Multivariate copula quantile mapping for bias correction of reanalysis air temperature data

Alidoost, Fakhereh; Stein, Alfred; Su, Zhongbo; Sharifi, Alimorad

doi:10.1080/14498596.2019.1601138

Cited by 7 publications

(5 citation statements)

References 31 publications

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“…The distance scales in the top left corner of the maps are given in units of kilometers. The basin topography is based on the high-resolution ETOPO1 Global Relief Model (Amante and Eakins, 2009), while coastlines, rivers and political borders are taken from the Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG, Wessel and Smith, 1996). The basin boundaries are based on the HydroSHEDS dataset (Lehner and Grill, 2013) with some slight modifications and adjustments for ensuring the consistency with boundary definitions from local authorities and stakeholders from the study regions.…”

Section: Seas5 Seasonal Forecastsmentioning

confidence: 99%

Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

Lorenz

Portele

Laux

et al. 2021

Earth Syst. Sci. Data

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Abstract. Seasonal forecasts have the potential to substantially improve water management particularly in water-scarce regions. However, global seasonal forecasts are usually not directly applicable as they are provided at coarse spatial resolutions of at best 36 km and suffer from model biases and drifts. In this study, we therefore apply a bias-correction and spatial-disaggregation (BCSD) approach to seasonal precipitation, temperature and radiation forecasts of the latest long-range seasonal forecasting system SEAS5 of the European Centre for Medium-Range Weather Forecasts (ECMWF). As reference we use data from the ERA5-Land offline land surface rerun of the latest ECMWF reanalysis ERA5. Thereby, we correct for model biases and drifts and improve the spatial resolution from 36 km to 0.1∘. This is performed for example over four predominately semi-arid study domains across the world, which include the river basins of the Karun (Iran), the São Francisco River (Brazil), the Tekeze–Atbara river and Blue Nile (Sudan, Ethiopia and Eritrea), and the Catamayo–Chira river (Ecuador and Peru). Compared against ERA5-Land, the bias-corrected and spatially disaggregated forecasts have a higher spatial resolution and show reduced biases and better agreement of spatial patterns than the raw forecasts as well as remarkably reduced lead-dependent drift effects. But our analysis also shows that computing monthly averages from daily bias-corrected forecasts particularly during periods with strong temporal climate gradients or heteroscedasticity can lead to remaining biases especially in the lowest- and highest-lead forecasts. Our SEAS5 BCSD forecasts cover the whole (re-)forecast period from 1981 to 2019 and include bias-corrected and spatially disaggregated daily and monthly ensemble forecasts for precipitation, average, minimum, and maximum temperature as well as for shortwave radiation from the issue date to the next 215 d and 6 months, respectively. This sums up to more than 100 000 forecasted days for each of the 25 (until the year 2016) and 51 (from the year 2017) ensemble members and each of the five analyzed variables. The full repository is made freely available to the public via the World Data Centre for Climate at https://doi.org/10.26050/WDCC/SaWaM_D01_SEAS5_BCSD (Domain D01, Karun Basin (Iran), Lorenz et al., 2020b), https://doi.org/10.26050/WDCC/SaWaM_D02_SEAS5_BCSD (Domain D02: São Francisco Basin (Brazil), Lorenz et al., 2020c), https://doi.org/10.26050/WDCC/SaWaM_D03_SEAS5_BCSD (Domain D03: basins of the Tekeze–Atbara and Blue Nile (Ethiopia, Eritrea, Sudan), Lorenz et al., 2020d), and https://doi.org/10.26050/WDCC/SaWaM_D04_SEAS5_BCSD (Domain D04: Catamayo–Chira Basin (Ecuador, Peru), Lorenz et al., 2020a). It is currently the first publicly available daily high-resolution seasonal forecast product that covers multiple regions and variables for such a long period. It hence provides a unique test bed for evaluating the performance of seasonal forecasts over semi-arid regions and as driving data for hydrological, ecosystem or climate impact models. Therefore, our forecasts provide a crucial contribution for the disaster preparedness and, finally, climate proofing of the regional water management in climatically sensitive regions.

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Section: Seas5 Seasonal Forecastsmentioning

confidence: 99%

Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

Lorenz

Portele

Laux

et al. 2021

Earth Syst. Sci. Data

View full text Add to dashboard Cite

show abstract

“…Schepen et al, 2018;Manzanas et al, 2019;Khajehei et al, 2018) or lower biases (e.g. Alidoost et al, 2019) as quantile mapping for example tends to produce negatively skillful forecasts when the raw forecasts are not significantly positively correlated with observations (Zhao et al, 2017), it should be considered that quantile mapping still serves as the reference method in most of the recent bias correction studies. In other words, there is currently no other bias correction method that is similarly widespread.…”

Section: Discussionmentioning

confidence: 99%

Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

Lorenz¹,

Portele²,

Laux³

et al. 2020

Preprint

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Abstract. Seasonal forecasts have the potential to substantially improve water management particularly in water scarce regions. However, global seasonal forecasts are usually not directly applicable as they are provided at coarse spatial resolutions of at best 36 km and suffer from model biases and drifts. In this study, we therefore apply a bias-correction and spatial-disaggregation (BCSD) approach to seasonal precipitation, temperature and radiation forecasts of the latest long-range seasonal forecasting system SEAS5 of the European Centre for Medium Range Weather Forecasts (ECMWF). As reference we use data from the ERA5-Land offline land surface re-run of the latest ECMWF reanalysis ERA5. By that, we correct for model biases and drifts and improve the spatial resolution from 36 km to 0.1°. This is exemplary performed over 4 predominately semi-arid study domains across the world, which include the river basins of the Karun (Iran), the São Francisco (Brazil), the Tekeze-Atbara and Blue Nile (Sudan, Ethiopia and Eritrea), and the Catamayo-Chira (Ecuador and Peru). Compared against ERA5-Land, the bias-corrected and spatially disaggregated forecasts have a higher spatial resolution and show reduced biases and better agreement of spatial patterns than the raw forecasts. Furthermore, the lead-dependent drift effects are remarkably reduced in the BCSD-forecasts. However, our analysis also showed that computing monthly averages from daily bias-corrected forecasts can lead to statistical inconsistencies particularly during periods and seasons with strong temporal climate gradients or heteroscedasticity. During such periods, particularly the lowest- and highest-lead forecasts can show remaining biases. Our dataset covers the whole (re-)forecast period from 1981 to 2019, for which we provide bias-corrected and spatially disaggregated daily ensemble forecasts for precipitation, average, minimum and maximum temperature as well as for shortwave radiation from the initial date to the coming 214 days. This sums up to more than 100,000 forecasted days for each of the 25 (until the year 2016) and 51 (from the year 2017) ensemble members and each of the 5 analyzed variables. The full repository is made freely available to the public via the World Data Centre for Climate at https://doi.org/10.26050/WDCC/SaWaM_D01_SEAS5_BCSD (Domain D01, Karun Basin (Iran), Lorenz et al., 2020b), https://doi. org/10.26050/WDCC/SaWaM_D02_SEAS5_BCSD (Domain D02: São Francisco Basin (Brazil), Lorenz et al., 2020c), https://doi.org/10.26050/WDCC/SaWaM_D03_SEAS5_BCSD (Domain D03: Tekeze-Atbara and Blue Nile Basins (Ethiopia, Eritrea, Sudan), Lorenz et al., 2020d), and https://doi.org/10.26050/WDCC/SaWaM_D04_SEAS5_BCSD (Domain D04: Catamayo-Chira Basin (Ecuador, Peru), Lorenz et al., 2020a). It is currently the first publicly available daily high-resolution seasonal forecast product that covers multiple regions and variables for such a long period. It hence provides a unique test-bed for evaluating the performance of seasonal forecasts over semi-arid regions and as driving data for hydrological, ecosystem or climate impact models. Therefore, our forecasts provide a crucial contribution for the disaster preparedness and, finally, climate proofing of the regional water management in climatically sensitive regions.

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“…Copula-based bias-correction method Alidoost et al. ( 2021 ) develops three multivariate copula-based quantile regression to map daily air temperature data. They (Khan et al.…”

Section: Introductionmentioning

confidence: 99%

A spatial copula interpolation in a random field with application in air pollution data

Thakur

Das

Shubhashree

2022

Model. Earth Syst. Environ.

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Interpolating a skewed conditional spatial random field with missing data is cumbersome in the absence of Gaussianity assumptions. Copulas can capture different types of joint tail characteristics beyond the Gaussian paradigm. Maintaining spatial homogeneity and continuity around the observed random spatial point is also challenging. Especially when interpolating along a spatial surface, the boundary points also demand focus in forming a neighborhood. As a result, importing the concept of hierarchical clustering on the spatial random field is necessary for developing the copula model with the interface of the Expectation-Maximization algorithm and concurrently utilizing the idea of the Bayesian framework. This article introduces a spatial cluster-based C-vine copula and a modified Gaussian distance kernel to derive a novel spatial probability distribution. To make spatial copula interpolation compatible and efficient, we estimate the parameter by employing different techniques. We apply the proposed spatial interpolation approach to the air pollution of Delhi as a crucial circumstantial study to demonstrate this newly developed novel spatial estimation technique.

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Multivariate copula quantile mapping for bias correction of reanalysis air temperature data

Cited by 7 publications

References 31 publications

Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

A spatial copula interpolation in a random field with application in air pollution data

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