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climateBR: Download Rainfall, Temperature, and Wind Data from Brazil

CRAN status Total downloads

climateBR helps social scientists study climate shocks in Brazil. It provides tools to download, process, and analyze historical meteorological data from the Brazilian National Institute of Meteorology (INMET).

Installation 

# From CRAN
install.packages("climateBR")

# Development version (latest features)
remotes::install_github("kaiorb52/climateBR")

Example: Maximum temperature in Rio de Janeiro state (2026)

First, download the historical INMET CSV files with download_inmet().

Next, convert the raw files into a partitioned Apache Arrow dataset with build_inmet_dataset(). This step is essential: each station’s CSV file has about 8,000 rows per year, and with more than 500 stations in Brazil, a single year can reach about 4 million observations. That is too much for most computers to handle efficiently as plain CSV files.

Finally, use read_inmet() to query the processed dataset.

library(climateBR)
library(dplyr)
data("mun_stations")

# The package includes datasets that speed up data aggregation.
# For example, `mun_stations` was created with `nearest_stations()` and links
# each of Brazil's 5,570 municipalities to its five nearest stations.

download_inmet(
  years = 2026,
  unzip_to = "data/raw/inmet"
)

build_inmet_dataset(
  input = "data/raw/inmet/",
  output = "data/processed/inmet/"
)

df_inmet <- read_inmet(path = "data/processed/inmet")

temp_rj <- df_inmet |> 
  group_by(ano, mes, codigo_wmo) |> 
  summarise(
    temp_max = max(
      temperatura_maxima_na_hora_ant_aut_c,
      na.rm = TRUE
    )
  ) |> 
  left_join(
    mun_stations |> 
      filter(station_order == 1) |> 
      select(state_muni, code_ibge7, code_wmo), 
    by = c("codigo_wmo" = "code_wmo")
  ) |> 
  filter(state_muni == "RJ") |> 
  collect() |> 
  filter(is.finite(temp_max)) |> # drop station-months with no readings
  arrange(-temp_max)
  
# A tibble: 644 × 6
# # Groups:   ano, mes [7]
#      ano   mes codigo_wmo temp_max state_muni code_ibge7
#    <int> <int> <chr>         <dbl> <chr>           <dbl>
#  1  2026     1 A601           41   RJ            3305554
#  2  2026     1 A601           41   RJ            3304144
#  3  2026     1 A601           41   RJ            3303609
#  4  2026     1 A601           41   RJ            3302270
#  5  2026     1 A601           41   RJ            3302007
#  6  2026     1 A621           40.8 RJ            3305109
#  7  2026     1 A621           40.8 RJ            3303500
#  8  2026     1 A621           40.8 RJ            3303203
#  9  2026     1 A621           40.8 RJ            3302858
# 10  2026     1 A621           40.8 RJ            3300456
# # ℹ 634 more rows
# # ℹ Use `print(n = ...)` to see more rows

library(ggplot2)

temp_rj |> 
  ggplot(aes(x = as.character(mes), y = temp_max)) +
  geom_boxplot() +
  theme_linedraw() +
  labs(y = "Max. temp. (°C)", x = "Month")
  


library(geobr)

mun_24 <- geobr::read_municipality(year = 2024)

mun_rj <- mun_24 |> 
  filter(abbrev_state == "RJ") |> 
  select(code_muni, geom)

# `temp_rj` already has one row per municipality and month
mun_temp_rj <- mun_rj |> 
  left_join(
    temp_rj, 
    by = c("code_muni" = "code_ibge7")
  )

mun_temp_rj$mes <- factor(
  mun_temp_rj$mes,
  levels = 1:12,
  labels = month.name
)

ggplot() +
  geom_sf(data = mun_temp_rj, aes(fill = temp_max)) +
  scale_fill_distiller(palette = "RdYlGn") +
  facet_wrap(.~mes) +
  labs(fill = "Max. temp. (°C)") +
  theme_void() +
  theme(
    legend.position = c(0.785, 0.185),
    plot.background = element_rect(fill = "white")
  )

License

This project is licensed under the MIT License.

Citation

To cite package ‘climateBR’ in publications use:


@Manual{,
  title = {climateBR: Download Rainfall, Temperature, and Wind Data from Brazil},
  author = {Kaio Bárbara},
  year = {2026},
  note = {R package version 0.2.5},
  url = {https://CRAN.R-project.org/package=climateBR},
}