On the bias correction of general circulation model output for Indian summer monsoon

Acharya, Nachiketa; Chattopadhyay, Surajit; Mohanty, U. C.; Dash, S. K.; Sahoo, L. N.

doi:10.1002/met.1294

Cited by 71 publications

(46 citation statements)

References 42 publications

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“…A simple bias correction is applied to each ERA-Interim time series through a rescaling that adjusts the first two moments (mean and variance) to the sample moments calculated for the corresponding observations (Acharya et al, 2013). Therefore, the bias correction is applied to the entire time series and it is not tailored to the extreme events only.…”

Section: Bias Correction Methodsmentioning

confidence: 99%

Return levels of temperature extremes in southern Pakistan

Zahid¹,

Blender²,

Lucarini

et al. 2017

Earth Syst. Dynam.

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Abstract. Southern Pakistan (Sindh) is one of the hottest regions in the world and is highly vulnerable to temperature extremes. In order to improve rural and urban planning, it is useful to gather information about the recurrence of temperature extremes. In this work, return levels of the daily maximum temperature T max are estimated, as well as the daily maximum wet-bulb temperature TW max extremes. We adopt the peaks over threshold (POT) method, which has not yet been used for similar studies in this region. Two main datasets are analyzed: temperatures observed at nine meteorological stations in southern Pakistan from 1980 to 2013, and the ERA-Interim (ECMWF reanalysis) data for the nearest corresponding locations. The analysis provides the 2-, 5-, 10-, 25-, 50-, and 100-year return levels (RLs) of temperature extremes. The 90 % quantile is found to be a suitable threshold for all stations. We find that the RLs of the observed T max are above 50 • C at northern stations and above 45 • C at the southern stations. The RLs of the observed TW max exceed 35 • C in the region, which is considered as a limit of survivability. The RLs estimated from the ERA-Interim data are lower by 3 to 5 • C than the RLs assessed for the nine meteorological stations. A simple bias correction applied to ERA-Interim data improves the RLs remarkably, yet discrepancies are still present. The results have potential implications for the risk assessment of extreme temperatures in Sindh.

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Section: Bias Correction Methodsmentioning

confidence: 99%

Return levels of temperature extremes in southern Pakistan

Zahid¹,

Blender²,

Lucarini

et al. 2017

Earth Syst. Dynam.

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“…The corrected data were first adjusted by finding the mean ratio between the recorded and computed data [34][35][36], as shown in Equation (7).…”

Section: Calibration and Validation Of Tsunami Modelmentioning

confidence: 99%

“…The calibration method, as recommended in this study, is meant to improve the initial water level using the mean ratio bias correction method. The corrected data were first adjusted by finding the mean ratio between the recorded and computed data [34][35][36], as shown in Equation (7).…”

Section: Calibration and Validation Of Tsunami Modelmentioning

confidence: 99%

Systematic Evaluation of Different Infrastructure Systems for Tsunami Defense in Sendai City

2018

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Abstract:The aim of this study is to assess the performances of different infrastructures as structural tsunami countermeasures in Sendai City, based on the lessons from the 11 March 2011, Great East Japan Tsunami, which is an example of a worst-case scenario. The tsunami source model Ver. 1.2 proposed by Tohoku University uses 10 subfaults, determined based on the tsunami height and the run-up heights measured for all tsunami affected areas. The TUNAMI-N2 model is used to simulate 24 cases of tsunami defense in Sendai City based on a combination of 5 scenarios of structural measures, namely, a seawall (existing and new seawall), a greenbelt, an elevated road and a highway. The results of a 2D tsunami numerical analysis show a significant difference in the tsunami inundations in the areas protected by several combinations of structures. The elevated road provides the highest performance of the single schemes, whereas the highest performance of the 2-layer schemes is the combination of an existing seawall and an elevated road. For the 3-layer scenarios, the highest performance is achieved by the grouping of an existing seawall, a new seawall, and an elevated road. The combination of an existing seawall, a new seawall, a greenbelt and an elevated road is the highest performing 4-layer scenario. The Sendai City plan, with a 5-layer scenario, reduces the tsunami inundation area by 20 sq. km with existing structural conditions. We found that the combination of an existing seawall, a greenbelt, an elevated road and a highway (a 4-layer scheme) is the optimum case to protect the city against a tsunami similar to the 2011 Great East Japan Tsunami. The proposed approach can be a guideline for future tsunami protection and the evaluation of countermeasure schemes.

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“…Hence, prior to improve signal from combination of all GCMs, the systematic bias of each GCM needs to remove. In view of this, standardized anomaly of each GCM has been calculated to remove the bias as proposed by Acharya et al ().…”

Section: Methodsmentioning

confidence: 99%

Prediction of Indian summer monsoon rainfall: a weighted multi-model ensemble to enhance probabilistic forecast skills

Acharya

Chattopadhyay²,

Mohanty

et al. 2013

Met. Apps

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India gets the maximum amount of rainfall during the months of June to September (JJAS) which is known as the summer monsoon season. The erratic nature of Indian summer monsoon rainfall (ISMR), in terms of both rainfall amount and distribution, is highly responsible for the interannual variability in agricultural production as well as occurrence of floods and droughts. Accurate seasonal predictions of ISMR are required for appropriate hydrological planning and disaster management systems. Studies have revealed that probabilistic prediction, based on the products of General Circulation Models (GCMs), can be generated in a parametric as well as non-parametric manner. The present paper discusses the enhancement of probabilistic prediction by improving the potential predictable signal obtained from these GCMs. A Singular-Value-Decomposition based multiple linear regression method (SVD-MLR) has been applied to improve the signal and a simple average of all GCMs (EM) has been used as the benchmark to examine the skill of the SVD-MLR method. The potential of the proposed method has been assessed through Brier Skill Score (BSS) and Rank Probability Skill Score (RPSS). A rigorous analysis has finally revealed that SVD-MLR method has better skill than EM in predicting the typical nature of observed monsoon rainfall in extreme years.

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On the bias correction of general circulation model output for Indian summer monsoon

Cited by 71 publications

References 42 publications

Return levels of temperature extremes in southern Pakistan

Return levels of temperature extremes in southern Pakistan

Systematic Evaluation of Different Infrastructure Systems for Tsunami Defense in Sendai City

Prediction of Indian summer monsoon rainfall: a weighted multi-model ensemble to enhance probabilistic forecast skills

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