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.

scholarly journals RELIC: a novel dye-bias correction method for Illumina Methylation BeadChip

BMC Genomics ◽  
2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Zongli Xu ◽  
Sabine A. S. Langie ◽  
Patrick De Boever ◽  
Jack A. Taylor ◽  
Liang Niu
Keyword(s):  
Bias Correction ◽  
Correction Method ◽  
Bias Correction Method

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

2007 ◽  
Vol 4 (1) ◽  
pp. 35-74 ◽  
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.

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