Optimization of Integrated Operation of Dams Using Ensemble Prediction

2016 ◽  
pp. 133-154
Author(s):  
Satoru Oishi
Keyword(s):  
Ensemble Prediction ◽  
Integrated Operation

Study of perturbing method in regional BGM ensemble prediction system

2012 ◽  
Vol 4 (1) ◽  
pp. 65
Author(s):  
Xiao Yu-Hua ◽  
He Guang-Bi ◽  
Chen Jing ◽  
Deng Guo
Keyword(s):  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System

scholarly journals Probabilistic Forecasting of the 500 hPa Geopotential Height over the Northern Hemisphere Using TIGGE Multi-model Ensemble Forecasts

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 253
Author(s):  
Luying Ji ◽  
Qixiang Luo ◽  
Yan Ji ◽  
Xiefei Zhi
Keyword(s):  
Northern Hemisphere ◽  
Geopotential Height ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Height Distribution ◽  
Height Field ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Geopotential Height Field

Bayesian model averaging (BMA) and ensemble model output statistics (EMOS) were used to improve the prediction skill of the 500 hPa geopotential height field over the northern hemisphere with lead times of 1–7 days based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and UK Met Office (UKMO) ensemble prediction systems. The performance of BMA and EMOS were compared with each other and with the raw ensembles and climatological forecasts from the perspective of both deterministic and probabilistic forecasting. The results show that the deterministic forecasts of the 500 hPa geopotential height distribution obtained from BMA and EMOS are more similar to the observed distribution than the raw ensembles, especially for the prediction of the western Pacific subtropical high. BMA and EMOS provide a better calibrated and sharper probability density function than the raw ensembles. They are also superior to the raw ensembles and climatological forecasts according to the Brier score and the Brier skill score. Comparisons between BMA and EMOS show that EMOS performs slightly better for lead times of 1–4 days, whereas BMA performs better for longer lead times. In general, BMA and EMOS both improve the prediction skill of the 500 hPa geopotential height field.

scholarly journals Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 405
Author(s):  
Anam Nawaz Khan ◽  
Naeem Iqbal ◽  
Rashid Ahmad ◽  
Do-Hyeun Kim
Keyword(s):  
Energy Consumption ◽  
Statistical Model ◽  
Residential Buildings ◽  
Residential Building ◽  
Experimental Results ◽  
Consumption Patterns ◽  
Ensemble Prediction ◽  
Short Term ◽  
Term Energy ◽  
Prediction Approach

With the development of modern power systems (smart grid), energy consumption prediction becomes an essential aspect of resource planning and operations. In the last few decades, industrial and commercial buildings have thoroughly been investigated for consumption patterns. However, due to the unavailability of data, the residential buildings could not get much attention. During the last few years, many solutions have been devised for predicting electric consumption; however, it remains a challenging task due to the dynamic nature of residential consumption patterns. Therefore, a more robust solution is required to improve the model performance and achieve a better prediction accuracy. This paper presents an ensemble approach based on learning to a statistical model to predict the short-term energy consumption of a multifamily residential building. Our proposed approach utilizes Long Short-Term Memory (LSTM) and Kalman Filter (KF) to build an ensemble prediction model to predict short term energy demands of multifamily residential buildings. The proposed approach uses real energy data acquired from the multifamily residential building, South Korea. Different statistical measures are used, such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score, to evaluate the performance of the proposed approach and compare it with existing models. The experimental results reveal that the proposed approach predicts accurately and outperforms the existing models. Furthermore, a comparative analysis is performed to evaluate and compare the proposed model with conventional machine learning models. The experimental results show the effectiveness and significance of the proposed approach compared to existing energy prediction models. The proposed approach will support energy management to effectively plan and manage the energy supply and demands of multifamily residential buildings.

scholarly journals Predictability of Deep Convection in Idealized and Operational Forecasts: Effects of Radar Data Assimilation, Orography, and Synoptic Weather Regime

2019 ◽  
Vol 148 (1) ◽  
pp. 63-81 ◽  
Author(s):  
Kevin Bachmann ◽  
Christian Keil ◽  
George C. Craig ◽  
Martin Weissmann ◽  
Christian A. Welzbacher
Keyword(s):  
Data Assimilation ◽  
Deep Convection ◽  
Radar Data ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Model Errors ◽  
Ensemble Prediction System ◽  
Beneficial Impact ◽  
The Impact ◽  
Radar Data Assimilation

Abstract We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.

scholarly journals Time Evolution of Initial Errors in Lorenz’s 05 Chaotic Model

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Hynek Bednář ◽  
Aleš Raidl ◽  
Jiří Mikšovský
Keyword(s):  
Weather Prediction ◽  
Prediction Method ◽  
Atmospheric Model ◽  
Ensemble Prediction ◽  
Asymptotic Value ◽  
Time Limits ◽  
Initial Errors ◽  
Asymptotic Values ◽  
Chaotic Model ◽  
Almost All

Initial errors in weather prediction grow in time and, as they become larger, their growth slows down and then stops at an asymptotic value. Time of reaching this saturation point represents the limit of predictability. This paper studies the asymptotic values and time limits in a chaotic atmospheric model for five initial errors, using ensemble prediction method (model’s data) as well as error approximation by quadratic and logarithmic hypothesis and their modifications. We show that modified hypotheses approximate the model’s time limits better, but not without serious disadvantages. We demonstrate how hypotheses can be further improved to achieve better match of time limits with the model. We also show that quadratic hypothesis approximates the model’s asymptotic value best and that, after improvement, it also approximates the model’s time limits better for almost all initial errors and time lengths.

Concept of a local integrated operation network for different information flows

1993 ◽  
Author(s):  
Anke Schmietainski ◽  
Bernd X. Weis ◽  
Heinz Schlesinger
Keyword(s):  
Information Flows ◽  
Integrated Operation

scholarly journals Evaluation of the Added Value of Regional Ensemble Forecasts on Global Ensemble Forecasts

2012 ◽  
Vol 27 (4) ◽  
pp. 972-987 ◽  
Author(s):  
Yong Wang ◽  
Simona Tascu ◽  
Florian Weidle ◽  
Karin Schmeisser
Keyword(s):  
Horizontal Resolution ◽  
Added Value ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Initial Condition ◽  
Weather Variables ◽  
Single Model ◽  
Ensemble Forecasts ◽  
Model Based ◽  
Surface Weather

Abstract The regional single-model-based Aire Limitée Adaptation Dynamique Développement International–Limited Area Ensemble Forecasting (ALADIN-LAEF) ensemble prediction system (EPS) is evaluated and compared with the global ECMWF-EPS to investigate the added value of regional to global EPS models. ALADIN-LAEF consists of 16 perturbed members at 18-km horizontal resolution, while ECMWF-EPS includes 50 perturbed members at 50-km horizontal resolution. In ALADIN-LAEF, the atmospheric initial condition uncertainty is quantified by using blending, which combines large-scale uncertainty generated by the ECMWF-EPS singular-vector approach with small-scale perturbations resolved by the ALADIN breeding technique. The surface initial condition perturbations are generated by use of the noncycling surface breeding (NCSB) technique, and different physics schemes are employed for different forecast members to account for model uncertainties. The verification and comparison have been carried out for a 2-month period during summer 2007 over central Europe. The results show a quite favorable level of performance for ALADIN-LAEF compared to ECMWF-EPS for surface weather variables. ALADIN-LAEF adds more value to precipitation forecasts and has greater skill for 10-m wind and mean sea level pressure results than does ECMWF-EPS. For 2-m temperature, ALADIN-LAEF forecasts have larger spread, are statistically more consistent, but also have less skill than ECMWF-EPS due to the strong cold bias in the ALADIN forecasts. For the upper-air weather parameters, the forecast of ALADIN-LAEF has a larger spread, but the forecast skill of ALADIN-LAEF is from neutral to slightly inferior compared to ECMWF-EPS. It may be concluded that a regional single-model-based EPS with fewer ensemble members could provide more added value in terms of greater skill for near-surface weather variables than the global EPS with larger ensemble size, whereas it may have limitations when applied to upper-air weather variables.

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