A bias correction method for identification of linear dynamic errors-in-variables models

2002 ◽  
Vol 47 (7) ◽  
pp. 1142-1147 ◽  
Author(s):  
Wei Xing Zheng
Keyword(s):  
Bias Correction ◽  
Correction Method ◽  
Errors In Variables ◽  
Linear Dynamic ◽  
Bias Correction Method ◽  
Dynamic Errors

BIAS CORRECTION METHOD FOR d4PDF TYPHOON TRACK DATASET AND FUTURE CHANGES IN TYPHOON CENTRAL PRESSURE

2021 ◽  
Vol 77 (2) ◽  
pp. I_973-I_978
Author(s):  
Junichi ARIMURA ◽  
Zhongrui QIU ◽  
Tetsuya OKAYASU ◽  
Koutarou CHICHIBU ◽  
Kunihiro WATANABE ◽  
...  
Keyword(s):  
Bias Correction ◽  
Correction Method ◽  
Central Pressure ◽  
Bias Correction Method ◽  
Typhoon Track

scholarly journals Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand

2007 ◽  
Vol 11 (4) ◽  
pp. 1373-1390 ◽  
Author(s):  
D. Sharma ◽  
A. Das Gupta ◽  
M. S. Babel
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
Correction Method ◽  
Hydrologic Model ◽  
Global Climate Models ◽  
Model Parameters ◽  
Bias Correction Method ◽  
Spatial Disaggregation ◽  
Grid Nodes

Abstract. Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.

Linear dynamic errors-in-variables models

1986 ◽  
Vol 23 (A) ◽  
pp. 23-39 ◽  
Author(s):  
M. Deistler
Keyword(s):  
Dynamical Systems ◽  
Transfer Functions ◽  
Errors In Variables ◽  
Linear Dynamical Systems ◽  
Linear Dynamic ◽  
Dynamic Errors ◽  
Non Gaussian ◽  
Inputs And Outputs

Linear dynamical systems where both inputs and outputs are contaminated by errors are considered. A characterization of the sets of all observationally equivalent transfer functions is given, the role of the causality assumption is investigated and conditions for identifiability in the case of Gaussian as well as non-Gaussian observations are derived.

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