From fab87617251adcc583cf881fe1a660a4b8d596d6 Mon Sep 17 00:00:00 2001 From: "Jose M. Gutierrez" Date: Tue, 2 Oct 2018 10:42:15 +0200 Subject: [PATCH 1/3] Update README.md --- README.md | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 81d7a4a..c0819c9 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,8 @@ # What is calibratoR? -[calibratoR](https://github.com/SantanderMetGroup/calibratoR) implements several methods for statistical calibration of climate forecasts (typically on a monthly/seasonal basis), which range from simple mean/variance adjustment to more sophisticated Ensemble Model Output Statistics (EMOS) options which take into account the existing correspondence between the ensemble mean and the observations in the calibration process. This R package works with the *grid* object developed for the [climate4R](http://www.meteo.unican.es/climate4r) bundle (see [Iturbide et al. 2018]() for details). Currently, only gridded data are supported (point-wise stations will be also supported in a future release). +[calibratoR](https://github.com/SantanderMetGroup/calibratoR) implements several methods for statistical calibration of climate forecasts (e.g. seasonal forecasts) on a monthly/seasonal basis. The implemented methods range from simple mean/variance adjustment to more sophisticated Ensemble Model Output Statistics (EMOS) options which take into account the temporal correspondence between the ensemble mean and the observations in the calibration process. This package is complementary with [downscaleR](https://github.com/SantanderMetGroup/downscaleR), which implements standard bias correction and (perfect prog) statistical downscaling on a daily basis. + +This package is fully compatible with the [climate4R](http://www.meteo.unican.es/climate4r) framework (see [Iturbide et al. 2018](https://doi.org/10.1016/j.envsoft.2018.09.009) for details). Currently, only gridded data are supported (point-wise stations will be also supported in a future release). The recommended installation procedure is to use the `install_github` command from the devtools R package: @@ -14,4 +16,11 @@ install.packages("SpecsVerification") ``` --- -**Reference and further information on the methods implemented:** [Manzanas et al. 2018]() +--- +**Reference and further information:** + +**[Application to the C3S dataset]** Manzanas et al. (2018) Bias adjustment and ensemble recalibration methods for seasonal forecasting: A comprehensive intercomparison using the C3S dataset. Submitted to Climate Dynamics. + +**[General description of the climate4R framework]** Iturbide et al. (2018) climate4R: An R-based Open Framework for Reproducible Climate Data Access and Post-processing. **Environmental Modelling and Software**. https://doi.org/10.1016/j.envsoft.2018.09.009 +Check out the companion notebooks for the two examples [GitHub](https://github.com/SantanderMetGroup/notebooks). + From fc75e08263c90b0543fa7abdb6973f74a09f3b88 Mon Sep 17 00:00:00 2001 From: Rodrigo Manzanas Date: Tue, 28 May 2019 14:16:34 +0200 Subject: [PATCH 2/3] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c0819c9..98a4a62 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ install.packages("SpecsVerification") --- **Reference and further information:** -**[Application to the C3S dataset]** Manzanas et al. (2018) Bias adjustment and ensemble recalibration methods for seasonal forecasting: A comprehensive intercomparison using the C3S dataset. Submitted to Climate Dynamics. +**[Application to the C3S dataset]** Manzanas et al. (2018) Bias adjustment and ensemble recalibration methods for seasonal forecasting: A comprehensive intercomparison using the C3S dataset. **Climate Dynamics**. https://doi.org/10.1007/s00382-019-04640-4 **[General description of the climate4R framework]** Iturbide et al. (2018) climate4R: An R-based Open Framework for Reproducible Climate Data Access and Post-processing. **Environmental Modelling and Software**. https://doi.org/10.1016/j.envsoft.2018.09.009 Check out the companion notebooks for the two examples [GitHub](https://github.com/SantanderMetGroup/notebooks). From b12c277c1f3ad8125e76081c3f0dd7362af5e3a5 Mon Sep 17 00:00:00 2001 From: Rodrigo Manzanas Date: Tue, 30 Jun 2020 11:56:09 +0200 Subject: [PATCH 3/3] Update README.md Slight modification of the description page --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 98a4a62..aec6708 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,6 @@ # What is calibratoR? -[calibratoR](https://github.com/SantanderMetGroup/calibratoR) implements several methods for statistical calibration of climate forecasts (e.g. seasonal forecasts) on a monthly/seasonal basis. The implemented methods range from simple mean/variance adjustment to more sophisticated Ensemble Model Output Statistics (EMOS) options which take into account the temporal correspondence between the ensemble mean and the observations in the calibration process. This package is complementary with [downscaleR](https://github.com/SantanderMetGroup/downscaleR), which implements standard bias correction and (perfect prog) statistical downscaling on a daily basis. - -This package is fully compatible with the [climate4R](http://www.meteo.unican.es/climate4r) framework (see [Iturbide et al. 2018](https://doi.org/10.1016/j.envsoft.2018.09.009) for details). Currently, only gridded data are supported (point-wise stations will be also supported in a future release). +[calibratoR](https://github.com/SantanderMetGroup/calibratoR) implements several methods for the statistical calibration of climate forecasts (e.g. seasonal forecasts) on a monthly/seasonal basis; from simple mean/variance adjustment to more sophisticated Ensemble Model Output Statistics (EMOS) options which take into account the temporal correspondence between the ensemble mean and the observations in the calibration process. This package is complementary with [downscaleR](https://github.com/SantanderMetGroup/downscaleR), which implements standard bias correction and (perfect prog) statistical downscaling on a daily basis. It is fully compatible with the [climate4R](http://www.meteo.unican.es/climate4r) framework (see [Iturbide et al. 2018](https://doi.org/10.1016/j.envsoft.2018.09.009) for details). Currently, only gridded data are supported (point-wise stations will be also supported in a future release). The recommended installation procedure is to use the `install_github` command from the devtools R package: @@ -19,7 +17,9 @@ install.packages("SpecsVerification") --- **Reference and further information:** -**[Application to the C3S dataset]** Manzanas et al. (2018) Bias adjustment and ensemble recalibration methods for seasonal forecasting: A comprehensive intercomparison using the C3S dataset. **Climate Dynamics**. https://doi.org/10.1007/s00382-019-04640-4 +**[Comparison of bias adjustment, ensemble recalibration, MOS and PP techniques]** Manzanas et al. (2020) Statistical adjustment, calibration and downscaling of seasonal forecasts: a case-study for Southeast Asia. **Climate Dynamics**. https://doi.org/10.1007/s00382-020-05145-1 + +**[Application to the C3S dataset]** Manzanas et al. (2019) Bias adjustment and ensemble recalibration methods for seasonal forecasting: A comprehensive intercomparison using the C3S dataset. **Climate Dynamics**. https://doi.org/10.1007/s00382-019-04640-4 **[General description of the climate4R framework]** Iturbide et al. (2018) climate4R: An R-based Open Framework for Reproducible Climate Data Access and Post-processing. **Environmental Modelling and Software**. https://doi.org/10.1016/j.envsoft.2018.09.009 Check out the companion notebooks for the two examples [GitHub](https://github.com/SantanderMetGroup/notebooks).