565-2577.

  13. Characterizing the Behavior of NOAA's Hydrologic Ensemble Forecast Service in California

    NASA Astrophysics Data System (ADS)

    He, M.; Whitin, B.; Brown, J.; Fickenscher, P.; Henkel, A.; Talanki, S.; Hartman, R.

    2014-12-01

    The National Oceanic and Atmospheric Administration (NOAA)'s National Weather Service (NWS) is implementing the Hydrologic Ensemble Forecast Service (HEFS) across the operating areas of the 13 NWS River Forecast Centers (RFCs). As the implementation progresses, hindcasting and validation is necessary to understand the strengths and weaknesses of the HEFS and to guide its operational use. Particularly in regions such as California that encompass a broad range of elevation, temperature, and precipitation gradients, the quality of the HEFS forecasts will vary geographically, and it is important to understand the degrees and controls on forecast quality in this context. This study aims to develop a comprehensive understanding of the quality of HEFS forecasts in California, with the aim of guiding and enhancing the implementation of the HEFS, as well as informing end-users about the expected quality of the HEFS forecasts. The HEFS was calibrated with temperature and precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environmental Prediction. Also, in order to determine forecast skill and to benchmark the HEFS against a simpler forecasting system, the HEFS was calibrated with a conditional ("resampled") climatology. The calibrated HEFS was used to generate retrospective forecasts of precipitation, temperature, and streamflow for a 25-year (1985-2009) period for six basins in the state. The forecast horizon was 1-14 days. The retrospective forecasts were verified conditionally on forecast lead time, magnitude, and season. Preliminary results indicate that HEFS forecasts are much more skillful when forced by inputs from the GEFS, rather than resampled climatology. However, there are noticeable differences in forecast quality among basins. These observations demonstrate the applicability of HEFS in a wide hydroclimatic gradient within California, while highlighting the difficulty in generalizing its behavior across the state.

  14. From Interannual Streamflow Forecasts to New Water Management Strategies for Ceara, N. E. Brazil

    NASA Astrophysics Data System (ADS)

    Lall, U.; Sharma, A.; Arumugam, S.; de Souza Filho, A. F.

    2002-12-01

    Extended range climate forecasts are opening the way for innovations in water marketing and management. Here, we first describe how climate predictors are used for forecasting multivariate streamflow sequences up to 18 months into the future. These forecasts then provide the basis for a new scheme of allocation of water to different sectors of use via annual reliable contracts, derived through reservoir optimization and simulation using policy and economic measures, the ensemble forecasts, and reliability levels and monthly demand patterns for each contract type that are specified by a "river basin water committee". These wholesale contracts are then disaggregated within each sector through an auction or bidding process, where the rights of economically disadvantaged users are protected via bidding through coalitions or the state bidding on their behalf. Contract holders have the option of exercising or trading their contracts. Subsequently, the reservoir system is operated to meet these contracts at the specified level of reliability, prioritize failures, and to allocate non-contract water using updated forecasts. The contracts function like an insurance policy in that the holder is given compensation as per initially agreed on terms, in the event of contract failure. The process is being applied in Ceara, N. E. Brazil, where forecast skill and vulnerability to drought are high.

  15. Ensemble hydro-meteorological forecasting for early warning of floods and scheduling of hydropower production

    NASA Astrophysics Data System (ADS)

    Solvang Johansen, Stian; Steinsland, Ingelin; Engeland, Kolbjørn

    2016-04-01

    Running hydrological models with precipitation and temperature ensemble forcing to generate ensembles of streamflow is a commonly used method in operational hydrology. Evaluations of streamflow ensembles have however revealed that the ensembles are biased with respect to both mean and spread. Thus postprocessing of the ensembles is needed in order to improve the forecast skill. The aims of this study is (i) to to evaluate how postprocessing of streamflow ensembles works for Norwegian catchments within different hydrological regimes and to (ii) demonstrate how post processed streamflow ensembles are used operationally by a hydropower producer. These aims were achieved by postprocessing forecasted daily discharge for 10 lead-times for 20 catchments in Norway by using EPS forcing from ECMWF applied the semi-distributed HBV-model dividing each catchment into 10 elevation zones. Statkraft Energi uses forecasts from these catchments for scheduling hydropower production. The catchments represent different hydrological regimes. Some catchments have stable winter condition with winter low flow and a major flood event during spring or early summer caused by snow melting. Others has a more mixed snow-rain regime, often with a secondary flood season during autumn, and in the coastal areas, the stream flow is dominated by rain, and the main flood season is autumn and winter. For post processing, a Bayesian model averaging model (BMA) close to (Kleiber et al 2011) is used. The model creates a predictive PDF that is a weighted average of PDFs centered on the individual bias corrected forecasts. The weights are here equal since all ensemble members come from the same model, and thus have the same probability. For modeling streamflow, the gamma distribution is chosen as a predictive PDF. The bias correction parameters and the PDF parameters are estimated using a 30-day sliding window training period. Preliminary results show that the improvement varies between catchments depending

  16. Hydrological-oriented verification for ensemble forecasting systems: the case of the PIT diagram

    NASA Astrophysics Data System (ADS)

    Bourgin, François; Ramos, Maria-Helena; Perrin, Charles; Renard, Benjamin

    2017-04-01

    The most common way to communicate uncertainty in streamflow predictions for water resources and risk management is through the use of ensemble scenarios or prediction intervals. While the advantages of probabilistic flow forecasting for decision-making are recognized, the evaluation of the quality of ensemble-based or probabilistic forecasts remains a challenge. Reliability is a fundamental attribute when evaluating the quality of probabilistic flow predictions. It is related to the statistical coherence of the associated uncertainty estimates. Reliable predictions are thus important for users who take actions based on prediction intervals (e.g., reservoir inflow volume forecasts) or on the forecast probability of a given critical event (e.g., exceedance of a flood threshold). However, forecast systems are usually developed to serve many users and, in general, they are evaluated without considering the user's specific decision-making problem. This means that a forecasting system must be reliable in all situations (for normal, high or low flows; for peak flow probabilities or volume probabilities of occurrence), regardless of the event of interest for the user. At the same time, users are often interested in knowing if a forecasting system performs well for their case of application. Application-focused evaluations of the quality of a forecast are thus also important to enhance the usefulness of a forecasting system. Here, we investigate the specificities of hydrological-oriented verification of reliability that is commonly assessed with the Probability Integral Transform (PIT) diagram. We applied an ensemble forecasting system to a large set of catchments in France to assess the impact of conditioning strategies used to stratifying the data on the evaluation of forecast performance. For example, we considered separating low and high flows, or focusing on rainfall-driven or recession parts of the hydrographs. We show that the use of conditioning strategies can

  17. Uncertainty in Dispersion Forecasting Using Meteorological Ensembles

    SciTech Connect

    Leach, M J; Chin, H-N

    2000-03-23

    A approach for quantifying meteorological uncertainty is via development of an ensemble of forecasts from slightly perturbed initial conditions (Sivillo et al., 1997) to predict the time evolution of the probability density function of atmospheric variables (Mullen and Baurnhefner, 1994). We create an ensemble of forecasts by varying the initial (and boundary) conditions for the COAMPS meteorological model. The variations in the initial conditions must be consistent with analysis error. Optimally, the range of initial conditions would encompass the ''true'' atmospheric state, but which is never actually known. Our method for creating varying initial conditions is to use different global data sets to derive the necessary data. We use two models from the National Weather Service (the AVN and ETA models) and one from the Navy (the NOGAPS model). In addition to those data sets we perturb the data from those models, using a normally distributed random number at each grid point in the COAMPS model. We perturb the (u,v) wind components, the temperature and the moisture. The size of the perturbation is determined by the variability within that variable field. The forecasts are run for 48 hours. We then use the output from the COAMPS model to drive a Lagrangian dispersion model (LODI) for simulated releases. The results from a simulated release from hour 33 are shown in Figure 1. The center of the domain is Oakland airport and the basic on-shore wind is from the southwest. In three of the simulations, the plume goes over the top of the hills to the northeast, and in the other three the plume hugs the coastline and goes around those hills The two solutions reflect a dependence on the Froude number, a ratio of the Kinetic energy to Potential energy. Higher Kinetic energy flow (Higher Froude number) flow goes over the top of the mountain, while lower Kinetic energy flow goes around the hills.

  18. Assimilation of soil moisture and streamflow observations to improve flood forecasting with considering runoff routing lags

    NASA Astrophysics Data System (ADS)

    Meng, Shanshan; Xie, Xianhong; Liang, Shunlin

    2017-07-01

    Assimilation of either soil moisture or streamflow has been well demonstrated to improve flood forecasting. However, it is difficult to assimilate two different types of observations into a rainfall-runoff model simultaneously because there is a time lag between soil moisture and streamflow owing to the runoff routing process. In this study, we developed an effective data assimilation scheme based on the ensemble Kalman filter and smoother (named as EnKF-S) to exploit the benefits of the two observation types while accounting for the runoff routing lag. To prove the importance of accounting for the time lag, a scheme named Dual-EnKF was used to compare. To demonstrate the schemes, we designed synthetic cases regarding two typical flood patterns, i.e., flash flood and gradual flood. The results show that EnKF-S can effectively improve flood forecasting compared with Dual-EnKF, particularly when the runoff routing has distinct time lags. For the synthetic cases, EnKF-S reduced root-mean-square error (RMSE) by more than 70% relative to the data assimilation scheme without considering runoff routing lags. Therefore, this effective data assimilation scheme holds great potential for short-term flood forecasting by merging observations from ground measurement and remote sensing retrievals.

  19. Application of quantitative precipitation forecasting and precipitation ensemble prediction for hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Tao, P.; Tie-Yuan, S.; Zhi-Yuan, Y.; Jun-Chao, W.

    2015-05-01

    The precipitation in the forecast period influences flood forecasting precision, due to the uncertainty of the input to the hydrological model. Taking the ZhangHe basin as the example, the research adopts the precipitation forecast and ensemble precipitation forecast product of the AREM model, uses the Xin Anjiang hydrological model, and tests the flood forecasts. The results show that the flood forecast result can be clearly improved when considering precipitation during the forecast period. Hydrological forecast based on Ensemble Precipitation prediction gives better hydrological forecast information, better satisfying the need for risk information for flood prevention and disaster reduction, and has broad development opportunities.

  20. A probabilistic approach to forecast the uncertainty with ensemble spread

    NASA Astrophysics Data System (ADS)

    Van Schaeybroeck, Bert; Vannitsem, Stéphane

    2015-04-01

    For most purposes the information gathered from an ensemble forecast is the ensemble mean and its uncertainty. The ensemble spread is commonly used as a measure of the uncertainty. We propose a method to assess whether the ensemble spread is a good measure of uncertainty and to bring forward an underlying spread-skill relationship. Forecasting the uncertainty should be probabilistic of nature. This implies that, if only the ensemble spread is available, a probability density function (PDF) for the uncertainty forecast must be reconstructed based on one parameter. Different models are introduced for the composition of such PDFs and evaluated for different spread-error metrics. The uncertainty forecast can then be verified based on probabilistic skill scores. For a perfectly reliable forecast the spread-error relationship is strongly heteroscedastic since the error can take a wide range of values, proportional to the ensemble spread. This makes a proper statistical assessment of the spread-skill relation intricate. However, it is shown that a logarithmic transformation of both spread and error allows for alleviating the heteroscedasticity. A linear regression analysis can then be performed to check whether the flow-dependent spread is a realistic indicator of the uncertainty and to what extent ensemble underdispersion or overdispersion depends on the ensemble spread. The methods are tested on the ensemble forecast of wind and geopotential height of the European Centre of Medium-range forecasts (ECMWF) over Europe and Africa. A comparison is also made with spread-skill analysis based on binning methods.

  1. Stochastic and dynamical downscaling of ensemble precipitation forecasts

    NASA Astrophysics Data System (ADS)

    Brussolo, E.; von Hardenberg, J.; Rebora, N.

    2009-04-01

    Forecasting hydrogeological risk in small basins requires quantitative forecasts and an estimate of the probability of occurrence of severe, localized precipitation events at spatial scales of the order of tens of kilometers or less, significantly smaller than those currently provided by large scale, global, ensemble forecasting systems (EPS). Dynamically based forecasts at these scales can be obtained extending EPS scenarios with high-resolution, non-hydrostatic, limited area ensemble prediction systems. An alternative is represented by the direct application of stochastic downscaling techniques to the large scale ensemble forecasts. This work compares the performances of these two very different ensemble forecast downscaling approaches. To this purpose we consider ensemble forecasts provided by the ECMWF EPS, downscaled in space using the RainFARM stochastic technique [1], and ensembles of forecasts obtained from the COSMO-LEPS limited area prediction system (which also uses ECMWF EPS ensemble members as boundary conditions), for three intense precipitation events over northern Italy in 2006. The statistical properties of the fields produced with these two techniques are compared and the skill of the resulting ensembles is verified against direct precipitation measurements from a dense network of rain gauges. Reference: 1. Rebora, N., L. Ferraris, J. von Hardenberg, and A. Provenzale, 2006: The RainFARM: Rainfall Downscaling by a Filtered AutoRegressive Model. J. Hydrometeorol., 7, 724-738.

  2. Value of long-term streamflow forecasts to reservoir operations for water supply in snow-dominated river catchments

    NASA Astrophysics Data System (ADS)

    Anghileri, D.; Voisin, N.; Castelletti, A.; Pianosi, F.; Nijssen, B.; Lettenmaier, D. P.

    2016-06-01

    We present a forecast-based adaptive management framework for water supply reservoirs and evaluate the contribution of long-term inflow forecasts to reservoir operations. Our framework is developed for snow-dominated river basins that demonstrate large gaps in forecast skill between seasonal and inter-annual time horizons. We quantify and bound the contribution of seasonal and inter-annual forecast components to optimal, adaptive reservoir operation. The framework uses an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity (VIC) hydrology model. We determine the optimal sequence of daily release decisions using the Model Predictive Control (MPC) optimization scheme. We then assess the forecast value by comparing system performance based on the ESP forecasts with the performances based on climatology and perfect forecasts. We distinguish among the relative contributions of the seasonal component of the forecast versus the inter-annual component by evaluating system performance based on hybrid forecasts, which are designed to isolate the two contributions. As an illustration, we first apply the forecast-based adaptive management framework to a specific case study, i.e., Oroville Reservoir in California, and we then modify the characteristics of the reservoir and the demand to demonstrate the transferability of the findings to other reservoir systems. Results from numerical experiments show that, on average, the overall ESP value in informing reservoir operation is 35% less than the perfect forecast value and the inter-annual component of the ESP forecast contributes 20-60% of the total forecast value.

  3. A simple method to improve ensemble-based ozone forecasts

    NASA Astrophysics Data System (ADS)

    Pagowski, M.; Grell, G. A.; McKeen, S. A.; Dévényi, D.; Wilczak, J. M.; Bouchet, V.; Gong, W.; McHenry, J.; Peckham, S.; McQueen, J.; Moffet, R.; Tang, Y.

    2005-04-01

    Forecasts from seven air quality models and ozone data collected over the eastern USA and southern Canada during July and August 2004 are used in creating a simple method to improve ensemble-based forecasts of maximum daily 1-hr and 8-hr averaged ozone concentrations. The method minimizes least-square error of ensemble forecasts by assigning weights for its members. The real-time ozone (O3) forecasts from this ensemble of models are statistically evaluated against the ozone observations collected for the AIRNow database comprising more than 350 stations. Application of this method is shown to significantly improve overall statistics (e.g., bias, root mean square error, and index of agreement) of the weighted ensemble compared to the averaged ensemble or any individual ensemble member. If a sufficient number of observations is available, we recommend that weights be calculated daily; if not, a longer training phase will still provide a positive benefit.

  4. Assessing the potential skill of seasonal streamflow forecasting for the River Rhine and the Upper Danube Basin

    NASA Astrophysics Data System (ADS)

    Klein, B.; Meissner, D.; Gerl, N.; Hemri, S.; Gneiting, T. J.

    2013-12-01

    the two basins. For the River Rhine with a catchment area of approx. 185.000 km2 the semi-distributed HBV model with a time-step of one day and for the Upper Danube Basin (102.000 km2) the water balance model COSERO with a time-step of 1 month are applied. As observed meteorological input the ERA Interim dataset is used, which is statistically downscaled from its relatively coarse grid resolution to the subbasins of the models. As meteorological forecast input two different approaches are used for the 30-year hindcast period in this study: (a) the Extended Streamflow Prediction ESP - a resampling approach of historical meteorology - which is applied to asses the potential predictability arising from the initial conditions and (b) the ensemble re-forecasts of the ECMWF seasonal forecast system 4 - a global coupled ocean-atmosphere general circulation model - used to quantify the potential benefit of numerical weather forecasts. Bayesian Model Averaging BMA and Ensemble-Model Output statistics EMOS are applied to the generated seasonal ensemble streamflow forecasts for calibration and the estimation of the predictive probability density function. Different skill measures are used to verify the potential skill of the seasonal forecasts of the different methods.

  5. Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the US Sunbelt

    NASA Astrophysics Data System (ADS)

    Mazrooei, A.; Sinha, T.; Kumar, S.; Peters-Lidard, C. D.; Arumugam, S.

    2014-12-01

    In order to better manage water demands from multiple uses (e.g., municipal water demands, hydroelectric power generation, and agricultural operations), water resources managers and operators are interested to know potential changes in seasonal streamflow based on climate forecasts. However, various sources of uncertainty in forecasting streamflow pose significant challenges to utilize streamflow forecasts in real time operations. In this study we systematically decompose various sources of errors in developing seasonal streamflow forecasts from multiple Land Surface Models (LSMs) forced with downscaled and disaggregated climate forecasts. The objectives of this study are: 1) Quantifying various sources of errors arising from each LSM, climate forecasts, and downscaling/disaggregation techniques employed in developing streamflow forecasts, and 2) Comparing the performance and the skill of different LSMs in streamflow forecasting over selected target basins in the study area. First, three-month ahead precipitation forecasts from ECHAM4.5 GCM for each season were statistically downscaled from 2.8° to 1/8° scale using Principal Component Regression (PCR) and then temporally disaggregated from monthly to daily time step using K-Nearest-Neighbor (K-NN) approach. For other climatic forcings excluding precipitation we obtained hourly climatology over almost 30 years (from 1979 to 2010) from NLDAS-2 dataset. Then LSMs such as NOAH3.2 and CLM2 were forced with precipitation forecasts and climatological forcings to develop retrospective seasonal streamflow forecast over the period of 20 years (1991-2010). Finally, the performance of different LSMs in forecasting streamflow under different schemes were analyzed to quantify sources of errors and to validate forecasted streamflow.

  6. Forecasting Global Point Rainfall using ECMWF's Ensemble Forecasting System

    NASA Astrophysics Data System (ADS)

    Pillosu, Fatima; Hewson, Timothy; Zsoter, Ervin; Baugh, Calum

    2017-04-01

    ECMWF (the European Centre for Medium range Weather Forecasts), in collaboration with the EFAS (European Flood Awareness System) and GLOFAS (GLObal Flood Awareness System) teams, has developed a new operational system that post-processes grid box rainfall forecasts from its ensemble forecasting system to provide global probabilistic point-rainfall predictions. The project attains a higher forecasting skill by applying an understanding of how different rainfall generation mechanisms lead to different degrees of sub-grid variability in rainfall totals. In turn this approach facilitates identification of cases in which very localized extreme totals are much more likely. This approach aims also to improve the rainfall input required in different hydro-meteorological applications. Flash flood forecasting, in particular in urban areas, is a good example. In flash flood scenarios precipitation is typically characterised by high spatial variability and response times are short. In this case, to move beyond radar based now casting, the classical approach has been to use very high resolution hydro-meteorological models. Of course these models are valuable but they can represent only very limited areas, may not be spatially accurate and may give reasonable results only for limited lead times. On the other hand, our method aims to use a very cost-effective approach to downscale global rainfall forecasts to a point scale. It needs only rainfall totals from standard global reporting stations and forecasts over a relatively short period to train it, and it can give good results even up to day 5. For these reasons we believe that this approach better satisfies user needs around the world. This presentation aims to describe two phases of the project: The first phase, already completed, is the implementation of this new system to provide 6 and 12 hourly point-rainfall accumulation probabilities. To do this we use a limited number of physically relevant global model parameters (i

  7. Research and operational applications in multi-center ensemble forecasting

    NASA Astrophysics Data System (ADS)

    Zhu, Y.; Toth, Z.

    2009-05-01

    The North American Ensemble Forecast System (NAEFS) was built up in 2004 by the Meteorological Service of Canada (MSC), the National Meteorological Service of Mexico (NMSM), and the US National Weather Service (NWS) as an operational multi-center ensemble forecast system. Currently it combines the 20-member MSC and NWS ensembles to form a joint ensemble of 40 members twice a day. The joint ensemble forecast, after bias correction and statistical downscaling, is used to generate a suite of products for CONUS, North America and for other regions of the globe. The THORPEX Interactive Grand Global Ensemble (TIGGE) project has been established a few years ago to collect operational global ensemble forecasts from world centers, and distribute to the scientific community, to encourage research leading to the acceleration of improvements in the skill and utility of high impact weather forecasts. TIGGE research is expected to advise the development of the operational NAEFS system and eventually the two projects are expected to converge into a single operational system, the Global Interactive Forecast System (GIFS). This presentation will review recent developments, the current status, and plans related to the TIGGE research and NAEFS operational multi-center ensemble projects.

  8. Seamless Hourly Rainfall Ensemble Forecasts for 0 - 10 days

    NASA Astrophysics Data System (ADS)

    Cooper, Shaun; Seed, Alan

    2014-05-01

    The Australian Bureau of Meteorology uses a number of Numerical Weather Prediction (NWP) models to generate deterministic rainfall forecasts over a range of lead-times, each with a different resolution in space and time and with different forecast domains. High resolution regional NWP models are used to generate forecasts for the first three days, and are typically more accurate than lower resolution Global NWP models that produce forecasts for longer lead times. Consequently, there is a requirement for a seamless forecast system that is able to blend the various NWP forecasts into a single forecast with a uniform resolution over the entire forecast period. NWP rainfall forecasts contain errors at scales that are significant for even large river basins, and ensemble hydrological prediction systems require ensembles of the order of 100 members, which is well beyond the size that can be generated by NWP ensemble systems. The idea, therefore, is to blend the NWP models in such a way that recognises the skill of the NWP at a particular scale and lead time and to use a stochastic model of forecast errors to perturb the blended deterministic forecast to generate a large ensemble. NWP uncertainties are scale and forecast lead time dependent, especially at long forecast lead times, and are characteristic to each model. By blending the models scale by scale it is possible to recognise the increased skill of the models at larger spatial scales and shorter lead times. The stochastic model is applied at each scale, adding increasingly more variability at smaller spatial scales, while preserving the space-time structure of rain. This process allows an ensemble to be generated by blending deterministic forecasts. Two NWP models from the Bureau, ACCESS-G (Global) (~40 km by ~40 km, 3 hourly out to 10 days) and ACCESS-R (Regional) (~12 km by ~12 km, 1 hourly out to 3 days), are downscaled and blended with the stochastic model to produce an ensemble of hourly forecasts out to 10

  9. Streamflow forecast uncertainty evolution and its effect on real-time reservoir operation

    NASA Astrophysics Data System (ADS)

    Chen, Lu; Singh, Vijay P.; Lu, Weiwei; Zhang, Junhong; Zhou, Jianzhong; Guo, Shenglian

    2016-09-01

    When employing streamflow forecasting in practical applications, such as reservoir operation, one important issue is to deal with the uncertainty involved in forecasting. Traditional studies dealing with the uncertainty in streamflow forecasting have been limited in describing the evolution of forecast uncertainty. This paper proposes a copula-based uncertainty evolution (CUE) model to describe the evolution of streamflow forecast uncertainty. The generated forecast uncertainty series fits the observed series well in terms of observed mean, standard deviation and skewness. Daily flow with forecast uncertainty are simulated and used to determine the effect of forecast uncertainty on real-time reservoir operation of the Three Gorges Reservoir (TGR), China. Results show that using the forecast inflow coupled with the pre-release module for reservoir operation of TGR in flood season cannot increase the flood risk.

  10. Evaluation of snow data assimilation using the ensemble Kalman filter for seasonal streamflow prediction in the western United States

    NASA Astrophysics Data System (ADS)

    Huang, Chengcheng; Newman, Andrew J.; Clark, Martyn P.; Wood, Andrew W.; Zheng, Xiaogu

    2017-01-01

    In this study, we examine the potential of snow water equivalent data assimilation (DA) using the ensemble Kalman filter (EnKF) to improve seasonal streamflow predictions. There are several goals of this study. First, we aim to examine some empirical aspects of the EnKF, namely the observational uncertainty estimates and the observation transformation operator. Second, we use a newly created ensemble forcing dataset to develop ensemble model states that provide an estimate of model state uncertainty. Third, we examine the impact of varying the observation and model state uncertainty on forecast skill. We use basins from the Pacific Northwest, Rocky Mountains, and California in the western United States with the coupled Snow-17 and Sacramento Soil Moisture Accounting (SAC-SMA) models. We find that most EnKF implementation variations result in improved streamflow prediction, but the methodological choices in the examined components impact predictive performance in a non-uniform way across the basins. Finally, basins with relatively higher calibrated model performance (> 0.80 NSE) without DA generally have lesser improvement with DA, while basins with poorer historical model performance show greater improvements.

  11. Seasonal Ensemble Forecasting: Using Bayesian Model Averaging and Constrained Fourier Smoothing

    NASA Astrophysics Data System (ADS)

    Sahu, M.; Lahari, S.; Khosa, R.

    2016-12-01

    Bayesian model averaging (BMA) is a statistical method that can be used to exploit the strength of individual models by combining them with suitable weights assigned to each member of the ensemble in the hope of achieving a more reliable forecast and together with quantification of uncertainty. In this paper, a methodology has been proposed for "online" real time streamflow forecasting using Bayesian averaging and constraint Fourier smoothing that seeks to correct for negative coefficients that are likely to arise in the traditional Fourier transform based approach. It is understood that the realization of any given state of a process under observation is expected to consist of a `non-observable' true signal which is corrupted by noise or random fluctuation that could potentially arise in nature for a diverse set of reasons and estimation of the expected state of the process has to be managed within this `noisy' environment. Further, in order to capture seasonal variation in streamflow, updated estimates of period specific variances are used to determine a weight function. Various models including the Multi-scale Coupled Wavelet Volterra models, ARMA models, Unit Hydrograph methods for forecasting, amongst others, are used to generate ensembles of forecasts and combined using Bayesian averaging.

  12. Using ensembles in water management: forecasting dry and wet episodes

    NASA Astrophysics Data System (ADS)

    van het Schip-Haverkamp, Tessa; van den Berg, Wim; van de Beek, Remco

    2015-04-01

    Extreme weather situations as droughts and extensive precipitation are becoming more frequent, which makes it more important to obtain accurate weather forecasts for the short and long term. Ensembles can provide a solution in terms of scenario forecasts. MeteoGroup uses ensembles in a new forecasting technique which presents a number of weather scenarios for a dynamical water management project, called Water-Rijk, in which water storage and water retention plays a large role. The Water-Rijk is part of Park Lingezegen, which is located between Arnhem and Nijmegen in the Netherlands. In collaboration with the University of Wageningen, Alterra and Eijkelkamp a forecasting system is developed for this area which can provide water boards with a number of weather and hydrology scenarios in order to assist in the decision whether or not water retention or water storage is necessary in the near future. In order to make a forecast for drought and extensive precipitation, the difference 'precipitation- evaporation' is used as a measurement of drought in the weather forecasts. In case of an upcoming drought this difference will take larger negative values. In case of a wet episode, this difference will be positive. The Makkink potential evaporation is used which gives the most accurate potential evaporation values during the summer, when evaporation plays an important role in the availability of surface water. Scenarios are determined by reducing the large number of forecasts in the ensemble to a number of averaged members with each its own likelihood of occurrence. For the Water-Rijk project 5 scenario forecasts are calculated: extreme dry, dry, normal, wet and extreme wet. These scenarios are constructed for two forecasting periods, each using its own ensemble technique: up to 48 hours ahead and up to 15 days ahead. The 48-hour forecast uses an ensemble constructed from forecasts of multiple high-resolution regional models: UKMO's Euro4 model,the ECMWF model, WRF and

  13. Streamflow forecasting and data assimilation: bias in precipitation, soil moisture states, and groundwater fluxes.

    NASA Astrophysics Data System (ADS)

    McCreight, J. L.; Gochis, D. J.; Hoar, T.; Dugger, A. L.; Yu, W.

    2014-12-01

    Uncertainty in precipitation forcing, soil moisture states, and model groundwater fluxes are first-order sources of error in streamflow forecasting. While near-surface estimates of soil moisture are now available from satellite, very few soil moisture observations below 5 cm depth or groundwater discharge estimates are available for operational forecasting. Radar precipitation estimates are subject to large biases, particularly during extreme events (e.g. Steiner et al., 2010) and their correction is not typically available in real-time. Streamflow data, however, are readily available in near-real-time and can be assimilated operationally to help constrain uncertainty in these uncertain states and improve streamflow forecasts. We examine the ability of streamflow observations to diagnose bias in the three most uncertain variables: precipitation forcing, soil moisture states, and groundwater fluxes. We investigate strategies for their subsequent bias correction. These include spinup and calibration strategies with and without the use of data assimilation and the determination of the proper spinup timescales. Global and spatially distributed multipliers on the uncertain states included in the assimilation state vector (e.g. Seo et al., 2003) will also be evaluated. We examine real cases and observing system simulation experiments for both normal and extreme rainfall events. One of our test cases considers the Colorado Front Range flood of September 2013 where the range of disagreement amongst five precipitation estimates spanned a factor of five with only one exhibiting appreciable positive bias (Gochis et al, submitted). Our experiments are conducted using the WRF-Hydro model with the NoahMP land surface component and the data assimilation research testbed (DART). A variety of ensemble data assimilation approaches (filters) are considered. ReferencesGochis, DJ, et al. "The Great Colorado Flood of September 2013" BAMS (Submitted 4-7-14). Seo, DJ, V Koren, and N

  14. Using ensemble streamflow prediction in the reservoir operation during drought by implicit and explicit stochastic optimization: case study in Shihmen Reservoir

    NASA Astrophysics Data System (ADS)

    Chou, Kuan-wen; Jiing-Yun You, Gene; Jang, Jiun-Huei

    2016-04-01

    One of the important goals of water resource management is the establishment of realistic reservoir operating policies for water allocation, especially during periods of drought. In the context of short-term (a few months) water management such as drought, we need to predict the future inflow and allocate current and future water resource to avoid severe economic loss. Because the future flow condition in uncertain, applying the stochastic optimization technique is common in guide reservoir operation. This study is trying to use the ensemble streamflow prediction (ESP) in reservoir operation during drought. We develop reservoir operation model based on two stochastic optimization frameworks, the explicit stochastic optimization (ESO) or implicit stochastic optimization (ISO). Because the forecast is updated time by time, the rolling process is adapted, the decision process is ''rolled over'' every periods and extended into the future. This study use Shihmen Reservoir as a case study. The ensemble streamflow prediction is produced and provided by National Science and Technology Center for Disaster Reduction (NCDR). Not only expect to provide an appropriate framework in integrating streamflow forecast a reservoir operation during drought, we also aim to compare the ISO and ESO to identify their advantages and disadvantages. As a result, the streamflow forecast can directly contribute, rather than just be kept in mind, in the reservoir operation during drought period.

  15. Streamflow Forecasts in Poorly Documented Basins from Meteorological Fields

    NASA Astrophysics Data System (ADS)

    Uribe, E. M.; Shuttleworth, J. W.; Gupta, H. V.; Mullen, S. L.; Zeng, X.

    2006-12-01

    This paper describes research undertaken in support of a project that seeks to enable the interpretation of predicted meteorological fields in terms of the streamflow in poorly documented catchments. The focus in this presentation is on addressing the issues involved in parameterizing and calibrating the hydrological model to do this in basins where available local data is limited. Hydrological modeling depends on the information available for basin characterization and model validation and calibration and most publicly available models require information not readily available in many regions of the world. Therefore, alternative approaches to the characterization of a basin are required so that stakeholders can benefit from climate forecasts via model estimates of streamflow. An alternative methodology for the parameterization of a basin using globally and publicly available data is proposed for the Modular Modeling System Precipitation Runoff Modeling System (MMS-PRMS). The methodology is based on deriving topography, soil and vegetation parameters from remote sensing and processed digital maps. In this study this model was forced with daily rainfall observations from a local rain gauge network between 1948 and 1978 and the resulting streamflow predictions evaluated and the model calibrated against long records of daily observations. The short-term predictive capabilities of the calibrated model are then tested using daily rainfall forecasts derived from the North American Regional Reanalysis (NARR) from 1979 to 1990. The methodology is tested in the basin of the "Rio Grijalva", which is located in southern Mexico. Use of the model in this basin benefits from the alternative characterization methods described because the local information available for this basin is not sufficient to parameterize physically-based hydrological models. Additionally, the events responsible for the rainfall variability during the wet-season in this basin include diverse weather

  16. An Ensemble Approach for Forecasting Net Interchange Schedule

    SciTech Connect

    Vlachopoulou, Maria; Gosink, Luke J.; Pulsipher, Trenton C.; Ferryman, Thomas A.; Zhou, Ning; Tong, Jianzhong

    2013-09-01

    The net interchange schedule (NIS) is the sum of the transactions (MW) between an ISO/RTO and its neighbors. Effective forecasting of the submitted NIS can improve grid operation efficiency. This paper applies a Bayesian model averaging (BMA) technique to forecast submitted NIS. As an ensemble approach, the BMA method aggregates different forecasting models in order to improve forecasting accuracy and consistency. In this study, the BMA method is compared to two alternative approaches: a stepwise regression method and an artificial neural network (ANN) trained for NIS forecasting. In our comparative analysis, we use field measurement data from the Pennsylvania, New Jersey, and Maryland (PJM) Regional Transmission Organization (RTO) to train and test each method. Our preliminary results indicate that ensemble-based methods can provide more accurate and consistent NIS forecasts in comparison to non-ensemble alternate methods.

  17. Toward Integrative Uncertainty Accounting in Operational Hydrologic Ensemble Forecasting

    NASA Astrophysics Data System (ADS)

    Seo, D.; Demargne, J.; Wu, L.; Brown, J. D.; Schaake, J. C.

    2007-12-01

    Operational hydrologic forecasts are subject to large meteorological and hydrologic uncertainties, i.e., uncertainties in the hydrologic initial and boundary conditions, future boundary conditions, and observations. To produce reliable and skillful hydrologic ensemble forecasts, it is essential that both meteorological and hydrologic uncertainties are accurately accounted for. Toward that goal, NWS is developing a prototype hydrologic ensemble forecasting capability referred to as the eXperimental Ensemble Forecast System (XEFS) for operation at the NWS River Forecast Centers (RFC). It is envisioned that all or parts of this system may be shared with the research community for collaborative research and development toward improved operational hydrologic forecasting. In this talk, we describe the XEFS framework for integrative uncertainty accounting, identify key issues and share initial results.

  18. Theory and Practice of Phase-aware Ensemble Forecasting

    NASA Astrophysics Data System (ADS)

    Schulte, J. A.; Georgas, N.

    2016-12-01

    The timing of events represents a source of uncertainty in ensemble forecasting that can produce misleading ensemble statistics. A general theory is presented to overcome drawbacks of traditional ensemble forecasting statistics that perform poorly in the presence of timing disagreements among ensemble members. It was shown, in particular, that ensemble forecasts containing substantial uncertainty in timing can produce non-trivial higher-order statistical moments, rendering the ensemble mean inappropriate as a best available estimate of the future state of the forecast parameter in question. A set of theoretical experiments showed that the existence of large timing differences among ensemble members can produce negative ensemble skewness even when the ensemble members are sinusoids whose amplitudes are drawn from a normal distribution: Consistently, the ensemble mean will tend to fall on the left tail of the normal distribution representing the originally sampled amplitudes, rather than at the mean or median. To remedy the left-tail placement problem of the ensemble mean, a new generally applicable ensemble statistic - the phase-aware ensemble mean - is proposed that is more robust against ensemble skewness resulting from timing spread. The computation of the phase-aware mean involves the transformation of all ensemble members to wavelet space and the subsequent inverse wavelet transformation of the product of the ensemble mean wavelet phase and modulus back to the time domain. The new methods were applied to storm surge reforecasts for Hurricane Irene and Sandy at 8 stations located around the New York City metropolitan area. The phase-aware ensemble mean was found to perform better at detecting the magnitude of events compared to the traditional ensemble mean, consistent with the results from theoretical experiments. The ensemble mean, moreover, was found to be consistently located on the left tail of distributions representing future peak storm surge outcomes. A

  19. Two Topics in Seasonal Streamflow Forecasting: Soil Moisture Initialization Error and Precipitation Downscaling

    NASA Technical Reports Server (NTRS)

    Koster, Randal; Walker, Greg; Mahanama, Sarith; Reichle, Rolf

    2012-01-01

    Continental-scale offline simulations with a land surface model are used to address two important issues in the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which the downscaling of seasonal precipitation forecasts, if it could be done accurately, would improve streamflow forecasts. The reduction in streamflow forecast skill (with forecasted streamflow measured against observations) associated with adding noise to a soil moisture field is found to be, to first order, proportional to the average reduction in the accuracy of the soil moisture field itself. This result has implications for streamflow forecast improvement under satellite-based soil moisture measurement programs. In the second and more idealized ("perfect model") analysis, precipitation downscaling is found to have an impact on large-scale streamflow forecasts only if two conditions are met: (i) evaporation variance is significant relative to the precipitation variance, and (ii) the subgrid spatial variance of precipitation is adequately large. In the large-scale continental region studied (the conterminous United States), these two conditions are met in only a somewhat limited area.

  20. Trends in the predictive performance of raw ensemble weather forecasts

    NASA Astrophysics Data System (ADS)

    Hemri, Stephan; Scheuerer, Michael; Pappenberger, Florian; Bogner, Konrad; Haiden, Thomas

    2015-04-01

    Over the last two decades the paradigm in weather forecasting has shifted from being deterministic to probabilistic. Accordingly, numerical weather prediction (NWP) models have been run increasingly as ensemble forecasting systems. The goal of such ensemble forecasts is to approximate the forecast probability distribution by a finite sample of scenarios. Global ensemble forecast systems, like the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble, are prone to probabilistic biases, and are therefore not reliable. They particularly tend to be underdispersive for surface weather parameters. Hence, statistical post-processing is required in order to obtain reliable and sharp forecasts. In this study we apply statistical post-processing to ensemble forecasts of near-surface temperature, 24-hour precipitation totals, and near-surface wind speed from the global ECMWF model. Our main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the post-processed forecasts. The ECMWF ensemble is under continuous development, and hence its forecast skill improves over time. Parts of these improvements may be due to a reduction of probabilistic bias. Thus, we first hypothesize that the gain by post-processing decreases over time. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations we generate post-processed forecasts by ensemble model output statistics (EMOS) for each station and variable. Parameter estimates are obtained by minimizing the Continuous Ranked Probability Score (CRPS) over rolling training periods that consist of the n days preceding the initialization dates. Given the higher average skill in terms of CRPS of the post-processed forecasts for all three variables, we analyze the evolution of the difference in skill between raw ensemble and EMOS forecasts. The fact that the gap in skill remains almost constant over time, especially for near

  1. Systematic uncertainty reduction strategies for developing streamflow forecasts utilizing multiple climate models and hydrologic models

    NASA Astrophysics Data System (ADS)

    Singh, Harminder; Sankarasubramanian, A.

    2014-02-01

    Recent studies show that multimodel combinations improve hydroclimatic predictions by reducing model uncertainty. Given that climate forecasts are available from multiple climate models, which could be ingested with multiple watershed models, what is the best strategy to reduce the uncertainty in streamflow forecasts? To address this question, we consider three possible strategies: (1) reduce the input uncertainty first by combining climate models and then use the multimodel climate forecasts with multiple watershed models (MM-P), (2) ingest the individual climate forecasts (without multimodel combination) with various watershed models and then combine the streamflow predictions that arise from all possible combinations of climate and watershed models (MM-Q), (3) combine the streamflow forecasts obtained from multiple watershed models based on strategy (1) to develop a single streamflow prediction that reduces uncertainty in both climate forecasts and watershed models (MM-PQ). For this purpose, we consider synthetic schemes that generate streamflow and climate forecasts, for comparing the performance of three strategies with the true streamflow generated by a given hydrologic model. Results from the synthetic study show that reducing input uncertainty first (MM-P) by combining climate forecasts results in reduced error in predicting the true streamflow compared to the error of multimodel streamflow forecasts obtained by combining streamflow forecasts from all-possible combination of individual climate model with various hydrologic models (MM-Q). Since the true hydrologic model structure is unknown, it is desirable to consider MM-PQ as an alternate choice that reduces both input uncertainty and hydrologic model uncertainty. Application on two watersheds in NC also indicates that reducing the input uncertainty first is critical before reducing the hydrologic model uncertainty.

  2. Optimising seasonal streamflow forecast lead time for operational decision making in Australia

    NASA Astrophysics Data System (ADS)

    Schepen, Andrew; Zhao, Tongtiegang; Wang, Q. J.; Zhou, Senlin; Feikema, Paul

    2016-10-01

    Statistical seasonal forecasts of 3-month streamflow totals are released in Australia by the Bureau of Meteorology and updated on a monthly basis. The forecasts are often released in the second week of the forecast period, due to the onerous forecast production process. The current service relies on models built using data for complete calendar months, meaning the forecast production process cannot begin until the first day of the forecast period. Somehow, the bureau needs to transition to a service that provides forecasts before the beginning of the forecast period; timelier forecast release will become critical as sub-seasonal (monthly) forecasts are developed. Increasing the forecast lead time to one month ahead is not considered a viable option for Australian catchments that typically lack any predictability associated with snowmelt. The bureau's forecasts are built around Bayesian joint probability models that have antecedent streamflow, rainfall and climate indices as predictors. In this study, we adapt the modelling approach so that forecasts have any number of days of lead time. Daily streamflow and sea surface temperatures are used to develop predictors based on 28-day sliding windows. Forecasts are produced for 23 forecast locations with 0-14- and 21-day lead time. The forecasts are assessed in terms of continuous ranked probability score (CRPS) skill score and reliability metrics. CRPS skill scores, on average, reduce monotonically with increase in days of lead time, although both positive and negative differences are observed. Considering only skilful forecast locations, CRPS skill scores at 7-day lead time are reduced on average by 4 percentage points, with differences largely contained within +5 to -15 percentage points. A flexible forecasting system that allows for any number of days of lead time could benefit Australian seasonal streamflow forecast users by allowing more time for forecasts to be disseminated, comprehended and made use of prior to

  3. Forecasting European Droughts using the North American Multi-Model Ensemble (NMME)

    NASA Astrophysics Data System (ADS)

    Thober, Stephan; Kumar, Rohini; Samaniego, Luis; Sheffield, Justin; Schäfer, David; Mai, Juliane

    2015-04-01

    Soil moisture droughts have the potential to diminish crop yields causing economic damage or even threatening the livelihood of societies. State-of-the-art drought forecasting systems incorporate seasonal meteorological forecasts to estimate future drought conditions. Meteorological forecasting skill (in particular that of precipitation), however, is limited to a few weeks because of the chaotic behaviour of the atmosphere. One of the most important challenges in drought forecasting is to understand how the uncertainty in the atmospheric forcings (e.g., precipitation and temperature) is further propagated into hydrologic variables such as soil moisture. The North American Multi-Model Ensemble (NMME) provides the latest collection of a multi-institutional seasonal forecasting ensemble for precipitation and temperature. In this study, we analyse the skill of NMME forecasts for predicting European drought events. The monthly NMME forecasts are downscaled to daily values to force the mesoscale hydrological model (mHM). The mHM soil moisture forecasts obtained with the forcings of the dynamical models are then compared against those obtained with the Ensemble Streamflow Prediction (ESP) approach. ESP recombines historical meteorological forcings to create a new ensemble forecast. Both forecasts are compared against reference soil moisture conditions obtained using observation based meteorological forcings. The study is conducted for the period from 1982 to 2009 and covers a large part of the Pan-European domain (10°W to 40°E and 35°N to 55°N). Results indicate that NMME forecasts are better at predicting the reference soil moisture variability as compared to ESP. For example, NMME explains 50% of the variability in contrast to only 31% by ESP at a six-month lead time. The Equitable Threat Skill Score (ETS), which combines the hit and false alarm rates, is analysed for drought events using a 0.2 threshold of a soil moisture percentile index. On average, the NMME

  4. Modification of input datasets for the Ensemble Streamflow Prediction based on large-scale climatic indices and weather generator

    NASA Astrophysics Data System (ADS)

    Šípek, Václav; Daňhelka, Jan

    2015-09-01

    Ensemble Streamflow Prediction (ESP) provides an efficient tool for seasonal hydrological forecasts. In this study, we propose a new modification of input data series for the ESP system used for the runoff volume prediction with a lead of one month. These series are not represented by short historical weather datasets but by longer generated synthetic weather data series. Before their submission to the hydrological model, their number is restricted by relations among observed meteorological variables (average monthly precipitation and temperature) and large-scale climatic patterns and indices (e.g. North Atlantic Oscillation, sea level pressure values and two geopotential heights). This modification was tested over a four-year testing period using the river basin in central Europe. The LARS-WG weather generator proved to be a suitable tool for the extension of the historical weather records. The modified ESP approach proved to be more efficient in the majority of months compared both to the original ESP method and reference forecast (based on probability distribution of historical discharges). The improvement over traditional ESP was most obvious in the narrower forecast interval of the expected runoff volume. The inefficient forecasts of the modified ESP scheme (compared to traditional ESP) were conditioned by an insufficient restriction of input synthetic weather datasets by the climate forecast.

  5. Seasonal forecasting of global hydrologic extremes using the North American Multi-model Ensemble system

    NASA Astrophysics Data System (ADS)

    Wood, Eric F.; Yuan, Xing; Roundy, Joshua K.; Sheffield, Justin

    2015-04-01

    Seasonal hydrologic extremes in the form of droughts and wet spells have devastating impacts on human and natural systems. Improving our understanding and predictive capability of hydrologic extremes, and facilitating adaptations through establishing climate service systems at regional to global scales, are among the grand challenges proposed by the World Climate Research Programme (WCRP), and are the core themes of the Regional Hydroclimate Projects (RHP) under the Global Energy and Water Exchanges Project (GEWEX). An experimental global seasonal hydrologic forecasting system has been developed, which is based on coupled climate forecast models participating in the North American Multi-Model Ensemble (NMME) project and an advanced land surface hydrologic model. The system is evaluated over major GEWEX/RHP river basins by comparing with Ensemble Streamflow Prediction (ESP). The multi-model seasonal forecast system provides higher detectability for soil moisture droughts, more reliable low and high flow ensemble forecasts, and better "real-time" prediction for the 2012 North American extreme drought. The association of the onset of extreme hydrologic events with oceanic and land precursors is also investigated based on the joint distribution of forecasts and observations. Climate models have a higher probability of missing the onset of hydrologic extremes when there is no oceanic precursor. But oceanic precursor alone is insufficient to guarantee a correct forecast, a land precursor is also critical in avoiding a false alarm for forecasting extremes. This study is targeted at providing the scientific underpinning for the predictability of hydrologic extremes over GEWEX/RHP basins, and serves as a prototype for seasonal hydrologic forecasts within the Global Framework for Climate Services (GFCS).

  6. Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

    NASA Technical Reports Server (NTRS)

    Mazrooei, Amirhossein; Sinah, Tusshar; Sankarasubramanian, A.; Kumar, Sujay V.; Peters-Lidard, Christa D.

    2015-01-01

    Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

  7. Decomposition of sources of errors in seasonal streamflow forecasting over the U.S. Sunbelt

    NASA Astrophysics Data System (ADS)

    Mazrooei, Amirhossein; Sinha, Tushar; Sankarasubramanian, A.; Kumar, Sujay; Peters-Lidard, Christa D.

    2015-12-01

    Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8° to 1/8° spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

  8. Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

    NASA Technical Reports Server (NTRS)

    Mazrooei, Amirhossein; Sinah, Tusshar; Sankarasubramanian, A.; Kumar, Sujay V.; Peters-Lidard, Christa D.

    2015-01-01

    Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

  9. Verification of the Forecast Errors Based on Ensemble Spread

    NASA Astrophysics Data System (ADS)

    Vannitsem, S.; Van Schaeybroeck, B.

    2014-12-01

    The use of ensemble prediction systems allows for an uncertainty estimation of the forecast. Most end users do not require all the information contained in an ensemble and prefer the use of a single uncertainty measure. This measure is the ensemble spread which serves to forecast the forecast error. It is however unclear how best the quality of these forecasts can be performed, based on spread and forecast error only. The spread-error verification is intricate for two reasons: First for each probabilistic forecast only one observation is substantiated and second, the spread is not meant to provide an exact prediction for the error. Despite these facts several advances were recently made, all based on traditional deterministic verification of the error forecast. In particular, Grimit and Mass (2007) and Hopson (2014) considered in detail the strengths and weaknesses of the spread-error correlation, while Christensen et al (2014) developed a proper-score extension of the mean squared error. However, due to the strong variance of the error given a certain spread, the error forecast should be preferably considered as probabilistic in nature. In the present work, different probabilistic error models are proposed depending on the spread-error metrics used. Most of these models allow for the discrimination of a perfect forecast from an imperfect one, independent of the underlying ensemble distribution. The new spread-error scores are tested on the ensemble prediction system of the European Centre of Medium-range forecasts (ECMWF) over Europe and Africa. ReferencesChristensen, H. M., Moroz, I. M. and Palmer, T. N., 2014, Evaluation of ensemble forecast uncertainty using a new proper score: application to medium-range and seasonal forecasts. In press, Quarterly Journal of the Royal Meteorological Society. Grimit, E. P., and C. F. Mass, 2007: Measuring the ensemble spread-error relationship with a probabilistic approach: Stochastic ensemble results. Mon. Wea. Rev., 135, 203

  10. Online probabilistic learning with an ensemble of forecasts

    NASA Astrophysics Data System (ADS)

    Thorey, Jean; Mallet, Vivien; Chaussin, Christophe

    2016-04-01

    Our objective is to produce a calibrated weighted ensemble to forecast a univariate time series. In addition to a meteorological ensemble of forecasts, we rely on observations or analyses of the target variable. The celebrated Continuous Ranked Probability Score (CRPS) is used to evaluate the probabilistic forecasts. However applying the CRPS on weighted empirical distribution functions (deriving from the weighted ensemble) may introduce a bias because of which minimizing the CRPS does not produce the optimal weights. Thus we propose an unbiased version of the CRPS which relies on clusters of members and is strictly proper. We adapt online learning methods for the minimization of the CRPS. These methods generate the weights associated to the members in the forecasted empirical distribution function. The weights are updated before each forecast step using only past observations and forecasts. Our learning algorithms provide the theoretical guarantee that, in the long run, the CRPS of the weighted forecasts is at least as good as the CRPS of any weighted ensemble with weights constant in time. In particular, the performance of our forecast is better than that of any subset ensemble with uniform weights. A noteworthy advantage of our algorithm is that it does not require any assumption on the distributions of the observations and forecasts, both for the application and for the theoretical guarantee to hold. As application example on meteorological forecasts for photovoltaic production integration, we show that our algorithm generates a calibrated probabilistic forecast, with significant performance improvements on probabilistic diagnostic tools (the CRPS, the reliability diagram and the rank histogram).

  11. Interactive 3D visualisation of ECMWF ensemble weather forecasts

    NASA Astrophysics Data System (ADS)

    Rautenhaus, Marc; Grams, Christian M.; Schäfler, Andreas; Westermann, Rüdiger

    2013-04-01

    We investigate the feasibility of interactive 3D visualisation of ensemble weather predictions in a way suited for weather forecasting during aircraft-based atmospheric field campaigns. The study builds upon our previous work on web-based, 2D visualisation of numerical weather prediction data for the purpose of research flight planning (Rautenhaus et al., Geosci. Model Dev., 5, 55-71, 2012). Now we explore how interactive 3D visualisation of ensemble forecasts can be used to quickly identify atmospheric features relevant to a flight and to assess their uncertainty. We use data from the European Centre for Medium Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) and present techniques to interactively visualise the forecasts on a commodity desktop PC with a state-of-the-art graphics card. Major objectives of this study are: (1) help the user transition from the ``familiar'' 2D views (horizontal maps and vertical cross-sections) to 3D visualisation by putting interactive 2D views into a 3D context and enriching them with 3D elements, at the same time (2) maintain a high degree of quantitativeness in the visualisation to facilitate easy interpretation; (3) exploitation of the Graphics Processing Unit (GPU) for maximum interactivity; (4) investigation of how visualisation can be performed directly from datasets on ECMWF hybrid model levels; (5) development of a basic forecasting tool that provides synchronized navigation through forecast base and lead times, as well as through the ensemble dimension and (6) interactive computation and visualisation of ensemble-based quantities. A prototype of our tool was used for weather forecasting during the aircraft-based T-NAWDEX-Falcon field campaign, which took place in October 2012 at the German Aerospace Centre's (DLR) Oberpfaffenhofen base. We reconstruct the forecast of a warm conveyor belt situation that occurred during the campaign and discuss challenges and opportunities posed by employing three

  12. Streamflow Forecasting Using Nuero-Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Nanduri, U. V.; Swain, P. C.

    2005-12-01

    The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A

  13. Soil Moisture Initialization Error and Subgrid Variability of Precipitation in Seasonal Streamflow Forecasting

    NASA Technical Reports Server (NTRS)

    Koster, Randal D.; Walker, Gregory K.; Mahanama, Sarith P.; Reichle, Rolf H.

    2013-01-01

    Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, e.g., through satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale streamflow forecasts only when evaporation variance is significant relative to the precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface modeling system as a tool for addressing the science of hydrological prediction.

  14. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

    SciTech Connect

    Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad

    2015-12-08

    Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

  15. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis

    SciTech Connect

    Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad

    2015-10-02

    Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

  16. Calibrated Ensemble Forecasts using Quantile Regression Forests and Ensemble Model Output Statistics.

    NASA Astrophysics Data System (ADS)

    Taillardat, Maxime; Mestre, Olivier; Zamo, Michaël; Naveau, Philippe

    2016-04-01

    Ensembles used for probabilistic weather forecasting tend to be biased and underdispersive. This presentation proposes a statistical method for postprocessing ensembles based on Quantile Regression Forests (QRF), a generalization of random forests for quantile regression. This method does not fit a parametric probability density function like in Ensemble Model Output Statistics (EMOS) but provides an estimation of desired quantiles. This is a non-parametric approach which eliminates any assumption on the variable subject to calibration. This method can estimate quantiles using not only members of the ensemble but any predictor available including statistics on other variables for example. The method is applied to the Météo-France 35-members ensemble forecast (PEARP) for surface temperature and wind-speed for available lead times from 3 up to 54 hours and compared to EMOS. All postprocessed ensembles are much better calibrated than the PEARP raw ensemble and experiments on real data also show that QRF performs better than EMOS, and can bring a real gain for forecasters compared to EMOS. QRF provides sharp and reliable probabilistic forecasts. At last, classical scoring rules to verify predictive forecasts are completed by the introduction of entropy as a general measure of reliability.

  17. Using satellite and multi-modeling for improving soil moisture and streamflow forecasting

    NASA Astrophysics Data System (ADS)

    Toll, David; Li, Bailing; Xiwu, Zhan; Brian, Cosgrove

    2010-05-01

    Work for this project is towards improving the stream flow forecasts for the NOAA River Forecast Centers (RFC) throughout the U.S. using multi-model capability primarily from the NASA Land Information System and remote sensing data provided by AMSR-E for soil moisture. The RFCs address a range of issues, including peak and low flow predictions as well as river floods and flash floods. The NASA Land Information System (LIS) provides a data integration framework for combining a range of ancillary and satellite data with state of the art data assimilation capabilities. We are currently including: 1) the Noah land surface model (LSM) simulates soil moisture (both liquid and frozen), soil temperature, skin temperature, snowpack water equivalent, snowpack density, canopy water content, and the traditional energy flux and water flux terms of the surface energy and surface water balance; 2) the Sacramento Distributed model is based on the lumped 'SAC-SMA' model used for hydrological simulations; and 3) the Catchment land surface model that is distinctive in the way land surface elements are depicted as hydrological catchments. Results from assimilating AMSR-E (Advances Microwave Sounding Radiometer) soil moisture with the Noah LSM using ensemble Kalman filter data assimilation. Results for a test site in Oklahoma, US show significant improvement for soil moisture estimation assimilating AMSR-E data. We used a conservation of mass procedure within a soil column to provide a more physically based approach to transfer observed soil moisture state to the lower soil moisture profiles. Overall the AMSR-e results shows improvement for improving the true spatial mean of soil moisture improvements. Noah LSM comparisons to determine if AMSR-E contributed to an improved streamflow showed inconclusive results. More accurate hydrologic improvements are expected from the new SMOS (Soil Moisture Ocean Salinity) and the future SMAP (Soil Moisture Active Passive). Future work will compare

  18. Verification of forecast ensembles in complex terrain including observation uncertainty

    NASA Astrophysics Data System (ADS)

    Dorninger, Manfred; Kloiber, Simon

    2017-04-01

    Traditionally, verification means to verify a forecast (ensemble) with the truth represented by observations. The observation errors are quite often neglected arguing that they are small when compared to the forecast error. In this study as part of the MesoVICT (Mesoscale Verification Inter-comparison over Complex Terrain) project it will be shown, that observation errors have to be taken into account for verification purposes. The observation uncertainty is estimated from the VERA (Vienna Enhanced Resolution Analysis) and represented via two analysis ensembles which are compared to the forecast ensemble. For the whole study results from COSMO-LEPS provided by Arpae-SIMC Emilia-Romagna are used as forecast ensemble. The time period covers the MesoVICT core case from 20-22 June 2007. In a first step, all ensembles are investigated concerning their distribution. Several tests have been executed (Kolmogorov-Smirnov-Test, Finkelstein-Schafer Test, Chi-Square Test etc.) showing no exact mathematical distribution. So the main focus is on non-parametric statistics (e.g. Kernel density estimation, Boxplots etc.) and also the deviation between "forced" normal distributed data and the kernel density estimations. In a next step the observational deviations due to the analysis ensembles are analysed. In a first approach scores are multiple times calculated with every single ensemble member from the analysis ensemble regarded as "true" observation. The results are presented as boxplots for the different scores and parameters. Additionally, the bootstrapping method is also applied to the ensembles. These possible approaches to incorporating observational uncertainty into the computation of statistics will be discussed in the talk.

  19. Impact of state updating and multi-parametric ensemble for streamflow hindcasting in European river basins

    NASA Astrophysics Data System (ADS)

    Noh, S. J.; Rakovec, O.; Kumar, R.; Samaniego, L. E.

    2015-12-01

    Accurate and reliable streamflow prediction is essential to mitigate social and economic damage coming from water-related disasters such as flood and drought. Sequential data assimilation (DA) may facilitate improved streamflow prediction using real-time observations to correct internal model states. In conventional DA methods such as state updating, parametric uncertainty is often ignored mainly due to practical limitations of methodology to specify modeling uncertainty with limited ensemble members. However, if parametric uncertainty related with routing and runoff components is not incorporated properly, predictive uncertainty by model ensemble may be insufficient to capture dynamics of observations, which may deteriorate predictability. Recently, a multi-scale parameter regionalization (MPR) method was proposed to make hydrologic predictions at different scales using a same set of model parameters without losing much of the model performance. The MPR method incorporated within the mesoscale hydrologic model (mHM, http://www.ufz.de/mhm) could effectively represent and control uncertainty of high-dimensional parameters in a distributed model using global parameters. In this study, we evaluate impacts of streamflow data assimilation over European river basins. Especially, a multi-parametric ensemble approach is tested to consider the effects of parametric uncertainty in DA. Because augmentation of parameters is not required within an assimilation window, the approach could be more stable with limited ensemble members and have potential for operational uses. To consider the response times and non-Gaussian characteristics of internal hydrologic processes, lagged particle filtering is utilized. The presentation will be focused on gains and limitations of streamflow data assimilation and multi-parametric ensemble method over large-scale basins.

  20. Short-term ensemble radar rainfall forecasts for hydrological applications

    NASA Astrophysics Data System (ADS)

    Codo de Oliveira, M.; Rico-Ramirez, M. A.

    2016-12-01

    Flooding is a very common natural disaster around the world, putting local population and economy at risk. Forecasting floods several hours ahead and issuing warnings are of main importance to permit proper response in emergency situations. However, it is important to know the uncertainties related to the rainfall forecasting in order to produce more reliable forecasts. Nowcasting models (short-term rainfall forecasts) are able to produce high spatial and temporal resolution predictions that are useful in hydrological applications. Nonetheless, they are subject to uncertainties mainly due to the nowcasting model used, errors in radar rainfall estimation, temporal development of the velocity field and to the fact that precipitation processes such as growth and decay are not taken into account. In this study an ensemble generation scheme using rain gauge data as a reference to estimate radars errors is used to produce forecasts with up to 3h lead-time. The ensembles try to assess in a realistic way the residual uncertainties that remain even after correction algorithms are applied in the radar data. The ensembles produced are compered to a stochastic ensemble generator. Furthermore, the rainfall forecast output was used as an input in a hydrodynamic sewer network model and also in hydrological model for catchments of different sizes in north England. A comparative analysis was carried of how was carried out to assess how the radar uncertainties propagate into these models. The first named author is grateful to CAPES - Ciencia sem Fronteiras for funding this PhD research.

  1. First Assessment of Itaipu Dam Ensemble Inflow Forecasting System

    NASA Astrophysics Data System (ADS)

    Mainardi Fan, Fernando; Machado Vieira Lisboa, Auder; Gomes Villa Trinidad, Giovanni; Rógenes Monteiro Pontes, Paulo; Collischonn, Walter; Tucci, Carlos; Costa Buarque, Diogo

    2017-04-01

    Inflow forecasting for Hydropower Plants (HPP) Dams is one of the prominent uses for hydrological forecasts. A very important HPP in terms of energy generation for South America is the Itaipu Dam, located in the Paraná River, between Brazil and Paraguay countries, with a drainage area of 820.000km2. In this work, we present the development of an ensemble forecasting system for Itaipu, operational since November 2015. The system is based in the MGB-IPH hydrological model, includes hydrodynamics simulations of the main river, and is run every day morning forced by seven different rainfall forecasts: (i) CPTEC-ETA 15km; (ii) CPTEC-BRAMS 5km; (iii) SIMEPAR WRF Ferrier; (iv) SIMEPAR WRF Lin; (v) SIMEPAR WRF Morrison; (vi) SIMEPAR WRF WDM6; (vii) SIMEPAR MEDIAN. The last one (vii) corresponds to the median value of SIMEPAR WRF model versions (iii to vi) rainfall forecasts. Besides the developed system, the "traditional" method for inflow forecasting generation for the Itaipu Dam is also run every day. This traditional method consists in the approximation of the future inflow based on the discharge tendency of upstream telemetric gauges. Nowadays, after all the forecasts are run, the hydrology team of Itaipu develop a consensus forecast, based on all obtained results, which is the one used for the Itaipu HPP Dam operation. After one year of operation a first evaluation of the Ensemble Forecasting System was conducted. Results show that the system performs satisfactory for rising flows up to five days lead time. However, some false alarms were also issued by most ensemble members in some cases. And not in all cases the system performed better than the traditional method, especially during hydrograph recessions. In terms of meteorological forecasts, some members usage are being discontinued. In terms of the hydrodynamics representation, it seems that a better information of rivers cross section could improve hydrographs recession curves forecasts. Those opportunities for

  2. Accounting for three sources of uncertainty in ensemble hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Thiboult, Antoine; Anctil, François; Boucher, Marie-Amélie

    2016-05-01

    Seeking more accuracy and reliability, the hydrometeorological community has developed several tools to decipher the different sources of uncertainty in relevant modeling processes. Among them, the ensemble Kalman filter (EnKF), multimodel approaches and meteorological ensemble forecasting proved to have the capability to improve upon deterministic hydrological forecast. This study aims to untangle the sources of uncertainty by studying the combination of these tools and assessing their respective contribution to the overall forecast quality. Each of these components is able to capture a certain aspect of the total uncertainty and improve the forecast at different stages in the forecasting process by using different means. Their combination outperforms any of the tools used solely. The EnKF is shown to contribute largely to the ensemble accuracy and dispersion, indicating that the initial conditions uncertainty is dominant. However, it fails to maintain the required dispersion throughout the entire forecast horizon and needs to be supported by a multimodel approach to take into account structural uncertainty. Moreover, the multimodel approach contributes to improving the general forecasting performance and prevents this performance from falling into the model selection pitfall since models differ strongly in their ability. Finally, the use of probabilistic meteorological forcing was found to contribute mostly to long lead time reliability. Particular attention needs to be paid to the combination of the tools, especially in the EnKF tuning to avoid overlapping in error deciphering.

  3. Probabilistic regional wind power forecasts based on calibrated Numerical Weather Forecast ensembles

    NASA Astrophysics Data System (ADS)

    Späth, Stephan; von Bremen, Lueder; Junk, Constantin; Heinemann, Detlev

    2014-05-01

    With increasing shares of installed wind power in Germany, accurate forecasts of wind speed and power get increasingly important for the grid integration of Renewable Energies. Applications like grid management and trading also benefit from uncertainty information. This uncertainty information can be provided by ensemble forecasts. These forecasts often exhibit systematic errors such as biases and spread deficiencies. The errors can be reduced by statistical post-processing. We use forecast data from the regional Numerical Weather Prediction model COSMO-DE EPS as input to regional wind power forecasts. In order to enhance the power forecast, we first calibrate the wind speed forecasts against the model analysis, so some of the model's systematic errors can be removed. Wind measurements at every grid point are usually not available and as we want to conduct grid zone forecasts, the model analysis is the best target for calibration. We use forecasts from the COSMO-DE EPS, a high-resolution ensemble prediction system with 20 forecast members. The model covers the region of Germany and surroundings with a vertical resolution of 50 model levels and a horizontal resolution of 0.025 degrees (approximately 2.8 km). The forecast range is 21 hours with model output available on an hourly basis. Thus, we use it for shortest-term wind power forecasts. The COSMO-DE EPS was originally designed with a focus on forecasts of convective precipitation. The COSMO-DE EPS wind speed forecasts at hub height were post-processed by nonhomogenous Gaussian regression (NGR; Thorarinsdottir and Gneiting, 2010), a calibration method that fits a truncated normal distribution to the ensemble wind speed forecasts. As calibration target, the model analysis was used. The calibration is able to remove some deficits of the COSMO-DE EPS. In contrast to the raw ensemble members, the calibrated ensemble members do not show anymore the strong correlations with each other and the spread-skill relationship

  4. On the forecast skill of a convection-permitting ensemble

    NASA Astrophysics Data System (ADS)

    Schellander-Gorgas, Theresa; Wang, Yong; Meier, Florian; Weidle, Florian; Wittmann, Christoph; Kann, Alexander

    2017-01-01

    The 2.5 km convection-permitting (CP) ensemble AROME-EPS (Applications of Research to Operations at Mesoscale - Ensemble Prediction System) is evaluated by comparison with the regional 11 km ensemble ALADIN-LAEF (Aire Limitée Adaption dynamique Développement InterNational - Limited Area Ensemble Forecasting) to show whether a benefit is provided by a CP EPS. The evaluation focuses on the abilities of the ensembles to quantitatively predict precipitation during a 3-month convective summer period over areas consisting of mountains and lowlands. The statistical verification uses surface observations and 1 km × 1 km precipitation analyses, and the verification scores involve state-of-the-art statistical measures for deterministic and probabilistic forecasts as well as novel spatial verification methods. The results show that the convection-permitting ensemble with higher-resolution AROME-EPS outperforms its mesoscale counterpart ALADIN-LAEF for precipitation forecasts. The positive impact is larger for the mountainous areas than for the lowlands. In particular, the diurnal precipitation cycle is improved in AROME-EPS, which leads to a significant improvement of scores at the concerned times of day (up to approximately one-third of the scored verification measure). Moreover, there are advantages for higher precipitation thresholds at small spatial scales, which are due to the improved simulation of the spatial structure of precipitation.

  5. Probabilistic aspects of meteorological and ozone regional ensemble forecasts

    SciTech Connect

    Monache, L D; Hacker, J; Zhou, Y; Deng, X; Stull, R

    2006-03-20

    This study investigates whether probabilistic ozone forecasts from an ensemble can be made with skill; i.e., high verification resolution and reliability. Twenty-eight ozone forecasts were generated over the Lower Fraser Valley, British Columbia, Canada, for the 5-day period 11-15 August 2004, and compared with 1-hour averaged measurements of ozone concentrations at five stations. The forecasts were obtained by driving the CMAQ model with four meteorological forecasts and seven emission scenarios: a control run, {+-} 50% NO{sub x}, {+-} 50% VOC, and {+-} 50% NO{sub x} combined with VOC. Probabilistic forecast quality is verified using relative operating characteristic curves, Talagrand diagrams, and a new reliability index. Results show that both meteorology and emission perturbations are needed to have a skillful probabilistic forecast system--the meteorology perturbation is important to capture the ozone temporal and spatial distribution, and the emission perturbation is needed to span the range of ozone-concentration magnitudes. Emission perturbations are more important than meteorology perturbations for capturing the likelihood of high ozone concentrations. Perturbations involving NO{sub x} resulted in a more skillful probabilistic forecast for the episode analyzed, and therefore the 50% perturbation values appears to span much of the emission uncertainty for this case. All of the ensembles analyzed show a high ozone concentration bias in the Talagrand diagrams, even when the biases from the unperturbed emissions forecasts are removed from all ensemble members. This result indicates nonlinearity in the ensemble, which arises from both ozone chemistry and its interaction with input from particular meteorological models.

  6. Probabilistic aspects of meteorological and ozone regional ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Delle Monache, Luca; Hacker, Joshua P.; Zhou, Yongmei; Deng, Xingxiu; Stull, Roland B.

    2006-12-01

    This study investigates whether probabilistic ozone forecasts from an ensemble can be made with skill: i.e., high verification resolution and reliability. Twenty-eight ozone forecasts were generated over the Lower Fraser Valley, British Columbia, Canada, for the 5-day period 11-15 August 2004 and compared with 1-hour averaged measurements of ozone concentrations at five stations. The forecasts were obtained by driving the Community Multiscale Air Quality Model (CMAQ) model with four meteorological forecasts and seven emission scenarios: a control run, ±50% NOx, ±50% volatile organic compounds (VOC), and ±50% NOx combined with VOC. Probabilistic forecast quality is verified using relative operating characteristic curves, Talagrand diagrams, and a new reliability index. Results show that both meteorology and emission perturbations are needed to have a skillful probabilistic forecast system: the meteorology perturbation is important to capture the ozone temporal and spatial distribution and the emission perturbation is needed to span the range of ozone concentration magnitudes. Emission perturbations are more important than meteorology perturbations for capturing the likelihood of high ozone concentrations. Perturbations involving NOx resulted in a more skillful probabilistic forecast for the episode analyzed, and therefore the 50% perturbation values appear to span much of the emission uncertainty for this case. All of the ensembles analyzed show a high ozone concentration bias in the Talagrand diagrams, even when the biases from the unperturbed emissions forecasts are removed from all ensemble members. This result indicates nonlinearity in the ensemble, which arises from both ozone chemistry and its interaction with input from particular meteorological models.

  7. Predictability Assessment and Improving Ensemble Forecasts

    DTIC Science & Technology

    2001-09-30

    system (EFS) output by artificial neural networks . c) Design of optimal EFS’s, with an emphasis on precipitation forecasts. d) Design of stochastic physics parameterizations that improve under-dispersion in EFS s.

  8. Should we use seasonnal meteorological ensemble forecasts for hydrological forecasting? A case study for nordic watersheds in Canada.

    NASA Astrophysics Data System (ADS)

    Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert; Guay, Catherine

    2017-04-01

    Hydro-electricity is a major source of energy for many countries throughout the world, including Canada. Long lead-time streamflow forecasts are all the more valuable as they help decision making and dam management. Different techniques exist for long-term hydrological forecasting. Perhaps the most well-known is 'Extended Streamflow Prediction' (ESP), which considers past meteorological scenarios as possible, often equiprobable, future scenarios. In the ESP framework, those past-observed meteorological scenarios (climatology) are used in turn as the inputs of a chosen hydrological model to produce ensemble forecasts (one member corresponding to each year in the available database). Many hydropower companies, including Hydro-Québec (province of Quebec, Canada) use variants of the above described ESP system operationally for long-term operation planning. The ESP system accounts for the hydrological initial conditions and for the natural variability of the meteorological variables. However, it cannot consider the current initial state of the atmosphere. Climate models can help remedy this drawback. In the context of a changing climate, dynamical forecasts issued from climate models seem to be an interesting avenue to improve upon the ESP method and could help hydropower companies to adapt their management practices to an evolving climate. Long-range forecasts from climate models can also be helpful for water management at locations where records of past meteorological conditions are short or nonexistent. In this study, we compare 7-month hydrological forecasts obtained from climate model outputs to an ESP system. The ESP system mimics the one used operationally at Hydro-Québec. The dynamical climate forecasts are produced by the European Center for Medium range Weather Forecasts (ECMWF) System4. Forecasts quality is assessed using numerical scores such as the Continuous Ranked Probability Score (CRPS) and the Ignorance score and also graphical tools such as the

  9. Multimodel Ensembling in Seasonal Climate Forecasting at IRI.

    NASA Astrophysics Data System (ADS)

    Barnston, Anthony G.; Mason, Simon J.; Goddard, Lisa; Dewitt, David G.; Zebiak, Stephen E.

    2003-12-01

    The International Research Institute (IRI) for Climate Prediction seasonal forecast system is based largely on the predictions of ensembles of several atmospheric general circulation models (AGCMs) forced by two versions of an SST prediction—one consisting of persisted SST anomalies from the current observations and one of evolving SST anomalies as predicted by a set of dynamical and statistical SST prediction models. Recently, an objective multimodel ensembling procedure has replaced a more laborious and subjective weighting of the predictions of the several AGCMs. Here the skills of the multimodel predictions produced retrospectively over the first 4 years of IRI forecasts are examined and compared with the skills of the more subjectively derived forecasts actually issued. The multimodel ensemble predictions are generally found to be an acceptable replacement, although the precipitation forecasts do benefit from inclusion of empirical forecast tools. Planned pattern-level model output statistics (MOS) corrections for systematic biases in the AGCM forecasts may render them more sufficient in their own right.

  10. Applying Seasonal Climate Forecasts to Project Streamflows and Water Storage of Reservoirs

    NASA Astrophysics Data System (ADS)

    Lin, Hsuan-Te

    2016-04-01

    It is important to estimate available water in advance for water resources management. The purpose of this study is to apply seasonal climate forecasts to project streamflows and the storage of reservoirs with the lead time of three months, which can further be used to analyzes drought risk and even to develop drought early warning system (DEWS). The Central Weather Bureau of Taiwan has developed a two-tier dynamical climate forecast system (CWB-2tier-GFS-T42L18), which combines two atmospheric general circulation models with two global sea surface temperature forecasts. The CWB system can be used to forecast temperature and precipitation with the lead time of three months. The climatic conditions are classified into three categories (Below Normal, Normal, and Above Normal). This research generates weather data based on the projected seasonal climate to input a hydrological model to estimate streamflows. The hydrological component of GWLF model is used to simulate streamflows. Furthermore, the simulated streamflows are used to calculate the inflow and storage of Baoshan Reservoir and Baoshan Second Reservoir. The reliability of using seasonal climate forecast to project streamflows and available water of reservoirs will be verified. Keywords: Seasonal Climate, Water Resources, Hydrology, Reservoir, Drought

  11. Quantifying Uncertainty in Flood Inundation Mapping Using Streamflow Ensembles and Multiple Hydraulic Modeling Techniques

    NASA Astrophysics Data System (ADS)

    Hosseiny, S. M. H.; Zarzar, C.; Gomez, M.; Siddique, R.; Smith, V.; Mejia, A.; Demir, I.

    2016-12-01

    The National Water Model (NWM) provides a platform for operationalize nationwide flood inundation forecasting and mapping. The ability to model flood inundation on a national scale will provide invaluable information to decision makers and local emergency officials. Often, forecast products use deterministic model output to provide a visual representation of a single inundation scenario, which is subject to uncertainty from various sources. While this provides a straightforward representation of the potential inundation, the inherent uncertainty associated with the model output should be considered to optimize this tool for decision making support. The goal of this study is to produce ensembles of future flood inundation conditions (i.e. extent, depth, and velocity) to spatially quantify and visually assess uncertainties associated with the predicted flood inundation maps. The setting for this study is located in a highly urbanized watershed along the Darby Creek in Pennsylvania. A forecasting framework coupling the NWM with multiple hydraulic models was developed to produce a suite ensembles of future flood inundation predictions. Time lagged ensembles from the NWM short range forecasts were used to account for uncertainty associated with the hydrologic forecasts. The forecasts from the NWM were input to iRIC and HEC-RAS two-dimensional software packages, from which water extent, depth, and flow velocity were output. Quantifying the agreement between output ensembles for each forecast grid provided the uncertainty metrics for predicted flood water inundation extent, depth, and flow velocity. For visualization, a series of flood maps that display flood extent, water depth, and flow velocity along with the underlying uncertainty associated with each of the forecasted variables were produced. The results from this study demonstrate the potential to incorporate and visualize model uncertainties in flood inundation maps in order to identify the high flood risk zones.

  12. Evaluation of model-based seasonal streamflow and water allocation forecasts for the Elqui Valley, Chile

    NASA Astrophysics Data System (ADS)

    Delorit, Justin; Cristian Gonzalez Ortuya, Edmundo; Block, Paul

    2017-09-01

    In many semi-arid regions, multisectoral demands often stress available water supplies. Such is the case in the Elqui River valley of northern Chile, which draws on a limited-capacity reservoir to allocate 25 000 water rights. Delayed infrastructure investment forces water managers to address demand-based allocation strategies, particularly in dry years, which are realized through reductions in the volume associated with each water right. Skillful season-ahead streamflow forecasts have the potential to inform managers with an indication of future conditions to guide reservoir allocations. This work evaluates season-ahead statistical prediction models of October-January (growing season) streamflow at multiple lead times associated with manager and user decision points, and links predictions with a reservoir allocation tool. Skillful results (streamflow forecasts outperform climatology) are produced for short lead times (1 September: ranked probability skill score (RPSS) of 0.31, categorical hit skill score of 61 %). At longer lead times, climatological skill exceeds forecast skill due to fewer observations of precipitation. However, coupling the 1 September statistical forecast model with a sea surface temperature phase and strength statistical model allows for equally skillful categorical streamflow forecasts to be produced for a 1 May lead, triggered for 60 % of years (1950-2015), suggesting forecasts need not be strictly deterministic to be useful for water rights holders. An early (1 May) categorical indication of expected conditions is reinforced with a deterministic forecast (1 September) as more observations of local variables become available. The reservoir allocation model is skillful at the 1 September lead (categorical hit skill score of 53 %); skill improves to 79 % when categorical allocation prediction certainty exceeds 80 %. This result implies that allocation efficiency may improve when forecasts are integrated into reservoir decision frameworks. The

  13. Streamflow forecast in the Alto do Rio Doce watershed in Brazil, using hydrological and atmospheric model

    NASA Astrophysics Data System (ADS)

    Silva, J. M.; Saad, S. I.; Palma, G.; Rocha, H.; Palmeira, R. M.; Silva, B. L.; Pessoa, A. A.; Ramos, C. G.; Cecchini, M. A.

    2013-05-01

    Electrical energy in Brazil depends essentially on the streamflow, as hydropowers accounts for up to 79% of the total electrical energy installed capacity. Therefore, streamflow forecasts are very important tools to assist in the planning and operation of Brazilian hydroelectric reservoirs. This study evaluated the performance of a distributed hydrological model, Soil and Water Assessment Tool (SWAT) daily streamflow forecasts into four Reservoirs sited in the Alto do Rio Doce Watershed, in Southeast of Brazil. SWAT model was used with precipitation forecast from the regional meteorological model MM5. The calibration and validation processes of SWAT were accomplished using data from four monitoring stations. The model has been run for the 2010-2012 period, and while the apr/2010-set/2011 period has been used for calibration conducted manually, the validation reached the rest of the period. The manual calibration was conducted by the means of sensibility tests of parameters that control surface runoff and groundwater flow, specially the surlag and alpha_bf, respectively the surface runoff lag coefficient and the baseflow recession constant. The daily and monthly Nash-Sutcliffe, R2 and the mean relative error performance indicators were used to assess the relative performance of the model. Results showed that streamflow forecast was very similar toobservations, except in reservoirs with lower drainage areas, where the model did not simulated the beginning of the flood (Dec-Feb). The streamflow forecasts was strongly dependent on the quality of precipitation forecasts used. Given that no correction in the simulated rainfall by the MM5 model in the Alto do Rio Doce watershed has been conducted and no automated calibration method was applied to the parameters of the hydrologic model, we can conclude that the application of the SWAT hydrologic model employing the output data from the MM5 atmospheric model for the streamflow forecast was shown to be a tool of great

  14. Hybrid vs Adaptive Ensemble Kalman Filtering for Storm Surge Forecasting

    NASA Astrophysics Data System (ADS)

    Altaf, M. U.; Raboudi, N.; Gharamti, M. E.; Dawson, C.; McCabe, M. F.; Hoteit, I.

    2014-12-01

    Recent storm surge events due to Hurricanes in the Gulf of Mexico have motivated the efforts to accurately forecast water levels. Toward this goal, a parallel architecture has been implemented based on a high resolution storm surge model, ADCIRC. However the accuracy of the model notably depends on the quality and the recentness of the input data (mainly winds and bathymetry), model parameters (e.g. wind and bottom drag coefficients), and the resolution of the model grid. Given all these uncertainties in the system, the challenge is to build an efficient prediction system capable of providing accurate forecasts enough ahead of time for the authorities to evacuate the areas at risk. We have developed an ensemble-based data assimilation system to frequently assimilate available data into the ADCIRC model in order to improve the accuracy of the model. In this contribution we study and analyze the performances of different ensemble Kalman filter methodologies for efficient short-range storm surge forecasting, the aim being to produce the most accurate forecasts at the lowest possible computing time. Using Hurricane Ike meteorological data to force the ADCIRC model over a domain including the Gulf of Mexico coastline, we implement and compare the forecasts of the standard EnKF, the hybrid EnKF and an adaptive EnKF. The last two schemes have been introduced as efficient tools for enhancing the behavior of the EnKF when implemented with small ensembles by exploiting information from a static background covariance matrix. Covariance inflation and localization are implemented in all these filters. Our results suggest that both the hybrid and the adaptive approach provide significantly better forecasts than those resulting from the standard EnKF, even when implemented with much smaller ensembles.

  15. Climatic fluctuations and forecasting of streamflow in the lower Colorado River Basin

    USGS Publications Warehouse

    Thomas, B.E.

    2007-01-01

    Water-resource managers need to forecast streamflow in the Lower Colorado River Basin to plan for water-resource projects and to operate reservoirs for water supply. Statistical forecasts of streamflow based on historical records of streamflow can be useful, but statistical assumptions, such as stationarity of flows, need to be evaluated. This study evaluated the relation between climatic fluctuations and stationarity and developed regression equations to forecast streamflow by using climatic fluctuations as explanatory variables. Climatic fluctuations were represented by the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), and Southern Oscillation Index (SOI). Historical streamflow within the 25- to 30-year positive or negative phases of AMO or PDO was generally stationary. Monotonic trends in annual mean flows were tested at the 21 sites evaluated in this study; 76% of the sites had no significant trends within phases of AMO and 86% of the sites had no significant trends within phases of PDO. As climatic phases shifted in signs, however, many sites had nonstationary flows; 67% of the sites had significant changes in annual mean flow as AMO shifted in signs. The regression equations developed in this study to forecast streamflow incorporate these shifts in climate and streamflow, thus that source of nonstationarity is accounted for. The R 2 value of regression equations that forecast individual years of annual flow for the central part of the study area ranged from 0.28 to 0.49 and averaged 0.39. AMO was the most significant variable, and a combination of indices from both the Atlantic and Pacific Oceans explained much more variation in flows than only the Pacific Ocean indices. The average R2 value for equations with PDO and SOI was 0.15. ?? 2007 American Water Resources Association.

  16. Use of AWS, GPS, and Radiosonde observations to improve the 0-36h streamflow forecasts

    NASA Astrophysics Data System (ADS)

    Wang, Lei; Koike, Toshio; Liu, Jianyu; Wang, Man; Sun, Jihua; Lu, Hui; Tsutsui, Hiroyuki; Tamagawa, Katsunori; Li, Juan; Xu, Xiangde

    2010-05-01

    The real-time streamflow forecasting is largely determined by the quantitative precipitation forecasting (QPF), but convective weather remains a significant challenge for numerical weather prediction systems. The unreliable accuracies of the streamflow forecasts largely contributed to the historical flood disasters all over the world, including the southwest of China. In this study, the 36-h real-time streamflow forecasts with the Advanced Research Weather Research and Forecasting Model (WRF-ARW) and a distributed biosphere hydrological model (WEB-DHM) on 01 July and 15 July 2008 are presented. Results in the upper Nanpan River Basin of China demonstrated that the assimilation of the new and integrated meteorological observations into the WRF-ARW model has led to significant improvements on the 0-36h real-time QPF, and thus more reliable short-term streamflow forecasting. The newly-assimilated data includes the observations obtained from the newly-built Automatic Weather Stations (AWS), Global Positioning System (GPS), and Radiosonde in southwest China, funded by the cooperative JICA project between Japan and China.

  17. Climate information based streamflow and rainfall forecasts for Huai River Basin using Hierarchical Bayesian Modeling

    NASA Astrophysics Data System (ADS)

    Chen, X.; Hao, Z.; Devineni, N.; Lall, U.

    2013-09-01

    A Hierarchal Bayesian model for forecasting regional summer rainfall and streamflow season-ahead using exogenous climate variables for East Central China is presented. The model provides estimates of the posterior forecasted probability distribution for 12 rainfall and 2 streamflow stations considering parameter uncertainty, and cross-site correlation. The model has a multilevel structure with regression coefficients modeled from a common multivariate normal distribution results in partial-pooling of information across multiple stations and better representation of parameter and posterior distribution uncertainty. Covariance structure of the residuals across stations is explicitly modeled. Model performance is tested under leave-10-out cross-validation. Frequentist and Bayesian performance metrics used include Receiver Operating Characteristic, Reduction of Error, Coefficient of Efficiency, Rank Probability Skill Scores, and coverage by posterior credible intervals. The ability of the model to reliably forecast regional summer rainfall and streamflow season-ahead offers potential for developing adaptive water risk management strategies.

  18. Climate information based streamflow and rainfall forecasts for Huai River basin using hierarchical Bayesian modeling

    NASA Astrophysics Data System (ADS)

    Chen, X.; Hao, Z.; Devineni, N.; Lall, U.

    2014-04-01

    A Hierarchal Bayesian model is presented for one season-ahead forecasts of summer rainfall and streamflow using exogenous climate variables for east central China. The model provides estimates of the posterior forecasted probability distribution for 12 rainfall and 2 streamflow stations considering parameter uncertainty, and cross-site correlation. The model has a multi-level structure with regression coefficients modeled from a common multi-variate normal distribution resulting in partial pooling of information across multiple stations and better representation of parameter and posterior distribution uncertainty. Covariance structure of the residuals across stations is explicitly modeled. Model performance is tested under leave-10-out cross-validation. Frequentist and Bayesian performance metrics used include receiver operating characteristic, reduction of error, coefficient of efficiency, rank probability skill scores, and coverage by posterior credible intervals. The ability of the model to reliably forecast season-ahead regional summer rainfall and streamflow offers potential for developing adaptive water risk management strategies.

  19. Improving water quality forecasting via data assimilation - Application of maximum likelihood ensemble filter to HSPF

    NASA Astrophysics Data System (ADS)

    Kim, Sunghee; Seo, Dong-Jun; Riazi, Hamideh; Shin, Changmin

    2014-11-01

    An ensemble data assimilation (DA) procedure is developed and evaluated for the Hydrologic Simulation Program - Fortran (HSPF), a widely used watershed water quality model. The procedure aims at improving the accuracy of short-range water quality prediction by updating the model initial conditions (IC) based on real-time observations of hydrologic and water quality variables. The observations assimilated include streamflow, biochemical oxygen demand (BOD), dissolved oxygen (DO), chlorophyll a (CHL-a), nitrate (NO3), phosphate (PO4) and water temperature (TW). The DA procedure uses the maximum likelihood ensemble filter (MLEF), which is capable of handling both nonlinear model dynamics and nonlinear observation equations, in a fixed-lag smoother formulation. For evaluation, the DA procedure was applied to the Kumho Catchment of the Nakdong River Basin in the Republic of Korea. A set of performance measures was used to evaluate analysis and prediction of streamflow and water quality variables. To remove systematic errors in the model simulation originating from structural and parametric errors, a parsimonious bias correction procedure is incorporated into the observation equation. The results show that the DA procedure substantially improves predictive skill for most variables; reduction in root mean square error ranges from 11% to 60% for Day-1 through 3 predictions for all observed variables except DO. It is seen that MLEF handles highly nonlinear hydrologic and biochemical observation equations very well, and that it is an effective DA technique for water quality forecasting.

  20. Spatial Ensemble Postprocessing of Precipitation Forecasts Using High Resolution Analyses

    NASA Astrophysics Data System (ADS)

    Lang, Moritz N.; Schicker, Irene; Kann, Alexander; Wang, Yong

    2017-04-01

    Ensemble prediction systems are designed to account for errors or uncertainties in the initial and boundary conditions, imperfect parameterizations, etc. However, due to sampling errors and underestimation of the model errors, these ensemble forecasts tend to be underdispersive, and to lack both reliability and sharpness. To overcome such limitations, statistical postprocessing methods are commonly applied to these forecasts. In this study, a full-distributional spatial post-processing method is applied to short-range precipitation forecasts over Austria using Standardized Anomaly Model Output Statistics (SAMOS). Following Stauffer et al. (2016), observation and forecast fields are transformed into standardized anomalies by subtracting a site-specific climatological mean and dividing by the climatological standard deviation. Due to the need of fitting only a single regression model for the whole domain, the SAMOS framework provides a computationally inexpensive method to create operationally calibrated probabilistic forecasts for any arbitrary location or for all grid points in the domain simultaneously. Taking advantage of the INCA system (Integrated Nowcasting through Comprehensive Analysis), high resolution analyses are used for the computation of the observed climatology and for model training. The INCA system operationally combines station measurements and remote sensing data into real-time objective analysis fields at 1 km-horizontal resolution and 1 h-temporal resolution. The precipitation forecast used in this study is obtained from a limited area model ensemble prediction system also operated by ZAMG. The so called ALADIN-LAEF provides, by applying a multi-physics approach, a 17-member forecast at a horizontal resolution of 10.9 km and a temporal resolution of 1 hour. The performed SAMOS approach statistically combines the in-house developed high resolution analysis and ensemble prediction system. The station-based validation of 6 hour precipitation sums

  1. Decision Support on the Sediments Flushing of Aimorés Dam Using Medium-Range Ensemble Forecasts

    NASA Astrophysics Data System (ADS)

    Mainardi Fan, Fernando; Schwanenberg, Dirk; Collischonn, Walter; Assis dos Reis, Alberto; Alvarado Montero, Rodolfo; Alencar Siqueira, Vinicius

    2015-04-01

    In the present study we investigate the use of medium-range streamflow forecasts in the Doce River basin (Brazil), at the reservoir of Aimorés Hydro Power Plant (HPP). During daily operations this reservoir acts as a "trap" to the sediments that originate from the upstream basin of the Doce River. This motivates a cleaning process called "pass through" to periodically remove the sediments from the reservoir. The "pass through" or "sediments flushing" process consists of a decrease of the reservoir's water level to a certain flushing level when a determined reservoir inflow threshold is forecasted. Then, the water in the approaching inflow is used to flush the sediments from the reservoir through the spillway and to recover the original reservoir storage. To be triggered, the sediments flushing operation requires an inflow larger than 3000m³/s in a forecast horizon of 7 days. This lead-time of 7 days is far beyond the basin's concentration time (around 2 days), meaning that the forecasts for the pass through procedure highly depends on Numerical Weather Predictions (NWP) models that generate Quantitative Precipitation Forecasts (QPF). This dependency creates an environment with a high amount of uncertainty to the operator. To support the decision making at Aimorés HPP we developed a fully operational hydrological forecasting system to the basin. The system is capable of generating ensemble streamflow forecasts scenarios when driven by QPF data from meteorological Ensemble Prediction Systems (EPS). This approach allows accounting for uncertainties in the NWP at a decision making level. This system is starting to be used operationally by CEMIG and is the one shown in the present study, including a hindcasting analysis to assess the performance of the system for the specific flushing problem. The QPF data used in the hindcasting study was derived from the TIGGE (THORPEX Interactive Grand Global Ensemble) database. Among all EPS available on TIGGE, three were

  2. Application of evolutionary computation on ensemble forecast of quantitative precipitation

    NASA Astrophysics Data System (ADS)

    Dufek, Amanda S.; Augusto, Douglas A.; Dias, Pedro L. S.; Barbosa, Helio J. C.

    2017-09-01

    An evolutionary computation algorithm known as genetic programming (GP) has been explored as an alternative tool for improving the ensemble forecast of 24-h accumulated precipitation. Three GP versions and six ensembles' languages were applied to several real-world datasets over southern, southeastern and central Brazil during the rainy period from October to February of 2008-2013. According to the results, the GP algorithms performed better than two traditional statistical techniques, with errors 27-57% lower than simple ensemble mean and the MASTER super model ensemble system. In addition, the results revealed that GP algorithms outperformed the best individual forecasts, reaching an improvement of 34-42%. On the other hand, the GP algorithms had a similar performance with respect to each other and to the Bayesian model averaging, but the former are far more versatile techniques. Although the results for the six ensembles' languages are almost indistinguishable, our most complex linear language turned out to be the best overall proposal. Moreover, some meteorological attributes, including the weather patterns over Brazil, seem to play an important role in the prediction of daily rainfall amount.

  3. Identifying needs for streamflow forecasting in the Incomati basin, Southern Africa

    NASA Astrophysics Data System (ADS)

    Sunday, Robert; Werner, Micha; Masih, Ilyas; van der Zaag, Pieter

    2013-04-01

    Despite being widely recognised as an efficient tool in the operational management of water resources, rainfall and streamflow forecasts are currently not utilised in water management practice in the Incomati Basin in Southern Africa. Although, there have been initiatives for forecasting streamflow in the Sabie and Crocodile sub-basins, the outputs of these have found little use because of scepticism on the accuracy and reliability of the information, or the relevance of the information provided to the needs of the water managers. The process of improving these forecasts is underway, but as yet the actual needs of the forecasts are unclear and scope of the ongoing initiatives remains very limited. In this study questionnaires and focused group interviews were used to establish the need, potential use, benefit and required accuracy of rainfall and streamflow forecasts in the Incomati Basin. Thirty five interviews were conducted with professionals engaged in water sector and detailed discussions were held with water institutions, including the Inkomati Catchment Management Agency (ICMA), Komati Basin Water Authority (KOBWA), South African Weather Service (SAWS), water managers, dam operators, water experts, farmers and other water users in the Basin. Survey results show that about 97% of the respondents receive weather forecasts. In contrast to expectations, only 5% have access to the streamflow forecast. In the weather forecast, the most important variables were considered to be rainfall and temperature at daily and weekly time scales. Moreover, forecasts of global climatic indices such as El Niño or La Niña were neither received nor demanded. There was limited demand and/or awareness of flood and drought forecasts including the information on their linkages with global climatic indices. While the majority of respondents indicate the need and indeed use the weather forecast, the provision, communication and interpretation were in general found to be with too

  4. Ensemble stream flow predictions using the ECMWF forecasts

    NASA Astrophysics Data System (ADS)

    Kiczko, Adam; Romanowicz, Renata; Osuch, Marzena; Pappenberger, Florian; Karamuz, Emilia

    2015-04-01

    Floods and low flows in rivers are seasonal phenomena that can cause several problems to society. To anticipate high and low flow events, flow forecasts are crucial. They are of particular importance in mountainous catchments, where the lead time of forecasts is usually short. In order to prolong the forecast lead-time, numerical weather predictions (NWPs) are used as a hydrological model driving force. The forecasted flow is commonly given as one value, even though it is uncertain. There is an increasing interest in accounting for the uncertainty in flood early warning and decision support systems. When NWP are given in the form of ensembles, such as the ECMWF forecasts, the uncertainty of these forecasts can be accounted for. Apart from the forecast uncertainty the uncertainty related to the hydrological model used also plays an important role in the uncertainty of the final flow prediction. The aim of this study is the development of a stream flow prediction system for the Biała Tarnowska, a mountainous catchment in the south of Poland. We apply two different hydrological models. One is a conceptual HBV model for rainfall-flow predictions, applied within a Generalised Likelihood Uncertainty Estimation (GLUE) framework, the second is a data-based DBM model, adjusted for Polish conditions by adding the Soil Moisture Accounting (SMA) and snow-melt modules. Both models provide the uncertainty of the predictions, but the DBM approach is much more numerically efficient, therefore more suitable for the real-time forecasting.. The ECMWF forecasts require bias reduction in order to correspond to observations. Therefore we applied Quantile Mapping with and without seasonal adjustment for bias correction. Up to seven-days ahead forecast skills are compared using the Relative Operation Characteristic (ROC) graphs, for the flood warning and flood alarm flow value thresholds. The ECMWF forecasts are obtained from the project TIGGE (http

  5. The Ensemble Framework for Flash Flood Forecasting: Global and CONUS Applications

    NASA Astrophysics Data System (ADS)

    Flamig, Z.; Vergara, H. J.; Clark, R. A.; Gourley, J. J.; Kirstetter, P. E.; Hong, Y.

    2015-12-01

    The Ensemble Framework for Flash Flood Forecasting (EF5) is a distributed hydrologic modeling framework combining water balance components such as the Variable Infiltration Curve (VIC) and Sacramento Soil Moisture Accounting (SAC-SMA) with kinematic wave channel routing. The Snow-17 snow pack model is included as an optional component in EF5 for basins where snow impacts are important. EF5 also contains the Differential Evolution Adaptive Metropolis (DREAM) parameter estimation scheme for model calibration. EF5 is made to be user friendly and as such training has been developed into a weeklong course. This course has been tested in modeling workshops held in Namibia and Mexico. EF5 has also been applied to specialized applications including the Flooded Locations and Simulated Hydrographs (FLASH) project. FLASH aims to provide flash flood monitoring and forecasting over the CONUS using Multi-Radar Multi-Sensor precipitation forcing. Using the extensive field measurements database from the 10,000 USGS measurement locations across the CONUS, parameters were developed for the kinematic wave routing in FLASH. This presentation will highlight FLASH performance over the CONUS on basins less than 1,000 km2 and discuss the development of simulated streamflow climatology over the CONUS for data mining applications. A global application of EF5 has also been developed using satellite based precipitation measurements combined with numerical weather prediction forecasts to produce flood and impact forecasts. The performance of this global system will be assessed and future plans detailed.

  6. Ensemble Ionospheric Total Electron Content Forecasting during Storms

    NASA Astrophysics Data System (ADS)

    Chartier, A.; Mitchell, C. N.; Lu, G.; Anderson, J. L.; Collins, N.; Hoar, T. J.; Bust, G. S.; Matsuo, T.

    2014-12-01

    Earth's ionosphere presents a threat to human activities such as satellite positioning and timing, radio communications and surveillance. Nowcasts and forecasts of the ionosphere could help mitigate these damaging effects. Recent advances in the field of ionospheric imaging, as well as new storm-time ionospheric forecasting results are presented here. The approach combines globally distributed GPS Total Electron Content (TEC) measurements with an ensemble of coupled thermosphere-ionosphere models in order to produce short-term forecasts during a storm. One-hour forecast accuracy is much better than a climatological model run. Using this ensemble approach, it is possible to infer the neutral O/N2 ratio from TEC measurements so that subsequent TEC forecasts are improved. A review of ionospheric physics and data assimilation will also be given. The term data assimilation refers to a group of techniques designed to estimate atmospheric or oceanic states. In practice, data assimilation techniques seek to improve modeled estimates of the atmospheric state by incorporating observations. The relationship between data assimilation and forecasting is explored with reference to the physics of the thermosphere-ionosphere system. The work presented here uses the Data Assimilation Research Testbed (DART), which is an ensemble Kalman filter data assimilation framework. This is combined with a version of the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) that has been modified to accept more detailed solar and geomagnetic driver specifications. Future directions of work include the inference of Solar and geomagnetic drivers from the data assimilation process as well as coupling with lower-atmospheric models.

  7. Medium Range Ensembles Flood Forecasts for Community Level Applications

    NASA Astrophysics Data System (ADS)

    Fakhruddin, S.; Kawasaki, A.; Babel, M. S.; AIT

    2013-05-01

    Early warning is a key element for disaster risk reduction. In recent decades, there has been a major advancement in medium range and seasonal forecasting. These could provide a great opportunity to improve early warning systems and advisories for early action for strategic and long term planning. This could result in increasing emphasis on proactive rather than reactive management of adverse consequences of flood events. This can be also very helpful for the agricultural sector by providing a diversity of options to farmers (e.g. changing cropping pattern, planting timing, etc.). An experimental medium range (1-10 days) flood forecasting model has been developed for Bangladesh which provides 51 set of discharge ensembles forecasts of one to ten days with significant persistence and high certainty. This could help communities (i.e. farmer) for gain/lost estimation as well as crop savings. This paper describe the application of ensembles probabilistic flood forecast at the community level for differential decision making focused on agriculture. The framework allows users to interactively specify the objectives and criteria that are germane to a particular situation, and obtain the management options that are possible, and the exogenous influences that should be taken into account before planning and decision making. risk and vulnerability assessment was conducted through community consultation. The forecast lead time requirement, users' needs, impact and management options for crops, livestock and fisheries sectors were identified through focus group discussions, informal interviews and questionnaire survey.

  8. Time-Hierarchical Clustering and Visualization of Weather Forecast Ensembles.

    PubMed

    Ferstl, Florian; Kanzler, Mathias; Rautenhaus, Marc; Westermann, Rudiger

    2017-01-01

    We propose a new approach for analyzing the temporal growth of the uncertainty in ensembles of weather forecasts which are started from perturbed but similar initial conditions. As an alternative to traditional approaches in meteorology, which use juxtaposition and animation of spaghetti plots of iso-contours, we make use of contour clustering and provide means to encode forecast dynamics and spread in one single visualization. Based on a given ensemble clustering in a specified time window, we merge clusters in time-reversed order to indicate when and where forecast trajectories start to diverge. We present and compare different visualizations of the resulting time-hierarchical grouping, including space-time surfaces built by connecting cluster representatives over time, and stacked contour variability plots. We demonstrate the effectiveness of our visual encodings with forecast examples of the European Centre for Medium-Range Weather Forecasts, which convey the evolution of specific features in the data as well as the temporally increasing spatial variability.

  9. Translating Ensemble Weather Forecasts into Probabilistic User-Relevant Information

    NASA Astrophysics Data System (ADS)

    Steiner, Matthias; Sharman, Robert; Hopson, Thomas; Liu, Yubao; Chapman, Michael

    2010-05-01

    Weather-related decisions increasingly rely on probabilistic information as a means of assessing the risk of one potential outcome over another. Ensemble forecasting presents one of the key approaches trying to grasp the uncertainty of weather forecasting. Moreover, in the future decision makers will rely on tools that fully integrate weather information into the decision making process. Through these decision support tools, weather information will be translated into impact information. This presentation will highlight the translation of gridded ensemble weather forecasts into probabilistic user-relevant information. Examples will be discussed that relate to the management of air traffic, noise and pollution dispersion, missile trajectory prediction, water resources and flooding, wind energy production, and road maintenance. The primary take-home message from these examples will be that weather forecasts have to be tailored with a specific user perspective in mind rather than a "one fits all" approach, where a standard forecast product gets thrown over the fence and the user has to figure out what to do with it.

  10. Copula Based Post-processing Method for Hydrologic Ensemble Forecast

    NASA Astrophysics Data System (ADS)

    Duan, Q.; Li, W.

    2016-12-01

    Hydrology forecasts often contain uncertainties from model inputs, model parameters and model structure. Post processing methods can be applied to the original hydrology ensemble forecasts to correct the bias and spread error. Most existing post-processing methods applied the Normal Quantile Transform (NQT) to transform the hydrology variables to normal distribution for convenient statistical inference. However, the NQT based algorithm suffer several problems, such as the extrapolation problem in back-transform process. In this research, a copula based post-processing method was developed. The copula function estimates the joint distribution of observation and model forecast directly, and then the conditional distribution of observation given the model forecasts could be obtained without NQT. The proposed post-processing method was tested and compared with two other popular methods based on NQT, namely the Hydrology Uncertainty Processor (HUP) and General Linear Model Post-Processor (GLMPP) using the observation and simulation dataset from the Model Parameter Estimation Experiment (MOPEX) project. The results show that the drawback of NQT based post-processing methods can be alleviated in the proposed algorithm. Some suitable conditions and suggestions on the application of copula based post-processing method for hydrology ensemble forecast were also provided.

  11. Verification of Advances in a Coupled Snow-runoff Modeling Framework for Operational Streamflow Forecasts

    NASA Astrophysics Data System (ADS)

    Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

    2011-12-01

    The National Oceanic and Atmospheric Administration's (NOAA's) River Forecast Centers (RFCs) issue hydrologic forecasts related to flood events, reservoir operations for water supply, streamflow regulation, and recreation on the nation's streams and rivers. The RFCs use the National Weather Service River Forecast System (NWSRFS) for streamflow forecasting which relies on a coupled snow model (i.e. SNOW17) and rainfall-runoff model (i.e. SAC-SMA) in snow-dominated regions of the US. Errors arise in various steps of the forecasting system from input data, model structure, model parameters, and initial states. The goal of the current study is to undertake verification of potential improvements in the SNOW17-SAC-SMA modeling framework developed for operational streamflow forecasts. We undertake verification for a range of parameters sets (i.e. RFC, DREAM (Differential Evolution Adaptive Metropolis)) as well as a data assimilation (DA) framework developed for the coupled models. Verification is also undertaken for various initial conditions to observe the influence of variability in initial conditions on the forecast. The study basin is the North Fork America River Basin (NFARB) located on the western side of the Sierra Nevada Mountains in northern California. Hindcasts are verified using both deterministic (i.e. Nash Sutcliffe efficiency, root mean square error, and joint distribution) and probabilistic (i.e. reliability diagram, discrimination diagram, containing ratio, and Quantile plots) statistics. Our presentation includes comparison of the performance of different optimized parameters and the DA framework as well as assessment of the impact associated with the initial conditions used for streamflow forecasts for the NFARB.

  12. Mesoscale Predictability and Error Growth in Short Range Ensemble Forecasts

    NASA Astrophysics Data System (ADS)

    Gingrich, Mark

    Although it was originally suggested that small-scale, unresolved errors corrupt forecasts at all scales through an inverse error cascade, some authors have proposed that those mesoscale circulations resulting from stationary forcing on the larger scale may inherit the predictability of the large-scale motions. Further, the relative contributions of large- and small-scale uncertainties in producing error growth in the mesoscales remain largely unknown. Here, 100 member ensemble forecasts are initialized from an ensemble Kalman filter (EnKF) to simulate two winter storms impacting the East Coast of the United States in 2010. Four verification metrics are considered: the local snow water equivalence, total liquid water, and 850 hPa temperatures representing mesoscale features; and the sea level pressure field representing a synoptic feature. It is found that while the predictability of the mesoscale features can be tied to the synoptic forecast, significant uncertainty existed on the synoptic scale at lead times as short as 18 hours. Therefore, mesoscale details remained uncertain in both storms due to uncertainties at the large scale. Additionally, the ensemble perturbation kinetic energy did not show an appreciable upscale propagation of error for either case. Instead, the initial condition perturbations from the cycling EnKF were maximized at large scales and immediately amplified at all scales without requiring initial upscale propagation. This suggests that relatively small errors in the synoptic-scale initialization may have more importance in limiting predictability than errors in the unresolved, small-scale initial conditions.

  13. Space weather forecasting with a Multimodel Ensemble Prediction System (MEPS)

    NASA Astrophysics Data System (ADS)

    Schunk, R. W.; Scherliess, L.; Eccles, V.; Gardner, L. C.; Sojka, J. J.; Zhu, L.; Pi, X.; Mannucci, A. J.; Butala, M.; Wilson, B. D.; Komjathy, A.; Wang, C.; Rosen, G.

    2016-07-01

    The goal of the Multimodel Ensemble Prediction System (MEPS) program is to improve space weather specification and forecasting with ensemble modeling. Space weather can have detrimental effects on a variety of civilian and military systems and operations, and many of the applications pertain to the ionosphere and upper atmosphere. Space weather can affect over-the-horizon radars, HF communications, surveying and navigation systems, surveillance, spacecraft charging, power grids, pipelines, and the Federal Aviation Administration (FAA's) Wide Area Augmentation System (WAAS). Because of its importance, numerous space weather forecasting approaches are being pursued, including those involving empirical, physics-based, and data assimilation models. Clearly, if there are sufficient data, the data assimilation modeling approach is expected to be the most reliable, but different data assimilation models can produce different results. Therefore, like the meteorology community, we created a Multimodel Ensemble Prediction System (MEPS) for the Ionosphere-Thermosphere-Electrodynamics (ITE) system that is based on different data assimilation models. The MEPS ensemble is composed of seven physics-based data assimilation models for the ionosphere, ionosphere-plasmasphere, thermosphere, high-latitude ionosphere-electrodynamics, and middle to low latitude ionosphere-electrodynamics. Hence, multiple data assimilation models can be used to describe each region. A selected storm event that was reconstructed with four different data assimilation models covering the middle and low latitude ionosphere is presented and discussed. In addition, the effect of different data types on the reconstructions is shown.

  14. Intermediate-term forecasting of aftershocks from an early aftershock sequence: Bayesian and ensemble forecasting approaches

    NASA Astrophysics Data System (ADS)

    Omi, Takahiro; Ogata, Yosihiko; Hirata, Yoshito; Aihara, Kazuyuki

    2015-04-01

    Because aftershock occurrences can cause significant seismic risks for a considerable time after the main shock, prospective forecasting of the intermediate-term aftershock activity as soon as possible is important. The epidemic-type aftershock sequence (ETAS) model with the maximum likelihood estimate effectively reproduces general aftershock activity including secondary or higher-order aftershocks and can be employed for the forecasting. However, because we cannot always expect the accurate parameter estimation from incomplete early aftershock data where many events are missing, such forecasting using only a single estimated parameter set (plug-in forecasting) can frequently perform poorly. Therefore, we here propose Bayesian forecasting that combines the forecasts by the ETAS model with various probable parameter sets given the data. By conducting forecasting tests of 1 month period aftershocks based on the first 1 day data after the main shock as an example of the early intermediate-term forecasting, we show that the Bayesian forecasting performs better than the plug-in forecasting on average in terms of the log-likelihood score. Furthermore, to improve forecasting of large aftershocks, we apply a nonparametric (NP) model using magnitude data during the learning period and compare its forecasting performance with that of the Gutenberg-Richter (G-R) formula. We show that the NP forecast performs better than the G-R formula in some cases but worse in other cases. Therefore, robust forecasting can be obtained by employing an ensemble forecast that combines the two complementary forecasts. Our proposed method is useful for a stable unbiased intermediate-term assessment of aftershock probabilities.

  15. Climate model forecast biases assessed with a perturbed physics ensemble

    NASA Astrophysics Data System (ADS)

    Mulholland, David P.; Haines, Keith; Sparrow, Sarah N.; Wallom, David

    2017-09-01

    Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the `equivalent parameter perturbations', which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies.

  16. Climate model forecast biases assessed with a perturbed physics ensemble

    NASA Astrophysics Data System (ADS)

    Mulholland, David P.; Haines, Keith; Sparrow, Sarah N.; Wallom, David

    2016-10-01

    Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the `equivalent parameter perturbations', which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies.

  17. Improved forecasting of thermospheric densities using multi-model ensembles

    NASA Astrophysics Data System (ADS)

    Elvidge, Sean; Godinez, Humberto C.; Angling, Matthew J.

    2016-07-01

    This paper presents the first known application of multi-model ensembles to the forecasting of the thermosphere. A multi-model ensemble (MME) is a method for combining different, independent models. The main advantage of using an MME is to reduce the effect of model errors and bias, since it is expected that the model errors will, at least partly, cancel. The MME, with its reduced uncertainties, can then be used as the initial conditions in a physics-based thermosphere model for forecasting. This should increase the forecast skill since a reduction in the errors of the initial conditions of a model generally increases model skill. In this paper the Thermosphere-Ionosphere Electrodynamic General Circulation Model (TIE-GCM), the US Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Exosphere 2000 (NRLMSISE-00), and Global Ionosphere-Thermosphere Model (GITM) have been used to construct the MME. As well as comparisons between the MMEs and the "standard" runs of the model, the MME densities have been propagated forward in time using the TIE-GCM. It is shown that thermospheric forecasts of up to 6 h, using the MME, have a reduction in the root mean square error of greater than 60 %. The paper also highlights differences in model performance between times of solar minimum and maximum.

  18. Data Analysis, Modeling, and Ensemble Forecasting to Support NOWCAST and Forecast Activities at the Fallon Naval Air Station

    DTIC Science & Technology

    2008-09-30

    Weather and Research Forecasting model (WRF); 3) To include at a later stage the Coastal Oceanic and Atmospheric Modeling Prediction System ( COAMPS ...charts and animations, Other useful links, Ensemble forecasting (in construction), Forecast of transport and dispersion of dust and pollutants, Model...regional­ mesoscale multi-model ( COAMPS , WRF, and MM5) ensemble forecasting (Lewis 2005). In this initial phase of the development of the multi-model

  19. An Application of Advanced Ensemble Streamflow Prediction Methods to Assess Potential Impacts of the 2015 - 2016 ENSO Event over the Colorado River Basin

    NASA Astrophysics Data System (ADS)

    Miller, W. P.; Lamb, K. W.; Piechota, T. C.; Lakshmi, V.; Santos, N. I.; Tootle, G. A.; Kalra, A.; Fayne, J.

    2015-12-01

    Water resource managers throughout the Western United States have struggled with persistent and severe drought since the early 2000s. In the Colorado River Basin, the National Oceanic and Atmospheric Administration's (NOAA's) Colorado Basin River Forecast Center (CBRFC) provides forecasts of water supply conditions to resource managers throughout the basin using Ensemble Streamflow Prediction (ESP) methods that are largely driven by historical observations of temperature and precipitation. Currently, the CBRFC does not have a way to incorporate information from climatic teleconnections such as the El Niño Southern Oscillation (ENSO). ENSO describes warming sea surface temperatures in the Pacific Ocean that typically correlate with cool and wet winter precipitation events in California and the Lower Colorado River Basin during an El Niño event. Past research indicates the potential to identify analog ENSO events to evaluate the impact to reservoir storage in the Colorado River Basin. Current forecasts indicate the potential for one of the strongest El Niño events on record this winter. In this study, information regarding the upcoming ENSO event is used to inform water supply forecasts over the Upper Colorado River Basin. These forecasts are then compared to traditionally derived water supply forecast in an attempt to evaluate the possible impact of the El Niño event to water supply over the Colorado River Basin.

  20. Visualizing Confidence in Cluster-based Ensemble Weather Forecast Analyses.

    PubMed

    Kumpf, Alexander; Tost, Bianca; Baumgart, Marlene; Riemer, Michael; Westermann, Rudiger; Rautenhaus, Marc

    2017-08-29

    In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we -a team of visualization scientists and meteorologists- deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of "Tropical Cyclone Karl", guiding the user towards the cluster robustness information required for subsequent ensemble analysis.

  1. Forecast improvement by interactive ensemble of atmospheric models

    NASA Astrophysics Data System (ADS)

    Basnarkov, L.; Duane, G. S.; Kocarev, L.

    2013-12-01

    The advances in weather forecast traditionally have been based on two lines of improvement: 1 - deepening the understanding of physical phenomena that underlies the atmospheric dynamics; and 2 - steady increase in computer power that enables use of finer grid resolution. The meteorological centers model dynamics of the atmosphere with the same basic physical laws, but sometimes take different approaches in capturing small-scale phenomena and generally use different grid sizes. As a result there are dozens operational models around the globe with various parameterizations of the unresolved processes. Newest attempts in forecast improvements are based on using ensemble prediction. Multiple outputs are taken from runs with perturbed initial conditions, or perturbed parameter values. A novel paradigm is exploiting dynamical exchange of variables between simultaneously running models. There are already simulations of models exchanging fluxes between ocean and atmospheric models, but examples with direct coupling of different atmospheric models are rather new. Within this approach the coupling schemes can be different, but as simplest appear those that combine corresponding dynamical variables or tendency components. In this work we present results with an artificial toy model-Lorenz 96 model. To make more faithful example as reality (the atmosphere) is considered one Lorenz 96 class III system, while as its imperfect models are taken three class II systems that have different forcing terms. These resemble the models used in three different meteorological centers. The interactive ensemble has tendency that is weighted combination of the individual models' tendencies. The weights are obtained with statistical techniques based on past observations that target to minimize the mismatch between the truth's and interactive ensemble's tendencies. By means of anomaly correlation it is numerically verified that this ensemble has longer range of forecast than the individual models.

  2. Hydro-meteorological evaluation of downscaled global ensemble rainfall forecasts

    NASA Astrophysics Data System (ADS)

    Gaborit, Étienne; Anctil, François; Fortin, Vincent; Pelletier, Geneviève

    2013-04-01

    Ensemble rainfall forecasts are of high interest for decision making, as they provide an explicit and dynamic assessment of the uncertainty in the forecast (Ruiz et al. 2009). However, for hydrological forecasting, their low resolution currently limits their use to large watersheds (Maraun et al. 2010). In order to bridge this gap, various implementations of the statistic-stochastic multi-fractal downscaling technique presented by Perica and Foufoula-Georgiou (1996) were compared, bringing Environment Canada's global ensemble rainfall forecasts from a 100 by 70-km resolution down to 6 by 4-km, while increasing each pixel's rainfall variance and preserving its original mean. For comparison purposes, simpler methods were also implemented such as the bi-linear interpolation, which disaggregates global forecasts without modifying their variance. The downscaled meteorological products were evaluated using different scores and diagrams, from both a meteorological and a hydrological view points. The meteorological evaluation was conducted comparing the forecasted rainfall depths against nine days of observed values taken from Québec City rain gauge database. These 9 days present strong precipitation events occurring during the summer of 2009. For the hydrologic evaluation, the hydrological models SWMM5 and (a modified version of) GR4J were implemented on a small 6 km2 urban catchment located in the Québec City region. Ensemble hydrologic forecasts with a time step of 3 hours were then performed over a 3-months period of the summer of 2010 using the original and downscaled ensemble rainfall forecasts. The most important conclusions of this work are that the overall quality of the forecasts was preserved during the disaggregation procedure and that the disaggregated products using this variance-enhancing method were of similar quality than bi-linear interpolation products. However, variance and dispersion of the different members were, of course, much improved for the

  3. An assessment of a North American Multi-Model Ensemble (NMME) based global drought early warning forecast system

    NASA Astrophysics Data System (ADS)

    Wood, E. F.; Yuan, X.; Sheffield, J.; Pan, M.; Roundy, J.

    2013-12-01

    One of the key recommendations of the WCRP Global Drought Information System (GDIS) workshop is to develop an experimental real-time global monitoring and prediction system. While great advances has been made in global drought monitoring based on satellite observations and model reanalysis data, global drought forecasting has been stranded in part due to the limited skill both in climate forecast models and global hydrologic predictions. Having been working on drought monitoring and forecasting over USA for more than a decade, the Princeton land surface hydrology group is now developing an experimental global drought early warning system that is based on multiple climate forecast models and a calibrated global hydrologic model. In this presentation, we will test its capability in seasonal forecasting of meteorological, agricultural and hydrologic droughts over global major river basins, using precipitation, soil moisture and streamflow forecasts respectively. Based on the joint probability distribution between observations using Princeton's global drought monitoring system and model hindcasts and real-time forecasts from North American Multi-Model Ensemble (NMME) project, we (i) bias correct the monthly precipitation and temperature forecasts from multiple climate forecast models, (ii) downscale them to a daily time scale, and (iii) use them to drive the calibrated VIC model to produce global drought forecasts at a 1-degree resolution. A parallel run using the ESP forecast method, which is based on resampling historical forcings, is also carried out for comparison. Analysis is being conducted over global major river basins, with multiple drought indices that have different time scales and characteristics. The meteorological drought forecast does not have uncertainty from hydrologic models and can be validated directly against observations - making the validation an 'apples-to-apples' comparison. Preliminary results for the evaluation of meteorological drought onset

  4. Generation of a Solar Wind Ensemble for Space Weather Forecasting

    NASA Astrophysics Data System (ADS)

    Hassan, E.; Morley, S.; Steinberg, J. T.

    2015-12-01

    Knowing the upstream solar wind conditions is essential in forecasting the variations in the geomangetic field and the status of the Earth's ionosphere. Most data-driven simulations or data-assimilation codes, used for space weather forecasting, are based on the solar wind measurements at 1 AU, or more specifically at the first Lagrangian orbit (L1), such as observations from the Advanced Composition Explorer (ACE). However, L1 measurements may not represent the solar wind conditions just outside the magnetosphere. As a result, time-series measurements from L1 by themselves are not adequate to run simulations to derive probabilistic forecasts of the magnetosphere and ionosphere. To obtain confidence levels and uncertainty estimates, a solar wind ensemble data set is desirable. Therefore we used three years of measurements atACE advected using the flat delay method to the Interplanetary Monitoring Platform (IMP8) spacecraft location. Then, we compared both measurements to establish Kernel Density Estimation (KDE) functions for IMP8 measurements based on ACE measurements. In addition, we used a 4-categorization scheme to sort the incoming solar wind into ejecta, coronal-hole-origin, sector-reversal-regions, and streamer-belt-origin categories at both ACE and IMP8. We established the KDE functions for each category and compared with the uncategorized KDE functions. The location of the IMP8 spacecraft allows us to use these KDE functions to generate ensemble of solar wind data close to Earth's magnetopause. The ensemble can then be used to forecast the state of the geomagnetic field and the ionosphere.

  5. Ensemble-based methods for forecasting census in hospital units

    PubMed Central

    2013-01-01

    Background The ability to accurately forecast census counts in hospital departments has considerable implications for hospital resource allocation. In recent years several different methods have been proposed forecasting census counts, however many of these approaches do not use available patient-specific information. Methods In this paper we present an ensemble-based methodology for forecasting the census under a framework that simultaneously incorporates both (i) arrival trends over time and (ii) patient-specific baseline and time-varying information. The proposed model for predicting census has three components, namely: current census count, number of daily arrivals and number of daily departures. To model the number of daily arrivals, we use a seasonality adjusted Poisson Autoregressive (PAR) model where the parameter estimates are obtained via conditional maximum likelihood. The number of daily departures is predicted by modeling the probability of departure from the census using logistic regression models that are adjusted for the amount of time spent in the census and incorporate both patient-specific baseline and time varying patient-specific covariate information. We illustrate our approach using neonatal intensive care unit (NICU) data collected at Women & Infants Hospital, Providence RI, which consists of 1001 consecutive NICU admissions between April 1st 2008 and March 31st 2009. Results Our results demonstrate statistically significant improved prediction accuracy for 3, 5, and 7 day census forecasts and increased precision of our forecasting model compared to a forecasting approach that ignores patient-specific information. Conclusions Forecasting models that utilize patient-specific baseline and time-varying information make the most of data typically available and have the capacity to substantially improve census forecasts. PMID:23721123

  6. Ensemble-based methods for forecasting census in hospital units.

    PubMed

    Koestler, Devin C; Ombao, Hernando; Bender, Jesse

    2013-05-30

    The ability to accurately forecast census counts in hospital departments has considerable implications for hospital resource allocation. In recent years several different methods have been proposed forecasting census counts, however many of these approaches do not use available patient-specific information. In this paper we present an ensemble-based methodology for forecasting the census under a framework that simultaneously incorporates both (i) arrival trends over time and (ii) patient-specific baseline and time-varying information. The proposed model for predicting census has three components, namely: current census count, number of daily arrivals and number of daily departures. To model the number of daily arrivals, we use a seasonality adjusted Poisson Autoregressive (PAR) model where the parameter estimates are obtained via conditional maximum likelihood. The number of daily departures is predicted by modeling the probability of departure from the census using logistic regression models that are adjusted for the amount of time spent in the census and incorporate both patient-specific baseline and time varying patient-specific covariate information. We illustrate our approach using neonatal intensive care unit (NICU) data collected at Women & Infants Hospital, Providence RI, which consists of 1001 consecutive NICU admissions between April 1st 2008 and March 31st 2009. Our results demonstrate statistically significant improved prediction accuracy for 3, 5, and 7 day census forecasts and increased precision of our forecasting model compared to a forecasting approach that ignores patient-specific information. Forecasting models that utilize patient-specific baseline and time-varying information make the most of data typically available and have the capacity to substantially improve census forecasts.

  7. United States streamflow probabilities based on forecasted La Nina, winter-spring 2000

    USGS Publications Warehouse

    Dettinger, M.D.; Cayan, D.R.; Redmond, K.T.

    1999-01-01

    Although for the last 5 months the TahitiDarwin Southern Oscillation Index (SOI) has hovered close to normal, the “equatorial” SOI has remained in the La Niña category and predictions are calling for La Niña conditions this winter. In view of these predictions of continuing La Niña and as a direct extension of previous studies of the relations between El NiñoSouthern Oscil-lation (ENSO) conditions and streamflow in the United States (e.g., Redmond and Koch, 1991; Cayan and Webb, 1992; Redmond and Cayan, 1994; Dettinger et al., 1998; Garen, 1998; Cayan et al., 1999; Dettinger et al., in press), the probabilities that United States streamflows from December 1999 through July 2000 will be in upper and lower thirds (terciles) of the historical records are estimated here. The processes that link ENSO to North American streamflow are discussed in detail in these diagnostics studies. Our justification for generating this forecast is threefold: (1) Cayan et al. (1999) recently have shown that ENSO influences on streamflow variations and extremes are proportionately larger than the corresponding precipitation teleconnections. (2) Redmond and Cayan (1994) and Dettinger et al. (in press) also have shown that the low-frequency evolution of ENSO conditions support long-lead correlations between ENSO and streamflow in many rivers of the conterminous United States. (3) In many rivers, significant (weeks-to-months) delays between precipitation and the release to streams of snowmelt or ground-water discharge can support even longer term forecasts of streamflow than is possible for precipitation. The relatively slow, orderly evolution of El Niño-Southern Oscillation episodes, the accentuated dependence of streamflow upon ENSO, and the long lags between precipitation and flow encourage us to provide the following analysis as a simple prediction of this year’s river flows.

  8. Comparison of cross-validation and bootstrap aggregating for building a seasonal streamflow forecast model

    NASA Astrophysics Data System (ADS)

    Schick, Simon; Rössler, Ole; Weingartner, Rolf

    2016-10-01

    Based on a hindcast experiment for the period 1982-2013 in 66 sub-catchments of the Swiss Rhine, the present study compares two approaches of building a regression model for seasonal streamflow forecasting. The first approach selects a single "best guess" model, which is tested by leave-one-out cross-validation. The second approach implements the idea of bootstrap aggregating, where bootstrap replicates are employed to select several models, and out-of-bag predictions provide model testing. The target value is mean streamflow for durations of 30, 60 and 90 days, starting with the 1st and 16th day of every month. Compared to the best guess model, bootstrap aggregating reduces the mean squared error of the streamflow forecast by seven percent on average. Thus, if resampling is anyway part of the model building procedure, bootstrap aggregating seems to be a useful strategy in statistical seasonal streamflow forecasting. Since the improved accuracy comes at the cost of a less interpretable model, the approach might be best suited for pure prediction tasks, e.g. as in operational applications.

  9. Theoretical basis for operational ensemble forecasting of coronal mass ejections

    NASA Astrophysics Data System (ADS)

    Pizzo, V. J.; Koning, C.; Cash, M.; Millward, G.; Biesecker, D. A.; Puga, L.; Codrescu, M.; Odstrcil, D.

    2015-10-01

    We lay out the theoretical underpinnings for the application of the Wang-Sheeley-Arge-Enlil modeling system to ensemble forecasting of coronal mass ejections (CMEs) in an operational environment. In such models, there is no magnetic cloud component, so our results pertain only to CME front properties, such as transit time to Earth. Within this framework, we find no evidence that the propagation is chaotic, and therefore, CME forecasting calls for different tactics than employed for terrestrial weather or hurricane forecasting. We explore a broad range of CME cone inputs and ambient states to flesh out differing CME evolutionary behavior in the various dynamical domains (e.g., large, fast CMEs launched into a slow ambient, and the converse; plus numerous permutations in between). CME propagation in both uniform and highly structured ambient flows is considered to assess how much the solar wind background affects the CME front properties at 1 AU. Graphical and analytic tools pertinent to an ensemble approach are developed to enable uncertainties in forecasting CME impact at Earth to be realistically estimated. We discuss how uncertainties in CME pointing relative to the Sun-Earth line affects the reliability of a forecast and how glancing blows become an issue for CME off-points greater than about the half width of the estimated input CME. While the basic results appear consistent with established impressions of CME behavior, the next step is to use existing records of well-observed CMEs at both Sun and Earth to verify that real events appear to follow the systematic tendencies presented in this study.

  10. Development of Ensemble Model Based Water Demand Forecasting Model

    NASA Astrophysics Data System (ADS)

    Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop

    2014-05-01

    In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)

  11. Ensemble hydrological forecast efficiency evolution over various issue dates and lead-time: case study for the Cheboksary reservoir (Volga River)

    NASA Astrophysics Data System (ADS)

    Gelfan, Alexander; Moreido, Vsevolod

    2017-04-01

    Ensemble hydrological forecasting allows for describing uncertainty caused by variability of meteorological conditions in the river basin for the forecast lead-time. At the same time, in snowmelt-dependent river basins another significant source of uncertainty relates to variability of initial conditions of the basin (snow water equivalent, soil moisture content, etc.) prior to forecast issue. Accurate long-term hydrological forecast is most crucial for large water management systems, such as the Cheboksary reservoir (the catchment area is 374 000 sq.km) located in the Middle Volga river in Russia. Accurate forecasts of water inflow volume, maximum discharge and other flow characteristics are of great value for this basin, especially before the beginning of the spring freshet season that lasts here from April to June. The semi-distributed hydrological model ECOMAG was used to develop long-term ensemble forecast of daily water inflow into the Cheboksary reservoir. To describe variability of the meteorological conditions and construct ensemble of possible weather scenarios for the lead-time of the forecast, two approaches were applied. The first one utilizes 50 weather scenarios observed in the previous years (similar to the ensemble streamflow prediction (ESP) procedure), the second one uses 1000 synthetic scenarios simulated by a stochastic weather generator. We investigated the evolution of forecast uncertainty reduction, expressed as forecast efficiency, over various consequent forecast issue dates and lead time. We analyzed the Nash-Sutcliffe efficiency of inflow hindcasts for the period 1982 to 2016 starting from 1st of March with 15 days frequency for lead-time of 1 to 6 months. This resulted in the forecast efficiency matrix with issue dates versus lead-time that allows for predictability identification of the basin. The matrix was constructed separately for observed and synthetic weather ensembles.

  12. Statistical Analysis of Ensemble Forecasts of Tropical Cyclone Tracks over the North Atlantic

    DTIC Science & Technology

    2012-06-01

    OF ENSEMBLE FORECASTS OF TROPICAL CYCLONE TRACKS OVER THE NORTH ATLANTIC by Christopher E. Nixon June 2012 Thesis Advisor: Patrick A...June 2012 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE Statistical Analysis of Ensemble Forecasts of Tropical Cyclone ...The skill of individual ensemble prediction systems (EPS) is evaluated in terms of the probability of a tropical cyclone (TC) track forecast being

  13. A Statistical Multimodel Ensemble Approach to Improving Long-Range Forecasting in Pakistan

    DTIC Science & Technology

    2012-03-01

    MULTIMODEL ENSEMBLE APPROACH TO IMPROVING LONG-RANGE FORECASTING IN PAKISTAN by Shane D. Gillies March 2012 Thesis Co-Advisors: Tom Murphree...DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE A Statistical Multimodel Ensemble Approach to Improving Long-Range Forecasting in Pakistan 5...was selected based on DoD and national intelligence priorities for long-range support. For this test case, the system uses 81 ensemble forecast

  14. Artificial intelligence based models for stream-flow forecasting: 2000-2015

    NASA Astrophysics Data System (ADS)

    Yaseen, Zaher Mundher; El-shafie, Ahmed; Jaafar, Othman; Afan, Haitham Abdulmohsin; Sayl, Khamis Naba

    2015-11-01

    The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.

  15. Using rainfall thresholds and ensemble precipitation forecasts to issue and improve urban inundation alerts

    NASA Astrophysics Data System (ADS)

    Yang, Tsun-Hua; Hwang, Gong-Do; Tsai, Chin-Cheng; Ho, Jui-Yi

    2016-11-01

    Urban inundation forecasting with extended lead times is useful in saving lives and property. This study proposes the integration of rainfall thresholds and ensemble precipitation forecasts to provide probabilistic urban inundation forecasts. Utilization of ensemble precipitation forecasts can extend forecast lead times to 72 h, predicting peak flows and to allow response agencies to take necessary preparatory measures. However, ensemble precipitation forecasting is time- and resource-intensive. Using rainfall thresholds to estimate urban areas' inundation risk can decrease this complexity and save computation time. This study evaluated the performance of this system using 352 townships in Taiwan and seven typhoons during the period 2013-2015. The levels of forecast probability needed to issue inundation alerts were addressed because ensemble forecasts are probability based. This study applied six levels of forecast probability and evaluated their performance using five measures. The results showed that this forecasting system performed better before a typhoon made landfall. Geography had a strong impact at the start of the numerical weather modeling, resulting in the underestimation of rainfall forecasts. Regardless of this finding, the inundation forecast performance was highly contingent on the rainfall forecast skill. This study then tested a hybrid approach of on-site observations and rainfall forecasts to decrease the influence of numerical weather predictions and improve the forecast performance. The results of this combined system showed that forecasts with a 24 h lead time improved significantly. These findings and the hybrid approach can be applied to other hydrometeorological early warning systems to improve hazard-related forecasts.

  16. Stochastic parameterization testing with NOAA's developmental Global Ensemble Forecast System

    NASA Astrophysics Data System (ADS)

    Hamill, Thomas M.

    2017-04-01

    In the next few years, the US National Weather Service will be switching the production of its global ensemble forecast system (GEFS) from the current spectrally based dynamical core to a finite-volume dynamical core (FV3). A suite of stochastic parameterizations, some developed at other centres, have been developed for the spectral and then adapted for the FV3 dynamical core. The stochastic parameterizations include the SPPT scheme developed at ECMWF and a stochastically perturbed boundary-layer humidity scheme (SHUM) developed within NOAA. The stochastic parameterizations appear more active in the FV3 developmental system with the same parameter settings used in the spectral-based system, and probabilistic skill scores are competitive with or better than with the old spectral core. This talk will review the particular implementation of the stochastic parameterizations in FV3, compare probabilistic forecasts between the old and new system, and discuss the underlying reasons for greater activity of stochastic parameterizations in FV3.

  17. Impacts of Forecasted Climate Change on Snowpack, Glacier Recession, and Streamflow in the Nooksack River Basin

    NASA Astrophysics Data System (ADS)

    Murphy, R. D.; Mitchell, R. J.; Bandaragoda, C.; Grah, O. J.

    2015-12-01

    Like many watersheds in the North Cascades Mountain range, streamflow in the Nooksack River is strongly influenced by precipitation and snowmelt in the spring and glacial melt in the warmer summer months. With a maritime climate and a high relief basin with glacial ice (3400 hectares), the streamflow response in the Nooksack is sensitive to increases in temperature, thus forecasting the basins response to future climate is of vital importance for water resources planning purposes. The watershed (2000 km2) in the northwest of Washington, USA, is a valuable freshwater resource for regional municipalities, industry, and agriculture, and provides critical habitat for endangered salmon species. Due to a lack of spatially distributed long-term historical weather observations in the basin for downscaling purposes, we apply publically available statistically derived 1/16 degree gridded surface data along with the Distributed Hydrology Soil Vegetation Model (DHSVM; Wigmosta et al., 1992) with newly developed coupled dynamic glacier model (Clarke et al., 2015) to simulate hydrologic processes in the Nooksack River basin. We calibrate and validate the DHSVM to observed glacial mass balance and glacial ice extent as well as to observed daily streamflow and SNOTEL data in the Nooksack basin. For the historical period, we model using a gridded meteorological forcing data set (1950-2010; Livneh et al., 2013). We simulate forecasted climate change impacts, including glacial recession on streamflow, using gridded daily statically downscaled data from global climate models of the CMIP5 with RCP4.5 and RCP8.5 forcing scenarios developed using the multivariate adaptive constructed analogs method (Abatzoglou and Brown, 2011). Simulation results project an increase in winter streamflows due to more rainfall rather than snow, and a decrease in summer flows with a general shift in peak spring flows toward earlier in the spring. Glacier melt contribution to streamflow initially increases

  18. Storm Surge Simulation and Ensemble Forecast for Hurricane Irene (2011)

    NASA Astrophysics Data System (ADS)

    Lin, N.; Emanuel, K.

    2012-12-01

    Hurricane Irene, raking the U.S. East Coast during the period of 26-30 August 2011, caused widespread damage estimated at $15.8 billion and was responsible for 49 direct deaths (Avila and Cangialosi, 2011). Although the most severe impact in the northeastern U.S. was catastrophic inland flooding, with its unusually large size, Irene also generated high waves and storm surges and caused moderate to major coastal flooding. The most severe surge damage occurred between Oregon Inlet and Cape Hatteras in North Carolina (NC). Significant storm surge damage also occurred along southern Chesapeake Bay, and moderate and high surges were observed along the coast from New Jersey (NJ) northward. A storm surge of 0.9-1.8 m caused hundreds of millions of dollars in property damage in New York City (NYC) and Long Island, despite the fact that the storm made landfall to the west of NYC with peak winds of no more than tropical storm strength. Making three U.S. landfalls (in NC, NJ, and NY), Hurricane Irene provides a unique case for studying storm surge along the eastern U.S. coastline. We apply the hydrodynamic model ADCIRC (Luettich et al. 1992) to conduct surge simulations for Pamlico Sound, Chesapeake Bay, and NYC, using best track data and parametric wind and pressure models. The results agree well with tidal-gauge observations. Then we explore a new methodology for storm surge ensemble forecasting and apply it to Irene. This method applies a statistical/deterministic hurricane model (Emanuel et al. 2006) to generate large numbers of storm ensembles under the storm environment described by the 51 ECMWF ensemble members. The associated surge ensembles are then generated with the ADCIRC model. The numerical simulation is computationally efficient, making the method applicable to real-time storm surge ensemble forecasting. We report the results for NYC in this presentation. The ADCIRC simulation using the best track data generates a storm surge of 1.3 m and a storm tide of 2.1 m

  19. Linking seasonal streamflow forecast performance to operational value in emergency response settings

    NASA Astrophysics Data System (ADS)

    Turner, Sean; Bennett, James; Robertson, David; Galelli, Stefano

    2017-04-01

    In this work we investigate the operational value of seasonal streamflow forecasts for water reservoirs. We consider two different operating settings, namely continually adjusted and emergency response operations—the former represents systems for which the water release decisions are adjusted at frequent intervals, while the latter is typical of systems for which key management decisions are made relatively infrequently (e.g., urban water supply systems). We define the operational value as the difference between the long-term operating objective provided by forecast-informed operations—a rolling horizon, adaptive control approach—and more traditional operating rules. In a first set of experiments carried out in four contrasting catchments located in Australia, we use synthetic forecasts of different quality to show that the operational value increases with the forecast skill for continuously adjusted operations. By contrast, we find a weaker relation between forecast skill and value for emergency response settings. In a second set of experiments carried out with a modern experimental forecasting systems, called Forecast Guided Stochastic Scenarios (FoGSS), we show that the forecast value for emergency response operations can be largely explained by the forecast skill at the time during which critical release decisions are made.

  20. Streamflow forecasts on seasonal and interannual time scales for reservoir management

    NASA Astrophysics Data System (ADS)

    Robertson, A. W.; Lu, M.; Lall, U.

    2014-12-01

    Seasonal climate forecasts are beginning to be complemented by improved forecasting capabilities at both sub-seasonal and interannual annual timescales, with the future prospect of seamless climate forecasts for water system operations. While seasonal predictability is often very limited by physical and modeling constraints, harnessing additional predictable components of the climate system may in some cases substantially increase their usable information content, and provide more flexible forecasts in terms of the kinds of management decisions that can be informed. Here we present an example of combining season and year-ahead streamflow forecasts as input to a multi-use reservoir optimization model, applied to the Bhakra Dam in NW India. Bi-timescale forecasts are made with a seasonal periodic autoregressive (PAR) model with exogenous climate-forecast inputs, together with an annual PAR model fit to observed flows used as a baseline for year-ahead forecasts. Annual net revenue from irrigation and hydropower supplies are calculated with contracts optimized using the reservoir optimization model. With Bhakra Dam inflows deriving from both winter storms/snow melt and the summer monsoon, it is found that net annual revenue is maximized when new contracts are initiated in March and June. We explore various choices of PARX model seasonal predictors based on climate model output and data and show that, with the choice of a good start date, even forecasts with relatively low skill can have value.

  1. Ovis: A Framework for Visual Analysis of Ocean Forecast Ensembles.

    PubMed

    Höllt, Thomas; Magdy, Ahmed; Zhan, Peng; Chen, Guoning; Gopalakrishnan, Ganesh; Hoteit, Ibrahim; Hansen, Charles D; Hadwiger, Markus

    2014-08-01

    We present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations of the sea surface height that is used in ocean forecasting. The position of eddies can be derived directly from the sea surface height and our visualization approach enables their interactive exploration and analysis.The behavior of eddies is important in different application settings of which we present two in this paper. First, we show an application for interactive planning of placement as well as operation of off-shore structures using real-world ensemble simulation data of the Gulf of Mexico. Off-shore structures, such as those used for oil exploration, are vulnerable to hazards caused by eddies, and the oil and gas industry relies on ocean forecasts for efficient operations. We enable analysis of the spatial domain, as well as the temporal evolution, for planning the placement and operation of structures.Eddies are also important for marine life. They transport water over large distances and with it also heat and other physical properties as well as biological organisms. In the second application we present the usefulness of our tool, which could be used for planning the paths of autonomous underwater vehicles, so called gliders, for marine scientists to study simulation data of the largely unexplored Red Sea.

  2. Long-lead probabilistic forecasting of streamflow using ocean-atmospheric and hydrological predictors

    NASA Astrophysics Data System (ADS)

    Araghinejad, Shahab; Burn, Donald H.; Karamouz, Mohammad

    2006-03-01

    A geostatistically based approach with a local regression method is used to predict the magnitude of seasonal streamflow using ocean-atmospheric signals and the hydrological condition of a basin as predictors. The model characterizes the stochastic behavior of a forecast variable by generating a conditional distribution of the predicted value for different hydroclimatic conditions. The correlation structure between dependent and independent variables is represented by the variography of the predicted values in which the distance variable in the variogram is determined by measuring the distance between the predictors. This variogram in a virtual field constructed from the predictors makes it possible to predict variables as unmeasured points while considering historic information as measurement points of the field. Different types of kriging, as well as a generalized linear model regression, are used to predict data in interpolation and extrapolation modes. The forecast skill is evaluated using a linear error in probability space score for different combinations of predictors and different kriging methods. The method is applied to a case study of the Zayandeh-rud River in Isfahan, Iran. The utility of the method is demonstrated for forecasting autumn-winter and spring streamflow using the Southern Oscillation Index, the North Atlantic Oscillation, serial correlation between seasonal streamflow series, and the snow budget. The study analyzes the application of the proposed method in comparison with a K-nearest neighbor regression method. The results of this study show that the proposed method can significantly improve the long-lead probabilistic forecast skill for a nonlinear relationship between hydroclimatic predictors and streamflow in a region.

  3. A new view of seasonal forecast skill: bounding boxes from the DEMETER ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Weisheimer, Antje; Smith, Leonard A.; Judd, Kevin

    2005-05-01

    Insight into the likely weather several months in advance would be of great economic and societal value. The DEMETER project has made coordinated multi-model, multi-initial-condition simulations of the global weather as observed over the last 40 years; transforming these model simulations into forecasts is non-trivial. One approach is to extract merely a single forecast (e.g. best-first-guess) designed to minimize some measure of forecast error. A second approach would be to construct a full probability forecast. This paper explores a third option, namely to see how often this collection of simulations can be said to capture the target value, in the sense that the target lies within the bounding box of the forecasts. The DEMETER forecast system is shown to often capture the 2-m temperature target in this sense over continental areas at lead times up to six months. The target is captured over 95% of the time at over a third of the grid points and maintains a bounding box range less than that of the local climatology. Such information is of immediate value from a user's perspective. Implications for the minimum ensemble size as well as open foundational issues in translating a set of multi-model multi-initial-condition simulations into a forecast are discussed; in particular, those involving 'bias correction' are considered.

  4. Artificial Neural Network Models for Long Lead Streamflow Forecasts using Climate Information

    NASA Astrophysics Data System (ADS)

    Kumar, J.; Devineni, N.

    2007-12-01

    Information on season ahead stream flow forecasts is very beneficial for the operation and management of water supply systems. Daily streamflow conditions at any particular reservoir primarily depend on atmospheric and land surface conditions including the soil moisture and snow pack. On the other hand recent studies suggest that developing long lead streamflow forecasts (3 months ahead) typically depends on exogenous climatic conditions particularly Sea Surface Temperature conditions (SST) in the tropical oceans. Examples of some oceanic variables are El Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). Identification of such conditions that influence the moisture transport into a given basin poses many challenges given the nonlinear dependency between the predictors (SST) and predictand (stream flows). In this study, we apply both linear and nonlinear dependency measures to identify the predictors that influence the winter flows into the Neuse basin. The predictor identification approach here adopted uses simple correlation coefficients to spearman rank correlation measures for detecting nonlinear dependency. All these dependency measures are employed with a lag 3 time series of the high flow season (January - February - March) using 75 years (1928-2002) of stream flows recorded in to the Falls Lake, Neuse River Basin. Developing streamflow forecasts contingent on these exogenous predictors will play an important role towards improved water supply planning and management. Recently, the soft computing techniques, such as artificial neural networks (ANNs) have provided an alternative method to solve complex problems efficiently. ANNs are data driven models which trains on the examples given to it. The ANNs functions as universal approximators and are non linear in nature. This paper presents a study aiming towards using climatic predictors for 3 month lead time streamflow forecast. ANN models representing the physical process of the system are

  5. Two-stage seasonal streamflow forecasts to guide water resources decisions and water rights allocation

    NASA Astrophysics Data System (ADS)

    Block, P. J.; Gonzalez, E.; Bonnafous, L.

    2011-12-01

    Decision-making in water resources is inherently uncertain producing copious risks, ranging from operational (present) to planning (season-ahead) to design/adaptation (decadal) time-scales. These risks include human activity and climate variability/change. As the risks in designing and operating water systems and allocating available supplies vary systematically in time, prospects for predicting and managing such risks become increasingly attractive. Considerable effort has been undertaken to improve seasonal forecast skill and advocate for integration to reduce risk, however only minimal adoption is evident. Impediments are well defined, yet tailoring forecast products and allowing for flexible adoption assist in overcoming some obstacles. The semi-arid Elqui River basin in Chile is contending with increasing levels of water stress and demand coupled with insufficient investment in infrastructure, taxing its ability to meet agriculture, hydropower, and environmental requirements. The basin is fed from a retreating glacier, with allocation principles founded on a system of water rights and markets. A two-stage seasonal streamflow forecast at leads of one and two seasons prescribes the probability of reductions in the value of each water right, allowing water managers to inform their constituents in advance. A tool linking the streamflow forecast to a simple reservoir decision model also allows water managers to select a level of confidence in the forecast information.

  6. A short-term ensemble wind speed forecasting system for wind power applications

    NASA Astrophysics Data System (ADS)

    Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

    2011-12-01

    This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

  7. Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling

    NASA Astrophysics Data System (ADS)

    Galelli, S.; Castelletti, A.

    2013-07-01

    Combining randomization methods with ensemble prediction is emerging as an effective option to balance accuracy and computational efficiency in data-driven modelling. In this paper, we investigate the prediction capability of extremely randomized trees (Extra-Trees), in terms of accuracy, explanation ability and computational efficiency, in a streamflow modelling exercise. Extra-Trees are a totally randomized tree-based ensemble method that (i) alleviates the poor generalisation property and tendency to overfitting of traditional standalone decision trees (e.g. CART); (ii) is computationally efficient; and, (iii) allows to infer the relative importance of the input variables, which might help in the ex-post physical interpretation of the model. The Extra-Trees potential is analysed on two real-world case studies - Marina catchment (Singapore) and Canning River (Western Australia) - representing two different morphoclimatic contexts. The evaluation is performed against other tree-based methods (CART and M5) and parametric data-driven approaches (ANNs and multiple linear regression). Results show that Extra-Trees perform comparatively well to the best of the benchmarks (i.e. M5) in both the watersheds, while outperforming the other approaches in terms of computational requirement when adopted on large datasets. In addition, the ranking of the input variable provided can be given a physically meaningful interpretation.

  8. Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling

    NASA Astrophysics Data System (ADS)

    Galelli, S.; Castelletti, A.

    2013-02-01

    Combining randomization methods with ensemble prediction is emerging as an effective option to balance accuracy and computational efficiency in data-driven modeling. In this paper we investigate the prediction capability of extremely randomized trees (Extra-Trees), in terms of accuracy, explanation ability and computational efficiency, in a streamflow modeling exercise. Extra-Trees are a totally randomized tree-based ensemble method that (i) alleviates the poor generalization property and tendency to overfitting of traditional standalone decision trees (e.g. CART); (ii) is computationally very efficient; and, (iii) allows to infer the relative importance of the input variables, which might help in the ex-post physical interpretation of the model. The Extra-Trees potential is analyzed on two real-world case studies (Marina catchment (Singapore) and Canning River (Western Australia)) representing two different morphoclimatic contexts comparatively with other tree-based methods (CART and M5) and parametric data-driven approaches (ANNs and multiple linear regression). Results show that Extra-Trees perform comparatively well to the best of the benchmarks (i.e. M5) in both the watersheds, while outperforming the other approaches in terms of computational requirement when adopted on large datasets. In addition, the ranking of the input variable provided can be given a physically meaningful interpretation.

  9. The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia

    NASA Astrophysics Data System (ADS)

    Pokhrel, Prafulla; Wang, Q. J.; Robertson, David E.

    2013-10-01

    Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the "best" model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with different lagged climate indices as predictors, to take advantage of different predictive strengths of the multiple models. The second strategy is to introduce additional candidate models, using rainfall and sea surface temperature predictions from a global climate model as predictors. This is to take advantage of the direct simulations of various dynamic processes. The results show that combining forecasts from multiple statistical models generally yields more skillful forecasts than using only the best model and appears to moderate the worst forecast errors. The use of rainfall predictions from the dynamical climate model marginally improves the streamflow forecasts when viewed over all the study catchments and seasons, but the use of sea surface temperature predictions provide little additional benefit.

  10. Using oceanic-atmospheric oscillations for long lead time streamflow forecasting

    NASA Astrophysics Data System (ADS)

    Kalra, Ajay; Ahmad, Sajjad

    2009-03-01

    We present a data-driven model, Support Vector Machine (SVM), for long lead time streamflow forecasting using oceanic-atmospheric oscillations. The SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach and has been used to predict a quantity forward in time on the basis of training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. The SVM model is applied to three gages, i.e., Cisco, Green River, and Lees Ferry in the Upper Colorado River Basin in the western United States. Annual oceanic-atmospheric indices, comprising Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and El Nino-Southern Oscillations (ENSO) for a period of 1906-2001 are used to generate annual streamflow volumes with 3 years lead time. The SVM model is trained with 86 years of data (1906-1991) and tested with 10 years of data (1992-2001). On the basis of correlation coefficient, root means square error, and Nash Sutcliffe Efficiency Coefficient the model shows satisfactory results, and the predictions are in good agreement with measured streamflow volumes. Sensitivity analysis, performed to evaluate the effect of individual and coupled oscillations, reveals a strong signal for ENSO and NAO indices as compared to PDO and AMO indices for the long lead time streamflow forecast. Streamflow predictions from the SVM model are found to be better when compared with the predictions obtained from feedforward back propagation artificial neural network model and linear regression.

  11. Systematic evaluation of autoregressive error models as post-processors for a probabilistic streamflow forecast system

    NASA Astrophysics Data System (ADS)

    Morawietz, Martin; Xu, Chong-Yu; Gottschalk, Lars; Tallaksen, Lena

    2010-05-01

    A post-processor that accounts for the hydrologic uncertainty in a probabilistic streamflow forecast system is necessary to account for the uncertainty introduced by the hydrological model. In this study different variants of an autoregressive error model that can be used as a post-processor for short to medium range streamflow forecasts, are evaluated. The deterministic HBV model is used to form the basis for the streamflow forecast. The general structure of the error models then used as post-processor is a first order autoregressive model of the form dt = αdt-1 + σɛt where dt is the model error (observed minus simulated streamflow) at time t, α and σ are the parameters of the error model, and ɛt is the residual error described through a probability distribution. The following aspects are investigated: (1) Use of constant parameters α and σ versus the use of state dependent parameters. The state dependent parameters vary depending on the states of temperature, precipitation, snow water equivalent and simulated streamflow. (2) Use of a Standard Normal distribution for ɛt versus use of an empirical distribution function constituted through the normalized residuals of the error model in the calibration period. (3) Comparison of two different transformations, i.e. logarithmic versus square root, that are applied to the streamflow data before the error model is applied. The reason for applying a transformation is to make the residuals of the error model homoscedastic over the range of streamflow values of different magnitudes. Through combination of these three characteristics, eight variants of the autoregressive post-processor are generated. These are calibrated and validated in 55 catchments throughout Norway. The discrete ranked probability score with 99 flow percentiles as standardized thresholds is used for evaluation. In addition, a non-parametric bootstrap is used to construct confidence intervals and evaluate the significance of the results. The main

  12. A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network

    NASA Astrophysics Data System (ADS)

    Humphrey, Greer B.; Gibbs, Matthew S.; Dandy, Graeme C.; Maier, Holger R.

    2016-09-01

    Monthly streamflow forecasts are needed to support water resources decision making in the South East of South Australia, where baseflow represents a significant proportion of the total streamflow and soil moisture and groundwater are important predictors of runoff. To address this requirement, the utility of a hybrid monthly streamflow forecasting approach is explored, whereby simulated soil moisture from the GR4J conceptual rainfall-runoff model is used to represent initial catchment conditions in a Bayesian artificial neural network (ANN) statistical forecasting model. To assess the performance of this hybrid forecasting method, a comparison is undertaken of the relative performances of the Bayesian ANN, the GR4J conceptual model and the hybrid streamflow forecasting approach for producing 1-month ahead streamflow forecasts at three key locations in the South East of South Australia. Particular attention is paid to the quantification of uncertainty in each of the forecast models and the potential for reducing forecast uncertainty by using the hybrid approach is considered. Case study results suggest that the hybrid models developed in this study are able to take advantage of the complementary strengths of both the ANN models and the GR4J conceptual models. This was particularly the case when forecasting high flows, where the hybrid models were shown to outperform the two individual modelling approaches in terms of the accuracy of the median forecasts, as well as reliability and resolution of the forecast distributions. In addition, the forecast distributions generated by the hybrid models were up to 8 times more precise than those based on climatology; thus, providing a significant improvement on the information currently available to decision makers.

  13. Evaluation of Precipitation Ensemble Forecasts at Environmental Modeling Center (EMC) of NCEP

    NASA Astrophysics Data System (ADS)

    Luo, Y.; Zhu, Y.

    2016-12-01

    A verification framework for evaluating the precipitation forecast performance of a global ensemble system was developed at NECP/EMC. The verification framework was initially applied to the case of NCEP global ensemble forecast system (GEFS) and over CONUS. The 24- hour accumulated precipitation forecasts out to 15days are verified against the Climatology-Calibrated Precipitation Analysis (CCPA). CCPA is a high resolution data set of precipitation analysis generated by statistical adjusting Stage IV toward CPC gauge-based analysis. A variety of performance metrics have been used to evaluate the accuracy and probabilistic skill of precipitation forecast. It produces not only deterministic and categorical scores including traditional error statistics and contingency table statistics for the ensemble mean, but also probabilistic skill scores such as Brier Skill Score and reliability diagram. The forecast skill is calculated relative to a reference from a 10-year CCPA daily climatology data. The framework has been also expanded to evaluate precipitation forecasts of a global ensemble system from several other world forecast centers. In this presentation, this framework is demonstrated from its application to the routine evaluation of ensemble forecasts from these centers since 2013 and the assessment of changes in ensemble performance from every GEFS upgrade. Based on available observational and forecast archives, an inter-comparison study among these centers is performed using this framework for a period of 2013-present. The verification methods included in this framework are found to give useful and detailed information about the forecasts performance.

  14. Evaluation of climate modeling factors impacting the variance of streamflow

    NASA Astrophysics Data System (ADS)

    Al Aamery, N.; Fox, J. F.; Snyder, M.

    2016-11-01

    The present contribution quantifies the relative importance of climate modeling factors and chosen response variables upon controlling the variance of streamflow forecasted with global climate model (GCM) projections, which has not been attempted in previous literature to our knowledge. We designed an experiment that varied climate modeling factors, including GCM type, project phase, emission scenario, downscaling method, and bias correction. The streamflow response variable was also varied and included forecasted streamflow and difference in forecast and hindcast streamflow predictions. GCM results and the Soil Water Assessment Tool (SWAT) were used to predict streamflow for a wet, temperate watershed in central Kentucky USA. After calibrating the streamflow model, 112 climate realizations were simulated within the streamflow model and then analyzed on a monthly basis using analysis of variance. Analysis of variance results indicate that the difference in forecast and hindcast streamflow predictions is a function of GCM type, climate model project phase, and downscaling approach. The prediction of forecasted streamflow is a function of GCM type, project phase, downscaling method, emission scenario, and bias correction method. The results indicate the relative importance of the five climate modeling factors when designing streamflow prediction ensembles and quantify the reduction in uncertainty associated with coupling the climate results with the hydrologic model when subtracting the hindcast simulations. Thereafter, analysis of streamflow prediction ensembles with different numbers of realizations show that use of all available realizations is unneeded for the study system, so long as the ensemble design is well balanced. After accounting for the factors controlling streamflow variance, results show that predicted average monthly change in streamflow tends to follow precipitation changes and result in a net increase in the average annual precipitation and

  15. Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system

    NASA Astrophysics Data System (ADS)

    Wei, Mozheng; Toth, Zoltan; Wobus, Richard; Zhu, Yuejian

    2008-01-01

    Since modern data assimilation (DA) involves the repetitive use of dynamical forecasts, errors in analyses share characteristics of those in short-range forecasts. Initial conditions for an ensemble prediction/forecast system (EPS or EFS) are expected to sample uncertainty in the analysis field. Ensemble forecasts with such initial conditions can therefore (a) be fed back to DA to reduce analysis uncertainty, as well as (b) sample forecast uncertainty related to initial conditions. Optimum performance of both DA and EFS requires a careful choice of initial ensemble perturbations. DA can be improved with an EFS that represents the dynamically conditioned part of forecast error covariance as accurately as possible, while an EFS can be improved by initial perturbations reflecting analysis error variance. Initial perturbation generation schemes that dynamically cycle ensemble perturbations reminiscent to how forecast errors are cycled in DA schemes may offer consistency between DA and EFS, and good performance for both. In this paper, we introduce an EFS based on the initial perturbations that are generated by the Ensemble Transform (ET) and ET with rescaling (ETR) methods to achieve this goal. Both ET and ETR are generalizations of the breeding method (BM). The results from ensemble systems based on BM, ET, ETR and the Ensemble Transform Kalman Filter (ETKF) method are experimentally compared in the context of ensemble forecast performance. Initial perturbations are centred around a 3D-VAR analysis, with a variance equal to that of estimated analysis errors. Of the four methods, the ETR method performed best in most probabilistic scores and in terms of the forecast error explained by the perturbations. All methods display very high time consistency between the analysis and forecast perturbations. It is expected that DA performance can be improved by the use of forecast error covariance from a dynamically cycled ensemble either with a variational DA approach (coupled

  16. Exploring the calibration of a wind forecast ensemble for energy applications

    NASA Astrophysics Data System (ADS)

    Heppelmann, Tobias; Ben Bouallegue, Zied; Theis, Susanne

    2015-04-01

    In the German research project EWeLiNE, Deutscher Wetterdienst (DWD) and Fraunhofer Institute for Wind Energy and Energy System Technology (IWES) are collaborating with three German Transmission System Operators (TSO) in order to provide the TSOs with improved probabilistic power forecasts. Probabilistic power forecasts are derived from probabilistic weather forecasts, themselves derived from ensemble prediction systems (EPS). Since the considered raw ensemble wind forecasts suffer from underdispersiveness and bias, calibration methods are developed for the correction of the model bias and the ensemble spread bias. The overall aim is to improve the ensemble forecasts such that the uncertainty of the possible weather deployment is depicted by the ensemble spread from the first forecast hours. Additionally, the ensemble members after calibration should remain physically consistent scenarios. We focus on probabilistic hourly wind forecasts with horizon of 21 h delivered by the convection permitting high-resolution ensemble system COSMO-DE-EPS which has become operational in 2012 at DWD. The ensemble consists of 20 ensemble members driven by four different global models. The model area includes whole Germany and parts of Central Europe with a horizontal resolution of 2.8 km and a vertical resolution of 50 model levels. For verification we use wind mast measurements around 100 m height that corresponds to the hub height of wind energy plants that belong to wind farms within the model area. Calibration of the ensemble forecasts can be performed by different statistical methods applied to the raw ensemble output. Here, we explore local bivariate Ensemble Model Output Statistics at individual sites and quantile regression with different predictors. Applying different methods, we already show an improvement of ensemble wind forecasts from COSMO-DE-EPS for energy applications. In addition, an ensemble copula coupling approach transfers the time-dependencies of the raw

  17. Ocean state and uncertainty forecasts using HYCOM with Local Ensemble Transfer Kalman Filter (LETKF)

    NASA Astrophysics Data System (ADS)

    Wei, Mozheng; Hogan, Pat; Rowley, Clark; Smedstad, Ole-Martin; Wallcraft, Alan; Penny, Steve

    2017-04-01

    An ensemble forecast system based on the US Navy's operational HYCOM using Local Ensemble Transfer Kalman Filter (LETKF) technology has been developed for ocean state and uncertainty forecasts. One of the advantages is that the best possible initial analysis states for the HYCOM forecasts are provided by the LETKF which assimilates the operational observations using ensemble method. The background covariance during this assimilation process is supplied with the ensemble, thus it avoids the difficulty of developing tangent linear and adjoint models for 4D-VAR from the complicated hybrid isopycnal vertical coordinate in HYCOM. Another advantage is that the ensemble system provides the valuable uncertainty estimate corresponding to every state forecast from HYCOM. Uncertainty forecasts have been proven to be critical for the downstream users and managers to make more scientifically sound decisions in numerical prediction community. In addition, ensemble mean is generally more accurate and skilful than the single traditional deterministic forecast with the same resolution. We will introduce the ensemble system design and setup, present some results from 30-member ensemble experiment, and discuss scientific, technical and computational issues and challenges, such as covariance localization, inflation, model related uncertainties and sensitivity to the ensemble size.

  18. Advancing the cyberinfrastructure for sustaining high resolution, real-time streamflow and flood forecasts at a national scale

    NASA Astrophysics Data System (ADS)

    Arctur, D. K.; Maidment, D. R.; Clark, E. P.; Gochis, D. J.; Somos-Valenzuela, M. A.; Salas, F. R.; Nelson, J.

    2015-12-01

    In just the last year, it has become feasible to generate and refresh national 15-hour forecasts of streamflow and flood inundation, every hour at high resolution (average 3km stream segments), based on a workflow integrating US National Weather Service forecasts, the WRF-Hydro land surface model, the RAPID streamflow routing model, and other models. This capability has come about through a collaboration of numerous agencies, academic research and data centers, and commercial software vendors. This presentation provides insights and lessons learned for the development and evolution of a scalable architecture for water observations and forecasts that should be sustained operationally.

  19. Observation impact in short-range ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Necker, Tobias; Weissmann, Martin; Sommer, Matthias

    2017-04-01

    Observation impact assessment offers a great potential for convective-scale data assimilation. It provides information on the contribution of various observations to the observing system and is crucial for the refinement of the observing network as well as the data assimilation system. In the framework of the Hans-Ertel Centre for Weather Research (HErZ), a method for an ensemble-based approximation of observation impact using an observation-based verification metric was developed over the past years. Instead of the subsequent analysis, the method uses subsequent observations for verification that are considerably more independent from the forecast. Recently, the method was adapted to use independent observation types for verification. Results of the impact assessment using radar-derived precipitation observations for verification are presented. Furthermore the impact time of different observation types is investigated. The study covers the high impact weather period in summer 2016 using the pre-operational convective-scale ensemble system of Deutscher Wetterdienst (KENDA/COSMO-DE).

  20. Statistical ensemble postprocessing for precipitation forecasting during the West African Monsoon

    NASA Astrophysics Data System (ADS)

    Vogel, Peter; Gneiting, Tilmann; Knippertz, Peter; Fink, Andreas H.; Schlüter, Andreas

    2017-04-01

    Precipitation forecasts for one up to several days are of high socioeconomic importance for agriculturally dominated societies in West Africa. In this contribution, we evaluate the performance of operational European Centre for Medium-Range Weather Forecasts (ECWMF) raw ensemble and statistically postprocessed against climatological precipitation forecasts for accumulation periods of 1 to 5 days for the monsoon periods (May to mid-October) from 2007 to 2014. We use Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS) as state-of-the-art postprocessing methods and verify against station and gridded Tropical Rainfall Measuring Mission (TRMM) observations. Based on a subset of past forecast—observation-pairs, statistical postprocessing corrects ensemble forecasts for biases and dispersion errors. For the midlatitudes, statistical postprocessing has demonstrated its added value for a wide range of meteorological quantities and this contribution is the first to apply it to precipitation forecasts over West Africa, where the high degree of convective organization at the mesoscale makes precipitation forecasts particularly challenging. The raw ECMWF ensemble predictions of accumulated precipitation are poor compared to climatological forecasts and exhibit strong dispersion errors and biases. For the Guinea Coast, we find a substantial wet bias of the ECMWF ensemble and more than every second ensemble forecast fails to capture the verifying observation within its forecast range. Postprocessed forecasts clearly outperform ECMWF raw ensemble forecasts by correcting for biases and dispersion errors, but disappointingly reveal only slight, if any, improvements compared to climatological forecasts. These results hold across verification regions and years, for 1 to 5-day accumulated precipitation forecasts, and for station and gridded observations. We investigate different spatial accumulation sizes from 0.25 x 0.25° to 5 x 2° longitude

  1. Dynamically downscaled multi-model ensemble seasonal forecasts over Ethiopia

    NASA Astrophysics Data System (ADS)

    Asharaf, Shakeel; Fröhlich, Kristina; Fernandez, Jesus; Cardoso, Rita; Nikulin, Grigory; Früh, Barbara

    2016-04-01

    Truthful and reliable seasonal rainfall predictions have an important social and economic value for the east African countries as their economy is highly dependent on rain-fed agriculture and pastoral systems. Only June to September (JJAS) seasonal rainfall accounts to more than 80% crop production in Ethiopia. Hence, seasonal foresting is a crucial concern for the region. The European Provision of Regional Impact Assessment on a seasonal to decadal timescale (EUPORIAS) project offers a common framework to understand hindcast uncertainties through the use of multi-model and multi-member simulations over east Africa. Under this program, the participating regional climate models (RCMs) were driven by the atmospheric-only version of the ECEARTH global climate model, which provides hindcasts of a five-months period (May to September) from 1991-2012. In this study the RCMs downscaled rainfall is evaluated with respect to the observed JJAS rainfall over Ethiopia. Both deterministic and probabilistic based forecast skills are assessed. Our preliminary results show the potential usefulness of multi-model ensemble simulations in forecasting the seasonal rainfall over the region.

  2. Streamflow forecasting using the modular modeling system and an object-user interface

    USGS Publications Warehouse

    Jeton, A.E.

    2001-01-01

    The U.S. Geological Survey (USGS), in cooperation with the Bureau of Reclamation (BOR), developed a computer program to provide a general framework needed to couple disparate environmental resource models and to manage the necessary data. The Object-User Interface (OUI) is a map-based interface for models and modeling data. It provides a common interface to run hydrologic models and acquire, browse, organize, and select spatial and temporal data. One application is to assist river managers in utilizing streamflow forecasts generated with the Precipitation-Runoff Modeling System running in the Modular Modeling System (MMS), a distributed-parameter watershed model, and the National Weather Service Extended Streamflow Prediction (ESP) methodology.

  3. Multi-objective Optimization Based Calibration of Hydrologic Model and Ensemble Hydrologic Forecast for Java Island, Indonesia

    NASA Astrophysics Data System (ADS)

    Yanto, M.; Kasprzyk, J. R.; Rajagopalan, B.; Livneh, B.

    2016-12-01

    This study explores the benefits of multi-objective optimization of Variable Infiltration Capacity (VIC) model for five watersheds in Java, the most populous island in Indonesia. Six objective functions: Nash Sutcliffe Efficiency (NSE), percent bias (PBIAS), logarithmic function of root mean square error (Log-RMSE), predictive efficiency (Pe), percent errors in peak (PEP) and slope of flow duration curve error (SFDCE) were selected as evaluation metrics. These metrics were optimized by tuning four VIC model parameters: infiltration shape parameter (b), fraction of maximum baseflow where nonlinear baseflow begin (Ds), thickness of soil layer 2 (thick2) and thickness of soil layer 3 (thick3). We employed Borg Multiobjective Evolutionary Algorithm (Borg MOEA), an automatic simulation-optimization algorithm, to search for non-dominated solutions. We identified the redundancy between NSE and Log-RMSE, Pe, and PEP through visual inspection of their sensitivity to parameters b and Ds of VIC model and to baseflow index (BFI). Accordingly, we proposed NSE, PBIAS and SFDCE as critical objective functions to represent hydrologic processes in tropical region of Java, Indonesia. Using these three objective functions, we culled the objective functions based on at least - NSE > 0.75, PBIAS < 15% and SFDCE < 15% - and showed that the number of optimized objective functions as well as model parameters in the ensemble are reduced but the value ranges are maintained. We used the culled model parameters, to run the VIC model using an ensemble of conditioned seasonal climate forecast to generate an ensemble streamflow prediction in the period 2001 - 2010, the time window when the seasonal climate forecasts and observed streamflow records overlaps. We measured the skill of this seasonal forecast by computing the rank probability skill score (RPSS) of seasonal total flows and extremes at three different thresholds, for the dry and wet seasons. We showed that the RPSS of seasonal flows

  4. Comparison of the performance and reliability of 18 lumped hydrological models driven by ECMWF rainfall ensemble forecasts: a case study on 29 French catchments

    NASA Astrophysics Data System (ADS)

    Velázquez, Juan Alberto; Anctil, François; Ramos, Maria-Helena; Perrin, Charles

    2010-05-01

    An ensemble forecasting system seeks to assess and to communicate the uncertainty of hydrological predictions by proposing, at each time step, an ensemble of forecasts from which one can estimate the probability distribution of the predictant (the probabilistic forecast), in contrast with a single estimate of the flow, for which no distribution is obtainable (the deterministic forecast). In the past years, efforts towards the development of probabilistic hydrological prediction systems were made with the adoption of ensembles of numerical weather predictions (NWPs). The additional information provided by the different available Ensemble Prediction Systems (EPS) was evaluated in a hydrological context on various case studies (see the review by Cloke and Pappenberger, 2009). For example, the European ECMWF-EPS was explored in case studies by Roulin et al. (2005), Bartholmes et al. (2005), Jaun et al. (2008), and Renner et al. (2009). The Canadian EC-EPS was also evaluated by Velázquez et al. (2009). Most of these case studies investigate the ensemble predictions of a given hydrological model, set up over a limited number of catchments. Uncertainty from weather predictions is assessed through the use of meteorological ensembles. However, uncertainty from the tested hydrological model and statistical robustness of the forecasting system when coping with different hydro-meteorological conditions are less frequently evaluated. The aim of this study is to evaluate and compare the performance and the reliability of 18 lumped hydrological models applied to a large number of catchments in an operational ensemble forecasting context. Some of these models were evaluated in a previous study (Perrin et al. 2001) for their ability to simulate streamflow. Results demonstrated that very simple models can achieve a level of performance almost as high (sometimes higher) as models with more parameters. In the present study, we focus on the ability of the hydrological models to

  5. Verification of Ensemble Forecasts for the New York City Operations Support Tool

    NASA Astrophysics Data System (ADS)

    Day, G.; Schaake, J. C.; Thiemann, M.; Draijer, S.; Wang, L.

    2012-12-01

    The New York City water supply system operated by the Department of Environmental Protection (DEP) serves nine million people. It covers 2,000 square miles of portions of the Catskill, Delaware, and Croton watersheds, and it includes nineteen reservoirs and three controlled lakes. DEP is developing an Operations Support Tool (OST) to support its water supply operations and planning activities. OST includes historical and real-time data, a model of the water supply system complete with operating rules, and lake water quality models developed to evaluate alternatives for managing turbidity in the New York City Catskill reservoirs. OST will enable DEP to manage turbidity in its unfiltered system while satisfying its primary objective of meeting the City's water supply needs, in addition to considering secondary objectives of maintaining ecological flows, supporting fishery and recreation releases, and mitigating downstream flood peaks. The current version of OST relies on statistical forecasts of flows in the system based on recent observed flows. To improve short-term decision making, plans are being made to transition to National Weather Service (NWS) ensemble forecasts based on hydrologic models that account for short-term weather forecast skill, longer-term climate information, as well as the hydrologic state of the watersheds and recent observed flows. To ensure that the ensemble forecasts are unbiased and that the ensemble spread reflects the actual uncertainty of the forecasts, a statistical model has been developed to post-process the NWS ensemble forecasts to account for hydrologic model error as well as any inherent bias and uncertainty in initial model states, meteorological data and forecasts. The post-processor is designed to produce adjusted ensemble forecasts that are consistent with the DEP historical flow sequences that were used to develop the system operating rules. A set of historical hindcasts that is representative of the real-time ensemble

  6. A Study on Forecast of Ensemble Average Insolation in Utility Service Area Considering Diversity of Forecast Error

    NASA Astrophysics Data System (ADS)

    Suzuki, Kouki; Kato, Takeyoshi; Suzuoki, Yasuo

    A photovoltaic power generation system (PVS) is one of the promising measures to develop a low carbon society. Because of the unstable power output characteristics, a robust forecast method must be employed for realizing the high penetration of PVS into an electric power system. Considering the difference in power output patterns among PVSs dispersed in the service area of electric power system, the forecast error would vary among locations, resulting in the reduced forecast error of the ensemble average power output of high penetration PVS. In this paper, by using the multi-point data of insolation observed in Chubu area during four months, we evaluated the forecast error of the ensemble average insolation of 11 districts, and compared it with the forecast error of individual district. As the results, the number of periods with the forecast error larger than the average insolation during four months is reduced by 16 hours for the ensemble average insolation compared with the average value of individual forecast. The largest forecast error during four months is also reduced to 0.45 kWh/m2 for the ensemble average insolation from 0.68 kWh/m2 on average of 11 districts.

  7. Seasonal streamflow forecasts in a semi-arid Andean watershed using remotely sensed snow cover data

    NASA Astrophysics Data System (ADS)

    Cartes, M.; McPhee, J.; Vargas, X.

    2009-04-01

    Forecasts of monthly streamflow during the snowmelt season are highly relevant for real-time decision making such as hydropower production scheduling, irrigation planning, and water transfers in market-driven water resource systems. The Chilean water bureau issues such forecasts, for a number of snowmelt-driven watersheds in northern and central Chile, based on measurements from a sparse network of snow course stations. This research aims at improving the accuracy of the government-issued seasonal forecasts by combining streamflow data and remotely sensed snow cover information through a recurrent neural network (RNN). The snow cover area (SCA) obtained from MODIS-Surface Reflectance product (MOD09) and the Normalized Differentiation Snow Index (NDSI), from 2000-2008 period, allow us to understand the variation of the snowmelt and accumulation processes in six different basins located in central Chile (32,5° - 34,5° south latitude; 69,5° -70,5° west longitude). For the three basins located at higher altitudes (> 1800 m.s.l.), after applying a cross-correlation procedure we determined a strong relation (r > 0.7) between SCA and the seasonal hydrograph, lagged around 4 months. The basin SCA, the NDSI at specific points inside the basin and past basin streamflow data are input to the RNN for recognizing the pattern variation of seasonal hydrograph through supervised learning. The determination coefficients for the validation period (r2 > 0.6) indicate a good support for the application of this methodology in normal-humid hydrological years. Particularly for the dryer years we obtain a considerable overestimation (around 30%) of the monthly snowmelt runoff. These results are limited by the availability of data for different types (dry, normal or humid) of hydrological years.

  8. Including Impacts of Climate Change in Long-Range Forecasts of Future Tuolumne River Streamflow

    NASA Astrophysics Data System (ADS)

    Kenward, T.; Crawford, N. H.; Dufour, A.; McGurk, B. J.; Monier, W.

    2011-12-01

    Future streamflow is assessed for the Tuolumne River, a representative watershed in Sierra Nevada Mountains in California that provides 85% of the San Francisco Public Utility Commission's water supply for 2.5 million Bay Area residents and water to 8000 agricultural customers and over 200,000 electrical customers of the Turlock and Modesto Irrigation Districts. The Hydrocomp Forecasting and Analysis Model (HFAM) is a hydrologic simulation model which provides probabilistic inflow forecasts based on a continuous long-term simulation of a watershed driven by a historical meteorological database. The HFAM model is used to assess potential changes in the timing and volume of streamflow by driving the model with a long-term meteorological database that has been altered to represent the future climate in a specific year, for a given climate change scenario. In the Tuolumne, historical trends show increases to daily minimum temperatures but not to daily maximum temperatures. A static meteorological database that represents the current climate condition is generated by removing trends from historical data. Climate change scenarios for paired changes in temperature and precipitation were developed based on the range of predictions by global climate models. For each future climate condition, hourly temperature increases to the static database are calculated that are consistent with historical trends in daily minimum temperatures, while retaining a reasonable daily range in temperatures. Changes in future seasonal and annual probabilistic inflow forecasts are given for the Tuolumne River for each climate change scenario. The results show the importance of using probabilistic methods to assess climate change rather than mean values because impacts are more significant in low streamflow years.

  9. Comparison of extended medium-range forecast skill between KMA ensemble, ocean coupled ensemble, and GloSea5

    NASA Astrophysics Data System (ADS)

    Park, Sangwook; Kim, Dong-Joon; Lee, Seung-Woo; Lee, Kie-Woung; Kim, Jongkhun; Song, Eun-Ji; Seo, Kyong-Hwan

    2017-08-01

    This article describes a three way inter-comparison of forecast skill on an extended medium-range time scale using the Korea Meteorological Administration (KMA) operational ensemble numerical weather prediction (NWP) systems (i.e., atmosphere-only global ensemble prediction system (EPSG) and ocean-atmosphere coupledEPSG) and KMA operational seasonal prediction system, the Global Seasonal forecast system version 5 (GloSea5). The main motivation is to investigate whether the ensemble NWP system can provide advantage over the existing seasonal prediction system for the extended medium-range forecast (30 days) even with putting extra resources in extended integration or coupling with ocean with NWP system. Two types of evaluation statistics are examined: the basic verification statistics - the anomaly correlation and RMSE of 500-hPa geopotential height and 1.5-meter surface temperature for the global and East Asia area, and the other is the Real-time Multivariate Madden and Julian Oscillation (MJO) indices (RMM1 and RMM2) - which is used to examine the MJO prediction skill. The MJO is regarded as a main source of forecast skill in the tropics linked to the mid-latitude weather on monthly time scale. Under limited number of experiment cases, the coupled NWP extends the forecast skill of the NWP by a few more days, and thereafter such forecast skill is overtaken by that of the seasonal prediction system. At present stage, it seems there is little gain from the coupled NWP even though more resources are put into it. Considering this, the best combination of numerical product guidance for operational forecasters for an extended medium-range is extension of the forecast lead time of the current ensemble NWP (EPSG) up to 20 days and use of the seasonal prediction system (GloSea5) forecast thereafter, though there exists a matter of consistency between the two systems.

  10. Real-time application of meteorological ensembles for Danube flood forecasting

    NASA Astrophysics Data System (ADS)

    Csík, A.; Gauzer, B.; Gnandt, B.; Balint, G.

    2009-04-01

    Flood forecasting schemes may have the most diverse structure depending on catchment size, response or concentration time and the availability of real time input data. The centre of weight of the hydrological forecasting system is often shifted from hydrological tools to the meteorological observation and forecasting systems. At lowland river sections simple flood routing techniques prevail where accuracy of discharge estimation might depend mostly on the accuracy of upstream discharge estimation. In large river basin systems both elements are present. Attempts are made enabling the use of ensemble of short and medium term meteorological forecast results for real-time flood forecasting by coupling meteorological and hydrological modelling tools. The system is designed in three parts covering the upper and central Danube. The large number of nodes (41) makes the system in fact semi distributed in basin scale. All of the nodes are prepared for forecast purposes. Real time mode runs are carried out in 6 hourly time steps. The available meteorological analysis and forecasting tools are linked to the flood forecasting system. Meteorological forecasts include 6 days and 12 days out of the ECMWF 10-14-day ahead EPS and VarEPS. The hydrological side of the system includes the data ingestion part producing semi distributed catchment wise input from gridded fields and rainfall-runoff, flood routing modules. Operational application of the of the ensemble system has been studied by the comparison of real time deterministic forecast and the experimental real time ensemble forecast results since the summer of 2008 on the river Danube. The period of June-October 2008 included mostly low water period interrupted by smaller floods. The real time ensemble hydrological forecasting experiment proved that the use of meteorological ensembles to produce sets of hydrological predictions increased the capability to issue forecasts with describing current uncertainties. As the result of the

  11. Application of a 3-D Super Ensemble to ocean forecast

    NASA Astrophysics Data System (ADS)

    Lenartz, F.; Barth, A.; Beckers, J.-M.; Vandenbulcke, L.; Rixen, M.

    2009-04-01

    Super Ensemble (SE) techniques have recently allowed improving the forecast of various important oceanographic parameters, such as the significant wave height, the speed of sound or the surface drift, by correcting the prediction at a single or multiple locations, where data were available during the whole training period. However, nowadays common observation systems, such as satellite imagery or drifters, do not always provide information at the exact same locations, hence it is necessary to generalize the approach in order to take benefit of every image or track available. In this study, we try and apply a SE, fed with remote sensing and gliders data, to 3-D hydrodynamic models. The basic idea on which rely the SE methods is that a certain combination of several model runs and possibly data could yield better results than just one single model, even if it has a higher temporal or spatial resolution. As the most efficient techniques are the ones using observations, they rapidly developed and increased in complexity by copying what had been done in the data assimilation community; getting from the simple ensemble mean of the model outputs to their linear combination based on a particle filter. In our present study, we have decided to use the Kalman filter (KF) as it alleviates the need of an a priori determination of the training period length, and does not require the run of a very large ensemble of members. In addition, we apply it in a 3-D framework in order to take benefit of the spatial information contained by each source of measurements. For example, satellite images of sea surface temperature (SST) are very useful to correct the value of this parameter, but depending on the structure of the water column, it can also give a precious guess of how warm or cold is the ocean at 20 m deep. In our experiment the domain of interest is the Ligurian Sea during the last week of September, when part of the set-up for the CalVal08 campaign (SiC Charles Trees) had

  12. Climatic changes, streamflow, and long-term forecasting of intraplate seismicity

    NASA Astrophysics Data System (ADS)

    Costain, J. K.; Bollinger, G. A.

    1996-10-01

    bisected by the Mississippi River, Illinois, and James River, Virginia, in the period range of 11-13 years that might be associated with sunspot activity. In addition, there is positive correlation between periods of above average values of the standard deviation of streamflow time series and periods of seismicity in the central Virginia seismic zone. Many aspects of the weather appear to be modulated by a 20-year cycle. We observe a similar periodicity (18-20 years) in seismicity in the central Virginia seismic zone. A good agreement is observed when a streamflow time series is superimposed on the record of the earthquake strain factor if a value of 50 km 2/year is assumed for crustal hydraulic diffusivity. In the central Virginia seismic zone, it is found that the number of earthquakes versus depth, ψ, is directly proportional to pressure fluctuations at the depth ψ. In addition, the fractal dimension determined from downward-continued streamflow is approximately the same as the fractal dimension of intraplate seismicity. Furthermore, using the Gutenberg-Richter relation and assuming that the earthquake data sets in the New Madrid and central Virginia seismic zones are complete for all magnitudes m ⩾ 2, the ratio of the number of earthquakes occurring per year in the New Madrid zone to the central Virginia zone is about 40. The ratio of the standard deviations of downward-continued Mississippi River streamflow (at Thebes, Illinois) to the James River streamflow is also about 40. One interpretation of this common ratio is that the number of intraplate earthquakes generated in a seismogenic crust is directly proportional to the standard deviation of vertical variations in the elevation of the water table. If the hydroseismicity hypothesis is correct, then long-term variations in streamflow can be used to forecast long-term statistical variations in intraplate seismic activity.

  13. Verification of Meteorological and Oceanographic Ensemble Forecasts in the U.S. Navy

    NASA Astrophysics Data System (ADS)

    Klotz, S.; Hansen, J.; Pauley, P.; Sestak, M.; Wittmann, P.; Skupniewicz, C.; Nelson, G.

    2013-12-01

    The Navy Ensemble Forecast Verification System (NEFVS) has been promoted recently to operational status at the U.S. Navy's Fleet Numerical Meteorology and Oceanography Center (FNMOC). NEFVS processes FNMOC and National Centers for Environmental Prediction (NCEP) meteorological and ocean wave ensemble forecasts, gridded forecast analyses, and innovation (observational) data output by FNMOC's data assimilation system. The NEFVS framework consists of statistical analysis routines, a variety of pre- and post-processing scripts to manage data and plot verification metrics, and a master script to control application workflow. NEFVS computes metrics that include forecast bias, mean-squared error, conditional error, conditional rank probability score, and Brier score. The system also generates reliability and Receiver Operating Characteristic diagrams. In this presentation we describe the operational framework of NEFVS and show examples of verification products computed from ensemble forecasts, meteorological observations, and forecast analyses. The construction and deployment of NEFVS addresses important operational and scientific requirements within Navy Meteorology and Oceanography. These include computational capabilities for assessing the reliability and accuracy of meteorological and ocean wave forecasts in an operational environment, for quantifying effects of changes and potential improvements to the Navy's forecast models, and for comparing the skill of forecasts from different forecast systems. NEFVS also supports the Navy's collaboration with the U.S. Air Force, NCEP, and Environment Canada in the North American Ensemble Forecast System (NAEFS) project and with the Air Force and the National Oceanic and Atmospheric Administration (NOAA) in the National Unified Operational Prediction Capability (NUOPC) program. This program is tasked with eliminating unnecessary duplication within the three agencies, accelerating the transition of new technology, such as multi

  14. Verification of Meteorological and Oceanographic Ensemble Forecasts in the U.S. Navy

    NASA Astrophysics Data System (ADS)

    Klotz, S. P.; Hansen, J.; Pauley, P.; Sestak, M.; Wittmann, P.; Skupniewicz, C.; Nelson, G.

    2012-12-01

    The Navy Ensemble Forecast Verification System (NEFVS) has been promoted recently to operational status at the U.S. Navy's Fleet Numerical Meteorology and Oceanography Center (FNMOC). NEFVS processes FNMOC and National Centers for Environmental Prediction (NCEP) meteorological and ocean wave ensemble forecasts, gridded forecast analyses, and innovation (observational) data output by FNMOC's data assimilation system. The NEFVS framework consists of statistical analysis routines, a variety of pre- and post-processing scripts to manage data and plot verification metrics, and a master script to control application workflow. NEFVS computes metrics that include forecast bias, mean-squared error, conditional error, conditional rank probability score, and Brier score. The system also generates reliability and Receiver Operating Characteristic diagrams. In this presentation we describe the operational framework of NEFVS and show examples of verification products computed from ensemble forecasts, meteorological observations, and forecast analyses. The construction and deployment of NEFVS addresses important operational and scientific requirements within Navy Meteorology and Oceanography (METOC). These include computational capabilities for assessing the reliability and accuracy of meteorological and ocean wave forecasts in an operational environment, for quantifying effects of changes and potential improvements to the Navy's forecast models, and for comparing the skill of forecasts from different forecast systems. NEFVS also supports the Navy's collaboration with the U.S. Air Force, NCEP, and Environment Canada in the North American Ensemble Forecast System (NAEFS) project and with the Air Force and the National Oceanic and Atmospheric Administration (NOAA) in the National Unified Operational Prediction Capability (NUOPC) program. This program is tasked with eliminating unnecessary duplication within the three agencies, accelerating the transition of new technology, such as

  15. Improving urban streamflow forecasting using a high-resolution large scale modeling framework

    NASA Astrophysics Data System (ADS)

    Read, Laura; Hogue, Terri; Gochis, David; Salas, Fernando

    2016-04-01

    Urban flood forecasting is a critical component in effective water management, emergency response, regional planning, and disaster mitigation. As populations across the world continue to move to cities (~1.8% growth per year), and studies indicate that significant flood damages are occurring outside the floodplain in urban areas, the ability to model and forecast flow over the urban landscape becomes critical to maintaining infrastructure and society. In this work, we use the Weather Research and Forecasting- Hydrological (WRF-Hydro) modeling framework as a platform for testing improvements to representation of urban land cover, impervious surfaces, and urban infrastructure. The three improvements we evaluate include: updating the land cover to the latest 30-meter National Land Cover Dataset, routing flow over a high-resolution 30-meter grid, and testing a methodology for integrating an urban drainage network into the routing regime. We evaluate performance of these improvements in the WRF-Hydro model for specific flood events in the Denver-Metro Colorado domain, comparing to historic gaged streamflow for retrospective forecasts. Denver-Metro provides an interesting case study as it is a rapidly growing urban/peri-urban region with an active history of flooding events that have caused significant loss of life and property. Considering that the WRF-Hydro model will soon be implemented nationally in the U.S. to provide flow forecasts on the National Hydrography Dataset Plus river reaches - increasing capability from 3,600 forecast points to 2.7 million, we anticipate that this work will support validation of this service in urban areas for operational forecasting. Broadly, this research aims to provide guidance for integrating complex urban infrastructure with a large-scale, high resolution coupled land-surface and distributed hydrologic model.

  16. Sequential correction of ensemble regional weather predictions for forecasting reference evapotranspiration

    NASA Astrophysics Data System (ADS)

    Pelosi, Anna; Medina Gonzalez, Hanoi; Villani, Paolo; D'Urso, Guido; Battista Chirico, Giovanni

    2016-04-01

    This study explores the performance of an adaptive procedure for correcting the ensemble numerical weather outputs applied to the probabilistic forecast of reference evapotranspiration (ETo). This procedure is proposed as an effective forecast correction method when the available dataset is not large enough for the calibration of statistical batch procedures. The numerical weather prediction outputs are those provided by COSMO-LEPS, an ensemble-based Limited Area Model, with 16 members and 7.5 km spatial resolution, with forecast lead-time up to 5 days. ETo forecasts are computed according to the FAO Penman-Monteith (FAO-PM) equation, which requires data of five weather variables: air temperature, relative humidity, solar radiation and wind speed. The performance of the proposed procedure is evaluated at eighteen monitoring stations, located in Campania region (Southern Italy), with two alternative strategies: i) correction applied to the raw ensemble forecasts of the five weather variables prior applying the FAO-PM equation; ii) correction applied to the ensemble output of the ETo forecasts obtained with FAO-PM equation after using the raw ensemble weather forecasts as input. In both cases the suggested post-processing procedure was able to significantly increase the accuracy and reduce the uncertainty of the ETo forecasts.

  17. Streamflow hindcasting in European river basins via multi-parametric ensemble of the mesoscale hydrologic model (mHM)

    NASA Astrophysics Data System (ADS)

    Noh, Seong Jin; Rakovec, Oldrich; Kumar, Rohini; Samaniego, Luis

    2016-04-01

    There have been tremendous improvements in distributed hydrologic modeling (DHM) which made a process-based simulation with a high spatiotemporal resolution applicable on a large spatial scale. Despite of increasing information on heterogeneous property of a catchment, DHM is still subject to uncertainties inherently coming from model structure, parameters and input forcing. Sequential data assimilation (DA) may facilitate improved streamflow prediction via DHM using real-time observations to correct internal model states. In conventional DA methods such as state updating, parametric uncertainty is, however, often ignored mainly due to practical limitations of methodology to specify modeling uncertainty with limited ensemble members. If parametric uncertainty related with routing and runoff components is not incorporated properly, predictive uncertainty by DHM may be insufficient to capture dynamics of observations, which may deteriorate predictability. Recently, a multi-scale parameter regionalization (MPR) method was proposed to make hydrologic predictions at different scales using a same set of model parameters without losing much of the model performance. The MPR method incorporated within the mesoscale hydrologic model (mHM, http://www.ufz.de/mhm) could effectively represent and control uncertainty of high-dimensional parameters in a distributed model using global parameters. In this study, we present a global multi-parametric ensemble approach to incorporate parametric uncertainty of DHM in DA to improve streamflow predictions. To effectively represent and control uncertainty of high-dimensional parameters with limited number of ensemble, MPR method is incorporated with DA. Lagged particle filtering is utilized to consider the response times and non-Gaussian characteristics of internal hydrologic processes. The hindcasting experiments are implemented to evaluate impacts of the proposed DA method on streamflow predictions in multiple European river basins

  18. Towards an improved ensemble precipitation forecast: A probabilistic post-processing approach

    NASA Astrophysics Data System (ADS)

    Khajehei, Sepideh; Moradkhani, Hamid

    2017-03-01

    Recently, ensemble post-processing (EPP) has become a commonly used approach for reducing the uncertainty in forcing data and hence hydrologic simulation. The procedure was introduced to build ensemble precipitation forecasts based on the statistical relationship between observations and forecasts. More specifically, the approach relies on a transfer function that is developed based on a bivariate joint distribution between the observations and the simulations in the historical period. The transfer function is used to post-process the forecast. In this study, we propose a Bayesian EPP approach based on copula functions (COP-EPP) to improve the reliability of the precipitation ensemble forecast. Evaluation of the copula-based method is carried out by comparing the performance of the generated ensemble precipitation with the outputs from an existing procedure, i.e. mixed type meta-Gaussian distribution. Monthly precipitation from Climate Forecast System Reanalysis (CFS) and gridded observation from Parameter-Elevation Relationships on Independent Slopes Model (PRISM) have been employed to generate the post-processed ensemble precipitation. Deterministic and probabilistic verification frameworks are utilized in order to evaluate the outputs from the proposed technique. Distribution of seasonal precipitation for the generated ensemble from the copula-based technique is compared to the observation and raw forecasts for three sub-basins located in the Western United States. Results show that both techniques are successful in producing reliable and unbiased ensemble forecast, however, the COP-EPP demonstrates considerable improvement in the ensemble forecast in both deterministic and probabilistic verification, in particular in characterizing the extreme events in wet seasons.

  19. Skill improvement of seasonal Arctic sea ice forecasts using bias-correction and ensemble calibration

    NASA Astrophysics Data System (ADS)

    Krikken, Folmer; Hazeleger, Wilco; Vlot, Willem; Schmeits, Maurice; Guemas, Virginie

    2016-04-01

    We explore the standard error and skill of dynamical seasonal sea ice forecasts of the Arctic using different bias-correction and ensemble calibration methods. The latter is often used in weather forecasting, but so far has not been applied to Arctic sea ice forecasts. We use seasonal predictions of Arctic sea ice of a 5-member ensemble forecast using the fully coupled GCM EC-Earth, with model initial states obtained by nudging towards ORAS4 and ERA-Interim. The raw model forecasts contain large biases in total sea ice area, especially during the summer months. This is mainly caused by a difference in average seasonal cycle between EC-Earth and observations, which translates directly into the forecasts yielding large biases. Further errors are introduced by the differences in long term trend between the observed sea ice, and the uninitialised EC-earth simulation. We find that extended logistic regression (ELR) and heteroscedastic extended logistic regression (HELR) both prove viable ensemble calibration methods, and improve the forecasts substantially compared to standard bias correction techniques. No clear distinction between ELR and HELR is found. Forecasts starting in May have higher skill (CRPSS > 0 up to 5 months lead time) than forecasts starting in August (2-3 months) and November (2-3 months), with trend-corrected climatology as reference. Analysis of regional skill in the Arctic shows distinct differences, where mainly the Arctic ocean and the Kara and Barents sea prove to be one of the more predictable regions with skilful forecasts starting in May up to 5-6 months lead time. Again, forecasts starting in August and November show much lower regional skill. Overall, it is still difficult to beat relative simple statistical forecasts, but by using ELR and HELR we are getting reasonably close to skilful seasonal forecasts up to 12 months lead time. These results show there is large potential, and need, for using ensemble calibration in seasonal forecasts of

  20. More reliable coastal SST forecasts from the North American multimodel ensemble

    NASA Astrophysics Data System (ADS)

    Hervieux, G.; Alexander, M. A.; Stock, C. A.; Jacox, M. G.; Pegion, K.; Becker, E.; Castruccio, F.; Tommasi, D.

    2017-03-01

    The skill of monthly sea surface temperature (SST) anomaly predictions for large marine ecosystems (LMEs) in coastal regions of the United States and Canada is assessed using simulations from the climate models in the North American Multimodel Ensemble (NMME). The forecasts based on the full ensemble are generally more skillful than predictions from even the best single model. The improvement in skill is particularly noteworthy for probability forecasts that categorize SST anomalies into upper (warm) and lower (cold) terciles. The ensemble provides a better estimate of the full range of forecast values than any individual model, thereby correcting for the systematic over-confidence (under-dispersion) of predictions from an individual model. Probability forecasts, including tercile predictions from the NMME, are used frequently in seasonal forecasts for atmospheric variables and may have many uses in marine resource management.

  1. Multi and Single Model Ensemble Forecasting in the Gulf of Mexico

    NASA Astrophysics Data System (ADS)

    Hogan, P.; Thoppil, P.; Rowley, C.; Coelho, E.

    2012-04-01

    The Navy Coastal Ocean Model (NCOM) has been configured for the Gulf of Mexico and used to investigate forecast error via ensemble forecasting methods. The models assimilate observations via the Navy Coupled Ocean Data Assimilation (NCODA) system. The model has ~3 km horizontal grid resolution, 46 levels in the vertical, boundary forcing from a global ocean model also based on NCOM, surface forcing from the Navy's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS), as well as tidal forcing and river runoff. A deterministic control run provides the forecast error which is used (via an ensemble transform) to perturb the ensemble members. The atmospheric forcing is also perturbed via a space-time deformation technique. 32 ensemble members are generated and each produces a 72 hours forecast. These are the so called single model ensembles. Other Navy forecast systems that include the Gulf of Mexico (global and regional) that differ primarily in horizontal and vertical resolution and boundary conditions (surface and lateral) are used to calculate the so called multi (or super) ensemble. For both cases statistics calculated across the ensemble members are shown and discussed. Limits of predictability are described and discussed, especially with respect to the Loop Current Eddy Shedding episode of early July 2010 (Eddy Franklin). Overall system performance is quantified and discussed, with emphasis on (but not limited to) the Deep Water Horizon oil spill timeframe. Longer term predictability (30 day) is also investigated and discussed.

  2. Analyses and forecasts of a tornadic supercell outbreak using a 3DVAR system ensemble

    NASA Astrophysics Data System (ADS)

    Zhuang, Zhaorong; Yussouf, Nusrat; Gao, Jidong

    2016-05-01

    As part of NOAA's "Warn-On-Forecast" initiative, a convective-scale data assimilation and prediction system was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The system was then evaluated using retrospective short-range ensemble analyses and probabilistic forecasts of the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA. A 36-member multi-physics ensemble system provided the initial and boundary conditions for a 3-km convective-scale ensemble system. Radial velocity and reflectivity observations from four WSR-88Ds were assimilated into the ensemble using the ARPS 3DVAR technique. Five data assimilation and forecast experiments were conducted to evaluate the sensitivity of the system to data assimilation frequencies, in-cloud temperature adjustment schemes, and fixed- and mixed-microphysics ensembles. The results indicated that the experiment with 5-min assimilation frequency quickly built up the storm and produced a more accurate analysis compared with the 10-min assimilation frequency experiment. The predicted vertical vorticity from the moist-adiabatic in-cloud temperature adjustment scheme was larger in magnitude than that from the latent heat scheme. Cycled data assimilation yielded good forecasts, where the ensemble probability of high vertical vorticity matched reasonably well with the observed tornado damage path. Overall, the results of the study suggest that the 3DVAR analysis and forecast system can provide reasonable forecasts of tornadic supercell storms.

  3. Quantifying The Effects of Initial Soil Moisture On Seasonal Streamflow Forecasts In The Columbia River Basin

    NASA Astrophysics Data System (ADS)

    Hamlet, A. F.; Wood, A.; Lettenmaier, D. P.

    The role of soil moisture storage in the hydrologic cycle is well understood at a funda- mental level. Antecedent conditions are known to have potentially significant effects on streamflow forecasts, especially for short (e.g., flood) lead times. For this reason, the U.S. Geological Survey defines its "water year" as extending from October through September, a time period selected because over most of the U.S., soil moisture is at a seasonal low at summer's end. The effects of carryover soil moisture storage in the Columbia River basin have usually been considered to be minimal when forecasts are made on a water year or seasonal basis. Our study demonstrates that the role of carry- over soil moisture storage can be important. Absent direct observations of ET and soil moisture that would permit a closing of the water balance from observations, we use a physically based hydrologic model to estimate the soil moisture state at the begin- ning of the forecast period (Oct 1). We then evaluate, in a self-consistent manner, the subsequent effects of interannual variations in fall soil moisture on streamflow during the subsequent spring and summer snowmelt season (April-September). We analyze the period from 1950-1999, and the subsequent effects to the seasonal water balance at The Dalles, OR for representative high, medium, and low water years. The effects of initial soil state in fall are remarkably persistent, with significant effects occurring in the summer of the following water year. For a representative low flow year (1992), the simulated variability of the soil moisture state in September produces a range of summer streamflows (April-September mean) equivalent to about 16 percent of the mean summer flows for all initial soil conditions, with analogous, but smaller, relative changes for medium and high flow years. Winter flows are also affected, and the rel- ative intensity of effects in winter and summer is variable, an effect that is probably attributable to the

  4. Short-term streamflow forecasting with global climate change implications A comparative study between genetic programming and neural network models

    NASA Astrophysics Data System (ADS)

    Makkeasorn, A.; Chang, N. B.; Zhou, X.

    2008-05-01

    SummarySustainable water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods may also result in property damages and the loss of life. To more efficiently use the limited amount of water under the changing world or to resourcefully provide adequate time for flood warning, the issues have led us to seek advanced techniques for improving streamflow forecasting on a short-term basis. This study emphasizes the inclusion of sea surface temperature (SST) in addition to the spatio-temporal rainfall distribution via the Next Generation Radar (NEXRAD), meteorological data via local weather stations, and historical stream data via USGS gage stations to collectively forecast discharges in a semi-arid watershed in south Texas. Two types of artificial intelligence models, including genetic programming (GP) and neural network (NN) models, were employed comparatively. Four numerical evaluators were used to evaluate the validity of a suite of forecasting models. Research findings indicate that GP-derived streamflow forecasting models were generally favored in the assessment in which both SST and meteorological data significantly improve the accuracy of forecasting. Among several scenarios, NEXRAD rainfall data were proven its most effectiveness for a 3-day forecast, and SST Gulf-to-Atlantic index shows larger impacts than the SST Gulf-to-Pacific index on the streamflow forecasts. The most forward looking GP-derived models can even perform a 30-day streamflow forecast ahead of time with an r-square of 0.84 and RMS error 5.4 in our study.

  5. Application of dynamic linear regression to improve the skill of ensemble-based deterministic ozone forecasts

    SciTech Connect

    Pagowski, M O; Grell, G A; Devenyi, D; Peckham, S E; McKeen, S A; Gong, W; Monache, L D; McHenry, J N; McQueen, J; Lee, P

    2006-02-02

    Forecasts from seven air quality models and surface ozone data collected over the eastern USA and southern Canada during July and August 2004 provide a unique opportunity to assess benefits of ensemble-based ozone forecasting and devise methods to improve ozone forecasts. In this investigation, past forecasts from the ensemble of models and hourly surface ozone measurements at over 350 sites are used to issue deterministic 24-h forecasts using a method based on dynamic linear regression. Forecasts of hourly ozone concentrations as well as maximum daily 8-h and 1-h averaged concentrations are considered. It is shown that the forecasts issued with the application of this method have reduced bias and root mean square error and better overall performance scores than any of the ensemble members and the ensemble average. Performance of the method is similar to another method based on linear regression described previously by Pagowski et al., but unlike the latter, the current method does not require measurements from multiple monitors since it operates on individual time series. Improvement in the forecasts can be easily implemented and requires minimal computational cost.

  6. Short-term optimal operation of water systems using ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Raso, L.; Schwanenberg, D.; van de Giesen, N. C.; van Overloop, P. J.

    2014-09-01

    Short-term water system operation can be realized using Model Predictive Control (MPC). MPC is a method for operational management of complex dynamic systems. Applied to open water systems, MPC provides integrated, optimal, and proactive management, when forecasts are available. Notwithstanding these properties, if forecast uncertainty is not properly taken into account, the system performance can critically deteriorate. Ensemble forecast is a way to represent short-term forecast uncertainty. An ensemble forecast is a set of possible future trajectories of a meteorological or hydrological system. The growing ensemble forecasts’ availability and accuracy raises the question on how to use them for operational management. The theoretical innovation presented here is the use of ensemble forecasts for optimal operation. Specifically, we introduce a tree based approach. We called the new method Tree-Based Model Predictive Control (TB-MPC). In TB-MPC, a tree is used to set up a Multistage Stochastic Programming, which finds a different optimal strategy for each branch and enhances the adaptivity to forecast uncertainty. Adaptivity reduces the sensitivity to wrong forecasts and improves the operational performance. TB-MPC is applied to the operational management of Salto Grande reservoir, located at the border between Argentina and Uruguay, and compared to other methods.

  7. A pan-African medium-range ensemble flood forecast system

    NASA Astrophysics Data System (ADS)

    Thiemig, Vera; Bisselink, Bernard; Pappenberger, Florian; Thielen, Jutta

    2015-04-01

    The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions of the ECMWF and critical hydrological thresholds. In this study the predictive capability is investigated, to estimate AFFS' potential as an operational flood forecasting system for the whole of Africa. This is done in a hindcast mode, by reproducing pan-African hydrological predictions for the whole year of 2003 where important flood events were observed. Results were analysed in two ways, each with its individual objective. The first part of the analysis is of paramount importance for the assessment of AFFS as a flood forecasting system, as it focuses on the detection and prediction of flood events. Here, results were verified with reports of various flood archives such as Dartmouth Flood Observatory, the Emergency Event Database, the NASA Earth Observatory and Reliefweb. The number of hits, false alerts and missed alerts as well as the Probability of Detection, False Alarm Rate and Critical Success Index were determined for various conditions (different regions, flood durations, average amount of annual precipitations, size of affected areas and mean annual discharge). The second part of the analysis complements the first by giving a basic insight into the prediction skill of the general streamflow. For this, hydrological predictions were compared against observations at 36 key locations across Africa and the Continuous Rank Probability Skill Score (CRPSS), the limit of predictability and reliability were calculated. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas

  8. Ensemble Model Earthquake Forecasts During the 2010-12 Canterbury, New Zealand, Earthquake Sequence

    NASA Astrophysics Data System (ADS)

    Taroni, M.; Werner, M. J.; Marzocchi, W.; Zechar, J. D.

    2016-12-01

    Ensemble models provide at least two advantages for earthquake forecasting. Firstly, they circumvent the question of which model to choose for operational purposes by objectively and transparently merging all available ones. And secondly, the optimally merged model may provide better forecasts than any single model. In addition, complementary or inconsistent models, hypotheses and input data sets can easily be combined according to defined rules. Our purpose here is to investigate ensemble modeling techniques in the context of the 2010-12 Canterbury, New Zealand, earthquake sequence, for which over a dozen time-dependent earthquake forecast models have been developed that are currently under evaluation by the Collaboratory for the Study of Earthquake Predictability (CSEP). The models include recently refined and developed physics-based Coulomb stress models as well as new statistical models and hybrid models that employ physics-based components within a statistical framework. Here, we explore ensemble modeling techniques to create optimal forecasts by merging all available model forecasts. The mixing of the models is determined by a dynamic, weighted average over all forecasts, where the weights are determined according to a continually updated measure of past predictive skill. The ensemble model thus changes from day to day, giving greater weight to more informative models. We compare several methods for assembling ensemble models in terms of their predictive skills during the sequence and compare the optimal models with the individual best models.

  9. Tree Structure Generation from Ensemble Forecasts for Short-Term Reservoir Optimization

    NASA Astrophysics Data System (ADS)

    Raso, L.; Schwanenberg, D.; Van De Giesen, N.

    2012-12-01

    In short-term reservoir management, weather forecasts enable water managers to look further ahead in time and anticipate on future system states. In this context, ensemble forecasts provide information about the uncertainty of the weather information. Tree-Based Model Predictive Control (TB-MPC) is an optimization scheme that embeds ensemble forecasts in a Multistage Stochastic Programming. TB-MPC requires a predefined tree structure that specifies when the ensemble trajectories diverge from each other. A correct tree structure is of critical importance because it strongly affects the performance of the optimization, and existing methods do not offer satisfactory results. We present a new methodology to generate a tree structure from the trajectories of an ensemble. The method models the information flow, considering which observations will become available along the forecast horizon, at which moment, and their level of uncertainty. It places a branching point when there is enough certainty on which trajectory is actually occurring. The method is well suited for trajectories that are close to each other at the beginning of the forecasting horizon, and spread out when progressing in time, as ensemble forecasts typically do. The method is compared to other tree structures (two-stage stochastic programming and others) in terms of performance by an application to the short-term management of the Salto Grande hydropower reservoir in River Uruguay along the Argentinean Uruguayan border.

  10. Ensemble forecasting for renewable energy applications - status and current challenges for their generation and verification

    NASA Astrophysics Data System (ADS)

    Pinson, Pierre

    2016-04-01

    The operational management of renewable energy generation in power systems and electricity markets requires forecasts in various forms, e.g., deterministic or probabilistic, continuous or categorical, depending upon the decision process at hand. Besides, such forecasts may also be necessary at various spatial and temporal scales, from high temporal resolutions (in the order of minutes) and very localized for an offshore wind farm, to coarser temporal resolutions (hours) and covering a whole country for day-ahead power scheduling problems. As of today, weather predictions are a common input to forecasting methodologies for renewable energy generation. Since for most decision processes, optimal decisions can only be made if accounting for forecast uncertainties, ensemble predictions and density forecasts are increasingly seen as the product of choice. After discussing some of the basic approaches to obtaining ensemble forecasts of renewable power generation, it will be argued that space-time trajectories of renewable power production may or may not be necessitate post-processing ensemble forecasts for relevant weather variables. Example approaches and test case applications will be covered, e.g., looking at the Horns Rev offshore wind farm in Denmark, or gridded forecasts for the whole continental Europe. Eventually, we will illustrate some of the limitations of current frameworks to forecast verification, which actually make it difficult to fully assess the quality of post-processing approaches to obtain renewable energy predictions.

  11. Evaluating the performance of real-time streamflow forecasting using multi-satellite precipitation products in the Upper Zambezi, Africa

    NASA Astrophysics Data System (ADS)

    Demaria, E. M.; Valdes, J. B.; Wi, S.; Serrat-Capdevila, A.; Valdés-Pineda, R.; Durcik, M.

    2016-12-01

    In under-instrumented basins around the world, accurate and timely forecasts of river streamflows have the potential of assisting water and natural resource managers in their management decisions. The Upper Zambezi river basin is the largest basin in southern Africa and its water resources are critical to sustainable economic growth and poverty reduction in eight riparian countries. We present a real-time streamflow forecast for the basin using a multi-model-multi-satellite approach that allows accounting for model and input uncertainties. Three distributed hydrologic models with different levels of complexity: VIC, HYMOD_DS, and HBV_DS are setup at a daily time step and a 0.25 degree spatial resolution for the basin. The hydrologic models are calibrated against daily observed streamflows at the Katima-Mulilo station using a Genetic Algorithm. Three real-time satellite products: Climate Prediction Center's morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Tropical Rainfall Measuring Mission (TRMM-3B42RT) are bias-corrected with daily CHIRPS estimates. Uncertainty bounds for predicted flows are estimated with the Inverse Variance Weighting method. Because concentration times in the basin range from a few days to more than a week, we include the use of precipitation forecasts from the Global Forecasting System (GFS) to predict daily streamflows in the basin with a 10-days lead time. The skill of GFS-predicted streamflows is evaluated and the usefulness of the forecasts for short term water allocations is presented.

  12. Usefulness of ECMWF system-4 ensemble seasonal climate forecasts for East Africa

    NASA Astrophysics Data System (ADS)

    Ogutu, Geoffrey; Franssen, Wietse; Supit, Iwan; Omondi, Philip; Hutjes, Ronald

    2016-04-01

    This study evaluates whether European Centre for Medium-Range Weather Forecast (ECMWF) system-4 seasonal forecasts can potentially be used as input for impact analysis over East Africa. To be of any use, these forecasts should have skill. We used the 15-member ensemble runs and tested potential forecast skill of precipitation (tp), near surface air temperature (tas) and surface downwelling shortwave radiation (rsds) for future use in impact models. Probabilistic measures verified the ECMWF ensemble forecasts against the WATCH Forcing Data methodology applied to ERA-Interim data (WFDEI) climatology for the period 1981-2010. The Ranked Probability Skill Score (RPSS) assesses the overall forecast skill, whereas the Relative Operating Curve Skill Score (ROCSS) analyses skill of the forecasted tercile at both grid cell and over sub-regions with homogeneous rainfall characteristics. The results show that predictability of the three variables varies with season, location and forecast month (lead-time) before start of the seasons. Quantile-quantile bias correction clears biases in the raw forecasts but does not improve probabilistic skills. The October-December (OND) tp forecasts show skill over a larger area up to lead-time of three months compared to the March-May (MAM) and June-August (JJA) seasons. Temperature forecasts are skillful up to a minimum three months lead-time in all seasons, while the rsds is less skillful. ROCSS analyses indicate high skill in simulation of upper- and lower-tercile forecasts compared to simulation of the middle-terciles. Upper- and lower-tercile precipitation forecasts are 20-80% better than climatology over a larger area at 0-3 month lead-time; tas forecasts are 40-100% better at shorter lead-times while rsds forecasts are less skillful in all seasons. The forecast system captures manifestations of strong El Niño and La Niña years in terms of precipitation but the skill scores are region dependent.

  13. A Kalman-filter bias correction of ozone deterministic, ensemble-averaged, and probabilistic forecasts

    SciTech Connect

    Monache, L D; Grell, G A; McKeen, S; Wilczak, J; Pagowski, M O; Peckham, S; Stull, R; McHenry, J; McQueen, J

    2006-03-20

    Kalman filtering (KF) is used to postprocess numerical-model output to estimate systematic errors in surface ozone forecasts. It is implemented with a recursive algorithm that updates its estimate of future ozone-concentration bias by using past forecasts and observations. KF performance is tested for three types of ozone forecasts: deterministic, ensemble-averaged, and probabilistic forecasts. Eight photochemical models were run for 56 days during summer 2004 over northeastern USA and southern Canada as part of the International Consortium for Atmospheric Research on Transport and Transformation New England Air Quality (AQ) Study. The raw and KF-corrected predictions are compared with ozone measurements from the Aerometric Information Retrieval Now data set, which includes roughly 360 surface stations. The completeness of the data set allowed a thorough sensitivity test of key KF parameters. It is found that the KF improves forecasts of ozone-concentration magnitude and the ability to predict rare events, both for deterministic and ensemble-averaged forecasts. It also improves the ability to predict the daily maximum ozone concentration, and reduces the time lag between the forecast and observed maxima. For this case study, KF considerably improves the predictive skill of probabilistic forecasts of ozone concentration greater than thresholds of 10 to 50 ppbv, but it degrades it for thresholds of 70 to 90 ppbv. Moreover, KF considerably reduces probabilistic forecast bias. The significance of KF postprocessing and ensemble-averaging is that they are both effective for real-time AQ forecasting. KF reduces systematic errors, whereas ensemble-averaging reduces random errors. When combined they produce the best overall forecast.

  14. The total probabilities from high-resolution ensemble forecasting of floods

    NASA Astrophysics Data System (ADS)

    Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

    2015-04-01

    Ensemble forecasting has for a long time been used in meteorological modelling, to give an indication of the uncertainty of the forecasts. As meteorological ensemble forecasts often show some bias and dispersion errors, there is a need for calibration and post-processing of the ensembles. Typical methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). To make optimal predictions of floods along the stream network in hydrology, we can easily use the ensemble members as input to the hydrological models. However, some of the post-processing methods will need modifications when regionalizing the forecasts outside the calibration locations, as done by Hemri et al. (2013). We present a method for spatial regionalization of the post-processed forecasts based on EMOS and top-kriging (Skøien et al., 2006). We will also look into different methods for handling the non-normality of runoff and the effect on forecasts skills in general and for floods in particular. Berrocal, V. J., Raftery, A. E. and Gneiting, T.: Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts, Mon. Weather Rev., 135(4), 1386-1402, doi:10.1175/MWR3341.1, 2007. Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T.: Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation, Mon. Weather Rev., 133(5), 1098-1118, doi:10.1175/MWR2904.1, 2005. Hemri, S., Fundel, F. and Zappa, M.: Simultaneous calibration of ensemble river flow predictions over an entire range of lead times, Water Resour. Res., 49(10), 6744-6755, doi:10.1002/wrcr.20542, 2013. Raftery, A. E., Gneiting, T., Balabdaoui, F. and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev

  15. Global Ocean Nowcast/Forecast SST's from Multi-model Ensembles

    NASA Astrophysics Data System (ADS)

    Mehra, A.; Spindler, T.

    2013-12-01

    Multiple global SST nowcasts/forecasts are now available from various ocean operational systems (OOS). It can also be safely assumed that these systems have complementary predictive skills. There is also now well-documented literature that shows combining multiple forecasts using simple combinations can help substantially increase accuracy (or reduce error) of such forecasts (Clemen, 1989, Galmarini et al., 2004). Daily global nowcast SST fields from five different OOS (HYCOM, FOAM, CFS, RTOFS & MERCATOR) are used for investigation of ensemble techniques. The employed techniques include weighted means, clustering algorithms (Hartigan, 1975; Arthur and Vassilivitski, 2006) and operational consensus forecasts (Woodcock & Engel, 2005). Preliminary results are presented and discussed along with their limitations. Other alternatives to building ensembles including forecasts from prior run cycles of the same OOS will also be considered.

  16. A Multitemporal Remote Sensing Approach to Streamflow Prediction and Flood Vulnerability Forecasting

    NASA Astrophysics Data System (ADS)

    Weissling, B. P.; Xie, H.

    2006-12-01

    precipitation, land surface temperature, and select vegetation indices accounted for 78% (R2adj = 0.78) of the variance of gage station observed streamflow for calendar year 2004. Efforts are underway to calibrate and validate this model for other time periods within the data availability window of MODIS imagery products, and for other watersheds of varying size and similar climatic regime within the Guadalupe River and neighboring basins. The success of this remote sensing approach will have implications for developing near real-time flood risk and vulnerability forecasting models for both gaged and ungaged watersheds, as well as water supply management in regions of the world with limited resources to undertake conventional ground-based hydrologic studies.

  17. Determining optimal clothing ensembles based on weather forecasts, with particular reference to outdoor winter military activities

    NASA Astrophysics Data System (ADS)

    Morabito, Marco; Pavlinic, Daniela Z.; Crisci, Alfonso; Capecchi, Valerio; Orlandini, Simone; Mekjavic, Igor B.

    2011-07-01

    Military and civil defense personnel are often involved in complex activities in a variety of outdoor environments. The choice of appropriate clothing ensembles represents an important strategy to establish the success of a military mission. The main aim of this study was to compare the known clothing insulation of the garment ensembles worn by soldiers during two winter outdoor field trials (hike and guard duty) with the estimated optimal clothing thermal insulations recommended to maintain thermoneutrality, assessed by using two different biometeorological procedures. The overall aim was to assess the applicability of such biometeorological procedures to weather forecast systems, thereby developing a comprehensive biometeorological tool for military operational forecast purposes. Military trials were carried out during winter 2006 in Pokljuka (Slovenia) by Slovene Armed Forces personnel. Gastrointestinal temperature, heart rate and environmental parameters were measured with portable data acquisition systems. The thermal characteristics of the clothing ensembles worn by the soldiers, namely thermal resistance, were determined with a sweating thermal manikin. Results showed that the clothing ensemble worn by the military was appropriate during guard duty but generally inappropriate during the hike. A general under-estimation of the biometeorological forecast model in predicting the optimal clothing insulation value was observed and an additional post-processing calibration might further improve forecast accuracy. This study represents the first step in the development of a comprehensive personalized biometeorological forecast system aimed at improving recommendations regarding the optimal thermal insulation of military garment ensembles for winter activities.

  18. Determining optimal clothing ensembles based on weather forecasts, with particular reference to outdoor winter military activities.

    PubMed

    Morabito, Marco; Pavlinic, Daniela Z; Crisci, Alfonso; Capecchi, Valerio; Orlandini, Simone; Mekjavic, Igor B

    2011-07-01

    Military and civil defense personnel are often involved in complex activities in a variety of outdoor environments. The choice of appropriate clothing ensembles represents an important strategy to establish the success of a military mission. The main aim of this study was to compare the known clothing insulation of the garment ensembles worn by soldiers during two winter outdoor field trials (hike and guard duty) with the estimated optimal clothing thermal insulations recommended to maintain thermoneutrality, assessed by using two different biometeorological procedures. The overall aim was to assess the applicability of such biometeorological procedures to weather forecast systems, thereby developing a comprehensive biometeorological tool for military operational forecast purposes. Military trials were carried out during winter 2006 in Pokljuka (Slovenia) by Slovene Armed Forces personnel. Gastrointestinal temperature, heart rate and environmental parameters were measured with portable data acquisition systems. The thermal characteristics of the clothing ensembles worn by the soldiers, namely thermal resistance, were determined with a sweating thermal manikin. Results showed that the clothing ensemble worn by the military was appropriate during guard duty but generally inappropriate during the hike. A general under-estimation of the biometeorological forecast model in predicting the optimal clothing insulation value was observed and an additional post-processing calibration might further improve forecast accuracy. This study represents the first step in the development of a comprehensive personalized biometeorological forecast system aimed at improving recommendations regarding the optimal thermal insulation of military garment ensembles for winter activities.

  19. Investigating Statistical Downscaling Methods and Applications for the NCEP/GEFS Ensemble Precipitation Forecasts

    NASA Astrophysics Data System (ADS)

    Luo, Y.; Zhu, Y.; Hou, D.

    2015-12-01

    Significant discrepancies exist when coarse resolution model precipitation forecast products on standard output grids are verified against high-resolution analyses, remaining a challenge for NWP model guidance products. To enhance the usefulness of the model products, tremendous efforts with various statistical post-processing techniques are being made to reduce those discrepancies and recover small scale features using observations and a long-term reforecast climatology as the baseline. Among them, downscaling ensemble using forecast analogs (Hamill et al., 2006) and multiplicative downscaling using Parameter-elevation Regressions on Independent Slopes Model (PRISM) Mountain Mapper by WPC show promising improvement in skill of forecasts. This work concentrates on these two commonly used statistical downscaling approaches along with the Frequency Matching Method (FMM, Zhu and Luo, 2015) developed at NCEP/EMC. In this work, these three approaches will be investigated when applied to the standard one degree NCEP Global Ensemble Forecast System (GEFS) ensemble precipitation forecasts based on the 5-km high resolution NCEP Climatology-Calibrated Precipitation Analysis (CCPA) and 18 years ensemble control only reforecast data from the latest version of GEFS (GEFS v11.0). We will explore the effectiveness and feasibility of these approaches and to discover their strengths and weaknesses, with a focus mainly on generation of much higher 5km NDGD grid GEFS ensemble precipitation forecasts over the CONUS. This work is also expected to identify factors that influence the performance for each approach, such as the choice of case matching methods, the sample size, weighting function, regime definition, etc. A summary of the investigations and outcomes will be shown. Suggestions for some possible directions to produce such a high resolution ensemble precipitation forecast products in the future will be provided.

  20. Time Series Forecasting of Daily Reference Evapotranspiration by Neural Network Ensemble Learning for Irrigation System

    NASA Astrophysics Data System (ADS)

    Manikumari, N.; Murugappan, A.; Vinodhini, G.

    2017-07-01

    Time series forecasting has gained remarkable interest of researchers in the last few decades. Neural networks based time series forecasting have been employed in various application areas. Reference Evapotranspiration (ETO) is one of the most important components of the hydrologic cycle and its precise assessment is vital in water balance and crop yield estimation, water resources system design and management. This work aimed at achieving accurate time series forecast of ETO using a combination of neural network approaches. This work was carried out using data collected in the command area of VEERANAM Tank during the period 2004 - 2014 in India. In this work, the Neural Network (NN) models were combined by ensemble learning in order to improve the accuracy for forecasting Daily ETO (for the year 2015). Bagged Neural Network (Bagged-NN) and Boosted Neural Network (Boosted-NN) ensemble learning were employed. It has been proved that Bagged-NN and Boosted-NN ensemble models are better than individual NN models in terms of accuracy. Among the ensemble models, Boosted-NN reduces the forecasting errors compared to Bagged-NN and individual NNs. Regression co-efficient, Mean Absolute Deviation, Mean Absolute Percentage error and Root Mean Square Error also ascertain that Boosted-NN lead to improved ETO forecasting performance.

  1. Season-ahead streamflow forecast informed tax strategies for semi-arid water rights markets

    NASA Astrophysics Data System (ADS)

    Delorit, J. D.; Block, P. J.

    2016-12-01

    In many semi-arid regions multisectoral demands stress available water supplies. The Elqui River valley of north central Chile, which draws on limited capacity reservoirs supplied largely by annually variable snowmelt, is one of these cases. This variability forces water managers to develop demand-based allocation strategies which have typically resulted in water right volume reductions, applied equally per right. Compounding this issue is often deferred or delayed infrastructure investments, which has been linked Chile's Coasian approach to water markets, under which rights holders do not pay direct procurement costs, non-use fees, nor taxes. Here we build upon our previous research using forecasts of likely water rights reductions, informed by season-ahead prediction models of October-January (austral growing season) streamflow, to construct annual, forecast-sensitive, per right tax. We believe this tax, to be borne by right holders, will improve the beneficial use of water resources by stimulating water rights trading and improving system efficiency by generating funds for infrastructure investment, thereby reducing free-ridership and conflict between rights holders. Research outputs will include sectoral per right tax assessments, tax revenue generation, Elqui River valley economic output, and water rights trading activity.

  2. GloFAS-Seasonal: Operational Seasonal Ensemble River Flow Forecasts at the Global Scale

    NASA Astrophysics Data System (ADS)

    Emerton, Rebecca; Zsoter, Ervin; Smith, Paul; Salamon, Peter

    2017-04-01

    Seasonal hydrological forecasting has potential benefits for many sectors, including agriculture, water resources management and humanitarian aid. At present, no global scale seasonal hydrological forecasting system exists operationally; although smaller scale systems have begun to emerge around the globe over the past decade, a system providing consistent global scale seasonal forecasts would be of great benefit in regions where no other forecasting system exists, and to organisations operating at the global scale, such as disaster relief. We present here a new operational global ensemble seasonal hydrological forecast, currently under development at ECMWF as part of the Global Flood Awareness System (GloFAS). The proposed system, which builds upon the current version of GloFAS, takes the long-range forecasts from the ECMWF System4 ensemble seasonal forecast system (which incorporates the HTESSEL land surface scheme) and uses this runoff as input to the Lisflood routing model, producing a seasonal river flow forecast out to 4 months lead time, for the global river network. The seasonal forecasts will be evaluated using the global river discharge reanalysis, and observations where available, to determine the potential value of the forecasts across the globe. The seasonal forecasts will be presented as a new layer in the GloFAS interface, which will provide a global map of river catchments, indicating whether the catchment-averaged discharge forecast is showing abnormally high or low flows during the 4-month lead time. Each catchment will display the corresponding forecast as an ensemble hydrograph of the weekly-averaged discharge forecast out to 4 months, with percentile thresholds shown for comparison with the discharge climatology. The forecast visualisation is based on a combination of the current medium-range GloFAS forecasts and the operational EFAS (European Flood Awareness System) seasonal outlook, and aims to effectively communicate the nature of a seasonal

  3. The Experimental Regional Ensemble Forecast System (ExREF): Its Use in NWS Forecast Operations and Preliminary Verification

    NASA Technical Reports Server (NTRS)

    Reynolds, David; Rasch, William; Kozlowski, Daniel; Burks, Jason; Zavodsky, Bradley; Bernardet, Ligia; Jankov, Isidora; Albers, Steve

    2014-01-01

    The Experimental Regional Ensemble Forecast (ExREF) system is a tool for the development and testing of new Numerical Weather Prediction (NWP) methodologies. ExREF is run in near-realtime by the Global Systems Division (GSD) of the NOAA Earth System Research Laboratory (ESRL) and its products are made available through a website, an ftp site, and via the Unidata Local Data Manager (LDM). The ExREF domain covers most of North America and has 9-km horizontal grid spacing. The ensemble has eight members, all employing WRF-ARW. The ensemble uses a variety of initial conditions from LAPS and the Global Forecasting System (GFS) and multiple boundary conditions from the GFS ensemble. Additionally, a diversity of physical parameterizations is used to increase ensemble spread and to account for the uncertainty in forecasting extreme precipitation events. ExREF has been a component of the Hydrometeorology Testbed (HMT) NWP suite in the 2012-2013 and 2013-2014 winters. A smaller domain covering just the West Coast was created to minimize band-width consumption for the NWS. This smaller domain has and is being distributed to the National Weather Service (NWS) Weather Forecast Office and California Nevada River Forecast Center in Sacramento, California, where it is ingested into the Advanced Weather Interactive Processing System (AWIPS I and II) to provide guidance on the forecasting of extreme precipitation events. This paper will review the cooperative effort employed by NOAA ESRL, NASA SPoRT (Short-term Prediction Research and Transition Center), and the NWS to facilitate the ingest and display of ExREF data utilizing the AWIPS I and II D2D and GFE (Graphical Software Editor) software. Within GFE is a very useful verification software package called BoiVer that allows the NWS to utilize the River Forecast Center's 4 km gridded QPE to compare with all operational NWP models 6-hr QPF along with the ExREF mean 6-hr QPF so the forecasters can build confidence in the use of the

  4. Multi-model ensemble forecasts of tropical cyclones in 2010 and 2011 based on the Kalman Filter method

    NASA Astrophysics Data System (ADS)

    He, Chengfei; Zhi, Xiefei; You, Qinglong; Song, Bin; Fraedrich, Klaus

    2015-08-01

    This study conducted 24- to 72-h multi-model ensemble forecasts to explore the tracks and intensities (central mean sea level pressure) of tropical cyclones (TCs). Forecast data for the northwestern Pacific basin in 2010 and 2011 were selected from the China Meteorological Administration, European Centre for Medium-Range Weather Forecasts (ECMWF), Japan Meteorological Agency, and National Centers for Environmental Prediction datasets of the Observing System Research and Predictability Experiment Interactive Grand Global Ensemble project. The Kalman Filter was employed to conduct the TC forecasts, along with the ensemble mean and super-ensemble for comparison. The following results were obtained: (1) The statistical-dynamic Kalman Filter, in which recent observations are given more importance and model weighting coefficients are adjusted over time, produced quite different results from that of the super-ensemble. (2) The Kalman Filter reduced the TC mean absolute track forecast error by approximately 50, 80 and 100 km in the 24-, 48- and 72-h forecasts, respectively, compared with the best individual model (ECMWF). Also, the intensity forecasts were improved by the Kalman Filter to some extent in terms of average intensity deviation (AID) and correlation coefficients with reanalysis intensity data. Overall, the Kalman Filter technique performed better compared to multi-models, the ensemble mean, and the super-ensemble in 3-day forecasts. The implication of this study is that this technique appears to be a very promising statistical-dynamic method for multi-model ensemble forecasts of TCs.

  5. Investigating the interactions between data assimilation and post-processing in hydrological ensemble forecasting

    NASA Astrophysics Data System (ADS)

    Bourgin, François; Ramos, Maria-Helena; Thirel, Guillaume; Andreassian, Vazken

    2015-04-01

    Statistical post-processing and data assimilation (also called real-time model updating in the engineering community), although generally dealt with separately, can be intrinsically related in the hydrological forecasting framework. Both represent techniques that may be used in a forecasting system to improve the quality of the forecasts (i.e., to provide more accurate and reliable forecasts) and to, ultimately, enhance the usefulness of the forecasts in decision-making. In this study, we investigate how data assimilation and post-processing contribute, either separately or together, to the skill of a hydrological ensemble forecasting system. Based on a set of 202 unregulated catchments spread over France, we compare four forecasting options: without data assimilation and post-processing, without data assimilation but with post-processing, with data assimilation but without post-processing, and with both data assimilation and post-processing. A short-range meteorological ensemble prediction system, the Météo-France PEARP EPS, and the GRP hydrological model, a continuous, lumped storage-type model specifically designed for flood forecasting, were used to produce hourly hydrological ensemble forecasts. The data assimilation procedure used exploits the last available observed discharge to directly update the routing store state of the hydrological model, and the last relative error to correct the model output with a multiplicative coefficient. For post-processing, we used a hydrological uncertainty processor (HUP) that assesses model simulation uncertainties and compute empirical uncertainty bounds to flow simulations. Results indicate that both strategies have complementary effects. Data assimilation has mainly a very positive effect on forecast accuracy. Its impact however decreases with increasing lead time. Post-processing, by accounting specifically for hydrological uncertainty, has a very positive and longer lasting effect on forecast reliability. As a

  6. On the Value of Climate Elasticity Indices to Assess the Impact of Climate Change on Streamflow Projection using an ensemble of bias corrected CMIP5 dataset

    NASA Astrophysics Data System (ADS)

    Demirel, Mehmet; Moradkhani, Hamid

    2015-04-01

    Changes in two climate elasticity indices, i.e. temperature and precipitation elasticity of streamflow, were investigated using an ensemble of bias corrected CMIP5 dataset as forcing to two hydrologic models. The Variable Infiltration Capacity (VIC) and the Sacramento Soil Moisture Accounting (SAC-SMA) hydrologic models, were calibrated at 1/16 degree resolution and the simulated streamflow was routed to the basin outlet of interest. We estimated precipitation and temperature elasticity of streamflow from: (1) observed streamflow; (2) simulated streamflow by VIC and SAC-SMA models using observed climate for the current climate (1963-2003); (3) simulated streamflow using simulated climate from 10 GCM - CMIP5 dataset for the future climate (2010-2099) including two concentration pathways (RCP4.5 and RCP8.5) and two downscaled climate products (BCSD and MACA). The streamflow sensitivity to long-term (e.g., 30-year) average annual changes in temperature and precipitation is estimated for three periods i.e. 2010-40, 2040-70 and 2070-99. We compared the results of the three cases to reflect on the value of precipitation and temperature indices to assess the climate change impacts on Columbia River streamflow. Moreover, these three cases for two models are used to assess the effects of different uncertainty sources (model forcing, model structure and different pathways) on the two climate elasticity indices.

  7. A novel hybrid ensemble learning paradigm for tourism forecasting

    NASA Astrophysics Data System (ADS)

    Shabri, Ani

    2015-02-01

    In this paper, a hybrid forecasting model based on Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) is proposed to forecast tourism demand. This methodology first decomposes the original visitor arrival series into several Intrinsic Model Function (IMFs) components and one residual component by EMD technique. Then, IMFs components and the residual components is forecasted respectively using GMDH model whose input variables are selected by using Partial Autocorrelation Function (PACF). The final forecasted result for tourism series is produced by aggregating all the forecasted results. For evaluating the performance of the proposed EMD-GMDH methodologies, the monthly data of tourist arrivals from Singapore to Malaysia are used as an illustrative example. Empirical results show that the proposed EMD-GMDH model outperforms the EMD-ARIMA as well as the GMDH and ARIMA (Autoregressive Integrated Moving Average) models without time series decomposition.

  8. Remote Sensing Applications to Improve Seasonal Forecasting of Streamflow and Reservoir Storage in the Upper Snake River Basin

    NASA Astrophysics Data System (ADS)

    McGuire, M.; Wood, A. W.; Andreadis, K.; van Rheenen, N.; Lettenmaier, D. P.

    2003-12-01

    Mountain snowmelt contributes over eighty percent of the water supply in the Western U.S. Accurate estimates and forecasts of snow cover and snowmelt are important to many local, state, and Federal agencies that have interests in agriculture, hydropower, and recreation. Water resource managers depend on accurate water supply predictions to allocate a finite supply of water to often competing demands. Although quantitative forecasts of seasonal snowmelt runoff have been made at many locations in the West for over 50 years, these forecasts are limited by accurate knowledge of winter season precipitation and snow accumulation in remote areas, which presently are estimated via in situ networks like the NRCS' SNOTEL. We evaluate the potential to improve on methods based solely on in situ observations through a strategy that combines nowcasts of soil moisture, snow water content, and other hydrologic variables using the Variable Infiltration Capacity (VIC) macroscale hydrologic model, updated with a combination of SNOTEL-based estimates of snow water equivalent and remote sensing (MODIS) estimates of snow areal extent. The method is evaluated for the Upper Snake River basin, for which a long-term retrospective VIC run for the period 1950-2002 provides a model climatology, and the basis for a retrospective evaluation of seasonal streamflow forecast skill absent updating. For winters 2001-2 and 2002-3, we evaluate the impact of both replacing and augmenting our updating scheme based on SNOTEL data with corrections from two remote sensing products: course spatial resolution MODIS snow-cover data and, on a more experimental basis, an AMSR snow water equivalent product. In addition to seasonal streamflow forecasts based on an adaptation of the Extended Streamflow Prediction (ESP) method, we implement and evaluate experimental reservoir forecasts produced with the SNAKESIM Snake River reservoir management model for forecasts made in the winters of 2001-2 and 2003-4 for the

  9. Global ensemble forecast system precipitation forecasts and the implications of statistical downscaling over the western United States

    NASA Astrophysics Data System (ADS)

    Lewis, Wyndam Robert

    Operational medium-range ensemble modeling systems produce quantitative precipitation forecasts (QPFs) that provide guidance for weather forecasters, yet these systems lack sufficient resolution to adequately resolve orographic influences on precipitation. In this study, we verify cool-season (Oct-Mar) Global Ensemble Forecast System (GEFS) QPFs using daily (24-h) western United States (U.S.) Snow Telemetry (SNOTEL) observations, which tend to be located at upper elevations where orographic enhancement of precipitation is more pronounced. Results indicate widespread dry biases, which reflect the infrequent production of larger 24-h precipitation events (≥ 22.9 mm in Pacific Ranges and ≥ 10.2 mm in the Interior Ranges) relative to observations. Performance metrics, such as equitable threat score (ETS), hit rate, and false alarm ratio, generally worsen from the coast toward the interior. The ensemble spread captures only 30% of upper-quartile events at Day 5, exhibits poor reliability, and is about as skillful over the interior compared to forecasts using climatological probabilities. In an effort to improve QPFs without exacerbating computing demands, we explore statistical downscaling based on high-resolution climatological precipitation analyses from the Parameter-elevation Relationships on Independent Slopes Model (PRISM), an approach frequently used by operational forecasters. Such downscaling improves model biases, ETSs, and hit rates. However, 50% of downscaled QPFs for upper-quartile events are false alarms at Day 1, and probabilistic QPFs still do not capture 40% of such events at Day 5. These results should help forecasters and hydrologists understand the capabilities and limitations of GEFS forecasts and statistical downscaling over the western U.S. and other regions of complex terrain.

  10. The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation

    NASA Astrophysics Data System (ADS)

    Li, Hui; Hong, Lu-Yao; Zhou, Qing; Yu, Hai-Jie

    2015-08-01

    The business failure of numerous companies results in financial crises. The high social costs associated with such crises have made people to search for effective tools for business risk prediction, among which, support vector machine is very effective. Several modelling means, including single-technique modelling, hybrid modelling, and ensemble modelling, have been suggested in forecasting business risk with support vector machine. However, existing literature seldom focuses on the general modelling frame for business risk prediction, and seldom investigates performance differences among different modelling means. We reviewed researches on forecasting business risk with support vector machine, proposed the general assisted prediction modelling frame with hybridisation and ensemble (APMF-WHAE), and finally, investigated the use of principal components analysis, support vector machine, random sampling, and group decision, under the general frame in forecasting business risk. Under the APMF-WHAE frame with support vector machine as the base predictive model, four specific predictive models were produced, namely, pure support vector machine, a hybrid support vector machine involved with principal components analysis, a support vector machine ensemble involved with random sampling and group decision, and an ensemble of hybrid support vector machine using group decision to integrate various hybrid support vector machines on variables produced from principle components analysis and samples from random sampling. The experimental results indicate that hybrid support vector machine and ensemble of hybrid support vector machines were able to produce dominating performance than pure support vector machine and support vector machine ensemble.

  11. An application of ensemble/multi model approach for wind power production forecast.

    NASA Astrophysics Data System (ADS)

    Alessandrini, S.; Decimi, G.; Hagedorn, R.; Sperati, S.

    2010-09-01

    model) seems to reach similar level of accuracy of those of the mesocale models (LAMI and RAMS). Finally we have focused on the possibility of using the ensemble model (ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first day ahead period. In fact low spreads often correspond to low forecast error. For longer forecast horizon the correlation between RMSE and ensemble spread decrease becoming too low to be used for this purpose.

  12. A statistical approach to generating perturbed physics ensemble forecasts using the WRF model

    NASA Astrophysics Data System (ADS)

    Duan, Q.; Shen, C. W.; Wang, C.

    2016-12-01

    The WRF model can be regarded as millions of different models contained in one framework. The potential number of different parameterization scheme combinations is over 2 million based on WRF3.7.1. Which scheme combinations are suitable for a particular location? This study aims to identify a set of parameterization scheme combinations to form a perturbed physics ensemble which is suitable for short-term summer precipitation forecasting in the Greater Beijing area. Using a number of statistical methods including variance analysis, Latin Hypercube sampling, Tukey Honest Significance Difference (TUKEY-HSD) test and some heuristics, we performed the screening of different scheme combinations. The screening is based on a number of performance criteria including the Enhanced Threat Score and the Bias. After several rounds of screening, we settled on a set of 24 scheme combinations that form the basis for the perturbed physics ensemble forecasting. The generated ensemble forecasts were validated based on several common skill metrics such as ranked probability scores, reliability operating characteristics curves, and Bryer Score. The results show that the performance skill of the ensemble forecasts is better than the deterministic forecasting used operationally currently by Beijing Institute of Urban Meteorology for the all rainfall intensities.

  13. Impact of improved meteorological forcing, profile of soil hydraulic conductivity and data assimilation on an operational Hydrological Ensemble Forecast System over France

    NASA Astrophysics Data System (ADS)

    Coustau, Mathieu; Rousset-Regimbeau, Fabienne; Thirel, Guillaume; Habets, Florence; Janet, Bruno; Martin, Eric; de Saint-Aubin, Céline; Soubeyroux, Jean-Michel

    2015-06-01

    A Hydrological Ensemble Forecasting System (HEFS) known as SIMPE has been run over France in real time by Météo-France since 2004. The system combines the 51-member, 10-day ECMWF EPS atmospheric forcing at a 1.5° resolution with the ISBA-MODCOU physically-based distributed hydrological model to provide streamflow forecasts over France. The initial conditions for all the HEFS runs are provided by SIM; i.e., the ISBA-MODCOU model forced by the outputs of the mesoscale meteorological analysis system SAFRAN. A previous study introduced and tested two improvements of this system over a past period. These modifications consisted of an improved representation of the profile of hydraulic conductivity and the implementation of a data assimilation subsystem. The purpose of the present study was to test the HEFS and its two modifications in operational mode, with the new higher-resolution ECMWF EPS atmospheric forcing at 0.25° resolution, available in real time on the Météo-France database, and with less observed discharge available for the data assimilation subsystem. The new ISBA physics scheme led to a notable improvement in the discharge simulation in western and northeastern France, where no aquifers were simulated by the MODCOU model. This improvement was not impacted by real-time conditions. Likewise, the improvement resulting from the data assimilation system applied over France was not significantly affected by real-time conditions. The propagation of the data assimilation correction to gauging stations located upstream or downstream of the assimilated stations limited the deterioration of forecasted streamflow due to real-time conditions. Finally, the ECMWF EPS high-resolution atmospheric forcing had a significant impact on the streamflow forecasts for small catchments, which increased with lead time.

  14. Using ensemble NWP wind power forecasts to improve national power system management

    NASA Astrophysics Data System (ADS)

    Cannon, D.; Brayshaw, D.; Methven, J.; Coker, P.; Lenaghan, D.

    2014-12-01

    National power systems are becoming increasingly sensitive to atmospheric variability as generation from wind (and other renewables) increases. As such, the days-ahead predictability of wind power has significant implications for power system management. At this time horizon, power system operators plan transmission line outages for maintenance. In addition, forecast users begin to form backup strategies to account for the uncertainty in wind power predictions. Under-estimating this uncertainty could result in a failure to meet system security standards, or in the worst instance, a shortfall in total electricity supply. On the other hand, overly conservative assumptions about the forecast uncertainty incur costs associated with the unnecessary holding of reserve power. Using the power system of Great Britain (GB) as an example, we construct time series of GB-total wind power output using wind speeds from either reanalyses or global weather forecasts. To validate the accuracy of these data sets, wind power reconstructions using reanalyses and forecast analyses over a recent period are compared to measured GB-total power output. The results are found to be highly correlated on time scales greater than around 6 hours. Results are presented using ensemble wind power forecasts from several national and international forecast centres (obtained through TIGGE). Firstly, the skill with which global ensemble forecasts can represent the uncertainty in the GB-total power output at up to 10 days ahead is quantified. Following this, novel ensemble forecast metrics are developed to improve estimates of forecast uncertainty within the context of power system operations, thus enabling the development of more cost effective strategies. Finally, the predictability of extreme events such as prolonged low wind periods or rapid changes in wind power output are examined in detail. These events, if poorly forecast, induce high stress scenarios that could threaten the security of the power

  15. CEREF: A hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system

    NASA Astrophysics Data System (ADS)

    Zhang, Hongbo; Singh, Vijay P.; Wang, Bin; Yu, Yinghao

    2016-09-01

    Hydrological forecasting is complicated by flow regime alterations in a coupled socio-hydrologic system, encountering increasingly non-stationary, nonlinear and irregular changes, which make decision support difficult for future water resources management. Currently, many hybrid data-driven models, based on the decomposition-prediction-reconstruction principle, have been developed to improve the ability to make predictions of annual streamflow. However, there exist many problems that require further investigation, the chief among which is the direction of trend components decomposed from annual streamflow series and is always difficult to ascertain. In this paper, a hybrid data-driven model was proposed to capture this issue, which combined empirical mode decomposition (EMD), radial basis function neural networks (RBFNN), and external forces (EF) variable, also called the CEREF model. The hybrid model employed EMD for decomposition and RBFNN for intrinsic mode function (IMF) forecasting, and determined future trend component directions by regression with EF as basin water demand representing the social component in the socio-hydrologic system. The Wuding River basin was considered for the case study, and two standard statistical measures, root mean squared error (RMSE) and mean absolute error (MAE), were used to evaluate the performance of CEREF model and compare with other models: the autoregressive (AR), RBFNN and EMD-RBFNN. Results indicated that the CEREF model had lower RMSE and MAE statistics, 42.8% and 7.6%, respectively, than did other models, and provided a superior alternative for forecasting annual runoff in the Wuding River basin. Moreover, the CEREF model can enlarge the effective intervals of streamflow forecasting compared to the EMD-RBFNN model by introducing the water demand planned by the government department to improve long-term prediction accuracy. In addition, we considered the high-frequency component, a frequent subject of concern in EMD

  16. Formulation and results from ensemble forecasting using Multimodels for Hurricanes, Global Weather and Seasonal Climate

    NASA Astrophysics Data System (ADS)

    Krishnamurthi, T. N.

    2006-11-01

    This paper carries a short review of a multimodel/multianalysis superensemble for weather and seasonal climate forecasts. This model was first developed by the authors in 1999 at Florida State University. This entails a large number of forecasts using these multimodels from past data sets, that is called a training phase of the superensemble. During this training phase statistical relation among the model forecasts and the observed fields is obtained using multiple regression methods. This training phase requires roughly 4 months of past daily forecasts for numerical weather prediction (NWP), approximately 6 years of past seasonal forecast and about 60 past hurricane/typhoon/tropical cyclone forecasts from each of the participating member models. The training phase is followed by a forecast phase where the member model forecasts (into the future) use the aforementioned statistics to construct multimodel superensemble forecasts. Our focus on NWP has been to examine the performance of the multimodel superensemble forecast against those of the member models, their ensemble mean and the bias removed ensemble means. We have noted an invariable much superior performance of the multimodel superensemble. We have noted that roughly a minimal number of 7 to 8 models are needed to carry out this exercise. We were also able to improve the database and the statistics of the training phase by rejecting poorer forecast days and optimizing the number of training days. The common metrics for forecast evaluation include root mean square error, anomaly correlation and equitable threat scores. Great impact on real time and experimental forecasts from the superensemble were noted for precipitation, sea level pressure, temperature and 500 hPa geopotential height fields. The improvements in forecasting heavy rains by the multimodel/multianalysis superensemble are found to provide useful guidance in flood events. In hurricane forecasts improvements in track position forecasts of the order

  17. The state of the art of flood forecasting - Hydrological Ensemble Prediction Systems

    NASA Astrophysics Data System (ADS)

    Thielen-Del Pozo, J.; Pappenberger, F.; Salamon, P.; Bogner, K.; Burek, P.; de Roo, A.

    2010-09-01

    Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Because of the specific characteristics of each catchment, varying data availability and end-user demands, the design of the best flood forecasting system may differ from catchment to catchment. However, despite the differences in concept and data needs, there is one underlying issue that spans across all systems. There has been an growing awareness and acceptance that uncertainty is a fundamental issue of flood forecasting and needs to be dealt with at the different spatial and temporal scales as well as the different stages of the flood generating processes. Today, operational flood forecasting centres change increasingly from single deterministic forecasts to probabilistic forecasts with various representations of the different contributions of uncertainty. The move towards these so-called Hydrological Ensemble Prediction Systems (HEPS) in flood forecasting represents the state of the art in forecasting science, following on the success of the use of ensembles for weather forecasting (Buizza et al., 2005) and paralleling the move towards ensemble forecasting in other related disciplines such as climate change predictions. The use of HEPS has been internationally fostered by initiatives such as "The Hydrologic Ensemble Prediction Experiment" (HEPEX), created with the aim to investigate how best to produce, communicate and use hydrologic ensemble forecasts in hydrological short-, medium- und long term prediction of hydrological processes. The advantages of quantifying the different contributions of uncertainty as well as the overall uncertainty to obtain reliable and useful flood forecasts also for extreme events

  18. Skill Assessment of National Multi-Model Ensemble Forecasts for Seasonal Drought Prediction in East Africa

    NASA Astrophysics Data System (ADS)

    Shukla, S.; Hoell, A.; Roberts, J. B.; Funk, C. C.; Robertson, F. R.

    2014-12-01

    The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as 2011, part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at a seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However, seasonal drought prediction in this region faces several challenges including lack of skillful seasonal rainfall forecasts. The National Multi-model Ensemble (NMME); a state-of-the-art dynamical climate forecast system is potentially a promising tool for drought prediction in this region. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ forecasts ensemble members. Recent studies have indicated that in general NMME offers improvement over forecasts from any of the individual model. However, thus far the skill of NMME for forecasting rainfall in a vulnerable region like East Africa has largely been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons of the region. (i.e. March-April-May, July-August-September, and October-November-December). Additionally we describe a hybrid approach that combines statistical method with NMME forecasts to improve rainfall forecast skill in the region when raw NMME forecasts skill is lacking. This approach uses constructed analog method to improve NMME's March-April-May rainfall forecast skill in East Africa.

  19. Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

    NASA Technical Reports Server (NTRS)

    Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

    2015-01-01

    The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

  20. Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

    NASA Technical Reports Server (NTRS)

    Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

    2014-01-01

    The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

  1. Dynamics and Predictability of Hurricane Humberto (2007) Revealed from Ensemble Analysis and Forecasting

    NASA Technical Reports Server (NTRS)

    Sippel, Jason A.; Zhang, Fuqing

    2009-01-01

    This study uses short-range ensemble forecasts initialized with an Ensemble-Kalman filter to study the dynamics and predictability of Hurricane Humberto, which made landfall along the Texas coast in 2007. Statistical correlation is used to determine why some ensemble members strengthen the incipient low into a hurricane and others do not. It is found that deep moisture and high convective available potential energy (CAPE) are two of the most important factors for the genesis of Humberto. Variations in CAPE result in as much difference (ensemble spread) in the final hurricane intensity as do variations in deep moisture. CAPE differences here are related to the interaction between the cyclone and a nearby front, which tends to stabilize the lower troposphere in the vicinity of the circulation center. This subsequently weakens convection and slows genesis. Eventually the wind-induced surface heat exchange mechanism and differences in landfall time result in even larger ensemble spread. 1

  2. A component-resampling approach for estimating probability distributions from small forecast ensembles

    USGS Publications Warehouse

    Dettinger, M.

    2006-01-01

    In many meteorological and climatological modeling applications, the availability of ensembles of predictions containing very large numbers of members would substantially ease statistical analyses and validations. This study describes and demonstrates an objective approach for generating large ensembles of "additional" realizations from smaller ensembles, where the additional ensemble members share important first-and second-order statistical characteristics and some dynamic relations within the original ensemble. By decomposing the original ensemble members into assuredly independent time-series components (using a form of principal component decomposition) that can then be resampled randomly and recombined, the component-resampling procedure generates additional time series that follow the large and small scale structures in the original ensemble members, without requiring any tuning by the user. The method is demonstrated by applications to operational medium-range weather forecast ensembles from a single NCEP weather model and application to a multi-model, multi-emission-scenarios ensemble of 21st Century climate-change projections. ?? Springer 2006.

  3. Wave ensemble forecast in the Western Mediterranean Sea, application to an early warning system.

    NASA Astrophysics Data System (ADS)

    Pallares, Elena; Hernandez, Hector; Moré, Jordi; Espino, Manuel; Sairouni, Abdel

    2015-04-01

    The Western Mediterranean Sea is a highly heterogeneous and variable area, as is reflected on the wind field, the current field, and the waves, mainly in the first kilometers offshore. As a result of this variability, the wave forecast in these regions is quite complicated to perform, usually with some accuracy problems during energetic storm events. Moreover, is in these areas where most of the economic activities take part, including fisheries, sailing, tourism, coastal management and offshore renewal energy platforms. In order to introduce an indicator of the probability of occurrence of the different sea states and give more detailed information of the forecast to the end users, an ensemble wave forecast system is considered. The ensemble prediction systems have already been used in the last decades for the meteorological forecast; to deal with the uncertainties of the initial conditions and the different parametrizations used in the models, which may introduce some errors in the forecast, a bunch of different perturbed meteorological simulations are considered as possible future scenarios and compared with the deterministic forecast. In the present work, the SWAN wave model (v41.01) has been implemented for the Western Mediterranean sea, forced with wind fields produced by the deterministic Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS). The wind fields includes a deterministic forecast (also named control), between 11 and 21 ensemble members, and some intelligent member obtained from the ensemble, as the mean of all the members. Four buoys located in the study area, moored in coastal waters, have been used to validate the results. The outputs include all the time series, with a forecast horizon of 8 days and represented in spaghetti diagrams, the spread of the system and the probability at different thresholds. The main goal of this exercise is to be able to determine the degree of the uncertainty of the wave forecast, meaningful

  4. Potentialities of ensemble strategies for flood forecasting over the Milano urban area

    NASA Astrophysics Data System (ADS)

    Ravazzani, Giovanni; Amengual, Arnau; Ceppi, Alessandro; Homar, Víctor; Romero, Romu; Lombardi, Gabriele; Mancini, Marco

    2016-08-01

    Analysis of ensemble forecasting strategies, which can provide a tangible backing for flood early warning procedures and mitigation measures over the Mediterranean region, is one of the fundamental motivations of the international HyMeX programme. Here, we examine two severe hydrometeorological episodes that affected the Milano urban area and for which the complex flood protection system of the city did not completely succeed. Indeed, flood damage have exponentially increased during the last 60 years, due to industrial and urban developments. Thus, the improvement of the Milano flood control system needs a synergism between structural and non-structural approaches. First, we examine how land-use changes due to urban development have altered the hydrological response to intense rainfalls. Second, we test a flood forecasting system which comprises the Flash-flood Event-based Spatially distributed rainfall-runoff Transformation, including Water Balance (FEST-WB) and the Weather Research and Forecasting (WRF) models. Accurate forecasts of deep moist convection and extreme precipitation are difficult to be predicted due to uncertainties arising from the numeric weather prediction (NWP) physical parameterizations and high sensitivity to misrepresentation of the atmospheric state; however, two hydrological ensemble prediction systems (HEPS) have been designed to explicitly cope with uncertainties in the initial and lateral boundary conditions (IC/LBCs) and physical parameterizations of the NWP model. No substantial differences in skill have been found between both ensemble strategies when considering an enhanced diversity of IC/LBCs for the perturbed initial conditions ensemble. Furthermore, no additional benefits have been found by considering more frequent LBCs in a mixed physics ensemble, as ensemble spread seems to be reduced. These findings could help to design the most appropriate ensemble strategies before these hydrometeorological extremes, given the computational

  5. Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq

    NASA Astrophysics Data System (ADS)

    Yaseen, Zaher Mundher; Jaafar, Othman; Deo, Ravinesh C.; Kisi, Ozgur; Adamowski, Jan; Quilty, John; El-Shafie, Ahmed

    2016-11-01

    Monthly stream-flow forecasting can yield important information for hydrological applications including sustainable design of rural and urban water management systems, optimization of water resource allocations, water use, pricing and water quality assessment, and agriculture and irrigation operations. The motivation for exploring and developing expert predictive models is an ongoing endeavor for hydrological applications. In this study, the potential of a relatively new data-driven method, namely the extreme learning machine (ELM) method, was explored for forecasting monthly stream-flow discharge rates in the Tigris River, Iraq. The ELM algorithm is a single-layer feedforward neural network (SLFNs) which randomly selects the input weights, hidden layer biases and analytically determines the output weights of the SLFNs. Based on the partial autocorrelation functions of historical stream-flow data, a set of five input combinations with lagged stream-flow values are employed to establish the best forecasting model. A comparative investigation is conducted to evaluate the performance of the ELM compared to other data-driven models: support vector regression (SVR) and generalized regression neural network (GRNN). The forecasting metrics defined as the correlation coefficient (r), Nash-Sutcliffe efficiency (ENS), Willmott's Index (WI), root-mean-square error (RMSE) and mean absolute error (MAE) computed between the observed and forecasted stream-flow data are employed to assess the ELM model's effectiveness. The results revealed that the ELM model outperformed the SVR and the GRNN models across a number of statistical measures. In quantitative terms, superiority of ELM over SVR and GRNN models was exhibited by ENS = 0.578, 0.378 and 0.144, r = 0.799, 0.761 and 0.468 and WI = 0.853, 0.802 and 0.689, respectively and the ELM model attained lower RMSE value by approximately 21.3% (relative to SVR) and by approximately 44.7% (relative to GRNN). Based on the findings of this

  6. On the performance of updating Stochastic Dynamic Programming policy using Ensemble Streamflow Prediction in a snow-covered region

    NASA Astrophysics Data System (ADS)

    Martin, A.; Pascal, C.; Leconte, R.

    2014-12-01

    Stochastic Dynamic Programming (SDP) is known to be an effective technique to find the optimal operating policy of hydropower systems. In order to improve the performance of SDP, this project evaluates the impact of re-updating the policy at every time step by using Ensemble Streamflow Prediction (ESP). We present a case study of the Kemano's hydropower system on the Nechako River in British Columbia, Canada. Managed by Rio Tinto Alcan (RTA), this system is subject to large streamflow volumes in spring due to important amount of snow depth during the winter season. Therefore, the operating policy should not only maximize production but also minimize the risk of flooding. The hydrological behavior of the system is simulated with CEQUEAU, a distributed and deterministic hydrological model developed by the Institut national de la recherche scientifique - Eau, Terre et Environnement (INRS-ETE) in Quebec, Canada. On each decision time step, CEQUEAU is used to generate ESP scenarios based on historical meteorological sequences and the current state of the hydrological model. These scenarios are used into the SDP to optimize the new release policy for the next time steps. This routine is then repeated over the entire simulation period. Results are compared with those obtained by using SDP on historical inflow scenarios.

  7. Flash flood warnings using the ensemble precipitation forecasting technique: A case study on forecasting floods in Taiwan caused by typhoons

    NASA Astrophysics Data System (ADS)

    Yang, Tsun-Hua; Yang, Sheng-Chi; Ho, Jui-Yi; Lin, Gwo-Fong; Hwang, Gong-Do; Lee, Cheng-Shang

    2015-01-01

    A flash flood is an event that develops rapidly. Given early warnings with sufficient lead time, flood forecasting can help people prepare disaster prevention measures. To provide this early warning, a statistics-based flood forecasting model was developed to evaluate the flooding potential in urban areas using ensemble quantitative precipitation forecasts (the Taiwan Cooperative Precipitation Ensemble Forecast Experiment, TAPEX). The proposed model uses different sources of information, such as (i) the designed capacity of storm sewer systems, (ii) a flood inundation potential database, and (iii) historical flooding observations, to evaluate the potential for flash flooding situations to occur. Using 24-, 48- and 72-h ahead precipitation forecasts from the TAPEX, the proposed model can assess the flooding potential with two levels of risk and at the township scale with a 3-day lead time. The proposed model is applied to Pingtung County, which includes 33 townships and is located in southern Taiwan. A dataset of typhoon storms from 2010 to 2014 was used to evaluate the model performance. The accuracy and threat score for testing events are 0.68 and 0.30, respectively, with a lead time of 24 h. The accuracy and threat score for training events are 0.82 and 0.31, respectively, with a lead time of 24 h. The model performance decreases when the lead time is extended. However, the model demonstrates its potential as a valuable reference to improve emergency responses to alleviate the loss of lives and property due to flooding.

  8. Real-time Ensemble Forecasting of Coronal Mass Ejections using the WSA-ENLIL+Cone Model

    NASA Astrophysics Data System (ADS)

    Mays, M. L.; Taktakishvili, A.; Pulkkinen, A. A.; MacNeice, P. J.; Rastaetter, L.; Kuznetsova, M. M.; Odstrcil, D.

    2013-12-01

    Ensemble forecasting of coronal mass ejections (CMEs) provides significant information in that it provides an estimation of the spread or uncertainty in CME arrival time predictions due to uncertainties in determining CME input parameters. Ensemble modeling of CME propagation in the heliosphere is performed by forecasters at the Space Weather Research Center (SWRC) using the WSA-ENLIL cone model available at the Community Coordinated Modeling Center (CCMC). SWRC is an in-house research-based operations team at the CCMC which provides interplanetary space weather forecasting for NASA's robotic missions and performs real-time model validation. A distribution of n (routinely n=48) CME input parameters are generated using the CCMC Stereo CME Analysis Tool (StereoCAT) which employs geometrical triangulation techniques. These input parameters are used to perform n different simulations yielding an ensemble of solar wind parameters at various locations of interest (satellites or planets), including a probability distribution of CME shock arrival times (for hits), and geomagnetic storm strength (for Earth-directed hits). Ensemble simulations have been performed experimentally in real-time at the CCMC since January 2013. We present the results of ensemble simulations for a total of 15 CME events, 10 of which were performed in real-time. The observed CME arrival was within the range of ensemble arrival time predictions for 5 out of the 12 ensemble runs containing hits. The average arrival time prediction was computed for each of the twelve ensembles predicting hits and using the actual arrival time an average absolute error of 8.20 hours was found for all twelve ensembles, which is comparable to current forecasting errors. Some considerations for the accuracy of ensemble CME arrival time predictions include the importance of the initial distribution of CME input parameters, particularly the mean and spread. When the observed arrivals are not within the predicted range, this

  9. Ensemble sea ice forecast for predicting compressive situations in the Baltic Sea

    NASA Astrophysics Data System (ADS)

    Lehtiranta, Jonni; Lensu, Mikko; Kokkonen, Iiro; Haapala, Jari

    2017-04-01

    Forecasting of sea ice hazards is important for winter shipping in the Baltic Sea. In current numerical models the ice thickness distribution and drift are captured well, but compressive situations are often missing from forecast products. Its inclusion is requested by the shipping community, as compression poses a threat to ship operations. As compressing ice is capable of stopping ships for days and even damaging them, its inclusion in ice forecasts is vital. However, we have found that compression can not be predicted well in a deterministic forecast, since it can be a local and a quickly changing phenomenon. It is also very sensitive to small changes in the wind speed and direction, the prevailing ice conditions, and the model parameters. Thus, a probabilistic ensemble simulation is needed to produce a meaningful compression forecast. An ensemble model setup was developed in the SafeWIN project for this purpose. It uses the HELMI multicategory ice model, which was amended for making simulations in parallel. The ensemble was built by perturbing the atmospheric forcing and the physical parameters of the ice pack. The model setup will provide probabilistic forecasts for the compression in the Baltic sea ice. Additionally the model setup provides insight into the uncertainties related to different model parameters and their impact on the model results. We have completed several hindcast simulations for the Baltic Sea for verification purposes. These results are shown to match compression reports gathered from ships. In addition, an ensemble forecast is in preoperational testing phase and its first evaluation will be presented in this work.

  10. Comparison of Ensemble Mean and Deterministic Forecasts for Long-Range Airlift Fuel Planning

    DTIC Science & Technology

    2014-03-27

    fluctuations of the general circulation. WMO -IUGG Symp. on Research and Development Aspects of Long- range Forecasting 66, World Meteorological...Organization, WMO . Lorenz, E., 1993: The Essence of Chaos. University of Washington Press. McLay, J. and C. H. Bishop, 2010: A local formulation of the ensemble

  11. Combining multiobjective optimization and Bayesian model averaging to calibrate forecast ensembles of soil hydraulic models

    NASA Astrophysics Data System (ADS)

    WöHling, Thomas; Vrugt, Jasper A.

    2008-12-01

    Most studies in vadose zone hydrology use a single conceptual model for predictive inference and analysis. Focusing on the outcome of a single model is prone to statistical bias and underestimation of uncertainty. In this study, we combine multiobjective optimization and Bayesian model averaging (BMA) to generate forecast ensembles of soil hydraulic models. To illustrate our method, we use observed tensiometric pressure head data at three different depths in a layered vadose zone of volcanic origin in New Zealand. A set of seven different soil hydraulic models is calibrated using a multiobjective formulation with three different objective functions that each measure the mismatch between observed and predicted soil water pressure head at one specific depth. The Pareto solution space corresponding to these three objectives is estimated with AMALGAM and used to generate four different model ensembles. These ensembles are postprocessed with BMA and used for predictive analysis and uncertainty estimation. Our most important conclusions for the vadose zone under consideration are (1) the mean BMA forecast exhibits similar predictive capabilities as the best individual performing soil hydraulic model, (2) the size of the BMA uncertainty ranges increase with increasing depth and dryness in the soil profile, (3) the best performing ensemble corresponds to the compromise (or balanced) solution of the three-objective Pareto surface, and (4) the combined multiobjective optimization and BMA framework proposed in this paper is very useful to generate forecast ensembles of soil hydraulic models.

  12. GEOWOW - Benefits of TIGGE ensemble forecast data for the GEOSS community

    NASA Astrophysics Data System (ADS)

    Zsoter, Ervin

    2014-05-01

    GEOWOW (GEOSS interoperability for Weather, Ocean and Water) is an EU-funded project with the main challenge to improve Earth Observation data discovery, accessibility and exploitability, and to evolve the Global Earth Observation System of Systems (GEOSS) for the benefit of all Societal Benefit Areas (SBAs) with focus on Weather, Ocean Ecosystems and Water. There is a particular focus on supporting multi-disciplinary interoperability across different SBAs, including the use of weather forecast ensembles in applications. The weather community in GEOWOW, led by ECMWF, addresses the elements of the GEO Capacity Building Strategy by improving the access to TIGGE (THORPEX Interactive Grand Global Ensemble) data in which global ensemble predictions from 10 leading global NWP centres are collected in near real-time to support research on ensemble prediction. GEOWOW extends the TIGGE archive with ensemble weather forecasts from limited area models and will build a multi-model ensemble time-series archive (Global and LAM containing data only for specific points) to increase the accessibility of TIGGE for a wider community. GEOWOW work also includes the development and demonstration of (multi-model) products using TIGGE data to support high impact weather forecasting in areas such as tropical cyclone tracks, heavy precipitation events and strong winds. Testing and delivery of these products is in collaboration with participants in the WMO Severe Weather Forecasting Demonstration Project (SWFDP). The SWFDP is a WMO capacity building project which uses a Cascading Forecasting Process to support a basic capability to issue severe weather warnings in developing and least-developed countries. A specific area of work covers the demonstration and documentation of the potential use of TIGGE data in high impact weather forecasting through mainly case studies. Analysed cases include various types of severe weather and different regions - for example rainfall and flooding in West

  13. Data Analysis, Modeling, and Ensemble Forecasting to Support NOWCAST and Forecast Activities at the Fallon Naval Station

    DTIC Science & Technology

    2009-09-01

    lived intense dust storm over the Fallon NAS is also being conducted as a preliminary to an ensemble forecast that emphasizes operational prediction...identify sources of error in dynamical prediction, and (2) analysis and prediction of dust storms over western U.S. Both projects are supported by...interdisciplinary project linking dust emission modeling, atmospheric predictions and Lagrangian Random Particle Dispersion modeling. Dr. Koracin is a Lead

  14. Informing Water Management by Direct Use of Snow Information as Surrogate of Medium-to-Long Range Streamflow Forecast

    NASA Astrophysics Data System (ADS)

    Denaro, S.; Giuliani, M.; Castelletti, A.

    2014-12-01

    Medium-to-long range streamflow forecast provide a key assistance in anticipating hydro- climatic adverse events and prompting effective adaptation measures. For instance, accurate medium-long range streamflow forecasts have a great potential to improve water reservoir operation by enabling more efficient allocation of water volumes in time (e.g. via hedging). Unfortunately, these forecasts often lacks reliability and accuracy, especially when low-frequency climate forcing (e.g. ENSO) is not intense enough to improve the forecast lead time (e.g. in Europe), and might be computationally very demanding, In this work, we explore the direct use of both rough snow data (e.g. snow depth) and snow water equivalent estimates as surrogate of medium-to-long range streamflow forecast to inform the operation of a regulated lake. The underlying idea is that snow data contains key information on current and future water availability throughout the snow melting season that might significantly improve the operation's anticipation potential. We adopt a three step methodology: First, we compute the upper bound of the system performance by assuming perfect foresight and we assess the value of additional information as the difference between this ideal solution and current operation. Using input variable selection, we then select the most relevant snow information to explain the release trajectory associated to the upper bound operating policy. Finally, we derive the optimal policy conditioned upon the selected variables by Multi-Objecting Evolutionary Direct Policy Search. The methodology is demonstrated on the snow-dominated Lake Como river basin, in the Italian Alps. Lake Como is a regulated lake primarily used to supply water to a large cultivated area and snowmelt from May-July is the most important contribution to the creation of the seasonal storage. Results show that using raw data or simple SWE estimates can largely improve anticipation capability in the daily operation of

  15. Ambiguity in Ensemble Forecasting: Evolution, Estimate Validation and Value

    DTIC Science & Technology

    2009-09-01

    I labored through my research and writing this dissertation. First, I could not have accomplished this daunting achievement without the love and...practical approach to estimating ambiguity. The explanation of the practical methods follows that given in Eckel and Allen (2009) for ease of writing ...representation of model uncertainties in the ECMWF Ensemble Prediction System. Quart. J. Roy. Meteor. Soc., 125, 2887–2907. Burgers, G., P. Jan van Leeuwen

  16. Operational water management of Rijnland water system and pilot of ensemble forecasting system for flood control

    NASA Astrophysics Data System (ADS)

    van der Zwan, Rene

    2013-04-01

    The Rijnland water system is situated in the western part of the Netherlands, and is a low-lying area of which 90% is below sea-level. The area covers 1,100 square kilometres, where 1.3 million people live, work, travel and enjoy leisure. The District Water Control Board of Rijnland is responsible for flood defence, water quantity and quality management. This includes design and maintenance of flood defence structures, control of regulating structures for an adequate water level management, and waste water treatment. For water quantity management Rijnland uses, besides an online monitoring network for collecting water level and precipitation data, a real time control decision support system. This decision support system consists of deterministic hydro-meteorological forecasts with a 24-hr forecast horizon, coupled with a control module that provides optimal operation schedules for the storage basin pumping stations. The uncertainty of the rainfall forecast is not forwarded in the hydrological prediction. At this moment 65% of the pumping capacity of the storage basin pumping stations can be automatically controlled by the decision control system. Within 5 years, after renovation of two other pumping stations, the total capacity of 200 m3/s will be automatically controlled. In critical conditions there is a need of both a longer forecast horizon and a probabilistic forecast. Therefore ensemble precipitation forecasts of the ECMWF are already consulted off-line during dry-spells, and Rijnland is running a pilot operational system providing 10-day water level ensemble forecasts. The use of EPS during dry-spells and the findings of the pilot will be presented. Challenges and next steps towards on-line implementation of ensemble forecasts for risk-based operational management of the Rijnland water system will be discussed. An important element in that discussion is the question: will policy and decision makers, operator and citizens adapt this Anticipatory Water

  17. Intelligent Ensemble Forecasting System of Stock Market Fluctuations Based on Symetric and Asymetric Wavelet Functions

    NASA Astrophysics Data System (ADS)

    Lahmiri, Salim; Boukadoum, Mounir

    2015-08-01

    We present a new ensemble system for stock market returns prediction where continuous wavelet transform (CWT) is used to analyze return series and backpropagation neural networks (BPNNs) for processing CWT-based coefficients, determining the optimal ensemble weights, and providing final forecasts. Particle swarm optimization (PSO) is used for finding optimal weights and biases for each BPNN. To capture symmetry/asymmetry in the underlying data, three wavelet functions with different shapes are adopted. The proposed ensemble system was tested on three Asian stock markets: The Hang Seng, KOSPI, and Taiwan stock market data. Three statistical metrics were used to evaluate the forecasting accuracy; including, mean of absolute errors (MAE), root mean of squared errors (RMSE), and mean of absolute deviations (MADs). Experimental results showed that our proposed ensemble system outperformed the individual CWT-ANN models each with different wavelet function. In addition, the proposed ensemble system outperformed the conventional autoregressive moving average process. As a result, the proposed ensemble system is suitable to capture symmetry/asymmetry in financial data fluctuations for better prediction accuracy.

  18. Ensemble forecasting of potential habitat for three invasive fishes

    USGS Publications Warehouse

    Poulos, Helen M.; Chernoff, Barry; Fuller, Pam L.; Butman, David

    2012-01-01

    Aquatic invasive species pose major ecological and economic threats to aquatic ecosystems worldwide via displacement, predation, or hybridization with native species and the alteration of aquatic habitats and hydrologic cycles. Modeling the habitat suitability of alien aquatic species through spatially explicit mapping is an increasingly important risk assessment tool. Habitat modeling also facilitates identification of key environmental variables influencing invasive species distributions. We compared four modeling methods to predict the potential continental United States distributions of northern snakehead Channa argus (Cantor, 1842), round goby Neogobius melanostomus (Pallas, 1814), and silver carp Hypophthalmichthys molitrix (Valenciennes, 1844) using maximum entropy (Maxent), the genetic algorithm for rule set production (GARP), DOMAIN, and support vector machines (SVM). We used inventory records from the USGS Nonindigenous Aquatic Species Database and a geographic information system of 20 climatic and environmental variables to generate individual and ensemble distribution maps for each species. The ensemble maps from our study performed as well as or better than all of the individual models except Maxent. The ensemble and Maxent models produced significantly higher accuracy individual maps than GARP, one-class SVMs, or DOMAIN. The key environmental predictor variables in the individual models were consistent with the tolerances of each species. Results from this study provide insights into which locations and environmental conditions may promote the future spread of invasive fish in the US.

  19. Seasonal forecasts of North Atlantic tropical cyclone activity in the North American Multi-Model Ensemble

    NASA Astrophysics Data System (ADS)

    Manganello, Julia V.; Cash, Benjamin A.; Hodges, Kevin I.; Kinter, James L.

    2017-04-01

    The North American Multi-Model Ensemble (NMME)-Phase II models are evaluated in terms of their retrospective seasonal forecast skill of the North Atlantic (NA) tropical cyclone (TC) activity, with a focus on TC frequency. The TC identification and tracking algorithm is modified to accommodate model data at daily resolution. It is also applied to three reanalysis products at the spatial and temporal resolution of the NMME-Phase II ensemble to allow for a more objective estimation of forecast skill. When used with the reanalysis data, the TC tracking generates realistic climatological distributions of the NA TC formation and tracks, and represents the interannual variability of the NA TC frequency quite well. Forecasts with the multi-model ensemble (MME) when initialized in April and later tend to have skill in predicting the NA seasonal TC counts (and TC days). At longer leads, the skill is low or marginal, although one of the models produces skillful forecasts when initialized as early as January and February. At short lead times, while demonstrating the highest skill levels the MME also tends to significantly outperform the individual models and attain skill comparable to the reanalysis. In addition, the short-lead MME forecasts are quite reliable. At regional scales, the skill is rather limited and mostly present in the western tropical NA and the Caribbean Sea. It is found that the overall MME forecast skill is limited by poor representation of the low-frequency variability in the predicted TC frequency, and large fluctuations in skill on decadal time scales. Addressing these deficiencies is thought to increase the value of the NMME ensemble in providing operational guidance.

  20. Application of object-oriented verification techniques to ensemble precipitation forecasts

    NASA Astrophysics Data System (ADS)

    Gallus, W.

    2009-04-01

    Both the Method for Diagnostic Evaluation (MODE) and Contiguous Rain Area (CRA) object-oriented verification techniques have been used to analyze precipitation forecasts from two sets of ensembles to determine if spread-skill behavior observed using traditional measures can be seen in the object parameters, and to examine several methods of obtaining forecast guidance from the object parameters. One set of ensembles consisted of two 8 member Weather Research and Forecasting (WRF) model ensembles, one having mixed physics and dynamics with common initial and lateral boundary conditions (Phys) and another using common physics and dynamic core but with perturbed initial and lateral boundary conditions (IC/LBC). Traditional measures had found that spread grows much faster in IC/LBC than in Phys such that although skill and spread initially are as large or larger in Phys than in IC/LBC, after roughly 24 hours, better skill and spread are found in IC/LBC. These measures also reflected the strong diurnal signal of precipitation dominating the central United States during the warm season. The other set of ensembles included 5 members of a 4 km grid spacing WRF ensemble (ENS4) and 5 members of a 20 km WRF ensemble (ENS20). Traditional measures applied to these ensembles suggested that the diurnal signal was better in ENS4 and spread increased more rapidly than in ENS20. Standard deviations (SDs) of four object parameters computed for the first set of ensembles showed the trend of enhanced spread growth in IC/LBC compared to Phys that had been observed in traditional measures, with areal coverage of precipitation exhibiting the greatest growth in spread with time. The two techniques did not produce identical results, although they did show the same general trends. CRA better showed differences between Phys and IC/LBC for SDs of rain rate, while MODE showed more of a difference for SDs of rain volume. A diurnal cycle had some influence on the SDs of all parameters, especially

  1. Probabilistic Solar Wind and Geomagnetic Forecasting Using an Analogue Ensemble or "Similar Day" Approach

    NASA Astrophysics Data System (ADS)

    Owens, M. J.; Riley, P.; Horbury, T. S.

    2017-05-01

    Effective space-weather prediction and mitigation requires accurate forecasting of near-Earth solar-wind conditions. Numerical magnetohydrodynamic models of the solar wind, driven by remote solar observations, are gaining skill at forecasting the large-scale solar-wind features that give rise to near-Earth variations over days and weeks. There remains a need for accurate short-term (hours to days) solar-wind forecasts, however. In this study we investigate the analogue ensemble (AnEn), or "similar day", approach that was developed for atmospheric weather forecasting. The central premise of the AnEn is that past variations that are analogous or similar to current conditions can be used to provide a good estimate of future variations. By considering an ensemble of past analogues, the AnEn forecast is inherently probabilistic and provides a measure of the forecast uncertainty. We show that forecasts of solar-wind speed can be improved by considering both speed and density when determining past analogues, whereas forecasts of the out-of-ecliptic magnetic field [BN] are improved by also considering the in-ecliptic magnetic-field components. In general, the best forecasts are found by considering only the previous 6 - 12 hours of observations. Using these parameters, the AnEn provides a valuable probabilistic forecast for solar-wind speed, density, and in-ecliptic magnetic field over lead times from a few hours to around four days. For BN, which is central to space-weather disturbance, the AnEn only provides a valuable forecast out to around six to seven hours. As the inherent predictability of this parameter is low, this is still likely a marked improvement over other forecast methods. We also investigate the use of the AnEn in forecasting geomagnetic indices Dst and Kp. The AnEn provides a valuable probabilistic forecast of both indices out to around four days. We outline a number of future improvements to AnEn forecasts of near-Earth solar-wind and geomagnetic

  2. System for NIS Forecasting Based on Ensembles Analysis

    SciTech Connect

    2014-01-02

    BMA-NIS is a package/library designed to be called by a script (e.g. Perl or Python). The software itself is written in the language of R. The software assists electric power delivery systems in planning resource availability and demand, based on historical data and current data variables. Net Interchange Schedule (NIS) is the algebraic sum of all energy scheduled to flow into or out of a balancing area during any interval. Accurate forecasts for NIS are important so that the Area Control Error (ACE) stays within an acceptable limit. To date, there are many approaches for forecasting NIS but all none of these are based on single models that can be sensitive to time of day and day of week effects.

  3. Applying Machine Learning to Climate Forcing of Streamflow: Regression Ensembles Illuminate Local Influences on Watershed Responses to ENSO.

    NASA Astrophysics Data System (ADS)

    Rice, J. S.; Emanuel, R. E.

    2016-12-01

    The El Nino Southern Oscillation (ENSO) is an important contributor to global climate variability and is also a major influence on the hydrological cycle, affecting precipitation and watershed hydrologic responses around the globe. Although regional patterns of hydrologic responses to ENSO climate forcing are generally clear, individual watersheds within a region respond very differently (or not at all) to ENSO. Our goal was to improve understanding of watershed scale hydrologic responses to ENSO and the factors that influence variability in those responses. This goal was addressed using a large dataset of watersheds (n=2731) within the conterminous United States together with an ensemble of various forms of regression. Specifically, we combine linear, non-linear, and tree-based regressors into a stacked ensemble of models to identify patterns in watershed scale hydrologic responses to ENSO. The resulting regression ensemble explained approximately 80% of the observed variability in monthly runoff responses to the multivariate ENSO index. We found that features related to the residence time of water within a watershed were associated with the expression of ENSO signals within streamflow. Our results have important implications for future hydrologic responses to ENSO within the U.S. given ongoing changes in hydroclimate. We identified other relationships that present opportunities for management based approaches to mediating watershed responses to future extremes in ENSO activity. Together, these results provide a prime example of the application of machine learning based methods in the hydrologic sciences to increase basic understanding of hydrologic behavior while also generating insight that may directly benefit water resource management activities.

  4. Verification of an ensemble prediction system for storm surge forecast in the Adriatic Sea

    NASA Astrophysics Data System (ADS)

    Mel, Riccardo; Lionello, Piero

    2014-12-01

    In the Adriatic Sea, storm surges present a significant threat to Venice and to the flat coastal areas of the northern coast of the basin. Sea level forecast is of paramount importance for the management of daily activities and for operating the movable barriers that are presently being built for the protection of the city. In this paper, an EPS (ensemble prediction system) for operational forecasting of storm surge in the northern Adriatic Sea is presented and applied to a 3-month-long period (October-December 2010). The sea level EPS is based on the HYPSE (hydrostatic Padua Sea elevation) model, which is a standard single-layer nonlinear shallow water model, whose forcings (mean sea level pressure and surface wind fields) are provided by the ensemble members of the ECMWF (European Center for Medium-Range Weather Forecasts) EPS. Results are verified against observations at five tide gauges located along the Croatian and Italian coasts of the Adriatic Sea. Forecast uncertainty increases with the predicted value of the storm surge and with the forecast lead time. The EMF (ensemble mean forecast) provided by the EPS has a rms (root mean square) error lower than the DF (deterministic forecast), especially for short (up to 3 days) lead times. Uncertainty for short lead times of the forecast and for small storm surges is mainly caused by uncertainty of the initial condition of the hydrodynamical model. Uncertainty for large lead times and large storm surges is mainly caused by uncertainty in the meteorological forcings. The EPS spread increases with the rms error of the forecast. For large lead times the EPS spread and the forecast error substantially coincide. However, the EPS spread in this study, which does not account for uncertainty in the initial condition, underestimates the error during the early part of the forecast and for small storm surge values. On the contrary, it overestimates the rms error for large surge values. The PF (probability forecast) of the EPS

  5. Ensemble Statistical Post-Processing of the National Air Quality Forecast Capability: Enhancing Ozone Forecasts in Baltimore, Maryland

    NASA Technical Reports Server (NTRS)

    Garner, Gregory G.; Thompson, Anne M.

    2013-01-01

    An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone. The user is provided the freedom to tailor the forecast to the decision at hand by using decision-specific probability thresholds that define a forecast for an ozone exceedance. Taking advantage of the ESP, the user not only receives an increase in value over the NAQFC, but also receives value for An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone

  6. A One-dimensional Ensemble Forecast and Assimilation System for Fog Prediction

    NASA Astrophysics Data System (ADS)

    Müller, M. D.; Schmutz, C.; Parlow, E.

    2007-06-01

    A probabilistic fog forecast system was designed based on two high resolution numerical 1-D models called COBEL and PAFOG. The 1-D models are coupled to several 3-D numerical weather prediction models and thus are able to consider the effects of advection. To deal with the large uncertainty inherent to fog forecasts, a whole ensemble of 1-D runs is computed using the two different numerical models and a set of different initial conditions in combination with distinct boundary conditions. Initial conditions are obtained from variational data assimilation, which optimally combines observations with a first guess taken from operational 3-D models. The design of the ensemble scheme computes members that should fairly well represent the uncertainty of the current meteorological regime. Verification for an entire fog season reveals the importance of advection in complex terrain. The skill of 1-D fog forecasts is significantly improved if advection is considered. Thus the probabilistic forecast system has the potential to support the forecaster and therefore to provide more accurate fog forecasts.

  7. North American Multi Model Ensemble (NMME) Performance of Monthly Precipitation Forecast over South Sulawesi, Indonesia

    NASA Astrophysics Data System (ADS)

    Setiawan, A. M.; Koesmaryono, Y.; Faqih, A.; Gunawan, D.

    2017-03-01

    The North American Multi-Model Ensemble (NMME) as one of the multi-model seasonal forecasting system, regularly generate monthly precipitation forecast for all globe with 0.5 - 11.5 months lead time. This useful information can be used as general input to regional and local precipitation forecast. This study quantifies monthly precipitation hindcast data performance in South Sulawesi provided by seven coupled models from the NMME during 29 years (1982 - 2010) period. Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset and the Standardized Verification System (SVS) for long-range forecasts (LRF) analysis applied to asses monthly precipitation prediction. Almost all individual model shows relatively high skill when used to make June - November monthly precipitation prediction and low skill when used to forecast monthly precipitation in December - May periods. Multi-Model Ensemble (MME) performed using Simple Composite Method (SCM) increased performance of monthly precipitation prediction. Nevertheless, this improvement only applies at short lead time (< 3.3 months). This result also shows that NMME is more promising when it used to make precipitation forecast application during dry period (i.e. drought prediction) rather than wet period over South Sulawesi region.

  8. Ensemble approach to wheat yield forecasting in Ukraine

    NASA Astrophysics Data System (ADS)

    Kussul, Nataliia; Kolotii, Andrii; Skakun, Sergii; Shelestov, Andrii; Kussul, Olga; Kravchenko, Oleksii

    2014-05-01

    Crop yield forecasting is an extremely important component of the agriculture monitoring domain. In our previous study [1], we assessed relative efficiency and feasibility of using an NDVI-based empirical model for winter wheat yield forecasting at oblast level in Ukraine. Though the NDVI-based model provides minimum data requirements, it has some limitations since NDVI is indirectly related just to biomass but not meteorological conditions. Therefore, it is necessary to assess satellite-derived parameters that incorporate meteorology while maintaining the requirement of minimum data inputs. The objective of the proposed paper is several-fold: (i) to assess efficiency of using biophysical satellite-derived parameters for crop yield forecasting for Ukraine and select the optimal ones based on rigorous feature selection procedure; (ii) to assimilate predictions made by models built on various satellite-derived parameters. Two new parameters are considered in the paper: (i) vegetation health index (VHI) at 4 km spatial resolution derived from a series of NOAA satellites; (ii) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) derived from SPOT-VEGETATION at 1 km spatial resolution. VHI data are provided as weekly composites and FAPAR data are provided as decadal composites. The particular advantage of using VHI is that it incorporates moisture and thermal conditions of vegetation canopy, while FAPAR is directly related to the primary productivity of photosynthesis It is required to find a day of the year for which a parameter is taken and used in the empirical model. For this purpose, a Random Forest feature selection procedure is applied. It is found that VHI and FAPAR values taken in April-May provided the minimum error value when comparing to the official statistics, thus enabling forecasts 2-3 months prior to harvest, and this corresponds to results derived from LOOCV procedure. The best timing for making reliable yield forecasts is nearly the same

  9. Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

    PubMed Central

    Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

    2014-01-01

    Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds. PMID:27382627

  10. Seasonal Hydrometeorological Ensemble Prediction System; Forecast of Irrigation Potentials in Denmark

    NASA Astrophysics Data System (ADS)

    Lucatero, D.; Jensen, K. H.; Madsen, H.; Refsgaard, J. C.; Kidmose, J.

    2015-12-01

    The European Center for Medium Weather Forecast seasonal ensemble prediction system (ECMWF-SEPS) of weather variables such as precipitation, temperature and evapotranspiration is used as input to an integrated surface-subsurface hydrological model based on the MIKE SHE system to generate probabilistic forecasts of the irrigation requirements in the Skjern river catchment in Denmark. We demonstrate the usability of the ECMWF-SEPS and discuss the challenges and areas of opportunities when issuing forecasts generated with this methodology. A simple bias-correction and downscaling technique, namely linear scaling, is applied to the raw inputs to remove the bias intrinsic in ensemble prediction systems and to downscale the data to a scale appropriate for hydrological modelling. The forecasts of the meteorological variables are analysed for accuracy and reliability by comparing them to meteorological observations. Additionally, weather ensembles will be generated using the nearest-neighbour resampling technique with the purpose of exploring additional possibilities of hydrometeorological system input for complementing situations where the SEPS is lacking skill.

  11. Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models.

    PubMed

    Alizadeh, Mohamad Javad; Jafari Nodoushan, Ehsan; Kalarestaghi, Naghi; Chau, Kwok Wing

    2017-10-09

    This study explores two ideas to made an improvement on the artificial neural network (ANN)-based models for suspended sediment forecasting in several time steps ahead. In this regard, both observed and forecasted time series are incorporated as input variables of the models when applied for more than one lead time. Secondly, least-square ensemble models employing multiple wavelet-ANN models are developed to increase the performance of the single model. For this purpose, different wavelet families are linked with the ANN model and performance of each model is evaluated using error measures. The Skagit River near Mount Vernon in Washington county is selected as the case study. The daily flow discharge and suspended sediment concentration (SSC) in the current day are considered as input variables to predict suspended sediment concentration in the next day. For more lead times, the input structure is updated by adding the forecast of SSC in the previous time step. Results of this study demonstrate that incorporating both observed and predicted variables in the input structure improves performance of conventional models in which those only employ observed time series as input variables. Moreover, ensemble model developed for each lead time outperforms the best single wavelet-ANN model which indicates superiority of the ensemble model over the other one. Findings of this study reveal that acceptable forecasts of daily suspended sediment concentration up to 3 days in advance can be achieved using the proposed methodology.

  12. Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

    PubMed

    Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

    2014-01-01

    Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

  13. Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature

    NASA Astrophysics Data System (ADS)

    Baran, Sándor; Möller, Annette

    2017-02-01

    Forecast ensembles are typically employed to account for prediction uncertainties in numerical weather prediction models. However, ensembles often exhibit biases and dispersion errors, thus they require statistical post-processing to improve their predictive performance. Two popular univariate post-processing models are the Bayesian model averaging (BMA) and the ensemble model output statistics (EMOS). In the last few years, increased interest has emerged in developing multivariate post-processing models, incorporating dependencies between weather quantities, such as for example a bivariate distribution for wind vectors or even a more general setting allowing to combine any types of weather variables. In line with a recently proposed approach to model temperature and wind speed jointly by a bivariate BMA model, this paper introduces an EMOS model for these weather quantities based on a bivariate truncated normal distribution. The bivariate EMOS model is applied to temperature and wind speed forecasts of the 8-member University of Washington mesoscale ensemble and the 11-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service and its predictive performance is compared to the performance of the bivariate BMA model and a multivariate Gaussian copula approach, post-processing the margins with univariate EMOS. While the predictive skills of the compared methods are similar, the bivariate EMOS model requires considerably lower computation times than the bivariate BMA method.

  14. Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature

    NASA Astrophysics Data System (ADS)

    Baran, Sándor; Möller, Annette

    2016-06-01

    Forecast ensembles are typically employed to account for prediction uncertainties in numerical weather prediction models. However, ensembles often exhibit biases and dispersion errors, thus they require statistical post-processing to improve their predictive performance. Two popular univariate post-processing models are the Bayesian model averaging (BMA) and the ensemble model output statistics (EMOS). In the last few years, increased interest has emerged in developing multivariate post-processing models, incorporating dependencies between weather quantities, such as for example a bivariate distribution for wind vectors or even a more general setting allowing to combine any types of weather variables. In line with a recently proposed approach to model temperature and wind speed jointly by a bivariate BMA model, this paper introduces an EMOS model for these weather quantities based on a bivariate truncated normal distribution. The bivariate EMOS model is applied to temperature and wind speed forecasts of the 8-member University of Washington mesoscale ensemble and the 11-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service and its predictive performance is compared to the performance of the bivariate BMA model and a multivariate Gaussian copula approach, post-processing the margins with univariate EMOS. While the predictive skills of the compared methods are similar, the bivariate EMOS model requires considerably lower computation times than the bivariate BMA method.

  15. Ensemble forecast of human West Nile virus cases and mosquito infection rates

    PubMed Central

    DeFelice, Nicholas B.; Little, Eliza; Campbell, Scott R.; Shaman, Jeffrey

    2017-01-01

    West Nile virus (WNV) is now endemic in the continental United States; however, our ability to predict spillover transmission risk and human WNV cases remains limited. Here we develop a model depicting WNV transmission dynamics, which we optimize using a data assimilation method and two observed data streams, mosquito infection rates and reported human WNV cases. The coupled model-inference framework is then used to generate retrospective ensemble forecasts of historical WNV outbreaks in Long Island, New York for 2001–2014. Accurate forecasts of mosquito infection rates are generated before peak infection, and >65% of forecasts accurately predict seasonal total human WNV cases up to 9 weeks before the past reported case. This work provides the foundation for implementation of a statistically rigorous system for real-time forecast of seasonal outbreaks of WNV. PMID:28233783

  16. Benchmark verification of deterministic and ensemble forecasts for the river Rhine

    NASA Astrophysics Data System (ADS)

    van Osnabrugge, Bart; Weerts, Albrecht; Verkade, Jan; den Toom, Matthijs; Uijlenhoet, Remko; Sprokkereef, Eric

    2016-04-01

    As part of the IMPREX project (IMproving PRedictions and management of EXtremes) we perform a benchmark verification study (tier 1) of an operational active hydrological forecasting system of the river Rhine. We assess the current forecast skill and contributions of current DA methods (e.g. error correction) to forecast skill. Our results will be compared with previous verification studies (Renner et al., 2009, Verkade et al., 2013) for the Rhine. Based on the results we will identify weaknesses and opportunities for future improvements. After model improvements and introduction of more advanced (ensemble) DA methods the benchmark will be conducted again later in the project (tier 2). From these experiments, we hope to show the relative importance of data assimilation, the availability of real-time hydrologic measured data in comparison with (improved) meteorological forecast skill and their effect on hydrologic predictability for the river Rhine.

  17. Ensemble forecast of human West Nile virus cases and mosquito infection rates

    NASA Astrophysics Data System (ADS)

    Defelice, Nicholas B.; Little, Eliza; Campbell, Scott R.; Shaman, Jeffrey

    2017-02-01

    West Nile virus (WNV) is now endemic in the continental United States; however, our ability to predict spillover transmission risk and human WNV cases remains limited. Here we develop a model depicting WNV transmission dynamics, which we optimize using a data assimilation method and two observed data streams, mosquito infection rates and reported human WNV cases. The coupled model-inference framework is then used to generate retrospective ensemble forecasts of historical WNV outbreaks in Long Island, New York for 2001-2014. Accu