scholarly journals Performance and robustness of probabilistic river forecasts computed with quantile regression based on multiple independent variables

2015 ◽  
Vol 19 (9) ◽  
pp. 3969-3990 ◽  
Author(s):  
F. Hoss ◽  
P. S. Fischbeck
Keyword(s):  
Quantile Regression ◽  
High Reliability ◽  
Forecast Error ◽  
Skill Score ◽  
Water Levels ◽  
Training Data ◽  
Forecast Errors ◽  
Data Set ◽  
Forecast Performance ◽  
Independent Variables

Abstract. This study applies quantile regression (QR) to predict exceedance probabilities of various water levels, including flood stages, with combinations of deterministic forecasts, past forecast errors and rates of water level rise as independent variables. A computationally cheap technique to estimate forecast uncertainty is valuable, because many national flood forecasting services, such as the National Weather Service (NWS), only publish deterministic single-valued forecasts. The study uses data from the 82 river gauges, for which the NWS' North Central River Forecast Center issues forecasts daily. Archived forecasts for lead times of up to 6 days from 2001 to 2013 were analyzed. Besides the forecast itself, this study uses the rate of rise of the river stage in the last 24 and 48 h and the forecast error 24 and 48 h ago as predictors in QR configurations. When compared to just using the forecast as an independent variable, adding the latter four predictors significantly improved the forecasts, as measured by the Brier skill score and the continuous ranked probability score. Mainly, the resolution increases, as the forecast-only QR configuration already delivered high reliability. Combining the forecast with the other four predictors results in a much less favorable performance. Lastly, the forecast performance does not strongly depend on the size of the training data set but on the year, the river gauge, lead time and event threshold that are being forecast. We find that each event threshold requires a separate configuration or at least calibration.

scholarly journals Performance and robustness of probabilistic river forecasts computed with quantile regression based on multiple independent variables in the North Central USA

2014 ◽  
Vol 11 (10) ◽  
pp. 11281-11333
Author(s):  
F. Hoss ◽  
P. S. Fischbeck
Keyword(s):  
Quantile Regression ◽  
High Reliability ◽  
Forecast Error ◽  
Skill Score ◽  
Training Dataset ◽  
Forecast Performance ◽  
North Central ◽  
The North ◽  
Rise Rate ◽  
Using Data

Abstract. This study further develops the method of quantile regression (QR) to predict exceedance probabilities of flood stages by post-processing forecasts. Using data from the 82 river gages, for which the National Weather Service's North Central River Forecast Center issues forecasts daily, this is the first QR application to US American river gages. Archived forecasts for lead times up to six days from 2001–2013 were analyzed. Earlier implementations of QR used the forecast itself as the only independent variable (Weerts et al., 2011; López López et al., 2014). This study adds the rise rate of the river stage in the last 24 and 48 h and the forecast error 24 and 48 h ago to the QR model. Including those four variables significantly improved the forecasts, as measured by the Brier Skill Score (BSS). Mainly, the resolution increases, as the original QR implementation already delivered high reliability. Combining the forecast with the other four variables results in much less favorable BSSs. Lastly, the forecast performance does not depend on the size of the training dataset, but on the year, the river gage, lead time and event threshold that are being forecast. We find that each event threshold requires a separate model configuration or at least calibration.

scholarly journals Alternative configurations of quantile regression for estimating predictive uncertainty in water level forecasts for the upper Severn River: a comparison

2014 ◽  
Vol 18 (9) ◽  
pp. 3411-3428 ◽  
Author(s):  
P. López López ◽  
J. S. Verkade ◽  
A. H. Weerts ◽  
D. P. Solomatine
Keyword(s):  
Quantile Regression ◽  
Water Level ◽  
Skill Score ◽  
Water Levels ◽  
Normal Space ◽  
Numerical Verification ◽  
Relative Operating Characteristic ◽  
Hydrological Uncertainty ◽  
Quality Aspects ◽  
Normal Quantile Transformation

Abstract. The present study comprises an intercomparison of different configurations of a statistical post-processor that is used to estimate predictive hydrological uncertainty. It builds on earlier work by Weerts, Winsemius and Verkade (2011; hereafter referred to as WWV2011), who used the quantile regression technique to estimate predictive hydrological uncertainty using a deterministic water level forecast as a predictor. The various configurations are designed to address two issues with the WWV2011 implementation: (i) quantile crossing, which causes non-strictly rising cumulative predictive distributions, and (ii) the use of linear quantile models to describe joint distributions that may not be strictly linear. Thus, four configurations were built: (i) a ''classical" quantile regression, (ii) a configuration that implements a non-crossing quantile technique, (iii) a configuration where quantile models are built in normal space after application of the normal quantile transformation (NQT) (similar to the implementation used by WWV2011), and (iv) a configuration that builds quantile model separately on separate domains of the predictor. Using each configuration, four reforecasting series of water levels at 14 stations in the upper Severn River were established. The quality of these four series was intercompared using a set of graphical and numerical verification metrics. Intercomparison showed that reliability and sharpness vary across configurations, but in none of the configurations do these two forecast quality aspects improve simultaneously. Further analysis shows that skills in terms of the Brier skill score, mean continuous ranked probability skill score and relative operating characteristic score is very similar across the four configurations.

scholarly journals An assessment of Bayesian bias estimator for numerical weather prediction

2008 ◽  
Vol 15 (6) ◽  
pp. 1013-1022 ◽  
Author(s):  
J. Son ◽  
D. Hou ◽  
Z. Toth
Keyword(s):  
Time Series ◽  
Numerical Weather Prediction ◽  
Sampling Error ◽  
Weather Prediction ◽  
Training Data ◽  
Statistical Characteristics ◽  
Forecast Errors ◽  
Data Sets ◽  
Data Set ◽  
Numerical Weather

Abstract. Various statistical methods are used to process operational Numerical Weather Prediction (NWP) products with the aim of reducing forecast errors and they often require sufficiently large training data sets. Generating such a hindcast data set for this purpose can be costly and a well designed algorithm should be able to reduce the required size of these data sets. This issue is investigated with the relatively simple case of bias correction, by comparing a Bayesian algorithm of bias estimation with the conventionally used empirical method. As available forecast data sets are not large enough for a comprehensive test, synthetically generated time series representing the analysis (truth) and forecast are used to increase the sample size. Since these synthetic time series retained the statistical characteristics of the observations and operational NWP model output, the results of this study can be extended to real observation and forecasts and this is confirmed by a preliminary test with real data. By using the climatological mean and standard deviation of the meteorological variable in consideration and the statistical relationship between the forecast and the analysis, the Bayesian bias estimator outperforms the empirical approach in terms of the accuracy of the estimated bias, and it can reduce the required size of the training sample by a factor of 3. This advantage of the Bayesian approach is due to the fact that it is less liable to the sampling error in consecutive sampling. These results suggest that a carefully designed statistical procedure may reduce the need for the costly generation of large hindcast datasets.

EFFICIENT CLASSIFICATION OF SCANNED MEDIA USING SPATIAL STATISTICS

2010 ◽  
Vol 24 (06) ◽  
pp. 917-946
Author(s):  
GOZDE UNAL ◽  
GAURAV SHARMA ◽  
REINER ESCHBACH
Keyword(s):  
Spatial Statistics ◽  
High Reliability ◽  
Image Data ◽  
Training Data ◽  
Automated Classification ◽  
Data Set ◽  
Statistical Measures ◽  
Scanned Image ◽  
Scanned Images

Photography, lithography, xerography, and inkjet printing are the dominant technologies for color printing. Images produced on these "different media" are often scanned either for the purpose of copying or creating an electronic representation. For an improved color calibration during scanning, a media identification from the scanned image data is desirable. In this paper, we propose an efficient algorithm for automated classification of input media into four major classes corresponding to photographic, lithographic, xerographic and inkjet. Our technique exploits the strong correlation between the type of input media and the spatial statistics of corresponding images, which are observed in the scanned images. We adopt ideas from spatial statistics literature, and design two spatial statistical measures of dispersion and periodicity, which are computed over spatial point patterns generated from blocks of the scanned image, and whose distributions provide the features for making a decision. We utilize extensive training data and determined well separated decision regions to classify the input media. We validate and tested our classification technique results over an independent extensive data set. The results demonstrate that the proposed method is able to distinguish between the different media with high reliability.

scholarly journals Alternative configurations of Quantile Regression for estimating predictive uncertainty in water level forecasts for the Upper Severn River: a comparison

2014 ◽  
Vol 11 (4) ◽  
pp. 3811-3855 ◽  
Author(s):  
P. López López ◽  
J. S. Verkade ◽  
A. H. Weerts ◽  
D. P. Solomatine
Keyword(s):  
Quantile Regression ◽  
Water Level ◽  
Skill Score ◽  
Water Levels ◽  
Normal Space ◽  
Numerical Verification ◽  
Relative Operating Characteristic ◽  
Hydrological Uncertainty ◽  
Is Implementation ◽  
Quality Aspects

Abstract. The present study comprises an inter-comparison of different configurations of a statistical post-processor that is used to estimate predictive hydrological uncertainty. It builds on earlier work by Weerts et al. (2011, herinafter referred to as wwv2011), who used the Quantile Regression technique to estimate predictive hydrological uncertainty using a deterministic water level forecast as a predictor. The various configurations are designed to address two issues with the wwv2011 implementation: (i) quantile crossing, which causes non-strictly rising cumulative predictive distributions, and (ii) the use of linear quantile models to describe joint distributions that may not be strictly linear. Thus, four configurations were built: (i) the "as is" implementation used by wwv2011, (ii) a configuration that implements a non-crossing quantile technique, (iii) a configuration where quantile models are built in Normal space after application of the Normal Quantile Transform, and (iv) a configuration that builds quantile model separately on separate domains of the predictor. Using each, four re-forecasting series of water levels at fourteen stations in the Upper Severn River were established. The quality of these four series was inter-compared using a set of graphical and numerical verification metrics. Intercomparison showed that reliability and sharpness vary across configurations, but in none of the configurations do these two forecast quality aspects improve simultaneously. Further analysis shows that skills in terms of Brier Skill Score, mean Continuous Ranked Probability Skill Score and Relative Operating Characteristic Score is very similar across the four configurations.

scholarly journals Are futures prices good price forecasts? Underestimation of price reversion in the soybean complex

2019 ◽  
Vol 47 (1) ◽  
pp. 178-199 ◽  
Author(s):  
Joshua Huang ◽  
Teresa Serra ◽  
Philip Garcia
Keyword(s):  
Quantile Regression ◽  
Forecast Errors ◽  
Central Tendency ◽  
Futures Prices ◽  
Forecast Performance ◽  
Market Participants ◽  
Price Distribution ◽  
Forecasting Performance ◽  
Futures Price ◽  
Good Price

Abstract Using quantile regression, we evaluate the forecasting performance of futures prices in the soybean complex. The procedure provides a more complete picture of the distribution of forecasts than mainstream methods that only focus on central tendency measures. Forecast performance differs by location in the futures price distribution. Futures forecast perform well in the centre of the distribution. However, futures prices tend to over-forecast when futures prices are high and under-forecast when futures prices are low, suggesting that futures prices tend to under-estimate price reversion towards the centre of the distribution. Forecast errors are larger when futures prices are high. The findings are related to theories in the literature used to explain pricing bias, and their implications for market participants are discussed.

scholarly journals Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

2016 ◽  
Vol 16 (5) ◽  
pp. 3399-3412 ◽  
Author(s):  
Thomas Schmidt ◽  
John Kalisch ◽  
Elke Lorenz ◽  
Detlev Heinemann
Keyword(s):  
Solar Radiation ◽  
Forecast Error ◽  
Forecast Skill ◽  
Small Scale ◽  
Surface Solar Radiation ◽  
Data Set ◽  
Forecast Performance ◽  
Electric Power Supply ◽  
Sky Imager ◽  
Spatio Temporal

Abstract. Clouds are the dominant source of small-scale variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the worldwide electric power supply increases the need for accurate solar radiation forecasts. In this work, we present results of a very short term global horizontal irradiance (GHI) forecast experiment based on hemispheric sky images. A 2-month data set with images from one sky imager and high-resolution GHI measurements from 99 pyranometers distributed over 10 km by 12 km is used for validation. We developed a multi-step model and processed GHI forecasts up to 25 min with an update interval of 15 s. A cloud type classification is used to separate the time series into different cloud scenarios. Overall, the sky-imager-based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depends strongly on the predominant cloud conditions. Especially convective type clouds lead to high temporal and spatial GHI variability. For cumulus cloud conditions, the analysis error is found to be lower than that introduced by a single pyranometer if it is used representatively for the whole area in distances from the camera larger than 1–2 km. Moreover, forecast skill is much higher for these conditions compared to overcast or clear sky situations causing low GHI variability, which is easier to predict by persistence. In order to generalize the cloud-induced forecast error, we identify a variability threshold indicating conditions with positive forecast skill.

scholarly journals Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales)

2011 ◽  
Vol 15 (1) ◽  
pp. 255-265 ◽  
Author(s):  
A. H. Weerts ◽  
H. C. Winsemius ◽  
J. S. Verkade
Keyword(s):  
Quantile Regression ◽  
Water Level ◽  
Flood Forecasting ◽  
Water Levels ◽  
Forecast Errors ◽  
Rainfall Runoff ◽  
England And Wales ◽  
Predictive Uncertainty ◽  
Hydrological Characteristics ◽  
Forecasting System

Abstract. In this paper, a technique is presented for assessing the predictive uncertainty of rainfall-runoff and hydraulic forecasts. The technique conditions forecast uncertainty on the forecasted value itself, based on retrospective Quantile Regression of hindcasted water level forecasts and forecast errors. To test the robustness of the method, a number of retrospective forecasts for different catchments across England and Wales having different size and hydrological characteristics have been used to derive in a probabilistic sense the relation between simulated values of water levels and matching errors. From this study, we can conclude that using Quantile Regression for estimating forecast errors conditional on the forecasted water levels provides a relatively simple, efficient and robust means for estimation of predictive uncertainty.

An Empirical Analysis of Forecast Performance of the GDP Growth in India

2019 ◽  
Vol 20 (2) ◽  
pp. 368-386 ◽  
Author(s):  
Monika Gupta ◽  
Mohammad Haris Minai
Keyword(s):  
Forecast Error ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Gdp Growth ◽  
Forecast Errors ◽  
Forecast Performance ◽  
Reserve Bank ◽  
Rules Of Thumb ◽  
Actual Outcome ◽  
High Degree

This article evaluates the accuracy of a forecast based on the properties of the forecast error. To measure how close the predictions of GDP growth are to the actual outcome in India, we have calculated three measures of forecast accuracy: mean absolute error (MAE), root mean square error (RMSE) and Theil’s U statistic. To evaluate the performance of the forecasts, we have compared them with naive forecast and common rules of thumb, using moving averages (MAs) as rules of thumb. The results are inconclusive regarding biasedness and also inefficient. Further, the forecasts have a high degree of correlation among themselves. The findings of forecast errors suggest that the performance of Reserve Bank of India (RBI) forecasts is favourable compared to other organizations, as well as with respect to the general international standard.

scholarly journals Estimation of predictive hydrological uncertainty using quantile regression: examples from the national flood forecasting system (England and Wales)

2010 ◽  
Vol 7 (4) ◽  
pp. 5547-5575 ◽  
Author(s):  
A. H. Weerts ◽  
H. C. Winsemius ◽  
J. S. Verkade
Keyword(s):  
Quantile Regression ◽  
Water Level ◽  
Uncertainty Estimation ◽  
Water Levels ◽  
Forecast Errors ◽  
Rainfall Runoff ◽  
England And Wales ◽  
Predictive Uncertainty ◽  
Hydrological Characteristics ◽  
Forecasting System

Abstract. In this paper, a is presented for assessing the predictive uncertainty of rainfall-runoff and hydraulic forecasts that conditions forecasts uncertainty on the forecasted value itself, based on retrospective quantile regression of hindcasted water level forecasts and forecast errors. To test the robustness of the method, a number of retrospective forecasts for different catchments across England and Wales having different size and hydrological characteristics have been used to derive in a probabilistic sense the relation between simulated values of discharges and water levels, and matching errors. From this study, we can conclude that using quantile regression for estimating forecast errors conditional on the forecasted water levels provides an extremely simple, efficient and robust means for uncertainty estimation of deterministic forecasts.

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