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)

load(system.file("extdata", "isd_history.rda", package = "GSODR"))

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

Oz

##              STNID                         NAME     LAT     LON CTRY STATE
##    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      
##   ---                                                                     
## 1038: 959890-99999      BICHENO (COUNCIL DEPOT) -41.867 148.300   AS      
## 1039: 959950-99999 LORD HOWE ISLAND WINDY POINT -31.533 159.067   AS      
## 1040: 959970-99999    HEARD ISLAND (ATLAS COVE) -53.017  73.400   AS      
## 1041: 996600-99999          ENVIRONM BUOY 55011 -40.800 144.300   AS      
## 1042: 999999-82101               NORTHWEST CAPE -22.333 114.050   AS      
##          BEGIN      END COUNTRY_NAME ISO2C ISO3C
##    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
##   ---                                           
## 1038: 19650101 20190602    AUSTRALIA    AU   AUS
## 1039: 20120920 20190831    AUSTRALIA    AU   AUS
## 1040: 19980301 20121220    AUSTRALIA    AU   AUS
## 1041: 19930221 19970403    AUSTRALIA    AU   AUS
## 1042: 19680305 19680430    AUSTRALIA    AU   AUS

# Look for a specific town in Australia
subset(Oz, grepl("TOOWOOMBA", NAME))

##           STNID              NAME     LAT     LON CTRY STATE    BEGIN
## 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
## 1: 20120503    AUSTRALIA    AU   AUS
## 2: 20190831    AUSTRALIA    AU   AUS

Download a Single Station and Year

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().

library(skimr)
tbar <- get_GSOD(years = 2010, station = "955510-99999")
skim(tbar)

## Skim summary statistics
##  n obs: 365 
##  n variables: 44 
## 
## ── Variable type:character ─────────────────────────────────────────────────────────────
##          variable missing complete   n min max empty n_unique
##              CTRY       0      365 365   2   2     0        1
##   DEWP_ATTRIBUTES       0      365 365   2   2     0        6
##    MAX_ATTRIBUTES     351       14 365   1   1     0        1
##    MIN_ATTRIBUTES     212      153 365   1   1     0        1
##              NAME       0      365 365  17  17     0        1
##   PRCP_ATTRIBUTES       1      364 365   1   1     0        5
##    SLP_ATTRIBUTES       0      365 365   2   2     0        4
##             STATE       0      365 365   0   0   365        1
##             STNID       0      365 365  12  12     0        1
##    STP_ATTRIBUTES       0      365 365   2   2     0        4
##   TEMP_ATTRIBUTES       0      365 365   2   2     0        4
##  VISIB_ATTRIBUTES       0      365 365   2   2     0        6
##   WDSP_ATTRIBUTES       0      365 365   2   2     0        4
## 
## ── Variable type:Date ──────────────────────────────────────────────────────────────────
##  variable missing complete   n        min        max     median n_unique
##  YEARMODA       0      365 365 2010-01-01 2010-12-31 2010-07-02      365
## 
## ── Variable type:integer ───────────────────────────────────────────────────────────────
##          variable missing complete   n     mean     sd    p0   p25   p50
##             BEGIN       0      365 365 2e+07      0    2e+07 2e+07 2e+07
##               DAY       0      365 365    15.72   8.81     1     8    16
##               END       0      365 365 2e+07      0    2e+07 2e+07 2e+07
##             I_FOG     365        0 365   NaN     NA       NA    NA    NA
##            I_HAIL     365        0 365   NaN     NA       NA    NA    NA
##    I_RAIN_DRIZZLE     365        0 365   NaN     NA       NA    NA    NA
##        I_SNOW_ICE     365        0 365   NaN     NA       NA    NA    NA
##         I_THUNDER     365        0 365   NaN     NA       NA    NA    NA
##  I_TORNADO_FUNNEL     365        0 365   NaN     NA       NA    NA    NA
##             MONTH       0      365 365     6.53   3.45     1     4     7
##              YDAY       0      365 365   183    105.51     1    92   183
##              YEAR       0      365 365  2010      0     2010  2010  2010
##    p75  p100     hist
##  2e+07 2e+07 ▁▁▁▇▁▁▁▁
##     23    31 ▇▇▇▇▆▇▇▇
##  2e+07 2e+07 ▁▁▁▇▁▁▁▁
##     NA    NA         
##     NA    NA         
##     NA    NA         
##     NA    NA         
##     NA    NA         
##     NA    NA         
##     10    12 ▇▅▇▃▅▇▅▇
##    274   365 ▇▇▇▇▇▇▇▇
##   2010  2010 ▁▁▁▇▁▁▁▁
## 
## ── Variable type:numeric ───────────────────────────────────────────────────────────────
##   variable missing complete   n    mean    sd      p0     p25     p50
##       DEWP       4      361 365   11.83  4.86   -2.6     8.7    13.1 
##         EA       4      361 365    1.45  0.42    0.5     1.1     1.5 
##  ELEVATION       0      365 365  642     0     642     642     642   
##         ES       0      365 365    1.93  0.5     0.9     1.5     2   
##       GUST     365        0 365  NaN    NA      NA      NA      NA   
##   LATITUDE       0      365 365  -27.55  0     -27.55  -27.55  -27.55
##  LONGITUDE       0      365 365  151.92  0     151.92  151.92  151.92
##        MAX       0      365 365   21.62  4.65   10.3    17.9    21.9 
##        MIN       0      365 365   12.13  4.8     0.1     8.2    13.2 
##      MXSPD       0      365 365    8.29  2.29    3.6     6.7     8.2 
##       PRCP       1      364 365    3.02  9.32    0       0       0   
##         RH       4      361 365   75.16 12.42   32      66.7    76.9 
##        SLP       0      365 365 1017.67  5.09 1003.5  1014.1  1018   
##       SNDP     365        0 365  NaN    NA      NA      NA      NA   
##        STP       0      365 365  944.68  4.19  932.6   942.1   944.9 
##       TEMP       0      365 365   16.45  4.21    6.2    12.9    17.3 
##      VISIB     264      101 365   14.98  9.14    0.5     7.4    12.6 
##       WDSP       0      365 365    5.93  1.97    2.2     4.4     5.7 
##      p75    p100     hist
##    15.4    20.3  ▁▂▃▃▅▇▇▂
##     1.7     2.4  ▂▃▅▅▆▇▃▁
##   642     642    ▁▁▁▇▁▁▁▁
##     2.3     3.3  ▂▆▃▇▆▂▂▁
##    NA      NA            
##   -27.55  -27.55 ▁▁▁▇▁▁▁▁
##   151.92  151.92 ▁▁▁▇▁▁▁▁
##    24.6    35.1  ▁▃▆▆▇▃▁▁
##    16.4    21.4  ▁▂▅▅▅▇▇▁
##     9.8    17    ▂▆▆▇▂▁▁▁
##     1      86.6  ▇▁▁▁▁▁▁▁
##    83.3   100    ▁▁▂▃▆▇▅▂
##  1021    1031    ▁▂▃▅▇▅▂▁
##    NA      NA            
##   947.4   955.6  ▁▂▃▆▇▅▂▁
##    19.5    25.7  ▁▃▆▅▇▇▅▂
##    23.3    36    ▅▆▇▃▃▇▂▂
##     7.2    15.3  ▃▇▇▅▁▁▁▁

Using nearest_stations()

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.

There are missing stations in this query. Not all that are listed and queried actually have files on the server.

tbar_stations <- nearest_stations(LAT = -27.5598,
                                  LON = 151.9507,
                                  distance = 50)

tbar <- get_GSOD(years = 2010, station = tbar_stations)
skim(tbar)

## Skim summary statistics
##  n obs: 1095 
##  n variables: 44 
## 
## ── Variable type:character ─────────────────────────────────────────────────────────────
##          variable missing complete    n min max empty n_unique
##              CTRY       0     1095 1095   2   2     0        1
##   DEWP_ATTRIBUTES       0     1095 1095   2   2     0       10
##    MAX_ATTRIBUTES    1052       43 1095   1   1     0        1
##    MIN_ATTRIBUTES     634      461 1095   1   1     0        1
##              NAME       0     1095 1095   5  31     0        3
##   PRCP_ATTRIBUTES       1     1094 1095   1   1     0        5
##    SLP_ATTRIBUTES       0     1095 1095   2   2     0        9
##             STATE       0     1095 1095   0   0  1095        1
##             STNID       0     1095 1095  12  12     0        3
##    STP_ATTRIBUTES       0     1095 1095   2   2     0        9
##   TEMP_ATTRIBUTES       0     1095 1095   2   2     0        8
##  VISIB_ATTRIBUTES       0     1095 1095   2   2     0        6
##   WDSP_ATTRIBUTES       0     1095 1095   2   2     0        8
## 
## ── Variable type:Date ──────────────────────────────────────────────────────────────────
##  variable missing complete    n        min        max     median n_unique
##  YEARMODA       0     1095 1095 2010-01-01 2010-12-31 2010-07-02      365
## 
## ── Variable type:integer ───────────────────────────────────────────────────────────────
##          variable missing complete    n     mean        sd    p0   p25
##             BEGIN       0     1095 1095 2e+07    131241.93 2e+07 2e+07
##               DAY       0     1095 1095    15.72      8.8      1     8
##               END       0     1095 1095 2e+07         0    2e+07 2e+07
##             I_FOG    1095        0 1095   NaN        NA       NA    NA
##            I_HAIL    1095        0 1095   NaN        NA       NA    NA
##    I_RAIN_DRIZZLE    1095        0 1095   NaN        NA       NA    NA
##        I_SNOW_ICE    1095        0 1095   NaN        NA       NA    NA
##         I_THUNDER    1095        0 1095   NaN        NA       NA    NA
##  I_TORNADO_FUNNEL    1095        0 1095   NaN        NA       NA    NA
##             MONTH       0     1095 1095     6.53      3.45     1     4
##              YDAY       0     1095 1095   183       105.41     1    92
##              YEAR       0     1095 1095  2010         0     2010  2010
##    p50   p75  p100     hist
##  2e+07 2e+07 2e+07 ▇▁▁▁▁▁▇▇
##     16    23    31 ▇▇▇▇▆▇▇▇
##  2e+07 2e+07 2e+07 ▁▁▁▇▁▁▁▁
##     NA    NA    NA         
##     NA    NA    NA         
##     NA    NA    NA         
##     NA    NA    NA         
##     NA    NA    NA         
##     NA    NA    NA         
##      7    10    12 ▇▅▇▃▅▇▅▇
##    183   274   365 ▇▇▇▇▇▇▇▇
##   2010  2010  2010 ▁▁▁▇▁▁▁▁
## 
## ── Variable type:numeric ───────────────────────────────────────────────────────────────
##   variable missing complete    n    mean      sd      p0     p25     p50
##       DEWP       4     1091 1095   12.34   5.12    -3.7     8.95   13.5 
##         EA       4     1091 1095    1.5    0.46     0.5     1.1     1.5 
##  ELEVATION       0     1095 1095  380.97 224.57    94      94     406.9 
##         ES       0     1095 1095    2.12   0.62     0.9     1.6     2.1 
##       GUST    1095        0 1095  NaN     NA       NA      NA      NA   
##   LATITUDE       0     1095 1095  -27.5    0.066  -27.55  -27.55  -27.55
##  LONGITUDE       0     1095 1095  152      0.25   151.74  151.74  151.92
##        MAX       0     1095 1095   24.03   5.09    10.3    20.5    24   
##        MIN       0     1095 1095   12.13   5.64    -6       7.6    13.3 
##      MXSPD       0     1095 1095    7.13   2.42     1.5     5.1     6.7 
##       PRCP       1     1094 1095    2.79   8.68     0       0       0   
##         RH       4     1091 1095   71.38  11.86    32      64.3    71.4 
##        SLP     365      730 1095 1017.38   5.13  1003    1013.9  1017.7 
##       SNDP    1095        0 1095  NaN     NA       NA      NA      NA   
##        STP     365      730 1095  957.4   13.44   932.6   944.9   956.6 
##       TEMP       0     1095 1095   17.78   4.74     5.9    14.2    18.2 
##      VISIB     797      298 1095   21.42   8.86     0.5    14.6    24.55
##       WDSP       0     1095 1095    4.47   1.97     0.6     2.95    4.1 
##      p75    p100     hist
##    16.2    22.4  ▁▁▃▃▅▇▆▁
##     1.8     2.7  ▂▆▆▇▆▆▂▁
##   642     642    ▇▁▁▁▇▁▁▇
##     2.5     4    ▂▇▇▇▆▃▁▁
##    NA      NA            
##   -27.41  -27.41 ▇▁▁▁▁▁▁▃
##   152.33  152.33 ▇▁▇▁▁▁▁▇
##    27.5    39.2  ▁▃▆▇▇▃▂▁
##    16.7    23.7  ▁▁▃▆▅▇▇▁
##     8.7    17    ▁▆▇▆▅▁▁▁
##     0.5    86.6  ▇▁▁▁▁▁▁▁
##    80     100    ▁▁▂▅▇▆▃▁
##  1020.7  1031    ▁▂▃▆▇▅▂▁
##    NA      NA            
##   970.38  981.9  ▂▇▇▁▂▆▇▂
##    21.4    28.8  ▁▃▆▆▇▇▃▁
##    29.58   36    ▁▂▂▂▂▅▇▁
##     5.6    15.3  ▃▇▆▃▁▁▁▁

If you wished to drop the stations, 949999-00170 and 949999-00183 from the query, you could do this and run the get_GSOD() query with the new station names.

remove <- c("949999-00170", "949999-00183")

tbar_stations <- tbar_stations[!tbar_stations %in% remove]

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 <-
  gather(tbar_temps, Measurement, gather_cols = TEMP:MIN)

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()

plot of chunk Ex5

Download and Process Files in Parallel

GSODR supports the future package for parallel processing on varied platforms with the user determining the parallel back end to be used. The most simple way is to use multisession as an argument for future::plan(), which will default to available local cores as workers for the run. This can greatly reduce the time necessary to process GSOD files.

future::plan("multisession")
global <- get_GSOD(years = 2010:2011)

skim(global)

## Skim summary statistics
##  n obs: 7245807 
##  n variables: 44 
## 
## ── Variable type:character ─────────────────────────────────────────────────────────────
##          variable missing complete       n min max   empty n_unique
##              CTRY  448502  6797305 7245807   2   2       0      233
##   DEWP_ATTRIBUTES       0  7245807 7245807   2   2       0       22
##    MAX_ATTRIBUTES 4625406  2620401 7245807   1   1       0        1
##    MIN_ATTRIBUTES 3938865  3306942 7245807   1   1       0        1
##              NAME  448502  6797305 7245807   2  53       0    10765
##   PRCP_ATTRIBUTES  707510  6538297 7245807   1   1       0        9
##    SLP_ATTRIBUTES       0  7245807 7245807   2   2       0       22
##             STATE  448502  6797305 7245807   0   2 5489797       65
##             STNID       0  7245807 7245807  12  12       0    11752
##    STP_ATTRIBUTES       0  7245807 7245807   2   2       0       22
##   TEMP_ATTRIBUTES       0  7245807 7245807   2   2       0       21
##  VISIB_ATTRIBUTES       0  7245807 7245807   2   2       0       22
##   WDSP_ATTRIBUTES       0  7245807 7245807   2   2       0       22
## 
## ── Variable type:Date ──────────────────────────────────────────────────────────────────
##  variable missing complete       n        min        max     median
##  YEARMODA       0  7245807 7245807 2010-01-01 2011-12-31 2010-12-25
##  n_unique
##       730
## 
## ── Variable type:integer ───────────────────────────────────────────────────────────────
##          variable missing complete       n     mean        sd          p0
##             BEGIN  448502  6797305 7245807 2e+07    246278.31     1.9e+07
##               DAY       0  7245807 7245807    15.73      8.79     1      
##               END  448502  6797305 7245807 2e+07     13408.72 2e+07      
##             I_FOG 4419747  2826060 7245807     1         0        1      
##            I_HAIL 7245807        0 7245807   NaN        NA       NA      
##    I_RAIN_DRIZZLE 7245807        0 7245807   NaN        NA       NA      
##        I_SNOW_ICE 7245807        0 7245807   NaN        NA       NA      
##         I_THUNDER 7245807        0 7245807   NaN        NA       NA      
##  I_TORNADO_FUNNEL 7245807        0 7245807   NaN        NA       NA      
##             MONTH       0  7245807 7245807     6.55      3.45     1      
##              YDAY       0  7245807 7245807   183.73    105.4      1      
##              YEAR       0  7245807 7245807  2010.49      0.5   2010      
##    p25   p50   p75  p100     hist
##  2e+07 2e+07 2e+07 2e+07 ▁▁▂▅▃▅▃▇
##      8    16    23    31 ▇▇▇▇▆▇▇▇
##  2e+07 2e+07 2e+07 2e+07 ▁▁▁▁▁▁▁▇
##      1     1     1     1 ▁▁▁▇▁▁▁▁
##     NA    NA    NA    NA         
##     NA    NA    NA    NA         
##     NA    NA    NA    NA         
##     NA    NA    NA    NA         
##     NA    NA    NA    NA         
##      4     7    10    12 ▇▅▇▃▅▇▅▇
##     92   184   275   365 ▇▇▇▇▇▇▇▇
##   2010  2010  2011  2011 ▇▁▁▁▁▁▁▇
## 
## ── Variable type:numeric ───────────────────────────────────────────────────────────────
##   variable missing complete       n    mean     sd       p0     p25
##       DEWP  398663  6847144 7245807    6.42  12.03   -82.9    -0.7 
##         EA  398663  6847144 7245807    1.24   0.84     0       0.6 
##  ELEVATION   15064  7230743 7245807  355.61 554.09  -999.9    28   
##         ES       0  7245807 7245807    1.87   1.25     0       0.9 
##       GUST 5496881  1748926 7245807   12.06   4.1      5       9.3 
##   LATITUDE  448502  6797305 7245807   32.42  28.58   -89      25.08
##  LONGITUDE  448502  6797305 7245807    3.72  87.13  -179.98  -80.27
##        MAX    5294  7240513 7245807   17.63  13.66   -78.3     9   
##        MIN    4628  7241179 7245807    7.58  12.96   -81.8     0.2 
##      MXSPD  391661  6854146 7245807    6.11   3.32     0.5     4   
##       PRCP  707510  6538297 7245807    1.89   7.64     0       0   
##         RH  430581  6815226 7245807   69.19  19.77     0      57.7 
##        SLP 2744059  4501748 7245807 1013.99   9.2    910.2  1008.9 
##       SNDP 6856908   388899 7245807  258.71 264.92    10.2    71.1 
##        STP 2727697  4518110 7245807  560.11 462.18     0      11.9 
##       TEMP       0  7245807 7245807   12.47  13.06   -79.3     4.7 
##      VISIB 1709680  5536127 7245807   15.81   9.8      0      10   
##       WDSP  318576  6927231 7245807    3.3    2.35     0       1.7 
##      p50     p75    p100     hist
##     7.4    15.1    32.2  ▁▁▁▁▁▅▇▃
##     1       1.7     4.8  ▇▇▅▃▂▁▁▁
##   140.51  418    7018    ▁▇▁▁▁▁▁▁
##     1.6     2.8     8.8  ▇▇▅▃▁▁▁▁
##    11.3    14      59.6  ▇▅▁▁▁▁▁▁
##    40.65   50.56   83.65 ▁▁▁▁▂▅▇▁
##    11.21   71.85  179.75 ▁▅▅▂▇▂▅▂
##    19.4    28.4    56    ▁▁▁▁▃▇▇▁
##     8.9    17.1    42.7  ▁▁▁▁▂▇▇▁
##     5.7     7.7    49.8  ▇▅▁▁▁▁▁▁
##     0       0.3   481.1  ▇▁▁▁▁▁▁▁
##    72      83.3   100    ▁▁▁▃▅▇▇▅
##  1013.7  1019.1  1077.7  ▁▁▁▁▇▆▁▁
##   190.5   370.8  2989.6  ▇▂▁▁▁▁▁▁
##   882.9   977.8   999.8  ▆▁▁▁▁▁▁▇
##    13.9    22.7    43.3  ▁▁▁▁▂▆▇▂
##    14      16.7   160    ▇▁▁▁▁▁▁▁
##     2.8     4.3    49.3  ▇▁▁▁▁▁▁▁

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 September 2019    
##                STNID YEAR  JAN  FEB  MAR  APR  MAY  JUN  JUL  AUG  SEP
##      1: 007018-99999 2011    0    0 2104 2797 2543 2614  382    0    0
##      2: 007018-99999 2013    0    0    0    0    0    0  710    0    0
##      3: 007026-99999 2012    0    0    0    0    0    0  367    0    0
##      4: 007026-99999 2014    0    0    0    0    0    0  180    0    4
##      5: 007026-99999 2016    0    0    0    0    0  794    0    0    0
##     ---                                                               
## 629794:   A51256-451 2015 2196 1866 2206 1909 2215 2090 2202 2191 2128
## 629795:   A51256-451 2016 2185 2047 2175 2131 2213 2139 2209 2216 2131
## 629796:   A51256-451 2017 2192 1883 2204 1910 2145 2113 2218 2204 2082
## 629797:   A51256-451 2018 2192 1887 2194 2113 2151 2095 2202 2197 1816
## 629798:   A51256-451 2019 2188 2000 2143 2105 2187 2131 2174 2080    1
##          OCT  NOV  DEC
##      1:    0    0    0
##      2:    0    0    0
##      3:    0    0    7
##      4:    0  552    0
##      5:    0    0    0
##     ---               
## 629794: 2192 2097 2093
## 629795: 2196 2131 1665
## 629796: 2192 2103 2174
## 629797: 2195 2063 2178
## 629798:    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 September 2019    
##            STNID YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
##  1: 955510-99999 1998   0   0 222 223 221 211 226 217 222 234 215 230
##  2: 955510-99999 1999 213 201 235 224 244 229 239 247 236 246 233 243
##  3: 955510-99999 2000 241 227 247 238 246 237 245 240 236 248 239 248
##  4: 955510-99999 2001 245 223 246 238 239 236 243 240 237 236 235 246
##  5: 955510-99999 2002 245 219 246 236 243 229 243 246 227 238 233 246
##  6: 955510-99999 2003 244 217 220 232 235 233 246 242 218 239 225 245
##  7: 955510-99999 2004 240 227 241 229 233 224 235 244 235 244 235 245
##  8: 955510-99999 2005 241 221 242 240 247 239 247 247 234 242 239 246
##  9: 955510-99999 2006 245 223 246 232 241 238 247 247 239 247 240 247
## 10: 955510-99999 2007 247 222 244 240 248 240 244 244 239 247 237 246
## 11: 955510-99999 2008 247 228 248 239 248 239 248 247 239 247 238 248
## 12: 955510-99999 2009 245 222 246 235 244 237 248 248 239 248 239 248
## 13: 955510-99999 2010 248 223 248 240 244 240 242 247 240 248 240 247
## 14: 955510-99999 2011 247 224 247 240 247 240 248 247 239 248 239 248
## 15: 955510-99999 2012 248 232 248 240 248 240 248 247 240 248 240 245
## 16: 955510-99999 2013 236 220 247 233 248 239 252 247 238 248 239 246
## 17: 955510-99999 2014 243 224 247 240 246 239 246 247 240 247 240 248
## 18: 955510-99999 2015 248 222 248  72 247 240 247 248 239 247 238 247
## 19: 955510-99999 2016 246 228 245 240 246 240 248 248 238 248 239 248
## 20: 955510-99999 2017 247 224 248 240 248 239 248 247 239 248 240 248
## 21: 955510-99999 2018 248 224 248 239 247 240 246 247 218 244 190 248
## 22: 955510-99999 2019 247 224 245 240 214 240 248 246   0   0   0   0
##            STNID YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Additional Climate Data Availability

Additional climate data, GSODRdata, formatted for use with GSOD data provided by GSODR are available as an R package, which can only be installed through GitHub due to the package size, >5Mb, being too large for CRAN.

if (!require(devtools)) {
  install.packages(
    "devtools",
    repos = c(CRAN = "https://cloud.r-project.org/")
  )
  library(devtools)
}
devtools::install_github("adamhsparks/GSODRdata")
library("GSODRdata")

Notes

WMO Resolution 40. NOAA Policy

Users of these data should take into account the following (from the NCEI website):

“The following data and products may have conditions placed on their international commercial use. They can be used within the U.S. or for non-commercial international activities without restriction. The non-U.S. data cannot be redistributed for commercial purposes. Re-distribution of these data by others must provide this same notification.” WMO Resolution 40. NOAA Policy

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;
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 YYYY-mm-dd 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 rrace 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;
ES - Mean daily saturation vapour pressure;
RH - Mean daily relative humidity.

Appendix 2: Map of Current GSOD Station Locations

plot of chunk unnamed-chunk-1

GSODR

Adam H Sparks

Introduction