Post-Processing of Climate Data for Health Applications

Obtain, transform and export climate data including reanalyses, (seasonal) forecasts and hindcasts, and weather stations for their use in epidemiological analyses. It is organised in three sequential blocks, input (download and load data), transform (downscaling, verification, spatiotemporal aggregation and threshold-based indicators) and output (visualising and exporting data). Downscaling methods include those described in Duzenli et al. (2026) and verification methods are based on those in Manubens et al. (2018) .

clim4health

Overview

clim4health is an R package designed to obtain, transform and export climate data for their use in epidemiological analyses and other types of applications. The package contains a series of functions structured in three sequential blocks: input, transformation, and output.

In the input block, clim4health provides functions to download several types of climate data including reanalyses, seasonal forecasts and hindcasts, and weather stations and load them into memory for their processing. The transformation block includes functions to downscale and verify climate data, perform spatiotemporal aggregations, as well as compute threshold-based suitability indicators. Finally, in the output block, functions to visualise and export the transformed data are provided.

clim4health is one of the packages developed by the Global Health Resilience (GHR) team at the Barcelona Supercomputing Center (BSC) within the HARMONIZE project, which comprises different R and Python libraries tailored for health, climate, environmental, and socioeconomic data acquisition, harmonisation, and visualization.

More information about the R packages developed at GHR for climate and health workflows and an online version of the package documentation can be found at the GHR tools website.

Installation

# Install from CRAN
install.packages("clim4health")

# Get the development version from Gitlab
devtools::install_git('https://earth.bsc.es/gitlab/ghr/clim4health.git')

Usage

# Load climate reanalysis temperature data included in the package
# and plot it for the study area
library("clim4health")
library("sf")

# Region municipalities
mun_path <- system.file("extdata", "areas", "munip_vallecauca.gpkg",
                        package = "clim4health")
mun <- read_sf(mun_path)

# Reanalysis data
rean_path <- system.file("extdata", "reanalysis", package = "clim4health")
rean <- c4h_load(rean_path, variable = "t2m", ext = 'nc')

# Convert units
rean <- c4h_convert_units(rean, to = "celsius")

# clim4health plot
c4h_plot(rean, 
         boundaries = mun,
         mask_boundaries = TRUE, 
         coordgrid = TRUE)

Vignettes

Several vignettes are available to explore clim4health. Vignettes loaded in the package can be accessed in R by typing vignette("vignettename") (e.g., vignette("clim4health_overview")).

The vignettes are:

• "clim4health_overview": a general introduction to the main functionalities of the package, including a worked example.
• "clim4health_get": an introduction to using c4h_get() to download data from the Copernicus Climate Data Store.
• "clim4health_glossary": a glossary of terms related to climate data, postprocessing, and verification, relevant within the clim4health package.
• "clim4health_s2dv_cubes": an introduction to the data class used within clim4health, and how to create your own object of this class if needed.
• "clim4health_downscaling": an introduction to downscaling methodologies and how they are applied within clim4health.
• "clim4health_verification": a guide to different verification metrics for seasonal forecasts and how they are calculated within the package.

Developers

Emily Ball, PhD
Barcelona Supercomputing Center
Climate Services Team

Carles Milà, PhD
Barcelona Supercomputing Center
Global Health Resilience

Alba Llabrés, PhD
Barcelona Supercomputing Center
Climate Services Team

Raúl Capellán Fernández, MSc
Barcelona Supercomputing Center
Earth Data and Diagnostics

Rebeca Nunes, MSc
Barcelona Supercomputing Center
Earth Data and Diagnostics

Daniela Lührsen, MSc
Barcelona Supercomputing Center
Global Health Resilience

Anna B. Kawiecki, PhD
Barcelona Supercomputing Center
Global Health Resilience

Rachel Lowe, PhD
Barcelona Supercomputing Center
Global Health Resilience (Group leader)