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  • 1
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    Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain (2023)
    Details
    Publication Date: 2024-02-28
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉High spatio‐temporal resolution near‐surface projected data is vital for climate change impact studies and adaptation. We derived the highest statistically downscaled resolution multivariate ensemble currently available: daily 1 km until the end of the century. Deep learning models were employed to develop transfer functions for precipitation, water vapor pressure, radiation, wind speed, and, maximum, mean and minimum temperature. Perfect prognosis is the particular statistical downscaling methodology applied, using a subset of the ReKIS data set for Saxony as predictands, the ERA5 reanalysis as during‐training predictors and the CORDEX‐EUR11 ensemble as projected predictors. The performance of the transfer functions was validated with the VALUE framework, yielding highly satisfactory results. Particular attention was given to the three major perfect prognosis assumptions, for which several tests were carried out and thoroughly discussed. From the latter, we corroborated their fulfillment to a high degree, thus, the derived projections are considered adequate and relevant for impact modelers. In total, 18 runs for RCP85, 1 for RCP45, and 4 for RCP26 were downscaled under both stochastic and deterministic approaches. This multivariate ensemble could drive more accurate and diverse impact studies in the region. Generally, the projected climatologies are in agreement with coarser resolution projections. Nevertheless, statistical particularities were observed for some projections, thus, a list of caveats for potential users is given. Due to the scalability of the presented methodology, further possible applications with additional datasets are proposed. Lastly, several potential improvement prospects are discussed toward the ideal subsequent iteration of the perfect prognosis statistical downscaling methodology.〈/p〉
    Description: Plain Language Summary: There is a great worldwide demand for high spatio‐temporal resolution projections to develop climate change adaptation and mitigation schemes. Despite recent improvements, the resolution of both global and regional climate models is still too coarse to properly represent local variability, particularly in complex terrains. Depending on the application, impact modelers and decision makers require kilometer‐scale projections, with a minimum daily temporal resolution, of near‐surface variables. To fill this information gap, we employed artificial intelligence algorithms to downscale, to a novel daily 1 km resolution, a projection ensemble until the end of the century consisting of precipitation, water vapor pressure, radiation, wind speed, and, maximum, mean and minimum temperature. The ensemble comprises 18 runs of the business‐as‐usual worst‐case scenario (RCP85), 1 run of the stabilization scenario (RCP45), and 4 of the optimistic low‐emissions scenario (RCP26). The main assumptions of the methodology were thoroughly tested and discussed. The validation carried out yielded highly satisfactory results. Thus, we consider the projections to be adequate and relevant for impact studies. The region studied is located in Saxony (Germany), still, the methodology shown is potentially applicable anywhere in the world.〈/p〉
    Description: Key Points: 〈list list-type="bullet"〉 〈list-item〉 〈p xml:lang="en"〉Highest statistically downscaled spatio‐temporal resolution multivariate ensemble currently available, consisting of 23 projection runs〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉We downscaled precipitation, water vapor pressure, radiation, wind speed, and, maximum, mean and minimum temperature〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉The methodology complied to a high degree with the three perfect prognosis assumptions and is scalable to other spatio‐temporal resolutions〈/p〉〈/list-item〉 〈/list〉 〈/p〉
    Description: European Social Fund, Freistaat Sachsen http://dx.doi.org/10.13039/501100004895
    Description: https://rekis.hydro.tu-dresden.de/
    Description: https://doi.org/10.5281/zenodo.7570247
    Description: https://doi.org/10.5281/zenodo.7559173
    Description: https://doi.org/10.5281/zenodo.7558945
    Description: https://doi.org/10.5281/zenodo.8059248
    Description: https://doi.org/10.5281/zenodo.8198925
    Keywords: ddc:551.6 ; climate change ; statistical downscaling ; perfect prognosis ; ERA5 ; CORDEX ; deep learning ; multivariate ensemble
    Language: English
    Type: doc-type:article
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  • 2
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    Configuration and intercomparison of deep learning neural models for statistical downscaling (2020)
    Copernicus
    Details
    Publication Date: 2020-04-28
    Description: Deep learning techniques (in particular convolutional neural networks, CNNs) have recently emerged as a promising approach for statistical downscaling due to their ability to learn spatial features from huge spatiotemporal datasets. However, existing studies are based on complex models, applied to particular case studies and using simple validation frameworks, which makes a proper assessment of the (possible) added value offered by these techniques difficult. As a result, these models are usually seen as black boxes, generating distrust among the climate community, particularly in climate change applications. In this paper we undertake a comprehensive assessment of deep learning techniques for continental-scale statistical downscaling, building on the VALUE validation framework. In particular, different CNN models of increasing complexity are applied to downscale temperature and precipitation over Europe, comparing them with a few standard benchmark methods from VALUE (linear and generalized linear models) which have been traditionally used for this purpose. Besides analyzing the adequacy of different components and topologies, we also focus on their extrapolation capability, a critical point for their potential application in climate change studies. To do this, we use a warm test period as a surrogate for possible future climate conditions. Our results show that, while the added value of CNNs is mostly limited to the reproduction of extremes for temperature, these techniques do outperform the classic ones in the case of precipitation for most aspects considered. This overall good performance, together with the fact that they can be suitably applied to large regions (e.g., continents) without worrying about the spatial features being considered as predictors, can foster the use of statistical approaches in international initiatives such as Coordinated Regional Climate Downscaling Experiment (CORDEX).
    Print ISSN: 1991-959X
    Electronic ISSN: 1991-9603
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 3
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    Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment (2020)
    Copernicus
    Details
    Publication Date: 2020-04-01
    Description: The increasing demand for high-resolution climate information has attracted growing attention to statistical downscaling (SDS) methods, due in part to their relative advantages and merits as compared to dynamical approaches (based on regional climate model simulations), such as their much lower computational cost and their fitness for purpose for many local-scale applications. As a result, a plethora of SDS methods is nowadays available to climate scientists, which has motivated recent efforts for their comprehensive evaluation, like the VALUE initiative (http://www.value-cost.eu, last access: 29 March 2020). The systematic intercomparison of a large number of SDS techniques undertaken in VALUE, many of them independently developed by different authors and modeling centers in a variety of languages/environments, has shown a compelling need for new tools allowing for their application within an integrated framework. In this regard, downscaleR is an R package for statistical downscaling of climate information which covers the most popular approaches (model output statistics – including the so-called “bias correction” methods – and perfect prognosis) and state-of-the-art techniques. It has been conceived to work primarily with daily data and can be used in the framework of both seasonal forecasting and climate change studies. Its full integration within the climate4R framework (Iturbide et al., 2019) makes possible the development of end-to-end downscaling applications, from data retrieval to model building, validation, and prediction, bringing to climate scientists and practitioners a unique comprehensive framework for SDS model development. In this article the main features of downscaleR are showcased through the replication of some of the results obtained in VALUE, placing an emphasis on the most technically complex stages of perfect-prognosis model calibration (predictor screening, cross-validation, and model selection) that are accomplished through simple commands allowing for extremely flexible model tuning, tailored to the needs of users requiring an easy interface for different levels of experimental complexity. As part of the open-source climate4R framework, downscaleR is freely available and the necessary data and R scripts to fully replicate the experiments included in this paper are also provided as a companion notebook.
    Print ISSN: 1991-959X
    Electronic ISSN: 1991-9603
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 4
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    Statistical downscaling with the downscaleR package: Contribution to the VALUE intercomparison experiment (2019)
    Copernicus
    Details
    Publication Date: 2019-09-16
    Description: The increasing demand for high-resolution climate information has attracted a growing attention for statistical downscaling methods (SD), due in part to their relative advantages and merits as compared to dynamical approaches (based on regional climate model simulations), such as their much lower computational cost and their fitness-for-purpose for many local-scale applications. As a result, a plethora of SD methods is nowadays available for climate scientists, which has motivated recent efforts for their comprehensive evaluation, like the VALUE Project (http://www.value-cost.eu). The systematic intercomparison of a large number of SD techniques undertaken in VALUE, many of them independently developed by different authors and modeling centers in a variety of languages/environments, has shown a compelling need for new tools allowing for their application within an integrated framework. With this regard, downscaleR is an R package for statistical downscaling of climate information which covers the most popular approaches (Model Output Statistics – including the so called 'bias correction' methods – and Perfect Prognosis) and state-of-the-art techniques. It has been conceived to work primarily with daily data and can be used in the framework of both seasonal forecasting and climate change studies. Its full integration within the climate4R framework (Iturbide et al. 2019) makes possible the development of end-to-end downscaling applications, from data retrieval to model building, validation and prediction, bringing to climate scientists and practitioners a unique comprehensive framework for SD model development. In this article the main features of downscaleR are showcased through the replication of some of the results obtained in the VALUE Project, making an emphasis in the most technically complex stages of perfect-prog model calibration (predictor screening, cross-validation and model selection) that are accomplished through simple commands allowing for extremely flexible model tuning, tailored to the needs of users requiring an easy interface for different levels of experimental complexity. As part of the open-source climate4R framework, downscaleR is freely available and the necessary data and R scripts to fully replicate the experiments included in this paper are also provided as a companion notebook.
    Print ISSN: 1991-9611
    Electronic ISSN: 1991-962X
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 5
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    On the suitability of deep convolutional neural networks for continental-wide downscaling of climate change projections (2021)
    Springer
    Details
    Publication Date: 2021-06-20
    Description: In a recent paper, Baño-Medina et al. (Configuration and Intercomparison of deep learning neural models for statistical downscaling. preprint, 2019) assessed the suitability of deep convolutional neural networks (CNNs) for downscaling of temperature and precipitation over Europe using large-scale ‘perfect’ reanalysis predictors. They compared the results provided by CNNs with those obtained from a set of standard methods which have been traditionally used for downscaling purposes (linear and generalized linear models), concluding that CNNs are well suited for continental-wide applications. That analysis is extended here by assessing the suitability of CNNs for downscaling future climate change projections using Global Climate Model (GCM) outputs as predictors. This is particularly relevant for this type of “black-box” models, whose results cannot be easily explained based on physical reasons and could potentially lead to implausible downscaled projections due to uncontrolled extrapolation artifacts. Based on this premise, we analyze in this work the two key assumptions that are made in perfect prognosis downscaling: (1) the predictors chosen to build the statistical model should be well reproduced by GCMs and (2) the statistical model should be able to reliably extrapolate out of sample (climate change) conditions. As a first step to test the suitability of these models, the latter assumption is assessed here by analyzing how the CNNs affect the raw GCM climate change signal (defined as the difference, or delta, between future and historical climate). Our results show that, as compared to well-established generalized linear models (GLMs), CNNs yield smaller departures from the raw GCM outputs for the end of century, resulting in more plausible downscaling results for climate change applications. Moreover, as a consequence of the automatic treatment of spatial features, CNNs are also found to provide more spatially homogeneous downscaled patterns than GLMs.
    Print ISSN: 0930-7575
    Electronic ISSN: 1432-0894
    Topics: Geosciences , Physics
    Published by Springer
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