Meteorological Data
Mattia Rossi
2019-10-04
Meteo.RmdExplore the Database
The database we use in this Package is the CKAN API for meteorological data exposed by the OpenData Portal South Tyrol.
Since Version 0.2 we can extract all Metainformation by using getMeteoInfo
## SCODE NAME_D NAME_I NAME_L NAME_E
## 1 00390SF Graun Schöneben Curon Belpiano Graun Schöneben Graun Schöneben
## 2 00390SF Graun Schöneben Curon Belpiano Graun Schöneben Graun Schöneben
## 3 00390SF Graun Schöneben Curon Belpiano Graun Schöneben Graun Schöneben
## 4 00390SF Graun Schöneben Curon Belpiano Graun Schöneben Graun Schöneben
## 5 00390SF Graun Schöneben Curon Belpiano Graun Schöneben Graun Schöneben
## 6 00390SF Graun Schöneben Curon Belpiano Graun Schöneben Graun Schöneben
## ALT LONG LAT TYPE DESC_D
## 1 2040 10.4979 46.8064 LT Lufttemperatur
## 2 2040 10.4979 46.8064 WR Windrichtung
## 3 2040 10.4979 46.8064 WG Windgeschwindigkeit
## 4 2040 10.4979 46.8064 HS Schneehöhe
## 5 2040 10.4979 46.8064 WG.BOE Windgeschwindigkeit Böe
## 6 2040 10.4979 46.8064 LF relative Luftfeuchte
## DESC_I DESC_L UNIT DATE
## 1 Temperatura dell´aria Temperatura dl’aria °C 2019-10-04T13:00:00CEST
## 2 Direzione del vento Direzion dl vënt ° 2019-10-04T13:00:00CEST
## 3 Velocità del vento Slune dl vënt m/s 2019-10-04T13:00:00CEST
## 4 Altezza neve al suolo Autëza dla nëif cm 2019-10-04T13:00:00CEST
## 5 Velocitá raffica Slune dl vënt m/s 2019-10-04T13:00:00CEST
## 6 Umidità relativa Tume relatif % 2019-10-04T13:00:00CEST
## VALUE
## 1 2.6
## 2 4.0
## 3 0.2
## 4 -1.1
## 5 1.4
## 6 64.4## Simple feature collection with 6 features and 13 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 10.4979 ymin: 46.8064 xmax: 10.4979 ymax: 46.8064
## epsg (SRID): 4326
## proj4string: +proj=longlat +datum=WGS84 +no_defs
## SCODE NAME_D NAME_I NAME_L NAME_E
## 1 00390SF Graun Schöneben Curon Belpiano Graun Schöneben Graun Schöneben
## 2 00390SF Graun Schöneben Curon Belpiano Graun Schöneben Graun Schöneben
## 3 00390SF Graun Schöneben Curon Belpiano Graun Schöneben Graun Schöneben
## 4 00390SF Graun Schöneben Curon Belpiano Graun Schöneben Graun Schöneben
## 5 00390SF Graun Schöneben Curon Belpiano Graun Schöneben Graun Schöneben
## 6 00390SF Graun Schöneben Curon Belpiano Graun Schöneben Graun Schöneben
## ALT TYPE DESC_D DESC_I
## 1 2040 LT Lufttemperatur Temperatura dell´aria
## 2 2040 WR Windrichtung Direzione del vento
## 3 2040 WG Windgeschwindigkeit Velocità del vento
## 4 2040 HS Schneehöhe Altezza neve al suolo
## 5 2040 WG.BOE Windgeschwindigkeit Böe Velocitá raffica
## 6 2040 LF relative Luftfeuchte Umidità relativa
## DESC_L UNIT DATE VALUE
## 1 Temperatura dl’aria °C 2019-10-04T13:00:00CEST 2.6
## 2 Direzion dl vënt ° 2019-10-04T13:00:00CEST 4.0
## 3 Slune dl vënt m/s 2019-10-04T13:00:00CEST 0.2
## 4 Autëza dla nëif cm 2019-10-04T13:00:00CEST -1.1
## 5 Slune dl vënt m/s 2019-10-04T13:00:00CEST 1.4
## 6 Tume relatif % 2019-10-04T13:00:00CEST 64.4
## geometry
## 1 POINT (10.4979 46.8064)
## 2 POINT (10.4979 46.8064)
## 3 POINT (10.4979 46.8064)
## 4 POINT (10.4979 46.8064)
## 5 POINT (10.4979 46.8064)
## 6 POINT (10.4979 46.8064)Download from Database
Analogous to the MONALISA Database in the example before we can access the database with the downloadMeteo function providing key input sich as the code of a single station, the sensor code corresponding to a product or a daterange. By default the data is stored in memory. If you want to download it you need to provide a path and set the csv variable as TRUE
s <- "2017-01-01 00:00"
e <- "2018-01-01 00:00"
a<-downloadMeteo2(station_code = "02500MS",
sensor_code = "LT",
datestart = s,
dateend = e)
a## # A tibble: 52,518 x 6
## TimeStamp SCODE Sensor Start End Value
## <dttm> <fct> <fct> <chr> <chr> <dbl>
## 1 2017-01-01 00:00:00 02500MS LT 20170101 20180101 1.9
## 2 2017-01-01 00:10:00 02500MS LT 20170101 20180101 1.8
## 3 2017-01-01 00:20:00 02500MS LT 20170101 20180101 1.6
## 4 2017-01-01 00:30:00 02500MS LT 20170101 20180101 1.5
## 5 2017-01-01 00:40:00 02500MS LT 20170101 20180101 1.5
## 6 2017-01-01 00:50:00 02500MS LT 20170101 20180101 1.6
## 7 2017-01-01 01:00:00 02500MS LT 20170101 20180101 1.4
## 8 2017-01-01 01:10:00 02500MS LT 20170101 20180101 1.5
## 9 2017-01-01 01:20:00 02500MS LT 20170101 20180101 1
## 10 2017-01-01 01:30:00 02500MS LT 20170101 20180101 1.1
## # ... with 52,508 more rowsPlot the Results
And now we can simply Plot the Results using GGplot2
As before the Plots can be done with Leaflet. This could be interesting to explore before the actual data retrieval to diminish the number of processed station data
Combine with other Data
Now if you want t import other data such as Shapefiles or point files indicating your area of interest you can use some predefined functions. In case yo would like to reduce the dimensionality of the acquired dataset by providing a buffer around apoint file you can use the buffmeteo function. If you want to plot the buffer Zone just add addCircles to the leaflet or use the getBuffershp option in the buffmeteo function. In this example we use a buffer of 10km around one point to see which stations lie within that range. This can be done either graphically
## Reading layer `Test' from data source `C:\Users\MRossi\Documents\07_Codes\MonalisR\vignettes\data\Test.shp' using driver `ESRI Shapefile'
## Simple feature collection with 115 features and 8 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 611860.4 ymin: 5122613 xmax: 757831.9 ymax: 5213918
## epsg (SRID): NA
## proj4string: +proj=utm +zone=32 +ellps=GRS80 +units=m +no_defsor by extracting the stations within the 10km buffer zone around the imported Point.
## Simple feature collection with 6 features and 13 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 11.1855 ymin: 46.2362 xmax: 11.1855 ymax: 46.2362
## epsg (SRID): 4326
## proj4string: +proj=longlat +datum=WGS84 +no_defs
## SCODE NAME_D NAME_I NAME_L NAME_E ALT TYPE DESC_D
## 615 88820MS Salurn Salorno Salurn Salurn 212 LD.RED Luftdruck
## 616 88820MS Salurn Salorno Salurn Salurn 212 SD Sonnenscheindauer
## 617 88820MS Salurn Salorno Salurn Salurn 212 LF relative Luftfeuchte
## 618 88820MS Salurn Salorno Salurn Salurn 212 LT Lufttemperatur
## 619 88820MS Salurn Salorno Salurn Salurn 212 GS Globalstrahlung
## 620 88820MS Salurn Salorno Salurn Salurn 212 N Niederschlag
## DESC_I DESC_L UNIT DATE
## 615 Pressione atmosferica Druch dl’aria hPa 2019-10-04T13:20:00CEST
## 616 Durata soleggiamento Dureda dl surëdl s 2019-10-04T13:20:00CEST
## 617 Umidità relativa Tume relatif % 2019-10-04T13:20:00CEST
## 618 Temperatura dell´aria Temperatura dl’aria °C 2019-10-04T13:20:00CEST
## 619 Radiazione globale Nraiazion globala W/m² 2019-10-04T13:20:00CEST
## 620 Precipitazioni plueia mm 2019-10-04T13:20:00CEST
## VALUE geometry
## 615 1014.1 POINT (11.1855 46.2362)
## 616 0.0 POINT (11.1855 46.2362)
## 617 54.8 POINT (11.1855 46.2362)
## 618 13.3 POINT (11.1855 46.2362)
## 619 71.2 POINT (11.1855 46.2362)
## 620 0.0 POINT (11.1855 46.2362)