Package {rmet}

Type:	Package
Title:	Download and Read Brazilian Meteorological Data from INMET
Version:	0.4.0
Description:	Automates the download and processing of historical hourly weather data from automatic stations of the Brazilian National Institute of Meteorology (INMET). It provides the official station catalogue with an offline snapshot and nearest-station search, validated and resumable downloads that detect updated archives, and a fast parser for the formatting and encoding inconsistencies of raw CSV files across years. It returns analysis-ready data frames with standardized column names and UTC timestamps. It flags implausible values with transparent quality-control checks, reports data completeness, and builds daily summaries in each station's local time with explicit rules for incomplete days. Data are retrieved from https://portal.inmet.gov.br/dadoshistoricos and https://apitempo.inmet.gov.br/estacoes/T.
Depends:	R (≥ 4.0.0)
Encoding:	UTF-8
LazyData:	true
Language:	en-US
License:	GPL (≥ 3)
URL:	https://github.com/rodrigosqrt3/rmet
BugReports:	https://github.com/rodrigosqrt3/rmet/issues
RoxygenNote:	7.3.3
Imports:	curl (≥ 4.0), jsonlite
Suggests:	spelling, testthat (≥ 3.0.0), knitr, rmarkdown, sf, withr
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	yes
Packaged:	2026-11-12 14:56:29 UTC; rodri
Author:	Rodrigo Fonseca Villa [aut, cre]
Maintainer:	Rodrigo Fonseca Villa <rodrigo03.villa@gmail.com>
Repository:	CRAN
Date/Publication:	2026-11-12 15:20:02 UTC

rmet: Access Historical Weather Data from INMET

Description

Tools to find stations and to download, cache, parse, and summarise historical hourly weather data from Brazil's National Institute of Meteorology (INMET).

Main functions

• inmet_get() downloads and reads data in one call.

• inmet_stations() and inmet_nearest() search the official station catalogue.

• inmet_qc() flags implausible values and inmet_completeness() reports missing data.

• inmet_daily() builds daily summaries in local time (inmet_timezone()).

• inmet_monthly(), inmet_et0() and inmet_derive() add monthly values, evapotranspiration and derived variables; inmet_pet_thornthwaite() and inmet_water_balance() give the water balance.

• inmet_indices(), inmet_spi(), inmet_normals() and inmet_anomaly() describe the climate; inmet_fill() fills short gaps.

• inmet_nearest(), inmet_stations_within() and inmet_interpolate() relate stations to locations and areas.

• inmet_availability() shows which stations are in the cached archives.

• inmet_as_sf() converts stations or data to spatial points.

• inmet_download(), inmet_read(), and inmet_extract() give step-by-step control.

• inmet_cache_status(), inmet_cache_clear() and inmet_cache_dir() manage the local cache.

Author(s)

Maintainer: Rodrigo Fonseca Villa rodrigo03.villa@gmail.com (ORCID)

See Also

Useful links:

• https://github.com/rodrigosqrt3/rmet

• Report bugs at https://github.com/rodrigosqrt3/rmet/issues

Anomalies relative to the climatological normal

Description

Subtracts (or divides by) the normal of each calendar month from the values of inmet_monthly().

Usage

inmet_anomaly(x, normals = NULL, variables = NULL, relative = FALSE, ...)

Arguments

Value

x with one ⁠<variable>_anom⁠ column per variable.

See Also

inmet_normals()

Examples

monthly <- inmet_monthly(inmet_daily(rmet_example))
anomalies <- inmet_anomaly(monthly, min_years = 1)
anomalies[, c("month", "temp_mean_c", "temp_mean_c_anom")]

Convert stations or data to spatial points

Description

Turns any data frame with latitude and longitude columns, such as the output of inmet_stations(), inmet_nearest(), inmet_read() or inmet_daily(), into an sf object for mapping and spatial joins. Requires the sf package.

Usage

inmet_as_sf(x, remove = FALSE)

Arguments

Value

An sf data frame with point geometries in WGS 84 (EPSG:4326). Rows with missing coordinates are dropped with a warning.

Examples

if (requireNamespace("sf", quietly = TRUE)) {
  rs <- inmet_stations(state = "RS", source = "bundled")
  points <- inmet_as_sf(rs)
  plot(points["elevation"])
}

Which stations and periods are in the cached archives

Description

Lists the station files inside the annual archives that were already downloaded, without reading any data. It is a quick way to see which stations have data in a year, and which period each one covers, before calling inmet_read().

Usage

inmet_availability(years = NULL, stations = NULL, dest_dir = inmet_cache_dir())

Arguments

Details

Only the file names are used, so the call is fast even for archives of 100 MB. The period reflects what INMET announces in the names; a station can still have gaps inside it (see inmet_completeness()).

Value

A data frame with one row per station and year: station_code, state, year, start and end (first and last day covered by the station files, from their names; NA when a name has no period) and n_files (the current-year archive has several cumulative files per station).

See Also

inmet_download(), inmet_stations(), inmet_cache_status()

Examples

# an empty cache gives an empty result
inmet_availability(dest_dir = file.path(tempdir(), "rmet-empty"))

Clear the rmet cache

Description

Clear the rmet cache

Usage

inmet_cache_clear(
  years = NULL,
  dest_dir = inmet_cache_dir(),
  ask = interactive(),
  catalog = FALSE
)

Arguments

Value

Invisible NULL.

Examples

inmet_cache_clear(dest_dir = file.path(tempdir(), "rmet"), ask = FALSE)

Default cache directory of rmet

Description

Returns the directory where rmet stores downloaded archives and the station catalogue. It is the first one that is set among the option rmet.cache_dir, the environment variable RMET_CACHE_DIR, and the user-level cache directory of R (tools::R_user_dir("rmet", "cache")). Every function that takes a dest_dir or cache_dir argument uses it as the default.

Usage

inmet_cache_dir()

Value

A single path.

See Also

inmet_cache_status(), inmet_cache_clear()

Examples

inmet_cache_dir()

# use a different directory for the whole session
old <- options(rmet.cache_dir = file.path(tempdir(), "rmet"))
inmet_cache_dir()
options(old)

Inspect downloaded INMET archives

Description

Inspect downloaded INMET archives

Usage

inmet_cache_status(dest_dir = inmet_cache_dir())

Arguments

Value

A data frame with one row per cached year: archive path, size in MB, whether the ZIP is valid, whether a partial download exists, and when the archive was downloaded (UTC, if recorded). Returned invisibly when the cache is empty.

Examples

inmet_cache_status(dest_dir = file.path(tempdir(), "rmet"))

Data completeness by station and period

Description

Summarises how complete hourly data are, to choose stations and periods before an analysis.

Usage

inmet_completeness(x, by = c("month", "year"), variables = NULL)

Arguments

Details

The expected number of hours is the full length of the period, so periods only partly covered by x (for example, the current month, or a period cut by start_date in inmet_read()) show low completeness. Global radiation is missing at night at most stations, so its completeness is usually around 50 %.

Value

A data frame with one row per station and period: station_code, period ("YYYY-MM" or "YYYY"), hours_expected (hours in the period), hours_present (hourly records in x), and, for each variable, the percentage of the expected hours with a valid value. Values flagged by inmet_qc() do not count as valid.

See Also

inmet_qc(), inmet_daily()

Examples

inmet_completeness(rmet_example, variables = c("temp_dry_c", "precip_mm"))

Convert units of solar radiation

Description

Converts daily solar radiation between the units used in agrometeorology.

Usage

inmet_convert_radiation(x, from, to)

Arguments

Value

A numeric vector.

See Also

inmet_daily()

Examples

inmet_convert_radiation(20, from = "mj_m2", to = "w_m2")

Daily summaries of hourly INMET data

Description

Aggregates the hourly output of inmet_read() or inmet_get() into one row per station and local calendar day, handling the conventions of the source data correctly.

Usage

inmet_daily(x, tz = "auto", min_hours = 20L)

Arguments

Details

INMET stamps every record with the end of the hour it describes, in UTC. A record is therefore assigned to the local day containing the middle of its hour (datetime minus 30 minutes): the value stamped 03:00 UTC, for example, describes 23:00 to 00:00 Brasilia time and belongs to the earlier day. Grouping with as.Date(datetime) instead would use UTC days, which in Brazil end at 21:00 or 22:00 local time.

Radiation is usually missing at night, so radiation_mj_m2 does not use min_hours. It is the sum of the available values on days with at least min_hours hourly records, and NA otherwise; daytime gaps make it an underestimate.

The function does not itself check data quality, but values flagged by inmet_qc() (non-missing ⁠<variable>_flag⁠ columns) are left out of the daily statistics.

Value

A data frame with one row per station and day, sorted by station and date, containing the station identifiers, date, time_zone, n_hours (number of hourly records for that day) and the daily variables that can be computed from the columns present:

Examples

daily <- inmet_daily(rmet_example)
head(daily[, c("date", "n_hours", "temp_max_c", "temp_min_c", "precip_mm")])

# after quality control, flagged hourly values are ignored
daily_qc <- inmet_daily(inmet_qc(rmet_example))

Derived hourly variables

Description

Adds commonly needed variables computed from the hourly measurements: vapour pressure, vapour pressure deficit, wind components and heat index. Values flagged by inmet_qc() are ignored.

Usage

inmet_derive(
  x,
  variables = c("vapor_pressure_kpa", "vpd_kpa", "wind_u_ms", "wind_v_ms",
    "heat_index_c")
)

Arguments

Value

x with the new columns.

Variables

Saturation vapour pressure uses the FAO-56 formula 0.6108 * exp(17.27 * T / (T + 237.3)).

See Also

inmet_et0(), inmet_daily()

Examples

hourly <- inmet_derive(rmet_example)
summary(hourly$vpd_kpa)

Design reference evapotranspiration

Description

Computes the reference evapotranspiration that is exceeded only on a given fraction of days, a common design value for irrigation systems.

Usage

inmet_design_et0(x, percentile = 0.8, by = NULL)

Arguments

Value

A data frame with one row per group, the grouping columns, n_days (days with a value) and et0_design_mm.

See Also

inmet_et0()

Examples

daily <- inmet_et0(inmet_daily(rmet_example))
inmet_design_et0(daily, by = "station_code")

Download INMET historical data archives

Description

Downloads annual ZIP archives (one per year, containing every automatic station) from INMET's historical data portal into a local cache.

Usage

inmet_download(
  years = .current_year(),
  dest_dir = inmet_cache_dir(),
  max_tries = 15L,
  quiet = FALSE,
  force = FALSE,
  check_updates = TRUE
)

Arguments

Details

Archives are written to ⁠<year>.zip.part⁠ and only renamed to ⁠<year>.zip⁠ after their size has been checked against the size reported by the server and the ZIP structure has been validated, so a partial file is never mistaken for a complete one. Interrupted downloads resume from where they stopped on the next call. A small ⁠<year>.zip.meta⁠ file records the server's version information.

The INMET server sends one archive at a time per client, so years are downloaded sequentially. Typical archives for recent years are 90 to 110 MB.

Value

Invisibly, a named character vector with the paths of the archives available after the call.

See Also

inmet_get(), inmet_read(), inmet_cache_status()

Examples

paths <- inmet_download(2023, dest_dir = file.path(tempdir(), "rmet"))

Reference evapotranspiration

Description

Adds the daily grass reference evapotranspiration et0_mm to the output of inmet_daily(). The default is the FAO Irrigation and Drainage Paper 56 Penman-Monteith equation; the Hargreaves-Samani equation needs only temperature.

Usage

inmet_et0(
  x,
  method = c("penman-monteith", "hargreaves"),
  estimate_radiation = FALSE,
  coastal = FALSE
)

Arguments

Details

Penman-Monteith follows FAO-56: mean temperature as the average of the daily extremes, actual vapour pressure from the mean dew point (or, if it is not available, from the mean relative humidity), net radiation from the measured global radiation with the clear-sky radiation of the station's latitude and elevation, no soil heat flux, and pressure from the elevation. INMET wind sensors are at 10 m, so wind speed is converted to 2 m with the logarithmic profile.

Hargreaves-Samani is 0.0023 * (Tmean + 17.8) * sqrt(Tmax - Tmin) * Ra with the extraterrestrial radiation Ra in mm/day. It is less accurate but does not depend on radiation, wind or humidity, which are often missing.

Daily radiation is the sum of the hourly values, and so is an underestimate on days with missing daytime hours; use the min_hours rule of inmet_daily() and check the result before using it in applications such as irrigation scheduling.

Value

x with the new column et0_mm (mm/day); NA on days when any input is missing.

See Also

inmet_daily(), inmet_monthly(), inmet_design_et0()

Examples

daily <- inmet_daily(rmet_example)
daily <- inmet_et0(daily)
summary(daily$et0_mm)

# temperature-only alternative
hs <- inmet_et0(daily, method = "hargreaves")

Extract INMET archives

Description

Copies the original station CSV files out of the annual archives, for use with other software.

Usage

inmet_extract(
  years,
  dest_dir = inmet_cache_dir(),
  out_dir = file.path(dest_dir, "csv"),
  stations = NULL,
  overwrite = FALSE,
  quiet = FALSE
)

Arguments

Value

Invisibly, the paths of the extracted files.

Examples

cache <- file.path(tempdir(), "rmet")
inmet_download(2023, dest_dir = cache)
inmet_extract(2023, dest_dir = cache, out_dir = file.path(tempdir(), "csv"),
              stations = "A801")

Fill short gaps in hourly or daily data

Description

Fills missing values by linear interpolation across short gaps and, for what remains, with the typical value for that day of the year (or hour of the day). Every filled value is marked in a ⁠<variable>_filled⁠ column, so the result can always be traced back to measurements.

Usage

inmet_fill(
  x,
  variables = NULL,
  max_gap = 3L,
  method = c("both", "linear", "climatology"),
  min_years = 3L
)

Arguments

Details

Each station is filled on its own. The typical value is the mean of the valid values of the same station on the same month and day (daily data) or month, day and UTC hour (hourly data), across all years in x; it needs several years of data to be meaningful, and it smooths out extreme days. Check how much was filled before an analysis: table(filled$temp_dry_c_filled, useNA = "ifany").

Value

x with the missing values filled and one ⁠<variable>_filled⁠ column per filled variable, holding "linear", "climatology" or NA. Rows that are absent from x are not created.

See Also

inmet_completeness(), inmet_qc()

Examples

hourly <- rmet_example
hourly$temp_dry_c[100:102] <- NA
filled <- inmet_fill(hourly, "temp_dry_c", method = "linear")
table(filled$temp_dry_c_filled, useNA = "ifany")

Download and read INMET data in one call

Description

The recommended high-level interface: downloads missing annual archives with inmet_download(), reuses valid cached files, and returns the result of inmet_read().

Usage

inmet_get(
  years,
  stations = NULL,
  dest_dir = inmet_cache_dir(),
  tz = "UTC",
  variables = NULL,
  start_date = NULL,
  end_date = NULL,
  drop_empty = TRUE,
  max_tries = 15L,
  force = FALSE,
  check_updates = TRUE,
  quiet = FALSE
)

Arguments

Value

A data frame as documented in inmet_read().

See Also

inmet_daily(), inmet_stations(), inmet_nearest()

Examples

weather <- inmet_get(
  years = 2023,
  stations = "A801",
  variables = c("temp_dry_c", "precip_mm"),
  dest_dir = file.path(tempdir(), "rmet")
)
daily <- inmet_daily(weather)

Climate indices from daily data

Description

Computes yearly or monthly climate indices from the output of inmet_daily(), in the spirit of the indices of the Expert Team on Climate Change Detection and Indices (ETCCDI), plus growing degree days.

Usage

inmet_indices(x, by = c("year", "month"), min_valid = 0.8, base_temp = 10)

Arguments

Details

Runs (cdd, cwd) are broken by missing days. rx5day uses five consecutive calendar days without missing values.

Value

A data frame with one row per station and period: the station identifiers, period (first day of the period), n_days (rows present) and the indices that can be computed:

See Also

inmet_daily(), inmet_spi()

Examples

inmet_indices(inmet_daily(rmet_example))

Interpolate station values to other locations

Description

Estimates a variable at any location (for example, the centroids of municipalities or the points of a grid) from the values observed at the stations, by inverse-distance weighting of the nearest stations.

Usage

inmet_interpolate(
  x,
  targets,
  variable,
  power = 2,
  k = 5L,
  max_distance_km = Inf
)

Arguments

Details

A target that coincides with a station gets that station's value. Inverse distance weighting is simple and makes no assumption about the spatial structure, but it cannot extrapolate beyond the observed values, ignores elevation, and is poor for rainfall in sparse networks; use it for a first estimate or for filling gaps, not for precise surfaces.

Value

targets with the interpolated column (named as variable), n_stations (stations used) and nearest_km (distance to the closest one).

See Also

inmet_nearest(), inmet_monthly()

Examples

stations <- inmet_stations(state = "RS", source = "bundled")
stations$elevation_obs <- stations$elevation
targets <- data.frame(latitude = c(-30, -29), longitude = c(-51, -53))
inmet_interpolate(stations, targets, "elevation_obs", k = 3)

Monthly summaries of daily INMET data

Description

Aggregates the output of inmet_daily() into one row per station and calendar month, with an explicit rule for incomplete months.

Usage

inmet_monthly(x, min_days = 20L)

Arguments

Details

Months are the calendar months of the date column, which inmet_daily() builds in each station's local time. Sums (precip_mm, rain_days, et0_mm) are not scaled up for missing days, so they are only comparable between months when min_days is close to the month length. Use inmet_completeness() to see how complete the hourly data are.

Value

A data frame with one row per station and month, sorted by station and month, with the station identifiers, month (the first day of the month, as a Date), n_days (days present in x) and the monthly variables that can be computed from the columns present:

See Also

inmet_daily(), inmet_et0()

Examples

daily <- inmet_daily(rmet_example)
monthly <- inmet_monthly(daily)
monthly[, c("month", "n_days", "temp_mean_c", "precip_mm", "rain_days")]

Find the stations closest to locations

Description

Ranks automatic stations by great-circle distance from one or many points, for example the centroids of municipalities.

Usage

inmet_nearest(latitude, longitude, n = 5L, ...)

Arguments

Value

The rows of inmet_stations() for the n nearest stations, with an added distance_km column, closest first. With several locations the rows of all of them are stacked, with an added first column location (the position of the point in latitude) and rank.

See Also

inmet_stations_within(), inmet_interpolate()

Examples

# Stations near Porto Alegre, from the bundled catalogue
inmet_nearest(-30.03, -51.23, n = 3, source = "bundled")

# several locations at once
inmet_nearest(c(-30.03, -23.55), c(-51.23, -46.63), n = 2, source = "bundled")

Climatological normals

Description

Computes the mean of each calendar month over the years available for each station, from the output of inmet_monthly().

Usage

inmet_normals(x, variables = NULL, years = NULL, min_years = 10L)

Arguments

Value

A data frame with one row per station and calendar month: station_code, month_of_year (1 to 12), n_years (the largest number of years behind any variable) and the mean of each variable.

See Also

inmet_anomaly(), inmet_monthly()

Examples

normals <- inmet_normals(inmet_monthly(inmet_daily(rmet_example)), min_years = 1)
normals[, c("month_of_year", "temp_mean_c", "precip_mm")]

Potential evapotranspiration by Thornthwaite

Description

Adds the monthly potential evapotranspiration pet_mm to the output of inmet_monthly(), from air temperature alone, with the method of Thornthwaite (1948) corrected for the day length and the number of days of each month.

Usage

inmet_pet_thornthwaite(x)

Arguments

Details

The annual heat index of each station is computed from its mean temperature of each calendar month over all the years in x, so x must have all 12 calendar months for the station; otherwise pet_mm is NA for it. Stations with several years give a stable index; with a single year the index is that of the year. Thornthwaite's method underestimates in humid tropical regions and overestimates in dry ones, and is best for monthly or longer periods; for daily needs see inmet_et0().

Value

x with the new column pet_mm (mm per month).

See Also

inmet_water_balance(), inmet_et0()

Examples

monthly <- inmet_monthly(inmet_daily(rmet_example))
monthly <- inmet_pet_thornthwaite(monthly)
monthly[, c("month", "temp_mean_c", "pet_mm")]

Quality control of hourly INMET data

Description

Flags implausible hourly values with simple, transparent checks. INMET publishes automatic-station data without consistency validation; these checks catch the most common errors (impossible values, sensor spikes, stuck sensors and internally inconsistent records) but are no substitute for checks tailored to an analysis.

Usage

inmet_qc(
  x,
  action = c("flag", "remove"),
  checks = c("range", "consistency", "spike", "persistence"),
  persistence_hours = 12L,
  quiet = FALSE
)

Arguments

Value

x with flag columns added (or flagged values removed). The attribute qc_summary is a data frame with the number and percentage of flagged values per variable and check.

Checks

• range: outside physical limits for Brazil, e.g. air temperature outside -15 to 48 degC, relative humidity outside 1 to 100 %, hourly precipitation outside 0 to 150 mm, station pressure outside 650 to 1060 hPa, or wind direction outside 0 to 360 degrees.

• consistency: hourly maximum below minimum (temperature, humidity, pressure; both flagged); air temperature outside the extremes of the hour it ends; dew point above air temperature; gust below mean wind speed. A tolerance of 0.5 units allows for rounding.

• spike: a value that departs from both adjacent hours in the same direction by more than 8 degC (air and dew-point temperature), 5 hPa (pressure) or 45 % (relative humidity). Changes that persist, such as a cold front, are not flagged.

• persistence: the same value in at least persistence_hours consecutive hours, for temperatures, humidity, pressure, wind and precipitation. Zero wind speed, zero precipitation, humidity of 90 % or more (fog, persistent rain, sensors that saturate below 100 %), and wind direction during calm hours (speed below 0.5 m/s) are not checked.

Neighbouring hours are only compared within a station and when exactly one hour apart.

See Also

inmet_daily(), inmet_completeness()

Examples

checked <- inmet_qc(rmet_example)
attr(checked, "qc_summary")
table(checked$temp_dry_c_flag, useNA = "ifany")

# daily values ignore flagged hours
daily <- inmet_daily(checked)

Read cached INMET historical data

Description

Parses the station CSV files inside annual INMET archives (downloaded with inmet_download()) into one data frame. Files are read directly from the ZIP archives; nothing is extracted to disk.

Usage

inmet_read(
  years,
  stations = NULL,
  dest_dir = inmet_cache_dir(),
  tz = "UTC",
  variables = NULL,
  start_date = NULL,
  end_date = NULL,
  drop_empty = TRUE,
  quiet = FALSE
)

Arguments

Value

A data frame with one row per station and hour, sorted by station code and time. Identifier columns come first: station_code, station_name, region, state, latitude, longitude, elevation (from each file's header) and datetime (POSIXct). Measurement columns are numeric; missing values are NA.

Repeated files

The archive of the current year contains several cumulative versions of each station file (for example, January to August and January to September). Each station and hour is returned once, taken from the most recent version that contains it.

Timestamps

Each row is stamped with the end of the hour it describes, in UTC. Values labelled "previous hour" (such as temp_max_prev_c and precip_mm) summarise the hour ending at datetime. Use inmet_daily() to build daily summaries in local time.

Columns

Unrecognised source columns are kept under a snake_case version of their original title.

See Also

inmet_get(), inmet_download(), inmet_daily(), inmet_stations()

Examples

cache <- file.path(tempdir(), "rmet")
inmet_download(2023, dest_dir = cache)
poa <- inmet_read(2023, stations = "A801", dest_dir = cache)

Standardized Precipitation Index

Description

Computes the Standardized Precipitation Index (SPI) of McKee et al. (1993), a measure of how wet or dry an accumulation of months is compared with the station's own history, from the output of inmet_monthly().

Usage

inmet_spi(x, scale = 3L, min_years = 20L)

Arguments

Details

For each station and calendar month, a gamma distribution is fitted to the accumulated precipitation by maximum likelihood (Thom's approximation), with the probability of zero accumulation treated separately, and the cumulative probability is transformed to a standard normal variable. Accumulations with any missing month are NA; months absent from x count as missing.

Value

x with the new column spi (and the attribute spi_scale). Values near 0 are normal, below -1 are dry and above 1 are wet.

See Also

inmet_monthly(), inmet_indices()

Examples

monthly <- inmet_monthly(inmet_daily(rmet_example))
# one year is too short for a meaningful SPI: use min_years = 1 only to see the output
inmet_spi(monthly, scale = 3, min_years = 1)[, c("month", "precip_mm", "spi")]

Search the INMET automatic-station catalogue

Description

Returns automatic weather stations published by INMET. By default, the function uses a cached copy of the official API response and refreshes stale caches periodically. If the service is unavailable, it falls back to an older cache or to a snapshot of the official catalogue bundled with the package (its date is stored in the snapshot_date attribute).

Usage

inmet_stations(
  region = NULL,
  state = NULL,
  search = NULL,
  status = NULL,
  entity = NULL,
  source = c("auto", "remote", "bundled"),
  refresh = FALSE,
  max_age = 30,
  cache_dir = inmet_cache_dir(),
  quiet = FALSE
)

Arguments

Value

A data frame with station code, name, region, state, coordinates, altitude, operational dates (UTC calendar dates) and status, provider metadata, and the local time_zone (see inmet_timezone()). The legacy start_year column is retained for compatibility. The attribute catalog_source records where the data came from ("cache", "INMET API", "stale cache", or "bundled fallback").

See Also

inmet_nearest(), inmet_get(), inmet_read()

Examples

# Offline, reproducible example using the bundled fallback
inmet_stations(state = "RS", source = "bundled")


# Current official catalogue (downloaded once and then cached)
inmet_stations(state = "RS", refresh = TRUE, cache_dir = tempdir())

Stations inside an area

Description

Selects the stations whose coordinates fall inside a bounding box or a polygon.

Usage

inmet_stations_within(area, ...)

Arguments

Value

The rows of inmet_stations() inside area.

See Also

inmet_nearest(), inmet_as_sf()

Examples

# stations in a box around Porto Alegre
inmet_stations_within(c(-52, -31, -50, -29), source = "bundled")

Local time zone of Brazilian stations

Description

Returns the Olson time-zone name for each station from its state and, where a state spans more than one historical zone, its longitude. The zones carry Brazil's full history of offsets and daylight saving time, so they give correct local times for any year since 2000.

Usage

inmet_timezone(state, longitude = NA_real_)

Arguments

Details

Zone boundaries inside Amazonas and Para follow municipal borders; the longitude limits reproduce them for INMET's stations but are an approximation elsewhere.

Value

A character vector of time-zone names, NA for unknown states.

Examples

inmet_timezone(c("RS", "MT", "AC", "AM", "AM"),
               c(-51.2, -56.1, -67.8, -60.0, -69.9))

Climatological water balance

Description

Sequential soil water balance of Thornthwaite and Mather (1955) for each station, from precipitation and potential evapotranspiration.

Usage

inmet_water_balance(x, awc = 100, pet = "pet_mm", initial = awc)

Arguments

Details

When precipitation is below the evapotranspiration, the soil dries exponentially: storage = previous * exp((P - PET) / awc); otherwise it recharges up to awc and the rest is excess. The first periods depend on initial, so use a series of at least a year before the period of interest. The result does not depend on the time step, but the usual choice is monthly.

Value

x with the columns storage_mm (soil water at the end of the period), etr_mm (actual evapotranspiration), deficit_mm (potential minus actual evapotranspiration) and excess_mm (surplus that percolates or runs off). Periods with missing precipitation or evapotranspiration have NA in the four columns and leave the storage unchanged.

See Also

inmet_pet_thornthwaite(), inmet_et0()

Examples

monthly <- inmet_pet_thornthwaite(inmet_monthly(inmet_daily(rmet_example)))
balance <- inmet_water_balance(monthly, awc = 100)
balance[, c("month", "precip_mm", "pet_mm", "storage_mm", "deficit_mm", "excess_mm")]

Classes of rmet data

Description

The data frames returned by inmet_read(), inmet_get(), inmet_daily() and inmet_monthly() have the classes rmet_hourly, rmet_daily and rmet_monthly, which all inherit from rmet_data and data.frame. They behave as ordinary data frames in every respect (indexing, merge(), dplyr, as.data.frame()), and add a compact print() and a summary().

Usage

## S3 method for class 'rmet_data'
print(x, ..., n = 10L)

## S3 method for class 'rmet_data'
summary(object, ...)

## S3 method for class 'summary.rmet_data'
print(x, ...)

## S3 method for class 'rmet_data'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

## S3 method for class 'rmet_data'
plot(
  x,
  y = NULL,
  variable = NULL,
  station = NULL,
  type = "l",
  xlab = "",
  ylab = NULL,
  ...
)

Arguments

Value

print() returns x invisibly. summary() returns a data frame with one row per station (class summary.rmet_data): number of rows, first and last time or date, and the percentage of non-missing values in the measurement columns. as.data.frame() removes the rmet classes.

Examples

class(rmet_example)
rmet_example
summary(rmet_example)

Hourly weather data from INMET station A801 (Porto Alegre), 2023

Description

One year of hourly observations from the automatic station A801 (Porto Alegre - Jardim Botanico, RS), exactly as returned by inmet_get(2023, stations = "A801"). Timestamps are in UTC. Hours with no measurements at all are not included.

Usage

rmet_example

Format

An rmet_hourly data frame (see rmet-classes) with 8,663 rows and 25 columns. See inmet_read() for the description of each column.

Source

INMET (Instituto Nacional de Meteorologia), https://portal.inmet.gov.br/dadoshistoricos. Generated by data-raw/rmet_example.R.

Examples

range(rmet_example$datetime)
head(inmet_daily(rmet_example))