Abstract. Despite the wealth of existing climate forecast data, only a small part is effectively exploited for sectoral applications. A major cause of this is the lack of integrated tools that allow the translation of data into useful and skilful climate information. This barrier is addressed through the development of an R package. CSTools is an easy-to-use toolbox designed and built to assess and improve the quality of climate forecasts for seasonal to multi–annual scales. The package contains process-based state-of-the-art methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. Due to the design of the toolbox in individual functions, the users can develop their own post-processing chain of functions as shown in the use cases presented in this manuscript: the analysis of an extreme wind speed event, the generation of seasonal forecasts of snow depth based on the SNOWPACK model and the post-processing of data to be used as input for the SCHEME hydrological model.
Abstract. Despite the wealth of existing climate forecast data, only a small part is effectively exploited for sectoral applications. A major cause of this is the lack of integrated tools that allow the translation of data into useful and skillful climate information. This barrier is addressed through the development of an R package. Climate Services Toolbox (CSTools) is an easy-to-use toolbox designed and built to assess and improve the quality of climate forecasts for seasonal to multi-annual scales. The package contains process-based, state-of-the-art methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination, and multivariate verification, as well as basic and advanced tools to obtain tailored products. Due to the modular design of the toolbox in individual functions, the users can develop their own post-processing chain of functions, as shown in the use cases presented in this paper, including the analysis of an extreme wind speed event, the generation of seasonal forecasts of snow depth based on the SNOWPACK model, and the post-processing of temperature and precipitation data to be used as input in impact models.
<p>The availability of climate data has never been larger, as evidenced by the development of the Copernicus Climate Change Service. However, availability of climate data does not automatically translate into usability and sophisticated post-processing is often required to turn these climate data into user-relevant climate information allowing them to develop and implement strategies of adaptation to climate variability and to trigger decisions.&#160;</p><p>Developed under the umbrella of the ERA4CS Medscope project by multiple European partners, here we present an R package currently in development, which aims to provide tools to exploit dynamical seasonal forecasts such as to provide information relevant to public and private stakeholders at the seasonal timescale. This toolbox, called CSTools (short for Climate Service Tools), contains process-based methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products.&#160;</p><p>In addition to presenting some of the tools that are contained in the package, we also present a short overview of the development strategy adopted for this toolbox. The latter relies on a version controlling system established such as to allow scientists and developers to work within a common framework using a platform where they can exchange with other developers, test the various functionalities and discuss issues arising from the work, amongst other things. Furthermore, we will also present some vignettes, which are one of the mechanisms that allows users to understand and visualize the capabilities of CSTools. For instance, CSTools contains a step by step vignette showing how to use and visualize the output of MultivarRMSE, which gives an indication of the forecast performance (RMSE) for multiple variables simultaneously.&#160;</p><p>While the extensive community of R users offers the opportunity of merging climate forecaster experts with final users, CSTools can also be used by other communities, such as Python users through the interface rpy. Finally, the publication of this package on CRAN (the Comprehensive R Archive Network) makes it easily accessible to interested users and ensures its proper functioning on different operational systems.&#160;</p>
<p>Climate forecasts need to be postprocessed to obtain user-relevant climate information, to develop and implement strategies of adaptation to climate variability and to trigger decisions. Several postprocessing methods are gathered into CSTools (short for Climate Service Tools) for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products.&#160;</p><p>Besides an overview of the methods and documentation available in CSTools, a practical example is demonstrated. The objective of this practical example is to postprocess a seasonal forecast with a set of CSTools functions in order to obtain the required data to produce forecasts of mountain snow resources. Quantile mapping bias-correction and RainFARM stochastic downscaling methods are applied to raw seasonal forecast daily precipitation data to derive 1 km resolution fields. Bias-adjusted and downscaled precipitation data are then employed to drive a snow model, SNOWPACK, and generate snow depth seasonal forecasts at selected high-elevation sites in North-Western Italian Alps.&#160;</p><p>The computational resources required by CSTools to process the forecasts will be discussed. This assessment is relevant given the memory requirements for the use case: while seasonal forecast data occupies ~10MB (8 x 8 grid cells, 215 forecast time steps for 30 different initializations with 25 members each), the data post-processed reaches ~1TB (the RainFARM downscaling requires a refinement factor 100 for the SNOWPACK model increasing the spatial resolution to 800 x 800 grid cells and creating 10 stochastic realizations for each ensemble member). In addition to one strategy using conventional loops, startR is introduced as an efficient alternative. startR is an R package that allows implementing the MapReduce paradigm, i.e. chunking the data and processing them either locally or remotely on high-performance computing systems, leveraging multi-node and multi-core parallelism where possible.</p>
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