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Sample records for ensemble streamflow forecasts

  1. Statistical calibration of seasonal ensemble streamflow forecasts

    NASA Astrophysics Data System (ADS)

    Wood, A. W.; Wiley, M.; Nijssen, B.

    2008-12-01

    Model-based streamflow forecast ensembles routinely exhibit errors due to input forecast uncertainty, initial condition uncertainty and hydrologic modeling error, and such errors can undermine the suitability of raw model forecast output for use in follow-on applications such as reservoir management. This presentation evaluates the use of quantile regression for modeling and correcting streamflow prediction errors at seasonal lead times. Quantile regression has been used rarely in the hydrologic forecasting area, yet its insensitivity to outliers and avoidance of parametric assumptions about forecast errors makes it suitable for representing quantiles of datasets (such as streamflow) that often have skewed or otherwise irregular distributions. For illustration, a local linear quantile regression framework is applied to seasonal streamflow hindcasts in the Pacific Northwest and elsewhere in the western US, and found to reduce forecast bias and improve reliability, albeit with a slight loss of forecast resolution.

  2. Streamflow Ensemble Generation using Climate Forecasts

    NASA Astrophysics Data System (ADS)

    Watkins, D. W.; O'Connell, S.; Wei, W.; Nykanen, D.; Mahmoud, M.

    2002-12-01

    Although significant progress has been made in understanding the correlation between large-scale atmospheric circulation patterns and regional streamflow anomalies, there is a general perception that seasonal climate forecasts are not being used to the fullest extent possible for optimal water resources management. Possible contributing factors are limited knowledge and understanding of climate processes and prediction capabilities, noise in climate signals and inaccuracies in forecasts, and hesitancy on the part of water managers to apply new information or methods that could expose them to greater liability. This work involves a decision support model based on streamflow ensembles developed for the Lower Colorado River Authority in Central Texas. Predicative skill is added to ensemble forecasts that are based on climatology by conditioning the ensembles on observable climate indicators, including streamflow (persistence), soil moisture, land surface temperatures, and large-scale recurrent patterns such as the El Ni¤o-Southern Oscillation, Pacific Decadal Oscillation, and the North Atlantic Oscillation. A Bayesian procedure for updating ensemble probabilities is outlined, and various skill scores are reviewed for evaluating forecast performance. Verification of the ensemble forecasts using a resampling procedure indicates a small but potentially significant improvement in forecast skill that could be exploited in seasonal water management decisions. The ultimate goal of this work will be explicit incorporation of climate forecasts in reservoir operating rules and estimation of the value of the forecasts.

  3. 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).

  4. 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

  5. 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.

  6. 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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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 ...

  8. 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

  9. A one year experience of an operationnal streamflow ensemble forecasting chain taking into account human expertise

    NASA Astrophysics Data System (ADS)

    Mathevet, T.; Moulin, L.; Gailhard, J.; Garçon, R.; Bernard, P.; Le Lay, M.; Zalachori, I.; Chardon, J.

    2012-04-01

    In the context of a national energy company (EDF : Electricité de France), hydro-meteorological forecasts are necessary to ensure safety of both power plants and dams, they also enables to meet environmental standards and to improve water resources management and decision making. Indeed, hydrometeorological ensemble forecasts allow a better representation of uncertainties from both meteorological and hydrological forecasts, which is essential to synthesize available information, coming from different meteorological and hydrological models and from human experience. An operational hydrological ensemble forecasting chain has been developed at EDF since 2008 and has been in use since 2011: last year more than 1200 ensemble streamflow forecasts have been made on more than 30 watersheds covering different spatial scales. This chain is specific because in one hand it takes into account both meteorological and hydrological uncertainties by pre/post-processing, and on the other hand, the human expertise is encouraged : the forecasters can modify forecasted distributions of mean daily rainfall, mean daily air temperature and streamflow. Firstly, this paper presents the different components of this operational hydrometeorological ensemble forecasting chain. Secondly, performances of this chain are assessed on some particular cases. The main steps of this chain are the following: (1) Pre-processing of meteorological ensembles (temperature and rainfall bias and reliability correction), (2) streamflow forecasts using a rainfall-runoff model and streamflow data assimilation and (3) post-processing of streamflow ensembles. The pre-processing of meteorological input is based on the correction (bias and reliability) of EPS-ECMWF forecasts by statistical analog forecasts based on past observed geopotential fields. The post-processing of streamflow forecasts is based on a simple statistical modelisation of the empirical model error by streamflow class and lead-time. The performance

  10. Navigating a Path Toward Operational, Short-term, Ensemble Based, Probablistic Streamflow Forecasts

    NASA Astrophysics Data System (ADS)

    Hartman, R. K.; Schaake, J.

    2004-12-01

    The National Weather Service (NWS) has federal responsibility for issuing public flood warnings in the United States. Additionally, the NWS has been engaged in longer range water resources forecasts for many years, particularly in the Western U.S. In the past twenty years, longer range forecasts have increasingly incorporated ensemble techniques. Ensemble techniques are attractive because they allow a great deal of flexibility, both temporally and in content. This technique also provides for the influence of additional forcings (i.e. ENSO), through either pre or post processing techniques. More recently, attention has turned to the use of ensemble techniques in the short-term streamflow forecasting process. While considerably more difficult, the development of reliable short-term probabilistic streamflow forecasts has clear application and value for many NWS customers and partners. During flood episodes, expensive mitigation actions are initialed or withheld and critical reservoir management decisions are made in the absence of uncertainty and risk information. Limited emergency services resources and the optimal use of water resources facilities necessitates the development of a risk-based decision making process. The development of reliable short-term probabilistic streamflow forecasts are an essential ingredient in the decision making process. This paper addresses the utility of short-term ensemble streamflow forecasts and the considerations that must be addressed as techniques and operational capabilities are developed. Verification and validation information are discussed from both a scientific and customer perspective. Education and training related to the interpretation and use of ensemble products are also addressed.

  11. Application of a Multi-Scheme Ensemble Prediction System and an Ensemble Classification Method to Streamflow Forecasting

    NASA Astrophysics Data System (ADS)

    Pahlow, M.; Moehrlen, C.; Joergensen, J.; Hundecha, Y.

    2007-12-01

    Europe has experienced a number of unusually long-lasting and intense rainfall events in the last decade, resulting in severe floods in most European countries. Ensemble forecasts emerged as a valuable resource to provide decision makers in case of emergency with adequate information to protect downstream areas. However, forecasts should not only provide a best guess of the state of the stream network, but also an estimate of the range of possible outcomes. Ensemble forecast techniques are a suitable tool to obtain the required information. Furthermore a wide range of uncertainty that may impact hydrological forecasts can be accounted for using an ensemble of forecasts. The forecasting system used in this study is based on a multi-scheme ensemble prediction method and forecasts the meteorological uncertainty on synoptic scales as well as the resulting forecast error in weather derived products. Statistical methods are used to directly transform raw weather output to derived products and thereby utilize the statistical capabilities of each ensemble forecast. The forecasting system MS-EPS (Multi-Scheme Ensemble Prediction System) used in this study is a limited area ensemble prediction system using 75 different numerical weather prediction (NWP) model parameterisations. These individual 'schemes' each differ in their formulation of the fast meteorological processes. The MS-EPS forecasts are used as input for a hydrological model (HBV) to generate an ensemble of streamflow forecasts. Determining the most probable forecast from an ensemble of forecasts requires suitable statistical tools. They must enable a forecaster to interpret the model output, to condense the information and to provide the desired product. For this purpose, a probabilistic multi-trend filter (pmt-filter) for statistical post-processing of the hydrological ensemble forecasts is used in this study. An application of the forecasting system is shown for a watershed located in the eastern part of

  12. 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.

  13. 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

  14. 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.

  15. 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

  16. 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.

  17. Combining stochastic weather generation and ensemble streamflow predictions for short to medium term flow forecasting over Quebec, Canada

    NASA Astrophysics Data System (ADS)

    Brissette, F.; Chen, J.; Li, Z.; Turcotte, R.

    2012-04-01

    Probabilistic streamflow forecasting has been an important research avenue over the past decade and such approaches are now more commonly being incorporated into operational forecasting systems within government agencies and industries dealing with water management. This work details a prototype for a streamflow forecast operational system in southern Quebec, Canada. The system uses ensemble meteorological forecasts for short term (less than 10 days) forecasting, switching to a stochastic weather generator for the period exceeding 10 days all the way to a three-month lead time. Precipitation and temperature series are then fed to one (or many) hydrological models to produce streamflow forecasts. The ensemble weather forecasts are corrected for biases and under dispersion using logistic regression. Results show that the ensemble streamflow forecasts resulting from the ensemble meteorological forecast have more skill than the deterministic forecasts. Preliminary results indicate that ensemble meteorological forecasts displayed skill for a period up to 5 days for precipitation and up to about 10 days for temperature. Past ten days, probabilistic streamflow forecasts are based on multiple synthetic times series obtained from a stochastic weather generator. The use of stochastic time series result in better forecasts then resampling the historical record and allows for better evaluation of extreme events. The weather generator can easily be linked to large scale seasonal global predictors, is such links exist. Over the tested basins (continental climate), the system forecast has skills up to a lead time of 4 weeks in the best case. For a lead-time between one and three months, using the forecast prototype yielded no better results than using the historical streamflow record. This work also investigated the uncertainty linked to the choice of one hydrology model and the ability of a multi-model approach to improve streamflow forecasting. Preliminary results showed that

  18. Physical controls on the scale-dependence of ensemble streamflow forecast dispersion

    NASA Astrophysics Data System (ADS)

    Mascaro, G.; Vivoni, E. R.; Deidda, R.

    2010-07-01

    The accuracy of ensemble streamflow forecasts (ESFs) is impacted by the propagation of uncertainty associated with quantitative precipitation forecasts (QPFs) through the physical processes occurring in the basin. In this study, we consider consistent ESFs (i.e., observations and ensemble members are equally likely) and we study the effect of basin area (A) and antecedent rainfall (AR) on the ESF dispersion, a metric of flood forecast skill. Results from a set of numerical experiments indicate that: (i) for small basins (≲180 km2), ESF dispersion is mainly dominated by the runoff generation process and does not depend on the basin size A; (ii) for larger areas, ESF dispersion decreases with A according to a log-linear relation due to the decreasing variability of ensemble QPFs and, possibly, to the channel routing process. In addition, we found that, regardless the basin size, the ESF dispersion decreases as AR increases, and that the influence of AR is larger for basins with fast response times. Physical controls (land cover, soil texture and morphometric features) on the analyzed basin response confirm these interpretations.

  19. Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

    This study develops a new error modelling method for ensemble short-term and real-time streamflow forecasting, called error reduction and representation in stages (ERRIS). The novelty of ERRIS is that it does not rely on a single complex error model but runs a sequence of simple error models through four stages. At each stage, an error model attempts to incrementally improve over the previous stage. Stage 1 establishes parameters of a hydrological model and parameters of a transformation function for data normalization, Stage 2 applies a bias correction, Stage 3 applies autoregressive (AR) updating, and Stage 4 applies a Gaussian mixture distribution to represent model residuals. In a case study, we apply ERRIS for one-step-ahead forecasting at a range of catchments. The forecasts at the end of Stage 4 are shown to be much more accurate than at Stage 1 and to be highly reliable in representing forecast uncertainty. Specifically, the forecasts become more accurate by applying the AR updating at Stage 3, and more reliable in uncertainty spread by using a mixture of two Gaussian distributions to represent the residuals at Stage 4. ERRIS can be applied to any existing calibrated hydrological models, including those calibrated to deterministic (e.g. least-squares) objectives.

  20. 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

  1. 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

  2. Real-time demonstration and evaluation of over-the-loop short to medium-range ensemble streamflow forecasting

    NASA Astrophysics Data System (ADS)

    Wood, A. W.; Clark, E.; Newman, A. J.; Nijssen, B.; Clark, M. P.; Gangopadhyay, S.; Arnold, J. R.

    2015-12-01

    The US National Weather Service River Forecasting Centers are beginning to operationalize short range to medium range ensemble predictions that have been in development for several years. This practice contrasts with the traditional single-value forecast practice at these lead times not only because the ensemble forecasts offer a basis for quantifying forecast uncertainty, but also because the use of ensembles requires a greater degree of automation in the forecast workflow than is currently used. For instance, individual ensemble member forcings cannot (practically) be manually adjusted, a step not uncommon with the current single-value paradigm, thus the forecaster is required to adopt a more 'over-the-loop' role than before. The relative lack of experience among operational forecasters and forecast users (eg, water managers) in the US with over-the-loop approaches motivates the creation of a real-time demonstration and evaluation platform for exploring the potential of over-the-loop workflows to produce usable ensemble short-to-medium range forecasts, as well as long range predictions. We describe the development and early results of such an effort by a collaboration between NCAR and the two water agencies, the US Army Corps of Engineers and the US Bureau of Reclamation. Focusing on small to medium sized headwater basins around the US, and using multi-decade series of ensemble streamflow hindcasts, we also describe early results, assessing the skill of daily-updating, over-the-loop forecasts driven by a set of ensemble atmospheric outputs from the NCEP GEFS for lead times from 1-15 days.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. Examining the Role Played by Meteorological Ensemble Forecasts, Ensemble Kalman Filter Streamflow Assimilation, and Multiple Hydrological Models within a Prediction System Accounting for Three Sources of Uncertainty

    NASA Astrophysics Data System (ADS)

    Anctil, F.; Thiboult, A.; Boucher, M. A.

    2015-12-01

    Building a hydrological ensemble prediction system (H-EPS) from an operational deterministic one may look like an easy task. Indeed, one has only to issue many forecasts at each time step instead of a single one. The problem gets much more complicated when that same person seeks the predictive distribution to be interpretable (reliable). This presentation examine the role played by meteorological ensemble forecasts (meteorological uncertainty), Ensemble Kalman Filter (EnKF) streamflow assimilation (initial conditions uncertainty), and multiple hydrological models (structural uncertainty), combined in a nearly reliable H-EPS. The EnKF is shown to contribute largely to the ensemble accuracy and dispersion, indicating that the initial condition 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 improve the general forecasting performance and prevents 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. The H-EPS was implemented on 38 catchments (Québec, Canada) characterized by a dominant spring freshet and tested for 9-day ahead forecasts over a 2-year period. Twenty lumped hydrological models, chosen for their structural and conceptual diversity, were available to the project as well as ECMWF probabilistic meteorological weather forecasts.

  8. 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

  9. 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

  10. 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

  11. 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

  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. Evaluation of ensemble streamflow predictions in Europe

    NASA Astrophysics Data System (ADS)

    Alfieri, Lorenzo; Pappenberger, Florian; Wetterhall, Fredrik; Haiden, Thomas; Richardson, David; Salamon, Peter

    2014-09-01

    In operational hydrological forecasting systems, improvements are directly related to the continuous monitoring of the forecast performance. An efficient evaluation framework must be able to spot issues and limitations and provide feedback to the system developers. In regional systems, the expertise of analysts on duty is a major component of the daily evaluation. On the other hand, large scale systems need to be complemented with semi-automated tools to evaluate the quality of forecasts equitably in every part of their domain. This article presents the current status of the monitoring and evaluation framework of the European Flood Awareness System (EFAS). For each grid point of the European river network, 10-day ensemble streamflow predictions are evaluated against a reference simulation which uses observed meteorological fields as input to a calibrated hydrological model. Performance scores are displayed over different regions, forecast lead times, basin sizes, as well as in time, considering average scores for moving 12-month windows of forecasts. Skilful predictions are found in medium to large rivers over the whole 10-day range. On average, performance drops significantly in river basins with upstream area smaller than 300 km2, partly due to underestimation of the runoff in mountain areas. Model limitations and recommendations to improve the evaluation framework are discussed in the final section.

  14. Probabilistic Downscaling Methods for Developing Categorical Streamflow Forecasts using Climate Forecasts

    NASA Astrophysics Data System (ADS)

    Mazrooei, A. H.

    2015-12-01

    Statistical information from climate forecast ensembles can be utilized in developing probabilistic streamflow forecasts for providing the uncertainty in streamflow forecast potential. This study examines the use of Multinomial Logistic Regression (MLR) in downscaling the probabilistic information from the large-scale climate forecast ensembles into a point-scale categorical streamflow forecasts. Performance of MLR in developing one-month lead categorical forecasts is evaluated for various river basins over the US Sunbelt. Comparison of MLR with the estimated categorical forecasts from Principle Component Regression (PCR) method under both cross-validation and split-sampling validation reveals that in general the forecasts from MLR has better performance and lower Rank Probability Score (RPS) compared to the PCR forecasts. In addition, MLR performs better than PCR method particularly in arid basins that exhibit strong skewness in seasonal flows with records of distinct dry years. A theoretical underpinning for this improved performance of MLR is also provided.

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. Assessing skill of a global bimonthly streamflow ensemble prediction system

    NASA Astrophysics Data System (ADS)

    van Dijk, A. I.; Peña-Arancibia, J.; Sheffield, J.; Wood, E. F.

    2011-12-01

    Ideally, a seasonal streamflow forecasting system might be conceived of as a system that ingests skillful climate forecasts from general circulation models and propagates these through thoroughly calibrated hydrological models that are initialised using hydrometric observations. In practice, there are practical problems with each of these aspects. Instead, we analysed whether a comparatively simple hydrological model-based Ensemble Prediction System (EPS) can provide global bimonthly streamflow forecasts with some skill and if so, under what circumstances the greatest skill may be expected. The system tested produces ensemble forecasts for each of six annual bimonthly periods based on the previous 30 years of global daily gridded 1° resolution climate variables and an initialised global hydrological model. To incorporate some of the skill derived from ocean conditions, a post-EPS analog method was used to sample from the ensemble based on El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO) index values observed prior to the forecast. Forecasts skill was assessed through a hind-casting experiment for the period 1979-2008. Potential skill was calculated with reference to a model run with the actual forcing for the forecast period (the 'perfect' model) and was compared to actual forecast skill calculated for each of the six forecast times for an average 411 Australian and 51 pan-tropical catchments. Significant potential skill in bimonthly forecasts was largely limited to northern regions during the snow melt period, seasonally wet tropical regions at the transition of wet to dry season, and the Indonesian region where rainfall is well correlated to ENSO. The actual skill was approximately 34-50% of the potential skill. We attribute this primarily to limitations in the model structure, parameterisation and global forcing data. Use of better climate forecasts and remote sensing

  20. Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms

    NASA Astrophysics Data System (ADS)

    Erdal, Halil Ibrahim; Karakurt, Onur

    2013-01-01

    SummaryStreamflow forecasting is one of the most important steps in the water resources planning and management. Ensemble techniques such as bagging, boosting and stacking have gained popularity in hydrological forecasting in the recent years. The study investigates the potential usage of two ensemble learning paradigms (i.e., bagging; stochastic gradient boosting) in building classification and regression trees (CARTs) ensembles to advance the streamflow prediction accuracy. The study, initially, investigates the use of classification and regression trees for monthly streamflow forecasting and employs a support vector regression (SVR) model as the benchmark model. The analytic results indicate that CART outperforms SVR in both training and testing phases. Although the obtained results of CART model in training phase are considerable, it is not in testing phase. Thus, to optimize the prediction accuracy of CART for monthly streamflow forecasting, we incorporate bagging and stochastic gradient boosting which are rooted in same philosophy, advancing the prediction accuracy of weak learners. Comparing with the results of bagged regression trees (BRTs) and stochastic gradient boosted regression trees (GBRTs) models possess satisfactory monthly streamflow forecasting performance than CART and SVR models. Overall, it is found that ensemble learning paradigms can remarkably advance the prediction accuracy of CART models in monthly streamflow forecasting.

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. Perturbation of convection-permitting NWP forecasts for flash-flood ensemble forecasting

    NASA Astrophysics Data System (ADS)

    Vincendon, B.; Ducrocq, V.; Nuissier, O.; Vié, B.

    2011-05-01

    Mediterranean intense weather events often lead to devastating flash-floods. Extending the forecasting lead times further than the watershed response times, implies the use of numerical weather prediction (NWP) to drive hydrological models. However, the nature of the precipitating events and the temporal and spatial scales of the watershed response make them difficult to forecast, even using a high-resolution convection-permitting NWP deterministic forecasting. This study proposes a new method to sample the uncertainties of high-resolution NWP precipitation forecasts in order to quantify the predictability of the streamflow forecasts. We have developed a perturbation method based on convection-permitting NWP-model error statistics. It produces short-term precipitation ensemble forecasts from single-value meteorological forecasts. These rainfall ensemble forecasts are then fed into a hydrological model dedicated to flash-flood forecasting to produce ensemble streamflow forecasts. The verification on two flash-flood events shows that this forecasting ensemble performs better than the deterministic forecast. The performance of the precipitation perturbation method has also been found to be broadly as good as that obtained using a state-of-the-art research convection-permitting NWP ensemble, while requiring less computing time.

  6. 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

  7. CME Ensemble Forecasting - A Primer

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

    SWPC has been evaluating various approaches for ensemble forecasting of Earth-directed CMEs. We have developed the software infrastructure needed to support broad-ranging CME ensemble modeling, including composing, interpreting, and making intelligent use of ensemble simulations. The first step is to determine whether the physics of the interplanetary propagation of CMEs is better described as chaotic (like terrestrial weather) or deterministic (as in tsunami propagation). This is important, since different ensemble strategies are to be pursued under the two scenarios. We present the findings of a comprehensive study of CME ensembles in uniform and structured backgrounds that reveals systematic relationships between input cone parameters and ambient flow states and resulting transit times and velocity/density amplitudes at Earth. These results clearly indicate that the propagation of single CMEs to 1 AU is a deterministic process. Thus, the accuracy with which one can forecast the gross properties (such as arrival time) of CMEs at 1 AU is determined primarily by the accuracy of the inputs. This is no tautology - it means specifically that efforts to improve forecast accuracy should focus upon obtaining better inputs, as opposed to developing better propagation models. In a companion paper (deKoning et al., this conference), we compare in situ solar wind data with forecast events in the SWPC operational archive to show how the qualitative and quantitative findings presented here are entirely consistent with the observations and may lead to improved forecasts of arrival time at Earth.

  8. 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.

  9. 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

  10. 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

  11. 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.

  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. 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.

  14. 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.

  15. 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

  16. 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

  17. 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.

  18. 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.

  19. Towards integrated error estimation and lag-aware data assimilation for operational streamflow forecasting

    NASA Astrophysics Data System (ADS)

    Li, Y.; Ryu, D.; Western, A. W.; Wang, Q.; Robertson, D.; Crow, W. T.

    2013-12-01

    Timely and reliable streamflow forecasting with acceptable accuracy is fundamental for flood response and risk management. However, streamflow forecasting models are subject to uncertainties from inputs, state variables, model parameters and structures. This has led to an ongoing development of methods for uncertainty quantification (e.g. generalized likelihood and Bayesian approaches) and methods for uncertainty reduction (e.g. sequential and variational data assimilation approaches). These two classes of methods are distinct yet related, e.g., the validity of data assimilation is essentially determined by the reliability of error specification. Error specification has been one of the most challenging areas in hydrologic data assimilation and there is a major opportunity for implementing uncertainty quantification approaches to inform both model and observation uncertainties. In this study, ensemble data assimilation methods are combined with the maximum a posteriori (MAP) error estimation approach to construct an integrated error estimation and data assimilation scheme for operational streamflow forecasting. We contrast the performance of two different data assimilation schemes: a lag-aware ensemble Kalman smoother (EnKS) and the conventional ensemble Kalman filter (EnKF). The schemes are implemented for a catchment upstream of Myrtleford in the Ovens river basin, Australia to assimilate real-time discharge observations into a conceptual catchment model, modèle du Génie Rural à 4 paramètres Horaire (GR4H). The performance of the integrated system is evaluated in both a synthetic forecasting scenario with observed precipitation and an operational forecasting scenario with Numerical Weather Prediction (NWP) forecast rainfall. The results show that the error parameters estimated by the MAP approach generates a reliable spread of streamflow prediction. Continuous state updating reduces uncertainty in initial states and thereby improves the forecasting accuracy

  20. 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

  1. Relating seasonal streamflow forecast skill to uncertainties in initial conditions, future forcings, and hydrologic modeling

    NASA Astrophysics Data System (ADS)

    Wood, A. W.; Hopson, T. M.; Newman, A. J.; Sampson, K. M.; Brekke, L. D.; Arnold, J.; Raff, D. A.; Clark, M. P.

    2013-12-01

    Skill in model-based hydrologic forecasting depends on the ability to estimate a watershed's initial moisture and energy conditions, to forecast future weather and climate inputs, and on the quality of the hydrologic model's representation of watershed processes. The impact of these factors on prediction skill varies regionally, seasonally, and by model. We investigate these influences in a series of predictability experiments using calibrated hydrologic simulation models for a 630-watershed dataset that spans the continental US (CONUS), and using the current major simulation models of National Weather Service streamflow forecasting operations. Earlier work in this area (Wood and Lettenmaier, GRL 2008) outlined an ensemble-based strategy for attributing streamflow forecast uncertainty between two endpoints representing zero information about future forcings (ie, the NWS ensemble streamflow prediction, or ESP approach) versus zero information about initial conditions (termed ';reverse-ESP'). This study adopts a more comprehensive approach to characterize the effects of varying levels of uncertainty, from zero knowledge to perfect information in the model world, on streamflow prediction uncertainty. Ensemble hindcasts reflecting varying levels of uncertainty are initialized on a monthly basis for the basins' periods of record, creating background sensitivity information that helps to decompose total hydrologic prediction error into the three components identified above. Observed streamflow prediction errors are then coupled with estimates of realistic uncertainties in future forcing and with model simulation error to infer initial condition errors. This presentation reports findings from the predictability experiments, summarizing the relative importance of uncertainties in basin initial conditions and weather and climate forecasts, and their dependence on forecast lead time, initiation date and regional hydroclimate characteristics.

  2. 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.

  3. Evaluating and Intercomparison of Statistical and Probabilistic Seasonal Streamflow Forecasting Procedures - A Western US Case Study

    NASA Astrophysics Data System (ADS)

    Meier, M. J.; Moradkhani, H.

    2008-12-01

    Over the past decade the western region of the US has experienced explosive population growth, which has created a demand for not only more municipal water supply, but also an increase in water for hydropower, recreation, and aquatic habitat and restoration. In order to provide an uninterrupted, dependable water supply to meet all of these downstream needs, many water resource managers will rely on seasonal streamflow forecasts issued by the National Weather Service (NWS) and the National Resources Conservation Service (NRCS) for planning and decision making purposes. These forecasts are possible in this region because much of the streamflow in this area is a result of the collection of seasonal snowpack over the winter months and the melting of this snowpack over the spring and summer. This pattern of snow accumulation and melt makes it possible for a forecaster to develop water supply forecasts with lead-times of several months. The primary goal of this research is to compare and analyze the results from four different procedures used for producing long-lead streamflow forecasts. The models and techniques covered in this work include Principal Component Analysis (PCA), z-score regression, Independent Component Analysis (ICA), and the Ensemble Streamflow Prediction (ESP) System. These procedures were tested on a few watersheds in the Pacific Northwest and the Colorado River Basin. This work investigates the merits and disadvantages of each procedure, and useful recommendations are made for providing more accurate, reliable and skillful seasonal forecasts.

  4. 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.

  5. 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

  6. Ensemble flood forecasting on the Tocantins River - Brazil

    NASA Astrophysics Data System (ADS)

    Fan, Fernando; Collischonn, Walter; Jiménez, Karena; Sorribas, Mino; Buarque, Diogo; Siqueira, Vinicius

    2014-05-01

    The Tocantins River basin is located in the northern region of Brazil and has about 300.000 km2 of drainage area upstream of its confluence with river Araguaia, its major tributary. The Tocantins River is intensely used for hydropower production, with seven major dams, including Tucuruí, world's fourth largest in terms of installed capacity. In this context, the use of hydrological streamflow forecasts at this basin is very useful to support the decision making process for reservoir operation, and can produce benefits by reducing damages from floods, increasing dam safety and upgrading efficiency in power generation. The occurrence of floods along the Tocantins River is a relatively frequent event, where one recent example is the year of 2012, when a large flood occurred in the Tocantins River with discharge peaks exceeding 16.000m³/s, and causing damages to cities located along the river. After this flooding event, a hydrological forecasting system was developed and is operationally in use since mid-2012 in order to assist the decision making of dam operation along the river basin. The forecasting system is based on the MGB-IPH model, a large scale distributed hydrological model, and initially used only telemetric data as observed information and deterministic rainfall forecasts from the Brazilian Meteorological Forecasting Centre (CPTEC) with 7-days lead time as input. Since August-2013 the system has been updated and now works with two new features: (i) a technique for merging satellite TRMM real-time precipitation estimative with gauged information is applied to reduce the uncertainty due to the lack of observed information over a portion of the basin, since the total number of rain gages available is scarce compared to the total basin area; (ii) rainfall ensemble forecasts with 16-days lead time provided by the Global Ensemble Forecasting System (GEFs), from the 2nd Generation of NOAA Global Ensemble Reforecast Data Set, maintained by the National Center for

  7. Hydrologic Ensemble Forecast Service for Operational Short-to-Long Range Hydrometeorological and Hydrologic Ensembles in the U.S. National Weather Service

    NASA Astrophysics Data System (ADS)

    Demargne, Julie; Brown, James; Wu, Limin; Regonda, Satish; He, Minxue; Fresch, Mark

    2013-04-01

    In order to quantify the main sources of uncertainty in hydrologic forecasts for a wide range of practical applications (e.g. flood risk management, water supply management, streamflow regulation, and recreation planning), the NOAA's National Weather Service (NWS) is implementing a short- to long-range Hydrologic Ensemble Forecast Service (HEFS). The HEFS extends the existing hydrologic ensemble services to include short-range forecasts and incorporate additional weather and climate information. It provides, at forecast horizons ranging from 6-hr to about a year, hydrometeorological and hydrologic ensemble forecasts that are reasonably unbiased and skillful over a wide range of spatio-temporal scales. Based on separate modeling of the input and hydrologic uncertainties, the HEFS includes: 1) the Meteorological Ensemble Forecast Processor (MEFP), which ingests weather and climate forecasts from multiple numerical weather prediction models to produce bias-corrected forcing ensembles at the hydrologic basin scales; 2) the hydrologic Ensemble Post-processor (EnsPost), which models the collective hydrologic uncertainty and corrects for systematic biases in streamflow; 3) the Ensemble Verification Service, which verifies the forcing and streamflow ensembles to help identify the main sources of skill and error in the forecasts and provides forecast quality information for forecasters and users; and 4) the Graphics Generator, which enables forecasters to create configurable plots for analysis and delivery to the public. The implementation started in 2011 and now five NWS River Forecast Centers are testing the HEFS in real-time over a large number of basins. The New York City Department of Environmental Protection is currently transitioning its water supply system for New York City to make use of the HEFS ensembles for more efficient and effective water management. This presentation describes recent verification results from multi-year hindcasting based on precipitation and

  8. 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.

  9. Ozone ensemble forecast with machine learning algorithms

    NASA Astrophysics Data System (ADS)

    Mallet, Vivien; Stoltz, Gilles; Mauricette, Boris

    2009-03-01

    We apply machine learning algorithms to perform sequential aggregation of ozone forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system Polyphemus. The ensemble simulations are obtained by changes in the physical parameterizations, the numerical schemes, and the input data to the models. The simulations are carried out for summer 2001 over western Europe in order to forecast ozone daily peaks and ozone hourly concentrations. On the basis of past observations and past model forecasts, the learning algorithms produce a weight for each model. A convex or linear combination of the model forecasts is then formed with these weights. This process is repeated for each round of forecasting and is therefore called sequential aggregation. The aggregated forecasts demonstrate good results; for instance, they always show better performance than the best model in the ensemble and they even compete against the best constant linear combination. In addition, the machine learning algorithms come with theoretical guarantees with respect to their performance, that hold for all possible sequences of observations, even nonstochastic ones. Our study also demonstrates the robustness of the methods. We therefore conclude that these aggregation methods are very relevant for operational forecasts.

  10. 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

  11. 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

  12. Reliable probabilistic forecasts from an ensemble reservoir inflow forecasting system

    NASA Astrophysics Data System (ADS)

    Bourdin, Dominique R.; Nipen, Thomas N.; Stull, Roland B.

    2014-04-01

    This paper describes a probabilistic reservoir inflow forecasting system that explicitly attempts to sample from major sources of uncertainty in the modeling chain. Uncertainty in hydrologic forecasts arises due to errors in the hydrologic models themselves, their parameterizations, and in the initial and boundary conditions (e.g., meteorological observations or forecasts) used to drive the forecasts. The Member-to-Member (M2M) ensemble presented herein uses individual members of a numerical weather model ensemble to drive two different distributed hydrologic models, each of which is calibrated using three different objective functions. An ensemble of deterministic hydrologic states is generated by spinning up the daily simulated state using each model and parameterization. To produce probabilistic forecasts, uncertainty models are used to fit probability distribution functions (PDF) to the bias-corrected ensemble. The parameters of the distribution are estimated based on statistical properties of the ensemble and past verifying observations. The uncertainty model is able to produce reliable probability forecasts by matching the shape of the PDF to the shape of the empirical distribution of forecast errors. This shape is found to vary seasonally in the case-study watershed. We present an "intelligent" adaptation to a Probability Integral Transform (PIT)-based probability calibration scheme that relabels raw cumulative probabilities into calibrated cumulative probabilities based on recent past forecast performance. As expected, the intelligent scheme, which applies calibration corrections only when probability forecasts are deemed sufficiently unreliable, improves reliability without the inflation of ignorance exhibited in certain cases by the original PIT-based scheme.

  13. How can we best use climate information and hydrologic initial conditions to improve seasonal streamflow forecasts?

    NASA Astrophysics Data System (ADS)

    Mendoza, P. A.; Wood, A. W.; Rothwell, E.; Clark, M. P.; Brekke, L. D.; Arnold, J. R.

    2015-12-01

    Over the last decades, a number of forecasting centers around the world have offered seasonal streamflow predictions, using methodologies that span a wide range of data requirements and complexity. In the western United States, two primary approaches have been adopted for operational purposes: (i) development of regression equations between future streamflow and in situ observations (e.g. rainfall, snow water equivalent), and (ii) ensemble hydrologic model simulations that combine initial watershed moisture states with historically observed weather sequences for the forecast period (e.g., Ensemble Streamflow Prediction, ESP). Nevertheless, none of these methodologies makes use of analyzed or forecast climate information, which might increase the skill of seasonal predictions. Further, there is a need to better understand the marginal benefits of using more complex methods (from statistical to dynamical) and different types of information. In this work, we provide a systematic intercomparison of various seasonal streamflow forecasting techniques, including: (1) a dynamical approach based on conceptual hydrologic modeling and ESP, (2) statistical regression using climate information and/or initial hydrologic conditions, (3) an ESP trace weighting scheme based on analog climatic conditions, and (4) combination of dynamical and statistical forecasts (i.e. hybrid approach). Climate information is taken from the NCEP CFSv2 reanalysis and reforecast datasets. These methods are tested for predicting spring (e.g., May-September) runoff volumes at case study basins located in the US Pacific Northwest, and results obtained for several initialization times are evaluated in terms of accuracy, probabilistic skill and statistical consistency. Preliminary results show that for earlier initialization times (October 1 to December 1), statistical and hybrid techniques that make use of climate information outperform ESP in terms of correlation and probabilistic skill. Although ESP at

  14. 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).

  15. 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

  16. Evaluation of ensemble streamflow predictions of the European Flood Awareness System (EFAS)

    NASA Astrophysics Data System (ADS)

    Alfieri, Lorenzo; Pappenberger, Florian; Wetterhall, Fredrik; Haiden, Thomas; Richardson, David; Salamon, Peter; Thielen, Jutta

    2014-05-01

    In operational hydrological forecasting systems, improvements are directly related to the continuous monitoring of the forecast performance. An efficient evaluation framework must be able to spot issues and limitations and provide feedback to the system developers. In regional systems, the expertise of analysts on duty is a major component of the daily evaluation. On the other hand, large scale systems need to be complemented with semi-automated tools to evaluate the quality of forecasts equitably in every part of their domain. This work presents the current status of the monitoring and evaluation framework of the European Flood Awareness System (EFAS). Twice per day, EFAS performs hydrological simulation of ensemble weather predictions over Europe and detects river sections where forecast streamflow is likely to exceed flood warning thresholds in the coming days. In each 5x5 km2 grid point of the European river network, 10-day ensemble streamflow predictions driven by ECMWF weather forecasts are evaluated against a reference simulation which uses observed meteorological fields as input to a calibrated hydrological model. Performance scores are displayed spatially on maps and plotted against their forecast lead time, basin size, as well as in time, considering average scores for 12-month moving windows of forecasts. Results indicate skillful predictions in medium to large river basins over the 10-day range. An evaluation of 12-month average scores over the past 5 years suggests a moderate improvement for all 12-month forecasts ending from the beginning of 2013 onwards. Such improvement occurred notwithstanding an increasing negative forecast bias in mountain regions. On average, performance drops significantly in river basins with upstream area smaller than 300 km2, due to resolution issues and to the underestimation of the runoff in mountain areas. On the other hand, performance in rivers with large upstream area (i.e., 10,000 km2 and above) shows highly positive

  17. 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

  18. Ensemble forecasts of monthly catchment rainfall out to long lead times by post-processing coupled general circulation model output

    NASA Astrophysics Data System (ADS)

    Schepen, Andrew; Wang, Q. J.

    2014-11-01

    Monthly streamflow forecasts with long lead time are being sought by water managers in Australia. In this study, we take a first step towards a monthly streamflow modelling approach by harnessing a coupled ocean-atmosphere general circulation model (CGCM) to produce monthly rainfall forecasts for three catchments across Australia. Bayesian methodologies are employed to produce forecasts based on CGCM raw rainfall forecasts and also CGCM sea surface temperature forecasts. The Schaake Shuffle is used to connect forecast ensemble members of individual months to form ensemble monthly time series forecasts. Monthly forecasts and three-monthly forecasts of rainfall are assessed for lead times of 0-6 months, based on leave-one-year-out cross-validation for 1980-2010. The approach is shown to produce well-calibrated ensemble forecasts that source skill from both the atmospheric and ocean modules of the CGCM. Although skill is generally low, moderate skill scores are observed in some catchments for lead times of up to 6 months. In months and catchments where there is limited skill, the forecasts revert to climatology. Thus the forecasts developed can be considered suitable for continuously forecasting time series of streamflow to long lead times, when coupled with a suitable monthly hydrological model.

  19. 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

  20. 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.

  1. 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.

  2. 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

  3. 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

  4. Water balance models in one-month-ahead streamflow forecasting.

    USGS Publications Warehouse

    Alley, W.M.

    1985-01-01

    Techniques are tested that incorporate information from water balance models in making 1-month-ahead streamflow forecasts in New Jersey. The results are compared to those based on simple autoregressive time series models. The relative performance of the models is dependent on the month of the year in question. -from Author

  5. 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.

  6. Why large-scale seasonal streamflow forecasts are feasible

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

    Seasonal forecasts of precipitation and temperature, using either statistical or dynamic prediction, have been around for almost 2 decades. The skill of these forecasts differ both in space and time, with highest skill in areas heavily influenced by SST anomalies such as El Nino or areas where land surface properties have a major impact on e.g. Monsoon strength, such as the vegetation cover of the Sahel region or the snow cover of the Tibetan plateau. However, the skill of seasonal forecasts is limited in most regions, with anomaly correlation coefficients varying between 0.2 and 0.5 for 1-3 month precipitation totals. This raises the question whether seasonal hydrological forecasting is feasible. Here, we make the case that it is. Using the example of statistical forecasts of NAO-strength and related precipitation anomalies over Europe, we show that the skill of large-scale streamflow forecasts is generally much higher than the precipitation forecasts itself, provided that the initial state of the system is accurately estimated. In the latter case, even the precipitation climatology can produce skillful results. This is due to the inertia of the hydrological system rooted in the storage of soil moisture, groundwater and snow pack, as corroborated by a recent study using snow observations for seasonal streamflow forecasting in the Western US. These examples seem to suggest that for accurate seasonal hydrological forecasting, correct state estimation is more important than accurate seasonal meteorological forecasts. However, large-scale estimation of hydrological states is difficult and validation of large-scale hydrological models often reveals large biases in e.g. streamflow estimates. Fortunately, as shown with a validation study of the global model PCR-GLOBWB, these biases are of less importance when seasonal forecasts are evaluated in terms of their ability to reproduce anomalous flows and extreme events, i.e. by anomaly correlations or categorical quantile

  7. 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

  8. National Weather Service (NWS) Implementation of the Hydrologic Ensemble Forecast Service

    NASA Astrophysics Data System (ADS)

    Hartman, R. K.; Fresch, M. A.; Wells, E.

    2015-12-01

    Operational hydrologic forecasters as well as the communities that they serve have long recognized the value of including uncertainty in hydrologic projections. While single value (deterministic) forecasts are easy to understand and link to specific mitigation actions, the potential for using modern risk management strategies is very limited. This is particularly evident at lead times beyond a few days when forecast skill may be low but the value (and costs) of mitigation actions may be quite high. Based on nearly ten years of research and development, the NWS's National Water Center (NWC, formerly the Office of Hydrologic Development) implemented and evaluated the Hydrologic Ensemble Forecast Service (HEFS, see Demargne et al. 2014 Brown et al., 2013, Brown et al., 2014a/b/c). The HEFS provides hydrologic forecasts that reflect the total uncertainty, including that contributed by the meteorological forcing and the hydrologic modeling. The HEFS leverages the skill in weather and climate forecasts to produce ensemble forecasts of precipitation, temperature and streamflow at forecast lead times ranging from one hour to one year. The resulting ensembles represent a rich dataset from which a wide variety of risk-based decision support information can be derived. The NWS River Forecast Centers (RFCs) are starting to incorporate the Hydrologic Ensemble Forecast Service (HEFS,) into their routine operations. In 2012, five (of thirteen) RFCs began running and testing HEFS in an experimental mode. In 2015, HEFS was deployed (including training and software support) to the eight remaining RFCs. Currently, all RFCs are running the HEFS every day in real-time for an increasing number of forecast locations. Eventually, forecasts from the HEFS will be integrated into the warning/hazard services at the NWS Weather Forecast Offices (WFOs). This contribution describes the HEFS framework, the development and deployment strategy, and the operational plans for HEFS going forward.

  9. 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

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. Can multimodel ensembles improve medium range precipitation forecasts?

    NASA Astrophysics Data System (ADS)

    Flowerdew, J.

    2010-09-01

    Ensemble forecasts aim to improve decision-making by predicting the full distribution of possible outcomes. Forecasts from a single ensemble may suffer from systematic errors due to the model and assimilation processes used. By combining results from multiple ensembles which have different systematic errors and are more skilful in different situations, it should be possible to produce a composite forecast superior to that from any individual ensemble. The value of ensemble combination has been demonstrated by several authors for variables such as surface temperature, but there has been relatively little investigation of the potential benefit for the key forecast variable of precipitation. This work uses a subset of the TIGGE dataset to investigate the benefit of multimodel ensembles over single ensembles for medium range forecasts of precipitation. Early results verifying raw output against short range control forecasts show noticeable benefit across a range of score measures and thresholds, particularly for the reliability component of the Brier Skill Score. We plan to extend this verification to radar- and gauge-based observations, examining the nature and independence of the forecast errors to understand how multimodel combination might be beneficial. Further work would consider appropriate methods of calibration, along with the question of whether the multimodel advantage survives against calibrated single ensembles.

  15. 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.

  16. Ensemble seasonal hydrological forecasting at the pan-European scale

    NASA Astrophysics Data System (ADS)

    Pechlivanidis, Ilias; Spångmyr, Henrik; Bosshard, Thomas; Gustafsson, David; Olsson, Jonas

    2015-04-01

    Recent advances in understanding and forecasting of climate and climate change have resulted into skillful and useful meteorological predictions, which can consequently increase the confidence of hydrological prognosis and awareness from an end-user perspective. However, the majority of seasonal impact modelling has commonly been conducted at only one or a limited number of basins limiting the need to understand large systems which are heavily influenced by human activities. In here, we complement the "deep" knowledge from basin based modelling using large scale multi-basin modelling, which is capable of representing human influences (i.e. irrigation, reservoirs and groundwater use). We analyse the seasonal predictive skill along Europe's hydro-climatic gradient using the pan-European E-HYPE v3.0 multi-basin hydrological model. Forcing data (mean daily precipitation and temperature) are derived from the WFDEI product for the period 1979-2010 and used to initialise the hydrological model (level in surface water, i.e. reservoirs, lakes and wetlands, soil moisture, snow depth). Re-forecast forcing data (daily mean precipitation and temperature for the period 1981-2010) from ECMWF's System 4 (15 members initialised every month) are firstly bias corrected using a modified version of the Distribution Based Scaling (DBS) method to account for drifting conditioning the bias correction on the lead month, and further used to drive E-HYPE. The predictive skill of river runoff for a number of European basins is assessed on seasonal timescales. Seasonal re-forecasts are evaluated with respect to their accuracy against observed impact variables, i.e. streamflow, at different space and time-scales; the value of the predictions are assessed using various performance metrics. Verification points (around 2600 stations) are used to represent various climatologies, soil-types, land uses, altitudes and basin scales within Europe. We finally identify regions of similar hydrological

  17. Ensemble approach for hydrological forecasting in ungauged catchments

    NASA Astrophysics Data System (ADS)

    Randrianasolo, Annie; Ramos, Maria-Helena; Andreassian, Vazken

    2013-04-01

    This study focuses on the application of ensemble approaches to forecast flows in ungauged catchments. The aim is to study the best strategy to search for information in gauged "donor" basins and to transfer it to the ungauged site. We investigate what information is needed to set up a rainfall-runoff model and to perform forecast updating in real time. These two components of a flood forecasting system are thus decoupled in our approach. The methodology adopted integrates the scenarios of regional transfer of information and the scenarios of ensemble weather forecasting together in a forecasting system. The approach of ensemble forecasting is thus generalised to the particular case of hydrological forecasting in ungauged basins. The study is based on 211 catchments in France and on an archive of about 4.5 years of ensemble forecasts of rainfall, which are used for hydrological modelling on a daily time step. Flow forecasts are evaluated with special attention paid to the attributes of reliability and accuracy of the forecasts. The results show that forecast reliability in ungauged sites can be improved by using several sets of parameters from neighbour catchments, while forecast accuracy is improved with the transfer of updating information from gauged neighbour catchments.

  18. 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.

  19. A Sequential Monte Carlo Approach for Streamflow Forecasting

    NASA Astrophysics Data System (ADS)

    Hsu, K.; Sorooshian, S.

    2008-12-01

    As alternatives to traditional physically-based models, Artificial Neural Network (ANN) models offer some advantages with respect to the flexibility of not requiring the precise quantitative mechanism of the process and the ability to train themselves from the data directly. In this study, an ANN model was used to generate one-day-ahead streamflow forecasts from the precipitation input over a catchment. Meanwhile, the ANN model parameters were trained using a Sequential Monte Carlo (SMC) approach, namely Regularized Particle Filter (RPF). The SMC approaches are known for their capabilities in tracking the states and parameters of a nonlinear dynamic process based on the Baye's rule and the proposed effective sampling and resampling strategies. In this study, five years of daily rainfall and streamflow measurement were used for model training. Variable sample sizes of RPF, from 200 to 2000, were tested. The results show that, after 1000 RPF samples, the simulation statistics, in terms of correlation coefficient, root mean square error, and bias, were stabilized. It is also shown that the forecasted daily flows fit the observations very well, with the correlation coefficient of higher than 0.95. The results of RPF simulations were also compared with those from the popular back-propagation ANN training approach. The pros and cons of using SMC approach and the traditional back-propagation approach will be discussed.

  20. 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.

  1. Ensemble Bayesian forecasting system Part I: Theory and algorithms

    NASA Astrophysics Data System (ADS)

    Herr, Henry D.; Krzysztofowicz, Roman

    2015-05-01

    The ensemble Bayesian forecasting system (EBFS), whose theory was published in 2001, is developed for the purpose of quantifying the total uncertainty about a discrete-time, continuous-state, non-stationary stochastic process such as a time series of stages, discharges, or volumes at a river gauge. The EBFS is built of three components: an input ensemble forecaster (IEF), which simulates the uncertainty associated with random inputs; a deterministic hydrologic model (of any complexity), which simulates physical processes within a river basin; and a hydrologic uncertainty processor (HUP), which simulates the hydrologic uncertainty (an aggregate of all uncertainties except input). It works as a Monte Carlo simulator: an ensemble of time series of inputs (e.g., precipitation amounts) generated by the IEF is transformed deterministically through a hydrologic model into an ensemble of time series of outputs, which is next transformed stochastically by the HUP into an ensemble of time series of predictands (e.g., river stages). Previous research indicated that in order to attain an acceptable sampling error, the ensemble size must be on the order of hundreds (for probabilistic river stage forecasts and probabilistic flood forecasts) or even thousands (for probabilistic stage transition forecasts). The computing time needed to run the hydrologic model this many times renders the straightforward simulations operationally infeasible. This motivates the development of the ensemble Bayesian forecasting system with randomization (EBFSR), which takes full advantage of the analytic meta-Gaussian HUP and generates multiple ensemble members after each run of the hydrologic model; this auxiliary randomization reduces the required size of the meteorological input ensemble and makes it operationally feasible to generate a Bayesian ensemble forecast of large size. Such a forecast quantifies the total uncertainty, is well calibrated against the prior (climatic) distribution of

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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

  9. 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

  10. 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

  11. 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.

  12. 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).

  13. 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).

  14. 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

  15. 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.

  16. Addendum to Bayesian ensemble forecast of river stages and ensemble size requirements

    NASA Astrophysics Data System (ADS)

    Herr, Henry D.; Krzysztofowicz, Roman

    2014-07-01

    Ensemble size requirements were analyzed using Bayesian ensemble forecasts (Herr and Krzysztofowicz, 2010). These forecasts were generated using output from an analytic-numerical Bayesian forecasting system (BFS) within a Monte-Carlo algorithm. Empirical distributions were estimated from the ensemble forecasts and compared with the analytic distributions output by the BFS. Using the maximum absolute difference as a measure of the quality of fit, it was shown that a single graph described the relationship between the ensemble size and the expected maximum absolute difference. Within the scope of the experiments, the graph was insensitive to forecast point, precipitation event (rain or no rain), and lead time. However, the graph only applied to probabilistic river stage forecast and probabilistic flood forecast, but not to probabilistic stage transition forecast, which is specified by a family of conditional predictive one-step transition distributions. Herein, further mathematical analysis of the estimation algorithm shows that, with a small modification, the single graph can be applied to all three types of forecasts.

  17. 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

  18. 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.

  19. 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.

  20. Evaluating reliability and resolution of ensemble forecasts using information theory

    NASA Astrophysics Data System (ADS)

    Weijs, Steven; van de Giesen, Nick

    2010-05-01

    Ensemble forecasts are increasingly popular for the communication of uncertainty towards the public and decision makers. Ideally, an ensemble forecast reflects both the uncertainty and the information in a forecast, which means that the spread in the ensemble should accurately represent the true uncertainty. For ensembles to be useful, they should be probabilistic, as probability is the language to precisely describe an incomplete state of knowledge, that is typical for forecasts. Information theory provides the ideal tools to deal with uncertainty and information in forecasts. Essential to the use and development of models and forecasts are ways to evaluate their quality. Without a proper definition of what is good, it is impossible to improve forecasts. In contrast to forecast value, which is user dependent, forecast quality, which is defined as the correspondence between forecasts and observations, can be objectively defined, given the question that is asked. The evaluation of forecast quality is known as forecast verification. Numerous techniques for forecast verification have been developed over the past decades. The Brier score (BS) and the derived Ranked Probability Score (RPS) are among the most widely used scores for measuring forecast quality. Both of these scores can be split into three additive components: uncertainty, reliability and resolution. While the first component, uncertainty, just depends on the inherent variability in the forecasted event, the latter two measure different aspects of the quality of forecasts themselves. Resolution measures the difference between the conditional probabilities and the marginal probabilities of occurrence. The third component, reliability, measures the conditional bias in the probability estimates, hence unreliability would be a better name. In this work, we argue that information theory should be adopted as the correct framework for measuring quality of probabilistic ensemble forecasts. We use the information

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

    NASA Astrophysics Data System (ADS)

    Thiboult, A.; Anctil, F.; Boucher, M.-A.

    2015-07-01

    Seeking for 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, multimodel approaches and meteorological ensemble forecasting proved to have the capability to improve upon deterministic hydrological forecast. This study aims at untangling the sources of uncertainty by studying the combination of these tools and assessing their 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 stage in the forecasting process by using different means. Their combination outperforms any of the tool used solely. The EnKF is shown to contribute largely to the ensemble accuracy and dispersion, indicating that the initial condition 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 improve the general forecasting performance and prevents 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 Ensemble Kalman Filter tuning to avoid overlapping in error deciphering.

  2. 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.

  3. Implementation of stochastic parameterization of surface variables in ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Weidle, F.; Wang, Y.; Wittmann, C.; Tang, J.; Xia, F.

    2012-04-01

    To represent uncertainties in the numerical weather prediction, various ensemble forecasting systems have been recently developed by different weather services around the world. Most ensemble systems account for uncertainties in the initial conditions and for limited area ensemble systems also errors in the lateral boundary conditions are considered by using one or more global ensemble systems as coupling model. To account for uncertainties in the model a stochastic physic scheme is one possibility which is in use in some ensemble systems. In forecast models surface processes are typically treated by parameterizations to represent physical processes at the interface between surface and atmosphere. Especially for the forecast quality of near surface fields these processes can play a crucial role but also for the formation of precipitation in the atmosphere. However, uncertainties in the forecast due to errors in the representation of surface processes, which can feed back to atmospheric processes are not yet considered in any operational ensemble system. The aim of the work is to investigate whether the representation of uncertainties in the surface scheme of an ensemble system can improve the probabilistic forecast. Therefore a stochastic physics scheme for surface processes, similar to the stochastic physics scheme that has been used for several years in the ECMWF-EPS, is implemented in ALADIN-LAEF, the operational limited area ensemble model of the Austrian Weather service. The stochastic physic method is used to randomly perturb tendencies of surface variables, for example soil moisture and surface temperature in the surface scheme, during the forecast. In the presentation the implementation in ALADIN-LAEF is introduced and simulations of high impact weather situations as well as a verification over 3 months against forecasts without perturbing surface fields will be presented. To verify the importance of the representation of uncertainties due to surface

  4. Improving ensemble forecasting with q-norm bred vectors

    NASA Astrophysics Data System (ADS)

    Pazo, Diego; Lopez, Juan Manuel; Rodriguez, Miguel Angel

    2016-04-01

    Error breeding is a popular and simple method to generate initial perturbations for use in ensemble forecasting that is used for operational purposes in many weather/climate centres worldwide. There is a widespread belief among practitioners that the type of norm used in the periodic normalizations of BVs does not have an effect on the performance of ensemble forecasting systems. However, we have recently reported that BVs constructed with different norms have indeed very different dynamical and spatial properties. In particular, BVs constructed with the 0-norm or geometric norm has nice properties (e.g. enhancement of the ensemble diversity), which in principle render it more adequate to construct ensembles than other norm types like the Euclidean one. These advantages are clearly demonstrated here in a simple experiment of ensemble forecasting for the Lorenz-96 model with ensembles of BVs. Our simple numerical assimilation experiment shows how the increased statistical diversity of geometric BVs leads to improved scores regarding forecasting capabilities as compared with BVs constructed with the standard Euclidean norm.

  5. Skill forecasting from different wind power ensemble prediction methods

    NASA Astrophysics Data System (ADS)

    Pinson, Pierre; Nielsen, Henrik Aa; Madsen, Henrik; Kariniotakis, George

    2007-07-01

    This paper presents an investigation on alternative approaches to the providing of uncertainty estimates associated to point predictions of wind generation. Focus is given to skill forecasts in the form of prediction risk indices, aiming at giving a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the dispersion of ensemble members for a single prediction horizon, or over a set of successive look-ahead times. It is shown on the test case of a Danish offshore wind farm how prediction risk indices may be related to several levels of forecast uncertainty (and energy imbalances). Wind power ensemble predictions are derived from the transformation of ECMWF and NCEP ensembles of meteorological variables to power, as well as by a lagged average approach alternative. The ability of risk indices calculated from the various types of ensembles forecasts to resolve among situations with different levels of uncertainty is discussed.

  6. 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.

  7. Probabilistic flood warning using grand ensemble weather forecasts

    NASA Astrophysics Data System (ADS)

    He, Y.; Wetterhall, F.; Cloke, H.; Pappenberger, F.; Wilson, M.; Freer, J.; McGregor, G.

    2009-04-01

    As the severity of floods increases, possibly due to climate and landuse change, there is urgent need for more effective and reliable warning systems. The incorporation of numerical weather predictions (NWP) into a flood warning system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and can lead to a high number of false or missed warnings. An ensemble of weather forecasts from one Ensemble Prediction System (EPS), when used on catchment hydrology, can provide improved early flood warning as some of the uncertainties can be quantified. EPS forecasts from a single weather centre only account for part of the uncertainties originating from initial conditions and stochastic physics. Other sources of uncertainties, including numerical implementations and/or data assimilation, can only be assessed if a grand ensemble of EPSs from different weather centres is used. When various models that produce EPS from different weather centres are aggregated, the probabilistic nature of the ensemble precipitation forecasts can be better retained and accounted for. The availability of twelve global EPSs through the 'THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for the design of an improved probabilistic flood forecasting framework. This work presents a case study using the TIGGE database for flood warning on a meso-scale catchment. The upper reach of the River Severn catchment located in the Midlands Region of England is selected due to its abundant data for investigation and its relatively small size (4062 km2) (compared to the resolution of the NWPs). This choice was deliberate as we hypothesize that the uncertainty in the forcing of smaller catchments cannot be represented by a single EPS with a very limited number of ensemble members, but only through the variance given by a large number ensembles

  8. 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.

  9. 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.

  10. Ensemble Forecasts for Water Management Optimization Procedures (EOP)

    NASA Astrophysics Data System (ADS)

    Howard, C. D.

    2007-12-01

    Science and technology support water management through weather forecasts, hydrologic forecasts, and water management models. In modern water management practice these three tools and their data are integrated into seamless "Decision Support Systems" with convenient user interfaces. This paper discusses the last step in such systems for reservoir operations management. This is an optimization step that incorporates uncertainties into suggestions for making the best possible decisions. Ensemble weather forecasts typically provide a set of alternative equally likely weather sequences over the forecast period. These are used as inputs to hydrologic models to provide a suite of hydrologic time series that represent equally likely alternative water supply and flood forecasts that are conditional on current weather and watershed initial conditions. The decision making process at the water management level includes the following steps: evaluate risks, develop contingency plans, implement mitigative actions, determine optimal operating decisions. These steps each require a probabilistic analysis that may be optimized with an appropriate procedure for the specific application. If the right programs and data management tools are available, and readily accessible, hydrologic ensemble forecasts can provide inputs to an Ensemble Optimization Procedure (EOP) to support decision making. This paper discusses practical implementation issues and demonstrates three EOP methods for scheduling operation of a hypothetical hydroelectric reservoir.

  11. Regionalization of post-processed ensemble runoff forecasts

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

    For many years, meteorological models have been run with perturbated initial conditions or parameters to produce ensemble forecasts that are used as a proxy of the uncertainty of the forecasts. However, the ensembles are usually both biased (the mean is systematically too high or too low, compared with the observed weather), and has dispersion errors (the ensemble variance indicates a too low or too high confidence in the forecast, compared with the observed weather). The ensembles are therefore commonly post-processed to correct for these shortcomings. Here we look at one of these techniques, referred to as Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). Originally, the post-processing parameters were identified as a fixed set of parameters for a region. The application of our work is the European Flood Awareness System (http://www.efas.eu), where a distributed model is run with meteorological ensembles as input. We are therefore dealing with a considerably larger data set than previous analyses. We also want to regionalize the parameters themselves for other locations than the calibration gauges. The post-processing parameters are therefore estimated for each calibration station, but with a spatial penalty for deviations from neighbouring stations, depending on the expected semivariance between the calibration catchment and these stations. The estimated post-processed parameters can then be used for regionalization of the postprocessing parameters also for uncalibrated locations using top-kriging in the rtop-package (Skøien et al., 2006, 2014). We will show results from cross-validation of the methodology and although our interest is mainly in identifying exceedance probabilities for certain return levels, we will also show how the rtop package can be used for creating a set of post-processed ensembles through simulations.

  12. Bayesian Processor of Ensemble for Precipitation Forecasting: A Development Plan

    NASA Astrophysics Data System (ADS)

    Toth, Z.; Krzysztofowicz, R.

    2006-05-01

    The Bayesian Processor of Ensemble (BPE) is a new, theoretically-based technique for probabilistic forecasting of weather variates. It is a generalization of the Bayesian Processor of Output (BPO) developed by Krzysztofowicz and Maranzano for processing single values of multiple predictors into a posterior distribution function of a predictand. The BPE processes an ensemble of a predictand generated by multiple integrations of a numerical weather prediction (NWP) model, and optimally fuses the ensemble with climatic data in order to quantify uncertainty about the predictand. As is well known, Bayes theorem provides the optimal theoretical framework for fusing information from different sources and for obtaining the posterior distribution function of a predictand. Using a family of such distribution functions, a given raw ensemble can be mapped into a posterior ensemble, which is well calibrated, has maximum informativeness, and preserves the spatio-temporal and cross-variate dependence structure of the NWP output fields. The challenge is to develop and test the BPE suitable for operational forecasting. This talk will present the basic design components of the BPE, along with a discussion of the climatic and training data to be used in its potential application at the National Centers for Environmental Prediction (NCEP). The technique will be tested first on quasi-normally distributed variates and next on precipitation variates. For reasons of economy, the BPE will be applied on the relatively coarse resolution grid corresponding to the ensemble output, and then the posterior ensemble will be downscaled to finer grids such as that of the National Digital Forecast Database (NDFD).

  13. Combined use of SOM-classification and Feed-Forward Networks for multinetwork streamflow forecasting

    NASA Astrophysics Data System (ADS)

    Toth, E.

    2009-04-01

    The contribution presents the results of a modular approach for real-time streamflow forecasting, that applies different rainfall-runoff models, on the basis of the hydro-meteorological situation characterising each forecast instant. Modular neural networks or multi-network modelling for streamflow forecasting have been successfully applied in the recent years (e.g. Abrahart and See, 2000; Corzo and Solomatine, 2007; Parasuraman and Elshorbagy 2007). The hydrological and meteorological conditions of the watershed in the instant in which the forecast is issued determine, in fact, which hydrological processes will be dominant in the following period: the future evolution of the streamflow values is then simulated with a rainfall-runoff model that is specific for each forecast instant, parameterised on the basis of the evolution of the similar situations observed in the past. In the present work, the hydro-meteorological conditions are classified with a clustering technique based on unsupervised artificial neural networks, namely self-organisation maps (SOMs) or Kohonen networks. Following the SOM classification, the streamflow forecasts for an Italian mid-sized mountain watershed are issued by specific multilayer feed-forward artificial neural network (FFN). The results confirm that an adequate distinction of the hydro-meteorological conditions characterising the basin at the forecast instant, thus including additional knowledge on the forthcoming hydrological processes, may considerably improve the rainfall-runoff modelling performance.

  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. 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.

  16. 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.

  17. Combining Observations with a Distributed Hydrological Model for Imporved Streamflow Forecasting

    NASA Astrophysics Data System (ADS)

    Small, S.

    2015-12-01

    The Iowa Flood Center operates a real-time flood forecasting system for the state of Iowa based upon a distributed hydrological model. This model partitions the landscape into individual control volumes called hillslopes, which are determined from a 90 meter DEM. In addition to the results of this hydrological model, streamflow observations are available at more than 300 locations, including measurements from USGS operated streamflow gauges and Iowa Flood Center operated bridge sensors. Augmenting the model outputs with available observations can improve forecast accuracy. Combining these sources of information requires computing sensitivities of model states at each location to upstream states. These sensitivities greatly increase the number of computations and require additional computational power to maintain real-time usability.This presentation documents developments with a real-time distributed streamflow forecasting model with assimilated data. The forecasting system applied to the State of Iowa (about 140,000 square kilometers) will be detailed. A comparison of streamflow forecasts with model states influenced by observations to forecasts without influence by observations is given to show the effectiveness of our methods.

  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. 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

  20. 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

  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. 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.

  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. 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.

  5. Automatic state updating for operational streamflow forecasting via variational data assimilation

    NASA Astrophysics Data System (ADS)

    Seo, Dong-Jun; Cajina, Lee; Corby, Robert; Howieson, Tracy

    2009-04-01

    SummaryIn operational hydrologic forecasting, to account for errors in the initial and boundary conditions, and in parameters and structures of the hydrologic models, the forecasters routinely make adjustments in real-time to the hydrometeorological input, hydrologic model states and, in certain cases, model parameters based on streamflow observations. Though a great deal of effort has been made in recent years to automate such "run-time modifications" (MOD) by human forecasters to a possible extent, automatic state updating of hydrologic models is yet to be widely accepted or routinely practiced in operational hydrology for a range of reasons. In this paper, we describe a state updating procedure intended specifically for operational streamflow forecasting for gauged headwater basins, and compare its performance with human forecaster MOD through a real-time forecasting experiment. The procedure is based on variational assimilation (VAR) of streamflow, precipitation and potential evaporation (PE) data into lumped soil moisture accounting and routing models operating at a 1-h timestep. The procedure has been in experimental operation since 2003 at the National Weather Service's (NWS) West Gulf River Forecast Center (WGRFC) in Fort Worth, TX. Also described is a novel parameter estimation and optimization tool, the Adjoint-Based OPTimizer (AB_OPT), used for lumped hydrologic modeling at a 1-h timestep necessary for VAR.

  6. 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.

  7. 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.

  8. Toward post-processing ensemble forecasts based on hindcasts

    NASA Astrophysics Data System (ADS)

    Van Schaeybroeck, B.; Vannitsem, S.

    2012-04-01

    Having in mind an operational implementation of post-processing at the Royal Meteorological Institute of Belgium (RMI), we study possible approaches of correcting the ECMWF ensemble forecast for stations in Belgium using the ensemble hindcast data set. This data set is each week enlarged by eighteen independent five-member ensemble forecasts using the current operational system. Therefore, the hindcasts constitute an ideal basis for the training cycle of post-processing. Combined with a forward predictor-selection procedure we propose to use a post-processing technique called error-in-variables model output statistics or EVMOS. This technique was recently proposed and is based on linear regression and suited for correcting ensemble forecasts. The corrected forecasts are produced for nine synoptic stations in Belgium. Different factors which influence the correction quality and which we aim to optimize are the number of weeks of training data, the number of predictors and the clustering of daily training data. We also investigate the influence of the training period, that is, the period of days over which the training forecasts are initialized. More specifically, we compare a training window which is centered around the forecast day with the case where the days of training precede the forecast day. Different results for the different training periods arise due to seasonal effects. We validate the different approaches against the bias-corrected forecasts using observations at nine stations for the ten-meter zonal and meridional wind speed and the two-meter temperature and for lead times up to one week. This is performed by cross-validation for a period of fourteen weeks. For the inland stations and for all lead times, a mean-square-error (MSE) improvement of around 1.5 m2/s2 and 0.5 (°C)2 for wind and temperature, respectively, is obtained. The MSE gain for wind at the two coastal stations is lower, especially for the meridional wind. Systematic biases are

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. 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

  14. 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)

  15. 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.

  16. 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.

  17. 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.

  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. 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.

  20. 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.

  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. PMID:26357365

  2. Evaluation of ensemble forecast uncertainty using a new proper score: application to medium-range and seasonal forecasts

    NASA Astrophysics Data System (ADS)

    Christensen, Hannah; Moroz, Irene; Palmer, Tim

    2015-04-01

    Forecast verification is important across scientific disciplines as it provides a framework for evaluating the performance of a forecasting system. In the atmospheric sciences, probabilistic skill scores are often used for verification as they provide a way of unambiguously ranking the performance of different probabilistic forecasts. In order to be useful, a skill score must be proper -- it must encourage honesty in the forecaster, and reward forecasts which are reliable and which have good resolution. A new score, the Error-spread Score (ES), is proposed which is particularly suitable for evaluation of ensemble forecasts. It is formulated with respect to the moments of the forecast. The ES is confirmed to be a proper score, and is therefore sensitive to both resolution and reliability. The ES is tested on forecasts made using the Lorenz '96 system, and found to be useful for summarising the skill of the forecasts. The European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) is evaluated using the ES. Its performance is compared to a perfect statistical probabilistic forecast -- the ECMWF high resolution deterministic forecast dressed with the observed error distribution. This generates a forecast that is perfectly reliable if considered over all time, but which does not vary from day to day with the predictability of the atmospheric flow. The ES distinguishes between the dynamically reliable EPS forecasts and the statically reliable dressed deterministic forecasts. Other skill scores are tested and found to be comparatively insensitive to this desirable forecast quality. The ES is used to evaluate seasonal range ensemble forecasts made with the ECMWF System 4. The ensemble forecasts are found to be skilful when compared with climatological or persistence forecasts, though this skill is dependent on region and time of year.

  3. 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

  4. How good do seasonal streamflow forecasts need to be to improve reservoir operation?

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

    Reservoir operating rules inform release decisions based on competing demands, priorities, available storage, and reservoir characteristics. Reservoir inflow forecasts over a range of forecast lead times (from days or less to seasons or longer) can improve release decisions and lead to more efficient use of reservoir storage. However, the utility or value of inflow forecasts is directly related to forecast quality. Through a case study of the Oroville-Thermalito reservoir complex in the Feather River Basin, California, we explore the effect of forecast quality on optimal release strategies. Streamflow in the Upper Feather Basin is strongly seasonal signal, with most of the flow occurring during the winter (mostly from rainfall at lower elevations) and spring (from melt of the previous winter's snow accumulation). Accurate prediction of the volume and timing of snowmelt (which is possible via various means, including monitoring of accumulated winter precipitation, and measurements of high elevation snowpack) has the potential to improve reservoir operation. In this study, we use Deterministic Dynamic Programming to optimize medium-range and seasonal reservoir operation based on different forecasts of reservoir inflows. We determine maximum reservoir performance by forcing the optimization with observed inflows, which is equivalent to a perfect forecast. The forecast quality is then progressively degraded to allow forecast skill to be related to changes in release decisions and to determine the minimum forecast skill that is required to affect decision-making. We generate forecasted inflow sequences using the Variable Infiltration Capacity (VIC) hydrology model. Forecast initial conditions are created using observed meteorology, including streamflow and snow data assimilation, while inflow forecasts are based on seasonal climate forecasts. Although the specific forecast skill level is specific to the Feather River basin, the methodology should be transferable to

  5. 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.

  6. 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

  7. 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.

  8. Visualization of 3D ensemble weather forecasts to predict uncertain warm conveyor belt situations

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

    We present the application of interactive 3D visualization of ensemble weather predictions to forecasting warm conveyor belt (WCB) situations during aircraft-based atmospheric research campaigns under consideration of uncertainty in the forecast. Based on requirements of the 2012 T-NAWDEX-Falcon campaign, a method based on ensemble Lagrangian particle trajectories has been developed to predict 3D probabilities of the spatial occurrence of WCBs. The method has been integrated into the new open-source 3D ensemble visualization tool Met.3D. The integration facilitates interactive visual exploration of predicted WCB features and derived probabilities in the context of ensemble forecasts from the European Centre for Medium Range Weather Forecasts. To judge forecast uncertainty, Met.3D's interactivity enables the user to compute and visualize ensemble statistical quantities on-demand and to navigate the ensemble members. A new visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region assists the forecaster in interpreting the obtained probabilities. In this presentation, we focus on a case study that illustrates how we envision the use of 3D ensemble visualization for weather forecasting. The case study revisits a forecast case from T-NAWDEX-Falcon and demonstrates the practical application of the proposed uncertainty visualization methods.

  9. Seasonal Streamflow Forecast for the Yangtze River at the Three Gorges Dam, China

    NASA Astrophysics Data System (ADS)

    Xu, K.; Brown, C.; Kwon, H.; Lall, U.; Zhang, J.; Hayashi, S.

    2005-12-01

    Water is a vital resource for human as well as the natural ecosystems. The availability of water is greatly influenced by climate conditions that vary on seasonal, interannual, and decadal time scales. The severe flood and drought events that frequently occur during the summer monsoon have generated interest in seasonal climate forecasting. In particular, the relationship between tropical sea surface temperatures and the East Asian monsoon has been the subject of many studies in the past decade. However there are no reports of streamflow prediction using climate indices in East Asian rivers. In this study, we attempt to identify major modes of variability in global climate variables that may be used to produce seasonal forecasts of Yangtze River streamflow, which represents the inflow into the Three Gorges Dam (TGD). Streamflow at Yichang hydrological station (YHS) during the summer monsoon was analyzed for the period 1882-2003. Simple methods of statistical analysis were used to describe the relationship between summer (June-July-August (JJA)) streamflow and preceding climate variables and to evaluate predictive ability. Linear correlation maps were constructed using (March-April-May (MAM)) values (3-month ahead) to identify regions that exhibit teleconnections with streamflow at YHS. The climate indices were used in multivariate linear and quadratic regression of YHS JJA streamflow. The results indicated that the correlation coefficients of the multivariate regression ranged from 0.72-0.78, showing a good agreement with the observed. From the cross-validation analysis, the quadratic regression model for JJA streamflow at YHS with the first EOFs of two SST boxes in the Eastern Indian and Western Pacific was best. The results suggested that the strong teleconnections between the streamflow and the ENSO-related sea surface temperature, outgoing longwave radiation, ocean precipitation, and sea level pressure should provide helpful information for improving the

  10. Ensemble forecast of typhoon generated by orthogonal conditional nonlinear optimal perturbations

    NASA Astrophysics Data System (ADS)

    Huo, Zhenhua; Duan, Wansuo; Zhou, Feifan

    2016-04-01

    Orthogonal conditional nonlinear optimal perturbations (CNOPs) are the initial perturbations that have the largest impact on the forecast results in orthogonal subspaces of the initial perturbation space. Previous studies demonstrate the successful application of orthogonal CNOPs in ensemble forecasting. And further analysis indicates that orthogonal CNOPs may be more adapt to the prediction of strong events, among which typhoon events occur in the tropical or subtropical areas where the diabatic physical processes is very important and has strong nonlinear behavior. For these reasons, this paper focuses on the application of orthogonal CNOPs in ensemble forecast of typhoon. In this study, orthogonal CNOPs, orthogonal singular vectors (SVs), bred vectors (BVs) and random perturbations (RPs) are applied for typhoon ensemble forecasts using MM5 model. The results show that, for typhoons Matsa in 2005 and Sepat in 2007, ensemble forecasts generated by orthogonal CNOPs greatly improve the control forecast, successfully predicts the landing location of Matsa, and gives the warning information of the landing of Sepat. In detail, for the ensemble mean associated with orthogonal CNOPs, the averaging track forecast error over 5 days is decreased by 45.58 km for Matsa and 87.8 km for Sepat, compared with control forecast. However, ensemble forecasts generated by other three methods could not successfully predict the landing location of Matsa and give the warning information of the landing of Sepat. Compared with orthogonal SVs, BVs and RPs, ensemble forecasts generated by orthogonal CNOPs corresponds to the largest ensemble spread, improves the control forecast at the largest extent, and best samples the distribution of initial analysis errors. All these results show that orthogonal CNOPs may provide another useful technique for ensemble forecast of typhoon.

  11. Downscaling medium-range ensemble forecasts using a neural network approach

    NASA Astrophysics Data System (ADS)

    Ohba, M.; Kadokura, S.; Yoshida, Y.; Nohara, D.; Toyoda, Y.

    2015-06-01

    In this study, we present an application of self-organizing maps (SOMs) to downscaling weekly ensemble forecasts for probabilistic prediction of local precipitation in Japan. SOM is simultaneously employed on four elemental variables derived from the JRA55 reanalysis over area of study (Southwestern Japan), whereby a two-dimensional lattice of weather patterns (WPs) dominated during the 1958-2008 period is obtained. Downscaling weekly ensemble forecasts to local precipitation are conducted by using the obtained SOM lattice based on the WPs of the global model ensemble forecast. A probabilistic local precipitation is easily and quickly obtained from the ensemble forecast. The predictability skill of the ensemble forecasts for the precipitation is significantly improved under the downscaling technique.

  12. 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

  13. 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.

  14. 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.

  15. 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.

  16. A New Multivariate Approach in Generating Ensemble Meteorological Forcings for Hydrological Forecasting

    NASA Astrophysics Data System (ADS)

    Khajehei, Sepideh; Moradkhani, Hamid

    2015-04-01

    Producing reliable and accurate hydrologic ensemble forecasts are subject to various sources of uncertainty, including meteorological forcing, initial conditions, model structure, and model parameters. Producing reliable and skillful precipitation ensemble forecasts is one approach to reduce the total uncertainty in hydrological applications. Currently, National Weather Prediction (NWP) models are developing ensemble forecasts for various temporal ranges. It is proven that raw products from NWP models are biased in mean and spread. Given the above state, there is a need for methods that are able to generate reliable ensemble forecasts for hydrological applications. One of the common techniques is to apply statistical procedures in order to generate ensemble forecast from NWP-generated single-value forecasts. The procedure is based on the bivariate probability distribution between the observation and single-value precipitation forecast. However, one of the assumptions of the current method is fitting Gaussian distribution to the marginal distributions of observed and modeled climate variable. Here, we have described and evaluated a Bayesian approach based on Copula functions to develop an ensemble precipitation forecast from the conditional distribution of single-value precipitation forecasts. Copula functions are known as the multivariate joint distribution of univariate marginal distributions, which are presented as an alternative procedure in capturing the uncertainties related to meteorological forcing. Copulas are capable of modeling the joint distribution of two variables with any level of correlation and dependency. This study is conducted over a sub-basin in the Columbia River Basin in USA using the monthly precipitation forecasts from Climate Forecast System (CFS) with 0.5x0.5 Deg. spatial resolution to reproduce the observations. The verification is conducted on a different period and the superiority of the procedure is compared with Ensemble Pre

  17. 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

  18. 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.

  19. Development and application of an atmospheric-hydrologic-hydraulic flood forecasting model driven by TIGGE ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Bao, Hongjun; Zhao, Linna

    2012-02-01

    A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction (NWP) information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centers for Environmental Prediction (NCEP). The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a

  20. Long-range seasonal streamflow forecasting over the Iberian Peninsula using large-scale atmospheric and oceanic information

    NASA Astrophysics Data System (ADS)

    Hidalgo-Muñoz, J. M.; Gámiz-Fortis, S. R.; Castro-Díez, Y.; Argüeso, D.; Esteban-Parra, M. J.

    2015-05-01

    Identifying the relationship between large-scale climate signals and seasonal streamflow may provide a valuable tool for long-range seasonal forecasting in regions under water stress, such as the Iberian Peninsula (IP). The skill of the main teleconnection indices as predictors of seasonal streamflow in the IP was evaluated. The streamflow database used was composed of 382 stations, covering the period 1975-2008. Predictions were made using a leave-one-out cross-validation approach based on multiple linear regression, combining Variance Inflation Factor and Stepwise Backward selection to avoid multicollinearity and select the best subset of predictors. Predictions were made for four forecasting scenarios, from one to four seasons in advance. The correlation coefficient (RHO), Root Mean Square Error Skill Score (RMSESS), and the Gerrity Skill Score (GSS) were used to evaluate the forecasting skill. For autumn streamflow, good forecasting skill (RHO>0.5, RMSESS>20%, GSS>0.4) was found for a third of the stations located in the Mediterranean Andalusian Basin, the North Atlantic Oscillation of the previous winter being the main predictor. Also, fair forecasting skill (RHO>0.44, RMSESS>10%, GSS>0.2) was found in stations in the northwestern IP (16 of these located in the Douro and Tagus Basins) with two seasons in advance. For winter streamflow, fair forecasting skill was found for one season in advance in 168 stations, with the Snow Advance Index as the main predictor. Finally, forecasting was poorer for spring streamflow than for autumn and winter, since only 16 stations showed fair forecasting skill in with one season in advance, particularly in north-western of IP.

  1. 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

  2. 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

  3. Are hybrid models integrated with data preprocessing techniques suitable for monthly streamflow forecasting? Some experiment evidences

    NASA Astrophysics Data System (ADS)

    Zhang, Xiaoli; Peng, Yong; Zhang, Chi; Wang, Bende

    2015-11-01

    A number of hydrological studies have proven the superior prediction performance of hybrid models coupled with data preprocessing techniques. However, many studies first decompose the entire data series into components and later divide each component into calibration and validation datasets to establish models, which sends some amount of future information into the decomposition and reconstruction processes. As a consequence, the resulting components used to forecast the value of a particular moment are computed using information from future values, which are not available at that particular moment in a forecasting exercise. Since most papers don't present their model framework in detail, it is difficult to identify whether they are performing a real forecast or not. Even though several other papers have explicitly stated which experiment they are performing, a comparison between results in the hindcast and forecast experiments is still missing. Therefore, it is necessary to investigate and compare the performance of these hybrid models in the two experiments in order to estimate whether they are suitable for real forecasting. With the combination of three preprocessing techniques, such as wavelet analysis (WA), empirical mode decomposition (EMD) and singular spectrum analysis (SSA), and two modeling methods (i.e. ANN model and ARMA model), six hybrid models are developed in this study, including WA-ANN, WA-ARMA, EMD-ANN, EMD-ARMA, SSA-ANN and SSA-ARMA. Preprocessing techniques are used to decompose the data series into sub-series, and then these sub-series are modeled using ANN and ARMA models. These models are examined in hindcasting and forecasting of the monthly streamflow of two sites in the Yangtze River of China. The results of this study indicate that the six hybrid models perform better in the hindcast experiment compared with the original ANN and ARMA models, while the hybrid models in the forecast experiment perform worse than the original models and the

  4. 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.

  5. Estimating and communicating hydrometeorological uncertainty in a context of operational hydrological ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Mathevet, T.; Ramos, M.; Gailhard, J.; Bernard, P.; Garçon, R.

    2010-12-01

    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. Given that the actual quality of meteorological and hydrological forecasts do not allow decision-making in a certain future, meteorological and hydrological ensemble forecasts allow a better representation of forecasts uncertainties. Ensemble forecasts improve the human expertise of hydrological forecasts, which is essential to synthesize available informations, coming from different meteorological and hydrological models and human experience. In this context, the good estimation and communication of hydrological forecasts uncertainties is an essential step to improve the efficient use of forecasts by end-users. This communication is based on operational experiences and focuses on the estimation and communication of uncertain hydro-meteorological forecasts. First, an operational hydro-meteorological ensemble chain developed at EDF is introduced. This chain takes into account both meteorological and hydrological uncertainties, in order to achieve a good probabilistic calibration of forecasts. Probabilistic calibration is absolutely necessary to avoid misrepresentation of uncertainties and under-confidence of forecasts by forecasters and end-users. Then, typical case-studies based on rare hydro-meteorological events will illustrate forecasters difficulty to estimate and communicate forecast uncertainties. Examples on the Durance (Alps) and Loire (Cevennes) watersheds show the cascading and mixing of uncertainties. Forecasters are used to face rather complex situations and cope with uncertain spatio-temporal meteorological forecasts, uncertain rainfall-runoff models and their own expertise. This communication illustrate the daily forecaster experience of hydrometeorological uncertainties and the difficulties to

  6. 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

  7. 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

  8. 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

  9. Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction

    NASA Astrophysics Data System (ADS)

    Xuan, Y.; Cluckie, I. D.; Wang, Y.

    2009-03-01

    Advances in mesoscale numerical weather predication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate high-resolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however, that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a short-range high-resolution mesoscale weather model. The results show that: (1) The hydrological model driven by QPF can produce forecasts comparable with those from a raingauge-driven one; (2) The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3) the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.

  10. Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction

    NASA Astrophysics Data System (ADS)

    Cluckie, I. D.; Xuan, Y.; Wang, Y.

    2006-10-01

    Advances in meso-scale numerical weather predication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate high-resolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however, that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a short-range high-resolution mesoscale weather model. The results show that: (1) The hydrological model driven by QPF can produce forecasts comparable with those from a raingauge-driven one; (2) The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3) the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.

  11. 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.

  12. Retrospective analysis and forecasting of streamflows using a shifting level model

    NASA Astrophysics Data System (ADS)

    Fortin, V.; Perreault, L.; Salas, J. D.

    2004-08-01

    Shifting level models have been suggested in the literature since the late 1970's for stochastic simulation of streamflow data. Parameter estimation for these models has been generally based on the method of moments. While this estimation approach has been useful for simulation studies, some limitations are apparent. One is the difficulty of evaluating the uncertainty of the model parameters, and another one is that the proposed model is not amenable to forecasting because the underlying mean of the process, which changes with time, is not estimated. In this paper, we reformulate the original shifting level model to conform to the so-called Hidden Markov Chain models (HMMs). These models are increasingly used in applied statistics and techniques such as Monte-Carlo Markov chain, and in particular Gibbs sampling, are well suited for estimating the parameters of HMMs. We use Gibbs sampling in a Bayesian framework for parameter estimation and show the applicability of the reformulated shifting level model for detection of abrupt regime changes and forecasting of annual streamflow series. The procedure is illustrated using annual flows of the Senegal River in Africa.

  13. 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

  14. 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.

  15. Performance comparison of meso-scale ensemble wave forecasting systems for Mediterranean sea states

    NASA Astrophysics Data System (ADS)

    Pezzutto, Paolo; Saulter, Andrew; Cavaleri, Luigi; Bunney, Christopher; Marcucci, Francesca; Torrisi, Lucio; Sebastianelli, Stefano

    2016-08-01

    This paper compares the performance of two wind and wave short range ensemble forecast systems for the Mediterranean Sea. In particular, it describes a six month verification experiment carried out by the U.K. Met Office and Italian Air Force Meteorological Service, based on their respective systems: the Met Office Global-Regional Ensemble Prediction System and the Nettuno Ensemble Prediction System. The latter is the ensemble version of the operational Nettuno forecast system. Attention is focused on the differences between the two implementations (e.g. grid resolution and initial ensemble members sampling) and their effects on the prediction skill. The cross-verification of the two ensemble systems shows that from a macroscopic point of view the differences cancel out, suggesting similar skill. More in-depth analysis indicates that the Nettuno wave forecast is better resolved but, on average, slightly less reliable than the Met Office product. Assessment of the added value of the ensemble techniques at short range in comparison with the deterministic forecast from Nettuno, reveals that adopting the ensemble approach has small, but substantive, advantages.

  16. 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.

  17. 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.

  18. Improved sub-seasonal meteorological forecast skill using weighted multi-model ensemble simulations

    NASA Astrophysics Data System (ADS)

    Wanders, Niko; Wood, Eric F.

    2016-09-01

    Sub-seasonal to seasonal weather and hydrological forecasts have the potential to provide vital information for a variety of water-related decision makers. Here, we investigate the skill of four sub-seasonal forecast models from phase-2 of the North American Multi-Model Ensemble using reforecasts for the period 1982–2012. Two weighted multi-model ensemble means from the models have been developed for predictions of both sub-seasonal precipitation and temperature. By combining models through optimal weights, the multi-model forecast skill is significantly improved compared to a ‘standard’ equally weighted multi-model forecast mean. We show that optimal model weights are robust and the forecast skill is maintained for increased length of time and regions with a low initial forecast skill show significant skill after optimal weighting of the individual model forecast. The sub-seasonal model forecasts models show high skill over the tropics, approximating their skill at monthly resolution. Using the weighted approach, a significant increase is found in the forecast skill for dry, wet, cold and warm extreme events. The weighted mean approach brings significant advances to sub-seasonal forecasting due to its reduced uncertainty in the forecasts with a gain in forecast skill. This significantly improves their value for end-user applications and our ability to use them to prepare for upcoming extreme conditions, like floods and droughts.

  19. Improved sub-seasonal meteorological forecast skill using weighted multi-model ensemble simulations

    NASA Astrophysics Data System (ADS)

    Wanders, Niko; Wood, Eric F.

    2016-09-01

    Sub-seasonal to seasonal weather and hydrological forecasts have the potential to provide vital information for a variety of water-related decision makers. Here, we investigate the skill of four sub-seasonal forecast models from phase-2 of the North American Multi-Model Ensemble using reforecasts for the period 1982-2012. Two weighted multi-model ensemble means from the models have been developed for predictions of both sub-seasonal precipitation and temperature. By combining models through optimal weights, the multi-model forecast skill is significantly improved compared to a ‘standard’ equally weighted multi-model forecast mean. We show that optimal model weights are robust and the forecast skill is maintained for increased length of time and regions with a low initial forecast skill show significant skill after optimal weighting of the individual model forecast. The sub-seasonal model forecasts models show high skill over the tropics, approximating their skill at monthly resolution. Using the weighted approach, a significant increase is found in the forecast skill for dry, wet, cold and warm extreme events. The weighted mean approach brings significant advances to sub-seasonal forecasting due to its reduced uncertainty in the forecasts with a gain in forecast skill. This significantly improves their value for end-user applications and our ability to use them to prepare for upcoming extreme conditions, like floods and droughts.

  20. 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

  1. Uncertainty Analysis of the Ensemble Hydrological Forecasts in the Coupled Meteorological-Hydrological Modelling Environment

    NASA Astrophysics Data System (ADS)

    Xuan, Y.; Cluckie, I. D.

    2006-12-01

    The advances in meso-scale numerical weather predication render hydrologists the capability to incorporate high-resolution NWP directly into flood forecasting systems in order to obtain an extended lead time. However, such a direct application of rainfall outputs from the NWP model can contribute considerable uncertainties to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be highlighted by the scaling process. In this research, the ensemble hydrological forecasts driven by the ensemble weather prediction are investigated in an effort trying to understand both the potential and the implication of the ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. A data-rich catchment facilitated with dense rainguage network as well as high resolution weather radar was chosen to run the ensemble hydrological simulations of a distributed hydrological model driven by the high resolution NWP predictions. The uncertainties of the amount and the location/timing of the rainfall prediction are discussed whith the results showing that: (1) the hydrological model driven by the short-range NWP can produce forecasts comparable with those from a raingauge-driven one; (2) the ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3) the uncertainties as well as systematical biases sometimes are significantly large and, as such, extra efforts need to be made to improve the quality of such a system.

  2. 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.

  3. Increasing streamflow forecast lead time for snowmelt-driven catchment based on large-scale climate patterns

    NASA Astrophysics Data System (ADS)

    Kalra, Ajay; Ahmad, Sajjad; Nayak, Anurag

    2013-03-01

    This study focuses on improving the spring-summer streamflow forecast lead time using large scale climate patterns. An artificial intelligence type data-driven model, Support Vector Machine (SVM), was developed incorporating oceanic-atmospheric oscillations to increase the forecast lead time. The application of SVM model is tested on three unimpaired gages in the North Platte River Basin. Seasonal averages of oceanic-atmospheric indices for the period of 1940-2007 are used to generate spring-summer streamflow volumes with 3-, 6- and 9-month lead times. The results reveal a strong association between coupled indices compared to their individual effects. The best streamflow estimates are obtained at 6-month compared to 3-month and 9-month lead times. The proposed modeling technique is expected to provide useful information to water managers and help in better managing the water resources and the operation of water systems.

  4. Improving the accuracy of flood forecasting with transpositions of ensemble NWP rainfall fields considering orographic effects

    NASA Astrophysics Data System (ADS)

    Yu, Wansik; Nakakita, Eiichi; Kim, Sunmin; Yamaguchi, Kosei

    2016-08-01

    The use of meteorological ensembles to produce sets of hydrological predictions increased the capability to issue flood warnings. However, space scale of the hydrological domain is still much finer than meteorological model, and NWP models have challenges with displacement. The main objective of this study to enhance the transposition method proposed in Yu et al. (2014) and to suggest the post-processing ensemble flood forecasting method for the real-time updating and the accuracy improvement of flood forecasts that considers the separation of the orographic rainfall and the correction of misplaced rain distributions using additional ensemble information through the transposition of rain distributions. In the first step of the proposed method, ensemble forecast rainfalls from a numerical weather prediction (NWP) model are separated into orographic and non-orographic rainfall fields using atmospheric variables and the extraction of topographic effect. Then the non-orographic rainfall fields are examined by the transposition scheme to produce additional ensemble information and new ensemble NWP rainfall fields are calculated by recombining the transposition results of non-orographic rain fields with separated orographic rainfall fields for a generation of place-corrected ensemble information. Then, the additional ensemble information is applied into a hydrologic model for post-flood forecasting with a 6-h interval. The newly proposed method has a clear advantage to improve the accuracy of mean value of ensemble flood forecasting. Our study is carried out and verified using the largest flood event by typhoon 'Talas' of 2011 over the two catchments, which are Futatsuno (356.1 km2) and Nanairo (182.1 km2) dam catchments of Shingu river basin (2360 km2), which is located in the Kii peninsula, Japan.

  5. 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.

  6. 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.

  7. 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

  8. Forecasting the Solar photospheric magnetic field using solar flux transport model and local ensemble Kalman filtering

    NASA Astrophysics Data System (ADS)

    Zhang, Y.

    2015-12-01

    Accurate forecasting the solar photospheric magnetic field distribution play an important role in the estimates of the inner boundary conditions of the coronal and solar wind model. Forecasting solar photospheric magnetic field using the solar flux transport (SFT) model can achieve an acceptable match to the actual field. The observations from ground-based or spacecraft instruments can be assimilated to update the modeled flux. The local ensemble Kalman filtering (LEnKF) method is utilized to improve forecasts and characterize their uncertainty by propagating the SFT model with different model parameters forward in time to control the evolution of the solar photospheric magnetic field. Optimal assimilation of measured data into the ensemble produces an improvement in the fit of the forecast to the actual field. Our approach offers a method to improve operational forecasting of the solar photospheric magnetic field. The LEnKF method also allows sensitivity analysis of the SFT model to noise and uncertainty within the physical representation.

  9. Forecasting the solar photospheric magnetic field using solar flux transport model and local ensemble Kalman filtering

    NASA Astrophysics Data System (ADS)

    Zhang, Ying; Du, Aimin; Feng, Xueshang

    2015-04-01

    Accurate forecasting the solar photospheric magnetic field distribution play an important role in the estimates of the inner boundary conditions of the coronal and solar wind model. Forecasting solar photospheric magnetic field using the solar flux transport (SFT) model can achieve an acceptable match to the actual field. The observations from ground-based or spacecraft instruments can be assimilated to update the modeled flux. The local ensemble Kalman filtering (LEnKF) method is utilized to improve forecasts and characterize their uncertainty by propagating the SFT model with different model parameters forward in time to control the evolution of the solar photospheric magnetic field. Optimal assimilation of measured data into the ensemble produces an improvement in the fit of the forecast to the actual field. Our approach offers a method to improve operational forecasting of the solar photospheric magnetic field. The LEnKF method also allows sensitivity analysis of the SFT model to noise and uncertainty within the physical representation.

  10. Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast

    NASA Astrophysics Data System (ADS)

    Bao, H.-J.; Zhao, L.-N.; He, Y.; Li, Z.-J.; Wetterhall, F.; Cloke, H. L.; Pappenberger, F.; Manful, D.

    2011-02-01

    The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for flood forecast. The Grid-Xinanjiang distributed hydrological model, which is based on the Xinanjiang model theory and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the Grid-Xinanjiang model and the TIGGE database gives a promising tool for an early warning of flood events several days ahead.

  11. Ensemble Volcanic Aerosol Forecasting in Hawaii using a Parallelized version of the Hysplit Dispersion Model

    NASA Astrophysics Data System (ADS)

    Pattantyus, A.; Businger, S.

    2013-12-01

    A transition from deterministic to probabilistic forecasts of the dispersion of emissions from the Kilauea Volcano on the Island of Hawaii is under way. Operational forecasts of volcanic smog (vog) have been produced for 3 years by a custom version of NOAA's Hysplit dispersion model (vog model hereafter), a Lagrangian transport model that uses high-resolution WRF-ARW model output for initial conditions run at the University of Hawaii at Manoa. The vog model has been successful in predicting which locations in the State of Hawaii will be impacted by the vog plume. Initial concentrations of emissions from the volcano are set empirically based on downstream observations provided by the Hawaiian Volcano Observatory. Fast changing meteorological conditions and/or rapid variations in emissions rates cause forecast errors to increase. Recent efforts aim to leverage the parallelism of Hysplit to run ensemble forecasts with various initial condition configurations to better quantify the forecast uncertainty. The ensemble will contain 28 members each with perturbed heights and locations of initial aerosol concentrations. Forecast sulfur dioxide and sulfate aerosol concentrations follow Air Resources Laboratory's Air Quality Index (AQI). The resulting probabilistic forecasts will provide probability of exceedance plots and concentration-probability plots for each AQI level. Because some people are extremely sensitive to low concentrations of sulfate aerosols, the lowest AQI levels will be distinguished in the exceedance map output. Downstream observations at Pahala and Kona will be used to validate the ensemble results, which will also be compared to the results of deterministic forecasts.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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

  10. 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

  11. The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models

    NASA Astrophysics Data System (ADS)

    Demirel, M. C.; Booij, M. J.; Hoekstra, A. Y.

    2015-01-01

    This paper investigates the skill of 90-day low-flow forecasts using two conceptual hydrological models and one data-driven model based on Artificial Neural Networks (ANNs) for the Moselle River. The three models, i.e. HBV, GR4J and ANN-Ensemble (ANN-E), all use forecasted meteorological inputs (precipitation P and potential evapotranspiration PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low-flow forecasts for five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the models. The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the models are compared based on their skill of low-flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90-day-ahead low flows in the very dry year 2003 without precipitation data. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET

  12. 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.

  13. 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

  14. An application of ensemble/multi model approach for wind power production forecasting

    NASA Astrophysics Data System (ADS)

    Alessandrini, S.; Pinson, P.; Hagedorn, R.; Decimi, G.; Sperati, S.

    2011-02-01

    The wind power forecasts of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast applied in this study is based on meteorological models that provide the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. For this purpose a training of a Neural Network (NN) to link directly the forecasted meteorological data and the power data has been performed. One wind farm has been examined located in a mountain area in the south of Italy (Sicily). First we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by the combination of models (RAMS, ECMWF deterministic, LAMI). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error (normalized by nominal power) of at least 1% compared to the singles models approach. Finally we have focused on the possibility of using the ensemble model system (EPS by 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 three days ahead period.

  15. A non-parametric postprocessor for bias-correcting multi-model ensemble forecasts of hydrometeorological and hydrologic variables

    NASA Astrophysics Data System (ADS)

    Brown, James; Seo, Dong-Jun

    2010-05-01

    Operational forecasts of hydrometeorological and hydrologic variables often contain large uncertainties, for which ensemble techniques are increasingly used. However, the utility of ensemble forecasts depends on the unbiasedness of the forecast probabilities. We describe a technique for quantifying and removing biases from ensemble forecasts of hydrometeorological and hydrologic variables, intended for use in operational forecasting. The technique makes no a priori assumptions about the distributional form of the variables, which is often unknown or difficult to model parametrically. The aim is to estimate the conditional cumulative distribution function (ccdf) of the observed variable given a (possibly biased) real-time ensemble forecast from one or several forecasting systems (multi-model ensembles). The technique is based on Bayesian optimal linear estimation of indicator variables, and is analogous to indicator cokriging (ICK) in geostatistics. By developing linear estimators for the conditional expectation of the observed variable at many thresholds, ICK provides a discrete approximation of the full ccdf. Since ICK minimizes the conditional error variance of the indicator expectation at each threshold, it effectively minimizes the Continuous Ranked Probability Score (CRPS) when infinitely many thresholds are employed. However, the ensemble members used as predictors in ICK, and other bias-correction techniques, are often highly cross-correlated, both within and between models. Thus, we propose an orthogonal transform of the predictors used in ICK, which is analogous to using their principal components in the linear system of equations. This leads to a well-posed problem in which a minimum number of predictors are used to provide maximum information content in terms of the total variance explained. The technique is used to bias-correct precipitation ensemble forecasts from the NCEP Global Ensemble Forecast System (GEFS), for which independent validation results

  16. 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.

  17. Three-dimensional visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

    NASA Astrophysics Data System (ADS)

    Rautenhaus, M.; Grams, C. M.; Schäfler, A.; Westermann, R.

    2015-07-01

    We present the application of interactive three-dimensional (3-D) visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX - North Atlantic Waveguide and Downstream Impact Experiment) campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs) has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF) ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off).

  18. 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

  19. 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.

  20. Impact of EOS MLS ozone data on medium-extended range ensemble weather forecasts

    NASA Astrophysics Data System (ADS)

    Cheung, J. C. H.; Haigh, J. D.; Jackson, D. R.

    2014-08-01

    As the stratosphere is largely characterized by its ozone abundance, the quality of the ozone field is important for a realistic representation of the stratosphere in weather and climate models. While the stratosphere is directly affected by radiative heating from ozone photodissociation, ozone abundance might also impact the representation of the troposphere since the stratosphere and troposphere are dynamically linked. In this paper, we examine the potential benefits of using ozone data from the Earth Observing System (EOS) Microwave Limb Sounder (MLS) for medium-extended range tropospheric forecasts in a current numerical weather prediction system. The global component of the Met Office Global and Regional Ensemble Prediction System is used, which is run at a resolution of N216 L85 with 24 ensemble members. We compare two scenarios of 31 day forecasts covering the same period, one with the current operational ozone climatology and the other with a monthly mean zonally averaged ozone field computed from the MLS data set. In the extreme case of the Arctic "ozone hole" of March 2011, our results show a general reduction in stratospheric forecast errors in the tropics and Southern Hemisphere as a result of the improved representation of ozone. However, even in such a scenario, where the MLS ozone field is much superior to that of the control, we find that tropospheric forecast errors in the medium-extended range are dominated by the spread of ensemble members and no significant reduction in the root-mean-square forecast errors.

  1. 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

  2. Ensemble-based air quality forecasts: A multimodel approach applied to ozone

    NASA Astrophysics Data System (ADS)

    Mallet, Vivien; Sportisse, Bruno

    2006-09-01

    The potential of ensemble techniques to improve ozone forecasts is investigated. Ensembles with up to 48 members (models) are generated using the modeling system Polyphemus. Members differ in their physical parameterizations, their numerical approximations, and their input data. Each model is evaluated during 4 months (summer 2001) over Europe with hundreds of stations from three ozone-monitoring networks. We found that several linear combinations of models have the potential to drastically increase the performances of model-to-data comparisons. Optimal weights associated with each model are not robust in time or space. Forecasting these weights therefore requires relevant methods, such as selection of adequate learning data sets, or specific learning algorithms. Significant performance improvements are accomplished by the resulting forecasted combinations. A decrease of about 10% of the root-mean-square error is obtained on ozone daily peaks. Ozone hourly concentrations show stronger improvements.

  3. 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.

  4. System for NIS Forecasting Based on Ensembles Analysis

    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 aremore » 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.« less

  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. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. A Preliminary Application of Ensemble Transform Kalman Filter System to Rainfall Forecast

    NASA Astrophysics Data System (ADS)

    Tie, Wang

    2014-05-01

    WangTie(1,2),YangXiaofeng(1),ZangXin(1),GengLining(1),ZhouYan(1) (1)NanJing China-Spacenet Satellite Telecom CO., Ltd, Nanjing, 210061, China,(2)Institute of Atmospheric Physics, Chinese Academy of Sciences, LASG, Beijing, 100029, China The Ensemble Transform Kalman Filter(ETKF) method is applied to WRF model(Weather Research & Forecast Model system) Version 3.5.1, and the ETKF based data assimilation system is constructed. The accurate of forecast result and the sensitivity of initial data error on a heavy rainfall from 5th to 7th July 2003 in Jiangsu province China are investigated. The numerical result indicated that the ETKF method could decrease the forecast error and improves the forecast result either the synoptic scale system and the meso-scale system, the RMSEs of wind decrease less than 18 percent, while the temperature and humidity decrease about 6 precent. The forecast of precipitation, especially the 12h accumulated precipitation is improved significant after ETKF assimilation. Keywords: intense rainfall, prediction error, Ensemble Transform Kalman Filter(ETKF), WRF model

  12. Development and Evaluation of Storm Surge Ensemble Forecasting for the Philippines Using JMA Storm Surge Model

    NASA Astrophysics Data System (ADS)

    Lapidez, J. P. B.; Tablazon, J. P.; Lagmay, A. M. F. A.; Suarez, J. K. B.; Santiago, J. T.

    2014-12-01

    The Philippines is one of the countries most vulnerable to storm surge. It is located in the North-western Pacific basin which is the most active basin in the planet. An average of 20 tropical cyclones enters the Philippine area of responsibility (PAR) every year. The archipelagic nature of the country with regions having gently sloping coasts and shallow bays also contribute to the formation of extreme surges. Last November 2013, storm surge brought by super typhoon Haiyan severely damaged several coastal regions in the Visayan Islands. Haiyan left more than 6 300 casualties and damages amounting to more than $ 2 billion. Extreme storm surge events such as this highlight the need to establish a storm surge early warning system for the country. This study explores the development and evaluation of storm surge ensemble forecasting for the Philippines using the Japan Meteorological Agency (JMA) storm surge model. 36-hour, 24-hour, and 12-hour tropical cyclone forecasts are used to generate an ensemble storm surge forecast to give the most probable storm surge height at a specific point brought by an incoming tropical cyclone. The result of the storm surge forecast is compared to tide gauge record to evaluate the accuracy. The total time of computation and dissemination of forecast result is also examined to assess the feasibility of using the JMA storm surge model for operational purposes.

  13. The effects of land surface process perturbations in a global ensemble forecast system

    NASA Astrophysics Data System (ADS)

    Deng, Guo; Zhu, Yuejian; Gong, Jiandong; Chen, Dehui; Wobus, Richard; Zhang, Zhe

    2016-10-01

    Atmospheric variability is driven not only by internal dynamics, but also by external forcing, such as soil states, SST, snow, sea-ice cover, and so on. To investigate the forecast uncertainties and effects of land surface processes on numerical weather prediction, we added modules to perturb soil moisture and soil temperature into NCEP's Global Ensemble Forecast System (GEFS), and compared the results of a set of experiments involving different configurations of land surface and atmospheric perturbation. It was found that uncertainties in different soil layers varied due to the multiple timescales of interactions between land surface and atmospheric processes. Perturbations of the soil moisture and soil temperature at the land surface changed sensible and latent heat flux obviously, as compared to the less or indirect land surface perturbation experiment from the day-to-day forecasts. Soil state perturbations led to greater variation in surface heat fluxes that transferred to the upper troposphere, thus reflecting interactions and the response to atmospheric external forcing. Various verification scores were calculated in this study. The results indicated that taking the uncertainties of land surface processes into account in GEFS could contribute a slight improvement in forecast skill in terms of resolution and reliability, a noticeable reduction in forecast error, as well as an increase in ensemble spread in an under-dispersive system. This paper provides a preliminary evaluation of the effects of land surface processes on predictability. Further research using more complex and suitable methods is needed to fully explore our understanding in this area.

  14. Application of an ensemble technique based on singular spectrum analysis to daily rainfall forecasting.

    PubMed

    Baratta, Daniela; Cicioni, Giovambattista; Masulli, Francesco; Studer, Léonard

    2003-01-01

    In previous work, we have proposed a constructive methodology for temporal data learning supported by results and prescriptions related to the embedding theorem, and using the singular spectrum analysis both in order to reduce the effects of the possible discontinuity of the signal and to implement an efficient ensemble method. In this paper we present new results concerning the application of this approach to the forecasting of the individual rain-fall intensities series collected by 135 stations distributed in the Tiber basin. The average RMS error of the obtained forecasting is less than 3mm of rain. PMID:12672433

  15. ASCAT soil moisture data assimilation through the Ensemble Kalman Filter for improving streamflow simulation in Mediterranean catchments

    NASA Astrophysics Data System (ADS)

    Loizu, Javier; Massari, Christian; Álvarez-Mozos, Jesús; Casalí, Javier; Goñi, Mikel

    2016-04-01

    Assimilation of Surface Soil Moisture (SSM) observations obtained from remote sensing techniques have been shown to improve streamflow prediction at different time scales of hydrological modeling. Different sensors and methods have been tested for their application in SSM estimation, especially in the microwave region of the electromagnetic spectrum. The available observation devices include passive microwave sensors such as the Advanced Microwave Scanning Radiometer - Earth Observation System (AMSR-E) onboard the Aqua satellite and the Soil Moisture and Ocean Salinity (SMOS) mission. On the other hand, active microwave systems include Scatterometers (SCAT) onboard the European Remote Sensing satellites (ERS-1/2) and the Advanced Scatterometer (ASCAT) onboard MetOp-A satellite. Data assimilation (DA) include different techniques that have been applied in hydrology and other fields for decades. These techniques include, among others, Kalman Filtering (KF), Variational Assimilation or Particle Filtering. From the initial KF method, different techniques were developed to suit its application to different systems. The Ensemble Kalman Filter (EnKF), extensively applied in hydrological modeling improvement, shows its capability to deal with nonlinear model dynamics without linearizing model equations, as its main advantage. The objective of this study was to investigate whether data assimilation of SSM ASCAT observations, through the EnKF method, could improve streamflow simulation of mediterranean catchments with TOPLATS hydrological complex model. The DA technique was programmed in FORTRAN, and applied to hourly simulations of TOPLATS catchment model. TOPLATS (TOPMODEL-based Land-Atmosphere Transfer Scheme) was applied on its lumped version for two mediterranean catchments of similar size, located in northern Spain (Arga, 741 km2) and central Italy (Nestore, 720 km2). The model performs a separated computation of energy and water balances. In those balances, the soil

  16. A one-way coupled atmospheric-hydrological modeling system with combination of high-resolution and ensemble precipitation forecasting

    NASA Astrophysics Data System (ADS)

    Wu, Zhiyong; Wu, Juan; Lu, Guihua

    2016-09-01

    Coupled hydrological and atmospheric modeling is an effective tool for providing advanced flood forecasting. However, the uncertainties in precipitation forecasts are still considerable. To address uncertainties, a one-way coupled atmospheric-hydrological modeling system, with a combination of high-resolution and ensemble precipitation forecasting, has been developed. It consists of three high-resolution single models and four sets of ensemble forecasts from the THORPEX Interactive Grande Global Ensemble database. The former provides higher forecasting accuracy, while the latter provides the range of forecasts. The combined precipitation forecasting was then implemented to drive the Chinese National Flood Forecasting System in the 2007 and 2008 Huai River flood hindcast analysis. The encouraging results demonstrated that the system can clearly give a set of forecasting hydrographs for a flood event and has a promising relative stability in discharge peaks and timing for warning purposes. It not only gives a deterministic prediction, but also generates probability forecasts. Even though the signal was not persistent until four days before the peak discharge was observed in the 2007 flood event, the visualization based on threshold exceedance provided clear and concise essential warning information at an early stage. Forecasters could better prepare for the possibility of a flood at an early stage, and then issue an actual warning if the signal strengthened. This process may provide decision support for civil protection authorities. In future studies, different weather forecasts will be assigned various weight coefficients to represent the covariance of predictors and the extremes of distributions.

  17. Comparing Non-homogeneous Gaussian Regression and Bayesian Model Averaging for post-processing hydrological ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Smith, Paul; Pappenberger, Florian

    2015-04-01

    Bayesian Model Averaging and Non-homogeneous Gaussian Regression have been proposed as techniques for post-processing ensemble forecasts into predictive probability distributions. Both methods make use of past forecast data for which observations are available to propose weights for the ensemble members along with bias and dispersion corrections. The mathematical basis and application of these methods though differs significantly. In this work we contrast the forecast results derived using these methods within the European Flood Awareness System, an operational flood forecasting system covering Europe. The performance of the different methods at lead times up to 15 days is compared at multiple sites and for notable flood events.

  18. High resolution ensemble forecasting for the Gulf of Mexico eddies and fronts

    NASA Astrophysics Data System (ADS)

    Counillon, F.; Bertino, L.

    2007-05-01

    As oil production moves further into deeper waters, the costs related to strong current hazards are increasing accordingly, and accurate three-dimensional forecasts of currents are urgently needed. To be useful, models have to locate eddies and fronts to an accuracy of 30 km at a nowcast stage, which is almost impossible to accomplish with the use of satellite data of the same accuracy. The use of stochastic forecast allows us to give confidence of our prediction. We are using a nested configuration of the Hybrid coordinate ocean model (HYCOM), where the TOPAZ system, which covers the Atlantic and the Artic, gives lateral boundary condition to a high-resolution (5km) model of the Gulf of Mexico (GOM). TOPAZ is a real-time forecasting coupled ocean-ice model, which assimilates sea level anomaly (SLA), sea surface temperature, and sea ice concentration, with the ensemble Kalman filter. The high- resolution model assimilates SLA using the ensemble optimal interpolation, which updates accordingly the currents, salinity, temperature, and layer interface at all depths. Here, we evaluate the ensemble forecast capabilities of our high-resolution model, for eddy Extreme that has been observed from altimeters around the 15th of July. We run 6 successive ensemble runs composed of 10 members of equal likelihood. Members differ by perturbations of the initial state, of the lateral boundary conditions, and of the atmospheric boundary conditions. We have started the experiment 1 month prior to the shedding event, because it was the time necessary for perturbation of boundary conditions to spread uniformly and reach a significant level across the GOM. The ensemble reproduces well the dynamics of the eddy shedding and produces a significant spread at the boundary of the eddy, but underestimates the RMS error of the SLA. Prior to the shedding time, the error growth increase, induced by the highly non-linear growth of cyclonic eddies at the boundary of the Loop Current. Additionally

  19. Multi-model ensemble forecasting of North Atlantic tropical cyclone activity

    NASA Astrophysics Data System (ADS)

    Villarini, Gabriele; Luitel, Beda; Vecchi, Gabriel A.; Ghosh, Joyee

    2016-09-01

    North Atlantic tropical cyclones (TCs) and hurricanes are responsible for a large number of fatalities and economic damage. Skillful seasonal predictions of the North Atlantic TC activity can provide basic information critical to our improved preparedness. This study focuses on the development of statistical-dynamical seasonal forecasting systems for different quantities related to the frequency and intensity of North Atlantic TCs. These models use only tropical Atlantic and tropical mean sea surface temperatures (SSTs) to describe the variability exhibited by the observational records because they reflect the importance of both local and non-local effects on the genesis and development of TCs in the North Atlantic basin. A set of retrospective forecasts of SSTs by six experimental seasonal-to-interannual prediction systems from the North American Multi-Model Ensemble are used as covariates. The retrospective forecasts are performed over the period 1982-2015. The skill of these statistical-dynamical models is quantified for different quantities (basin-wide number of tropical storms and hurricanes, power dissipation index and accumulated cyclone energy) for forecasts initialized as early as November of the year prior to the season to forecast. The results of this work show that it is possible to obtain skillful retrospective forecasts of North Atlantic TC activity with a long lead time. Moreover, probabilistic forecasts of North Atlantic TC activity for the 2016 season are provided.

  20. Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models

    NASA Astrophysics Data System (ADS)

    Tiwari, Mukesh K.; Adamowski, Jan

    2013-10-01

    A new hybrid wavelet-bootstrap-neural network (WBNN) model is proposed in this study for short term (1, 3, and 5 day; 1 and 2 week; and 1 and 2 month) urban water demand forecasting. The new method was tested using data from the city of Montreal in Canada. The performance of the WBNN method was compared with the autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average model with exogenous input variables (ARIMAX), traditional NNs, wavelet analysis-based NNs (WNN), bootstrap-based NNs (BNN), and a simple naïve persistence index model. The WBNN model was developed as an ensemble of several NNs built using bootstrap resamples of wavelet subtime series instead of raw data sets. The results demonstrated that the hybrid WBNN and WNN models produced significantly more accurate forecasting results than the traditional NN, BNN, ARIMA, and ARIMAX models. It was also found that the WBNN model reduces the uncertainty associated with the forecasts, and the performance of WBNN forecasted confidence bands was found to be more accurate and reliable than BNN forecasted confidence bands. It was found in this study that maximum temperature and total precipitation improved the accuracy of water demand forecasts using wavelet analysis. The performance of WBNN models was also compared for different numbers of bootstrap resamples (i.e., 25, 50, 100, 200, and 500) and it was found that WBNN models produced optimum results with different numbers of bootstrap resamples for different lead time forecasts with considerable variability.

  1. How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction

    NASA Astrophysics Data System (ADS)

    Pappenberger, F.; Ramos, M. H.; Cloke, H. L.; Wetterhall, F.; Alfieri, L.; Bogner, K.; Mueller, A.; Salamon, P.

    2015-03-01

    The skill of a forecast can be assessed by comparing the relative proximity of both the forecast and a benchmark to the observations. Example benchmarks include climatology or a naïve forecast. Hydrological ensemble prediction systems (HEPS) are currently transforming the hydrological forecasting environment but in this new field there is little information to guide researchers and operational forecasters on how benchmarks can be best used to evaluate their probabilistic forecasts. In this study, it is identified that the forecast skill calculated can vary depending on the benchmark selected and that the selection of a benchmark for determining forecasting system skill is sensitive to a number of hydrological and system factors. A benchmark intercomparison experiment is then undertaken using the continuous ranked probability score (CRPS), a reference forecasting system and a suite of 23 different methods to derive benchmarks. The benchmarks are assessed within the operational set-up of the European Flood Awareness System (EFAS) to determine those that are 'toughest to beat' and so give the most robust discrimination of forecast skill, particularly for the spatial average fields that EFAS relies upon. Evaluating against an observed discharge proxy the benchmark that has most utility for EFAS and avoids the most naïve skill across different hydrological situations is found to be meteorological persistency. This benchmark uses the latest meteorological observations of precipitation and temperature to drive the hydrological model. Hydrological long term average benchmarks, which are currently used in EFAS, are very easily beaten by the forecasting system and the use of these produces much naïve skill. When decomposed into seasons, the advanced meteorological benchmarks, which make use of meteorological observations from the past 20 years at the same calendar date, have the most skill discrimination. They are also good at discriminating skill in low flows and for all

  2. A regional air quality forecasting system over Europe: the MACC-II daily ensemble production

    NASA Astrophysics Data System (ADS)

    Marécal, V.; Peuch, V.-H.; Andersson, C.; Andersson, S.; Arteta, J.; Beekmann, M.; Benedictow, A.; Bergström, R.; Bessagnet, B.; Cansado, A.; Chéroux, F.; Colette, A.; Coman, A.; Curier, R. L.; Denier van der Gon, H. A. C.; Drouin, A.; Elbern, H.; Emili, E.; Engelen, R. J.; Eskes, H. J.; Foret, G.; Friese, E.; Gauss, M.; Giannaros, C.; Guth, J.; Joly, M.; Jaumouillé, E.; Josse, B.; Kadygrov, N.; Kaiser, J. W.; Krajsek, K.; Kuenen, J.; Kumar, U.; Liora, N.; Lopez, E.; Malherbe, L.; Martinez, I.; Melas, D.; Meleux, F.; Menut, L.; Moinat, P.; Morales, T.; Parmentier, J.; Piacentini, A.; Plu, M.; Poupkou, A.; Queguiner, S.; Robertson, L.; Rouïl, L.; Schaap, M.; Segers, A.; Sofiev, M.; Tarasson, L.; Thomas, M.; Timmermans, R.; Valdebenito, Á.; van Velthoven, P.; van Versendaal, R.; Vira, J.; Ung, A.

    2015-09-01

    This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in the MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The paper gives an overall picture of its status at the end of MACC-II (summer 2014) and analyses the performance of the multi-model ensemble. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs (non-methane volatile organic compounds) and PAN+PAN precursors) over eight vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performance of the system is assessed daily, weekly and every 3 months (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the ensemble median to forecast regional ozone pollution events. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10. The statistical indicators for ozone in summer 2014 show that the ensemble median gives on average the best performances compared to the seven models. There is very little degradation of the scores with the forecast day but there is a marked diurnal cycle, similarly to the individual models, that can be related partly to the prescribed diurnal variations of anthropogenic emissions in the models

  3. Forecasting skills of the ensemble hydro-meteorological system for the Po river floods

    NASA Astrophysics Data System (ADS)

    Ricciardi, Giuseppe; Montani, Andrea; Paccagnella, Tiziana; Pecora, Silvano; Tonelli, Fabrizio

    2013-04-01

    The Po basin is the largest and most economically important river-basin in Italy. Extreme hydrological events, including floods, flash floods and droughts, are expected to become more severe in the next future due to climate change, and related ground effects are linked both with environmental and social resilience. A Warning Operational Center (WOC) for hydrological event management was created in Emilia Romagna region. In the last years, the WOC faced challenges in legislation, organization, technology and economics, achieving improvements in forecasting skill and information dissemination. Since 2005, an operational forecasting and modelling system for flood modelling and forecasting has been implemented, aimed at supporting and coordinating flood control and emergency management on the whole Po basin. This system, referred to as FEWSPo, has also taken care of environmental aspects of flood forecast. The FEWSPo system has reached a very high level of complexity, due to the combination of three different hydrological-hydraulic chains (HEC-HMS/RAS - MIKE11 NAM/HD, Topkapi/Sobek), with several meteorological inputs (forecasted - COSMOI2, COSMOI7, COSMO-LEPS among others - and observed). In this hydrological and meteorological ensemble the management of the relative predictive uncertainties, which have to be established and communicated to decision makers, is a debated scientific and social challenge. Real time activities face professional, modelling and technological aspects but are also strongly interrelated with organization and human aspects. The authors will report a case study using the operational flood forecast hydro-meteorological ensemble, provided by the MIKE11 chain fed by COSMO_LEPS EQPF. The basic aim of the proposed approach is to analyse limits and opportunities of the long term forecast (with a lead time ranging from 3 to 5 days), for the implementation of low cost actions, also looking for a well informed decision making and the improvement of

  4. Forecasting of Severe Weather in Austria and Hungary Using High-Resolution Ensemble Prediction System

    NASA Astrophysics Data System (ADS)

    Szucs, Mihaly; Simon, Andre; Szintai, Balazs; Suklitsch, Martin; Wang, Yong; Wastl, Clemens; Boloni, Gergely

    2015-04-01

    The study presents and compares several approaches in EPS (ensemble prediction system) forecasting based on the non-hydrostatic, high resolution AROME model. The PEARP (global ARPEGE model EPS) was used for coupling. Besides, AROME-EPS was also generated upon hydrostatic ALADIN-EPS forecasts (LAEF), which were used as initial and lateral boundary conditions for each AROME-EPS run. The horizontal resolution of the AROME model is 2.5km and it uses 60 vertical levels for the vertical discretization. In most of the tests, the AROME-EPS run with 10+1 members in Hungarian and 16 members in Austrian implementation. The forecast length was usually set to 30-36 hours. The use of high-resolution EPS has advantages in almost all situations with severe convection (mostly in forecasting intense multicell thunderstorms or mesoscale convective systems of non-frontal origin). The possibility of severe thunderstorm was indicated by several EPS runs even if the deterministic (reference) AROME model failed to forecast the event. Similarly, it could be shown that the AROME-EPS can perform better than hydrostatic global or ALADIN-EPS models in situations with strong wind or heavy precipitation induced by large-scale circulation (mainly in mountain regions). Both EDA (Ensemble of Data Assimilation) and SPPT (Stochastically Perturbed Parameterized Tendencies) methods were tested as a potential perturbation generation method on limited area. The EDA method was able to improve the accuracy of single members through the reduction of the analysis error by applying local data assimilation. It was also able to increase the spread of the system in the early hours due to the additional analysis perturbations. The impact of the SPPT scheme was proven to be smaller in comparison to the impact of this method in global ensemble systems. Further possibilities of improving the assimilation methods and the setup of the AROME-EPS are also discussed.

  5. Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions

    NASA Astrophysics Data System (ADS)

    Perera, Kushan C.; Western, Andrew W.; Robertson, David E.; George, Biju; Nawarathna, Bandara

    2016-06-01

    Irrigation demands fluctuate in response to weather variations and a range of irrigation management decisions, which creates challenges for water supply system operators. This paper develops a method for real-time ensemble forecasting of irrigation demand and applies it to irrigation command areas of various sizes for lead times of 1 to 5 days. The ensemble forecasts are based on a deterministic time series model coupled with ensemble representations of the various inputs to that model. Forecast inputs include past flow, precipitation, and potential evapotranspiration. These inputs are variously derived from flow observations from a modernized irrigation delivery system; short-term weather forecasts derived from numerical weather prediction models and observed weather data available from automatic weather stations. The predictive performance for the ensemble spread of irrigation demand was quantified using rank histograms, the mean continuous rank probability score (CRPS), the mean CRPS reliability and the temporal mean of the ensemble root mean squared error (MRMSE). The mean forecast was evaluated using root mean squared error (RMSE), Nash-Sutcliffe model efficiency (NSE) and bias. The NSE values for evaluation periods ranged between 0.96 (1 day lead time, whole study area) and 0.42 (5 days lead time, smallest command area). Rank histograms and comparison of MRMSE, mean CRPS, mean CRPS reliability and RMSE indicated that the ensemble spread is generally a reliable representation of the forecast uncertainty for short lead times but underestimates the uncertainty for long lead times.

  6. Streamflow Forecasting using Satellite Products: A Benchmark Approach. Can We Reduce Uncertainty by using Multiple Products and Multiple Models?

    NASA Astrophysics Data System (ADS)

    Roy, T.; Serrat-Capdevila, A.; Gupta, H.; Valdes, J. B.

    2015-12-01

    Real-time satellite precipitation products can be used to drive hydrologic forecasts in downstream areas of poorly gauged basins. We present an improved approach to hydrologic modeling using satellite precipitation estimates to reduce uncertainty, consisting of: (1) bias-correction of satellite products, (2) re-calibration of hydrologic models using bias-corrected estimates, (3) bias-correction of streamflow outputs, and (4) plotting of uncertainty intervals. In addition, we evaluate the benefits of multi-product and model forecasts using four satellite precipitation products (CHIRPS, CMORPH, TMPA, and PERSIANN-CCS) to drive two hydrologic models (HYMOD and HBV-EDU), generating eight forecasts from different model-product-combinations following the approach described above. These probabilistic forecasts are then merged in an attempt to produce an improved forecast with higher accuracy and smaller uncertainty. These methods are applied in the Mara Basin in Kenya, facing serious water sustainability challenges, in an effort to support water management decisions balancing human and environmental needs, as part of the NASA SERVIR Applied Sciences Team.

  7. Development of a Multi-Model Ensemble Scheme for the Tropical Cyclone Forecast

    NASA Astrophysics Data System (ADS)

    Jun, S.; Lee, W. J.; Kang, K.; Shin, D. H.

    2015-12-01

    A Multi-Model Ensemble (MME) prediction scheme using selected and weighted method was developed and evaluated for tropical cyclone forecast. The analyzed tropical cyclone track and intensity data set provided by Korea Meteorological Administration and 11 numerical model outputs - GDAPS, GEPS, GFS (data resolution; 50 and 100 km), GFES, HWRF, IFS(data resolution; 50 and 100 km), IFS EPS, JGSM, and TEPS - during 2011-2014 were used for this study. The procedure suggested in this study was divided into two stages: selecting and weighting process. First several numerical models were chosen based on the past model's performances in the selecting stage. Next, weights, referred to as regression coefficients, for each model forecasts were calculated by applying the linear and nonlinear regression technique to past model forecast data in the weighting stage. Finally, tropical cyclone forecasts were determined by using both selected and weighted multi-model values at that forecast time. The preliminary result showed that selected MME's improvement rate (%) was more than 5% comparing with non-selected MME at 72 h track forecast.

  8. Can potential predictability characterize seasonal forecast skills of the East Asian summer monsoon in the ENSEMBLES coupled models?

    NASA Astrophysics Data System (ADS)

    Yang, Dejian; Tang, Youmin; Zhang, Yaocun; Yang, Xiuqun

    2013-04-01

    In ensemble forecasts, an important criterion for the reliability and calibration of a forecast system is the correspondence between two types of predictability metrics, the observation-free potential predictability measures and the observation-involved forecast skill measures. In this study, we investigate the ability of signal-to-noise ratio (SNR)-based potential predictability in characterizing seasonal forecast skills of the East Asian summer monsoon (EASM) in the ENSEMBLES models. It is found that in the ENSEMBLES multi-model ensemble (MME) system, SNR-based potential predictability can well characterize the spatial-temporal variations of seasonal forecast skills of the EASM, deterministic and probabilistic, which indicates the good capability of this MME system in capturing the underlying true seasonal predictability of the EASM . On the other hand, there exists a potential predictability-forecast skill contradiction when comparing the MME with the participating single model ensembles (SMEs), that is, MME outperforms individual models in terms of forecast skills, whereas potential predictability estimated using the former is lower than those estimated using the latter. A simple statistical model is used to explain this contradiction with moderate success.

  9. Stress transfer in earthquakes, hazard estimation and ensemble forecasting: Inferences from numerical simulations

    NASA Astrophysics Data System (ADS)

    Rundle, John B.; Rundle, Paul B.; Donnellan, Andrea; Li, P.; Klein, W.; Morein, Gleb; Turcotte, D. L.; Grant, Lisa

    2006-02-01

    Observations indicate that earthquake faults occur in topologically complex, multi-scale networks driven by plate tectonic forces. We present realistic numerical simulations, involving data-mining, pattern recognition, theoretical analyses and ensemble forecasting techniques, to understand how the observable space-time earthquake patterns are related to the fundamentally inaccessible and unobservable dynamics. Numerical simulations can also help us to understand how the different scales involved in earthquake physics interact and influence the resulting dynamics. Our simulations indicate that elastic interactions (stress transfer) combined with the nonlinearity in the frictional failure threshold law lead to the self-organization of the statistical dynamics, producing 1) statistical distributions for magnitudes and frequencies of earthquakes that have characteristics similar to those possessed by the Gutenberg-Richter magnitude-frequency distributions observed in nature; and 2) clear examples of stress transfer among fault activity described by stress shadows, in which an earthquake on one group of faults reduces the Coulomb failure stress on other faults, thereby delaying activity on those faults. In this paper, we describe the current state of modeling and simulation efforts for Virtual California, a model for all the major active strike slip faults in California. Noting that the Working Group on California Earthquake Probabilities (WGCEP) uses statistical distributions to produce earthquake forecast probabilities, we demonstrate that Virtual California provides a powerful tool for testing the applicability and reliability of the WGCEP statistical methods. Furthermore, we show how the simulations can be used to develop statistical earthquake forecasting techniques that are complementary to the methods used by the WGCEP, but improve upon those methods in a number of important ways. In doing so, we distinguish between the "official" forecasts of the WGCEP, and the

  10. Basis for a streamflow forecasting system to Rincón del Bonete and Salto Grande (Uruguay)

    NASA Astrophysics Data System (ADS)

    Talento, Stefanie; Terra, Rafael

    2013-10-01

    This paper presents the basis for the design of streamflow prediction systems for the hydroelectric dams of Rincón del Bonete (Uruguay) and Salto Grande (Uruguay-Argentina). The prediction is made, independently, for each reservoir and each month of the year with two methodologies: data-driven statistical models and hybrid downscaling that includes atmospheric predictors. We determine a set of potential predictors and then fit linear models coupled with variable selection techniques, under the hypothesis of perfectly known predictors. The predictive skill of the schemes outperforms the climatological forecast throughout the year in both reservoirs (except August in Rincón del Bonete). This remains the case even when the forecast lead does not allow for the use of preceding flows as predictors. While in Rincón del Bonete it is not possible to distinguish a period of high predictability, in Salto Grande, there is a robust signal in March-May and October-December.

  11. Integrating Hydrometeorological Information for Precipitation and Streamflow Forecasting Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Chiang, Y.; Chang, F.

    2008-12-01

    The major purpose of this study is to effectively construct artificial neural networks-based multistep ahead flood forecasting using hydrometeorological and numerical weather prediction (NWP) information. To achieve this goal, we first compare three mean areal precipitation forecasts: radar/NWP multisource-derived forecasts (Pr), NWP precipitation forecasts (Pn), and improved precipitation forecasts (Pm) by merging Pr and Pn. The analysis shows that the accuracy of Pm is higher than that of Pr and Pn. The analysis also indicates that the NWP precipitation forecasts do provide relative effectiveness to the merging procedure, particularly for forecast lead time of 4-6 h. In sum, the merged products performed well and captured the main tendency of rainfall pattern. Subsequently, a recurrent neural network (RNN)-based multistep ahead flood forecasting techniques is produced by feeding with the merged precipitation. The evaluation of 1-6 h flood forecasting schemes strongly shows that the proposed hydrological model provides accurate and stable flood forecasts in comparison with conventional case and significantly improves the peak flow forecasts and the time-lag problem. An important finding is the hydrologic model responses seem not sensitive to precipitation predictions in lead times of 1-3 h, whereas the runoff forecasts are highly dependent on predicted precipitation information for longer lead times (4-6 h). Overall, the results demonstrate that accurate and consistent multistep ahead flood forecasting can be obtained by integrating predicted precipitation information into ANNs modeling.

  12. Probabilistic rainfall and streamflow prediction in Japanese small basin using EnKF

    NASA Astrophysics Data System (ADS)

    Ushiyama, Tomoki; Sayama, Takahiro; Iwami, Yoichi; Miyoshi, Takemasa

    2014-05-01

    Probabilistic rainfall and streamflow forecasts have recently been recognized to have a highest potential for flood warning and disaster mitigation. Those forecast systems were proved to improve forecast skill considerably than deterministic approach. However, the smaller the target basins were chosen, the harder they forecast right amount of rainfall in the right place. The quantitative precipitation forecast (QPF) for strong rainfall in limited areas is still a challenging research targets. Although countries like Japan with complex terrain with steep slopes divided in small river basins needs QPF with high horizontal resolution, no such probabilistic streamflow forecasting system had been developed in Japan. We developed a probabilistic rainfall and streamflow forecast system, and applied it to a Japanese small basin for two typhoon events. Our objective is to examine the potential of this forecasting system. The numerical weather prediction (NWP) part is based on Weather Research and Forecasting (WRF) model combined with the Local Ensemble Transform Kalman Filter (LETKF) which is a sort of EnKF. The lateral boundary conditions were given by 5km resolution operational mesoscale model by Japan Meteorological Agency. The system assimilates PREPBUFR by NCEP and ground observed wind and air temperature. The WRF were run in 3km and 15km resolution, and 21 member ensemble run and deterministic runs were discussed. Predicted rainfall patterns were applied to our originally developed distributed hydrological model, named Rainfall-Runoff-Inundation (RRI) model, and we obtain streamflow pattern in the target basin, Hiyoshi Dam basin, in the center of Japan with a catchment size of 290km^2. Two typhoon cases, TALAS and ROKE in 2011, were examined which caused severe flood and landslide in this area. We implemented intermittent predictions with predictions updated every 6 hours, during about 5 day long period in both cases. The fraction skill scores (FSS) of the rainfall

  13. A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP)

    NASA Astrophysics Data System (ADS)

    Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.

    2016-08-01

    Drought is among the costliest natural hazards worldwide and extreme drought events in recent years have caused huge losses to various sectors. Drought prediction is therefore critically important for providing early warning information to aid decision making to cope with drought. Due to the complicated nature of drought, it has been recognized that the univariate drought indicator may not be sufficient for drought characterization and hence multivariate drought indices have been developed for drought monitoring. Alongside the substantial effort in drought monitoring with multivariate drought indices, it is of equal importance to develop a drought prediction method with multivariate drought indices to integrate drought information from various sources. This study proposes a general framework for multivariate multi-index drought prediction that is capable of integrating complementary prediction skills from multiple drought indices. The Multivariate Ensemble Streamflow Prediction (MESP) is employed to sample from historical records for obtaining statistical prediction of multiple variables, which is then used as inputs to achieve multivariate prediction. The framework is illustrated with a linearly combined drought index (LDI), which is a commonly used multivariate drought index, based on climate division data in California and New York in the United States with different seasonality of precipitation. The predictive skill of LDI (represented with persistence) is assessed by comparison with the univariate drought index and results show that the LDI prediction skill is less affected by seasonality than the meteorological drought prediction based on SPI. Prediction results from the case study show that the proposed multivariate drought prediction outperforms the persistence prediction, implying a satisfactory performance of multivariate drought prediction. The proposed method would be useful for drought prediction to integrate drought information from various sources

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

    NASA Astrophysics Data System (ADS)

    Thiemig, V.; Bisselink, B.; Pappenberger, F.; Thielen, J.

    2015-08-01

    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 by the ECMWF (European Centre for Medium-Ranged Weather Forecasts) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important floods were observed. Results were verified by ground measurements of 36 sub-catchments as well as by reports of various flood archives. 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 AFFS showed limitations for small-scale and short duration flood events. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe) illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African flood forecasting system, although issues related to the practical implication will still need to be investigated.

  15. Ensemble Modeling with Data Assimilation Models: A New Strategy for Space Weather Science, Specifications and Forecasts

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

    The Earth's Ionosphere-Thermosphere-Electrodynamics (I-T-E) system varies markedly on a range of spatial and temporal scales and these variations can have adverse effects on human operations and systems. Consequently, there is a need to both mitigate and forecast near-Earth space weather. Following the meteorologists, our goal is to specify and forecast the global I-T-E system with data assimilation models, because they are reliable and the models are already available. Currently, our team has first-principles-based data assimilation models for the ionosphere, ionosphere-plasmasphere, thermosphere, high-latitude ionosphere-electrodynamics, and mid-low latitude ionosphere-electrodynamics. These models assimilate a myriad of different ground- and space-based observations, and there are several data assimilation models for each near-Earth space domain. This enables us to conduct Multimodel Ensemble Data Assimilation of the I-T-E system that can account for different physical modeling assumptions, numerical techniques, and model initialization approaches. The application of ensemble modeling with several different data assimilation models will lead to a paradigm shift in how basic physical processes are studied in near-Earth space, and it should also lead to a significant advance space weather forecasting.

  16. Maximization of seasonal forecasts performance combining Grand Multi-Model Ensembles

    NASA Astrophysics Data System (ADS)

    Alessandri, Andrea; De Felice, Matteo; Catalano, Franco; Lee, Doo Young; Yoo, Jin Ho; Lee, June-Yi; Wang, Bin

    2014-05-01

    Multi-Model Ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single-model errors. Previous works suggested that the potential benefit that can be expected by using a MME amplify with the increase of the independence of the contributing Seasonal Prediction Systems. In this work we combine the two Multi Model Ensemble (MME) Seasonal Prediction Systems (SPSs) independently developed by the European (ENSEMBLES) and by the Asian-Pacific (CliPAS/APCC) communities. To this aim, all the possible multi-model combinations obtained by putting together the 5 models from ENSEMBLES and the 11 models from CliPAS/APCC have been evaluated. The grand ENSEMBLES-CliPAS/APCC Multi-Model enhances significantly the skill compared to previous estimates from the contributing MMEs. The combinations of SPSs maximizing the skill that is currently attainable for specific predictands/phenomena is evaluated. Our results show that, in general, the better combinations of SPSs are obtained by mixing ENSEMBLES and CliPAS/APCC models and that only a limited number of SPSs is required to obtain the maximum performance. The number and selection of models that perform better is usually different depending on the region/phenomenon under consideration. As an example for the tropical Pacific, the maximum performance is obtained with only the combination of 5-to-6 SPSs from the grand ENSEMBLES-CliPAS/APCC MME. With particular focus over Tropical Pacific, the relationship between performance and bias of the grand-MME combinations is evaluated. The skill of the grand-MME combinations over Euro-Mediterranean and East-Asia regions is further evaluated as a function of the capability of the selected contributing SPSs to forecast anomalies of the Polar/Siberian highs during winter and of the Asian summer monsoon precipitation during summer. Our results indicate that, combining SPSs from independent MME sources is a good

  17. Multi-model ensemble-based probabilistic prediction of tropical cyclogenesis using TIGGE model forecasts

    NASA Astrophysics Data System (ADS)

    Jaiswal, Neeru; Kishtawal, C. M.; Bhomia, Swati; Pal, P. K.

    2016-10-01

    An extended range tropical cyclogenesis forecast model has been developed using the forecasts of global models available from TIGGE portal. A scheme has been developed to detect the signatures of cyclogenesis in the global model forecast fields [i.e., the mean sea level pressure and surface winds (10 m horizontal winds)]. For this, a wind matching index was determined between the synthetic cyclonic wind fields and the forecast wind fields. The thresholds of 0.4 for wind matching index and 1005 hpa for pressure were determined to detect the cyclonic systems. These detected cyclonic systems in the study region are classified into different cyclone categories based on their intensity (maximum wind speed). The forecasts of up to 15 days from three global models viz., ECMWF, NCEP and UKMO have been used to predict cyclogenesis based on multi-model ensemble approach. The occurrence of cyclonic events of different categories in all the forecast steps in the grided region (10 × 10 km2) was used to estimate the probability of the formation of cyclogenesis. The probability of cyclogenesis was estimated by computing the grid score using the wind matching index by each model and at each forecast step and convolving it with Gaussian filter. The proposed method is used to predict the cyclogenesis of five named tropical cyclones formed during the year 2013 in the north Indian Ocean. The 6-8 days advance cyclogenesis of theses systems were predicted using the above approach. The mean lead prediction time for the cyclogenesis event of the proposed model has been found as 7 days.

  18. Forecasting European cold waves based on subsampling strategies of CMIP5 and Euro-CORDEX ensembles

    NASA Astrophysics Data System (ADS)

    Cordero-Llana, Laura; Braconnot, Pascale; Vautard, Robert; Vrac, Mathieu; Jezequel, Aglae

    2016-04-01

    Forecasting future extreme events under the present changing climate represents a difficult task. Currently there are a large number of ensembles of simulations for climate projections that take in account different models and scenarios. However, there is a need for reducing the size of the ensemble to make the interpretation of these simulations more manageable for impact studies or climate risk assessment. This can be achieved by developing subsampling strategies to identify a limited number of simulations that best represent the ensemble. In this study, cold waves are chosen to test different approaches for subsampling available simulations. The definition of cold waves depends on the criteria used, but they are generally defined using a minimum temperature threshold, the duration of the cold spell as well as their geographical extend. These climate indicators are not universal, highlighting the difficulty of directly comparing different studies. As part of the of the CLIPC European project, we use daily surface temperature data obtained from CMIP5 outputs as well as Euro-CORDEX simulations to predict future cold waves events in Europe. From these simulations a clustering method is applied to minimise the number of ensembles required. Furthermore, we analyse the different uncertainties that arise from the different model characteristics and definitions of climate indicators. Finally, we will test if the same subsampling strategy can be used for different climate indicators. This will facilitate the use of the subsampling results for a wide number of impact assessment studies.

  19. Ensemble Forecasting of Coronal Mass Ejections Using the WSA-ENLIL with CONED Model

    NASA Technical Reports Server (NTRS)

    Emmons, D.; Acebal, A.; Pulkkinen, A.; Taktakishvili, A.; MacNeice, P.; Odstricil, D.

    2013-01-01

    The combination of the Wang-Sheeley-Arge (WSA) coronal model, ENLIL heliospherical model version 2.7, and CONED Model version 1.3 (WSA-ENLIL with CONED Model) was employed to form ensemble forecasts for 15 halo coronal mass ejections (halo CMEs). The input parameter distributions were formed from 100 sets of CME cone parameters derived from the CONED Model. The CONED Model used image processing along with the bootstrap approach to automatically calculate cone parameter distributions from SOHO/LASCO imagery based on techniques described by Pulkkinen et al. (2010). The input parameter distributions were used as input to WSA-ENLIL to calculate the temporal evolution of the CMEs, which were analyzed to determine the propagation times to the L1 Lagrangian point and the maximum Kp indices due to the impact of the CMEs on the Earth's magnetosphere. The Newell et al. (2007) Kp index formula was employed to calculate the maximum Kp indices based on the predicted solar wind parameters near Earth assuming two magnetic field orientations: a completely southward magnetic field and a uniformly distributed clock-angle in the Newell et al. (2007) Kp index formula. The forecasts for 5 of the 15 events had accuracy such that the actual propagation time was within the ensemble average plus or minus one standard deviation. Using the completely southward magnetic field assumption, 10 of the 15 events contained the actual maximum Kp index within the range of the ensemble forecast, compared to 9 of the 15 events when using a uniformly distributed clock angle.

  20. Skill and predictability in multimodel ensemble forecasts for Northern Hemisphere regions with dominant winter precipitation

    NASA Astrophysics Data System (ADS)

    Ehsan, Muhammad Azhar; Tippett, Michael K.; Almazroui, Mansour; Ismail, Muhammad; Yousef, Ahmed; Kucharski, Fred; Omar, Mohamed; Hussein, Mahmoud; Alkhalaf, Abdulrahman A.

    2016-07-01

    Northern Hemisphere winter precipitation reforecasts from the European Centre for Medium Range Weather Forecast System-4 and six of the models in the North American Multi-Model Ensemble are evaluated, focusing on two regions (Region-A: 20°N-45°N, 10°E-65°E and Region-B: 20°N-55°N, 205°E-255°E) where winter precipitation is a dominant fraction of the annual total and where precipitation from mid-latitude storms is important. Predictability and skill (deterministic and probabilistic) are assessed for 1983-2013 by the multimodel composite (MME) of seven prediction models. The MME climatological mean and variability over the two regions is comparable to observation with some regional differences. The statistically significant decreasing trend observed in Region-B precipitation is captured well by the MME and most of the individual models. El Niño Southern Oscillation is a source of forecast skill, and the correlation coefficient between the Niño3.4 index and precipitation over region A and B is 0.46 and 0.35, statistically significant at the 95 % level. The MME reforecasts weakly reproduce the observed teleconnection. Signal, noise and signal to noise ratio analysis show that the signal variance over two regions is very small as compared to noise variance which tends to reduce the prediction skill. The MME ranked probability skill score is higher than that of individual models, showing the advantage of a multimodel ensemble. Observed Region-A rainfall anomalies are strongly associated with the North Atlantic Oscillation, but none of the models reproduce this relation, which may explain the low skill over Region-A. The superior quality of multimodel ensemble compared with individual models is mainly due to larger ensemble size.

  1. Reducing Uncertainties of Hydrologic Model Predictions Using a New Ensemble Pre-Processing Approach

    NASA Astrophysics Data System (ADS)

    Khajehei, S.; Moradkhani, H.

    2015-12-01

    Ensemble Streamflow Prediction (ESP) was developed to characterize the uncertainty in hydrologic predictions. However, ESP outputs are still prone to bias due to the uncertainty in the forcing data, initial condition, and model structure. Among these, uncertainty in forcing data has a major impact on the reliability of hydrologic simulations/forecasts. Major steps have been taken in generating less uncertain precipitation forecasts such as the Ensemble Pre-Processing (EPP) to achieve this goal. EPP is introduced as a statistical procedure based on the bivariate joint distribution between observation and forecast to generate ensemble climatologic forecast from single-value forecast. The purpose of this study is to evaluate the performance of pre-processed ensemble precipitation forecast in generating ensemble streamflow predictions. Copula functions used in EPP, model the multivariate joint distribution between univariate variables with any level of dependency. Accordingly, ESP is generated by employing both raw ensemble precipitation forecast as well as pre-processed ensemble precipitation. The ensemble precipitation forecast is taken from Climate Forecast System (CFS) generated by National Weather Service's (NWS) National Centers for Environmental Prediction (NCEP) models. Study is conducted using the precipitation Runoff Modeling System (PRMS) over two basins in the Pacific Northwest USA for the period of 1979 to 2013. Results reveal that applying this new EPP will lead to reduction of uncertainty and overall improvement in the ESP.

  2. Probabilistic maize yield simulation over East Africa using ensemble seasonal climate forecasts

    NASA Astrophysics Data System (ADS)

    Ogutu, Geoffrey; Supit, Iwan; Hutjes, Ronald

    2016-04-01

    Seasonal climate variability influences crop yields, especially in areas where rain fed agriculture is widely practiced such as in the East African region. Assuming that seasonal climate prediction skill would translate to similarly skillful prediction of impacts, an ensemble seasonal climate hindcast (ECMWF system4 EPS) for the period 1981 to 2010 at different initialization dates (lead months) before sowing is used to drive a crop simulation model: the World Food Studies (WOFOST) model, implemented for a single season Maize crop. The water-limited yield predictions were assessed against reference yields produced by the same crop model forced by the WATCH Forcing Data ERA-Interim (WFDEI) at grid point level. We focus on the main sowing dates of June/July (Northern region), March (Equatorial East Africa) and November (Southern region). Deviation of yields from the mean over the study period is used to indicate regions in which probabilistic yield forecasts would be useful while the Relative Operating Curve Skill Score (ROCSS) indicates prediction skill of above normal, near normal and below normal yield prediction. Equatorial regions of East Africa and coastal Kenya with sowing date in March show a mean deviation of ≥ 500 Kg/ha. Here probabilistic yield forecasts can potentially be useful as opposed to the northern and southern regions with less deviation. The high deviation in this region may also be due to the existence of more than one cropping season corresponding to the bi-modal rainfall regime since the model only simulates a single season. A positive ROCSS over a large extent of the equatorial region show predictability skill of all the tercile forecasts simulated by forecasts initialized at the start of sowing date (March i.e. lead 0 forecasts) and no predictability at longer lead months. Over Ethiopia in the northern region of East Africa, November harvests with a sowing date of June show predictability of the upper, lower and middle terciles at lead-0

  3. Forecast of Algal Bloom Dynamics in a River Using the Ensemble Kalman Filter

    NASA Astrophysics Data System (ADS)

    Kim, K.; Ryu, I.; Min, J.; Shin, K.; Park, L.

    2011-12-01

    A forecasting framework of algal bloom in a river channel was developed by employing two numerical models coupled in serial order to simulate a watershed and the main river channel. The HSPF model simulates flow discharge and water quality from the watershed and the EFDC model takes the results as inputs to simulate river channel hydrodynamics and water quality. The ensemble Kalman filter (EnKF) was applied for data assimilation in the framework, linking uncertainties of inputs, model structures and observations. The input precipitation and water quality from point sources were perturbed to form ensemble members of state variables. The perturbation generated the ensemble of flow discharges and concentrations of water quality variables including Chl-a from HSPF model simulations, which were fed into the EFDC model. Stochastic forcing terms to simulate model structural errors were added for both models. Also an error distribution was used to address the measurement error. The framework was applied to theYoungsan River watershed, which is located in southwestern area of the Korean Peninsula (about 3,470 km2). The weather forecast data provided by Korean Meteorological Agency, consisting of combined data from the UM-Regional and UM-Global models, were used through the forecasting simulations. The HSPF model was calibrated for the observed flow and water quality data of 2009 to 2010 and the EFDC model for the data of 2008 to 2009 before they are used in forecasting. Data assimilation was conducted with weekly Chl-a data sampled along the river channel by updating Chl-a concentrations on the EFDC model grids. To keep mass conservation of water quality constituents, the other variables are also updated correspondingly, considering the changes due to growth, death and respiration of algae. The results show that EnKF works efficiently for updating spatial distribution of Chl-a concentrations. However, since the ensemble inputs at the boundary locations produced by the HSPF

  4. Decision-relevant early-warning thresholds for ensemble flood forecasting systems

    NASA Astrophysics Data System (ADS)

    Stephens, Liz; Pappenberger, Florian; Cloke, Hannah; Alfieri, Lorenzo

    2014-05-01

    Over and under warning of potential future floods is problematic for decision-making, and could ultimately lead to trust being lost in the forecasts. The use of ensemble flood forecasting systems for early warning therefore requires a consideration of how to determine and implement decision-relevant thresholds for flood magnitude and probability. This study uses a year's worth of hindcasts from the Global Flood Awareness System (GloFAS) to explore the sensitivity of the warning system to the choice of threshold. We use a number of different methods for choosing these thresholds, building on current approaches that use model climatologies to determine the critical flow magnitudes, to those that can provide 'first guesses' of potential impacts (through integration with global-scale inundation mapping), as well as methods that could incorporate resource limitations.

  5. Comparative evaluation of ensemble Kalman filter, particle filter and variational techniques for river discharge forecast

    NASA Astrophysics Data System (ADS)

    Hirpa, F. A.; Gebremichael, M.; LEE, H.; Hopson, T. M.

    2012-12-01

    Hydrologic data assimilation techniques provide a means to improve river discharge forecasts through updating hydrologic model states and correcting the atmospheric forcing data via optimally combining model outputs with observations. The performance of the assimilation procedure, however, depends on the data assimilation techniques used and the amount of uncertainty in the data sets. To investigate the effects of these, we comparatively evaluate three data assimilation techniques, including ensemble Kalman filter (EnKF), particle filter (PF) and variational (VAR) technique, which assimilate discharge and synthetic soil moisture data at various uncertainty levels into the Sacramento Soil Moisture accounting (SAC-SMA) model used by the National Weather Service (NWS) for river forecasting in The United States. The study basin is Greens Bayou watershed with area of 178 km2 in eastern Texas. In the presentation, we summarize the results of the comparisons, and discuss the challenges of applying each technique for hydrologic applications.

  6. Three-dimensional visualization of ensemble weather forecasts - Part 1: The visualization tool Met.3D (version 1.0)

    NASA Astrophysics Data System (ADS)

    Rautenhaus, M.; Kern, M.; Schäfler, A.; Westermann, R.

    2015-07-01

    We present "Met.3D", a new open-source tool for the interactive three-dimensional (3-D) visualization of numerical ensemble weather predictions. The tool has been developed to support weather forecasting during aircraft-based atmospheric field campaigns; however, it is applicable to further forecasting, research and teaching activities. Our work approaches challenging topics related to the visual analysis of numerical atmospheric model output - 3-D visualization, ensemble visualization and how both can be used in a meaningful way suited to weather forecasting. Met.3D builds a bridge from proven 2-D visualization methods commonly used in meteorology to 3-D visualization by combining both visualization types in a 3-D context. We address the issue of spatial perception in the 3-D view and present approaches to using the ensemble to allow the user to assess forecast uncertainty. Interactivity is key to our approach. Met.3D uses modern graphics technology to achieve interactive visualization on standard consumer hardware. The tool supports forecast data from the European Centre for Medium Range Weather Forecasts (ECMWF) and can operate directly on ECMWF hybrid sigma-pressure level grids. We describe the employed visualization algorithms, and analyse the impact of the ECMWF grid topology on computing 3-D ensemble statistical quantities. Our techniques are demonstrated with examples from the T-NAWDEX-Falcon 2012 (THORPEX - North Atlantic Waveguide and Downstream Impact Experiment) campaign.

  7. 3-D visualization of ensemble weather forecasts - Part 1: The visualization tool Met.3D (version 1.0)

    NASA Astrophysics Data System (ADS)

    Rautenhaus, M.; Kern, M.; Schäfler, A.; Westermann, R.

    2015-02-01

    We present Met.3D, a new open-source tool for the interactive 3-D visualization of numerical ensemble weather predictions. The tool has been developed to support weather forecasting during aircraft-based atmospheric field campaigns, however, is applicable to further forecasting, research and teaching activities. Our work approaches challenging topics related to the visual analysis of numerical atmospheric model output - 3-D visualization, ensemble visualization, and how both can be used in a meaningful way suited to weather forecasting. Met.3D builds a bridge from proven 2-D visualization methods commonly used in meteorology to 3-D visualization by combining both visualization types in a 3-D context. We address the issue of spatial perception in the 3-D view and present approaches to using the ensemble to allow the user to assess forecast uncertainty. Interactivity is key to our approach. Met.3D uses modern graphics technology to achieve interactive visualization on standard consumer hardware. The tool supports forecast data from the European Centre for Medium Range Weather Forecasts and can operate directly on ECMWF hybrid sigma-pressure level grids. We describe the employed visualization algorithms, and analyse the impact of the ECMWF grid topology on computing 3-D ensemble statistical quantitites. Our techniques are demonstrated with examples from the T-NAWDEX-Falcon 2012 campaign.

  8. Ensemble Data Assimilation for Channel Flow Routing to Improve Operational Hydrologic Forecasting

    NASA Astrophysics Data System (ADS)

    Liu, Y.; Lee, H.; Seo, D.; Brown, J.; Corby, R.; Howieson, T.

    2008-12-01

    Channel flow routing, which predicts hydrograph transformation as water moves downstream, is a critical step in operational forecasting of floods and water resources. Like hydrologic modeling for headwater basins, routing modeling involves many kinds of uncertainties arising from observational data and the model itself. In addition to in-channel transformations, routing must also consider uncertainties from less-than-well-known sources and sinks along the channel. Data assimilation holds large potential in accounting for these different uncertainties in a dynamically and statistically consistent way. In this presentation, we describe an application of ensemble data assimilation for a hydrologic channel routing model based on the variable three-parameter Muskingum method, in which we consider errors in the inflow and outflow observations, and uncertainties in the initial conditions and Muskingum parameters. For data assimilation, we adopt the Maximum Likelihood Ensemble Filter (or MLEF, Zupanski 2005), which combines the strengths of variational data assimilation and ensemble filtering techniques. Results from applications to selected river sections in Texas in the WGRFC's service area will be presented, along with issues from research and operational perspectives.

  9. Maximum Likelihood Ensemble Filter-based Data Assimilation with HSPF for Improving Water Quality Forecasting

    NASA Astrophysics Data System (ADS)

    Kim, S.; Riazi, H.; Shin, C.; Seo, D.

    2013-12-01

    Due to the large dimensionality of the state vector and sparsity of observations, the initial conditions (IC) of water quality models are subject to large uncertainties. To reduce the IC uncertainties in operational water quality forecasting, an ensemble data assimilation (DA) procedure for the Hydrologic Simulation Program - Fortran (HSPF) model has been developed and evaluated for the Kumho River Subcatchment of the Nakdong River Basin in Korea. The procedure, referred to herein as MLEF-HSPF, uses maximum likelihood ensemble filter (MLEF) which combines strengths of variational assimilation (VAR) and ensemble Kalman filter (EnKF). The Control variables involved in the DA procedure include the bias correction factors for mean areal precipitation and mean areal potential evaporation, the hydrologic state variables, and the water quality state variables such as water temperature, dissolved oxygen (DO), biochemical oxygen demand (BOD), ammonium (NH4), nitrate (NO3), phosphate (PO4) and chlorophyll a (CHL-a). Due to the very large dimensionality of the inverse problem, accurately specifying the parameters for the DA procdedure is a challenge. Systematic sensitivity analysis is carried out for identifying the optimal parameter settings. To evaluate the robustness of MLEF-HSPF, we use multiple subcatchments of the Nakdong River Basin. In evaluation, we focus on the performance of MLEF-HSPF on prediction of extreme water quality events.

  10. Ensemble Data Assimilation with HSPF for Improved Real-Time Water Quality Forecasting

    NASA Astrophysics Data System (ADS)

    Kim, S.; Riazi, H.; rafieei nasab, A.; Shin, C.; Seo, D.

    2013-05-01

    An ensemble data assimilation (DA) procedure for the Hydrologic Simulation Program - Fortran (HSPF) model has been developed, tested and evaluated for implementation in real-time water quality forecasting. The procedure, referred to herein as MLEF-HSPF, uses maximum likelihood ensemble filter (MLEF) which combines strengths of variational assimilation (VAR) and ensemble Kalman filter (EnKF). To evaluate the procedure, MLEF-HSPF was run daily for a 2-yr period for the Kumho River Subbasin of the Nakdong River Basin in Korea. A set of performance measures was used to assess the marginal value of DA-aided predictions of stream flow and water quality variables such as water temperature, dissolved oxygen (DO), biochemical oxygen demand (BOD), ammonium (NH4), nitrate (NO3), phosphate (PO4) and chlorophyll a. Due to large dimensionality of the state vector and complexity of the biochemical processes involved, DA with HSPF poses additional challenges. In this presentation, we describe MLEF-HSPF, summarize the evaluation results and identify the challenges.

  11. Global and Limited-Area Ensemble Prediction Systems deployed for Wind Power Forecasting

    NASA Astrophysics Data System (ADS)

    Petroliagis, T. I.; Jacques, M.; Montani, A.; Bremen, L. V.; Heinemann, D.

    2010-09-01

    The integration of wind generation into power systems is affected by uncertainties in the forecasting of expected power output. Misestimating of meteorological conditions or large forecasting errors (phase errors, near cut-off speeds etc.) has proved to be very costly for infrastructures (i.e. unexpected loads on turbines) and reduces the value of wind energy for end-users. The state-of-the-art in wind power forecasting has focused so far on the 'usual' operating conditions rather than on extreme events. Thus, the current wind forecasting technology presents several strong bottlenecks. End-users urge for dedicated approaches to reduce large prediction errors or predict extremes from a local scale (gusts, shears) up to a European scale as extremes and forecast errors may propagate. The aim of the new European FP7 Project, namely SAFEWIND, is to substantially improve wind power predictability in challenging or extreme situations and at different temporal and spatial scales. One of the areas that SAFEWIND concentrates on is the use of Global and Local Area Model (LAM) Ensemble Prediction System (EPS) forecasts for improved wind predictions on local to regional scales. More specifically, the ECMWF (European Centre for Medium-Range Weather Forecasts) EPS has been deployed as the backbone Global EPS platform for the SAFEWIND. The operational ECMWF EPS uses perturbations based on initial and evolved singular vectors. Model uncertainties are presented currently by the stochastic physics scheme that perturbs the parametrised physics tendencies by multiplicative noise. The current horizontal resolution of the ECMWF EPS is roughly 32 km while its corresponding 'deterministic' IFS (Integrated Forecast System) forecast and analysis fields have a resolution of about 16 km. On the other hand, LEPS (Limited-Area Ensemble Prediction System) components have been provided by the COSMO-LEPS. This system is based on the non-hydrostatic COSMO-model developed within the COnsortium for

  12. A regional air quality forecasting system over Europe: the MACC-II daily ensemble production

    NASA Astrophysics Data System (ADS)

    Marécal, V.; Peuch, V.-H.; Andersson, C.; Andersson, S.; Arteta, J.; Beekmann, M.; Benedictow, A.; Bergström, R.; Bessagnet, B.; Cansado, A.; Chéroux, F.; Colette, A.; Coman, A.; Curier, R. L.; Denier van der Gon, H. A. C.; Drouin, A.; Elbern, H.; Emili, E.; Engelen, R. J.; Eskes, H. J.; Foret, G.; Friese, E.; Gauss, M.; Giannaros, C.; Guth, J.; Joly, M.; Jaumouillé, E.; Josse, B.; Kadygrov, N.; Kaiser, J. W.; Krajsek, K.; Kuenen, J.; Kumar, U.; Liora, N.; Lopez, E.; Malherbe, L.; Martinez, I.; Melas, D.; Meleux, F.; Menut, L.; Moinat, P.; Morales, T.; Parmentier, J.; Piacentini, A.; Plu, M.; Poupkou, A.; Queguiner, S.; Robertson, L.; Rouïl, L.; Schaap, M.; Segers, A.; Sofiev, M.; Thomas, M.; Timmermans, R.; Valdebenito, Á.; van Velthoven, P.; van Versendaal, R.; Vira, J.; Ung, A.

    2015-03-01

    This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. The paper gives an overall picture of its status at the end of MACC-II (summer 2014). This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs and PAN + PAN precursors) over 8 vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performances of the system are assessed daily, weekly and 3 monthly (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the median ensemble to forecast regional ozone pollution events. The time period of this case study is also used to illustrate that the median ensemble generally outperforms each of the individual models and that it is still robust even if two of the seven models are missing. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10 and show an overall improvement over time. The change of the skills of the ensemble over the past two summers for ozone and the past two winters for PM10 are discussed in the paper. While the evolution of the ozone scores is not significant, there are improvements of PM10 over the past two winters

  13. Stochastic Variation of Transient-wave Phase Speed: Effect on Forecast Ensemble Performance

    NASA Astrophysics Data System (ADS)

    McLay, J.; Hodyss, D.; Hansen, J.; Reynolds, C.; Bishop, C.

    2011-12-01

    Operational numerical weather prediction (NWP) models continue to produce substantial errors in the propagation of transient waves, e.g. phase errors of O(100km) at the 120h lead time in the case of synoptic extratropical cyclones (Froud 2009). The first part of this study examines the distribution of propagation speed errors for transient 500 hPa geopotential height anomalies in the Northern Hemisphere winter using two independent methods: A feature-shifting anomaly-correlation (AC) optimization method (after Brewster 2003 and Jablonowski and Williamson 2006), and a gradient-based AC optimization method. Among the findings, both methods are consistent in indicating that the spatio-temporal variance of the speed error distribution is substantial, and that the mean of the distribution is often not significantly different from zero. This implies that the speed error cannot be treated as a bias, but rather must be treated as a stochastic quantity. Based on this finding, the second part of this study tests the idea of stochastic variation of transient-wave phase speed in the framework of a 20-member forecast ensemble generated using the local ensemble transform (ET) technique in conjunction with a global spectral NWP model. A multivariate, spatially correlated Gaussian advective forcing is incorporated into the NWP integration, where the statistics of the forcing are guided by the aforementioned speed-error estimation results. The effect of the forcing on the forecast-ensemble performance is evaluated as a function of various distribution parameters including autoregression coefficient, mean, variance, and spatial correlation length scale.

  14. ENSO Forecasts in the North American Multi-Model Ensemble: Composite Analysis and Verification

    NASA Astrophysics Data System (ADS)

    Chen, L. C.

    2015-12-01

    In this study, we examine precipitation and temperature forecasts during El Nino/Southern Oscillation (ENSO) events in six models in the North American Multi-Model Ensemble (NMME), including the CFSv2, CanCM3, CanCM4, FLOR, GEOS5, and CCSM4 models, by comparing the model-based ENSO composites to the observed. The composite analysis is conducted using the 1982-2010 hindcasts for each of the six models with selected ENSO episodes based on the seasonal Ocean Nino Index (ONI) just prior to the date the forecasts were initiated. Two sets of composites are constructed over the North American continent: one based on precipitation and temperature anomalies, the other based on their probability of occurrence in a tercile-based system. The composites apply to monthly mean conditions in November, December, January, February, and March, respectively, as well as to the five-month aggregates representing the winter conditions. For the anomaly composites, we use the anomaly correlation coefficient and root-mean-square error against the observed composites for evaluation. For the probability composites, unlike conventional probabilistic forecast verification assuming binary outcomes to the observations, both model and observed composites are expressed in probability terms. Performance metrics for such validation are limited. Therefore, we develop a probability anomaly correlation measure and a probability score for assessment, so the results are comparable to the anomaly composite evaluation. We found that all NMME models predict ENSO precipitation patterns well during wintertime; however, some models have large discrepancies between the model temperature composites and the observed. The skill is higher for the multi-model ensemble, as well as the five-month aggregates. Comparing to the anomaly composites, the probability composites have superior skill in predicting ENSO temperature patterns and are less sensitive to the sample used to construct the composites, suggesting that

  15. Applications of satellite snow cover in computerized short-term streamflow forecasting. [Conejos River, Colorado

    NASA Technical Reports Server (NTRS)

    Leaf, C. F.

    1975-01-01

    A procedure is described whereby the correlation between: (1) satellite derived snow-cover depletion and (2) residual snowpack water equivalent, can be used to update computerized residual flow forecasts for the Conejos River in southern Colorado.

  16. Hydrologic Forecasting and Hydropower Production

    NASA Astrophysics Data System (ADS)

    Wigmosta, M. S.; Voisin, N.; Lettenmaier, D. P.; Coleman, A.; Mishra, V.; Schaner, N. A.

    2011-12-01

    Hydroelectric power production is one of many competing demands for available water along with other priority uses such as irrigation, thermoelectric cooling, municipal, recreation, and environmental performance. Increasingly, hydroelectric generation is being used to offset the intermittent nature of some renewable energy sources such as wind-generated power. An accurate forecast of the magnitude and timing of water supply assists managers in integrated planning and operations to balance competing water uses against current and future supply while protecting against the possibility of water or energy shortages and excesses with real-time actions. We present a medium-range to seasonal ensemble streamflow forecasting system where uncertainty in forecasts is addressed explicitly. The integrated forecast system makes use of remotely-sensed data and automated spatial and temporal data assimilation. Remotely-sensed snow cover, observed snow water equivalent, and observed streamflow data are used to update the hydrologic model state prior to the forecast. In forecast mode, the hydrology model is forced by calibrated ensemble weather/climate forecasts. This system will be fully integrated into a water optimization toolset to inform reservoir and power operations, and guide environmental performance decision making. This flow forecast system development is carried out in agreement with the National Weather Service so that the system can later be incorporated into the NOAA eXperimental Ensemble Forecast Service (XEFS).

  17. Met.3D - a new open-source tool for interactive 3D visualization of ensemble weather forecasts

    NASA Astrophysics Data System (ADS)

    Rautenhaus, Marc; Kern, Michael; Schäfler, Andreas; Westermann, Rüdiger

    2015-04-01

    We introduce Met.3D, a new open-source tool for the interactive 3D visualization of numerical ensemble weather predictions. The tool has been developed to support weather forecasting during aircraft-based atmospheric field campaigns, however, is applicable to further forecasting, research and teaching activities. Our work approaches challenging topics related to the visual analysis of numerical atmospheric model output -- 3D visualisation, ensemble visualization, and how both can be used in a meaningful way suited to weather forecasting. Met.3D builds a bridge from proven 2D visualization methods commonly used in meteorology to 3D visualization by combining both visualization types in a 3D context. It implements methods that address the issue of spatial perception in the 3D view as well as approaches to using the ensemble in order to assess forecast uncertainty. Interactivity is key to the Met.3D approach. The tool uses modern graphics hardware technology to achieve interactive visualization of present-day numerical weather prediction datasets on standard consumer hardware. Met.3D supports forecast data from the European Centre for Medium Range Weather Forecasts and operates directly on ECMWF hybrid sigma-pressure level grids. In this presentation, we provide an overview of the software --illustrated with short video examples--, and give information on its availability.

  18. Real-time variational assimilation of streamflow and radar-based precipitation data into operational hydrologic forecasting

    NASA Astrophysics Data System (ADS)

    Seo, D.; Koren, V.; Cajina, L.; Corby, R.; Finn, B.; Bell, F.

    2003-04-01

    To deal with various sources of error on the initial and boundary conditions, and in model parameters and structure, some form of state updating is necessary in operational forecasting that makes use of real-time streamflow observations. Here we analyze the benefit of variational assimilation as an automatic updating technique in an operational setting. Compared to state space-based techniques (e.g. Kalman filtering), variational assimilation (VAR)-based techniques offer at least two important advantages in state updating of operational hydrologic models that are, in particular, driven by radar-based precipitation input. 1) Because VAR does not require the hydrologic model to be rendered into a state-space form, no modifications are necessary to the model. Hence, the model parameters are completely transferable between calibration and state updating/assimilation. 2) Because VAR is a smoother, as opposed to a filter, VAR is very effective in assimilating data that are subject to significant biases, such as radar-based precipitation estimates. Following long-term off-line evaluation, a prototype VAR algorithm has been implemented recently at the National Weather Service West Gulf River Forecast Center (NWS/WGRFC). In this presentation, we describe the operational experience to date and the issues identified, and offer directions for further improvement.

  19. Using GEFS ensemble forecasts for decision making in reservoir management in California

    NASA Astrophysics Data System (ADS)

    Scheuerer, M.; Hamill, T.; Webb, R. S.

    2015-12-01

    Reservoirs such as Lake Mendocino in California's Russian River Basin provide flood control, water supply, recreation, and environmental stream flow regulation. Many of these reservoirs are operated by the U.S. Army Corps of Engineers (Corps) according to water control manuals that specify elevations for an upper volume of reservoir storage that must be kept available for capturing storm runoff and reducing flood risk, and a lower volume of storage that may be used for water supply. During extreme rainfall events, runoff is captured by these reservoirs and released as quickly as possible to create flood storage space for another potential storm. These flood control manuals are based on typical historical weather patterns - wet during the winter, dry otherwise - but are not informed directly by weather prediction. Alternative reservoir management approaches such as Forecast-Informed Reservoir Operations (FIRO), which seek to incorporate advances in weather prediction, are currently being explored as means to improve water supply availability while maintaining flood risk reduction and providing additional ecosystem benefits.We present results from a FIRO proof-of-concept study investigating the reliability of post-processed GEFS ensemble forecasts to predict the probability that day 6-to-10 precipitation accumulations in certain areas in California exceed a high threshold. Our results suggest that reliable forecast guidance can be provided, and the resulting probabilities could be used to inform decisions to release or hold water in the reservoirs. We illustrate the potential of these forecasts in a case study of extreme event probabilities for the Russian River Basin in California.

  20. Evaluation of precipitation forecasts over the Alps using the D-PHASE multi-model ensemble

    NASA Astrophysics Data System (ADS)

    Weusthoff, T.; Arpagaus, M.; Rotach, M. W.

    2010-09-01

    In the scope of the WWRP Forecast and Demonstration Project D-PHASE a real-time end-to-end forecasting system for heavy precipitation and subsequent flood events in the Alpine region was set up. Based on probabilistic and deterministic atmospheric and hydrological models as well as on nowcasting tools, warnings were issued for specified river catchments. The forecast data of the participating numerical models, including high resolution convection resolving models and models with parameterized convection, are stored in a central data archive in the World Data Center for Climate in Hamburg. They provide a huge amount of data which is well suited to investigate the use of atmospheric/hydrological models for flood forecasting in mountainous regions. Various methods are applied to get an objective verification of the model forecasts and their ability to correctly predict precipitation amounts and spatial patterns. The focus is hereby on a comparison of models with different horizontal mesh-sizes. The evaluation is done for the full D-PHASE observation period (DOP, June - November 2007) and for the whole D-PHASE domain covering basically the Alpine region. Amongst others, neighborhood verification techniques, where the forecast is evaluated on increasing spatial scales by defining an event based on the statistics of moving window domains, are applied with gridded VERA data serving as reference observation. The respective VERA analyses have been calculated by the University of Vienna based on the Joint D-PHASE/COPS (JDC) data consisting of more than 1000 GTS stations and more than 10000 non-GTS stations collected from national and regional weather services of Central Europe. A key feature of VERA is that no first guess is necessary to run the analysis - this makes the system Numerical Weather Prediction (NWP) model independent and therefore suitable for verification. This way, the multi-model ensemble of NWP models contributing to D-PHASE is evaluated for the highly

  1. Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA

    SciTech Connect

    Barker, D.; Huang, X. Y.; Liu, Z. Q.; Auligne, T.; Zhang, X.; Rugg, S.; Ajjaji, R.; Bourgeois, A.; Bray, J.; Chen, Y. S.; Demirtas, M.; Guo, Y. R.; Henderson, T.; Huang, W.; Lin, H. C.; Michalakes, J.; Rizvi, S.; Zhang, X. Y.

    2012-06-01

    Data assimilation is the process by which observations are combined with short-range NWP model output to produce an analysis of the state of the atmosphere at a specified time. Since its inception in the late 1990s, the multiagency Weather Research and Forecasting (WRF) model effort has had a strong data assimilation component, dedicating two working groups to the subject. This article documents the history of the WRF data assimilation effort, and discusses the challenges associated with balancing academic, research, and operational data assimilation requirements in the context of the WRF effort to date. The WRF Model's Community Variational/Ensemble Data Assimilation System (WRFDA) has evolved over the past 10 years, and has resulted in over 30 refereed publications to date, as well as implementation in a wide range of real-time and operational NWP systems.

  2. Combining multi-objective optimization and bayesian model averaging to calibrate forecast ensembles of soil hydraulic models

    SciTech Connect

    Vrugt, Jasper A; Wohling, Thomas

    2008-01-01

    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 multi-objective 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 multi-objective 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 post-processed 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 multi-objective optimization and BMA framework proposed in this paper is very useful to generate forecast ensembles of soil hydraulic models.

  3. Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting

    PubMed Central

    Owens, M J; Horbury, T S; Wicks, R T; McGregor, S L; Savani, N P; Xiong, M

    2014-01-01

    Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind “noise,” which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical “downscaling” of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme. Key Points Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations PMID:26213518

  4. On the combination of Stochastic Pertubation of Physical Tendencies and parameter perturbation for convection-permitting ensemble forecast

    NASA Astrophysics Data System (ADS)

    Marsigli, Chiara; Montani, Andrea; Paccagnella, Tiziana; Torrisi, Lucio; Marcucci, Francesca

    2016-04-01

    A convection-permitting ensemble based on the COSMO model (COSMO-IT-EPS) has been developed for Italy. The ensemble is run at 2.8 km of horizontal resolution, with 10 members, and receive initial and boundary conditions from a coarser resolution ensemble covering the entire Mediterranean area. A deficiency in the spread/skill relation of the ensemble in terms of near-surface weather parameter had been found in a previous study, in agreement with results reported in literature for similar limited-area ensemble systems. In order to address this issue, the physics perturbation methodology applied to the ensemble is here studied, with the aim of combining different sources of model uncertainties. Three configurations of the ensemble have been run for one month period in Autumn 2015: i) a control configuration, which is a pure downscaling ensemble, ii) a configuration where the model physics is perturbed by making use of the Stochastic Pertubation of Physical Tendencies (SPPT) scheme implemented in the COSMO model and iii) a configuration where the SPPT scheme is combined with perturbed physics parameters. The aim is to assess the relative impact of SPPT and parameter perturbation and to study their complementarity, both in a statistical way and on selected events. Objective evaluation of the forecast quality is performed for 2-meter temperature and humidity, against data from the SYNOP network, as well as for precipitation, using high density raingauge data to allow the application of spatial verification methods.

  5. Assessing the add value of ensemble forecast in a drought early warning

    NASA Astrophysics Data System (ADS)

    Calmanti, Sandro; Bosi, Lorenzo; Fernandez, Jesus; De Felice, Matteo

    2015-04-01

    The EU-FP7 project EUPORIAS is developing a prototype climate service to enhance the existing food security drought early warning system in Ethiopia. The Livelihoods, Early Assessment and Protection (LEAP) system is the Government of Ethiopia's national food security early warning system, established with the support of WFP and the World Bank in 2008. LEAP was designed to increase the predictability and timeliness of response to drought-related food crises in Ethiopia. It combines early warning with contingency planning and contingency funding, to allow the government, WFP and other partners to provide early assistance in anticipation of an impending catastrophes. Currently, LEAP uses satellite based rainfall estimates to monitor drought conditions and to compute needs. The main aim of the prototype is to use seasonal hindcast data to assess the added value of using ensemble climate rainfall forecasts to estimate the cost of assistance of population hit by major droughts. We outline the decision making process that is informed by the prototype climate service, and we discuss the analysis of the expected and skill of the available rainfall forecast data over Ethiopia. One critical outcome of this analysis is the strong dependence of the expected skill on the observational estimate assumed as reference. A preliminary evaluation of the full prototype products (drought indices and needs estimated) using hindcasts data will also be presented.

  6. High resolution WRF ensemble forecasting for irrigation: Multi-variable evaluation

    NASA Astrophysics Data System (ADS)

    Kioutsioukis, Ioannis; de Meij, Alexander; Jakobs, Hermann; Katragkou, Eleni; Vinuesa, Jean-Francois; Kazantzidis, Andreas

    2016-01-01

    An ensemble of meteorological simulations with the WRF model at convection-allowing resolution (2 km) is analysed in a multi-variable evaluation framework over Europe. Besides temperature and precipitation, utilized variables are relative humidity, boundary layer height, shortwave radiation, wind speed, convective and large-scale precipitation in view of explaining some of the biases. Furthermore, the forecast skill of evapotranspiration and irrigation water need is ultimately assessed. It is found that the modelled temperature exhibits a small but significant negative bias during the cold period in the snow-covered northeast regions. Total precipitation exhibits positive bias during all seasons but autumn, peaking in the spring months. The varying physics configurations resulted in significant differences for the simulated minimum temperature, summer rainfall, relative humidity, solar radiation and planetary boundary layer height. The interaction of the temperature and moisture profiles with the different microphysics schemes, results in excess convective precipitation using MYJ/WSM6 compared to YSU/Thompson. With respect to evapotranspiration and irrigation need, the errors using the MYJ configuration were in opposite directions and eventually cancel out, producing overall smaller biases. WRF was able to dynamically downscale global forecast data into finer resolutions in space and time for hydro-meteorological applications such as the irrigation management. Its skill was sensitive to the geographical location and physical configuration, driven by the variable relative importance of evapotranspiration and rainfall.

  7. Evaluation of Sub-monthly Forecast Skill from Global Ensemble Prediction Systems

    NASA Astrophysics Data System (ADS)

    Li, S.; Robertson, A. W.

    2013-12-01

    The prediction skill of precipitation over sub-monthly time scale is investigated based on hindcasts from three global ensemble prediction systems (EPS). The results valid for up to four weeks indicate good skill or predictability over some regions during the boreal summer monsoon season (e.g, June through September), particularly over southeastern Asia and the maritime continent. The hindcasts from all the three models correspond to high predictability over the first week compared to the following three weeks. The ECMWF forecast system tends to yield higher prediction skill than the other two systems, in terms of both anomaly correlation and mean squared skill score. The sources of sub-monthly predictability are examined over the maritime continent with focus on the intra-seasonal MJO and interannual ENSO phenomena in the EPS hindcasts. Rainfall variations for neutral-ENSO years are found to correspond well with the dominant MJO phase, whereas for moderate/strong ENSO events, the relationship of rainfall anomaly with MJO appears to become weaker, while the contribution of ENSO to the sub-monthly skill is substantial. However, there is exception that if a moderate/strong MJO event propagates from Indian Ocean to the maritime continent during typical ENSO years, the MJO impact can become overwhelming, regardless of how strong the ENSO event is. These results support the concept that 'windows of opportunity' of high forecast skill exist as a function of ENSO and the MJO in certain locations and seasons, that may lead to subseasonal to seasonal forecasts of substantial societal value in the future.

  8. Assessing Decadal Predictability of the Regional MiKlip Forecast Ensemble for Europe (1961-2010)

    NASA Astrophysics Data System (ADS)

    Mieruch-Schnuelle, S.; Schädler, G.; Feldmann, H.; Lenz, C.; Kothe, S.; Kottmeier, C.

    2013-12-01

    The aim of the large German Ministry for Education and Research (BMBF) project MiKlip (Mittelfristige Klimaprognose, Decadal Climate Prediction) is to develop a decadal forecast system. To explore the feasability and prospects of global and regional predictions on decadal time scales, global as well as regional predictive ensemble hindcasts have been generated. The focus of this contribution lies on the description of the 10 member regional hindcast ensemble for Europe (1961-2010) at 25 km resolution. The regional model COSMO-CLM has been driven by CMIP5 MPI-ESM simulations, which have been generated by 1-day time-lagged initialization of the atmosphere and an anomaly initialization for the ocean. The study aims at the assessment of the decadal variability and predictability against observations. After removing the long term bias as well as the long term linear trend from the data, we applied low pass filters to the original data to separate the decadal climate signal from high frequency noise. The decadal variability and predictability assessment is applied to temperature and precipitation data for the summer and winter half-year averages/sums. The best results have been found for the prediction of decadal temperature anomalies, i.e.~we have detected a distinct predictive skill and reasonable reliability. Hence it is possible to predict temperature variability on decadal timescales, However, the situation is less satisfactory for precipitation. Here we have found regions showing good predictability, but also regions without any predictive skill. Summer half-year temperature anomalies of E-OBS observations (lilac) and CCLM (orange) at a single grid point near Lodz (Poland). The original data are shown as thin lines, whereas the 9 year moving average filters are plotted as thick lines. The gray shaded area depicts the CCLM ensemble spread, i.e. the standard deviation over the ensemble. Additionally, because of the decadal initialization, the decades have been

  9. The value of the North American Multi Model Ensemble phase 2 for sub-seasonal hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Wanders, Niko; Wood, Eric

    2016-04-01

    Sub-seasonal to seasonal weather and hydrological forecasts have the potential to provide vital information for a variety of water-related decision makers. For example, seasonal forecasts of drought risk can enable farmers to make adaptive choices on crop varieties, labour usage, and technology investments. Seasonal and sub-seasonal predictions can increase preparedness to hydrological extremes that regularly occur in all regions of the world with large impacts on society. We investigated the skill of six seasonal forecast models from the NMME-2 ensemble coupled to two global hydrological models (VIC and PCRGLOBWB) for the period 1982-2012. The 31 years of NNME-2 hindcast data is used in combination with an ensemble mean and ESP forecast, to forecast important hydrological variables (e.g. soil moisture, groundwater storage, snow, reservoir levels and river discharge). By using two global hydrological models we are able to quantify both the uncertainty in the meteorological input and the uncertainty created by the different hydrological models. We show that the NMME-2 forecast outperforms the ESP forecasts in terms of anomaly correlation and brier skill score for all forecasted hydrological variables, with a low uncertainty in the performance amongst the hydrological models. However, the continuous ranked probability score (CRPS) of the NMME-2 ensemble is inferior to the ESP due to a large spread between the individual ensemble members. We use a cost analysis to show that the damage caused by floods and droughts in large scale rivers can globally be reduced by 48% (for leads from 1-2 months) to 20% (for leads between 6-9 months) when precautions are taken based on the NMME-2 ensemble instead of an ESP forecast. In collaboration with our local partner in West Africa (AGHRYMET), we looked at the performance of the sub-seasonal forecasts for crop planting dates and high flow season in West Africa. We show that the uncertainty in the optimal planting date is reduced

  10. Combined assimilation of soil moisture and streamflow data by an ensemble Kalman filter in a coupled model of surface-subsurface flow.

    NASA Astrophysics Data System (ADS)

    Camporese, M.; Paniconi, C.; Putti, M.; Salandin, P.

    2007-12-01

    Hydrologic models can largely benefit from the use of data assimilation algorithms, which allow to update the modeled system state incorporating in the solution of the model itself information coming from experimental measurements of various quantities, as soon as the data become available. In this context, data assimilation seems to be well fit for coupled surface--subsurface models, which, considering the watershed as the ensemble of surface and subsurface domains, allow a more accurate description of the hydrological processes at the catchment scale, where soil moisture largely influences the partitioning of rain between runoff and infiltration and thus controls the flow at the outlet. The need for a better determination of the variables of interest (streamflow at the outlet section, water table, soil water content, etc.) has led to a many efforts focused on the development of coupled numerical models, together with field and laboratory observations. Nevertheless, uncertainty in the schematic description of physical processes and inaccuracies on source data collection induce errors in the model predictions. The ensemble Kalman filter (EnKF) represents an extension to nonlinear problems of the classic Kalman filter by means of a Monte Carlo approach. A sequential assimilation procedure based on EnKF is developed and integrated in a process-based numerical model, which couples a three-dimensional finite element Richards equation solver for variably saturated porous media and a finite difference diffusion wave approximation based on a digital elevation data for surface water dynamics. A detailed analysis of the data assimilation algorithm behavior within the coupled model has been carried out on a synthetic 1D test case in order to verify the correct implementation and derive a series of fundamental parameters, such as the minimum ensemble size that can ensure a sufficient accuracy in the statistical estimates. The assimilation frequency, as well as the effects

  11. Application of Ensemble Sensitivity Analysis to Observation Targeting for Short-term Wind Speed Forecasting

    SciTech Connect

    Zack, J; Natenberg, E; Young, S; Manobianco, J; Kamath, C

    2010-02-21

    measure in order to get the maximum positive impact on forecast performance for a particular site and short-term look-ahead period. Both tools rely on the use of NWP models to assess the sensitivity of a forecast for a particular location to measurements made at a prior time (i.e. the look-ahead period) at points surrounding the target location. The fundamental hypothesis is that points and variables with high sensitivity are good candidates for measurements since information at those points are likely to have the most impact on the forecast for the desired parameter, location and look-ahead period. One approach is called the adjoint method (Errico and Vukicevic, 1992; Errico, 1997) and the other newer approach is known as Ensemble Sensitivity Analysis (ESA; Ancell and Hakim 2007; Torn and Hakim 2008). Both approaches have been tested on large-scale atmospheric prediction problems (e.g. forecasting pressure or precipitation over a relatively large region 24 hours ahead) but neither has been applied to mesoscale space-time scales of winds or any other variables near the surface of the earth. A number of factors suggest that ESA is better suited for short-term wind forecasting applications. One of the most significant advantages of this approach is that it is not necessary to linearize the mathematical representation of the processes in the underlying atmospheric model as required by the adjoint approach. Such a linearization may be especially problematic for the application of short-term forecasting of boundary layer winds in complex terrain since non-linear shifts in the structure of boundary layer due to atmospheric stability changes are a critical part of the wind power production forecast problem. The specific objective of work described in this paper is to test the ESA as a tool to identify measurement locations and variables that have the greatest positive impact on the accuracy of wind forecasts in the 0- to 6-hour look-ahead periods for the wind generation area of

  12. Optimization of precipitation and streamflow forecasts in the southwest Contiguous US for warm season convection

    NASA Astrophysics Data System (ADS)

    Lahmers, T.; Castro, C. L.; Gupta, H. V.; Gochis, D. J.; ElSaadani, M.

    2015-12-01

    Warm season convection associated with the North American Monsoon (NAM) provides an important source of precipitation for much of the Southwest Contiguous US (CONUS) and Northwest Mexico. Convection associated with the NAM can also result in flash flooding, a hazard to metropolitan areas such as Tucson and Phoenix, as well as rural areas where washouts of main roads can sever critical transportation infrastructure. In order to mitigate the effects of this problem, the National Oceanic and Atmospheric Administration (NOAA) National Water Center (NWC) is developing a national distributed hydrologic model using the WRF-Hydro framework with forcing from the High Resolution Rapid Refresh (HRRR) mesoscale atmospheric model. We aim to improve this National hydrologic and atmospheric modeling framework through the calibration of the WRF-Hydro model for the southwest CONUS and the optimization of planetary boundary layer and cloud microphysics schemes for the Weather Research and Forecasting (WRF) model in the same region. The WRF-Hydro model, with a similar structure as the national configuration used by the NWC, has been set up for the Gila River basin in southern Arizona. We demonstrate the utility of the model for forecasting high impact precipitation events in catchments with limited human modification. The WRF-Hydro model is spun up using past precipitation from the NCEP Stage-IV records and TRMM estimates. Atmospheric forcing for WRF-Hydro comes from the NASA Phase 2 North American Land Data Assimilation (NLDAS-2) dataset. WRF-Hydro is forced for selected high-impact events using a 3-km grid resolution Advanced Research WRF (WRF-ARW) atmospheric simulation. WRF-ARW is forced with the operational National Center for Environmental Prediction (NCEP) Global Forecasting System (GFS) operational model. This methodology demonstrates the modeling framework that will be used for future parameter calibration of WRF-Hydro and optimization of WRF-ARW.

  13. Impact of precipitation forecast uncertainties and initial soil moisture conditions on a probabilistic flood forecasting chain

    NASA Astrophysics Data System (ADS)

    Silvestro, Francesco; Rebora, Nicola

    2014-11-01

    One of the main difficulties that flood forecasters are faced with is evaluating how errors and uncertainties in forecasted precipitation propagate into streamflow forecast. These errors, must be combined with the effects of different initial soil moisture conditions that generally have a significant impact on the final results of a flood forecast. This is further complicated by the fact that a probabilistic approach is needed, especially when small and medium size basins are considered (the variability of the streamflow scenarios is in fact strongly influenced by the aforementioned factors). Moreover, the ensemble size is a degree of freedom when a precipitation downscaling algorithm is part of the forecast chain. In fact, a change of ensemble size could lead to different final results once the other inputs and parameters are fixed. In this work, a series of synthetic experiments have been designed and implemented to test an operational probabilistic flood forecast system in order to augment the knowledge of how streamflow forecasts can be affected by errors and uncertainties associated with the three aforementioned elements: forecasted rainfall, soil moisture initial conditions, and ensemble size.

  14. Probabilistic streamflow forecasting for hydroelectricity production: A comparison of two non-parametric system identification algorithms

    NASA Astrophysics Data System (ADS)

    Pande, Saket; Sharma, Ashish

    2014-05-01

    This study is motivated by the need to robustly specify, identify, and forecast runoff generation processes for hydroelectricity production. It atleast requires the identification of significant predictors of runoff generation and the influence of each such significant predictor on runoff response. To this end, we compare two non-parametric algorithms of predictor subset selection. One is based on information theory that assesses predictor significance (and hence selection) based on Partial Information (PI) rationale of Sharma and Mehrotra (2014). The other algorithm is based on a frequentist approach that uses bounds on probability of error concept of Pande (2005), assesses all possible predictor subsets on-the-go and converges to a predictor subset in an computationally efficient manner. Both the algorithms approximate the underlying system by locally constant functions and select predictor subsets corresponding to these functions. The performance of the two algorithms is compared on a set of synthetic case studies as well as a real world case study of inflow forecasting. References: Sharma, A., and R. Mehrotra (2014), An information theoretic alternative to model a natural system using observational information alone, Water Resources Research, 49, doi:10.1002/2013WR013845. Pande, S. (2005), Generalized local learning in water resource management, PhD dissertation, Utah State University, UT-USA, 148p.

  15. Evaluation of CMAQ and CAMx Ensemble Air Quality Forecasts during the 2015 MAPS-Seoul Field Campaign

    NASA Astrophysics Data System (ADS)

    Kim, E.; Kim, S.; Bae, C.; Kim, H. C.; Kim, B. U.

    2015-12-01

    The performance of Air quality forecasts during the 2015 MAPS-Seoul Field Campaign was evaluated. An forecast system has been operated to support the campaign's daily aircraft route decisions for airborne measurements to observe long-range transporting plume. We utilized two real-time ensemble systems based on the Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Comprehensive Air quality Model with extensions (CAMx) modeling framework and WRF-SMOKE- Community Multi_scale Air Quality (CMAQ) framework over northeastern Asia to simulate PM10 concentrations. Global Forecast System (GFS) from National Centers for Environmental Prediction (NCEP) was used to provide meteorological inputs for the forecasts. For an additional set of retrospective simulations, ERA Interim Reanalysis from European Centre for Medium-Range Weather Forecasts (ECMWF) was also utilized to access forecast uncertainties from the meteorological data used. Model Inter-Comparison Study for Asia (MICS-Asia) and National Institute of Environment Research (NIER) Clean Air Policy Support System (CAPSS) emission inventories are used for foreign and domestic emissions, respectively. In the study, we evaluate the CMAQ and CAMx model performance during the campaign by comparing the results to the airborne and surface measurements. Contributions of foreign and domestic emissions are estimated using a brute force method. Analyses on model performance and emissions will be utilized to improve air quality forecasts for the upcoming KORUS-AQ field campaign planned in 2016.

  16. Development of web-based services for an ensemble flood forecasting and risk assessment system

    NASA Astrophysics Data System (ADS)

    Yaw Manful, Desmond; He, Yi; Cloke, Hannah; Pappenberger, Florian; Li, Zhijia; Wetterhall, Fredrik; Huang, Yingchun; Hu, Yuzhong

    2010-05-01

    Flooding is a wide spread and devastating natural disaster worldwide. Floods that took place in the last decade in China were ranked the worst amongst recorded floods worldwide in terms of the number of human fatalities and economic losses (Munich Re-Insurance). Rapid economic development and population expansion into low lying flood plains has worsened the situation. Current conventional flood prediction systems in China are neither suited to the perceptible climate variability nor the rapid pace of urbanization sweeping the country. Flood prediction, from short-term (a few hours) to medium-term (a few days), needs to be revisited and adapted to changing socio-economic and hydro-climatic realities. The latest technology requires implementation of multiple numerical weather prediction systems. The availability of twelve global ensemble weather prediction systems through the ‘THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a good opportunity for an effective state-of-the-art early forecasting system. A prototype of a Novel Flood Early Warning System (NEWS) using the TIGGE database is tested in the Huai River basin in east-central China. It is the first early flood warning system in China that uses the massive TIGGE database cascaded with river catchment models, the Xinanjiang hydrologic model and a 1-D hydraulic model, to predict river discharge and flood inundation. The NEWS algorithm is also designed to provide web-based services to a broad spectrum of end-users. The latter presents challenges as both databases and proprietary codes reside in different locations and converge at dissimilar times. NEWS will thus make use of a ready-to-run grid system that makes distributed computing and data resources available in a seamless and secure way. An ability to run or function on different operating systems and provide an interface or front that is accessible to broad spectrum of end-users is additional requirement. The aim is to achieve robust interoperability

  17. Utilizing satellite precipitation estimates for streamflow forecasting via adjustment of mean field bias in precipitation data and assimilation of streamflow observations

    NASA Astrophysics Data System (ADS)

    Lee, Haksu; Zhang, Yu; Seo, Dong-Jun; Xie, Pingping

    2015-10-01

    This study explores mitigating bias in satellite quantitative precipitation estimates (SQPE) and improving hydrologic predictions at ungauged locations via adjustment of the mean field bias (MFB) in SQPE and data assimilation (DA) of streamflow observations in a distributed hydrologic model. In this study, a variational procedure is used to adjust MFB in Climate Prediction Center MORPHing (CMORPH) SQPE and assimilate streamflow observations at the outlet of Elk River Basin in Missouri into the distributed Sacramento Soil Moisture Accounting (SAC-SMA) and kinematic wave routing models. The benefits of assimilation are assessed by comparing the streamflow predictions with or without DA at both the outlet and an upstream location, and by comparing the soil moisture grids forced by CMORPH SQPE against those forced by higher-quality multisensor quantitative precipitation estimates (MQPE) from National Weather Service. Special attention is given to the dependence of the efficacy of DA on the quality and latency of the SQPE, and the impact of dynamic correction of MFB in the SQPE via DA. The results show that adjusting MFB in CMORPH SQPE in addition to assimilating outlet flow reduces 66% of the bias in the CMORPH SQPE analysis and the RMSE of 12-h streamflow predictions by 81% at the outlet and 34-62% at interior locations of the catchment. Compared to applying a temporally invariant MFB for the entire storm, the DA-based, dynamic MFB correction reduces the RMSE of 6-h streamflow prediction by 63% at the outlet and 39-69% at interior locations. It is also shown that the accuracy of streamflow prediction deteriorates if the delineation of the precipitation area by CMORPH SQPE is significantly different, as measured by the Hausdorff distance, from that by MQPE. When compared with adjusting MFB in the CMORPH SQPE over the entire assimilation window, adjusting the MFB for all but the latest 18 h (i.e., the latency of CMORPH SQPE) within the assimilation window reduces the

  18. Quantifying the Usefulness of Ensemble-Based Precipitation Forecasts with Respect to Water Use and Yield during a Field Trial

    NASA Astrophysics Data System (ADS)

    Christ, E.; Webster, P. J.; Collins, G.; Byrd, S.

    2014-12-01

    Recent droughts and the continuing water wars between the states of Georgia, Alabama and Florida have made agricultural producers more aware of the importance of managing their irrigation systems more efficiently. Many southeastern states are beginning to consider laws that will require monitoring and regulation of water used for irrigation. Recently, Georgia suspended issuing irrigation permits in some areas of the southwestern portion of the state to try and limit the amount of water being used in irrigation. However, even in southern Georgia, which receives on average between 23 and 33 inches of rain during the growing season, irrigation can significantly impact crop yields. In fact, studies have shown that when fields do not receive rainfall at the most critical stages in the life of cotton, yield for irrigated fields can be up to twice as much as fields for non-irrigated cotton. This leads to the motivation for this study, which is to produce a forecast tool that will enable producers to make more efficient irrigation management decisions. We will use the ECMWF (European Centre for Medium-Range Weather Forecasts) vars EPS (Ensemble Prediction System) model precipitation forecasts for the grid points included in the 1◦ x 1◦ lat/lon square surrounding the point of interest. We will then apply q-to-q bias corrections to the forecasts. Once we have applied the bias corrections, we will use the check-book method of irrigation scheduling to determine the probability of receiving the required amount of rainfall for each week of the growing season. These forecasts will be used during a field trial conducted at the CM Stripling Irrigation Research Park in Camilla, Georgia. This research will compare differences in yield and water use among the standard checkbook method of irrigation, which uses no precipitation forecast knowledge, the weather.com forecast, a dry land plot, and the ensemble-based forecasts mentioned above.

  19. Hydrologic scales, cloud variability, remote sensing, and models: Implications for forecasting snowmelt and streamflow

    USGS Publications Warehouse

    Simpson, James J.; Dettinger, M.D.; Gehrke, F.; McIntire, T.J.; Hufford, Gary L.

    2004-01-01

    Accurate prediction of available water supply from snowmelt is needed if the myriad of human, environmental, agricultural, and industrial demands for water are to be satisfied, especially given legislatively imposed conditions on its allocation. Robust retrievals of hydrologic basin model variables (e.g., insolation or areal extent of snow cover) provide several advantages over the current operational use of either point measurements or parameterizations to help to meet this requirement. Insolation can be provided at hourly time scales (or better if needed during rapid melt events associated with flooding) and at 1-km spatial resolution. These satellite-based retrievals incorporate the effects of highly variable (both in space and time) and unpredictable cloud cover on estimates of insolation. The insolation estimates are further adjusted for the effects of basin topography using a high-resolution digital elevation model prior to model input. Simulations of two Sierra Nevada rivers in the snowmelt seasons of 1998 and 1999 indicate that even the simplest improvements in modeled insolation can improve snowmelt simulations, with 10%-20% reductions in root-mean-square errors. Direct retrieval of the areal extent of snow cover may mitigate the need to rely entirely on internal calculations of this variable, a reliance that can yield large errors that are difficult to correct until long after the season is complete and that often leads to persistent underestimates or overestimates of the volumes of the water to operational reservoirs. Agencies responsible for accurately predicting available water resources from the melt of snowpack [e.g., both federal (the National Weather Service River Forecast Centers) and state (the California Department of Water Resources)] can benefit by incorporating concepts developed herein into their operational forecasting procedures. ?? 2004 American Meteorological Society.

  20. Seasonal River Flow Forecasting Using Multi-model Ensemble Climate Data

    NASA Astrophysics Data System (ADS)

    Lavers, D.; Prudhomme, C.; Hannah, D.; Troccoli, A.

    2007-12-01

    Developing skilful seasonal forecasting of river flows is important for many societal applications. Long-lead forecasts have potential to aid water management decision making and preparation for human response to hydrological extremes. The seasonal prediction of river flows has been a topic of increasing interest due to the recent 2004-06 drought and 2007 floods experienced in the UK. We compare the relative skill of predictions of river flow using: (1) a multi-Global Climate Model (GCM) ensemble data set and (2) downscaled multi-GCM data as input to a hydrological model. The period considered is 1980- 2001. The River Dyfi basin in West Wales, UK is the focus of this research. This basin is near natural, hence the climate-flow signal should be clearer. The DEMETER project is the source of the multi-model climate data, and this consists of 7 GCMs each with 9 ensemble members. Hindcasts with lead times up to 6 months are available from 1st February, 1st May, 1st August and 1st November initial conditions. Each hindcast was split into the first 3 and last 3 months, and the subsequent concatenation of the split hindcasts produced 2 time series (total of 7×9×2 ensemble series), which were run through the Probability Distributed Model (PDM). PDM is a lumped rainfall-runoff model that transforms rainfall and potential evaporation data to river flow at the basin outlet. PDM was calibrated with observations from 1980-1990, and then validated from 1991-2001. The coarse resolution of the DEMETER data (standardised to 2.5° × 2.5° resolution) means that the atmospheric motions at sub-grid scales are not captured by the models. The large spatial disparity between the GCM grids and the scale of the study (471.3 km2) lead to underestimation of precipitation by DEMETER models. This difference is addressed through the use of a statistical downscaling tool, the Statistical Downscaling Model (SDSM). The SDSM was calibrated on the ERA-40 re-analysis data set (from the ECMWF), as

  1. Ensemble Models

    EPA Science Inventory

    Ensemble forecasting has been used for operational numerical weather prediction in the United States and Europe since the early 1990s. An ensemble of weather or climate forecasts is used to characterize the two main sources of uncertainty in computer models of physical systems: ...

  2. Ensemble Canonical Correlation Prediction of Seasonal Precipitation Over the United States: Raising the Bar for Dynamical Model Forecasts

    NASA Technical Reports Server (NTRS)

    Lau, William K. M.; Kim, Kyu-Myong; Shen, S. P.

    2001-01-01

    This paper presents preliminary results of an ensemble canonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate downscaling studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into non-overlapping sectors. The canonical SST modes for each sector are used as the predictor for the ensemble hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all the regions of the US in every season compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible to the enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduces the spring predictability barrier over all the regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and additional local observations. The enhanced ECC forecast skill provides a new benchmark for evaluating dynamical model forecasts.

  3. Development of web-based services for a novel ensemble flood forecasting and risk assessment system

    NASA Astrophysics Data System (ADS)

    He, Y.; Manful, D. Y.; Cloke, H. L.; Wetterhall, F.; Li, Z.; Bao, H.; Pappenberger, F.; Wesner, S.; Schubert, L.; Yang, L.; Hu, Y.

    2009-12-01

    Flooding is a wide spread and devastating natural disaster worldwide. Floods that took place in the last decade in China were ranked the worst amongst recorded floods worldwide in terms of the number of human fatalities and economic losses (Munich Re-Insurance). Rapid economic development and population expansion into low lying flood plains has worsened the situation. Current conventional flood prediction systems in China are neither suited to the perceptible climate variability nor the rapid pace of urbanization sweeping the country. Flood prediction, from short-term (a few hours) to medium-term (a few days), needs to be revisited and adapted to changing socio-economic and hydro-climatic realities. The latest technology requires implementation of multiple numerical weather prediction systems. The availability of twelve global ensemble weather prediction systems through the ‘THORPEX Interactive Grand Global Ensemble’ (TIGGE) offers a good opportunity for an effective state-of-the-art early forecasting system. A prototype of a Novel Flood Early Warning System (NEWS) using the TIGGE database is tested in the Huai River basin in east-central China. It is the first early flood warning system in China that uses the massive TIGGE database cascaded with river catchment models, the Xinanjiang hydrologic model and a 1-D hydraulic model, to predict river discharge and flood inundation. The NEWS algorithm is also designed to provide web-based services to a broad spectrum of end-users. The latter presents challenges as both databases and proprietary codes reside in different locations and converge at dissimilar times. NEWS will thus make use of a ready-to-run grid system that makes distributed computing and data resources available in a seamless and secure way. An ability to run or function on different operating systems and provide an interface or front that is accessible to broad spectrum of end-users is additional requirement. The aim is to achieve robust

  4. [Estimation and forecast of chlorophyll a concentration in Taihu Lake based on ensemble square root filters].

    PubMed

    Li, Yuan; Li, Yun-Mei; Wang, Qiao; Zhang, Zhuo; Guo, Fei; Lü, Heng; Bi, Kun; Huang, Chang-Chun; Guo, Yu-Long

    2013-01-01

    Chlorophyll a concentration is one of the important parameters for the characterization of water quality, which reflects the degree of eutrophication and algae content in the water body. It is also an important factor in determining water spectral reflectance. Chlorophyll a concentration is an important water quality parameter in water quality remote sensing. Remote sensing quantitative retrieval of chlorophyll a concentration can provide new ideas and methods for the monitoring and evaluation of lake water quality. In this work, we developed a data assimilation scheme based on ensemble square root filters and three-dimensional numerical modeling for wind-driven circulation and pollutant transport to assimilate the concentration of chlorophyll a. We also conducted some assimilation experiments using buoy observation data on May 20, 2010. We estimated the concentration of chlorophyll a in Taihu Lake, and then used this result to forecast the concentration of chlorophyll a. During the assimilation stage, the root mean square error reduced from 1.58, 1.025, and 2.76 to 0.465, 0.276, and 1.01, respectively, and the average relative error reduced from 0.2 to 0.05, 0.046, and 0.069, respectively. During the prediction stage, the root mean square error reduced from 1.486, 1.143, and 2.38 to 0.017, 0.147, and 0.23, respectively, and the average relative error reduced from 0.2 to 0.002, 0.025, and 0.019, respectively. The final results indicate that the method of data assimilation can significantly improve the accuracy in the estimation and prediction of chlorophyll a concentration in Taihu Lake.

  5. Prediction and uncertainty of Hurricane Sandy (2012) explored through a real-time cloud-permitting ensemble analysis and forecast system assimilating airborne Doppler radar observations

    NASA Astrophysics Data System (ADS)

    Munsell, Erin B.; Zhang, Fuqing

    2014-03-01

    the Pennsylvania State University (PSU) real-time convection-permitting hurricane analysis and forecasting system (WRF-EnKF) that assimilates airborne Doppler radar observations, the sensitivity and uncertainty of forecasts initialized several days prior to landfall of Hurricane Sandy (2012) are assessed. The performance of the track and intensity forecasts of both the deterministic and ensemble forecasts by the PSU WRF-EnKF system show significant skill and are comparable to or better than forecasts produced by operational dynamical models, even at lead times of 4-5 days prior to landfall. Many of the ensemble members correctly capture the interaction of Sandy with an approaching midlatitude trough, which precedes Sandy's forecasted landfall in the Mid-Atlantic region of the United States. However, the ensemble reveals considerable forecast uncertainties in the prediction of Sandy. For example, in the ensemble forecast initialized at 0000 UTC 26 October 2012, 10 of the 60 members do not predict a United States landfall. Using ensemble composite and sensitivity analyses, the essential dynamics and initial condition uncertainties that lead to forecast divergence among the members in tracks and precipitation are examined. It is observed that uncertainties in the environmental steering flow are the most impactful factor on the divergence of Sandy's track forecasts, and its subsequent interaction with the approaching midlatitude trough. Though the midlatitude system does not strongly influence the final position of Sandy, differences in the timing and location of its interactions with Sandy lead to considerable differences in rainfall forecasts, especially with respect to heavy precipitation over land.

  6. Convective-scale data assimilation in the Weather Research and Forecasting model using a nonlinear ensemble filter

    NASA Astrophysics Data System (ADS)

    Poterjoy, Jonathan; Anderson, Jeffrey; Sobash, Ryan

    2016-04-01

    Assimilating measurements of convective-scale processes poses a large challenge for data assimilation techniques currently used in atmospheric science. A part of this challenge lies in the nonlinear system dynamics, as well as nonlinearity in the mapping between the model state and remotely sensed data used to provide evidence of the true system state. In this presentation, we discuss recent applications of a nonlinear data assimilation system, based on the particle filter (PF), for convective-scale data assimilation in the Weather Research and Forecasting model. The new data assimilation technique, denoted the local PF, operates in a manner similar to traditional PF methods, except the impact of observations on posterior particles (or ensemble members) is restricted to local neighborhoods of observations. We compare the local PF with a conventional ensemble Kalman filtering method in idealized data assimilation experiments performed for a developing mesoscale convective system. In these experiments, the local PF provides improved representations of cloud properties in posterior particles, which leads to a reduction in short-range forecast errors over the ensemble Kalman filter. This study presents the first successful application of a particle filter in a high-resolution weather prediction model.

  7. Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models

    NASA Astrophysics Data System (ADS)

    Khalil, Bahaa; Broda, Stefan; Adamowski, Jan; Ozga-Zielinski, Bogdan; Donohoe, Amanda

    2015-02-01

    Several groundwater-level forecasting studies have shown that data-driven models are simpler, faster to develop, and provide more accurate and precise results than physical or numerical-based models. Five data-driven models were examined for the forecasting of groundwater levels as a result of recharge via tailings from an abandoned mine in Quebec, Canada, for lead times of 1 day, 1 week and 1 month. The five models are: a multiple linear regression (MLR); an artificial neural network (ANN); two models that are based on de-noising the model predictors using the wavelet-transform (W-MLR, W-ANN); and a W-ensemble ANN (W-ENN) model. The tailing recharge, total precipitation, and mean air temperature were used as predictors. The ANN models performed better than the MLR models, and both MLR and ANN models performed significantly better after de-noising the predictors using wavelet-transforms. Overall, the W-ENN model performed best for each of the three lead times. These results highlight the ability of wavelet-transforms to decompose non-stationary data into discrete wavelet-components, highlighting cyclic patterns and trends in the time-series at varying temporal scales, rendering the data readily usable in forecasting. The good performance of the W-ENN model highlights the usefulness of ensemble modeling, which ensures model robustness along with improved reliability by reducing variance.

  8. Multi-initial-conditions and Multi-physics Ensembles in the Weather Research and Forecasting Model to Improve Coastal Stratocumulus Forecasts for Solar Power Integration

    NASA Astrophysics Data System (ADS)

    Yang, H.

    2015-12-01

    used to create a multi-parameter and multi-physics ensemble. The ensemble forecast system is implemented operationally for San Diego Gas & Electric Company to improve system operations.

  9. Ensemble-based snow data assimilation for an operational snow model

    NASA Astrophysics Data System (ADS)

    Liu, Y.; He, M.; Seo, D.; Laurine, D.; Lee, H.

    2010-12-01

    In mountainous regions of the western United States, seasonal snow pack evolution dominates the generation of snowmelt and streamflow. In the National Weather Service (NWS), the conceptual SNOW-17 model is used for operational forecasting of snowmelt, which then serves as an input to a rainfall-runoff model for streamflow forecasts in snow-affected areas. To improve snowmelt estimates and therefore streamflow forecasts, some River Forecast Centers (RFCs) of the NWS operate a snow updating system to update areal Snow Water Equivalent (SWE) estimates by using a regression technique to reconcile the differences between the observed SWE (e.g., from SNOTEL stations) and the modeled SWE. While this method is parsimonious and easy to use in operations, it does not capitalize on the full capabilities offered by advanced data assimilation techniques to quantify, reduce, and propagate forecast uncertainty in a statistically and dynamically consistent fashion. This study describes an application of the ensemble Kalman filter (EnKF) which automatically and systematically assimilates SNOTEL SWE observations into the SNOW-17 model to reduce uncertainties in model initial conditions. The robustness of the ensemble filter as compared to the operational regression-based method is evaluated for both snow and streamflow forecasts at several operational basins in the service area of the Northwest River Forecast Center (NWRFC). This presentation describes the implementation of the EnKF into the SNOW-17 model and summarizes the preliminary evaluation results.

  10. Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of anomalous hydrometeorological events over East Africa

    NASA Astrophysics Data System (ADS)

    Shukla, Shraddhanand; Roberts, Jason; Hoell, Andrew; Funk, Christopher C.; Robertson, Franklin; Kirtman, Ben

    2016-07-01

    The skill of North American multimodel ensemble (NMME) seasonal forecasts in East Africa (EA), which encompasses one of the most food and water insecure areas of the world, is evaluated using deterministic, categorical, and probabilistic evaluation methods. The skill is estimated for all three primary growing seasons: March-May (MAM), July-September (JAS), and October-December (OND). It is found that the precipitation forecast skill in this region is generally limited and statistically significant over only a small part of the domain. In the case of MAM (JAS) [OND] season it exceeds the skill of climatological forecasts in parts of equatorial EA (Northern Ethiopia) [equatorial EA] for up to 2 (5) [5] months lead. Temperature forecast skill is generally much higher than precipitation forecast skill (in terms of deterministic and probabilistic skill scores) and statistically significant over a majority of the region. Over the region as a whole, temperature forecasts also exhibit greater reliability than the precipitation forecasts. The NMME ensemble forecasts are found to be more skillful and reliable than the forecast from any individual model. The results also demonstrate that for some seasons (e.g. JAS), the predictability of precipitation signals varies and is higher during certain climate events (e.g. ENSO). Finally, potential room for improvement in forecast skill is identified in some models by comparing homogeneous predictability in individual NMME models with their respective forecast skill.

  11. A two-stage method of quantitative flood risk analysis for reservoir real-time operation using ensemble-based hydrologic forecasts

    NASA Astrophysics Data System (ADS)

    Liu, P.

    2013-12-01

    Quantitative analysis of the risk for reservoir real-time operation is a hard task owing to the difficulty of accurate description of inflow uncertainties. The ensemble-based hydrologic forecasts directly depict the inflows not only the marginal distributions but also their persistence via scenarios. This motivates us to analyze the reservoir real-time operating risk with ensemble-based hydrologic forecasts as inputs. A method is developed by using the forecast horizon point to divide the future time into two stages, the forecast lead-time and the unpredicted time. The risk within the forecast lead-time is computed based on counting the failure number of forecast scenarios, and the risk in the unpredicted time is estimated using reservoir routing with the design floods and the reservoir water levels of forecast horizon point. As a result, a two-stage risk analysis method is set up to quantify the entire flood risks by defining the ratio of the number of scenarios that excessive the critical value to the total number of scenarios. The China's Three Gorges Reservoir (TGR) is selected as a case study, where the parameter and precipitation uncertainties are implemented to produce ensemble-based hydrologic forecasts. The Bayesian inference, Markov Chain Monte Carlo, is used to account for the parameter uncertainty. Two reservoir operation schemes, the real operated and scenario optimization, are evaluated for the flood risks and hydropower profits analysis. With the 2010 flood, it is found that the improvement of the hydrologic forecast accuracy is unnecessary to decrease the reservoir real-time operation risk, and most risks are from the forecast lead-time. It is therefore valuable to decrease the avarice of ensemble-based hydrologic forecasts with less bias for a reservoir operational purpose.

  12. A centralized real-time controller for the reservoir's management on the Seine River using ensemble weather forecasting

    NASA Astrophysics Data System (ADS)

    Ficchi, Andrea; Raso, Luciano; Jay-Allemand, Maxime; Dorchies, David; Malaterre, Pierre-Olivier; Pianosi, Francesca; Van Overloop, Peter-Jules

    2013-04-01

    The reservoirs on the Seine River, upstream of Paris, are regulated with the objective of reducing floods and supporting low flows. The current management of these reservoirs is empirical, reactive, and decentralized, mainly based on filling curves, constructed from an analysis of historical floods and low flows. When inflows are significantly different from their seasonal average, this management strategy proves inefficient. Climate change is also a challenge, for the possible modification of future hydrologic conditions. To improve such management strategy, in this study we investigate the use of Tree-Based Model Predictive Control (TB-MPC), a proactive and centralized method that uses all the information available in real-time, including ensemble weather forecasting. In TB-MPC, a tree is generated from an ensemble of weather forecast. The tree structure summarizes the information contained in the ensemble, specifying the time, along the optimization horizon, when forecast trajectories diverge and thus uncertainty is expected to be resolved. This information is then used in the model predictive control framework. The TB-MPC controller is implemented in combination with the integrated model of the water system, including a semi-distributed hydrologic model of the watershed, a simplified hydraulic model of the river network, and the four reservoir models. Optimization takes into account the cost associated to floods and low-flows, and a penalty cost based on the final reservoir storages. The performances of the TB-MPC controller will be simulated and compared with those of deterministic MPC and with the actual management performances. This work is part of the Climaware European project (2010-2013) set up to develop and to assess measures for sustainable water resources management regarding adaptation to climate change.

  13. Incorporate Hydrologic Forecast for Real-Time Reservoir Operations

    NASA Astrophysics Data System (ADS)

    Zhao, T.; Cai, X.; Zhao, J.

    2011-12-01

    Advances in weather forecasting, hydrologic modeling, and hydro-climatic teleconnection relationships have significantly improved streamflow forecast precision and lead-time. The advances provide great opportunities to improve the operation rules of water resources systems, for example, updating reservoir operation curves using long-term forecast, or even replacing operation rules by real-time optimization and simulation models utilizing various streamflow forecast products. However, incorporation of forecast for real-time optimization of reservoir operation needs more understanding of the forecast uncertainty (FU) evolution with forecast horizon (FH, the advance time of a forecast) and the complicating effect of FU and FH. Increasing horizon may provide more information for decision making in a long time framework but with increasing error and less reliable information. This presentation addresses the challenges on the use of hydrologic forecast for real-time reservoir operations through the following two particular studies: 1) Evaluating the effectiveness of the various hydrological forecast products for reservoir operation with an explicit simulation of dynamic evolution of uncertainties involved in those products. A hypothetical example shows that optimal reservoir operation varies with the hydrologic forecast products. The utility of the reservoir operation with ensemble or probabilistic streamflow forecast (with a probabilistic uncertainty distribution) is the highest compared to deterministic streamflow forecast (DSF) with the forecast uncertainty represented in the form of deterministic forecast errors and DSF-based probabilistic streamflow forecast with the forecast uncertainty represented by a conditional distribution of forecast uncertainty for a given DSF. 2) Identifying an effective forecast horizon (EFH) under a limited inflow forecast considering the complicating effect of FH and FU, as well as streamflow variability and reservoir characteristics

  14. Uncertainty analysis of neural network based flood forecasting models: An ensemble based approach for constructing prediction interval

    NASA Astrophysics Data System (ADS)

    Kasiviswanathan, K.; Sudheer, K.

    2013-05-01

    Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived

  15. Ensemble Forecasting of Return Flow over the Gulf of Mexico: Value of the Single Upper Air Obseration

    NASA Astrophysics Data System (ADS)

    Lewis, J. M.; Lakshmivarahan, S.; Hu, J.; Weiss, S.

    2014-12-01

    Abstract A case study of return flow over the Gulf of Mexico has been conducted with the intention of determining the value of a single upper-air observation. This case study makes use of a set of upper-air observations collected by the U. S. Coast Guard ship Salvia that followed the trajectory of return-flow air parcels—essentially collecting observations in a Lagrangian frame of reference. A mixed layer model is used to make an ensemble forecast during the outflow phase of the phenomenon—that period when surface-driven buoyancy is the dynamical mechanism that transports moisture and heat into the atmospheric boundary layer. With this low-order nonlinear model, the contributions to forecast uncertainty that stem from initial conditions, boundary conditions, and physical/empirical parameters can be determined separately. These uncertainties serve as input to a three-dimensional variational (3D-Var) data assimilation scheme. Results indicate that the uncertainty in initial conditions dominates the full complement of uncertainties. An experiment is conducted to evaluate the value of a single set of upper-air observations over the Gulf (at a point near the onset of return flow). It is clear from this experiment that observations near the initial onset point significantly improves the forecast of mixing ratio and offers hope for improvement in operational forecasting with a modest increase in resources.

  16. Using a deterministic time-lagged ensemble forecast with a probabilistic threshold for improving 6-15 day summer precipitation prediction in China

    NASA Astrophysics Data System (ADS)

    Jie, Weihua; Wu, Tongwen; Wang, Jun; Li, Weijing; Polivka, Thomas

    2015-04-01

    A Deterministic Time-lagged Ensemble Forecast using a Probabilistic Threshold (DEFPT) method is suggested for improving summer 6-15 day categorical precipitation prediction in China from the Beijing Climate Center Atmospheric General Circulation Model version 2.1 (BCC_AGCM2.1). It is based on a time-lagged ensemble system that consists of 13 ensemble members separated sequentially at 6 hour intervals lagging the last three days. The DEFPT is not intended to predict the probability of rainfall, but rather to forecast rainfall (yes/no) occurrence for different categories of precipitation at any model grid box. A given categorical precipitation is forecasted to occur at one gridbox only when the ensemble probability for that categorical precipitation exceeds a certain threshold. This method is useful for providing an estimate of whether precipitation events will occur to decision-makers based on probabilistic forecasts during days 6-15. A large number of hindcast experiments for 1996-2005 summers reveal that this threshold can be best (and empirically) set as 5/13 and 4/13 respectively for the 6-15 day prediction of 1 + mm (i.e., above 1 mm per day) and 5 + mm rainfall events, using the Relative Operating Characteristic (ROC) curve, the Equitable Threat Score (ETS), the Hanssen and Kuipers (HK) score, and frequency bias (BIA) to achieve best prediction performance. With this set of thresholds, the DEFPT shows skill improvement over the corresponding single deterministic forecast using one initial value and the Time-Lagged Average Forecast (LAF) ensemble method. Similar improvements by the DEFPT are also found for the prediction of several other categories of precipitation between 1 + mm and 10 + mm per day. Application of DEFPT to larger ensemble size and BCC_AGCM version 2.2 with a higher horizontal resolution also demonstrates the effectiveness of the DEFPT for 6-15 day categorical precipitation forecasts.

  17. Determining the importance of model calibration for forecasting absolute/relative changes in streamflow from LULC and climate changes

    USGS Publications Warehouse

    Niraula, Rewati; Meixner, Thomas; Norman, Laura M.

    2015-01-01

    Land use/land cover (LULC) and climate changes are important drivers of change in streamflow. Assessing the impact of LULC and climate changes on streamflow is typically done with a calibrated and validated watershed model. However, there is a debate on the degree of calibration required. The objective of this study was to quantify the variation in estimated relative and absolute changes in streamflow associated with LULC and climate changes with different calibration approaches. The Soil and Water Assessment Tool (SWAT) was applied in an uncalibrated (UC), single outlet calibrated (OC), and spatially-calibrated (SC) mode to compare the relative and absolute changes in streamflow at 14 gaging stations within the Santa Cruz River Watershed in southern Arizona, USA. For this purpose, the effect of 3 LULC, 3 precipitation (P), and 3 temperature (T) scenarios were tested individually. For the validation period, Percent Bias (PBIAS) values were >100% with the UC model for all gages, the values were between 0% and 100% with the OC model and within 20% with the SC model. Changes in streamflow predicted with the UC and OC models were compared with those of the SC model. This approach implicitly assumes that the SC model is “ideal”. Results indicated that the magnitude of both absolute and relative changes in streamflow due to LULC predicted with the UC and OC results were different than those of the SC model. The magnitude of absolute changes predicted with the UC and SC models due to climate change (both P and T) were also significantly different, but were not different for OC and SC models. Results clearly indicated that relative changes due to climate change predicted with the UC and OC were not significantly different than that predicted with the SC models. This result suggests that it is important to calibrate the model spatially to analyze the effect of LULC change but not as important for analyzing the relative change in streamflow due to climate change. This

  18. Improvement of rainfall and flood forecasts by blending ensemble NWP rainfall with radar prediction considering orographic rainfall

    NASA Astrophysics Data System (ADS)

    Yu, Wansik; Nakakita, Eiichi; Kim, Sunmin; Yamaguchi, Kosei

    2015-12-01

    Many basins in Japan are characterized by steep mountainous regions, generating orographic rainfall events. Orographic rainfall may cause localized heavy rainfall to induce flash floods and sediment disasters. However, the accuracy of radar-based rainfall prediction was not enough because of the complex geographical pattern of the mountainous areas. In order to reduce damage due to localized heavy rainfall, characteristics of orographic rainfall must be identified into a short-term rainfall prediction procedure. The accuracy of radar-based rainfall prediction performs best for very short lead time, however the accuracy of radar prediction rapidly decreases with increasing lead times. At longer lead times, higher accuracy QPFs are produced by Numerical Weather Prediction (NWP) models, which solve the dynamics and physics of the atmosphere. This study proposes hybrid blending system of ensemble information from radar-based prediction and numerical weather prediction (NWP) to improve the accuracy of rainfall and flood forecasting. First, an improved radar image extrapolation method, which is comprised of the orographic rainfall identification and the error ensemble scheme, is introduced. Then, ensemble NWP outputs are updated based on mean bias of the error fields considering error structure. Finally, the improved radar-based prediction and updated NWP rainfall considering bias correction are blended dynamically with changing weight functions, which are computed from the expected skill of each radar prediction and updated NWP rainfall. The proposed method is verified temporally and spatially through a target event and is applied to the hybrid flood forecasting for updating with 1 h intervals. The newly proposed method shows sufficient reproducibility in peak discharge value, and could reduce the width of ensemble spread, which is expressed as the uncertainty, in the flood forecasting. Our study is carried out and verified using the largest flood event by typhoon

  19. Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting

    NASA Astrophysics Data System (ADS)

    Rubin, J. I.; Reid, J. S.; Hansen, J. A.; Anderson, J. L.; Collins, N.; Hoar, T. J.; Hogan, T.; Lynch, P.; McLay, J.; Reynolds, C. A.; Sessions, W. R.; Westphal, D. L.; Zhang, J.

    2015-10-01

    An ensemble-based forecast and data assimilation system has been developed for use in Navy aerosol forecasting. The system makes use of an ensemble of the Navy Aerosol Analysis Prediction System (ENAAPS) at 1° × 1°, combined with an Ensemble Adjustment Kalman Filter from NCAR's Data Assimilation Research Testbed (DART). The base ENAAPS-DART system discussed in this work utilizes the Navy Operational Global Analysis Prediction System (NOGAPS) meteorological ensemble to drive offline NAAPS simulations coupled with the DART Ensemble Kalman Filter architecture to assimilate bias-corrected MODIS Aerosol Optical Thickness (AOT) retrievals. This work outlines the optimization of the 20-member ensemble system, including consideration of meteorology and source-perturbed ensemble members as well as covariance inflation. Additional tests with 80 meteorological and source members were also performed. An important finding of this work is that an adaptive covariance inflation method, which has not been previously tested for aerosol applications, was found to perform better than a temporally and spatially constant covariance inflation. Problems were identified with the constant inflation in regions with limited observational coverage. The second major finding of this work is that combined meteorology and aerosol source ensembles are superior to either in isolation and that both are necessary to produce a robust system with sufficient spread in the ensemble members as well as realistic correlation fields for spreading observational information. The inclusion of aerosol source ensembles improves correlation fields for large aerosol source regions such as smoke and dust in Africa, by statistically separating freshly emitted from transported aerosol species. However, the source ensembles have limited efficacy during long range transport. Conversely, the meteorological ensemble produces sufficient spread at the synoptic scale to enable observational impact through the ensemble data

  20. Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting

    NASA Astrophysics Data System (ADS)

    Rubin, Juli I.; Reid, Jeffrey S.; Hansen, James A.; Anderson, Jeffrey L.; Collins, Nancy; Hoar, Timothy J.; Hogan, Timothy; Lynch, Peng; McLay, Justin; Reynolds, Carolyn A.; Sessions, Walter R.; Westphal, Douglas L.; Zhang, Jianglong

    2016-03-01

    An ensemble-based forecast and data assimilation system has been developed for use in Navy aerosol forecasting. The system makes use of an ensemble of the Navy Aerosol Analysis Prediction System (ENAAPS) at 1 × 1°, combined with an ensemble adjustment Kalman filter from NCAR's Data Assimilation Research Testbed (DART). The base ENAAPS-DART system discussed in this work utilizes the Navy Operational Global Analysis Prediction System (NOGAPS) meteorological ensemble to drive offline NAAPS simulations coupled with the DART ensemble Kalman filter architecture to assimilate bias-corrected MODIS aerosol optical thickness (AOT) retrievals. This work outlines the optimization of the 20-member ensemble system, including consideration of meteorology and source-perturbed ensemble members as well as covariance inflation. Additional tests with 80 meteorological and source members were also performed. An important finding of this work is that an adaptive covariance inflation method, which has not been previously tested for aerosol applications, was found to perform better than a temporally and spatially constant covariance inflation. Problems were identified with the constant inflation in regions with limited observational coverage. The second major finding of this work is that combined meteorology and aerosol source ensembles are superior to either in isolation and that both are necessary to produce a robust system with sufficient spread in the ensemble members as well as realistic correlation fields for spreading observational information. The inclusion of aerosol source ensembles improves correlation fields for large aerosol source regions, such as smoke and dust in Africa, by statistically separating freshly emitted from transported aerosol species. However, the source ensembles have limited efficacy during long-range transport. Conversely, the meteorological ensemble generates sufficient spread at the synoptic scale to enable observational impact through the ensemble data

  1. Efficiency of a real time flood forecasting system in the Alps and in the Apennines: deterministic versus ensemble predictions

    NASA Astrophysics Data System (ADS)

    Grossi, G.

    2009-04-01

    Real time hydrological forecasting is still a challenging task for most of the Italian territory, especially in mountain areas where both the topography and the meteorological forcing are affected by a strong spatial variability. Nevertheless there is an increasing request to provide some clues for the development of efficient real time flood forecasting systems, for warning population as well as for water management purposes. In this perspective the efficiency of a real time forecasting system needs to be investigated, with particular care to the uncertainty of the provided prediction and to how this prediction will be handled by water resources managers and land protection services. To this aim a real time flood forecasting system using both deterministic and ensemble meteorological predictions has been implemented at University of Brescia and applied to an Alpine area (the Toce River - Piemonte Region) and to an Apennine area (the Taro River - Emilia Romagna Region). The Map D- Phase experiment (autumn 2007) was a good test for the implemented system: daily rainfall fields provided by high resolution deterministic limited area meteorological models and esemble rainfall predictions provided by coarser resolution meteorological models could be used to force a hydrological model and produce either a single deterministic or an esemble of flood forecats. Namely only minor flood events occurred in the Alpine area in autumn 2007, while one major flood event affected the Taro river at the end of November 2007. Focusing on this major event the potentials of the forecasting system was tested and evaluated with reference also to the geographical and climatic characteristics of the investigated area.

  2. Progress in Multi-Center Probabilistic Wave Forecasting and Ensemble-Based Data Assimilation using LETKF at the US National Weather Service

    NASA Astrophysics Data System (ADS)

    Alves, Jose-Henrique; Bernier, Natacha; Etala, Paula; Wittmann, Paul

    2015-04-01

    The combination of ensemble predictions of Hs made by the US National Weather Service (NEW) and the US Navy Fleet Numerical Meteorological and Oceanography Center (FNMOC) has established the NFCENS, a probabilistic wave forecast system in operations at NCEP since 2011. Computed from 41 combined wave ensemble members, the new product outperforms deterministic and probabilistic forecasts and nowcasts of Hs issued separately at each forecast center, at all forecast ranges. The successful implementation of the NFCENS has brought new opportunities for collaboration with Environment Canada (EC). EC is in the process of adding new global wave model ensemble products to its existing suite of operational regional products. The planned upgrade to the current NFCENS wave multi-center ensemble includes the addition of 20 members from the Canadian WES. With this upgrade, the NFCENS will be renamed North American Wave Ensemble System (NAWES). As part of the new system implementation, new higher-resolution grids and upgrades to model physics using recent advances in source-term parameterizations are being tested. We provide results of a first validation of NAWES relative to global altimeter data, and buoy measurements of waves, as well as its ability to forecast waves during the 2012 North Atlantic hurricane Sandy. A second line of research involving wave ensembles at the NWS is the implementation of a LETKF-based data assimilation system developed in collaboration with the Argentinian Navy Meteorological Service. The project involves an implementation of the 4D-LETKF in the NWS global wave ensemble forecast system GWES. The 4-D scheme initializes a full 81-member ensemble in a 6-hour cycle. The LETKF determines the analysis ensemble locally in the space spanned by the ensemble, as a linear combination of the background perturbations. Observations from three altimeters and one scatterometer were used. Preliminary results for a prototype system running at the NWS, including

  3. How to pose the question matters: Behavioural Economics concepts in decision making on the basis of ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Alfonso, Leonardo; van Andel, Schalk Jan

    2014-05-01

    Part of recent research in ensemble and probabilistic hydro-meteorological forecasting analyses which probabilistic information is required by decision makers and how it can be most effectively visualised. This work, in addition, analyses if decision making in flood early warning is also influenced by the way the decision question is posed. For this purpose, the decision-making game "Do probabilistic forecasts lead to better decisions?", which Ramos et al (2012) conducted at the EGU General Assembly 2012 in the city of Vienna, has been repeated with a small group and expanded. In that game decision makers had to decide whether or not to open a flood release gate, on the basis of flood forecasts, with and without uncertainty information. A conclusion of that game was that, in the absence of uncertainty information, decision makers are compelled towards a more risk-averse attitude. In order to explore to what extent the answers were driven by the way the questions were framed, in addition to the original experiment, a second variant was introduced where participants were asked to choose between a sure value (for either loosing or winning with a giving probability) and a gamble. This set-up is based on Kahneman and Tversky (1979). Results indicate that the way how the questions are posed may play an important role in decision making and that Prospect Theory provides promising concepts to further understand how this works.

  4. Ensemble-based analysis of Front Range severe convection on 6-7 June 2012: Forecast uncertainty and communication of weather information to Front Range decision-makers

    NASA Astrophysics Data System (ADS)

    Vincente, Vanessa

    The variation of topography in Colorado not only adds to the beauty of its landscape, but also tests our ability to predict warm season severe convection. Deficient radar coverage and limited observations make quantitative precipitation forecasting quite a challenge. Past studies have suggested that greater forecast skill of mesoscale convection initiation and precipitation characteristics are achievable considering an ensemble with explicitly predicted convection compared to one that has parameterized convection. The range of uncertainty and probabilities in these forecasts can help forecasters in their precipitation predictions and communication of weather information to emergency managers (EMs). EMs serve an integral role in informing and protecting communities in anticipation of hazardous weather. An example of such an event occurred on the evening of 6 June 2012, where areas to the lee of the Rocky Mountain Front Range were impacted by flash-flood-producing severe convection that included heavy rain and copious amounts of hail. Despite the discrepancy in the timing, location and evolution of convection, the convection-allowing ensemble forecasts generally outperformed those of the convection-parameterized ensemble in representing the mesoscale processes responsible for the 6-7 June severe convective event. Key features sufficiently reproduced by several of the convection-allowing ensemble members resembled the observations: 1) general location of a convergence boundary east of Denver, 2) convective initiation along the boundary, 3) general location of a weak cold front near the Wyoming/Nebraska border, and 4) cold pools and moist upslope characteristics that contributed to the backbuilding of convection. Members from the convection-parameterized ensemble that failed to reproduce these results displaced the convergence boundary, produced a cold front that moved southeast too quickly, and used the cold front for convective initiation. The convection

  5. Comparison between genetic programming and an ensemble Kalman filter as data assimilation techniques for probabilistic flood forecasting

    NASA Astrophysics Data System (ADS)

    Mediero, L.; Garrote, L.; Requena, A.; Chávez, A.

    2012-04-01

    Flood events are among the natural disasters that cause most economic and social damages in Europe. Information and Communication Technology (ICT) developments in last years have enabled hydrometeorological observations available in real-time. High performance computing promises the improvement of real-time flood forecasting systems and makes the use of post processing techniques easier. This is the case of data assimilation techniques, which are used to develop an adaptive forecast model. In this paper, a real-time framework for probabilistic flood forecasting is presented and two data assimilation techniques are compared. The first data assimilation technique uses genetic programming to adapt the model to the observations as new information is available, updating the estimation of the probability distribution of the model parameters. The second data assimilation technique uses an ensemble Kalman filter to quantify errors in both hydrologic model and observations, updating estimates of system states. Both forecast models take the result of the hydrologic model calibration as a starting point and adapts the individuals of this first population to the new observations in each operation time step. Data assimilation techniques have great potential when are used in hydrological distributed models. The distributed RIBS (Real-time Interactive Basin Simulator) rainfall-runoff model was selected to simulate the hydrological process in the basin. The RIBS model is deterministic, but it is run in a probabilistic way through Monte Carlo simulations over the probability distribution functions that best characterise the most relevant model parameters, which were identified by a probabilistic multi-objective calibration developed in a previous work. The Manzanares River basin was selected as a case study. Data assimilation processes are computationally intensive. Therefore, they are well suited to test the applicability of the potential of the Grid technology to

  6. Comparison of surface freshwater fluxes from different climate forecasts produced through different ensemble generation schemes.

    NASA Astrophysics Data System (ADS)

    Romanova, Vanya; Hense, Andreas; Wahl, Sabrina; Brune, Sebastian; Baehr, Johanna

    2016-04-01

    The decadal variability and its predictability of the surface net freshwater fluxes is compared in a set of retrospective predictions, all using the same model setup, and only differing in the implemented ocean initialisation method and ensemble generation method. The basic aim is to deduce the differences between the initialization/ensemble generation methods in view of the uncertainty of the verifying observational data sets. The analysis will give an approximation of the uncertainties of the net freshwater fluxes, which up to now appear to be one of the most uncertain products in observational data and model outputs. All ensemble generation methods are implemented into the MPI-ESM earth system model in the framework of the ongoing MiKlip project (www.fona-miklip.de). Hindcast experiments are initialised annually between 2000-2004, and from each start year 10 ensemble members are initialized for 5 years each. Four different ensemble generation methods are compared: (i) a method based on the Anomaly Transform method (Romanova and Hense, 2015) in which the initial oceanic perturbations represent orthogonal and balanced anomaly structures in space and time and between the variables taken from a control run, (ii) one-day-lagged ocean states from the MPI-ESM-LR baseline system (iii) one-day-lagged of ocean and atmospheric states with preceding full-field nudging to re-analysis in both the atmospheric and the oceanic component of the system - the baseline one MPI-ESM-LR system, (iv) an Ensemble Kalman Filter (EnKF) implemented into oceanic part of MPI-ESM (Brune et al. 2015), assimilating monthly subsurface oceanic temperature and salinity (EN3) using the Parallel Data Assimilation Framework (PDAF). The hindcasts are evaluated probabilistically using fresh water flux data sets from four different reanalysis data sets: MERRA, NCEP-R1, GFDL ocean reanalysis and GECCO2. The assessments show no clear differences in the evaluations scores on regional scales. However, on the

  7. A hydro-meteorological ensemble prediction system for real-time flood forecasting purposes in the Milano area

    NASA Astrophysics Data System (ADS)

    Ravazzani, Giovanni; Amengual, Arnau; Ceppi, Alessandro; Romero, Romualdo; Homar, Victor; Mancini, Marco

    2015-04-01

    Analysis of forecasting strategies that can provide a tangible basis for flood early warning procedures and mitigation measures over the Western Mediterranean region is one of the fundamental motivations of the European HyMeX programme. Here, we examine a set of hydro-meteorological episodes that affected the Milano urban area for which the complex flood protection system of the city did not completely succeed before the occurred flash-floods. Indeed, flood damages have exponentially increased in the area 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. The flood forecasting system tested in this work comprises the Flash-flood Event-based Spatially distributed rainfall-runoff Transformation, including Water Balance (FEST-WB) and the Weather Research and Forecasting (WRF) models, in order to provide a hydrological ensemble prediction system (HEPS). Deterministic and probabilistic quantitative precipitation forecasts (QPFs) have been provided by WRF model in a set of 48-hours experiments. HEPS has been generated by combining different physical parameterizations (i.e. cloud microphysics, moist convection and boundary-layer schemes) of the WRF model in order to better encompass the atmospheric processes leading to high precipitation amounts. We have been able to test the value of a probabilistic versus a deterministic framework when driving Quantitative Discharge Forecasts (QDFs). Results highlight (i) the benefits of using a high-resolution HEPS in conveying uncertainties for this complex orographic area and (ii) a better simulation of the most of extreme precipitation events, potentially enabling valuable probabilistic QDFs. Hence, the HEPS copes with the significant deficiencies found in the deterministic QPFs. These shortcomings would prevent to correctly forecast the location and timing of high precipitation rates and

  8. Development and Use of the Hydrologic Ensemble Forecast System by the National Weather Service to Support the New York City Water Supply

    NASA Astrophysics Data System (ADS)

    Shedd, R.; Reed, S. M.; Porter, J. H.

    2015-12-01

    The National Weather Service (NWS) has been working for several years on the development of the Hydrologic Ensemble Forecast System (HEFS). The objective of HEFS is to provide ensemble river forecasts incorporating the best precipitation and temperature forcings at any specific time horizon. For the current implementation, this includes the Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFSv2). One of the core partners that has been working with the NWS since the beginning of the development phase of HEFS is the New York City Department of Environmental Protection (NYCDEP) which is responsible for the complex water supply system for New York City. The water supply system involves a network of reservoirs in both the Delaware and Hudson River basins. At the same time that the NWS was developing HEFS, NYCDEP was working on enhancing the operations of their water supply reservoirs through the development of a new Operations Support Tool (OST). OST is designed to guide reservoir system operations to ensure an adequate supply of high-quality drinking water for the city, as well as to meet secondary objectives for reaches downstream of the reservoirs assuming the primary water supply goals can be met. These secondary objectives include fisheries and ecosystem support, enhanced peak flow attenuation beyond that provided natively by the reservoirs, salt front management, and water supply for other cities. Since January 2014, the NWS Northeast and Middle Atlantic River Forecast Centers have provided daily one year forecasts from HEFS to NYCDEP. OST ingests these forecasts, couples them with near-real-time environmental and reservoir system data, and drives models of the water supply system. The input of ensemble forecasts results in an ensemble of model output, from which information on the range and likelihood of possible future system states can be extracted. This type of probabilistic information provides system managers with additional

  9. A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

    NASA Astrophysics Data System (ADS)

    Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

    2016-06-01

    To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

  10. Mechanism and Predictability of Persistent Euro-Russian Blocking in Summer of 2010 Examined by Ensemble Hindcast and Forecast Dataset

    NASA Astrophysics Data System (ADS)

    Fujii, A.; Kuroda, Y.; Mukougawa, H.

    2012-04-01

    We investigate the mechanism and predictability of a sequence of strong Euro-Russian blocking with an extraordinary duration from mid-June to mid-August 2010 using a hindcast experiment performed on MRI (Meteorological Research Institute)/JMA (Japan Meteorological Agency) AGCM and the operational 1-month ensemble forecast of the JMA. First, a detailed examination on the time sequence of this blocking event based on JRA-25/JCDAS reanalysis dataset reveals that the persistent blocking could be decomposed into three evolutionary stages: (1) In Stage I from mid-June to late-June, the blocking resides over the Atlantic (around 10˚W) and is maintained due to the interaction with high-frequency synoptic disturbances of relatively high activity in comparison with the other two Stages. (2) In Stage II from late-June to late-July, the blocking is located over western Europe (around 30˚E). (3) At the beginning of Stage III from late-July to mid-August, the blocking shifts its location from western Europe to Russia (around 50˚E). The low-frequency components, rather than the high-frequency components, contribute to this transition and maintenance of the Russian blocking during this stage. Second, the time variation of the predictability of this blocking event is quantitatively investigated by a series of hindcast experiment carried out every 6 hours by MRI/JMA AGCM (TL95L40). Predictability of blocking occurrence is estimated every 6 hours by Brier Score constructed from recent 5-day initial conditions of 20 ensemble members in terms of the Lagged Average Forecasting (LAF) method. It is found that the predictability is considerably reduced during the transition period of the evolutionary stages such as late-June and late-July. The forecast skill also becomes significantly worse during the decay period of the Russian blocking in mid-August. Third, precursory signals of low-frequency components related to the enhancement of the blocking high over Russia in late-July are

  11. Quantitative precipitation and streamflow forecast for two recent extreme hydro-meteorological events in Southern Italy with a fully-coupled model system

    NASA Astrophysics Data System (ADS)

    Mendicino, Giuseppe; Senatore, Alfonso

    2016-04-01

    Two severe hydro-meteorological events affected Calabria Region (Southern Italy) in the second half of the year 2015. The first event, on August 12th, focused on a relatively small area near the northern Ionian coast, resulted in a rainfall intensity of about 230 mm in 24 hours involving flash flooding with several million Euros of damages. The second event mainly affected the southern Ionian coast, was more persistent (it lasted from October 30th to November 2nd), interested a wider area and led to recorded rainfall values up to 400 mm in 24 hours and 700 mm in 48 hours, resulting in severe flooding, landslides and a human loss. The fully two-way dynamically coupled atmosphere-hydrology modeling system WRF-Hydro is used to reproduce both the events, in order to assess its skill in forecasting both quantitative precipitation and streamflow with initial and lateral atmospheric boundary conditions given by the recently available 0.25° output resolution GFS grid dataset. Precipitation estimates provided by 2 km-resolution atmospheric model are compared with both ground-based data and observations from a National Civil Protection Department single-polarization Doppler radar. Discharge data from the rivers and creeks affected by heavy precipitation are not available, then streamflow results are compared with either official discharge estimates provided by authorities (first event) or recorded river stages (second event). Results show good performances of the fully-coupled hydrometeorological prediction system which allows an improved representation of the coupled atmospheric and terrestrial processes and provides an integrated solution for the regional water cycle modeling, from atmospheric processes to river outlets.

  12. Low Streamflow Forcasting using Minimum Relative Entropy

    NASA Astrophysics Data System (ADS)

    Cui, H.; Singh, V. P.

    2013-12-01

    Minimum relative entropy spectral analysis is derived in this study, and applied to forecast streamflow time series. Proposed method extends the autocorrelation in the manner that the relative entropy of underlying process is minimized so that time series data can be forecasted. Different prior estimation, such as uniform, exponential and Gaussian assumption, is taken to estimate the spectral density depending on the autocorrelation structure. Seasonal and nonseasonal low streamflow series obtained from Colorado River (Texas) under draught condition is successfully forecasted using proposed method. Minimum relative entropy determines spectral of low streamflow series with higher resolution than conventional method. Forecasted streamflow is compared to the prediction using Burg's maximum entropy spectral analysis (MESA) and Configurational entropy. The advantage and disadvantage of each method in forecasting low streamflow is discussed.

  13. An experimental seasonal hydrological forecasting system over the Yellow River basin - Part 2: The added value from climate forecast models

    NASA Astrophysics Data System (ADS)

    Yuan, Xing

    2016-06-01

    This is the second paper of a two-part series on introducing an experimental seasonal hydrological forecasting system over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, the added value from eight North American Multimodel Ensemble (NMME) climate forecast models with a grand ensemble of 99 members is assessed in this paper, with an implicit consideration of human-induced uncertainty in the hydrological models through a post-processing procedure. The forecast skill in terms of anomaly correlation (AC) for 2 m air temperature and precipitation does not necessarily decrease over leads but is dependent on the target month due to a strong seasonality for the climate over the Yellow River basin. As there is more diversity in the model performance for the temperature forecasts than the precipitation forecasts, the grand NMME ensemble mean forecast has consistently higher skill than the best single model up to 6 months for the temperature but up to 2 months for the precipitation. The NMME climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model regionalized over the Yellow River basin to produce forecasts of soil moisture, runoff and streamflow. And the NMME/VIC forecasts are compared with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month hindcast experiments for each calendar month during 1982-2010. As verified by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC for the soil moisture forecasts, and the former has higher skill than the latter only for the forecasts at long leads and for those initialized in the rainy season. The forecast skill for runoff is lower for both forecast approaches, but the added value from NMME/VIC is more obvious, with an increase of the average AC by 0.08-0.2. To compare with the observed

  14. Effect of model error on precipitation forecasts in the high-resolution limited area ensemble prediction system of the Korea Meteorological Administration

    NASA Astrophysics Data System (ADS)

    Kim, SeHyun; Kim, Hyun Mee

    2015-04-01

    In numerical weather prediction using convective-scale model resolution, forecast uncertainties are caused by initial condition error, boundary condition error, and model error. Because convective-scale forecasts are influenced by subgrid scale processes which cannot be resolved easily, the model error becomes more important than the initial and boundary condition errors. To consider the model error, multi-model and multi-physics methods use several models and physics schemes and the stochastic physics method uses random numbers to create a noise term in the model equations (e.g. Stochastic Perturbed Parameterization Tendency (SPPT), Stochastic Kinetic Energy Backscatter (SKEB), Stochastic Convective Vorticity (SCV), and Random Parameters (RP)). In this study, the RP method was used to consider the model error in the high-resolution limited area ensemble prediction system (EPS) of the Korea Meteorological Administration (KMA). The EPS has 12 ensemble members with 3 km horizontal resolution which generate 48 h forecasts. The initial and boundary conditions were provided by the global EPS of the KMA. The RP method was applied to microphysics and boundary layer schemes, and the ensemble forecasts using RP were compared with those without RP during July 2013. Both Root Mean Square Error (RMSE) and spread of wind at 10 m verified by surface Automatic Weather System (AWS) observations decreased when using RP. However, for 1 hour accumulated precipitation, the spread increased with RP and Equitable Threat Score (ETS) showed different results for each rainfall event.

  15. Using the Firefly optimization method to weight an ensemble of rainfall forecasts from the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS)

    NASA Astrophysics Data System (ADS)

    dos Santos, A. F.; Freitas, S. R.; de Mattos, J. G. Z.; de Campos Velho, H. F.; Gan, M. A.; da Luz, E. F. P.; Grell, G. A.

    2013-09-01

    In this paper we consider an optimization problem applying the metaheuristic Firefly algorithm (FY) to weight an ensemble of rainfall forecasts from daily precipitation simulations with the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) over South America during January 2006. The method is addressed as a parameter estimation problem to weight the ensemble of precipitation forecasts carried out using different options of the convective parameterization scheme. Ensemble simulations were performed using different choices of closures, representing different formulations of dynamic control (the modulation of convection by the environment) in a deep convection scheme. The optimization problem is solved as an inverse problem of parameter estimation. The application and validation of the methodology is carried out using daily precipitation fields, defined over South America and obtained by merging remote sensing estimations with rain gauge observations. The quadratic difference between the model and observed data was used as the objective function to determine the best combination of the ensemble members to reproduce the observations. To reduce the model rainfall biases, the set of weights determined by the algorithm is used to weight members of an ensemble of model simulations in order to compute a new precipitation field that represents the observed precipitation as closely as possible. The validation of the methodology is carried out using classical statistical scores. The algorithm has produced the best combination of the weights, resulting in a new precipitation field closest to the observations.

  16. A model to forecast short-term snowmelt runoff using synoptic observations of streamflow, temperature, and precipitation.

    USGS Publications Warehouse

    Tangborn, W.V.

    1980-01-01

    Snowmelt runoff is forecast with a statistical model that utilizes daily values of stream discharge, gaged precipitation, and maximum and minimum observations of air temperature. Synoptic observations of these variables are made at existing low- and medium-altitude weather stations, thus eliminating the difficulties and expense of new, high-altitude installations. 4 model development steps are used to demonstrate the influence on prediction accuracy of basin storage. -from Author

  17. Aerosol Observability and Predictability: From Research to Operations for Chemical Weather Forecasting. Lagrangian Displacement Ensembles for Aerosol Data Assimilation

    NASA Technical Reports Server (NTRS)

    da Silva, Arlindo

    2010-01-01

    A challenge common to many constituent data assimilation applications is the fact that one observes a much smaller fraction of the phase space that one wishes to estimate. For example, remotely sensed estimates of the column average concentrations are available, while one is faced with the problem of estimating 3D concentrations for initializing a prognostic model. This problem is exacerbated in the case of aerosols because the observable Aerosol Optical Depth (AOD) is not only a column integrated quantity, but it also sums over a large number of species (dust, sea-salt, carbonaceous and sulfate aerosols. An aerosol transport model when driven by high-resolution, state-of-the-art analysis of meteorological fields and realistic emissions can produce skillful forecasts even when no aerosol data is assimilated. The main task of aerosol data assimilation is to address the bias arising from inaccurate emissions, and Lagrangian misplacement of plumes induced by errors in the driving meteorological fields. As long as one decouples the meteorological and aerosol assimilation as we do here, the classic baroclinic growth of error is no longer the main order of business. We will describe an aerosol data assimilation scheme in which the analysis update step is conducted in observation space, using an adaptive maximum-likelihood scheme for estimating background errors in AOD space. This scheme includes e explicit sequential bias estimation as in Dee and da Silva. Unlikely existing aerosol data assimilation schemes we do not obtain analysis increments of the 3D concentrations by scaling the background profiles. Instead we explore the Lagrangian characteristics of the problem for generating local displacement ensembles. These high-resolution state-dependent ensembles are then used to parameterize the background errors and generate 3D aerosol increments. The algorithm has computational complexity running at a resolution of 1/4 degree, globally. We will present the result of

  18. Short and Medium Range Hydrometeorological Ensemble Prediction System: A mesoscale case study in Denmark

    NASA Astrophysics Data System (ADS)

    Lucatero, D.; Madsen, H.; Refsgaard, J.; Kidmose, J.; Jensen, K.; Feddersen, H.; Sass, B.

    2013-12-01

    Recent floods in Europe have increased the need to improve flood prediction systems that will allow, firstly, for proper analysis of the uncertainties in the hydrological predictions and, secondly, for the generation of probabilistic forecasts. The use of Meteorological Ensemble Prediction Systems (MEPS) to propagate climate input uncertainty throughout the hydrological modeling process is a state-of-the-art methodology for issuing probabilistic forecasts. MEPS have been made operational at meteorological centers around the globe for both short and medium range forecasting. The European Centre for Medium-Range Weather Forecasts (ECMWF) issues probabilistic forecasts with a resolution of approximately 25 kilometers using 50 ensemble members. The forecasts are issued twice per day with a lead time up to 15 days. Likewise, the Danish Meteorological Institute (DMI) issues probabilistic forecasts for a limited area covering Northern Europe with 25 ensemble members. DMI's MEPS has a spatial resolution of 5 kilometers, a lead time of 48 hours and is issued four times per day. Our research focuses on the implementation of both MEPS as input variables in a hydrological forecast model. We are aiming at generating seamless probabilistic forecasts of hydrological variables such as streamflow and groundwater levels for Silkeborg city in Denmark. A MIKE SHE coupled surface-groundwater hydrological model set up for the Silkeborg catchment is used for the hydrological forecast. Short and medium range forecasts for streamflows and groundwater levels of past events are analyzed in order to study the feasibility and skills of the flood forecasting system. Several measures of skill are described and analyzed in order to detect biases and lack of accuracy in our forecast system. A study of the reliability, skill over climatology/persistence forecasting, resolution, discrimination, etc., was made by means of the analysis of the joint distribution of the forecasts and observations of

  19. Diversity of Aerosol Optical Thickness in analysis and forecasting modes of the models from the International Cooperative for Aerosol Prediction Multi-Model Ensemble (ICAP-MME)

    NASA Astrophysics Data System (ADS)

    Lynch, P.

    2014-12-01

    With the emergence of global aerosol models intended for operational forecasting use at global numerical weather prediction (NWP) centers, the International Cooperative for Aerosol Prediction (ICAP) was founded in 2010. One of the objectives of ICAP is to develop a global multi-model aerosol forecasting ensemble (ICAP-MME) for operational and basic research use. To increase the accuracy of aerosol forecasts, several of the NWP centers have incorporated assimilation of satellite and/or ground-based observations of aerosol optical thickness (AOT), the most widely available and evaluated aerosol parameter. The ICAP models are independent in their underlying meteorology, as well as aerosol sources, sinks, microphysics and chemistry. The diversity of aerosol representations in the aerosol forecast models results in differences in AOT. In addition, for models that include AOT assimilations, the diversity in assimilation methodology, the observed AOT data to be assimilated, and the pre-assimilation treatments of input data also leads to differences in the AOT analyses. Drawing from members of the ICAP latest generation of quasi-operational aerosol models, five day AOT forecasts and AOT analyses are analyzed from four multi-species models which have AOT assimilations: ECMWF, JMA, NASA GSFC/GMAO, and NRL/FNMOC. For forecast mode only, we also include the dust products from NOAA NGAC, BSC, and UK Met office in our analysis leading to a total of 7 dust models. AOT at 550nm from all models are validated at regionally representative Aerosol Robotic Network (AERONET) sites and a data assimilation grade multi-satellite aerosol analysis. These analyses are also compared with the recently developed AOT reanalysis at NRL. Here we will present the basic verification characteristics of the ICAP-MME, and identify regions of diversity between model analyses and forecasts. Notably, as in many other ensemble environments, the multi model ensemble consensus mean outperforms all of the

  20. Assessing the value of post-processed state-of-the-art long-term weather forecast ensembles for agricultural water management mediated by farmers' behaviours

    NASA Astrophysics Data System (ADS)

    Li, Yu; Giuliani, Matteo; Castelletti, Andrea

    2016-04-01

    Recent advances in modelling of coupled ocean-atmosphere dynamics significantly improved skills of long-term climate forecast from global circulation models (GCMs). These more accurate weather predictions are supposed to be a valuable support to farmers in optimizing farming operations (e.g. crop choice, cropping and watering time) and for more effectively coping with the adverse impacts of climate variability. Yet, assessing how actually valuable this information can be to a farmer is not straightforward and farmers' response must be taken into consideration. Indeed, in the context of agricultural systems potentially useful forecast information should alter stakeholders' expectation, modify their decisions, and ultimately produce an impact on their performance. Nevertheless, long-term forecast are mostly evaluated in terms of accuracy (i.e., forecast quality) by comparing hindcast and observed values and only few studies investigated the operational value of forecast looking at the gain of utility within the decision-making context, e.g. by considering the derivative of forecast information, such as simulated crop yields or simulated soil moisture, which are essential to farmers' decision-making process. In this study, we contribute a step further in the assessment of the operational value of long-term weather forecasts products by embedding these latter into farmers' behavioral models. This allows a more critical assessment of the forecast value mediated by the end-users' perspective, including farmers' risk attitudes and behavioral patterns. Specifically, we evaluate the operational value of thirteen state-of-the-art long-range forecast products against climatology forecast and empirical prediction (i.e. past year climate and historical average) within an integrated agronomic modeling framework embedding an implicit model of the farmers' decision-making process. Raw ensemble datasets are bias-corrected and downscaled using a stochastic weather generator, in

  1. Using constructed analogs to improve the skill of National Multi-Model Ensemble March–April–May precipitation forecasts in equatorial East Africa

    USGS Publications Warehouse

    Shukla, Shraddhanand; Funk, Christopher C.; Hoell, Andrew

    2014-01-01

    In this study we implement and evaluate a simple 'hybrid' forecast approach that uses constructed analogs (CA) to improve the National Multi-Model Ensemble's (NMME) March–April–May (MAM) precipitation forecasts over equatorial eastern Africa (hereafter referred to as EA, 2°S to 8°N and 36°E to 46°E). Due to recent declines in MAM rainfall, increases in population, land degradation, and limited technological advances, this region has become a recent epicenter of food insecurity. Timely and skillful precipitation forecasts for EA could help decision makers better manage their limited resources, mitigate socio-economic losses, and potentially save human lives. The 'hybrid approach' described in this study uses the CA method to translate dynamical precipitation and sea surface temperature (SST) forecasts over the Indian and Pacific Oceans (specifically 30°S to 30°N and 30°E to 270°E) into terrestrial MAM precipitation forecasts over the EA region. In doing so, this approach benefits from the post-1999 teleconnection that exists between precipitation and SSTs over the Indian and tropical Pacific Oceans (Indo-Pacific) and EA MAM rainfall. The coupled atmosphere-ocean dynamical forecasts used in this study were drawn from the NMME. We demonstrate that while the MAM precipitation forecasts (initialized in February) skill of the NMME models over the EA region itself is negligible, the ranked probability skill score of hybrid CA forecasts based on Indo-Pacific NMME precipitation and SST forecasts reach up to 0.45.

  2. Ensemble and Bias-Correction Techniques for Air-Quality Model Forecasts of Surface O3 and PM2.5 during the TEXAQS-II Experiment of 2006

    EPA Science Inventory

    Several air quality forecasting ensembles were created from seven models, running in real-time during the 2006 Texas Air Quality (TEXAQS-II) experiment. These multi-model ensembles incorporated a diverse set of meteorological models, chemical mechanisms, and emission inventories...

  3. Skill of global hydrological forecasting system FEWS GLOWASIS using climatic ESP forecasts

    NASA Astrophysics Data System (ADS)

    Weerts, A. H.; Candogan, N.; Winsemius, H. C.; van Beek, R.; Westerhoff, R.

    2012-04-01

    Forecasting of water availability and scarcity is a prerequisite for the management of hydropower reservoirs, basin-scale management of water resources, agriculture and disaster relief. The EU 7th Framework Program project Global Water Scarcity Information Service (GLOWASIS) aims to pre-validate a service that provides real-time global-scale information on water scarcity. In this contribution, we demonstrate what skill (compared to a climatology) may be reached with a global seasonal ensemble forecasting system consisting of: a) a global hydrological model PCR-GLOBWB; b) an updating procedure for initial forecasting states, based on the best-guess global rainfall information. As best guess, a combination of ERA-Interim Reanalysis rainfall and Global Precipitation Climatology Project (GPCP) observations is being used; c) a forecast based on Ensemble Streamflow Prediction (ESP)procedure and reverse ESP procedure (Wood and Lettenmaier, 2008). In the ESP procedure, a meteorological forecast ensemble is generated based on past years of observation series (i.e. climatological observations). As observations, the combination of ERA-Interim and GPCP is used. In reverse ESP, an ensemble is generated by using a set of initial states from a large sample of updates at the specific month of interest, and forecasts are produced using one observed set. This analysis allows us to measure how much skill may be expected from hydrological inertia and climatology alone, leaving aside for the moment potential skill improvement by using seasonal meteorological forecasts. In future work, we will measure how much skill improvement compared to the forecasts mentioned above may be reached, when ECMWF Seasonal forecasts are used. This will allow us to measure the contributions to skill of each component (initial state inertia, hydrology and meteorological inputs) of the forecast system.

  4. Forecasting forecast skill

    NASA Technical Reports Server (NTRS)

    Kalnay, Eugenia; Dalcher, Amnon

    1987-01-01

    It is shown that it is possible to predict the skill of numerical weather forecasts - a quantity which is variable from day to day and region to region. This has been accomplished using as predictor the dispersion (measured by the average correlation) between members of an ensemble of forecasts started from five different analyses. The analyses had been previously derived for satellite-data-impact studies and included, in the Northern Hemisphere, moderate perturbations associated with the use of different observing systems. When the Northern Hemisphere was used as a verification region, the prediction of skill was rather poor. This is due to the fact that such a large area usually contains regions with excellent forecasts as well as regions with poor forecasts, and does not allow for discrimination between them. However, when regional verifications were used, the ensemble forecast dispersion provided a very good prediction of the quality of the individual forecasts.

  5. On the proper use of Ensembles for Predictive Uncertainty assessment

    NASA Astrophysics Data System (ADS)

    Todini, Ezio; Coccia, Gabriele; Ortiz, Enrique

    2015-04-01

    uncertainty of the ensemble mean and that of the ensemble spread. The results of this new approach are illustrated by using data and forecasts from an operational real time flood forecasting. Coccia, G. and Todini, E. 2011. Recent developments in predictive uncertainty assessment based on the Model Conditional Processor approach. Hydrology and Earth System Sciences, 15, 3253-3274. doi:10.5194/hess-15-3253-2011. Krzysztofowicz, R. 1999 Bayesian theory of probabilistic forecasting via deterministic hydrologic model, Water Resour. Res., 35, 2739-2750. Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005. Using Bayesian model averaging to calibrate forecast ensembles, Mon. Weather Rev., 133, 1155-1174. Reggiani, P., Renner, M., Weerts, A., and van Gelder, P., 2009. Uncertainty assessment via Bayesian revision of ensemble streamflow predictions in the operational river Rhine forecasting system, Water Resour. Res., 45, W02428, doi:10.1029/2007WR006758. Todini E. 2004. Role and treatment of uncertainty in real-time flood forecasting. Hydrological Processes 18(14), 2743_2746 Todini, E. 2008. A model conditional processor to assess predictive uncertainty in flood forecasting. Intl. J. River Basin Management, 6(2): 123-137.

  6. Calibration and parameterization of a semi-distributed hydrological model to support sub-daily ensemble flood forecasting; a watershed in southeast Brazil

    NASA Astrophysics Data System (ADS)

    de Almeida Bressiani, D.; Srinivasan, R.; Mendiondo, E. M.

    2013-12-01

    The use of distributed or semi-distributed models to represent the processes and dynamics of a watershed in the last few years has increased. These models are important tools to predict and forecast the hydrological responses of the watersheds, and they can subside disaster risk management and planning. However they usually have a lot of parameters, of which, due to the spatial and temporal variability of the processes, are not known, specially in developing countries; therefore a robust and sensible calibration is very important. This study conduced a sub-daily calibration and parameterization of the Soil & Water Assessment Tool (SWAT) for a 12,600 km2 watershed in southeast Brazil, and uses ensemble forecasts to evaluate if the model can be used as a tool for flood forecasting. The Piracicaba Watershed, in São Paulo State, is mainly rural, but has about 4 million of population in highly relevant urban areas, and three cities in the list of critical cities of the National Center for Natural Disasters Monitoring and Alerts. For calibration: the watershed was divided in areas with similar hydrological characteristics, for each of these areas one gauge station was chosen for calibration; this procedure was performed to evaluate the effectiveness of calibrating in fewer places, since areas with the same group of groundwater, soil, land use and slope characteristics should have similar parameters; making calibration a less time-consuming task. The sensibility analysis and calibration were performed on the software SWAT-CUP with the optimization algorithm: Sequential Uncertainly Fitting Version 2 (SUFI-2), which uses Latin hypercube sampling scheme in an iterative process. The performance of the models to evaluate the calibration and validation was done with: Nash-Sutcliffe efficiency coefficient (NSE), determination coefficient (r2), root mean square error (RMSE), and percent bias (PBIAS), with monthly average values of NSE around 0.70, r2 of 0.9, normalized RMSE of 0

  7. Hydro-economic assessment of hydrological forecasting systems

    NASA Astrophysics Data System (ADS)

    Boucher, M.-A.; Tremblay, D.; Delorme, L.; Perreault, L.; Anctil, F.

    2012-01-01

    SummaryAn increasing number of publications show that ensemble hydrological forecasts exhibit good performance when compared to observed streamflow. Many studies also conclude that ensemble forecasts lead to a better performance than deterministic ones. This investigation takes one step further by not only comparing ensemble and deterministic forecasts to observed values, but by employing the forecasts in a stochastic decision-making assistance tool for hydroelectricity production, during a flood event on the Gatineau River in Canada. This allows the comparison between different types of forecasts according to their value in terms of energy, spillage and storage in a reservoir. The motivation for this is to adopt the point of view of an end-user, here a hydroelectricity production society. We show that ensemble forecasts exhibit excellent performances when compared to observations and are also satisfying when involved in operation management for electricity production. Further improvement in terms of productivity can be reached through the use of a simple post-processing method.

  8. September streamflow

    NASA Astrophysics Data System (ADS)

    Above-average streamflows dominated water conditions in the West for the 15th consecutive month, according to the September check of national water conditions by the U.S. Geological Survey (USGS). High flows were reported from Washington to southern California and eastward to central Colorado (see map, courtesy of USGS).In contrast, drier than usual conditions—with streamflows well below average—prevailed in the East and South. Low streamflows were also reported in the Ohio River valley, Kentucky, and Tennessee, and drought conditions worsened in west Texas, where no appreciable rainfall has occurred this year.

  9. A stochastic ensemble-based model to predict crop water requirements from numerical weather forecasts and VIS-NIR high resolution satellite images in Southern Italy

    NASA Astrophysics Data System (ADS)

    Pelosi, Anna; Falanga Bolognesi, Salvatore; De Michele, Carlo; Medina Gonzalez, Hanoi; Villani, Paolo; D'Urso, Guido; Battista Chirico, Giovanni

    2015-04-01

    Irrigation agriculture is one the biggest consumer of water in Europe, especially in southern regions, where it accounts for up to 70% of the total water consumption. The EU Common Agricultural Policy, combined with the Water Framework Directive, imposes to farmers and irrigation managers a substantial increase of the efficiency in the use of water in agriculture for the next decade. Ensemble numerical weather predictions can be valuable data for developing operational advisory irrigation services. We propose a stochastic ensemble-based model providing spatial and temporal estimates of crop water requirements, implemented within an advisory service offering detailed maps of irrigation water requirements and crop water consumption estimates, to be used by water irrigation managers and farmers. The stochastic model combines estimates of crop potential evapotranspiration retrieved from ensemble numerical weather forecasts (COSMO-LEPS, 16 members, 7 km resolution) and canopy parameters (LAI, albedo, fractional vegetation cover) derived from high resolution satellite images in the visible and near infrared wavelengths. The service provides users with daily estimates of crop water requirements for lead times up to five days. The temporal evolution of the crop potential evapotranspiration is simulated with autoregressive models. An ensemble Kalman filter is employed for updating model states by assimilating both ground based meteorological variables (where available) and numerical weather forecasts. The model has been applied in Campania region (Southern Italy), where a satellite assisted irrigation advisory service has been operating since 2006. This work presents the results of the system performance for one year of experimental service. The results suggest that the proposed model can be an effective support for a sustainable use and management of irrigation water, under conditions of water scarcity and drought. Since the evapotranspiration term represents a staple

  10. Intercomparison of streamflow postprocessing techniques: first results of a HEPEX community experiment

    NASA Astrophysics Data System (ADS)

    Brown, James; Ramos, Maria-Helena; Voisin, Nathalie

    2013-04-01

    Hydrological ensemble forecasts comprise uncertainties originating from multiple sources. Often, they are characterized by biases in the mean, spread or higher moments of their associated probability distributions. These biases may be unconditional or conditional upon several factors, such as the magnitude of the observed or forecast variable, season, or forecast lead time, and may be manifest in either the volume or timing of streamflow. The different biases may be more or less critical for practical applications ranging from flood prediction to reservoir regulation, which often require (or assume) unbiasedness. To some degree, statistical post-processors can correct for these biases. They include a broad range of statistical techniques that may target particular types of bias or a range of unconditional and conditional biases. For example, when viewed conditionally upon the forecast variable, a common aim is to produce sharp forecasts that are also reliable, i.e. have an appropriate amount of spread to accommodate forecast errors. Post-processing techniques also vary in whether they lump together all sources of bias and uncertainty or address the meteorological and hydrologic uncertainties separately. In the last couple of decades, several postprocessing techniques have been developed and tested for hydrological prediction. Applied to single-valued (deterministic) predictions as well as to multi-scenario (ensemble or multi-model) predictions for short- to long-term forecasts, they have however rarely been examined together, within an extensive and coherent inter-comparison framework. In order to promote a better understanding of the strengths and weaknesses of several of those techniques, and whether the choice of technique really matters, an inter-comparison was initiated in June 2012 under the auspices of the Hydrologic Ensemble Prediction Experiment HEPEX (van Andel et al., 2013). This experiment is fully described at http://www.hepex.org/. The inter

  11. Ensemble-based simultaneous emission estimates and improved forecast of radioactive pollution from nuclear power plant accidents: application to ETEX tracer experiment.

    PubMed

    Zhang, X L; Li, Q B; Su, G F; Yuan, M Q

    2015-04-01

    The accidental release of radioactive materials from nuclear power plant leads to radioactive pollution. We apply an augmented ensemble Kalman filter (EnKF) with a chemical transport model to jointly estimate the emissions of Perfluoromethylcyclohexane (PMCH), a tracer substitute for radionuclides, from a point source during the European Tracer Experiment, and to improve the forecast of its dispersion downwind. We perturb wind fields to account for meteorological uncertainties. We expand the state vector of PMCH concentrations through continuously adding an a priori emission rate for each succeeding assimilation cycle. We adopt a time-correlated red noise to simulate the temporal emission fluctuation. The improved EnKF system rapidly updates (and reduces) the excessively large initial first-guess emissions, thereby significantly improves subsequent forecasts (r = 0.83, p < 0.001). It retrieves 94% of the total PMCH released and substantially reduces transport error (>80% average reduction of the normalized mean square error).

  12. Development of a real time streamflow monitoring system for the Indian sub-continental basins

    NASA Astrophysics Data System (ADS)

    Shah, H. L.; Mishra, V.

    2015-12-01

    Real-time streamflow monitoring is essential in the Indian sub-continental river basins as a large population is affected by floods. Moreover, streamflow monitoring may help in managing the water resources in the agriculture dominated region. In the Indian sub-continental basins, it is challenging to obtain the real time information of streamflow, which is valuable for reservoir operations, water management, and flood forecasts. We setup the Variable Infiltration Capacity (VIC) hydrological model at daily temporal resolution and 0.25◦ spatial resolution using the bias corrected satellite precipitation product from the Tropical rainfall Measurement Mission Real Time (TRMM-3B42RTV7) and bias corrected temperature product from the Global Ensemble Forecast System (GEFS), version 2. Near-real-time precipitation and temperatures are bias corrected using the historic precipitation and temperature data from the India Meteorological Department (IMD). Moreover, we evaluated data assimilation approaches to improve the real-time monitoring of streamflow in the sub-continental basins.

  13. Real-time Reservoir Operation Based on a Combination of Long-term and Short-term Optimization and Hydrological Ensemble Forecasts

    NASA Astrophysics Data System (ADS)

    Meier, P.; Tilmant, A.; Boucher, M.; Anctil, F.

    2012-12-01

    In a reservoir system, benefits are usually increased if the system is operated in a coordinated manner. However, despite ever increasing computational power available to users, the optimization of a large system of reservoirs and hydropower stations remains a challenge, especially if uncertainties are included. When applying optimization methods, such as stochastic dynamic programming, the size of a problem becomes quickly too large to be solved. This situation is also known as the curse of dimensionality which limits the applicability of SDP to systems involving only two to three reservoirs. The fact that by design most reservoirs serve multiple purposes adds another difficulty when the operation is to be optimized. A method which is able to address the optimization of multi-purpose reservoirs even in large systems is stochastic dual dynamic programming (SDDP). This approximative dynamic programming technique represents the future benefit function with a number of hyperplanes. The SDDP model developed in this study maximizes the expected net benefits associated with the operation of a reservoir system on a midterm horizon (several years, monthly time step). SDDP provides, at each time step, estimates of the marginal water value stored in each reservoir. Reservoir operators, however, are interested in day-to-day decisions. To provide an operational optimization framework tailored for short-term decision support, the SDDP optimization can be coupled with a short-term nonlinear programming optimization using hydrological ensemble forecasts. The short-term objective therefore consists of the total electricity production within the forecast horizon and the total value of water stored in all the reservoirs. Thus, maximizing this objective ensures that a short-term decision does not contradict the strategic planning. This optimization framework is implemented for the Gatineau river basin, a sub-basin of the Ottawa river north of the city of Ottawa. The Gatineau river

  14. Drought Monitoring and Forecasting Using the Princeton/U Washington National Hydrologic Forecasting System

    NASA Astrophysics Data System (ADS)

    Wood, E. F.; Yuan, X.; Roundy, J. K.; Lettenmaier, D. P.; Mo, K. C.; Xia, Y.; Ek, M. B.

    2011-12-01

    observed atmospheric forcing. The forecast skills from the dynamical seasonal models (CFSv1, CFSv2, EUROSIP) and CPC are also compared with forecasts based on the Ensemble Streamflow Prediction (ESP) method, which uses initial conditions and historical forcings to generate seasonal forecasts. The skill of the system to predict drought, drought recovery and related hydrological conditions such as low-flows is assessed, along with quantified uncertainty.

  15. ASSESSMENT OF AN ENSEMBLE OF SEVEN REAL-TIME OZONE FORECASTS OVER EASTERN NORTH AMERICA DURING THE SUMMER OF 2004

    EPA Science Inventory

    The real-time forecasts of ozone (O3) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during July and August of 2004 (53 days) through the Aerometric Information Retrieval Now (AIRNow) network at roughly 340 mon...

  16. Requirements of Operational Verification of the NWSRFS-ESP Forecasts

    NASA Astrophysics Data System (ADS)

    Imam, B.; Werner, K.; Hartmann, H.; Sorooshian, S.; Pritchard, E.

    2006-12-01

    Forecast verification is the process of determining the quality of forecasts. This requires the utilization of quality measures that summarize one or more aspects of the relationship between forecasts and observations. Technically, the three main objectives of forecast verification are (a) monitoring, (b) improving, and (c) comparing the quality of different forecasting systems. However, users of forecast verification results range from administrators, who want to know the value of investing in forecast system improvement to forecasters and modelers, who want to assess areas of improving their own predictions, to forecast users, who weigh their decision based not only on the forecast but also on the perceived quality of such forecast. Our discussions with several forecasters and hydrologists in charge at various River Forecast Centers (RFCs) indicated that operational hydrologists view verification in a broader sense than their counterparts within the meteorological community. Their view encompasses verification as a possible tool in determining whether a forecast is ready for issuance as an "official" product or that it needs more work. In addition to the common challenges associated with verification of monthly and seasonal probabilistic forecasts. which include determining and obtaining the appropriate size of "forecast-observation" pairs data set, operational verification also requires the consideration of verification strategies for short-term forecasts. Under such condition, the identification of conditional verification (i.e., similar conditions) samples, tracking model states, input, and output, relative to their climatology, and the establishment of links between the forecast issuance, verification, and simulation components of the forecast system become important. In this presentation, we address the impacts of such view on the potential requirements of an operational verification system for the Ensemble Streamflow Prediction (ESP) component of the

  17. Evaluating National Weather Service Seasonal Forecast Products in Reservoir Operation Case Studies

    NASA Astrophysics Data System (ADS)

    Nielson, A.; Guihan, R.; Polebistki, A.; Palmer, R. N.; Werner, K.; Wood, A. W.

    2014-12-01

    Forecasts of future weather and streamflow can provide valuable information for reservoir operations and water management. A challenge confronting reservoir operators today is how to incorporate both climate and streamflow products into their operations and which of these forecast products are most informative and useful for optimized water management. This study incorporates several reforecast products provided by the Colorado Basin River Forecast Center (CBRFC) which allows a complete retrospective analysis of climate forecasts, resulting in an evaluation of each product's skill in the context of water resources management. The accuracy and value of forecasts generated from the Climate Forecast System version 2 (CFSv2) are compared to the accuracy and value of using an Ensemble Streamflow Predictions (ESP) approach. Using the CFSv2 may offer more insight when responding to climate driven extremes than the ESP approach because the CFSv2 incorporates a fully coupled climate model into its forecasts rather than using all of the historic climate record as being equally probable. The role of forecast updating frequency will also be explored. Decision support systems (DSS) for both Salt Lake City Parley's System and the Snohomish County Public Utility Department's (SnoPUD) Jackson project will be used to illustrate the utility of forecasts. Both DSS include a coupled simulation and optimization model that will incorporate system constraints, operating policies, and environmental flow requirements. To determine the value of the reforecast products, performance metrics meaningful to the managers of each system are to be identified and quantified. Without such metrics and awareness of seasonal operational nuances, it is difficult to identify forecast improvements in meaningful ways. These metrics of system performance are compared using the different forecast products to evaluate the potential benefits of using CFSv2 seasonal forecasts in systems decision making.

  18. Probabilistic Forecast for 21st Century Climate Based on an Ensemble of Simulations using a Business-As-Usual Scenario

    NASA Astrophysics Data System (ADS)

    Scott, J. R.; Forest, C. E.; Sokolov, A. P.; Dutkiewicz, S.

    2011-12-01

    The behavior of the climate system is examined in an ensemble of runs using an Earth System Model of intermediate complexity. Climate "parameters" varied are the climate sensitivity, the aerosol forcing, and the strength of ocean heat uptake. Variations in the latter were accomplished by changing the strength of the oceans' background vertical mixing. While climate sensitivity and aerosol forcing can be varied over rather wide ranges, it is more difficult to create such variation in heat uptake while maintaining a realistic overturning ocean circulation. Therefore, separate ensembles were carried out for a few values of the vertical diffusion coefficient. Joint probability distributions for climate sensitivity and aerosol forcing are constructed by comparing results from 20th century simulations with available observational data. These distributions are then used to generate ensembles of 21st century simulations; results allow us to construct probabilistic distributions for changes in important climate change variables such as surface air temperature, sea level rise, and magnitude of the AMOC. Changes in the rate of air-sea flux of CO2 and the export of carbon into the deep ocean are also examined.

  19. Potential drought stress in a Swiss mountain catchment—Ensemble forecasting of high mountain soil moisture reveals a drastic decrease, despite major uncertainties

    NASA Astrophysics Data System (ADS)

    RöSsler, Ole; Diekkrüger, Bernd; LöFfler, JöRg

    2012-04-01

    Climate change is expected to profoundly influence the hydrosphere of mountain ecosystems. The focus of current process-based research is centered on the reaction of glaciers and runoff to climate change; spatially explicit impacts on soil moisture remain widely neglected. We spatio-temporally analyzed the impact of the climate on soil moisture in a mesoscale high mountain catchment to facilitate the development of mitigation and adaptation strategies at the level of vegetation patterns. Two regional climate models were downscaled using three different approaches (statistical downscaling, delta change, and direct use) to drive a hydrological model (WaSiM-ETH) for reference and scenario period (1960-1990 and 2070-2100), resulting in an ensemble forecast of six members. For all ensembles members we found large changes in temperature, resulting in decreasing snow and ice storage and earlier runoff, but only small changes in evapotranspiration. The occurrence of downscaled dry spells was found to fluctuate greatly, causing soil moisture depletion and drought stress potential to show high variability in both space and time. In general, the choice of the downscaling approach had a stronger influence on the results than the applied regional climate model. All of the results indicate that summer soil moisture decreases, which leads to more frequent declines below a critical soil moisture level and an advanced evapotranspiration deficit. Forests up to an elevation of 1800 m a.s.l. are likely to be threatened the most, while alpine areas and most pastures remain nearly unaffected. Nevertheless, the ensemble variability was found to be extremely high and should be interpreted as a bandwidth of possible future drought stress situations.

  20. The Schaake shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields

    USGS Publications Warehouse

    Clark, M.; Gangopadhyay, S.; Hay, L.; Rajagopalan, B.; Wilby, R.

    2004-01-01

    A number of statistical methods that are used to provide local-scale ensemble forecasts of precipitation and temperature do not contain realistic spatial covariability between neighboring stations or realistic temporal persistence for subsequent forecast lead times. To demonstrate this point, output from a global-scale numerical weather prediction model is used in a stepwise multiple linear regression approach to downscale precipitation and temperature to individual stations located in and around four study basins in the United States. Output from the forecast model is downscaled for lead times up to 14 days. Residuals in the regression equation are modeled stochastically to provide 100 ensemble forecasts. The precipitation and temperature ensembles from this approach have a poor representation of the spatial variability and temporal persistence. The spatial correlations for downscaled output are considerably lower than observed spatial correlations at short forecast lead times (e.g., less than 5 days) when there is high accuracy in the forecasts. At longer forecast lead times, the downscaled spatial correlations are close to zero. Similarly, the observed temporal persistence is only partly present at short forecast lead times. A method is presented for reordering the ensemble output in order to recover the space-time variability in precipitation and temperature fields. In this approach, the ensemble members for a given forecast day are ranked and matched with the rank of precipitation and temperature data from days randomly selected from similar dates in the historical record. The ensembles are then reordered to correspond to the original order of the selection of historical data. Using this approach, the observed intersite correlations, intervariable correlations, and the observed temporal persistence are almost entirely recovered. This reordering methodology also has applications for recovering the space-time variability in modeled streamflow. ?? 2004 American

  1. Using seasonal forecasts in a drought forecasting system for water management: case-study of the Arzal dam in Brittany

    NASA Astrophysics Data System (ADS)

    Crochemore, Louise; Ramos, Maria-Helena; Perrin, Charles; Penasso, Aldo

    2014-05-01

    The Arzal dam is located at the outlet of the Vilaine River basin (10,000 km2) in Brittany, France. It controls a reservoir (50 hm3) managed for multiple water uses: drinking water, flood control, irrigation, sailing and fish by-passing. Its location in the estuary creates a physical divide between upstream freshwater and downstream saline water. The reservoir thus plays an essential role in the regional water management system. Its operational management during the summer season poses several challenges, mainly related to the quantification of future water inflows and the risks of having restricted water availability for its different uses. Indeed, the occurrence of severe drought periods between May and October may increase the risk of salt intrusion and drinking water contamination due to lock operations. Therefore it is important to provide decision-makers with reliable low-flow forecasts and risk-based visualization tools, which will support their choice of the best strategy for allocation of water among different users and stakeholders. This study focuses on an integrated hydro-meteorological forecasting system developed to forecast low flows upstream the Arzal dam and based on a lumped hydrological model. Medium-range meteorological forecasts from the ECMWF ensemble prediction system (51 scenarios up to 9 days ahead) are combined with seasonal meteorological forecasts also from ECMWF to provide extended streamflow forecasts for the summer period. The performance of the forecasts obtained by this method is compared with the performance of two benchmarks: (i) flow forecasts obtained using an ensemble of past observed precipitation series as precipitation scenarios, i.e. without any use of forecasts from meteorological models and (ii) flow forecasts obtained using the seasonal forecasts only, i.e. without medium-term information. First, the performance of ensemble forecasts is evaluated and compared by means of probabilistic scores. Then, a risk

  2. Operational Hydrologic Forecasts in the Columbia River Basin

    NASA Astrophysics Data System (ADS)

    Shrestha, K. Y.; Curry, J. A.; Webster, P. J.; Toma, V. E.; Jelinek, M.

    2013-12-01

    The Columbia River Basin (CRB) covers an area of ~670,000 km2 and stretches across parts of seven U.S. states and one Canadian province. The basin is subject to a variable climate, and moisture stored in snowpack during the winter is typically released in spring and early summer. These releases contribute to rapid increases in flow. A number of impoundments have been constructed on the Columbia River main stem and its tributaries for the purposes of flood control, navigation, irrigation, recreation, and hydropower. Storage reservoirs allow water managers to adjust natural flow patterns to benefit water and energy demands. In the past decade, the complexity of water resource management issues in the basin has amplified the importance of streamflow forecasting. Medium-range (1-10 day) numerical weather forecasts of precipitation and temperature can be used to drive hydrological models. In this work, probabilistic meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) are used to force the Variable Infiltration Capacity (VIC) model. Soil textures were obtained from FAO data; vegetation types / land cover information from UMD land cover data; stream networks from USGS HYDRO1k; and elevations from CGIAR version 4 SRTM data. The surface energy balance in 0.25° (~25 km) cells is closed through an iterative process operating at a 6 hour timestep. Output fluxes from a number of cells in the basin are combined through one-dimensional flow routing predicated on assumptions of linearity and time invariance. These combinations lead to daily mean streamflow estimates at key locations throughout the basin. This framework is suitable for ingesting daily numerical weather prediction data, and was calibrated using USGS mean daily streamflow data at the Dalles Dam (TDA). Operational streamflow forecasts in the CRB have been active since October 2012. These are 'naturalized' or unregulated forecasts. In 2013, increases of ~2600 m3/s (~48% of

  3. Ensemble bayesian model averaging using markov chain Monte Carlo sampling

    SciTech Connect

    Vrugt, Jasper A; Diks, Cees G H; Clark, Martyn P

    2008-01-01

    Bayesian model averaging (BMA) has recently been proposed as a statistical method to calibrate forecast ensembles from numerical weather models. Successful implementation of BMA however, requires accurate estimates of the weights and variances of the individual competing models in the ensemble. In their seminal paper (Raftery etal. Mon Weather Rev 133: 1155-1174, 2(05)) has recommended the Expectation-Maximization (EM) algorithm for BMA model training, even though global convergence of this algorithm cannot be guaranteed. In this paper, we compare the performance of the EM algorithm and the recently developed Differential Evolution Adaptive Metropolis (DREAM) Markov Chain Monte Carlo (MCMC) algorithm for estimating the BMA weights and variances. Simulation experiments using 48-hour ensemble data of surface temperature and multi-model stream-flow forecasts show that both methods produce similar results, and that their performance is unaffected by the length of the training data set. However, MCMC simulation with DREAM is capable of efficiently handling a wide variety of BMA predictive distributions, and provides useful information about the uncertainty associated with the estimated BMA weights and variances.

  4. Scale effects on information content and complexity of streamflows

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Understanding temporal and spatial variations of streamflows is important for flood forecasting, water resources management, and revealing interactions between hydrologic processes (e.g., precipitation, evapotranspiration, and soil water and groundwater flows.) The information theory has been used i...

  5. A hybrid model for PM₂.₅ forecasting based on ensemble empirical mode decomposition and a general regression neural network.

    PubMed

    Zhou, Qingping; Jiang, Haiyan; Wang, Jianzhou; Zhou, Jianling

    2014-10-15

    Exposure to high concentrations of fine particulate matter (PM₂.₅) can cause serious health problems because PM₂.₅ contains microscopic solid or liquid droplets that are sufficiently small to be ingested deep into human lungs. Thus, daily prediction of PM₂.₅ levels is notably important for regulatory plans that inform the public and restrict social activities in advance when harmful episodes are foreseen. A hybrid EEMD-GRNN (ensemble empirical mode decomposition-general regression neural network) model based on data preprocessing and analysis is firstly proposed in this paper for one-day-ahead prediction of PM₂.₅ concentrations. The EEMD part is utilized to decompose original PM₂.₅ data into several intrinsic mode functions (IMFs), while the GRNN part is used for the prediction of each IMF. The hybrid EEMD-GRNN model is trained using input variables obtained from principal component regression (PCR) model to remove redundancy. These input variables accurately and succinctly reflect the relationships between PM₂.₅ and both air quality and meteorological data. The model is trained with data from January 1 to November 1, 2013 and is validated with data from November 2 to November 21, 2013 in Xi'an Province, China. The experimental results show that the developed hybrid EEMD-GRNN model outperforms a single GRNN model without EEMD, a multiple linear regression (MLR) model, a PCR model, and a traditional autoregressive integrated moving average (ARIMA) model. The hybrid model with fast and accurate results can be used to develop rapid air quality warning systems. PMID:25089688

  6. Verification of Precipitation Forecasts from Two Numerical Weather Prediction Models for the Middle- and North-Eastern Region of the USA

    NASA Astrophysics Data System (ADS)

    Siddique, R.; Brown, J.; Reed, S. M.; Mejia, A.

    2014-12-01

    Accurate precipitation and temperature forecasts are the pre-requisites to produce skillful flood forecasts. The structures of different precipitation forecasting systems are constantly evolving, for example, with improvements in the forecasting techniques, increases in spatial and temporal resolution, improvements in model physics and numerical techniques, and better understanding of uncertainty. Hence, routine verification is necessary to understand the quality of forecasts at particular times and locations, and as inputs to hydrologic modeling. Hydrologic forecasters in the National Weather Service are evaluating precipitation and temperature forecasts from a wide range of numerical prediction models to improve the streamflow forecasts. To assist in this effort, our goal here is to verify the operational precipitation forecasts from the National Centers for Environmental Prediction 21-member Short Range Ensemble Forecast (SREF) system, together with precipitation reforecasts from the 11-member Global Ensemble Forecast System (GEFS). The verification is done for the middle- and north-eastern region of the United States and for mean areal precipitation forecasts conditioned on precipitation amounts, lead times, seasonality, and accumulation periods. Multi-sensor precipitation estimates are used as observed data. The effect of different basin sizes on forecast quality is also studied by simply choosing areal extents of varying sizes. Although flood forecasting is the main context of this study, separate analyses are presented for moderate and large precipitation events with a view towards providing additional information to forecasters. The summary of verification statistics indicates similar forecasting performance for both SREF and GEFS reforecasts even though GEFS reforecasts are valid for much longer lead times and coarser grid resolution. Precipitation forecasts from both of these models show greater skill in large basins than in relatively smaller ones

  7. Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill

    SciTech Connect

    Shukla, Shraddhanand; Voisin, Nathalie; Lettenmaier, D. P.

    2012-08-15

    We investigated the contribution of medium range weather forecasts with lead times up to 14 days to seasonal hydrologic prediction skill over the Conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP)-based experiments were performed for the period 1980-2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980-2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 days of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts, for runoff (SM) forecasts generally varies from 0 to 0.8 (0 to 0.5) as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.

  8. National Streamflow Information Program: Implementation Status Report

    USGS Publications Warehouse

    Norris, J. Michael

    2009-01-01

    The U.S. Geological Survey (USGS) operates and maintains a nationwide network of about 7,500 streamgages designed to provide and interpret long-term, accurate, and unbiased streamflow information to meet the multiple needs of many diverse national, regional, state, and local users. The National Streamflow Information Program (NSIP) was initiated in 2003 in response to Congressional and stakeholder concerns about (1) the decrease in the number of operating streamgages, including a disproportionate loss of streamgages with a long period of record; (2) the inability of the USGS to continue operating high-priority streamgages in an environment of reduced funding through partnerships; and (3) the increasing demand for streamflow information due to emerging resource-management issues and new data-delivery capabilities. The NSIP's mission is to provide the streamflow information and understanding required to meet national, regional, state, and local needs. Most of the existing streamgages are funded through partnerships with more than 850 other Federal, state, tribal, and local agencies. Currently, about 90 percent of the streamgages send data to the World Wide Web in near-real time (some information is transmitted within 15 minutes, whereas some lags by about 4 hours). The streamflow information collected at USGS streamgages is used for many purposes: *In water-resource appraisals and allocations - to determine how much water is available and how it is being allocated; *To provide streamflow information required by interstate agreements, compacts, and court decrees; *For engineering design of reservoirs, bridges, roads, culverts, and treatment plants; *For the operation of reservoirs, the operation of locks and dams for navigation purposes, and power production; *To identify changes in streamflow resulting from changes in land use, water use, and climate; *For streamflow forecasting, flood planning, and flood forecasting; *To support water-quality programs by allowing

  9. Value assessment of a global hydrological forecasting system

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

    The inter-annual variability in streamflow presents risks and opportunities in the management of water resources systems. Reliable hydrological forecasts, effective communication and proper response allow several sectors to make more informed management decisions. In many developing regions of the world, there are no efficient hydrological forecasting systems. A global forecasting system which indicates increased probabilities of streamflow excesses or shortages over long lead-times can be of great value for these regions. FEWS-World system is developed for this purpose. It is based on the Delft-FEWS (flood early warning system) developed by Deltares and incorporates the global hydrological model PCR-GLOBWB. 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 as its usefulness for possible users and ultimately for affected populations. Skill is assessed in historical simulation mode as well as retroactive forecasting mode. For the assessment in historical simulation mode a meteorological forcing based on observations from the Climate Research Unit of the University of East Anglia and the ERA-40 reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF) was used. For the assessment in retroactive forecasting mode the model was forced with ensemble forecasts from the seasonal forecast archives of ECMWF. The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts from ECMWF. The results will be disseminated on the internet, and hopefully provide information that is valuable for users in data and model-poor regions of the world. The results of the preliminary assessment show that although forecasting skill decreases with increasing lead time, the value of forecasts does not necessarily decrease. The forecast requirements and response options of several water related sectors was

  10. Calibrating the FloodMap Model to Improve the Integrated HydroProg-FloodMap Real-Time Multimodel Ensemble System for Forecasting Inundation

    NASA Astrophysics Data System (ADS)

    Świerczyńska, M. G.; Yu, D.; Miziński, B.; Niedzielski, T.; Latocha, A.; Parzóch, K.

    2015-12-01

    HydroProg is a novel system (research project no. 2011/01/D/ST10/04171 of the National Science Centre of Poland) which produces early warnings against peak flows. It works in real time and uses outputs from multiple hydrologic models to compute the multimodel ensemble prediction of riverflow, i.e. the hydrograph. The system has been experimentally implemented for the upper Nysa Kłodzka river basin (SW Poland). We also integrated the system with the well-established hydrodynamic model, known as FloodMap, to forecast flood inundation (HydroProg computes hydrograph prediction and FloodMap maps the hydrograph prognosis into terrain). The HydroProg-FloodMap solution works at five sites. The real-time experimental forecasts are available at http://www.klodzko.hydroprog.uni.wroc.pl/. The FloodMap model is calibrated at each site on a basis of the available Digital Elevation Model (DEM) or Digital Surface Model (DSM) and hydrograph data. However, since the launch of the HydroProg-FloodMap solution no true data on inundation has been available to check the model outputs against observation, and hence to redo the calibration if necessary. If we consider past events, which occurred before the launch of the system, there exists the observed inundation map for the Żelazno site. It was produced by geomorphological mapping of consequences of the flood in June 2009. The aim of the study is therefore to use this specific data set for a single site, calibrate the FloodMap model using inundation data, and identify the physical-geographical characteristics of terrain under which we are allowed to extrapolate the parameters to the other four sites. We conducted a spatial analysis of land use (based on Polish national database of topographical objects) and topography (based on DEM/DSM from the Light Detection and Ranging (LiDAR)) in order to identify similarities of the studied areas and hence to improve the estimates of the Manning's roughness coefficient.

  11. Hydrological drought forecasting and skill assessment for the Limpopo river basin, Southern Africa

    NASA Astrophysics Data System (ADS)

    Trambauer, Patricia; Werner, Micha; Winsemius, Hessel; Maskey, Shreedhar; Dutra, Emanuel

    2014-05-01

    Ensemble hydrological predictions are normally obtained by forcing hydrological models with ensembles of atmospheric forecasts produced by climate models, and frequently applied for flood-forecasting purposes. The approach can be equally applied to forecasting drought at the seasonal time scale. Although this approach has to date not been applied widely, the importance of the subject is recognised and is gaining attention by the research community. The semi-arid Limpopo river basin in Southern Africa has experienced severe droughts in the past, which resulted in crop failures, high economic losses and the need for humanitarian aid. This paper addresses the seasonal prediction of hydrological drought for the Limpopo river basin by testing three forecasting systems. We seek to provide operational guidance to farmers and water mangers within the basin at the seasonal time scale by predicting the availability of water for irrigation and water supply, which are the most important water uses in the basin. In the first system (FS_S4) the PCR-GLOBWB hydrological model was forced with the output of a global atmospheric model, the ECMWF seasonal forecast system S4 (atmosphere-ocean coupled), which has a spatial resolution of about 70 km. An ensemble of hindcasts from the meteorological forecasting system was first obtained for the summer rainfall season over southern Africa for the period 1981 to 2010 to allow for testing of the system. The second forecasting system (FS_ESP) is based on the Ensemble Streamflow Prediction (ESP) procedure. In the ESP procedure the ensemble meteorological forecast is generated with re-sampled historical meteorological data. The hydrological model is then forced with this re-sampled data. FS_ESP allows measuring the skill that can be expected from initial states combined with climatology, and the comparison with the first system allows measuring the improvement when using the ECMWF seasonal forecast S4 compared to using the ESP forecast. A third

  12. Conditional Monthly Weather Resampling Procedure for Operational Seasonal Water Resources Forecasting

    NASA Astrophysics Data System (ADS)

    Beckers, J.; Weerts, A.; Tijdeman, E.; Welles, E.; McManamon, A.

    2013-12-01

    To provide reliable and accurate seasonal streamflow forecasts for water resources management several operational hydrologic agencies and hydropower companies around the world use the Extended Streamflow Prediction (ESP) procedure. The ESP in its original implementation does not accommodate for any additional information that the forecaster may have about expected deviations from climatology in the near future. Several attempts have been conducted to improve the skill of the ESP forecast, especially for areas which are affected by teleconnetions (e,g. ENSO, PDO) via selection (Hamlet and Lettenmaier, 1999) or weighting schemes (Werner et al., 2004; Wood and Lettenmaier, 2006; Najafi et al., 2012). A disadvantage of such schemes is that they lead to a reduction of the signal to noise ratio of the probabilistic forecast. To overcome this, we propose a resampling method conditional on climate indices to generate meteorological time series to be used in the ESP. The method can be used to generate a large number of meteorological ensemble members in order to improve the stati