Copula-based statistical refinement of precipitation in RCM simulations over complex terrain

Laux, Patrick; Vogl, Stefanie; Qiu, Wei; Knoche, Hans Richard; Kunstmann, Harald

doi:10.5194/hess-15-2401-2011

Cited by 117 publications

(72 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The term "conditional" implies the level of association on the past sequence of observations and the "autoregressive" describes the feedback mechanism that incorporates past observations into the present (Laux et al, 2011).…”

Section: Generalizedmentioning

confidence: 99%

Daily Crude Oil Price Forecasting Model Using Arima, Generalized Autoregressive Conditional Heteroscedastic and Support Vector Machines

Ahmed¹

2014

American Journal of Applied Sciences

View full text Add to dashboard Cite

Crude oil price forecasting is gaining increased interest globally. This interest is due mainly to the economic value attached to the product. For this reason, new forecasting methods are proposed in the literature. This paper proposes a novel technique for forecasting crude oil price based on Support Vector Machines (SVM). The study adopts the data on crude oil price of West Texas Intermediate (WTI) for its experimental purposes. This is because many studies have previously used this same data and it will afford a common basis for assessment. To evaluate the performance of the model, the study employs two measures, RMSE and MAE. These are used to compare the performance of the proposed technique and that of ARIMA and GARCH methods for the most efficient in crude oil price forecasting. The results reveal that the proposed method outperforms the other two in terms of forecast accuracy while it achieved a forecast error of 0.8684 that of ARIMA and GARCH were 0.9856 and 1.0134 respectively judging by their RMSE.

show abstract

Section: Generalizedmentioning

confidence: 99%

Daily Crude Oil Price Forecasting Model Using Arima, Generalized Autoregressive Conditional Heteroscedastic and Support Vector Machines

Ahmed¹

2014

American Journal of Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Copula-based bias correction techniques have been originally introduced by Laux et al (2011) andVogl et al (2012), and are extended in this study by investigating gridded precipitation fields instead of individual and unevenly distributed stations. The Copula models are estimated for each single grid cell instead of choosing the most dominant model for the whole domain.…”

Section: Introductionmentioning

confidence: 99%

Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data

Mao

Vogl

Laux

et al. 2015

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Dynamically downscaled precipitation fields from regional climate models (RCMs) often cannot be used directly for regional climate studies. Due to their inherent biases, i.e., systematic over-or underestimations compared to observations, several correction approaches have been developed. Most of the bias correction procedures such as the quantile mapping approach employ a transfer function that is based on the statistical differences between RCM output and observations. Apart from such transfer functionbased statistical correction algorithms, a stochastic bias correction technique, based on the concept of Copula theory, is developed here and applied to correct precipitation fields from the Weather Research and Forecasting (WRF) model. For dynamically downscaled precipitation fields we used high-resolution (7 km, daily) WRF simulations for Germany driven by ERA40 reanalysis data for 1971-2000. The REG-NIE (REGionalisierung der NIEderschlagshöhen) data set from the German Weather Service (DWD) is used as gridded observation data (1 km, daily) and aggregated to 7 km for this application. The 30-year time series are split into a calibration (1971-1985) and validation (1986-2000) period of equal length. Based on the estimated dependence structure (described by the Copula function) between WRF and REGNIE data and the identified respective marginal distributions in the calibration period, separately analyzed for the different seasons, conditional distribution functions are derived for each time step in the validation period. This finally allows to get additional information about the range of the statistically possible bias-corrected values. The results show that the Copula-based approach efficiently corrects most of the errors in WRF derived precipitation for all seasons. It is also found that the Copula-based correction performs better for wet bias correction than for dry bias correction. In autumn and winter, the correction introduced a small dry bias in the northwest of Germany. The average relative bias of daily mean precipitation from WRF for the validation period is reduced from 10 % (wet bias) to −1 % (slight dry bias) after the application of the Copula-based correction. The bias in different seasons is corrected from 32 % March-April-May (MAM), −15 % June-July-August (JJA), 4 % September-October-November (SON) and 28 % December-January-February (DJF) to 16 % (MAM), −11 % (JJA), −1 % (SON) and −3 % (DJF), respectively. Finally, the Copula-based approach is compared to the quantile mapping correction method. The root mean square error (RMSE) and the percentage of the corrected time steps that are closer to the observations are analyzed. The Copula-based correction derived from the mean of the sampled distribution reduces the RMSE significantly, while, e.g., the quantile mapping method results in an increased RMSE for some regions.

show abstract

“…A practical advantage of the proposed methods is that they are not restricted to remove autocorrelation and heteroscedasticity in time series (Laux et al 2011) and the time series of the air temperature at each station were successfully reproduced by applying the bias correction separately at each day. Another aspect is the ability of the new methods to reproduce the moments of the marginal distribution of the observed variable.…”

Section: Discussion 25mentioning

confidence: 99%

“…Here, to obtain a single value for air temperature, a choice for either the mean, or the median or the mode of a simulation provides a single value ̂1. In the literature, the mean value of the simulations is considered as a single realization (Laux et al 2011;Vogl et al 2012). The number of samples in the simulations, however, influences the 25 simulation of conditional quantiles.…”

Section: Simulation Of Conditional Quantilementioning

confidence: 99%

See 1 more Smart Citation

Three novel copula-based bias correction methods for daily ECMWF air temperature data

Alidoost

Stein

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract. Data retrieved from global weather forecast systems are typically biased with respect to measurements at local weather stations. This paper presents three copula-based methods for bias correction of daily air temperature data derived from the European Centre for Medium-range Weather Forecasts (ECMWF). The aim is to predict conditional copula quantiles at different unvisited locations, assuming spatial stationarity of the underlying random field. The three new methods are: bivariate copula quantile mapping (types I and II), and a quantile search. These are compared with commonly applied methods, using 10 data from an agricultural area in the Qazvin Plain in Iran containing five weather stations. Cross-validation is carried out to assess the performance. The study shows that the new methods are able to predict the conditional quantiles at unvisited locations, improve the higher order moments of marginal distributions, and take the spatial variabilities of the bias-corrected variable into account. It further illustrates how a choice of the bias correction method affects the bias-corrected variable and highlights both theoretical and practical issues of the methods. We conclude that the three new methods improve local 15 refinement of weather data, in particular if a low number of observations is available.

show abstract

Copula-based statistical refinement of precipitation in RCM simulations over complex terrain

Cited by 117 publications

References 51 publications

Daily Crude Oil Price Forecasting Model Using Arima, Generalized Autoregressive Conditional Heteroscedastic and Support Vector Machines

Daily Crude Oil Price Forecasting Model Using Arima, Generalized Autoregressive Conditional Heteroscedastic and Support Vector Machines

Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data

Three novel copula-based bias correction methods for daily ECMWF air temperature data

Contact Info

Product

Resources

About