scholarly journals Regionalizing non-parametric precipitation amount models on different temporal scales

2016 ◽  
Author(s):  
Tobias Mosthaf ◽  
András Bárdossy
Keyword(s):  
Precipitation Amount ◽  
Distribution Functions ◽  
Parametric Models ◽  
Water Volume ◽  
Federal State ◽  
Interpolation Scheme ◽  
Study Region ◽  
Model Precipitation ◽  
Parametric Interpolation ◽  
Non Parametric

Abstract. Parametric distribution functions are commonly used to model precipitation amounts. Precipitation amounts themselves are crucial for stochastic rainfall generators or weather generators. Non-parametric kernel density estimates (KDEs) offer a more flexible way to model precipitation amounts. As it is already stated in their name, these models do not exhibit a parameter which can easily be regionalized to run rainfall generators at ungauged locations as well as at gauged locations. To overcome this deficiency we present a new interpolation scheme for non-parametric models and evaluate it for different temporal resolutions ranging from hourly to monthly. During the evaluation the non-parametric methods are compared to commonly used parametric models like the two parameter gamma and the mixed exponential distribution. As water volume is considered to be an essential parameter for applications like flood modeling, a Lorenz-curve based criterion is also introduced. To add value to the estimation of data at sub-daily resolutions, we incorporated the plentiful daily measurements in the interpolation scheme and this idea was evaluated. The study region is the federal state of Baden-Württemberg in the southwest of Germany with more than 500 rain gauges. The validation results show that the newly proposed non-parametric interpolation scheme works, and additionally seems to be more robust compared to parametric interpolation schemes, and that the incorporation of daily values in the regionalization of sub-daily models is very beneficial.

scholarly journals Regionalizing nonparametric models of precipitation amounts on different temporal scales

2017 ◽  
Vol 21 (5) ◽  
pp. 2463-2481 ◽  
Author(s):  
Tobias Mosthaf ◽  
András Bárdossy
Keyword(s):  
Distribution Functions ◽  
Parametric Models ◽  
Water Volume ◽  
Federal State ◽  
Interpolation Scheme ◽  
Study Region ◽  
Nonparametric Models ◽  
Nonparametric Kernel Density ◽  
Nonparametric Kernel ◽  
Model Precipitation

Abstract. Parametric distribution functions are commonly used to model precipitation amounts corresponding to different durations. The precipitation amounts themselves are crucial for stochastic rainfall generators and weather generators. Nonparametric kernel density estimates (KDEs) offer a more flexible way to model precipitation amounts. As already stated in their name, these models do not exhibit parameters that can be easily regionalized to run rainfall generators at ungauged locations as well as at gauged locations. To overcome this deficiency, we present a new interpolation scheme for nonparametric models and evaluate it for different temporal resolutions ranging from hourly to monthly. During the evaluation, the nonparametric methods are compared to commonly used parametric models like the two-parameter gamma and the mixed-exponential distribution. As water volume is considered to be an essential parameter for applications like flood modeling, a Lorenz-curve-based criterion is also introduced. To add value to the estimation of data at sub-daily resolutions, we incorporated the plentiful daily measurements in the interpolation scheme, and this idea was evaluated. The study region is the federal state of Baden-Württemberg in the southwest of Germany with more than 500 rain gauges. The validation results show that the newly proposed nonparametric interpolation scheme provides reasonable results and that the incorporation of daily values in the regionalization of sub-daily models is very beneficial.

A Non Parametric Model for Magneto Rheological Dampers

1999 ◽  
Author(s):  
Mehdi Ahmadian ◽  
Xubin Song
Keyword(s):  
Test Data ◽  
Mr Damper ◽  
Parametric Model ◽  
Parametric Models ◽  
Magnetic Saturation ◽  
Mr Dampers ◽  
Electro Magnetic ◽  
Magneto Rheological ◽  
Force Characteristics ◽  
Non Parametric

Abstract A non-parametric model for magneto-rheological (MR) dampers is presented. After discussing the merits of parametric and non-parametric models for MR dampers, the test data for a MR damper is used to develop a non-parametric model. The results of the model are compared with the test data to illustrate the accuracy of the model. The comparison shows that the non-parametric model is able to accurately predict the damper force characteristics, including the damper non-linearity and electro-magnetic saturation. It is further shown that the parametric model can be numerically solved more efficiently than the parametric models.

Bayesian non-parametric models for regional prevalence estimation

2008 ◽  
Vol 35 (5) ◽  
pp. 567-582 ◽  
Author(s):  
Adam J. Branscum ◽  
Timothy E. Hanson ◽  
Ian A. Gardner
Keyword(s):  
Parametric Models ◽  
Prevalence Estimation ◽  
Non Parametric

scholarly journals The use of personal weather station observations to improve precipitation estimation and interpolation

2021 ◽  
Vol 25 (2) ◽  
pp. 583-601
Author(s):  
András Bárdossy ◽  
Jochen Seidel ◽  
Abbas El Hachem
Keyword(s):  
Spatial Interpolation ◽  
Empirical Distribution ◽  
Precipitation Amount ◽  
Distribution Functions ◽  
Quantile Mapping ◽  
Novel Approach ◽  
Precipitation Estimation ◽  
Weather Stations ◽  
High Precipitation ◽  
Empirical Distribution Functions

Abstract. The number of personal weather stations (PWSs) with data available through the internet is increasing gradually in many parts of the world. The purpose of this study is to investigate the applicability of these data for the spatial interpolation of precipitation using a novel approach based on indicator correlations and rank statistics. Due to unknown errors and biases of the observations, rainfall amounts from the PWS network are not considered directly. Instead, it is assumed that the temporal order of the ranking of these data is correct. The crucial step is to find the stations which fulfil this condition. This is done in two steps – first, by selecting the locations using the time series of indicators of high precipitation amounts. Then, the remaining stations are then checked for whether they fit into the spatial pattern of the other stations. Thus, it is assumed that the quantiles of the empirical distribution functions are accurate. These quantiles are then transformed to precipitation amounts by a quantile mapping using the distribution functions which were interpolated from the information from the German National Weather Service (Deutscher Wetterdienst – DWD) data only. The suggested procedure was tested for the state of Baden-Württemberg in Germany. A detailed cross validation of the interpolation was carried out for aggregated precipitation amount of 1, 3, 6, 12 and 24 h. For each of these temporal aggregations, nearly 200 intense events were evaluated, and the improvement of the interpolation was quantified. The results show that the filtering of observations from PWSs is necessary as the interpolation error after the filtering and data transformation decreases significantly. The biggest improvement is achieved for the shortest temporal aggregations.

Non-parametric lactation curves

1989 ◽  
Vol 48 (2) ◽  
pp. 331-339 ◽  
Author(s):  
D. A. Elston ◽  
C. A. Glasbey ◽  
D. R. Neilson
Keyword(s):  
Parametric Method ◽  
Parametric Models ◽  
Summary Statistics ◽  
Parametric Curves ◽  
Biased Estimation ◽  
Derivatives Of ◽  
Non Parametric

ABSTRACTLactation curves are fitted to data as a preliminary to estimating summary statistics. Two widely quoted curves are atbe-ct (Wood, 1967) and a(1 - e-bt) - ct (Cobby and Le Du, 1978), each of which has three parameters. Restriction to either of these curves imposes limitations on the fit to the data and can result in biased estimation of summary statistics. Alternatively, lactation curves can be generated by the use of a non-parametric method which requires only weak assumptions about the signs of derivatives of the curves. Because the non-parametric curves are more flexible, estimates of summary statistics are less likely to be biased than those based on parametric models. Use of the non-parametric curves is particularly advantageous around the time of peak yield, where the curves of Wood and Cobby and Le Du are known to fit data poorly.

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