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fix: typos in clim_recal.md and dyme.md
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src/content/project/clim_recal.md

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Regional climate models (RCMs) contain systematic errors, or biases in their output [1]. Biases arise in RCMs for a number of reasons, such as the assumptions in the general circulation models (GCMs), and in the downscaling process from GCM to RCM [1,2].
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Regional climate models (RCMs) contain systematic errors, or biases in their output [^1]. Biases arise in RCMs for a number of reasons, such as the assumptions in the general circulation models (GCMs), and in the downscaling process from GCM to RCM.
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Researchers, policy-makers and other stakeholders wishing to use publicly available RCMs need to consider a range of "bias correction” methods (sometimes referred to as "bias adjustment" or "recalibration"). Bias correction methods offer a means of adjusting the outputs of RCM in a manner that might better reflect future climate change signals whilst preserving the natural and internal variability of climate [2].
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Researchers, policy-makers and other stakeholders wishing to use publicly available RCMs need to consider a range of "bias correction” methods (sometimes referred to as "bias adjustment" or "recalibration"). Bias correction methods offer a means of adjusting the outputs of RCM in a manner that might better reflect future climate change signals whilst preserving the natural and internal variability of climate [^2].
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The aim of **clim-recal** is therefore to:
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- To create accessible information on bias adjustment methods for non quantitative researchers and lay-audience stakeholders
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We are working in partnership with the MetOffice to ensure the propriety, quality, and usability of our work. We're focusing on the UKCP18 suite of products, with the first dataset of focus their ground-breaking local-scale (2.2km) [Convection Permitting Model (CPM)](https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/UKCP-Convection-permitting-model-projections-report.pdf).
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[^1]: Senatore et al., 2022, <https://doi.org/10.1016/j.ejrh.2022.101120>
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[^2]: Ayar et al., 2021, <https://doi.org/10.1038/s41598-021-82715-1>

src/content/project/dyme.md

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team:
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- fernando_benitez.md
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- fernando_benitez
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- ruth_bowyer
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- camila_rangel_smith.md
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- camila_rangel_smith
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- jennifer_ding
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- harry_meacher
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- greg_mingas

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