The Experts below are selected from a list of 2127 Experts worldwide ranked by ideXlab platform

Massimiliano Zappa - One of the best experts on this subject based on the ideXlab platform.

  • Technical note: Combining quantile forecasts and predictive distributions of streamflows
    Hydrology and Earth System Sciences, 2017
    Co-Authors: Konrad Bogner, Katharina Liechti, Massimiliano Zappa
    Abstract:

    The enhanced availability of many different hydro-meteorological Modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future stream-flow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple stream-flow forecast information will be aggregated based on several different predictive distributions, resp. quantile forecasts. For this combination the Bayesian Model Averaging (BMA) approach, the Nonhomogeneous Gaussian Regression (NGR), also known as Ensemble Model Output Statistic (EMOS) Model and a novel method called Beta transformed Linear Pooling (BLP) will be applied. By the help of the Quantile Score (QS) and the Continuous Ranked Probability Score (CRPS), the combination results for the Sihl river in Switzerland with about five years of forecast data will be compared and the differences between the raw and the optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resources management.

  • Technical Note: Combining Quantile Forecasts and Predictive Distributions of Stream-flows
    2017
    Co-Authors: Konrad Bogner, Katharina Liechti, Massimiliano Zappa
    Abstract:

    Abstract. The enhanced availability of many different hydro-meteorological Modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future stream-flow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple stream-flow forecast information will be aggregated based on several different predictive distributions, resp. quantile forecasts. For this combination the Bayesian Model Averaging (BMA) approach, the Nonhomogeneous Gaussian Regression (NGR), also known as Ensemble Model Output Statistic (EMOS) Model and a novel method called Beta transformed Linear Pooling (BLP) will be applied. By the help of the Quantile Score (QS) and the Continuous Ranked Probability Score (CRPS), the combination results for the Sihl river in Switzerland with about five years of forecast data will be compared and the differences between the raw and the optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resources management.

  • Technical note: Combining quantile forecasts and predictive distributions of streamflows
    Copernicus Publications, 2017
    Co-Authors: Konrad Bogner, Katharina Liechti, Massimiliano Zappa
    Abstract:

    The enhanced availability of many different hydro-meteorological Modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future streamflow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple streamflow forecast information will be aggregated based on several different predictive distributions, and quantile forecasts. For this combination the Bayesian Model averaging (BMA) approach, the non-homogeneous Gaussian regression (NGR), also known as the ensemble Model Output Statistic (EMOS) techniques, and a novel method called Beta-transformed linear pooling (BLP) will be applied. By the help of the quantile score (QS) and the continuous ranked probability score (CRPS), the combination results for the Sihl River in Switzerland with about 5 years of forecast data will be compared and the differences between the raw and optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resource management

Konrad Bogner - One of the best experts on this subject based on the ideXlab platform.

  • Technical note: Combining quantile forecasts and predictive distributions of streamflows
    Hydrology and Earth System Sciences, 2017
    Co-Authors: Konrad Bogner, Katharina Liechti, Massimiliano Zappa
    Abstract:

    The enhanced availability of many different hydro-meteorological Modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future stream-flow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple stream-flow forecast information will be aggregated based on several different predictive distributions, resp. quantile forecasts. For this combination the Bayesian Model Averaging (BMA) approach, the Nonhomogeneous Gaussian Regression (NGR), also known as Ensemble Model Output Statistic (EMOS) Model and a novel method called Beta transformed Linear Pooling (BLP) will be applied. By the help of the Quantile Score (QS) and the Continuous Ranked Probability Score (CRPS), the combination results for the Sihl river in Switzerland with about five years of forecast data will be compared and the differences between the raw and the optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resources management.

  • Technical Note: Combining Quantile Forecasts and Predictive Distributions of Stream-flows
    2017
    Co-Authors: Konrad Bogner, Katharina Liechti, Massimiliano Zappa
    Abstract:

    Abstract. The enhanced availability of many different hydro-meteorological Modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future stream-flow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple stream-flow forecast information will be aggregated based on several different predictive distributions, resp. quantile forecasts. For this combination the Bayesian Model Averaging (BMA) approach, the Nonhomogeneous Gaussian Regression (NGR), also known as Ensemble Model Output Statistic (EMOS) Model and a novel method called Beta transformed Linear Pooling (BLP) will be applied. By the help of the Quantile Score (QS) and the Continuous Ranked Probability Score (CRPS), the combination results for the Sihl river in Switzerland with about five years of forecast data will be compared and the differences between the raw and the optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resources management.

  • Technical note: Combining quantile forecasts and predictive distributions of streamflows
    Copernicus Publications, 2017
    Co-Authors: Konrad Bogner, Katharina Liechti, Massimiliano Zappa
    Abstract:

    The enhanced availability of many different hydro-meteorological Modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future streamflow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple streamflow forecast information will be aggregated based on several different predictive distributions, and quantile forecasts. For this combination the Bayesian Model averaging (BMA) approach, the non-homogeneous Gaussian regression (NGR), also known as the ensemble Model Output Statistic (EMOS) techniques, and a novel method called Beta-transformed linear pooling (BLP) will be applied. By the help of the quantile score (QS) and the continuous ranked probability score (CRPS), the combination results for the Sihl River in Switzerland with about 5 years of forecast data will be compared and the differences between the raw and optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resource management

Katharina Liechti - One of the best experts on this subject based on the ideXlab platform.

  • Technical note: Combining quantile forecasts and predictive distributions of streamflows
    Hydrology and Earth System Sciences, 2017
    Co-Authors: Konrad Bogner, Katharina Liechti, Massimiliano Zappa
    Abstract:

    The enhanced availability of many different hydro-meteorological Modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future stream-flow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple stream-flow forecast information will be aggregated based on several different predictive distributions, resp. quantile forecasts. For this combination the Bayesian Model Averaging (BMA) approach, the Nonhomogeneous Gaussian Regression (NGR), also known as Ensemble Model Output Statistic (EMOS) Model and a novel method called Beta transformed Linear Pooling (BLP) will be applied. By the help of the Quantile Score (QS) and the Continuous Ranked Probability Score (CRPS), the combination results for the Sihl river in Switzerland with about five years of forecast data will be compared and the differences between the raw and the optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resources management.

  • Technical Note: Combining Quantile Forecasts and Predictive Distributions of Stream-flows
    2017
    Co-Authors: Konrad Bogner, Katharina Liechti, Massimiliano Zappa
    Abstract:

    Abstract. The enhanced availability of many different hydro-meteorological Modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future stream-flow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple stream-flow forecast information will be aggregated based on several different predictive distributions, resp. quantile forecasts. For this combination the Bayesian Model Averaging (BMA) approach, the Nonhomogeneous Gaussian Regression (NGR), also known as Ensemble Model Output Statistic (EMOS) Model and a novel method called Beta transformed Linear Pooling (BLP) will be applied. By the help of the Quantile Score (QS) and the Continuous Ranked Probability Score (CRPS), the combination results for the Sihl river in Switzerland with about five years of forecast data will be compared and the differences between the raw and the optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resources management.

  • Technical note: Combining quantile forecasts and predictive distributions of streamflows
    Copernicus Publications, 2017
    Co-Authors: Konrad Bogner, Katharina Liechti, Massimiliano Zappa
    Abstract:

    The enhanced availability of many different hydro-meteorological Modelling and forecasting systems raises the issue of how to optimally combine this great deal of information. Especially the usage of deterministic and probabilistic forecasts with sometimes widely divergent predicted future streamflow values makes it even more complicated for decision makers to sift out the relevant information. In this study multiple streamflow forecast information will be aggregated based on several different predictive distributions, and quantile forecasts. For this combination the Bayesian Model averaging (BMA) approach, the non-homogeneous Gaussian regression (NGR), also known as the ensemble Model Output Statistic (EMOS) techniques, and a novel method called Beta-transformed linear pooling (BLP) will be applied. By the help of the quantile score (QS) and the continuous ranked probability score (CRPS), the combination results for the Sihl River in Switzerland with about 5 years of forecast data will be compared and the differences between the raw and optimally combined forecasts will be highlighted. The results demonstrate the importance of applying proper forecast combination methods for decision makers in the field of flood and water resource management

Akira Mano - One of the best experts on this subject based on the ideXlab platform.

  • Uncertainty assessment for short-term flood forecasts in Central Vietnam
    River Basin Management VI, 2011
    Co-Authors: Hoai Nam, Keiko Udo, Akira Mano
    Abstract:

    Accurate flood forecasts with greater lead-times are very important in development of flood mitigation measures, especially in short response catchments. The flood forecasts based on numerical weather prediction (NWP) and runoff Models have demonstrated its breakthrough to extend the forecast lead-time over traditional flood forecast methods, for instance, those are based on rainfall information from rain-gages. However, given the imperfectness either in the specification of initial states or in the formulation of NWP Models, rainfall prediction for example, the driving factor for flood forecast, has been recognised as a major source of uncertainty in the generation of river flow. This paper presents the uncertainty assessment for a short-term flood forecast Model that is coupled by the short-range global NWP Model, 0.5 degree spatial resolution, with the distributed rainfall runoff Model, for a large sized basin (Thu Bon River, 3,150km) located in Central Vietnam. To reduce uncertainty of runoff forecasts by means of increasing the rainfall prediction skill, first the Model Output Statistic technique has been employed to downscale the large scale prediction forecasts directly derived from the NWP Model Output to the basin scale by using the artificial neural network with the back-propagation method. Skill scores of the downscaled precipitation are investigated with increasing lead-time and compared to those obtained using the large scale precipitation forecasts. Uncertainties of runoff prediction are assessed by quantifying the relative error of forecasts and estimates of confidence interval for the mean error. Results show that larger uncertainties along with the forecast lead-times are observed; however, the Model is able to predict reliable river flows with lead-time of the order of 6-18 hours. This demonstrates great benefits in flood forecasting practices for many developing countries where ground weather observation is scarce and access to high resolution NWP Models is limited.

  • DEVELOPMENT OF SHORT-TERM FLOOD FORECAST Model - A CASE STUDY FOR CENTRAL VIETNAM
    2010
    Co-Authors: Hoai Nam, Keiko Udo, Akira Mano
    Abstract:

    This paper presents the development of a short-term flood forecast Model by coupling the relatively high resolution (0.5 O ) global numerical weather prediction Model (NWP) with the distributed rainfall runoff Model. The case study was conducted for a medium sized basin (the Ve River) located in Central Vietnam. Model Output Statistic (MOS) was applied to improve quantitative precipitation forecast (QPF) derived from the NWP Model. Separate regression equations for single storm events and continuous storm events were formulated based on training data of the wet season, 2008. Results of 24-hour lead time flood forecast using MOS-derived QPF were comparable to those obtained using raingages. Model validation demonstrated that the short-term flood forecast Model is encouraging for the further extension of flood forecast lead time at global-scale applicability.

Alexandre H. Thiery - One of the best experts on this subject based on the ideXlab platform.

  • Probabilistic forecasting of day-ahead solar irradiance using quantile gradient boosting
    Solar Energy, 2018
    Co-Authors: Hadrien Verbois, Andrivo Rusydi, Alexandre H. Thiery
    Abstract:

    Abstract Due to the chaotic nature of the underlying physical processes, even state-of-the-art Models cannot perfectly forecast the solar irradiance at the surface of the earth. There is, therefore, a growing interest in the research community for forecasting methods that can quantify their own uncertainty. This paper proposes a novel probabilistic framework for forecasting day-ahead hourly solar irradiance. A principal component analysis (PCA) is used to tightly combine a high-resolution mesoscale numerical weather prediction (NWP) Model with a quantile gradient boosting algorithm. A thorough evaluation of the deterministic and probabilistic properties of the Model is conducted for a full year in the tropical island of Singapore. The impact of the sky conditions on its performance is also considered. Furthermore, a rigorous Statistical framework is employed to systematically benchmark our Model against two state of the art methods, a Lasso Model Output Statistic procedure and an analog ensemble (AnEn). Our Model significantly improves the numerical weather prediction Model: it achieves a 41% reduction of the MAE and 39% reduction of the RMSE. It is also slightly more accurate than Lasso and has a CRPS 4% lower than that of AnEn.