Walkthrough

Set Up

After installing the package one can call it in a new session as any other package. A good practice is to define the cdo_token one intends to use for that session.

using NCEI
const cdo_token = ENV["cdo_token"]
# A token has form: r"[A-Za-z]{32}"

Datasets

One should first inspect the datasets available to select the ones the appropriate ones. Good information about a dataset is its ID which one needs to query data from that dataset, the temporal coverage, and documentation that can be accessed through the uid. For example, the daily summaries dataset (GHCND) has uid: C00861. The information for this dataset can be accessed at: https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.ncdc:C00861.

# Fetch all available datasets
cdo_datasets(cdo_token)

11 rows × 6 columns

	uid	mindate	maxdate	name	datacoverage	id
	String	Date	Date	String	Float64	String
1	gov.noaa.ncdc:C00861	1763-01-01	2019-02-11	Daily Summaries	1.0	GHCND
2	gov.noaa.ncdc:C00946	1763-01-01	2019-01-01	Global Summary of the Month	1.0	GSOM
3	gov.noaa.ncdc:C00947	1763-01-01	2018-01-01	Global Summary of the Year	1.0	GSOY
4	gov.noaa.ncdc:C00345	1991-06-05	2019-02-10	Weather Radar (Level II)	0.95	NEXRAD2
5	gov.noaa.ncdc:C00708	1994-05-20	2019-02-08	Weather Radar (Level III)	0.95	NEXRAD3
6	gov.noaa.ncdc:C00821	2010-01-01	2010-01-01	Normals Annual/Seasonal	1.0	NORMAL_ANN
7	gov.noaa.ncdc:C00823	2010-01-01	2010-12-31	Normals Daily	1.0	NORMAL_DLY
8	gov.noaa.ncdc:C00824	2010-01-01	2010-12-31	Normals Hourly	1.0	NORMAL_HLY
9	gov.noaa.ncdc:C00822	2010-01-01	2010-12-01	Normals Monthly	1.0	NORMAL_MLY
10	gov.noaa.ncdc:C00505	1970-05-12	2014-01-01	Precipitation 15 Minute	0.25	PRECIP_15
11	gov.noaa.ncdc:C00313	1900-01-01	2014-01-01	Precipitation Hourly	1.0	PRECIP_HLY

# Fetch all information about the GSOY dataset specifically
cdo_datasets(cdo_token, "GSOY")

1 rows × 5 columns

	mindate	maxdate	name	datacoverage	id
	Date	Date	String	Float64	String
1	1763-01-01	2018-01-01	Global Summary of the Year	1.0	GSOY

# Fetch all available datasets with the Temperature at the time of observation (TOBS) data type
cdo_datasets(cdo_token, datatypes = "TOBS")

1 rows × 6 columns

	uid	mindate	maxdate	name	datacoverage	id
	String	Date	Date	String	Float64	String
1	gov.noaa.ncdc:C00861	1763-01-01	2019-02-11	Daily Summaries	1.0	GHCND

# Fetch all available datasets with data for a given set of stations
cdo_datasets(cdo_token, stations = "GHCND:USC00010008")

6 rows × 6 columns

	uid	mindate	maxdate	name	datacoverage	id
	String	Date	Date	String	Float64	String
1	gov.noaa.ncdc:C00861	1763-01-01	2019-02-11	Daily Summaries	1.0	GHCND
2	gov.noaa.ncdc:C00946	1763-01-01	2019-01-01	Global Summary of the Month	1.0	GSOM
3	gov.noaa.ncdc:C00947	1763-01-01	2018-01-01	Global Summary of the Year	1.0	GSOY
4	gov.noaa.ncdc:C00821	2010-01-01	2010-01-01	Normals Annual/Seasonal	1.0	NORMAL_ANN
5	gov.noaa.ncdc:C00823	2010-01-01	2010-12-31	Normals Daily	1.0	NORMAL_DLY
6	gov.noaa.ncdc:C00822	2010-01-01	2010-12-01	Normals Monthly	1.0	NORMAL_MLY

Data Categories

The next step is to find the data categories one might need (e.g., temperature vs precipitation).

# Fetch all available data categories
cdo_datacategories(cdo_token)

42 rows × 2 columns

	name	id
	String	String
1	Annual Agricultural	ANNAGR
2	Annual Degree Days	ANNDD
3	Annual Precipitation	ANNPRCP
4	Annual Temperature	ANNTEMP
5	Autumn Agricultural	AUAGR
6	Autumn Degree Days	AUDD
7	Autumn Precipitation	AUPRCP
8	Autumn Temperature	AUTEMP
9	Computed	COMP
10	Computed Agricultural	COMPAGR
11	Degree Days	DD
12	Dual-Pol Moments	DUALPOLMOMENT
13	Echo Tops	ECHOTOP
14	Evaporation	EVAP
15	Hydrometeor Type	HYDROMETEOR
16	Land	LAND
17	Miscellany	MISC
18	Other	OTHER
19	Overlay	OVERLAY
20	Precipitation	PRCP
21	Pressure	PRES
22	Reflectivity	REFLECTIVITY
23	Sky cover & clouds	SKY
24	Spring Agricultural	SPAGR
⋮	⋮	⋮

# Fetch all information about the Annual Agricultural dataset specifically
cdo_datacategories(cdo_token, "ANNAGR")

1 rows × 2 columns

	name	id
	String	String
1	Annual Agricultural	ANNAGR

# Fetch data categories for a given set of locations
cdo_datacategories(cdo_token, locations = ["FIPS:37", "CITY:US390029"])

42 rows × 2 columns

	name	id
	String	String
1	Annual Agricultural	ANNAGR
2	Annual Degree Days	ANNDD
3	Annual Precipitation	ANNPRCP
4	Annual Temperature	ANNTEMP
5	Autumn Agricultural	AUAGR
6	Autumn Degree Days	AUDD
7	Autumn Precipitation	AUPRCP
8	Autumn Temperature	AUTEMP
9	Computed	COMP
10	Computed Agricultural	COMPAGR
11	Degree Days	DD
12	Dual-Pol Moments	DUALPOLMOMENT
13	Echo Tops	ECHOTOP
14	Evaporation	EVAP
15	Hydrometeor Type	HYDROMETEOR
16	Land	LAND
17	Miscellany	MISC
18	Other	OTHER
19	Overlay	OVERLAY
20	Precipitation	PRCP
21	Pressure	PRES
22	Reflectivity	REFLECTIVITY
23	Sky cover & clouds	SKY
24	Spring Agricultural	SPAGR
⋮	⋮	⋮

Data Types

Now we can inspect which variables we want to query from what data set.

# Fetch available data types
cdo_datatypes(cdo_token)

1,527 rows × 5 columns

	mindate	maxdate	name	datacoverage	id
	Date	Date	String	Float64⍰	String
1	1994-03-19	1996-05-28	Average cloudiness midnight to midnight from 30-second ceilometer data	1.0	ACMC
2	1965-01-01	2005-12-31	Average cloudiness midnight to midnight from manual observations	1.0	ACMH
3	1994-02-01	1996-05-28	Average cloudiness sunrise to sunset from 30-second ceilometer data	1.0	ACSC
4	1965-01-01	2005-12-31	Average cloudiness sunrise to sunset from manual observations	1.0	ACSH
5	1991-06-05	2019-02-10	Base Data	0.95	ALL
6	2010-01-01	2010-01-01	Long-term averages of annual cooling degree days with base 45F	1.0	ANN-CLDD-BASE45
7	2010-01-01	2010-01-01	Long-term averages of annual cooling degree days with base 50F	1.0	ANN-CLDD-BASE50
8	2010-01-01	2010-01-01	Long-term averages of annual cooling degree days with base 55F	1.0	ANN-CLDD-BASE55
9	2010-01-01	2010-01-01	Long-term averages of annual cooling degree days with base 57F	1.0	ANN-CLDD-BASE57
10	2010-01-01	2010-01-01	Long-term averages of annual cooling degree days with base 60F	1.0	ANN-CLDD-BASE60
11	2010-01-01	2010-01-01	Long-term averages of annual cooling degree days with base 70F	1.0	ANN-CLDD-BASE70
12	2010-01-01	2010-01-01	Long-term averages of annual cooling degree days with base 72F	1.0	ANN-CLDD-BASE72
13	2010-01-01	2010-01-01	Long-term averages of annual cooling degree days with base 65F	1.0	ANN-CLDD-NORMAL
14	2010-01-01	2010-01-01	Long-term averages of annual diurnal temperature range	1.0	ANN-DUTR-NORMAL
15	2010-01-01	2010-01-01	Long-term averages of annual growing degree days with base 40F	1.0	ANN-GRDD-BASE40
16	2010-01-01	2010-01-01	Long-term averages of annual growing degree days with base 45F	1.0	ANN-GRDD-BASE45
17	2010-01-01	2010-01-01	Long-term averages of annual growing degree days with base 50F	1.0	ANN-GRDD-BASE50
18	2010-01-01	2010-01-01	Long-term averages of annual growing degree days with base 55F	1.0	ANN-GRDD-BASE55
19	2010-01-01	2010-01-01	Long-term averages of annual growing degree days with base 57F	1.0	ANN-GRDD-BASE57
20	2010-01-01	2010-01-01	Long-term averages of annual growing degree days with base 60F	1.0	ANN-GRDD-BASE60
21	2010-01-01	2010-01-01	Long-term averages of annual growing degree days with base 65F	1.0	ANN-GRDD-BASE65
22	2010-01-01	2010-01-01	Long-term averages of annual growing degree days with base 70F	1.0	ANN-GRDD-BASE70
23	2010-01-01	2010-01-01	Long-term averages of annual growing degree days with base 72F	1.0	ANN-GRDD-BASE72
24	2010-01-01	2010-01-01	Long-term averages of annual growing degree days with truncated bases 48F and 86F	1.0	ANN-GRDD-TB4886
⋮	⋮	⋮	⋮	⋮	⋮

# Fetch more information about the ACMH data type id
cdo_datatypes(cdo_token, "ACMH")

1 rows × 4 columns

	mindate	maxdate	datacoverage	id
	Date	Date	Float64	String
1	1965-01-01	2005-12-31	1.0	ACMH

# Fetch data types with the air temperature data category
cdo_datatypes(cdo_token, datacategories = "TEMP")

59 rows × 5 columns

	mindate	maxdate	name	datacoverage	id
	Date	Date	String	Float64⍰	String
1	1763-01-01	2019-01-01	Cooling Degree Days Season to Date	1.0	CDSD
2	1863-05-04	2019-01-30	Number of days included in the multiday minimum temperature (MDTN)	1.0	DATN
3	1863-05-04	2019-01-29	Number of days included in the multiday maximum temperature (MDTX)	1.0	DATX
4	2010-01-01	2010-12-31	Long-term averages of daily diurnal temperature range	1.0	DLY-DUTR-NORMAL
5	2010-01-01	2010-12-31	Long-term standard deviations of daily diurnal temperature range	1.0	DLY-DUTR-STDDEV
6	2010-01-01	2010-12-31	Long-term averages of daily average temperature	1.0	DLY-TAVG-NORMAL
7	2010-01-01	2010-12-31	Long-term standard deviations of daily average temperature	1.0	DLY-TAVG-STDDEV
8	2010-01-01	2010-12-31	Long-term averages of daily maximum temperature	1.0	DLY-TMAX-NORMAL
9	2010-01-01	2010-12-31	Long-term standard deviations of daily maximum temperature	1.0	DLY-TMAX-STDDEV
10	2010-01-01	2010-12-31	Long-term averages of daily minimum temperature	1.0	DLY-TMIN-NORMAL
11	2010-01-01	2010-12-31	Long-term standard deviations of daily minimum temperature	1.0	DLY-TMIN-STDDEV
12	1763-01-01	2019-01-01	Extreme minimum temperature for the period.	1.0	EMNT
13	1763-01-01	2019-01-01	Extreme maximum temperature for the period.	1.0	EMXT
14	1763-07-01	2019-01-01	Heating Degree Days Season to Date	1.0	HDSD
15	2010-01-01	2010-12-31	Dew point 10th percentile	1.0	HLY-DEWP-10PCTL
16	2010-01-01	2010-12-31	Dew point 90th percentile	1.0	HLY-DEWP-90PCTL
17	2010-01-01	2010-12-31	Dew point mean	1.0	HLY-DEWP-NORMAL
18	2010-01-01	2010-12-31	Heat index mean	1.0	HLY-HIDX-NORMAL
19	2010-01-01	2010-12-31	Temperature 10th percentile	1.0	HLY-TEMP-10PCTL
20	2010-01-01	2010-12-31	Temperature 90th percentile	1.0	HLY-TEMP-90PCTL
21	2010-01-01	2010-12-31	Temperature mean	1.0	HLY-TEMP-NORMAL
22	2010-01-01	2010-12-31	Wind chill mean	1.0	HLY-WCHL-NORMAL
23	1763-01-01	2019-02-08	Highest minimum temperature	missing	HTMN
24	1763-01-01	2019-02-08	Highest maximum temperature	missing	HTMX
⋮	⋮	⋮	⋮	⋮	⋮

# Fetch data types that support a given set of stations
cdo_datatypes(cdo_token, stations = ["COOP:310090", "COOP:310184", "COOP:310212"])

21 rows × 5 columns

	mindate	maxdate	name	datacoverage	id
	Date	Date	String	Float64⍰	String
1	1763-01-01	2019-01-01	Cooling Degree Days	1.0	CLDD
2	1781-01-01	2019-01-01	Number of days with greater than or equal to 0.1 inch of precipitation	1.0	DP01
3	1781-01-01	2017-05-01	Number of days with greater than or equal to 0.5 inch of precipitation	1.0	DP05
4	1781-01-01	2019-01-01	Number of days with greater than or equal to 1.0 inch of precipitation	1.0	DP10
5	1888-12-01	2015-11-01	Departure from normal monthly precipitation.	1.0	DPNP
6	1888-06-01	2015-11-01	Departure from normal monthly temperature.	1.0	DPNT
7	1840-05-01	2019-01-01	Number days with snow depth > 1 inch.	1.0	DSNW
8	1763-01-01	2019-01-01	Number days with minimum temperature less than or equal to 0.0 F	1.0	DT00
9	1763-01-01	2019-01-01	Number days with minimum temperature less than or equal to 32.0 F	1.0	DT32
10	1763-01-01	2016-03-01	Number days with maximum temperature greater than or equal 90.0 F	1.0	DT90
11	1763-01-01	2019-01-01	Number days with maximum temperature < 32 F.	1.0	DX32
12	1763-01-01	2019-01-01	Extreme minimum temperature for the period.	1.0	EMNT
13	1781-01-01	2019-01-01	Extreme maximum precipitation for the period.	1.0	EMXP
14	1763-01-01	2019-01-01	Extreme maximum temperature for the period.	1.0	EMXT
15	1763-01-01	2019-01-01	Heating degree days	1.0	HTDD
16	1763-01-01	2016-03-01	Monthly Mean minimum temperature	1.0	MMNT
17	1763-01-01	2016-03-01	Monthly Mean maximum temperature	1.0	MMXT
18	1763-01-01	2016-03-01	Monthly mean temperature	1.0	MNTM
19	1857-01-01	2016-03-01	Maximum snow depth	1.0	MXSD
20	1781-01-01	2016-03-01	Total precipitation	1.0	TPCP
21	1840-05-01	2016-03-01	Total snow fall	1.0	TSNW

Location Categories

We must identify the spatial constraints of the search and that can be accomplished at various levels (e.g., State vs Zip code).

# Fetch all available location categories
cdo_locationcategories(cdo_token)

12 rows × 2 columns

	name	id
	String	String
1	City	CITY
2	Climate Division	CLIM_DIV
3	Climate Region	CLIM_REG
4	Country	CNTRY
5	County	CNTY
6	Hydrologic Accounting Unit	HYD_ACC
7	Hydrologic Cataloging Unit	HYD_CAT
8	Hydrologic Region	HYD_REG
9	Hydrologic Subregion	HYD_SUB
10	State	ST
11	US Territory	US_TERR
12	Zip Code	ZIP

# Fetch more information about the climate region location category
cdo_locationcategories(cdo_token, "CLIM_REG")

1 rows × 2 columns

	name	id
	String	String
1	Climate Region	CLIM_REG

# Fetch available location categories that have data after 1970
cdo_locationcategories(cdo_token, startdate = Date(1970, 1, 1))

12 rows × 2 columns

	name	id
	String	String
1	City	CITY
2	Climate Division	CLIM_DIV
3	Climate Region	CLIM_REG
4	Country	CNTRY
5	County	CNTY
6	Hydrologic Accounting Unit	HYD_ACC
7	Hydrologic Cataloging Unit	HYD_CAT
8	Hydrologic Region	HYD_REG
9	Hydrologic Subregion	HYD_SUB
10	State	ST
11	US Territory	US_TERR
12	Zip Code	ZIP

Locations

Now select which locations are of interest.

# Fetch available locations
cdo_locations(cdo_token)

38,859 rows × 5 columns

	mindate	maxdate	name	datacoverage	id
	Date	Date	String	Float64	String
1	1983-01-01	2019-01-30	Abu Dhabi, AE	1.0	CITY:AE000001
2	1944-03-01	2019-01-30	Ajman, AE	0.9991	CITY:AE000002
3	1944-03-01	2019-01-30	Dubai, AE	0.9991	CITY:AE000003
4	1944-03-01	2019-01-30	Sharjah, AE	0.9991	CITY:AE000006
5	1966-03-02	2019-01-30	Kabul, AF	0.9969	CITY:AF000007
6	1973-01-02	2019-01-30	Kandahar, AF	0.9819	CITY:AF000008
7	1877-04-01	2019-01-30	Algiers, AG	1.0	CITY:AG000001
8	1909-11-01	2019-01-30	Annaba, AG	1.0	CITY:AG000002
9	1973-04-01	2019-02-05	Batna, AG	0.9993	CITY:AG000003
10	1957-01-01	2019-02-05	Bechar, AG	1.0	CITY:AG000004
11	1909-11-01	2019-02-05	Bejaia, AG	1.0	CITY:AG000005
12	1880-05-01	2019-02-05	Constantine, AG	1.0	CITY:AG000006
13	1995-10-01	2019-02-05	Guelma, AG	1.0	CITY:AG000007
14	1888-01-01	2019-01-30	Laghouat, AG	1.0	CITY:AG000008
15	1995-10-01	2019-02-05	Medea, AG	0.9993	CITY:AG000009
16	1976-04-01	2019-02-05	Mostaganem, AG	0.9995	CITY:AG000010
17	1957-01-01	2019-02-05	Oran, AG	1.0	CITY:AG000011
18	1985-02-01	2019-02-05	Oum el Bouaghi, AG	1.0	CITY:AG000012
19	1981-01-01	2019-02-05	Saida, AG	0.9973	CITY:AG000013
20	1995-10-01	2019-02-05	Sidi-Bel-Abbes, AG	1.0	CITY:AG000014
21	1957-12-31	2019-02-05	Skikda, AG	1.0	CITY:AG000015
22	1940-01-01	2014-02-01	Tamanrasset, AG	0.9973	CITY:AG000016
23	1981-01-01	2019-02-05	Tlemcen, AG	1.0	CITY:AG000017
24	1881-07-01	1992-01-31	Baku, AJ	0.991	CITY:AJ000001
⋮	⋮	⋮	⋮	⋮	⋮

# Fetch more information about location id FIPS:37
cdo_locations(cdo_token, "FIPS:37")

1 rows × 5 columns

	mindate	maxdate	name	datacoverage	id
	Date	Date	String	Float64	String
1	1869-03-01	2019-02-10	North Carolina	1.0	FIPS:37

# Fetch available locations for the GHCND (Daily Summaries) dataset
cdo_locations(cdo_token, datasets = "GHCND")

28,450 rows × 5 columns

	mindate	maxdate	name	datacoverage	id
	Date	Date	String	Float64	String
1	1983-01-02	2019-01-30	Abu Dhabi, AE	0.9959	CITY:AE000001
2	1944-03-20	2019-01-30	Ajman, AE	0.9973	CITY:AE000002
3	1944-03-20	2019-01-30	Dubai, AE	0.9973	CITY:AE000003
4	1944-03-20	2019-01-30	Sharjah, AE	0.9973	CITY:AE000006
5	1966-03-02	2019-01-30	Kabul, AF	0.5559	CITY:AF000007
6	1973-01-02	2019-01-30	Kandahar, AF	0.4529	CITY:AF000008
7	1877-04-07	2019-01-30	Algiers, AG	1.0	CITY:AG000001
8	1909-11-23	2019-01-30	Annaba, AG	0.9252	CITY:AG000002
9	1973-04-03	2019-02-05	Batna, AG	0.8649	CITY:AG000003
10	1957-01-09	2019-02-05	Bechar, AG	1.0	CITY:AG000004
11	1909-11-23	2019-02-05	Bejaia, AG	1.0	CITY:AG000005
12	1880-05-18	2019-02-05	Constantine, AG	0.7634	CITY:AG000006
13	1995-10-25	2019-02-05	Guelma, AG	1.0	CITY:AG000007
14	1888-01-01	2019-01-30	Laghouat, AG	0.4328	CITY:AG000008
15	1995-10-25	2019-02-05	Medea, AG	0.739	CITY:AG000009
16	1976-04-28	2019-02-05	Mostaganem, AG	0.5775	CITY:AG000010
17	1957-01-01	2019-02-05	Oran, AG	1.0	CITY:AG000011
18	1985-02-07	2019-02-05	Oum el Bouaghi, AG	0.637	CITY:AG000012
19	1981-01-01	2019-02-05	Saida, AG	0.5947	CITY:AG000013
20	1995-10-25	2019-02-05	Sidi-Bel-Abbes, AG	0.7411	CITY:AG000014
21	1957-12-31	2019-02-05	Skikda, AG	0.6945	CITY:AG000015
22	1940-01-01	2008-12-31	Tamanrasset, AG	0.9404	CITY:AG000016
23	1981-01-01	2019-02-05	Tlemcen, AG	1.0	CITY:AG000017
24	1881-07-17	1992-01-31	Baku, AJ	0.9797	CITY:AJ000001
⋮	⋮	⋮	⋮	⋮	⋮

# Fetch all U.S. States
cdo_locations(cdo_token, locationcategories = "ST")

51 rows × 5 columns

	mindate	maxdate	name	datacoverage	id
	Date	Date	String	Float64	String
1	1888-02-01	2019-02-10	Alabama	1.0	FIPS:01
2	1893-09-01	2019-02-10	Alaska	1.0	FIPS:02
3	1867-08-01	2019-02-10	Arizona	1.0	FIPS:04
4	1871-07-01	2019-02-10	Arkansas	1.0	FIPS:05
5	1850-10-01	2019-02-10	California	1.0	FIPS:06
6	1852-10-01	2019-02-10	Colorado	1.0	FIPS:08
7	1884-11-01	2019-02-10	Connecticut	1.0	FIPS:09
8	1893-01-01	2019-02-10	Delaware	1.0	FIPS:10
9	1872-01-01	2019-02-10	District of Columbia	1.0	FIPS:11
10	1871-10-01	2019-02-10	Florida	1.0	FIPS:12
11	1849-01-01	2019-02-10	Georgia	1.0	FIPS:13
12	1905-01-01	2019-02-10	Hawaii	1.0	FIPS:15
13	1892-06-01	2019-02-10	Idaho	1.0	FIPS:16
14	1870-10-15	2019-02-10	Illinois	1.0	FIPS:17
15	1886-02-01	2019-02-10	Indiana	1.0	FIPS:18
16	1888-06-01	2019-02-10	Iowa	1.0	FIPS:19
17	1857-04-01	2019-02-10	Kansas	1.0	FIPS:20
18	1872-10-01	2019-02-10	Kentucky	1.0	FIPS:21
19	1882-07-01	2019-02-10	Louisiana	1.0	FIPS:22
20	1885-06-01	2019-02-10	Maine	1.0	FIPS:23
21	1880-01-01	2019-02-10	Maryland	1.0	FIPS:24
22	1831-02-01	2019-02-10	Massachusetts	1.0	FIPS:25
23	1887-06-01	2019-02-10	Michigan	1.0	FIPS:26
24	1886-01-01	2019-02-10	Minnesota	1.0	FIPS:27
⋮	⋮	⋮	⋮	⋮	⋮

Stations

Lastly, one can obtain the relevant stations and verify their spatial information, temporal coverage, and data quality. To select from feasible weather stations one can use packages such as Distances.jl to obtain the nearest acceptable weather station to the desired location.

# Fetch a list of stations that support a given set of data types
cdo_stations(cdo_token, datatypes = ["EMNT", "EMXT", "HTMN"])

12,832 rows × 9 columns

	elevation	mindate	maxdate	latitude	name	datacoverage	id	elevationUnit	longitude
	Float64⍰	Date	Date	Float64	String	Float64	String	String⍰	Float64
1	34.0	1944-03-01	2019-01-30	25.333	SHARJAH INTER. AIRP, AE	0.7247	GHCND:AE000041196	METERS	55.517
2	10.4	1983-01-01	2019-01-30	25.255	DUBAI INTERNATIONAL, AE	0.9973	GHCND:AEM00041194	METERS	55.364
3	26.8	1983-01-01	2019-01-30	24.433	ABU DHABI INTERNATIONAL, AE	1.0	GHCND:AEM00041217	METERS	54.651
4	24.0	1940-01-01	2019-01-30	36.7167	ALGER DAR EL BEIDA, AG	1.0	GHCND:AG000060390	METERS	3.25
5	397.0	1892-01-01	2019-02-05	30.5667	EL GOLEA, AG	0.7834	GHCND:AG000060590	METERS	2.8667
6	561.0	1958-10-01	2019-02-05	28.05	IN AMENAS, AG	1.0	GHCND:AG000060611	METERS	9.6331
7	1362.0	1940-01-01	2014-02-01	22.8	TAMANRASSET, AG	0.9911	GHCND:AG000060680	METERS	5.4331
8	50.0	1852-01-01	1966-12-31	35.7297	ORAN HOPITAL MILITAIRE, AG	0.6507	GHCND:AGE00135039	METERS	0.65
9	59.0	1877-04-01	1938-12-29	36.78	ALGIERS VILLE UNIVERSITE	0.9784	GHCND:AGE00147705	METERS	3.07
10	222.0	1879-01-01	2019-02-05	36.72	TIZI OUZOU, AG	0.5094	GHCND:AGE00147708	METERS	4.05
11	942.0	1879-01-01	1938-12-29	36.63	FORT NATIONAL, AG	0.932	GHCND:AGE00147709	METERS	4.2
12	660.0	1880-05-01	1938-12-30	36.3697	CONSTANTINE, AG	0.8736	GHCND:AGE00147711	METERS	6.62
13	112.0	1879-01-01	1938-12-28	36.17	ORLEANSVILLE CHLEF, AG	0.9084	GHCND:AGE00147712	METERS	1.34
14	1081.0	1878-01-01	1938-12-29	36.18	SETIF, AG	0.7132	GHCND:AGE00147713	METERS	5.4
15	863.0	1879-01-01	1938-12-29	35.42	TEBESSA, AG	0.6473	GHCND:AGE00147715	METERS	8.1197
16	83.0	1878-01-01	2019-02-05	35.1	NEMOURS GHAZAOUET, AG	0.5214	GHCND:AGE00147716	METERS	-1.85
17	476.0	1880-01-01	1938-12-27	35.2	SIDI BEL ABBES, AG	0.791	GHCND:AGE00147717	METERS	0.63
18	125.0	1880-01-01	2019-02-05	34.85	BISKRA, AG	0.7064	GHCND:AGE00147718	METERS	5.72
19	767.0	1888-01-01	2019-01-30	33.7997	LAGHOUAT, AG	0.5047	GHCND:AGE00147719	METERS	2.89
20	1320.0	1888-01-01	1938-12-28	33.68	GERYVILLE EL BAYADH, AG	0.8432	GHCND:AGE00147720	METERS	1.0
21	11.0	1981-11-01	2019-02-05	36.795	JIJEL, AG	0.9928	GHCND:AGM00060351	METERS	5.874
22	7.0	1957-12-31	2019-02-05	36.933	SKIKDA	0.7656	GHCND:AGM00060355	METERS	6.95
23	4.9	1945-06-09	2019-01-30	36.822	ANNABA, AG	0.8749	GHCND:AGM00060360	METERS	7.809
24	6.1	1973-01-01	2019-02-05	36.712	SOUMMAM, AG	0.9885	GHCND:AGM00060402	METERS	5.07
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮

# Fetch all information about the Abbeville AL station specifically
cdo_stations(cdo_token, "COOP:010008")

1 rows × 9 columns

	elevation	mindate	maxdate	latitude	name	datacoverage	id	elevationUnit	longitude
	Float64	Date	Date	Float64	String	Float64	String	String	Float64
1	139.0	1948-01-01	2014-01-01	31.5702	ABBEVILLE, AL US	0.8813	COOP:010008	METERS	-85.2482

# Fetch all the stations in North Carolina, US (FIPS:37)
cdo_stations(cdo_token, locations = "FIPS:37")

2,596 rows × 9 columns

	elevation	mindate	maxdate	latitude	name	datacoverage	id	elevationUnit	longitude
	Float64⍰	Date	Date	Float64	String	Float64	String	String⍰	Float64
1	185.9	1931-01-01	2015-11-01	35.3992	ALBEMARLE, NC US	0.9647	COOP:310090	METERS	-80.1994
2	841.9	1931-01-01	1962-03-01	35.9	ALTAPASS, NC US	0.9308	COOP:310160	METERS	-82.0167
3	533.1	1931-01-01	2005-09-01	35.2014	ANDREWS, NC US	0.9811	COOP:310184	METERS	-83.8386
4	519.1	1949-01-01	1962-03-01	35.0667	ANDREWS DAM, NC US	1.0	COOP:310192	METERS	-83.9167
5	137.2	1993-07-01	2015-11-01	35.7426	APEX, NC US	0.9739	COOP:310212	METERS	-78.8369
6	100.6	1931-01-01	2015-11-01	36.2911	ARCOLA, NC US	0.8881	COOP:310241	METERS	-77.9822
7	265.2	1931-01-01	2015-11-01	35.7044	ASHEBORO 2 W, NC US	0.9941	COOP:310286	METERS	-79.8378
8	637.9	1946-02-01	1958-06-01	35.4333	ASHEVILLE HENDERSONVILLE AIRPORT, NC US	0.9468	COOP:310296	METERS	-82.4833
9	645.3	1964-09-01	2015-11-01	35.4319	ASHEVILLE AIRPORT, NC US	1.0	COOP:310300	METERS	-82.5375
10	682.1	1902-08-01	2015-11-01	35.5954	ASHEVILLE, NC US	1.0	COOP:310301	METERS	-82.5568
11	571.5	1948-01-01	2014-01-01	35.8922	ASHFORD, NC US	1.0	COOP:310312	METERS	-81.9352
12	604.1	1973-09-01	1980-11-27	35.9	ASHFORD 2 N, NC US	0.0429	COOP:310314	METERS	-81.95
13	1.2	2004-07-01	2015-11-01	34.6997	ATLANTIC BEACH WATER PLANT, NC US	1.0	COOP:310356	METERS	-76.7381
14	6.1	1973-01-01	2015-11-01	35.3872	AURORA 6 N, NC US	0.9845	COOP:310375	METERS	-76.7763
15	182.9	1931-01-01	2014-01-01	35.4591	BADIN, NC US	0.9318	COOP:310438	METERS	-80.1827
16	755.9	1949-01-01	1962-03-01	36.0167	BAKERSVILLE, NC US	1.0	COOP:310466	METERS	-82.1667
17	1066.8	1949-01-01	1995-05-01	35.4167	BALSAM, NC US	0.3142	COOP:310490	METERS	-83.0833
18	1149.1	1931-01-01	2015-11-01	36.1616	BANNER ELK, NC US	0.9804	COOP:310506	METERS	-81.8741
19	707.1	1949-01-01	1962-03-01	35.76	BARNARDSVILLE 2 SE, NC US	1.0	COOP:310530	METERS	-82.4325
20	707.1	2011-01-01	2015-11-01	35.76	BARNARDSVILLE 2 SE, NC US	0.9835	COOP:310531	METERS	-82.4325
21	3.0	1954-07-01	1967-06-01	35.45	BATH 2 WSW, NC US	0.9359	COOP:310562	METERS	-76.8333
22	1.5	1968-07-01	2015-11-01	35.1311	BAYBORO 3 SW, NC US	0.9613	COOP:310576	METERS	-76.816
23	641.0	1949-01-01	1951-03-01	35.2333	BEAVERDAM CREEK, NC US	1.0	COOP:310634	METERS	-84.0833
24	573.0	1951-04-01	1962-03-01	35.2167	BEAVERDAM CREEK, NC US	0.9999	COOP:310635	METERS	-84.1
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮

Data

The final step is to obtain the raw data itself. A few transformations are recommended before proceeding with the analysis of the data (e.g., transforming the dataframe from long to short and filling the missing records with missing values for those observations). Read the documentation to interpret the various flags under the attributes column.

# Fetch data from the GHCND dataset (Daily Summaries) for zip code 28801, May 1st of 2010
cdo_data(cdo_token, "GHCND", Date(2010, 5, 1), Date(2010, 5, 1), locations = "ZIP:28801")

8 rows × 5 columns

	date	datatype	station	attributes	value
	DateTime	String	String	String⍰	Float64
1	2010-05-01T00:00:00	PRCP	GHCND:US1NCBC0005	,,N,	0.0
2	2010-05-01T00:00:00	SNOW	GHCND:US1NCBC0005	,,N,	0.0
3	2010-05-01T00:00:00	PRCP	GHCND:USW00013872	,,0,2400	0.3
4	2010-05-01T00:00:00	SNOW	GHCND:USW00013872	,,0,	0.0
5	2010-05-01T00:00:00	SNWD	GHCND:USW00013872	,,0,	0.0
6	2010-05-01T00:00:00	TMAX	GHCND:USW00013872	,,0,2400	26.7
7	2010-05-01T00:00:00	TMIN	GHCND:USW00013872	,,0,2400	13.9
8	2010-05-01T00:00:00	TOBS	GHCND:USW00013872	,,0,2400	20.6

# Fetch data from the PRECIP_15 dataset (Precipitation 15 Minute) for COOP station 010008, for May of 2010 with metric units
cdo_data(cdo_token, "PRECIP_15", Date(2010, 5, 1), Date(2010, 5, 31), stations = "COOP:010008")

63 rows × 5 columns

	date	datatype	station	attributes	value
	DateTime	String	String	String⍰	Float64
1	2010-05-01T00:15:00	QGAG	COOP:010008	g,,HT	0.0
2	2010-05-01T00:15:00	QPCP	COOP:010008	g,,HT	0.0
3	2010-05-03T14:00:00	QGAG	COOP:010008	,,HT	109.73
4	2010-05-03T14:15:00	QGAG	COOP:010008	,,HT	110.74
5	2010-05-03T14:15:00	QPCP	COOP:010008	,,HT	10.16
6	2010-05-03T14:30:00	QGAG	COOP:010008	,,HT	111.51
7	2010-05-03T14:30:00	QPCP	COOP:010008	,,HT	7.62
8	2010-05-03T14:45:00	QGAG	COOP:010008	,,HT	112.01
9	2010-05-03T14:45:00	QPCP	COOP:010008	,,HT	5.08
10	2010-05-03T15:00:00	QGAG	COOP:010008	,,HT	112.27
11	2010-05-03T15:00:00	QPCP	COOP:010008	,,HT	2.54
12	2010-05-03T15:15:00	QGAG	COOP:010008	,,HT	113.03
13	2010-05-03T15:15:00	QPCP	COOP:010008	,,HT	7.62
14	2010-05-03T15:30:00	QGAG	COOP:010008	,,HT	113.54
15	2010-05-03T15:30:00	QPCP	COOP:010008	,,HT	5.08
16	2010-05-03T16:00:00	QGAG	COOP:010008	,,HT	113.79
17	2010-05-03T16:00:00	QPCP	COOP:010008	,,HT	2.54
18	2010-05-03T17:00:00	QGAG	COOP:010008	,,HT	114.05
19	2010-05-03T17:00:00	QPCP	COOP:010008	,,HT	2.54
20	2010-05-03T17:15:00	QGAG	COOP:010008	,,HT	114.3
21	2010-05-03T17:15:00	QPCP	COOP:010008	,,HT	2.54
22	2010-05-03T17:30:00	QGAG	COOP:010008	,,HT	114.55
23	2010-05-03T17:30:00	QPCP	COOP:010008	,,HT	2.54
24	2010-05-03T17:45:00	QGAG	COOP:010008	,,HT	114.81
⋮	⋮	⋮	⋮	⋮	⋮

# Fetch data from the GSOM dataset (Global Summary of the Month) for GHCND station USC00010008, for May of 2010 with standard units
cdo_data(cdo_token, "GSOM", Date(2010, 5, 1), Date(2010, 5, 31),
         stations = "GHCND:USC00010008", metric = false)

10 rows × 5 columns

	date	datatype	station	attributes	value
	DateTime	String	String	String⍰	Float64
1	2010-05-01T00:00:00	DP01	GHCND:USC00010008	,0	4.0
2	2010-05-01T00:00:00	DP10	GHCND:USC00010008	,0	4.0
3	2010-05-01T00:00:00	DP1X	GHCND:USC00010008	,0	2.0
4	2010-05-01T00:00:00	DSND	GHCND:USC00010008	,0	0.0
5	2010-05-01T00:00:00	DSNW	GHCND:USC00010008	,0	0.0
6	2010-05-01T00:00:00	EMSD	GHCND:USC00010008	,,0,31,+	0.0
7	2010-05-01T00:00:00	EMSN	GHCND:USC00010008	,,0,31,+	0.0
8	2010-05-01T00:00:00	EMXP	GHCND:USC00010008	,,0,04,	4.0
9	2010-05-01T00:00:00	PRCP	GHCND:USC00010008	,,,0	6.6
10	2010-05-01T00:00:00	SNOW	GHCND:USC00010008	,,,0	0.0