rainOrSnowTools

The goal of the rainOrSnowTools R package is to support analysis for the Mountain Rain or Snow citizen science project.

🌨️🌧️👉 The processed data are available on our public-facing dashboard 👈🌧️🌨️

You can learn more about how to use the data on the dashboard’s User Guide tab.

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rainOrSnowTools provides:

1. Access to meteorological data from the HADS, LCD, WCC, and MADIS networks.
2. Modeled meteorological data (air/dew point/wet bulb temperature and relative humidity) for an observation point.
3. GPM IMERG probability of liquid precipitation (PLP) data for an observation point.
4. Geographical data (elevation, state, and Ecoregion 3/4) for an observation point.
5. QC of processed observation data.

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Installation and Loading

You can install rainOrSnowTools with:

# install.packages("devtools")
devtools::install_github("LynkerIntel/rainOrSnowTools")

Load in the rainOrSnowTools library:

library(rainOrSnowTools)

Package Functions

Each observation is geotagged with a datetime, location (latitude, longitude) and phase observation.

In this example, we will use the following sample: datetime: 2025-04-10 12:30:00 UTC,lat = 40,lon = -105, phase = "Snow"

Tagging an observation with ancillary data:

Geography-related:

Need to provide a lat/lon

Function	Description
get_elev	Elevation in meters, derived from the USGS 3DEP 10m product
get_eco_level3	EPA Ecoregion Level 3
get_eco_level4	EPA Ecoregion Level 4
get_state	U.S. State

# Elevation data in meters:
elev <- rainOrSnowTools::get_elev(lon_obs = lon,
                                  lat_obs = lat)
# [1] 1590.199

Meteorology-related:

Need to provide datetime and lat/lon

Function	Description
get_imerg	GPM IMERG Probability of Liquid Precipitation value (closer to 0 = likely snow, closer to 100 = likely rain)
model_meteo	This final function uses data collected from helper functions to model met variables for an observation point. Will only provide modeled data for points with >= 5 station data. Helper functions = access_meteo, qc_meteo, select_meteo, gather_meta

• access_meteo = Access data (+/- 1 hour) from the met network of choice (HADS, LCD, WCC, MADIS, or ALL). Required variables include datetime, lat, lon, deg_filter (search radius)
  • Note that LCD stations are no longer an available network after August 2025
• qc_meteo = QC the met data collected using standard thresholds
• select_meteo = Final selection of the met data from the network(s) of choice, filtering data to grab the closest in time
• gather_meta = Gathers the metadata associated for each station from select_meteo

# Get GPM IMERG probability of liquid precipitation:
plp <- rainOrSnowTools::get_imerg(datetime_utc    = datetime,
                                  lon_obs         = lon,
                                  lat_obs         = lat,
                                  product_version = 'GPM_3IMERGHHL.07') # this is the default version

# [1] 2
# This values agrees with the Snow phase report

Note that the GPM IMERG algorithm has deprecated the version 6 as of late 2024, see here

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Example workflow:

Get modeled meteorological variables, harnessing data from the HADS, WCC, and MADIS met networks.

# Define the static vars
met_networks = "ALL" # Call the HADS, WCC, and MADIS stations
degree_filter = 1 # 1º radius

meteo <- rainOrSnowTools::access_meteo(networks         = met_networks,
                                       datetime_utc_obs = datetime,
                                       lon_obs          = lon,
                                       lat_obs          = lat,
                                       deg_filter       = degree_filter)
#> Warning in file(file, "rt"): cannot open URL
#> 'https://www.ncei.noaa.gov/access/services/data/v1?dataset=local-climatological-data&stations=72438504828,72220012832,72539614815,72218604892,99999963898,72743604863,72635594871,72541614864,72437503893,72638414817,72743404870,99999954810,72324013882,72541404886,72436313803,72538794899,72090300441,72054300167,72535404806,72648094853,72225013829,72424013807,72533014827,72034054818,72041500140,72073600265,72438814829,72635094860,72628494836,72744014858,72539404839,72436553896,72438453842,72057500173,72438093819,A0735900240,72635794815,72438754807,74780703821,72438614835,72096100336,72423513810,72423093821,72636404883,72638594894,72540804881,74421494852,72743094850,72533694895,72019854813,72636014840,72639404874,72533594833,72320093801,72435653866,72535014848,72234354826,72538354827,72437303868,72037154850,72638714850,99999900430,72026654809,72067400249,72405853818,72540404847,72060100193,72521014895,72430314813,72216013869,72316013870,72540554816,72639094849,72319093846,72
#> [... truncated]

# List of Observations ("met") and Metadata ("metadata")

met <- meteo[["met"]]

# QC the meteo data
meteo_qc <- rainOrSnowTools::qc_meteo(met)
## All data pass QC

# Subset the data to select data points closest in time, and average the measured value
meteo_subset <- rainOrSnowTools:::select_meteo(meteo_qc, datetime)

# Get metadata for each station ID
metadata <- meteo$metadata %>%
          dplyr::filter(id %in% meteo_subset$id)

# `metadata` can get the number of data points from each network

#>      hads_counts wcc_counts madis_counts
#>       43         22          357

# `meteo_subset` can get the number of data points for each met variable

#>    rh temp_air temp_dew temp_wet
#>   360      404      333        0

# Finally, get modeled meteorological variables
met_vars <- rainOrSnowTools::model_meteo(id           = "example",
                                         lon_obs      = lon,
                                         lat_obs      = lat,
                                         elevation    = elev,
                                         datetime_utc = datetime,
                                         meteo_df     = meteo_subset,
                                         meta_df      = metadata)

# Output of 37 variables, does not include the QC flags

# Temperature units = ºC and RH unit = %
dplyr::tibble(met_vars)
#> # A tibble: 1 × 37
#>   id      temp_air_idw_lapse_const temp_air_idw_lapse_var temp_air_nearest_sit…¹
#>   <chr>                      <dbl>                  <dbl>                  <dbl>
#> 1 example                     5.50                   5.31                   7.43
#> # ℹ abbreviated name: ¹​temp_air_nearest_site_const
#> # ℹ 33 more variables: temp_air_nearest_site_var <dbl>, temp_air_avg_obs <dbl>,
#> #   temp_air_min_obs <dbl>, temp_air_max_obs <dbl>, temp_air_lapse_var <dbl>,
#> #   temp_air_lapse_var_r2 <dbl>, temp_air_lapse_var_pval <dbl>,
#> #   temp_air_n_stations <int>, temp_air_avg_time_gap <dbl>,
#> #   temp_air_avg_dist <dbl>, temp_air_nearest_id <chr>,
#> #   temp_air_nearest_elev <dbl>, temp_air_nearest_dist <dbl>, …

Data Citations:

Kantor, Diana; Casey, Nancy W.; Menne, Matthew J.; Buddenberg, Andrew. 2023. Local Climatological Data (LCD), Version 1. NOAA National Centers for Environmental Information. https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C01689. [before 29 Aug 2025]

NOAA National Centers for Environmental Prediction. 2006. Hydrometeorological Automated Data System (HADS). NOAA National Centers for Environmental Information.

NOAA. (n.d.). MADIS Meteorological Surface Dataset. Meteorological Assimilation Data Ingest System (MADIS). https://madis.ncep.noaa.gov/

NRCS. (n.d.). Snow and Climate Monitoring rRports and Maps. U.S. Department of Agriculture. https://www.nrcs.usda.gov/wps/portal/wcc/home/