Jacqueline Wingen: Getting temperature data

Temperature data is crucial for phenology and chill models, as it serves as a key input for these calculations. However, accessing weather data is often challenging due to restrictions and high costs, even when the data is publicly funded.

The chillR package facilitates access to global and California-specific weather databases, helping researchers obtain necessary temperature records.

Global Summary of the Day (GSOD)

The National Centers for Environmental Information (NCEI) provides temperature data through the GSOD database. While retrieving data manually can be cumbersome, chillR offers a streamlined solution with the function handle_gsod().

Listing Weather Stations

To retrieve a list of weather stations near a specific location, sorted by proximity, use:

station_list <- handle_gsod(action = "list_stations", 
                            location = c(7.10, 50.73), 
                            time_interval = c(1990, 2020))

This function returns a table containing station codes, available data years, and the percentage of the selected time period covered.

Downloading Weather Data

Once a suitable station is identified, its chillR_code can be used to download temperature records:

weather <- handle_gsod(action = "download_weather", 
                       location = station_list$chillR_code[4], 
                       time_interval = c(1990,2020))

This returns a list where weather[[1]] contains metadata, and weather[[2]] holds the actual temperature dataset.

Cleaning Weather Data

Raw GSOD data contains unnecessary variables and is recorded in Fahrenheit. chillR provides a function to clean and convert these records:

cleaned_weather <- handle_gsod(weather)

Temperature values are converted using the formula:

\(Temperature[°C]=(Temperature[°F]-32)\cdot\frac{5}{9}\)

This results in a more usable dataset suitable for further analysis.

Saving Data for Future Use

write.csv(station_list, "data/station_list.csv", row.names=FALSE)
write.csv(weather[[1]], "data/Bonn_raw_weather.csv", row.names=FALSE)
write.csv(cleaned_weather[[1]], "data/Bonn_chillR_weather.csv", row.names=FALSE)

`Exercises` on getting temperature data

Choose a location of interest and find the 25 closest weather stations using the handle_gsod function

station_list_Yakima <- handle_gsod(action = "list_stations",
                                   location = c(long = -120.50, lat = 46.60), 
                                   time_interval = c(1990, 2020))

chillR_code	STATION.NAME	CTRY	Lat	Long	BEGIN	END	Distance	Overlap_years	Perc_interval_covered
72781024243	YAKIMA AIR TERMINAL/MCALSR FIELD AP	US	46.564	-120.535	19730101	20250304	4.82	31.00	100
99999924243	YAKIMA AIR TERMINAL	US	46.568	-120.543	19480101	19721231	4.85	0.00	0
72781399999	VAGABOND AAF / YAKIMA TRAINING CENTER WASHINGTON USA	US	46.667	-120.454	20030617	20081110	8.25	5.40	17
72056299999	RANGE OP 13 / YAKIMA TRAINING CENTER	US	46.800	-120.167	20080530	20170920	33.79	9.31	30
72788399999	BOWERS FLD	US	47.033	-120.531	20000101	20031231	48.26	4.00	13
72788324220	BOWERS FIELD AIRPORT	US	47.034	-120.531	19880106	20250304	48.37	31.00	100
99999924220	ELLENSBURG BOWERS FI	US	47.034	-120.530	19480601	19550101	48.37	0.00	0
72784094187	HANFORD AIRPORT	US	46.567	-119.600	20060101	20130326	68.96	7.23	23
72784099999	HANFORD	US	46.567	-119.600	19730101	19971231	68.96	8.00	26
72782594239	PANGBORN MEMORIAL AIRPORT	US	47.397	-120.201	20000101	20250304	91.58	21.00	68
72782599999	PANGBORN MEM	US	47.399	-120.207	19730101	19971231	91.69	8.00	26
72788499999	RICHLAND AIRPORT	US	46.306	-119.304	19810203	20250303	97.39	31.00	100
72781524237	STAMPASS PASS FLTWO	US	47.277	-121.337	19730101	20250304	98.63	31.00	100
99999924237	STAMPEDE PASS	US	47.277	-121.337	19480101	19721231	98.63	0.00	0
72790024141	EPHRATA MUNICIPAL AIRPORT	US	47.308	-119.516	20050101	20250304	108.64	16.00	52
72782624141	EPHRATA MUNICIPAL	US	47.308	-119.515	19420101	19971231	108.69	8.00	26
99999924141	EPHRATA AP FCWOS	US	47.308	-119.515	19480101	19550101	108.69	0.00	0
72782724110	GRANT COUNTY INTL AIRPORT	US	47.193	-119.315	19430610	20250304	111.73	31.00	100
72782799999	MOSES LAKE/GRANT CO	US	47.200	-119.317	20000101	20031231	112.06	4.00	13
72784524163	TRI-CITIES AIRPORT	US	46.270	-119.118	19730101	20250304	112.21	31.00	100
72784599999	TRI CITIES	US	46.267	-119.117	20000101	20031231	112.40	4.00	13
99999924163	PASCO NAS	US	46.267	-119.117	19450401	19460601	112.40	0.00	0
72698824219	MUNICIPAL AIRPORT	US	45.619	-121.166	19730101	20250304	120.70	31.00	100
99999924219	THE DALLES MUNICIPAL ARPT	US	45.619	-121.166	19480101	19650101	120.70	0.00	0
72688399999	HERMISTON MUNI	US	45.828	-119.259	19980514	20051231	128.55	7.64	25

Download weather data for the most promising station on the list

weather_Yakima <- handle_gsod(action = "download_weather",
                              location = station_list_Yakima$chillR_code[1],
                              time_interval = c(1990, 2020))

weather_Yakima[[1]][1:20,]

DATE	Date	Year	Month	Day	Tmin	Tmax	Tmean	Prec	YEARMODA
1990-01-01 12:00:00	1990-01-01	1990	1	1	-1.722	1.722	-0.500	0.000	19900101
1990-01-02 12:00:00	1990-01-02	1990	1	2	-5.611	6.111	0.056	0.000	19900102
1990-01-03 12:00:00	1990-01-03	1990	1	3	-5.611	7.778	0.056	0.000	19900103
1990-01-04 12:00:00	1990-01-04	1990	1	4	-3.278	13.889	2.444	0.000	19900104
1990-01-05 12:00:00	1990-01-05	1990	1	5	-3.278	13.889	2.556	0.000	19900105
1990-01-06 12:00:00	1990-01-06	1990	1	6	-3.278	11.722	6.222	0.000	19900106
1990-01-07 12:00:00	1990-01-07	1990	1	7	-2.222	11.722	9.000	3.048	19900107
1990-01-08 12:00:00	1990-01-08	1990	1	8	2.778	12.222	6.722	9.652	19900108
1990-01-09 12:00:00	1990-01-09	1990	1	9	1.111	11.111	4.111	15.748	19900109
1990-01-10 12:00:00	1990-01-10	1990	1	10	1.111	12.222	7.167	2.794	19900110
1990-01-11 12:00:00	1990-01-11	1990	1	11	0.000	8.278	2.778	0.000	19900111
1990-01-12 12:00:00	1990-01-12	1990	1	12	0.000	6.111	2.833	0.254	19900112
1990-01-13 12:00:00	1990-01-13	1990	1	13	-2.778	6.111	0.944	0.254	19900113
1990-01-14 12:00:00	1990-01-14	1990	1	14	-3.889	5.000	-0.111	0.000	19900114
1990-01-15 12:00:00	1990-01-15	1990	1	15	-3.889	4.389	0.889	0.000	19900115
1990-01-16 12:00:00	1990-01-16	1990	1	16	-0.611	7.222	3.556	0.000	19900116
1990-01-17 12:00:00	1990-01-17	1990	1	17	-4.389	7.778	1.111	0.000	19900117
1990-01-18 12:00:00	1990-01-18	1990	1	18	-7.778	8.278	-2.667	0.000	19900118
1990-01-19 12:00:00	1990-01-19	1990	1	19	-7.778	3.278	0.389	0.000	19900119
1990-01-20 12:00:00	1990-01-20	1990	1	20	-2.222	2.222	0.778	0.000	19900120

Convert the weather data into chillR format

cleaned_weather_Yakima <- handle_gsod(weather_Yakima)

cleaned_weather_Yakima[[1]][1:20,]

Date	Year	Month	Day	Tmin	Tmax	Tmean	Prec
1990-01-01	1990	1	1	-1.722	1.722	-0.500	0.000
1990-01-02	1990	1	2	-5.611	6.111	0.056	0.000
1990-01-03	1990	1	3	-5.611	7.778	0.056	0.000
1990-01-04	1990	1	4	-3.278	13.889	2.444	0.000
1990-01-05	1990	1	5	-3.278	13.889	2.556	0.000
1990-01-06	1990	1	6	-3.278	11.722	6.222	0.000
1990-01-07	1990	1	7	-2.222	11.722	9.000	3.048
1990-01-08	1990	1	8	2.778	12.222	6.722	9.652
1990-01-09	1990	1	9	1.111	11.111	4.111	15.748
1990-01-10	1990	1	10	1.111	12.222	7.167	2.794
1990-01-11	1990	1	11	0.000	8.278	2.778	0.000
1990-01-12	1990	1	12	0.000	6.111	2.833	0.254
1990-01-13	1990	1	13	-2.778	6.111	0.944	0.254
1990-01-14	1990	1	14	-3.889	5.000	-0.111	0.000
1990-01-15	1990	1	15	-3.889	4.389	0.889	0.000
1990-01-16	1990	1	16	-0.611	7.222	3.556	0.000
1990-01-17	1990	1	17	-4.389	7.778	1.111	0.000
1990-01-18	1990	1	18	-7.778	8.278	-2.667	0.000
1990-01-19	1990	1	19	-7.778	3.278	0.389	0.000
1990-01-20	1990	1	20	-2.222	2.222	0.778	0.000

dir.create("Yakima")
write.csv(station_list_Yakima,"Yakima/station_list.csv", row.names = FALSE)
write.csv(weather_Yakima[[1]],"Yakima/raw_weather.csv", row.names = FALSE)
write.csv(cleaned_weather_Yakima[[1]],"Yakima/chillR_weather.csv", row.names = FALSE)