Ensemble forecast post-processing over Belgium: comparison of deterministic-like and ensemble regression methods

2011 ◽  
Vol 18 (1) ◽  
pp. 94-104 ◽  
Author(s):  
Stéphane Vannitsem ◽  
Renate Hagedorn
Keyword(s):  
Ensemble Forecast ◽  
Post Processing ◽  
Regression Methods ◽  
Ensemble Regression

Seasonal Ensemble Forecast Post-processing

2019 ◽  
pp. 819-845
Author(s):  
Andy Wood ◽  
A. Sankarasubramanian ◽  
Pablo Mendoza
Keyword(s):  
Ensemble Forecast ◽  
Post Processing

Short-Range Ensemble Forecast Post-processing

2019 ◽  
pp. 795-818
Author(s):  
Marie-Amélie Boucher ◽  
Emmanuel Roulin ◽  
Vincent Fortin
Keyword(s):  
Short Range ◽  
Ensemble Forecast ◽  
Post Processing

Seasonal Ensemble Forecast Post-processing

2018 ◽  
pp. 1-27
Author(s):  
Andy Wood ◽  
A. Sankarasubramanian ◽  
Pablo Mendoza
Keyword(s):  
Ensemble Forecast ◽  
Post Processing

Regime-dependent statistical post-processing of ensemble forecasts

2020 ◽  
Author(s):  
Sam Allen ◽  
Christopher Ferro ◽  
Frank Kwasniok
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Systematic Errors ◽  
Ensemble Forecast ◽  
State Variables ◽  
Post Processing ◽  
Ensemble Forecasts ◽  
Nwp Model ◽  
Processing Techniques ◽  
Geographical Locations

<p>A number of realizations of one or more numerical weather prediction (NWP) models, initialised at a variety of initial conditions, compose an ensemble forecast. These forecasts exhibit systematic errors and biases that can be corrected by statistical post-processing. Post-processing yields calibrated forecasts by analysing the statistical relationship between historical forecasts and their corresponding observations. This article aims to extend post processing methodology to incorporate atmospheric circulation. The circulation, or flow, is largely responsible for the weather that we experience and it is hypothesized here that relationships between the NWP model and the atmosphere depend upon the prevailing flow. Numerous studies have focussed on the tendency of this flow to reduce to a set of recognisable arrangements, known as regimes, which recur and persist at fixed geographical locations. This dynamical phenomenon allows the circulation to be categorized into a small number of regime states. In a highly idealized model of the atmosphere, the Lorenz ‘96 system, ensemble forecasts are subjected to well-known post-processing techniques conditional on the system's underlying regime. Two different variables, one of the state variables and one related to the energy of the system, are forecasted and considerable improvements in forecast skill upon standard post-processing are seen when the distribution of the predictand varies depending on the regime. Advantages of this approach and its inherent challenges are discussed, along with potential extensions for operational forecasters.</p>

Seasonal Ensemble Forecast Post-processing

2018 ◽  
pp. 1-27
Author(s):  
Andrew W. Wood ◽  
A. Sankarasubramanian ◽  
Pablo Mendoza
Keyword(s):  
Ensemble Forecast ◽  
Post Processing

scholarly journals Post-processing through linear regression

2011 ◽  
Vol 18 (2) ◽  
pp. 147-160 ◽  
Author(s):  
B. Van Schaeybroeck ◽  
S. Vannitsem
Keyword(s):  
Linear Regression ◽  
Forecast Error ◽  
Geometric Mean ◽  
Least Square Method ◽  
Least Square ◽  
Lead Times ◽  
Model Errors ◽  
Post Processing ◽  
Ensemble Forecasts ◽  
Regression Methods

Abstract. Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

Short-Range Ensemble Forecast Post-processing

2018 ◽  
pp. 1-25
Author(s):  
M. -A. Boucher ◽  
Emmanuel Roulin ◽  
Vincent Fortin
Keyword(s):  
Short Range ◽  
Ensemble Forecast ◽  
Post Processing

scholarly journals Spatial Predictability of Heavy Rainfall Events in East China and the Application of Spatial-Based Methods of Probabilistic Forecasting

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 490 ◽  
Author(s):  
Xiaoran Zhuang ◽  
Haonan Zhu ◽  
Jinzhong Min ◽  
Liu Zhang ◽  
Naigen Wu ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Probabilistic Approach ◽  
Ensemble Forecast ◽  
Post Processing ◽  
Probabilistic Forecasting ◽  
Rainfall Events ◽  
Verification Methods ◽  
Huai River ◽  
Convective Scale ◽  
Heavy Rainfall Events

One of the major issues in developing convective-scale ensemble forecasts is what is widely known as under-dispersion. This can be addressed through the consideration of spatial uncertainties via post-processing, motivating the development of various techniques to represent the spatial uncertainties of ensembles. In this study, a recently developed fraction-based approach (the ensemble agreement scale, EAS) is applied to characterize the spatial predictability and spread–skill performances of precipitation forecasts using a WRF-EnKF convective-scale ensemble forecast system over the Yangtze and Huai river valleys, China. Fourteen heavy rainfall events during the Meiyu season of 2013 and 2014 were classified into two categories—strong forcing (SF) and weak forcing (WF)—using the convective adjustment timescale. The results show that the spatial predictability and spread–skill relationship are highly regime-dependent and that both exhibit lower values under WF. Furthermore, a new object-based probabilistic approach (OBJ_NEP) is proposed as a supplement to traditional neighborhood ensemble probability (NEP) and a recently proposed fraction-based approach (EAS_NEP). The results of the application of OBJ_NEP are evaluated, and a comparison is made between NEP and EAS_NEP for the analysis of experiments involving both idealized and ‘real’ events by using objective verification methods. The results imply that OBJ_NEP can be effectively employed under different large-scale forcings. Consequently, these results can aid the understanding of spatial-based approaches to probabilistic forecasting, which has been widely applied to post-processing processes of convective-scale ensemble forecast systems (CSEFs) in recent years.

PENGARUH FAKTOR-FAKTOR INTERNAL PERBANKAN TERHADAP NON PERFORMING FINANCING PADA BANK UMUM SYARIAH

2019 ◽  
Vol 6 (2) ◽  
pp. 115-130
Author(s):  
Eka Ambara Harci Putranta ◽  
Lilik Ambarwati
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Capital Adequacy ◽  
Annual Reports ◽  
Regression Methods ◽  
Capital Adequacy Ratio ◽  
Negative Effect ◽  
Independent Variable

The study aims to analyze the influence of internal banking factors in the form of: Capital Adequency Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing at Sharia Banks. This research method used multiple linear regression analysis with the help of SPSS 16.00 software which is used to see the influence between the independent variables in the form of Capital Adequacy Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing. The sample of this study was 3 Islamic Commercial Banks, so there were 36 annual reports obtained through purposive sampling, then analyzed using multiple linear regression methods. The results showed that based on the F Test, the independent variable had an effect on the NPF, indicated by the F value of 17,016 and significance of 0,000, overall the independent variable was able to explain the effect of 69.60%. While based on the partial t test, showed that CAR has a significant negative effect, Total assets have a significant positive effect with a significance value below 0.05 (5%). Meanwhile FDR does not affect NPF.

APPLYING THE ISF TECHNOLOGY TO PRODUCE THE CAR PART MODELS

2010 ◽  
Vol 13 (4) ◽  
pp. 91-98
Author(s):  
Tuan Dinh Phan ◽  
Binh Thien Nguyen ◽  
Dien Khanh Le ◽  
Phuong Hoang Pham
Keyword(s):  
Rapid Prototyping ◽  
Incremental Sheet Forming ◽  
Cad Model ◽  
Post Processing ◽  
Technological Parameters ◽  
Batch Production ◽  
Complex Products ◽  
3D Cad ◽  
Real Size ◽  
Single Batch

The paper presents an application the research results previously done by group on the influence of technological parameters to the deformation angle and finish surface quality in order to choose technology parameters for the incremental sheet forming (ISF) process to produce products for the purpose of rapid prototyping or single-batch production, including all steps from design and process 3D CAD model, calculate and select the technological parameters, setting up manufacturing and the stage of post-processing. The samples formed successfully showed high applicability of this technology to practical work, the complex products with the real size can be produced in industries: automotive, motorcycle, civil...

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