GSODR

Adam H. Sparks

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 six main functions for facilitating this:

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.

For more information see the description of the data provided by NCEI, https://www.ncei.noaa.gov/data/global-summary-of-the-day/doc/readme.txt.

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
## Key: <STNID>
##              STNID                         NAME     LAT     LON ELEV(M)   CTRY  STATE
##             <char>                       <char>   <num>   <num>   <num> <char> <char>
##    1: 695023-99999          HORN ISLAND   (HID) -10.583 142.300      NA     AS       
##    2: 749430-99999           AIDELAIDE RIVER SE -13.300 131.133   131.0     AS       
##    3: 749432-99999    BATCHELOR FIELD AUSTRALIA -13.049 131.066   107.0     AS       
##    4: 749438-99999         IRON RANGE AUSTRALIA -12.700 143.300    18.0     AS       
##    5: 749439-99999     MAREEBA AS/HOEVETT FIELD -17.050 145.400   443.0     AS       
##   ---                                                                                
## 1415: 959890-99999      BICHENO (COUNCIL DEPOT) -41.867 148.300    11.0     AS       
## 1416: 959950-99999 LORD HOWE ISLAND WINDY POINT -31.533 159.067     4.0     AS       
## 1417: 959970-99999    HEARD ISLAND (ATLAS COVE) -53.017  73.400     4.0     AS       
## 1418: 996600-99999          ENVIRONM BUOY 55011 -40.800 144.300     0.0     AS       
## 1419: 999999-82101               NORTHWEST CAPE -22.333 114.050    38.1     AS       
##          BEGIN      END COUNTRY_NAME  ISO2C  ISO3C
##          <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
##   ---                                             
## 1415: 19650101 20240717    AUSTRALIA     AU    AUS
## 1416: 20120920 20240718    AUSTRALIA     AU    AUS
## 1417: 19980301 20121220    AUSTRALIA     AU    AUS
## 1418: 19930221 19970403    AUSTRALIA     AU    AUS
## 1419: 19680305 19680430    AUSTRALIA     AU    AUS
# Look for a specific town in Australia
subset(Oz, grepl("TOOWOOMBA", NAME))
## Key: <STNID>
##           STNID              NAME     LAT     LON ELEV(M)   CTRY  STATE    BEGIN
##          <char>            <char>   <num>   <num>   <num> <char> <char>    <int>
## 1: 945510-99999         TOOWOOMBA -27.583 151.933     676     AS        19561231
## 2: 955510-99999 TOOWOOMBA AIRPORT -27.550 151.917     642     AS        19980301
##         END COUNTRY_NAME  ISO2C  ISO3C
##       <int>       <char> <char> <char>
## 1: 19971231    AUSTRALIA     AU    AUS
## 2: 20240718    AUSTRALIA     AU    AUS

Download a Single Station and Year Using get_GSOD()

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  20240718 20240718 20240718 20240718 20240718 20240718 20240718 20240718 20240718 20240718 ...
##  $ 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.

Warning messages will be generated as not all stations have data for the requested year.

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

tbar <- get_GSOD(years = 2010, station = tbar_stations)
## Warning: 
## This station, 945510-99999, only provides data for years 1956 to 1997.
## Please send a request that falls within these years.
## Warning: 
## This station, 949999-00170, only provides data for years 1971 to 1984.
## Please send a request that falls within these years.
## Warning: 
## This station, 949999-00183, only provides data for years 1983 to 1984.
## Please send a request that falls within these years.
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  20240718 20240718 20240718 20240718 20240718 20240718 20240718 20240718 20240718 20240718 ...
##  $ 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()
plot of chunk Ex5

plot of chunk Ex5

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 get_updates()

{GSODR} provides a function, get_updates(), to retrieve the changelog for the GSOD data and return it in order from newest to oldest changes to the data set.

Following is an example how to use this function.

{r Ex17, eval=TRUE, message=FALSE}' get_updates()

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 July 2024  
## Key: <STNID>
##                STNID                NAME    LAT    LON ELEV(M)   CTRY  STATE    BEGIN
##               <char>              <char>  <num>  <num>   <num> <char> <char>    <int>
##      1: 008415-99999                <NA>     NA     NA      NA   <NA>   <NA>       NA
##      2: 010010-99999 JAN MAYEN(NOR-NAVY) 70.933 -8.667       9     NO        19310101
##      3: 010010-99999 JAN MAYEN(NOR-NAVY) 70.933 -8.667       9     NO        19310101
##      4: 010010-99999 JAN MAYEN(NOR-NAVY) 70.933 -8.667       9     NO        19310101
##      5: 010010-99999 JAN MAYEN(NOR-NAVY) 70.933 -8.667       9     NO        19310101
##     ---                                                                              
## 141732:   A51256-451                <NA>     NA     NA      NA   <NA>   <NA>       NA
## 141733:   A51256-451                <NA>     NA     NA      NA   <NA>   <NA>       NA
## 141734:   A51256-451                <NA>     NA     NA      NA   <NA>   <NA>       NA
## 141735:   A51256-451                <NA>     NA     NA      NA   <NA>   <NA>       NA
## 141736:   A51256-451                <NA>     NA     NA      NA   <NA>   <NA>       NA
##              END COUNTRY_NAME  ISO2C  ISO3C  YEAR   JAN   FEB   MAR   APR   MAY   JUN
##            <int>       <char> <char> <char> <int> <int> <int> <int> <int> <int> <int>
##      1:       NA         <NA>   <NA>   <NA>  2020     0     0    14     0     0     0
##      2: 20240718       NORWAY     NO    NOR  2020   736   695   744   717   744   718
##      3: 20240718       NORWAY     NO    NOR  2021   686   562   729   710   733   654
##      4: 20240718       NORWAY     NO    NOR  2022   549   513   292    98     0     0
##      5: 20240718       NORWAY     NO    NOR  2023   738   657   715   713   735   666
##     ---                                                                              
## 141732:       NA         <NA>   <NA>   <NA>  2020  2165  1455  2144  2125  2199  2123
## 141733:       NA         <NA>   <NA>   <NA>  2021  2085  1992  2217  1975  2146  2092
## 141734:       NA         <NA>   <NA>   <NA>  2022  2203  1937  2204  2144  2218  2119
## 141735:       NA         <NA>   <NA>   <NA>  2023  2006  1988  2172  1993  2063  2088
## 141736:       NA         <NA>   <NA>   <NA>  2024  2223  1956  2215  2152  2221  2004
##           JUL   AUG   SEP   OCT   NOV   DEC
##         <int> <int> <int> <int> <int> <int>
##      1:     0     0     0     0     0     0
##      2:   743   742   718   694   708   740
##      3:   726   717   712   737   714   630
##      4:   137     0   292   709   708   724
##      5:   735   726   693   729   698   741
##     ---                                    
## 141732:  2112  2192  2083  2079  2074  2187
## 141733:  2227  2170  2080  2163  2120  2168
## 141734:  2224  2209  2137  1743  2126  2201
## 141735:  2189  2182  2147  2199  2120  2197
## 141736:  1369     0     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 July 2024  
## Key: <STNID>
##           STNID              NAME    LAT     LON ELEV(M)   CTRY  STATE    BEGIN
##          <char>            <char>  <num>   <num>   <num> <char> <char>    <int>
## 1: 955510-99999 TOOWOOMBA AIRPORT -27.55 151.917     642     AS        19980301
## 2: 955510-99999 TOOWOOMBA AIRPORT -27.55 151.917     642     AS        19980301
## 3: 955510-99999 TOOWOOMBA AIRPORT -27.55 151.917     642     AS        19980301
## 4: 955510-99999 TOOWOOMBA AIRPORT -27.55 151.917     642     AS        19980301
## 5: 955510-99999 TOOWOOMBA AIRPORT -27.55 151.917     642     AS        19980301
##         END COUNTRY_NAME  ISO2C  ISO3C  YEAR   JAN   FEB   MAR   APR   MAY   JUN   JUL
##       <int>       <char> <char> <char> <int> <int> <int> <int> <int> <int> <int> <int>
## 1: 20240718    AUSTRALIA     AU    AUS  2020   246   232   248   238   248   348   493
## 2: 20240718    AUSTRALIA     AU    AUS  2021   485   483   742   720   743   716   744
## 3: 20240718    AUSTRALIA     AU    AUS  2022   743   672   739   716   739   716   728
## 4: 20240718    AUSTRALIA     AU    AUS  2023   738   663   730   715   737   701   733
## 5: 20240718    AUSTRALIA     AU    AUS  2024   741   691   626   662   714   703   429
##      AUG   SEP   OCT   NOV   DEC
##    <int> <int> <int> <int> <int>
## 1:   492   480   496   475   496
## 2:   737   719   744   720   726
## 3:   742   716   726   713   726
## 4:   729   700   730   710   744
## 5:     0     0     0     0     0

Using update_internal_isd_history()

{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_internal_isd_history()

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:

Appendix 2: Map of Current GSOD Station Locations

GSOD Station Locations. Data comes from US NCEI GSOD and CIA World DataBank II

GSOD Station Locations. Data comes from US NCEI GSOD and CIA World DataBank II

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.