Introduction

The GSOD or Global Surface Summary of the Day (GSOD) data provided by the US National Centers for Environmental Information (NCEI) are a valuable source of weather data with global coverage. However, the data files are cumbersome and difficult to work with. GSODR aims to make it easy to find, transfer and format the data you need for use in analysis and provides four main functions for facilitating this:

• get_GSOD() - this function queries and transfers files from the NCEI’s webpage, reformats them and returns a tidy data frame in R.

• reformat_GSOD() - this function takes individual station files from the local disk and re-formats them returning a tidy data frame in R.

• nearest_stations() - this function returns a vector of station IDs that fall within the given radius (kilometres) of a point given as latitude and longitude in order from nearest to farthest.

• update_station_list() - this function downloads the latest station list from the NCEI’s server updates the package’s internal database of stations and their metadata.

• get_inventory() - this function downloads the latest station inventory information from the NCEI’s server and returns the header information about the latest version as a message in the console and a data frame of the stations’ inventories for each year and month that data are reported.

When reformatting data either with get_GSOD() or reformat_GSOD(), all units are converted from United States Customary System (USCS) to International System of Units (SI), e.g., inches to millimetres and Fahrenheit to Celsius. Data in the R session summarise each year by station, which also includes vapour pressure and relative humidity elements calculated from existing data in GSOD.

Using get_GSOD()

Find Stations in or near Toowoomba, Queensland, Australia

GSODR provides lists of weather station locations and elevation values. It’s easy to find all stations in Australia.

library("GSODR")

# create data.frame for Australia only
Oz <- subset(isd_history, COUNTRY_NAME == "AUSTRALIA")

Oz
## Key: <STNID>
##              STNID                         NAME     LAT     LON   CTRY
##             <char>                       <char>   <num>   <num> <char>
##    1: 695023-99999          HORN ISLAND   (HID) -10.583 142.300     AS
##    2: 749430-99999           AIDELAIDE RIVER SE -13.300 131.133     AS
##    3: 749432-99999    BATCHELOR FIELD AUSTRALIA -13.049 131.066     AS
##    4: 749438-99999         IRON RANGE AUSTRALIA -12.700 143.300     AS
##    5: 749439-99999     MAREEBA AS/HOEVETT FIELD -17.050 145.400     AS
##   ---
## 1044: 959890-99999      BICHENO (COUNCIL DEPOT) -41.867 148.300     AS
## 1045: 959950-99999 LORD HOWE ISLAND WINDY POINT -31.533 159.067     AS
## 1046: 959970-99999    HEARD ISLAND (ATLAS COVE) -53.017  73.400     AS
## 1047: 996600-99999          ENVIRONM BUOY 55011 -40.800 144.300     AS
## 1048: 999999-82101               NORTHWEST CAPE -22.333 114.050     AS
##        STATE    BEGIN      END COUNTRY_NAME  ISO2C  ISO3C
##       <char>    <int>    <int>       <char> <char> <char>
##    1:        19420804 20030816    AUSTRALIA     AU    AUS
##    2:        19430228 19440821    AUSTRALIA     AU    AUS
##    3:        19421231 19430610    AUSTRALIA     AU    AUS
##    4:        19420917 19440930    AUSTRALIA     AU    AUS
##    5:        19420630 19440630    AUSTRALIA     AU    AUS
##   ---
## 1044:        19650101 20210115    AUSTRALIA     AU    AUS
## 1045:        20120920 20210116    AUSTRALIA     AU    AUS
## 1046:        19980301 20121220    AUSTRALIA     AU    AUS
## 1047:        19930221 19970403    AUSTRALIA     AU    AUS
## 1048:        19680305 19680430    AUSTRALIA     AU    AUS
# Look for a specific town in Australia
subset(Oz, grepl("TOOWOOMBA", NAME))
## Key: <STNID>
##           STNID              NAME     LAT     LON   CTRY  STATE    BEGIN
##          <char>            <char>   <num>   <num> <char> <char>    <int>
## 1: 945510-99999         TOOWOOMBA -27.583 151.933     AS        19561231
## 2: 955510-99999 TOOWOOMBA AIRPORT -27.550 151.917     AS        19980301
##         END COUNTRY_NAME  ISO2C  ISO3C
##       <int>       <char> <char> <char>
## 1: 20120503    AUSTRALIA     AU    AUS
## 2: 20210116    AUSTRALIA     AU    AUS

Now that we’ve seen where the reporting stations are located, we can download weather data from the station Toowoomba, Queensland, Australia for 2010 by using the STNID in the station parameter of get_GSOD().

tbar <- get_GSOD(years = 2010, station = "955510-99999")
str(tbar)
## Classes 'data.table' and 'data.frame':   365 obs. of  47 variables:
##  $STNID : chr "955510-99999" "955510-99999" "955510-99999" "955510-99999" ... ##$ NAME            : chr  "TOOWOOMBA AIRPORT" "TOOWOOMBA AIRPORT" "TOOWOOMBA AIRPORT" "TOOWOOMBA AIRPORT" ...
##  $CTRY : chr "AS" "AS" "AS" "AS" ... ##$ COUNTRY_NAME    : chr  "AUSTRALIA" "AUSTRALIA" "AUSTRALIA" "AUSTRALIA" ...
##  $ISO2C : chr "AU" "AU" "AU" "AU" ... ##$ ISO3C           : chr  "AUS" "AUS" "AUS" "AUS" ...
##  $STATE : chr "" "" "" "" ... ##$ LATITUDE        : num  -27.6 -27.6 -27.6 -27.6 -27.6 ...
##  $LONGITUDE : num 152 152 152 152 152 ... ##$ ELEVATION       : num  642 642 642 642 642 642 642 642 642 642 ...
##  $BEGIN : int 19980301 19980301 19980301 19980301 19980301 19980301 19980301 19980301 19980301 19980301 ... ##$ END             : int  20210116 20210116 20210116 20210116 20210116 20210116 20210116 20210116 20210116 20210116 ...
##  $YEARMODA : Date, format: "2010-01-01" "2010-01-02" ... ##$ YEAR            : int  2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 ...
##  $MONTH : int 1 1 1 1 1 1 1 1 1 1 ... ##$ DAY             : int  1 2 3 4 5 6 7 8 9 10 ...
##  $YDAY : int 1 2 3 4 5 6 7 8 9 10 ... ##$ TEMP            : num  21.2 23.2 21.4 18.9 20.5 21.9 21.3 20.9 21.9 22.3 ...
##  $TEMP_ATTRIBUTES : int 8 8 8 8 8 8 8 8 8 8 ... ##$ DEWP            : num  17.9 19.4 18.9 16.4 16.4 18.7 17.4 17.1 16.2 14.9 ...
##  $DEWP_ATTRIBUTES : int 8 8 8 8 8 8 8 8 8 8 ... ##$ SLP             : num  1013 1010 1012 1016 1016 ...
##  $SLP_ATTRIBUTES : int 8 8 8 8 8 8 8 8 8 8 ... ##$ STP             : num  942 939 941 944 944 ...
##  $STP_ATTRIBUTES : int 8 8 8 8 8 8 8 8 8 8 ... ##$ VISIB           : num  NA NA 14.3 23.3 NA NA NA NA NA NA ...
##  $VISIB_ATTRIBUTES: int 0 0 6 4 0 0 0 0 0 0 ... ##$ WDSP            : num  4.3 3.7 7.6 8.7 7.5 6.3 7.8 7.5 6.8 6.3 ...
##  $WDSP_ATTRIBUTES : int 8 8 8 8 8 8 8 8 8 8 ... ##$ MXSPD           : num  6.7 5.1 10.3 10.3 10.8 7.7 8.7 8.7 8.2 7.2 ...
##  $GUST : num NA NA NA NA NA NA NA NA NA NA ... ##$ MAX             : num  25.8 26.5 28.7 24.1 24.6 26.8 26.1 26.5 27.4 28.7 ...
##  $MAX_ATTRIBUTES : chr NA NA NA NA ... ##$ MIN             : num  17.8 19.1 19.3 16.9 16.7 17.5 19.1 18.5 17.8 17.7 ...
##  $MIN_ATTRIBUTES : chr NA NA "*" "*" ... ##$ PRCP            : num  1.52 0.25 19.81 1.02 0.25 ...
##  $PRCP_ATTRIBUTES : chr "G" "G" "G" "G" ... ##$ SNDP            : num  NA NA NA NA NA NA NA NA NA NA ...
##  $I_FOG : num 0 0 1 0 0 1 1 0 1 1 ... ##$ I_RAIN_DRIZZLE  : num  0 0 1 0 0 0 0 0 0 0 ...
##  $I_SNOW_ICE : num 0 0 0 0 0 0 0 0 0 0 ... ##$ I_HAIL          : num  0 0 0 0 0 0 0 0 0 0 ...
##  $I_THUNDER : num 0 0 0 0 0 0 0 0 0 0 ... ##$ I_TORNADO_FUNNEL: num  0 0 0 0 0 0 0 0 0 0 ...
##  $EA : num 2 2.2 2.2 1.9 1.9 2.2 2 1.9 1.8 1.7 ... ##$ ES              : num  2.5 2.8 2.5 2.2 2.4 2.6 2.5 2.5 2.6 2.7 ...
##  $RH : num 81.5 79.2 85.7 85.4 77.3 82.1 78.5 78.9 70.1 62.9 ... ## - attr(*, ".internal.selfref")=<externalptr> Using nearest_stations() to Download Multiple Stations at Once Using the nearest_stations() function, you can find stations closest to a given point specified by latitude and longitude in decimal degrees. This can be used to generate a vector to pass along to get_GSOD() and download the stations of interest. tbar_stations <- nearest_stations(LAT = -27.5598, LON = 151.9507, distance = 50) tbar <- get_GSOD(years = 2010, station = tbar_stations) str(tbar) ## Classes 'data.table' and 'data.frame': 1095 obs. of 47 variables: ##$ STNID           : chr  "945520-99999" "945520-99999" "945520-99999" "945520-99999" ...
##  $NAME : chr "OAKEY" "OAKEY" "OAKEY" "OAKEY" ... ##$ CTRY            : chr  "AS" "AS" "AS" "AS" ...
##  $COUNTRY_NAME : chr "AUSTRALIA" "AUSTRALIA" "AUSTRALIA" "AUSTRALIA" ... ##$ ISO2C           : chr  "AU" "AU" "AU" "AU" ...
##  $ISO3C : chr "AUS" "AUS" "AUS" "AUS" ... ##$ STATE           : chr  "" "" "" "" ...
##  $LATITUDE : num -27.4 -27.4 -27.4 -27.4 -27.4 ... ##$ LONGITUDE       : num  152 152 152 152 152 ...
##  $ELEVATION : num 407 407 407 407 407 ... ##$ BEGIN           : int  19730430 19730430 19730430 19730430 19730430 19730430 19730430 19730430 19730430 19730430 ...
##  $END : int 20210116 20210116 20210116 20210116 20210116 20210116 20210116 20210116 20210116 20210116 ... ##$ YEARMODA        : Date, format: "2010-01-01" "2010-01-02" ...
##  $YEAR : int 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 ... ##$ MONTH           : int  1 1 1 1 1 1 1 1 1 1 ...
##  $DAY : int 1 2 3 4 5 6 7 8 9 10 ... ##$ YDAY            : int  1 2 3 4 5 6 7 8 9 10 ...
##  $TEMP : num 23.4 26.2 24.5 21.6 22.6 24.7 24 23.3 24.4 25.1 ... ##$ TEMP_ATTRIBUTES : int  16 16 16 16 16 16 16 16 16 16 ...
##  $DEWP : num 18.4 19.4 19.4 16.8 16.9 18.7 17.1 17.1 15.7 13.6 ... ##$ DEWP_ATTRIBUTES : int  16 16 16 16 16 16 16 16 16 16 ...
##  $SLP : num 1012 1009 1011 1015 1015 ... ##$ SLP_ATTRIBUTES  : int  16 16 16 16 16 16 16 16 16 16 ...
##  $STP : num 967 964 966 969 969 ... ##$ STP_ATTRIBUTES  : int  16 16 16 16 16 16 16 16 16 16 ...
##  $VISIB : num NA NA NA NA NA NA NA NA NA NA ... ##$ VISIB_ATTRIBUTES: int  0 0 0 0 0 0 0 0 0 0 ...
##  $WDSP : num 4.3 4.1 6.1 7.5 4.4 4.3 5.8 6.2 5.6 4.5 ... ##$ WDSP_ATTRIBUTES : int  16 16 16 16 16 16 16 16 16 16 ...
##  $MXSPD : num 7.2 6.2 8.7 9.8 7.7 6.2 8.2 9.3 7.7 7.2 ... ##$ GUST            : num  NA NA NA NA NA NA NA NA NA NA ...
##  $MAX : num 28.5 31.2 33.6 27.1 27.8 30.4 30 30.5 31.9 33.2 ... ##$ MAX_ATTRIBUTES  : chr  NA NA NA NA ...
##  $MIN : num 19.5 20.5 21.3 18.8 18.4 18.6 20.6 18.6 17.2 16.2 ... ##$ MIN_ATTRIBUTES  : chr  NA NA "*" "*" ...
##  $PRCP : num 0.51 0 3.3 0 0 0 0 0.25 0 0 ... ##$ PRCP_ATTRIBUTES : chr  "G" "G" "G" "G" ...
##  $SNDP : num NA NA NA NA NA NA NA NA NA NA ... ##$ I_FOG           : num  0 0 0 0 0 0 0 0 0 0 ...
##  $I_RAIN_DRIZZLE : num 0 0 0 0 0 0 0 0 0 0 ... ##$ I_SNOW_ICE      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $I_HAIL : num 0 0 0 0 0 0 0 0 0 0 ... ##$ I_THUNDER       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $I_TORNADO_FUNNEL: num 0 0 0 0 0 0 0 0 0 0 ... ##$ EA              : num  2.1 2.2 2.2 1.9 1.9 2.2 1.9 1.9 1.8 1.6 ...
##  $ES : num 2.9 3.4 3.1 2.6 2.7 3.1 3 2.9 3.1 3.2 ... ##$ RH              : num  73.5 66.2 73.3 74.2 70.2 69.3 65.3 68.2 58.4 48.9 ...
##  - attr(*, ".internal.selfref")=<externalptr>

Plot Maximum and Minimum Temperature Values

Using the first data downloaded for a single station, 955510-99999, plot the temperature for 2010.

library("ggplot2")
library("tidyr")

# Create a dataframe of just the date and temperature values that we want to
# plot
tbar_temps <- tbar[, c("YEARMODA", "TEMP", "MAX", "MIN")]

# Gather the data from wide to long
tbar_temps <-
pivot_longer(tbar_temps, cols = TEMP:MIN, names_to = "Measurement")

ggplot(data = tbar_temps, aes(x = YEARMODA,
y = value,
colour = Measurement)) +
geom_line() +
scale_color_brewer(type = "qual", na.value = "black") +
scale_y_continuous(name = "Temperature") +
scale_x_date(name = "Date") +
ggtitle(label = "Max, min and mean temperatures for Toowoomba, Qld, AU",
subtitle = "Data: U.S. NCEI GSOD") +
theme_classic()

Using reformat_GSOD()

You may have already downloaded GSOD data or may just wish to use your browser to download the files from the server to you local disk and not use the capabilities of get_GSOD(). In that case the reformat_GSOD() function is useful.

There are two ways, you can either provide reformat_GSOD() with a list of specified station files or you can supply it with a directory containing all of the “STATION.csv” station files or “YEAR.zip” annual files that you wish to reformat.

Note Any .csv file provided to reformat_GSOD() will be imported, if it is not a GSOD data file, this will lead to an error. Make sure the directory and file lists are clean.

Reformat a List of Local Files

In this example two STATION.csv files are in subdirectories of user’s home directory and are listed for reformatting as a string.

y <- c("~/GSOD/gsod_1960/20049099999.csv",
"~/GSOD/gsod_1961/20049099999.csv")
x <- reformat_GSOD(file_list = y)

Reformat all Local Files Found in Directory

In this example all STATION.csv files in the sub-folder GSOD/gsod_1960 will be imported and reformatted.

x <- reformat_GSOD(dsn = "~/GSOD/gsod_1960")

Using update_station_list()

GSODR uses internal databases of station data from the NCEI to provide location and other metadata, e.g. elevation, station names, WMO codes, etc. to make the process of querying for weather data faster. This database is created and packaged with GSODR for distribution and is updated with new releases. Users have the option of updating these databases after installing GSODR. While this option gives the users the ability to keep the database up-to-date and gives GSODR’s authors flexibility in maintaining it, this also means that reproducibility may be affected since the same version of GSODR may have different databases on different machines. If reproducibility is necessary, care should be taken to ensure that the version of the databases is the same across different machines.

The database file isd_history.rda can be located on your local system by using the following command, paste0(.libPaths(), "/GSODR/extdata")[1], unless you have specified another location for library installations and installed GSODR there, in which case it would still be in GSODR/extdata.

To update GSODR’s internal database of station locations simply use update_station_list(), which will update the internal station database according to the latest data available from the NCEI.

update_station_list()

Using get_inventory()

GSODR provides a function, get_inventory() to retrieve an inventory of the number of weather observations by station-year-month for the beginning of record through to current.

Following is an example of how to retrieve the inventory and check a station in Toowoomba, Queensland, Australia, which was used in an earlier example.

inventory <- get_inventory()

inventory
##   *** FEDERAL CLIMATE COMPLEX INTEGRATED SURFACE DATA INVENTORY ***
##    This inventory provides the number of weather observations by
##    STATION-YEAR-MONTH for beginning of record through August 2022
## Key: <STNID>
##               STNID                NAME    LAT    LON   CTRY  STATE
##              <char>              <char>  <num>  <num> <char> <char>
##     1: 008415-99999                <NA>     NA     NA   <NA>   <NA>
##     2: 010010-99999 JAN MAYEN(NOR-NAVY) 70.933 -8.667     NO
##     3: 010010-99999 JAN MAYEN(NOR-NAVY) 70.933 -8.667     NO
##     4: 010010-99999 JAN MAYEN(NOR-NAVY) 70.933 -8.667     NO
##     5: 010014-99999          SORSTOKKEN 59.792  5.341     NO
##    ---
## 40333:   A51255-445                <NA>     NA     NA   <NA>   <NA>
## 40334:   A51255-445                <NA>     NA     NA   <NA>   <NA>
## 40335:   A51256-451                <NA>     NA     NA   <NA>   <NA>
## 40336:   A51256-451                <NA>     NA     NA   <NA>   <NA>
## 40337:   A51256-451                <NA>     NA     NA   <NA>   <NA>
##           BEGIN      END COUNTRY_NAME  ISO2C  ISO3C  YEAR   JAN   FEB
##           <int>    <int>       <char> <char> <char> <int> <int> <int>
##     1:       NA       NA         <NA>   <NA>   <NA>  2020     0     0
##     2: 19310101 20210116       NORWAY     NO    NOR  2020   736   695
##     3: 19310101 20210116       NORWAY     NO    NOR  2021   686   562
##     4: 19310101 20210116       NORWAY     NO    NOR  2022   549   513
##     5: 19861120 20210116       NORWAY     NO    NOR  2020   546   513
##    ---
## 40333:       NA       NA         <NA>   <NA>   <NA>  2021  2106  2009
## 40334:       NA       NA         <NA>   <NA>   <NA>  2022  2189  2004
## 40335:       NA       NA         <NA>   <NA>   <NA>  2020  2165  1455
## 40336:       NA       NA         <NA>   <NA>   <NA>  2021  2085  1992
## 40337:       NA       NA         <NA>   <NA>   <NA>  2022  2203  1937
##          MAR   APR   MAY   JUN   JUL   AUG   SEP   OCT   NOV   DEC
##        <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
##     1:    14     0     0     0     0     0     0     0     0     0
##     2:   744   717   744   718   743   742   718   694   708   740
##     3:   729   710   733   654   726   717   712   737   714   630
##     4:   292    31     0     0    84     0     0     0     0     0
##     5:   401   251   228   288   320   452   484   448   494   442
##    ---
## 40333:  2163  1970  2209  2105  2194  2087  2098  2150  2122  2183
## 40334:  2199  2149  2217  2136  2228   751     0     0     0     0
## 40335:  2144  2125  2199  2123  2112  2192  2083  2079  2074  2187
## 40336:  2217  1975  2146  2092  2227  2170  2080  2163  2120  2168
## 40337:  2204  2144  2218  2119  2224   751     0     0     0     0
subset(inventory, STNID %in% "955510-99999")
##   *** FEDERAL CLIMATE COMPLEX INTEGRATED SURFACE DATA INVENTORY ***
##    This inventory provides the number of weather observations by
##    STATION-YEAR-MONTH for beginning of record through August 2022
## Key: <STNID>
##           STNID              NAME    LAT     LON   CTRY  STATE    BEGIN
##          <char>            <char>  <num>   <num> <char> <char>    <int>
## 1: 955510-99999 TOOWOOMBA AIRPORT -27.55 151.917     AS        19980301
## 2: 955510-99999 TOOWOOMBA AIRPORT -27.55 151.917     AS        19980301
## 3: 955510-99999 TOOWOOMBA AIRPORT -27.55 151.917     AS        19980301
##         END COUNTRY_NAME  ISO2C  ISO3C  YEAR   JAN   FEB   MAR   APR   MAY
##       <int>       <char> <char> <char> <int> <int> <int> <int> <int> <int>
## 1: 20210116    AUSTRALIA     AU    AUS  2020   246   232   248   238   248
## 2: 20210116    AUSTRALIA     AU    AUS  2021   485   483   742   720   743
## 3: 20210116    AUSTRALIA     AU    AUS  2022   743   672   739   716   739
##      JUN   JUL   AUG   SEP   OCT   NOV   DEC
##    <int> <int> <int> <int> <int> <int> <int>
## 1:   348   493   492   480   496   475   496
## 2:   716   744   737   719   744   720   726
## 3:   716   728   230     0     0     0     0

Notes

WMO Resolution 40. NOAA Policy

The data summaries provided here are based on data exchanged under the World Meteorological Organization (WMO) World Weather Watch Program according to WMO Resolution 40 (Cg-XII). This allows WMO member countries to place restrictions on the use or re-export of their data for commercial purposes outside of the receiving country. Data for selected countries may, at times, not be available through this system. Those countries’ data summaries and products which are available here are intended for free and unrestricted use in research, education, and other non-commercial activities. However, for non-U.S. locations’ data, the data or any derived product shall not be provided to other users or be used for the re-export of commercial services.

Appendices

Appendix 1: GSODR Final Data Format, Contents and Units

GSODR formatted data include the following fields and units:

• STNID - Station number (WMO/DATSAV3 number) for the location;

• NAME - Unique text identifier;

• CTRY - Country in which the station is located. This field is the original FIPS code that NCEI provides;

• COUNTRY_NAME - Country in which the station is located. This field is the country name in English language;

• ISO2C - Country in which the station is located. This field is the two letter ISO country code;

• ISO3C - Country in which the station is located. This field is the three letter ISO country code;

• LAT - Latitude. Station dropped in cases where values are < -90 or > 90 degrees or Lat = 0 and Lon = 0;

• LON - Longitude. Station dropped in cases where values are < -180 or > 180 degrees or Lat = 0 and Lon = 0;

• ELEVATION - Elevation in metres;

• YEARMODA - Date in YYYYMMDD format;

• YEAR - The year (YYYY);

• MONTH - The month (mm);

• DAY - The day (dd);

• YDAY - Sequential day of year (not in original GSOD);

• TEMP - Mean daily temperature converted to degrees C to tenths. Missing = NA;

• TEMP_ATTRIBUTES - Number of observations used in calculating mean daily temperature;

• DEWP - Mean daily dew point converted to degrees C to tenths. Missing = NA;

• DEWP_ATTRIBUTES - Number of observations used in calculating mean daily dew point;

• SLP - Mean sea level pressure in millibars to tenths. Missing = NA;

• SLP_ATTRIBUTES - Number of observations used in calculating mean sea level pressure;

• STP - Mean station pressure for the day in millibars to tenths. Missing = NA;

• STP_ATTRIBUTES - Number of observations used in calculating mean station pressure;

• VISIB - Mean visibility for the day converted to kilometres to tenths. Missing = NA;

• VISIB_ATTRIBUTES - Number of observations used in calculating mean daily visibility;

• WDSP - Mean daily wind speed value converted to metres/second to tenths. Missing = NA;

• WDSP_ATTRIBUTES - Number of observations used in calculating mean daily wind speed;

• MXSPD - Maximum sustained wind speed reported for the day converted to metres/second to tenths. Missing = NA;

• GUST - Maximum wind gust reported for the day converted to metres/second to tenths. Missing = NA;

• MAX - Maximum temperature reported during the day converted to Celsius to tenths–time of max temp report varies by country and region, so this will sometimes not be the max for the calendar day. Missing = NA;

• MAX_ATTRIBUTES - Blank indicates max temp was taken from the explicit max temp report and not from the ‘hourly’ data. An “*” indicates max temp was derived from the hourly data (i.e., highest hourly or synoptic-reported temperature);

• MIN - Minimum temperature reported during the day converted to Celsius to tenths–time of min temp report varies by country and region, so this will sometimes not be the max for the calendar day. Missing = NA;

• MIN_ATTRIBUTES - Blank indicates max temp was taken from the explicit min temp report and not from the ‘hourly’ data. An “*” indicates min temp was derived from the hourly data (i.e., highest hourly or synoptic-reported temperature);

• PRCP - Total precipitation (rain and/or melted snow) reported during the day converted to millimetres to hundredths; will usually not end with the midnight observation, i.e., may include latter part of previous day. A value of “.00” indicates no measurable precipitation (includes a trace). Missing = NA; Note: Many stations do not report ‘0’ on days with no precipitation– therefore, NA will often appear on these days. For example, a station may only report a 6-hour amount for the period during which rain fell. See FLAGS_PRCP column for source of data;

• PRCP_ATTRIBUTES -

• A = 1 report of 6-hour precipitation amount;

• B = Summation of 2 reports of 6-hour precipitation amount;

• C = Summation of 3 reports of 6-hour precipitation amount;

• D = Summation of 4 reports of 6-hour precipitation amount;

• E = 1 report of 12-hour precipitation amount;

• F = Summation of 2 reports of 12-hour precipitation amount;

• G = 1 report of 24-hour precipitation amount;

• H = Station reported ‘0’ as the amount for the day (e.g. from 6-hour reports), but also reported at least one occurrence of precipitation in hourly observations–this could indicate a trace occurred, but should be considered as incomplete data for the day;

• I = Station did not report any precipitation data for the day and did not report any occurrences of precipitation in its hourly observations–it’s still possible that precipitation occurred but was not reported;

• SNDP - Snow depth in millimetres to tenths. Missing = NA;

• I_FOG - Indicator for fog, (1 = yes, 0 = no/not reported) for the occurrence during the day;

• I_RAIN_DRIZZLE - Indicator for rain or drizzle, (1 = yes, 0 = no/not reported) for the occurrence during the day;

• I_SNOW_ICE - Indicator for snow or ice pellets, (1 = yes, 0 = no/not reported) for the occurrence during the day;

• I_HAIL - Indicator for hail, (1 = yes, 0 = no/not reported) for the occurrence during the day;

• I_THUNDER - Indicator for thunder, (1 = yes, 0 = no/not reported) for the occurrence during the day;

• I_TORNADO_FUNNEL - Indicator for tornado or funnel cloud, (1 = yes, 0 = no/not reported) for the occurrence during the day;

• EA - Mean daily actual vapour pressure as calculated using improved August-Roche-Magnus approximation (Alduchov and Eskridge 1996). Missing = NA;

• ES - Mean daily saturation vapour pressure as calculated using improved August-Roche-Magnus approximation (Alduchov and Eskridge 1996). Missing = NA;

• RH - Mean daily relative humidity as calculated using improved August-Roche-Magnus approximation (Alduchov and Eskridge 1996). Missing = NA.

References

Alduchov, Oleg A., and Robert E. Eskridge. 1996. “Improved Magnus Form Approximation of Saturation Vapor Pressure.” Journal of Applied Meteorology 35 (4): 601–9.