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CSTools:Assessing Skill of Climate Forecasts on Seasonal-to-Decadal Timescales

Exploits dynamical seasonal forecasts in order to provide information relevant to stakeholders at the seasonal timescale. The package 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. This package was developed in the context of the 'ERA4CS' project 'MEDSCOPE' and the 'H2020 S2S4E' project and includes contributions from 'ArticXchange' project founded by 'EU-PolarNet 2'. 'Pérez-Zanón et al. (2022) <doi:10.5194/gmd-15-6115-2022>'. 'Doblas-Reyes et al. (2005) <doi:10.1111/j.1600-0870.2005.00104.x>'. 'Mishra et al. (2018) <doi:10.1007/s00382-018-4404-z>'. 'Sanchez-Garcia et al. (2019) <doi:10.5194/asr-16-165-2019>'. 'Straus et al. (2007) <doi:10.1175/JCLI4070.1>'. 'Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>'. 'Torralba et al. (2017) <doi:10.1175/JAMC-D-16-0204.1>'. 'D'Onofrio et al. (2014) <doi:10.1175/JHM-D-13-096.1>'. 'Verfaillie et al. (2017) <doi:10.5194/gmd-10-4257-2017>'. 'Van Schaeybroeck et al. (2019) <doi:10.1016/B978-0-12-812372-0.00010-8>'. 'Yiou et al. (2013) <doi:10.1007/s00382-012-1626-3>'.

Maintained by Victoria Agudetse. Last updated 1 years ago.

fortran

2 stars 4.48 score 1 dependents

cran

CSIndicators:Climate Services' Indicators Based on Sub-Seasonal to Decadal Predictions

Set of generalised tools for the flexible computation of climate related indicators defined by the user. Each method represents a specific mathematical approach which is combined with the possibility to select an arbitrary time period to define the indicator. This enables a wide range of possibilities to tailor the most suitable indicator for each particular climate service application (agriculture, food security, energy, water management, ...). This package is intended for sub-seasonal, seasonal and decadal climate predictions, but its methods are also applicable to other time-scales, provided the dimensional structure of the input is maintained. Additionally, the outputs of the functions in this package are compatible with 'CSTools'. This package is described in 'Pérez-Zanón et al. (2023) <doi:10.1016/j.cliser.2023.100393>' and it was developed in the context of 'H2020 MED-GOLD' (776467) and 'S2S4E' (776787) projects. See 'Lledó et al. (2019) <doi:10.1016/j.renene.2019.04.135>' and 'Chou et al., 2023 <doi:10.1016/j.cliser.2023.100345>' for details.

Maintained by Victòria Agudetse. Last updated 1 years ago.

3.00 score

cran

s2dv:A Set of Common Tools for Seasonal to Decadal Verification

The advanced version of package 's2dverification'. It is intended for 'seasonal to decadal' (s2d) climate forecast verification, but it can also be used in other kinds of forecasts or general climate analysis. This package is specially designed for the comparison between the experimental and observational datasets. The functionality of the included functions covers from data retrieval, data post-processing, skill scores against observation, to visualization. Compared to 's2dverification', 's2dv' is more compatible with the package 'startR', able to use multiple cores for computation and handle multi-dimensional arrays with a higher flexibility. The CDO version used in development is 1.9.8.

Maintained by Ariadna Batalla. Last updated 6 months ago.

1.95 score 3 dependents

cran

startR:Automatically Retrieve Multidimensional Distributed Data Sets

Tool to automatically fetch, transform and arrange subsets of multi- dimensional data sets (collections of files) stored in local and/or remote file systems or servers, using multicore capabilities where possible. The tool provides an interface to perceive a collection of data sets as a single large multidimensional data array, and enables the user to request for automatic retrieval, processing and arrangement of subsets of the large array. Wrapper functions to add support for custom file formats can be plugged in/out, making the tool suitable for any research field where large multidimensional data sets are involved.

Maintained by Victoria Agudetse. Last updated 6 months ago.

1.78 score 2 dependents

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