scholarly journals Adaptive correction of deterministic models to produce probabilistic forecasts

2012 ◽  
Vol 16 (8) ◽  
pp. 2783-2799 ◽  
Author(s):  
P. J. Smith ◽  
K. J. Beven ◽  
A. H. Weerts ◽  
D. Leedal
Keyword(s):  
Efficient Method ◽  
Flood Forecasting ◽  
Forecasting Model ◽  
Computationally Efficient ◽  
Deterministic Models ◽  
Probabilistic Forecasts ◽  
Adaptive Correction ◽  
System Output ◽  
Forecasting System

Abstract. This paper considers the correction of deterministic forecasts given by a flood forecasting model. A stochastic correction based on the evolution of an adaptive, multiplicative, gain is presented. A number of models for the evolution of the gain are considered and the quality of the resulting probabilistic forecasts assessed. The techniques presented offer a computationally efficient method for providing probabilistic forecasts based on existing flood forecasting system output.

scholarly journals Adaptive correction of deterministic models to produce accurate probabilistic forecasts

2012 ◽  
Vol 9 (1) ◽  
pp. 595-627 ◽  
Author(s):  
P. J. Smith ◽  
K. Beven ◽  
A. Weerts ◽  
D. Leedal
Keyword(s):  
Efficient Method ◽  
Flood Forecasting ◽  
Forecasting Model ◽  
Computationally Efficient ◽  
Deterministic Models ◽  
Probabilistic Forecasts ◽  
Adaptive Correction ◽  
System Output ◽  
Forecasting System

Abstract. This paper considers the correction of deterministic forecasts given by a flood forecasting model. A stochastic correction based on the evolution of an adaptive, multiplicative, gain is presented. A number of models for the evolution of the gain are considered and the quality of the resulting probabilistic forecasts assessed. The techniques presented offer, in certain situations, an effective and computationally efficient method for providing probabilistic forecasts based on existing flood forecasting system output.

scholarly journals Probabilistic photovoltaic power forecasting model based on deterministic forecasts

2020 ◽  
Vol 152 ◽  
pp. 01003
Author(s):  
L. Alfredo Fernandez-Jimenez ◽  
Sonia Terreros-Olarte ◽  
Pedro J. Zorzano-Santamaria ◽  
Montserrat Mendoza-Villena ◽  
Eduardo Garcia-Garrido
Keyword(s):  
Genetic Algorithm ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Deterministic Models ◽  
Conditional Quantile ◽  
Normal Distributions ◽  
Probabilistic Forecasts ◽  
Mixture Of Normal Distributions ◽  
Quantile Regression Method ◽  
Power Forecasting

This paper presents an original probabilistic photovoltaic (PV) power forecasting model for the day-ahead hourly generation in a PV plant. The probabilistic forecasting model is based on 12 deterministic models developed with different techniques. An optimization process, ruled by a genetic algorithm, chooses the forecasts of the deterministic models in order to achieve the probability distribution function (PDF) for the PV generation in each one of the daylight hours of the following day in a parametric approach. The PDFs, which constitute the probabilistic forecasts, are a mixture of normal distributions, each one centred in the forecasts of the selected deterministic models. The genetic algorithm chooses the deterministic forecasts, the variance of the normal distributions and their weights in the mixture. In a case study the proposed model achieves better forecasting results than the obtained with the conditional quantile regression method applied to the same data used to develop the deterministic forecasting models.

scholarly journals Towards an efficient storm surge and inundation forecasting system over the Bengal delta: chasing the Supercyclone Amphan

2021 ◽  
Vol 21 (8) ◽  
pp. 2523-2541
Author(s):  
Md. Jamal Uddin Khan ◽  
Fabien Durand ◽  
Xavier Bertin ◽  
Laurent Testut ◽  
Yann Krien ◽  
...  
Keyword(s):  
Real Time ◽  
Storm Surge ◽  
Coastal Region ◽  
Computationally Efficient ◽  
Bengal Delta ◽  
Modelling Framework ◽  
Forecasting System ◽  
Effective Decision Making ◽  
Storm Surge Forecasting

Abstract. The Bay of Bengal is a well-known breeding ground to some of the deadliest cyclones in history. Despite recent advancements, the complex morphology and hydrodynamics of this large delta and the associated modelling complexity impede accurate storm surge forecasting in this highly vulnerable region. Here we present a proof of concept of a physically consistent and computationally efficient storm surge forecasting system tractable in real time with limited resources. With a state-of-the-art wave-coupled hydrodynamic numerical modelling system, we forecast the recent Supercyclone Amphan in real time. From the available observations, we assessed the quality of our modelling framework. We affirmed the evidence of the key ingredients needed for an efficient, real-time surge and inundation forecast along this active and complex coastal region. This article shows the proof of the maturity of our framework for operational implementation, which can particularly improve the quality of localized forecast for effective decision-making over the Bengal delta shorelines as well as over other similar cyclone-prone regions.

scholarly journals Towards an efficient storm surge and inundation forecasting system over the Bengal delta: Chasing the super-cyclone Amphan

2020 ◽  
Author(s):  
Md Jamal Uddin Khan ◽  
Fabien Durand ◽  
Xavier Bertin ◽  
Laurent Testut ◽  
Yann Krien ◽  
...  
Keyword(s):  
Real Time ◽  
Storm Surge ◽  
Coastal Region ◽  
Computationally Efficient ◽  
Bengal Delta ◽  
Modelling Framework ◽  
Forecasting System ◽  
Super Cyclone ◽  
Storm Surge Forecasting

Abstract. The Bay of Bengal is a well-known breeding ground to some of the deadliest cyclones in history. Despite recent advancements, the complex morphology and hydrodynamics of this large delta and the associated modelling computational costs impede the storm surge forecasting in this highly vulnerable region. Here we present a proof of concept of a physically consistent and computationally efficient storm surge forecasting system tractable in real-time with limited resources. With a state-of-the-art wave-coupled hydrodynamic numerical modelling system, we forecast the recent super cyclone Amphan in real-time. From the available observations, we assessed the quality of our modelling framework. We affirmed the evidence of the key ingredients needed for an efficient, real-time surge and inundation forecast along this active and complex coastal region. This article shows the proof of the maturity of our framework for operational implementation, which can particularly improve the quality of localized forecast for effective decision-making.

scholarly journals Application of Stochastic Flood Forecasting Model Using Regression Method for Kelantan Catchment

2018 ◽  
Vol 203 ◽  
pp. 07001
Author(s):  
Sazali Osman ◽  
Norizan Abdul Aziz ◽  
Nurul Husaif ◽  
Lariyah Mohd Sidek ◽  
Aminah Shakirah ◽  
...  
Keyword(s):  
River Basin ◽  
Heavy Rain ◽  
Flood Forecasting ◽  
Peninsular Malaysia ◽  
Regression Method ◽  
Least Square ◽  
Mitigation Measures ◽  
Forecasting Model ◽  
Kelantan River Basin ◽  
Forecasting System

Flood is without doubt the most devastating natural disasters, striking numerous regions in Malaysia each year. During the last decades, the trend in flood damages has been growing exponentially. This is a consequence of the increasing frequency of heavy rain, changes in upstream land-use and a continuously increasing concentration of population and assets in flood prone areas. Malaysia, periodically, have faced with huge floods since previous years. Kelantan River basin, which located in the Northeast of Peninsular Malaysia, is prone to flood events in Malaysia. Kelantan River is the principal cause of flooding because it is constricted at its lower reaches. The capacity of the river at the downstream coastal area is less than 10,000 m3/s, therefore flood that exceeds this capacity will overspill the banks and discharge overland to the sea. Realizing the seriousness of the problems, it is vital in providing in time useful information for making crucial decisions especially to provide warning for any potential flood occurrence. In this study, stochastic flood forecasting model using stage regression method was applied to Kelantan River basin, in which the regression coefficients and equations was derived from the least square principle. The stochastic model were calibrated and validated which then shows that the equations derived are suitable to predict the hydrograph in Kelantan River basin. In conclusion, establishing a flood forecasting system would enhance the effectiveness of all other mitigation measures by providing time for appropriate actions. This has increased the importance of flood modelling for flood forecasts to issue advance warning in severe storm situations to reduce loss of lives and property damage.

scholarly journals Probabilistic Prediction of Tropical Cyclones. Part II: Intensity

2005 ◽  
Vol 133 (7) ◽  
pp. 1853-1864 ◽  
Author(s):  
Harry C. Weber
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Numerical Models ◽  
Probabilistic Prediction ◽  
Wind Speed Prediction ◽  
Probabilistic Forecasts ◽  
Prediction Time ◽  
Speed Prediction ◽  
Forecasting System

Abstract A new objective aid for operational probabilistic intensity (defined as maximum wind speed) prediction of tropical cyclones is presented. Based on statistical analyses of the performance of all operationally available numerical models (using datasets of the U.S. Navy’s Automated Tropical Cyclone Forecasting System) during training periods defined by the years 2000 and 2001, probabilistic and, as a by-product, deterministic intensity predictions were carried out for all global tropical-cyclone events during subsequent forecast periods defined by the years 2001 and 2002, respectively. The annual mean deterministic intensity errors of the years 2001 (2002) at 24-, 48-, 72-, 96-, and 120-h prediction time were found to be 6.2 (6.5), 9.6 (10.6), 11.7 (12.4), 15.4 (15.3), and 17.2 (17.1) m s−1, respectively. On average, the deterministic forecasts were of approximately the same quality as those of all current consensus approaches and of superior quality than those of the majority of all operational dynamical models. The quality of the probabilistic forecasts, provided in the form of intensity probability intervals at given prediction times, was assessed by the annual mean sizes of given probability intervals. For example, in the years 2001 (2002) the annual mean sizes of the 66% confidence intervals at 24-, 48-, 72-, 96-, and 120-h prediction times were found to be 12.6 (13.3), 19.7 (21.5), 24.4 (24.8), 39.6 (27.8), and 40.4 (29.0) m s−1, respectively. Postanalyses showed that the sizes of all intensity probability intervals represented conservative and reliable estimates of future storm intensities in that the observed percentages of storm intensities inside given intervals were larger than the corresponding expected percentages.

scholarly journals An Effective Potential for Frenkel Excitons

2020 ◽  
Author(s):  
Bartosz Błasiak ◽  
Wojciech Bartkowiak ◽  
Robert Władysław Góra
Keyword(s):  
Energy Transfer ◽  
Excitation Energy ◽  
Efficient Method ◽  
Effective Potential ◽  
Excitation Energy Transfer ◽  
Computationally Efficient ◽  
Frenkel Excitons

Excitation energy transfer (EET) is a ubiquitous process in life and materials sciences. Here, a new and computationally efficient method of evaluating the electronic EET couplings between interacting chromophores is...

scholarly journals A pan-African medium-range ensemble flood forecast system

2015 ◽  
Vol 19 (8) ◽  
pp. 3365-3385 ◽  
Author(s):  
V. Thiemig ◽  
B. Bisselink ◽  
F. Pappenberger ◽  
J. Thielen
Keyword(s):  
Large Scale ◽  
Practical Implication ◽  
River Basins ◽  
Flood Forecasting ◽  
Small Scale ◽  
Flood Forecast ◽  
Flood Events ◽  
Forecast System ◽  
Pan African ◽  
Forecasting System

Abstract. 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.

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