TIN‐Copula bias‐correction method for model‐derived maximum temperature in the MENA region

2021 ◽  
Author(s):  
Georgia Lazoglou ◽  
George Zittis ◽  
Panos Hadjinicolaou ◽  
Jos Lelieveld
Keyword(s):  
Bias Correction ◽  
Correction Method ◽  
Maximum Temperature ◽  
Mena Region ◽  
Bias Correction Method

scholarly journals Climate Projections for Precipitation and Temperature Indicators in the Douro Wine Region: The Importance of Bias Correction

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 990
Author(s):  
Joana Martins ◽  
Helder Fraga ◽  
André Fonseca ◽  
João Andrade Santos
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Model ◽  
Correction Method ◽  
Maximum Temperature ◽  
Lower Sensitivity ◽  
Climate Projections ◽  
Quantile Mapping ◽  
Bias Correction Method ◽  
Wine Region

The implications of weather and climate extremes on the viticulture and winemaking sector can be particularly detrimental and acquire more relevance under a climate change context. A four-member ensemble of the Regional Climate Model-Global Climate Model chain simulations is used to evaluate the potential impacts of climate change on indices of extreme temperature and precipitation, as well as on agroclimatic indices of viticultural suitability in the Douro Wine Region, Portugal, under current and future climate conditions, following the RCP8.5 anthropogenic radiative forcing scenario. Historical (1989–2005) and future (2051–2080) periods are considered for this purpose. Although model outputs are bias-corrected to improve the accuracy of the results, owing to the sensitivity of the climatic indicators to the specific bias correction method, the performance of the linear and quantile mapping methods are compared. The results hint at the importance of choosing the most accurate method (quantile mapping), not only in replicating extremes events but also in reproducing the accumulated agroclimatic indices. Significant differences between the bias correction methods are indeed found for the number of extremely warm days (maximum temperature > 35 °C), number of warm spells, number of warm spell days, number of consecutive dry days, the Dryness Index, and growing season precipitation. The Huglin Index reveals lower sensitivity, thus being more robust to the choice of the method. Hence, an unsuitable bias correction method may hinder the accuracy of climate change projections in studies heavily relying on derived extreme indices and agroclimatic indicators, such as in viticulture. Regarding the climate change signal, significant warming and drying trends are projected throughout the target region, which is supported by previous studies, but also accompanied by an increase of intensity, frequency, and duration of extreme events, namely heatwaves and dry spells. These findings thereby corroborate the need to adopt timely and effective adaptation strategies by the regional winemaking sector to warrant its future sustainability and enhance climate resilience.

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 Improved EOF-based bias correction method for seasonal forecasts and its application in IAP AGCM4.1

2018 ◽  
Vol 11 (6) ◽  
pp. 499-508
Author(s):  
Yue YU ◽  
Zhao-Hui LIN ◽  
Zheng-Kun QIN
Keyword(s):  
Bias Correction ◽  
Correction Method ◽  
Seasonal Forecasts ◽  
Bias Correction Method

A bias correction method for fractional closed-loop system identification

2015 ◽  
Vol 33 ◽  
pp. 25-36 ◽  
Author(s):  
Z. Yakoub ◽  
M. Chetoui ◽  
M. Amairi ◽  
M. Aoun
Keyword(s):  
System Identification ◽  
Bias Correction ◽  
Closed Loop ◽  
Correction Method ◽  
Loop System ◽  
Closed Loop System ◽  
Bias Correction Method

A bias correction method for Arctic satellite sea surface temperature observations

2014 ◽  
Vol 146 ◽  
pp. 201-213 ◽  
Author(s):  
Jacob L. Høyer ◽  
Pierre Le Borgne ◽  
Steinar Eastwood
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Bias Correction ◽  
Correction Method ◽  
Sea Surface ◽  
Bias Correction Method ◽  
Temperature Observations
Sign in / Sign up

Export Citation Format

Share Document