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1

Intercomparison of bias correction approaches for simulated temperature by multiple climatic models over southern India

Pandey Brij Kishor, Chandrakar Ayush, Vivek Balachandrakartha

Acta Geophysica

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2023

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Vol. 71, no. 4

1995--2008

EN

Climate model, a complex numerical representation of the global climate system, has been developed to simulate current climate and used to project future climatic conditions. Simulated climatic variables from climate models often exhibit significant deviations from observations. In climate projections, different approaches were introduced to deal with systematic deviations and random model errors. This paper demonstrates the intercomparison of four bias correction approaches (linear scaling, delta change correction, distribution mapping, and variance scaling) underlying the assumptions of stationary output from climate models. Mean monthly temperatures derived from five global climate models were corrected by four bias correction approaches for five states of southern India. The suitability of correction approaches depends upon the climate models and regional framework. The applied approaches improve the mean values and other statistical properties. The results show that all four bias techniques significantly improved the simulated data, but distribution mapping and variance scaling were more effective in removing systematic model biases.

2

Validation of CHIRPS satellite based precipitation data against the in situ observations using the Copula method: a case study of Kosar Dam basin, Iran

Mokhtari Shirin, Sharafati Ahmad, Raziei Tayeb

Acta Geophysica

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2022

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Vol. 70, no 1

465--484

EN

The present study compares the daily and monthly precipitation estimates of the CHIRPS satellite data with the in situ measurements at four stations scattered over the Kosar Dam basin in southwestern Iran. The uncertainty of the satellite precipitation estimates was calculated through simulation with the Copula functions. For this purpose, 55% of the stations, daily and monthly rainfall data relative to the 1987–2012 period were used for training (simulation), and the other 45% were used for testing (validation) the performance of the Copula model. First, the daily, monthly, and annual satellite precipitation estimates were statistically compared with precipitation observed at the stations and the whole basin using the Pearson correlation coefficient (CC), root mean square error (RMSE), and Bias statistics. The computed CC between the areal average of observed and satellite precipitation estimation at the basin is 0.49, 0.82, and 0.33 for daily, monthly, and annual time scales, respectively. The difference (biases) between the satellite estimates and in situ measurements was then calculated for daily, monthly, and annual time scales over the training period. The obtained biases were subsequently fitted with the General Extreme Value distribution function coupled with the Gaussian Copula model to generate a series of similar random biases for all precipitation events. Then, the generated random biases were summed with the original satellite estimates to correct the associated biases. The bias-corrected precipitation for the training period was then compared to the original estimates of the satellite at the stations and the whole basin using the P-factor, R-factor, Bias, RMSE, and CC statistics. The statistics show that the random biases generated by the Copula method for the monthly CHIRPS satellite data relative to the 14-year training period have reduced the error rate of the satellite data by 74 to 95 percent when compared to observations. The satellite precipitation estimates of the 11-year test period were also corrected using the generated random biases in the training period. The results show that the bias correction considerably improved the monthly estimates and reduced the error rate of the satellite estimation by about 76 percent. In general, the simulation of the satellite precipitation with the Gaussian Copula model was performed satisfactorily at the monthly time scale, but it was less efficient at the daily time scale.

3

Satellite-based streamflow simulation using CHIRPS satellite precipitation product in Shah Bahram Basin, Iran

Shirin Mokhtari, Ahmad Sharafati, Tayeb Raziei

Acta Geophysica

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2022

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Vol. 70, no 1

385--398

EN

Precipitation is the most important climate variable in hydrological practices, so accurate estimation of its intensity and volume is very crucial for hydrological applications. Remote sensing precipitation estimations have recently been widely employed in water resources management due to the lack of observed precipitation measurements in remote areas. However, remote sensing precipitation estimations are not free from systematic errors. This study aims to bias-correct the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) satellite precipitation estimations using the Gaussian-Copula approach and illustrates how it improves the simulated flow characteristics in the Shah Bahram basin in Kohgiluyeh and Boyer-Ahmad Province, southwestern Iran. The Nash-Sutclif Efciency (NSE) calculated between the original CHIRPS precipitation estimation and observation in the Shah Bahram basin equals −0.14; however, when bias-corrected CHIRPS data was compared to observation, the NSE increased to 0.23, suggesting about 158% improvement in the CHIRPS precipitation estimation when bias-corrected with the Gaussian-Copula approach. Next, the bias-corrected precipitation time series were utilized as the hydrologic modeling system inputs to simulate flow specifications such as discharge and peak value. Then, the simulation of the flow parameters was carried out with both original and bias-corrected CHIRPS satellite precipitation estimations and the ground-based precipitation. Though the NSE statistic of the simulation for the testing period has not changed significantly, the Pbias statistic has considerably improved. The result of the study indicates the good performance of the proposed bias correction approach in reducing the CHIRPS satellite estimations errors, concluding that it is a suitable approach for bias correction of the other satellite precipitation estimations in areas that suffer from the lack of ground-based observations necessary for food forecasting and other hydrological practices.

4

A feedback weighted fusion algorithm with dynamic sensor bias correction for gyroscope array

Yuan Ding, Qin Yongyuan, Shen Xiaowei, Wu Zongwei

Metrology and Measurement Systems

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2021

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Vol. 28, nr 1

161--179

EN

Low-cost Micro-Electromechanical System (MEMS) gyroscopes are known to have a smaller size, lower weight, and less power consumption than their more technologically advanced counterparts. However, current low-grade MEMS gyroscopes have poor performance and cannot compete with quality sensors in high accuracy navigational and guidance applications. The main focus of this paper is to investigate performance improvements by fusing multiple homogeneous MEMS gyroscopes. These gyros are transformed into a virtual gyro using a feedback weighted fusion algorithm with dynamic sensor bias correction. The gyroscope array combines eight homogeneous gyroscope units on each axis and divides them into two layers of differential configuration. The algorithm uses the gyroscope array estimation value to remove the gyroscope bias and then correct the gyroscope array measurement value. Then the gyroscope variance is recalculated in real time according to the revised measurement value and the weighted coefficients and state estimation of each gyroscope are deduced according to the least square principle. The simulations and experiments showed that the proposed algorithm could further reduce the drift and improve the overall accuracy beyond the performance limitations of individual gyroscopes. The maximum cumulative angle error was -2.09 degrees after 2000 seconds in the static test, and the standard deviation (STD) of the output fusion value of the proposed algorithm was 0.006 degrees/s in the dynamic test, which was only 1.7% of the STD value of an individual gyroscope.

5

Influence of climate change on flood magnitude and seasonality in the Arga River catchment in Spain

Garijo C., Mediero L.

Acta Geophysica

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2018

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Vol. 66, no. 4

769--790

EN

Climate change projections suggest that extremes, such as floods, will modify their behaviour in the future. Detailed catchment-scale studies are needed to implement the European Union Floods Directive and give recommendations for flood management and design of hydraulic infrastructure. In this study, a methodology to quantify changes in future flood magnitude and seasonality due to climate change at a catchment scale is proposed. Projections of 24 global climate models are used, with 10 being downscaled by the Spanish Meteorological Agency (Agencia Estatal de Meteorologı´a, AEMET) and 14 from the EURO-CORDEX project, under two representative concentration pathways (RCPs) 4.5 and 8.5, from the Fifth Assessment Report provided by the Intergovernmental Panel on Climate Change. Downscaled climate models provided by the AEMET were corrected in terms of bias. The HBV rainfall-runoff model was selected to simulate the catchment hydrological behaviour. Simulations were analysed through both annual maximum and peaks-over-threshold (POT) series. The results show a decrease in the magnitude of extreme floods for the climate model projections downscaled by the AEMET. However, results for the climate model projections downscaled by EURO-CORDEX show differing trends, depending on the RCP. A small decrease in the flood magnitude was noticed for the RCP 4.5, while an increase was found for the RCP 8.5. Regarding the monthly seasonality analysis performed by using the POT series, a delay in the flood timing from late-autumn to late-winter is identified supporting the findings of recent studies performed with observed data in recent decades.

6

Advances in antithetic time series analysis: separating fact from artifact

Ridley D.

Operations Research and Decisions

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2016

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Vol. 26, No. 3

57--68

EN

The problem of biased time series mathematical model parameter estimates is well known to be insurmountable. When used to predict future values by extrapolation, even a de minimis bias will eventually grow into a large bias, with misleading results. This paper elucidates how combining antithetic time series solves this baffling problem of bias in the fitted and forecast values by dynamic bias cancellation. Instead of growing to infinity, the average error can converge to a constant.