Data from Aemet (the Spanish meteorological agency) meteorological masts.
There are daily summaries and monthly summaries since the installation of the masts until 2012, when AEMET decided to close the access to the data.
There are several folders in the present repo:
.
├── aemet_original_data
│  ├── daily
│  │  ├── by_station
│  │  └── by_year
│  └── monthly
│  ├── by_station
│  └── by_year
├── csv
│  ├── daily
│  │  ├── by_station
│  │  └── by_year
│  └── monthly
│  ├── by_station
│  └── by_year
├── hdf5
├── parquet
│  ├── daily
│  │  ├── by_station
│  │  └── by_year
│  └── monthly
│  ├── by_station
│  └── by_year
└── src
It is a collection of the data made by the community. It is downloaded from the forums on tiempo.com.
The data in the present repo is the same data that could be obtained from the link above but changing the folder names and including some more information in a README.md file. Please, read that README file to know more about the original data (in spanish), the license,...
It is a derived dataset with some modifications:
- Column names have changed to a more detailed description including units:
Daily files:
| Original column name | New column name in derived datasets | Description |
|---|---|---|
| 'Año' + 'Mes' + 'Dia' (*) | 'date' | Day when the records were measured |
| 'Indicativo' | 'station_code' | Aemet station code |
| (**) | 'synop_code' | International synop code |
| 'Nombre' | 'station_name' | Aemet station name |
| 'Provincia' | 'province' | Spanish province where the station is located |
| 'Altitud' | 'altitude' | Altitude above mean sea level |
| (**) | 'latitude' | Latitude of the station (positive values are North and negative values are South) |
| (**) | 'longitude' | Longitude of the station (positive values are East and negative values are West) |
| 'T.Max' | 'temperature_max[C]' | Maximum temperature recorded during the day in Celsius degrees |
| 'Hora' | 'temperature_max_timestamp' | Timestamp (UTC) when the maximum temperature was recorded (time resolution: minutes) |
| 'T.Min' | 'temperature_min[C]' | Minimum temperature recorded during the day in Celsius degrees |
| 'Hora' | 'temperature_min_timestamp' | Timestamp (UTC) when the minimum temperature was recorded (time resolution: minutes) |
| 'T.Med' | 'temperature_avg[C]' | Average temperature recorded during the day in Celsius degrees |
| 'Racha' | 'windspeed_gust[m/s]' | Wind speed gust recorded during the day in meters per second |
| 'Dir' | 'winddirection_gust[degrees]' | Direction of the wind gust recorded during the day in degrees clockwise from the North |
| 'Hora' | 'wind_gust_timestamp' | Timestamp (UTC) when the wind gust was recorded (time resolution: minutes) |
| 'Vel.media' | 'windspeed_avg[m/s]' | Average wind speed recorded during the day in meters per second |
| 'Prec.' | 'rainfall[mm]' | Accumulated rainfall during the day in mm |
| 'Sol' | 'sun[h] | Accumulated hours of sun during the day |
| 'Pres.máx' | 'pressure_max[hPa]' | Maximum air pressure recorded during the day in hectopascals |
| 'Hora' | 'pressure_max_timestamp' | Timestamp (UTC) when the maximum air pressure was recorded (time resolution: hours) |
| 'Pres.min' | 'pressure_min[hPa]' | Minimum air pressure recorded during the day in hectopascals |
| 'Hora' | 'pressure_min_timestamp' | Timestamp (UTC) when the maximum air pressure was recorded (time resolution: hours) |
(*) Columns 'Año', 'Mes' and 'DĂa' indicating the day of the row records have been merged in column 'date'.
(**) These columns don't exist in the original files but in the postprocessed files have been included for convenience. It is more space but you don't have to open the maestro.csv file with the metadata of the stations.
Monthly files
| Original column name | New column name in derived datasets | Description |
|---|---|---|
| 'Año' + 'Mes' (*) | 'date' | Day when the records were measured |
| 'Indicativo' | 'station_code' | Aemet station code |
| (**) | 'synop_code' | International synop code |
| 'Nombre' | 'station_name' | Aemet station name |
| 'Provincia' | 'province' | Spanish province where the station is located |
| 'Altitud' | 'altitude' | Altitude above mean sea level |
| (**) | 'latitude' | Latitude of the station (positive values are North and negative values are South) |
| (*Âş*) | 'longitude' | Longitude of the station (positive values are East and negative values are West) |
| 'T.Med' | 'temperature_avg[C]' | Average temperature recorded during the month in Celsius degrees |
| 'T.Max.Med | 'temperature_max_avg[C]' | Average of the maximum daily temperatures recorded during the month in Celsius degrees |
| 'T.Min.Med | 'temperature_min_avg[C]' | Average of the minimum daily temperatures recorded during the month in Celsius degrees |
| 'T.Max.Abs | 'temperature_max[C]' | Maximum temperature recorded during the month in Celsius degrees |
| 'DĂa' | 'temperature_max_timestamp' | Timestamp (UTC) when the maximum temperature was recorded (time resolution: days) |
| 'T.Min.Abs' | 'temperature_min[C]' | Minimum temperature recorded during the month in Celsius degrees |
| 'DĂa' | 'temperature_min_timestamp' | Timestamp (UTC) when the minimum temperature was recorded (time resolution: days) |
| 'Min.Sup' | 'temperature_min_max[C]' | Maximum of the minimum daily temperatures recorded during the month in Celsius degrees |
| 'Max.Inf' | 'temperature_max_min[C]' | Minimum of the maximum daily temperatures recorded during the month in Celsius degrees |
| 'DĂasHelada' | 'frost_days_count' | Number of days with temperature below or equal to 0 Celsius degrees recorded during the month |
| 'Prec.total' | 'rainfall[mm]' | Accumulated rainfall during the month in mm |
| 'Prec.max' | 'rainfall_max[mm]' | Maximum daily accumulated rainfall recorded during the month in mm |
| 'DĂa' | 'rainfall_max_timestamp' | Timestamp (UTC) when the maximum daily accumulated rainfall was recorded (time resolution: days) |
| 'DiasPrec.Apre' | 'rainfall_days_001_count' | Number of days with accumulated rainfall above 0.1 mm |
| 'DiasPrecSup10' | 'rainfall_days_100_count' | Number of days with accumulated rainfall above 10.0 mm |
| 'DĂasLluvia' | 'rainfall_days_000_count' | Number of rainfall days recorded during the month |
| 'DĂasNieve' | 'snow_days_count' | Number of snow days recorded during the month |
| 'DĂasGranizo' | 'hail_days_count' | Number of hail days recorded during the month |
| 'VelRacha' | 'windspeed_gust[m/s]' | Wind speed gust recorded during the month in meters per second |
| 'DirRacha' | 'windirection gust[degrees]' | Direction of the wind gust recorded during the month in degrees clockwise from the North |
| 'Dia' + 'Hora' (***) | 'wind_gust_timestamp' | Timestamp (UTC) when the wind gust was recorded (time resolution: minutes) |
| 'DĂasRachaSup55' | 'windgust_days_55km/h_count' | Number of days with wind gust above 55 km/h |
| 'DĂasRachaSup91' | 'windgust_days_91km/h_count' | Number of days with wind gust above 91 km/h |
| 'Vel.media' | 'windspeed_avg[m/s]' | Average wind speed recorded during the month in meters second |
| 'Insol.media' | 'sun_avg[h]' | Average daily sun isolation recorded during the month in hours |
| '%Insol' | 'sun_avg[%]' | Average daily sun isolation recorded during the month in percentage |
| 'Pres.media' | 'pressure_avg[hPa]' | Average air pressure recorded during the month in hectopascals |
| 'Pres.Max.Abs' | 'pressure_max[hPa]' | Maximum air pressure recorded during the month in hectopascals |
| 'DĂa' | 'pressure_max_timestamp' | Timestamp (UTC) when the maximum air pressure was recorded (time resolution: days) |
| 'Pres.Min.Abs' | 'pressure_min[hPa]' | Minimum air pressure recorded during the month in hectopascals |
| 'DĂa' | 'pressure_min_timestamp' | Timestamp (UTC) when the maximum air pressure was recorded (time resolution: days) |
| 'Pres.MediaNivelMar | 'pressure_msl[hPa]' | Average mean sea level air pressure recorded during the the month in hectopascals |
(*) Columns 'Año' and 'Mes' indicating the month of the row records have been merged in column 'date'.
(**) These columns don't exist in the original files but in the postprocessed files have been included for convenience. It is more space but you don't have to open the maestro.csv file with the metadata of the stations.
(***) Columns 'DĂa' and 'Hora' indicating the day and hour of the maximum monthly wind gust have been merged in the column 'wind_gust_timestamp'.
- Rainfall values modifications
- In the original files the values of the rainfall colums are numeric, 'Ip' or 'Acum'. Numeric values have been maintained. 'Ip' means that there was rain but it was lower than 0.1 mm. 'Ip' values have been changed to
0.05in order to have numeric columns and do not mix types on the same colum but take into account that this value is invented and should be used only to filter the 'Ip' records. I Don't know what 'Acum' values are so these values arenans in the derived datasets.
- In the original files the values of the rainfall colums are numeric, 'Ip' or 'Acum'. Numeric values have been maintained. 'Ip' means that there was rain but it was lower than 0.1 mm. 'Ip' values have been changed to
All the files are similar to that on the csv folder but in parquet format (same column names, same issues with rainfall values,...). Read more about Apache Arrow here.
The only difference is that apache files maintain the index column. I didn't know how to remove it during the conversion :-( The parquet/arrow format is evolving.
Files are compressed using gzip.
To open the files in python read here.
There is a file called aemet.h5. It is included the daily and monthly files only by_station. Store also the by_year files is redundant. If you need the files by_year just file an issue explaining your motivation.
In this case, the columns with repeated values ('date', 'station_code', 'synop_code', 'station_name' , 'province', 'altitude', 'latitude', 'longitude') are not included in the file and this data is included as metadata. Also, the description of the columns is available as metadata.
To access the information with Python you can use Pandas (you will need also PyTables installed to work with HDF5):
import pandas as pd
store = pd.HDFStore('path/to/aemet.h5', mode='r')
# this will show all the dataframes included in the file
print(store)The output from the previous code should be something like:
<class 'pandas.io.pytables.HDFStore'>
File path: ../hdf5/aemet.h5
/daily/by_station/station0016A frame (shape->[24017,16])
/daily/by_station/station0076 frame (shape->[30378,16])
/daily/by_station/station0200E frame (shape->[33847,16])
/daily/by_station/station0367 frame (shape->[14374,16])
/daily/by_station/station0370B frame (shape->[18274,16])
/daily/by_station/station1014 frame (shape->[20864,16])
/daily/by_station/station1024E frame (shape->[30925,16])
/daily/by_station/station1082 frame (shape->[23884,16])
/daily/by_station/station1109 frame (shape->[20748,16])
/daily/by_station/station1110 frame (shape->[26761,16])
/daily/by_station/station1111 frame (shape->[11608,16])
/daily/by_station/station1208 frame (shape->[9044,16])
/daily/by_station/station1208A frame (shape->[13758,16])
/daily/by_station/station1208H frame (shape->[4227,16])
/daily/by_station/station1212E frame (shape->[16015,16])
/daily/by_station/station1249I frame (shape->[14549,16])
/daily/by_station/station1387 frame (shape->[29951,16])
/daily/by_station/station1387E frame (shape->[14915,16])
/daily/by_station/station1428 frame (shape->[25110,16])
/daily/by_station/station1484 frame (shape->[8036,16])
/daily/by_station/station1484C frame (shape->[9862,16])
/daily/by_station/station1495 frame (shape->[20607,16])
/daily/by_station/station1505 frame (shape->[12315,16])
/daily/by_station/station1549 frame (shape->[22554,16])
/daily/by_station/station1690A frame (shape->[16234,16])
/daily/by_station/station1690B frame (shape->[6360,16])
/daily/by_station/station2030 frame (shape->[25121,16])
/daily/by_station/station2331 frame (shape->[25264,16])
/daily/by_station/station2401 frame (shape->[12998,16])
/daily/by_station/station2422 frame (shape->[14245,16])
/daily/by_station/station2444 frame (shape->[10865,16])
/daily/by_station/station2444C frame (shape->[10665,16])
/daily/by_station/station2462 frame (shape->[24380,16])
/daily/by_station/station2465 frame (shape->[8766,16])
/daily/by_station/station2465A frame (shape->[17999,16])
/daily/by_station/station2539 frame (shape->[27666,16])
/daily/by_station/station2614 frame (shape->[33049,16])
/daily/by_station/station2661 frame (shape->[27302,16])
/daily/by_station/station2867 frame (shape->[24745,16])
/daily/by_station/station2870 frame (shape->[13504,16])
/daily/by_station/station3013 frame (shape->[22642,16])
/daily/by_station/station3129 frame (shape->[22554,16])
/daily/by_station/station3168A frame (shape->[12592,16])
/daily/by_station/station3168C frame (shape->[8556,16])
/daily/by_station/station3175 frame (shape->[18871,16])
/daily/by_station/station3191E frame (shape->[12025,16])
/daily/by_station/station3195 frame (shape->[33844,16])
/daily/by_station/station3196 frame (shape->[24625,16])
/daily/by_station/station3200 frame (shape->[22554,16])
/daily/by_station/station3259 frame (shape->[22186,16])
/daily/by_station/station3260B frame (shape->[11200,16])
/daily/by_station/station3469 frame (shape->[24472,16])
/daily/by_station/station3469A frame (shape->[10897,16])
/daily/by_station/station4121 frame (shape->[15310,16])
/daily/by_station/station4121C frame (shape->[17775,16])
/daily/by_station/station4452 frame (shape->[21093,16])
/daily/by_station/station4605 frame (shape->[23498,16])
/daily/by_station/station4642E frame (shape->[10349,16])
/daily/by_station/station5000A frame (shape->[17690,16])
/daily/by_station/station5000C frame (shape->[3349,16])
/daily/by_station/station5270 frame (shape->[21125,16])
/daily/by_station/station5270B frame (shape->[9930,16])
/daily/by_station/station5402 frame (shape->[18031,16])
/daily/by_station/station5514 frame (shape->[27238,16])
/daily/by_station/station5530E frame (shape->[14702,16])
/daily/by_station/station5783 frame (shape->[22554,16])
/daily/by_station/station5796 frame (shape->[22434,16])
/daily/by_station/station5910 frame (shape->[8796,16])
/daily/by_station/station5960 frame (shape->[23146,16])
/daily/by_station/station5973 frame (shape->[20010,16])
/daily/by_station/station6000A frame (shape->[17289,16])
/daily/by_station/station6001 frame (shape->[24077,16])
/daily/by_station/station6155A frame (shape->[25721,16])
/daily/by_station/station6297 frame (shape->[16505,16])
/daily/by_station/station6325O frame (shape->[16345,16])
/daily/by_station/station7031 frame (shape->[24898,16])
/daily/by_station/station7178I frame (shape->[10410,16])
/daily/by_station/station7228 frame (shape->[26329,16])
/daily/by_station/station8019 frame (shape->[16700,16])
/daily/by_station/station8025 frame (shape->[27047,16])
/daily/by_station/station8096 frame (shape->[22522,16])
/daily/by_station/station8175 frame (shape->[26603,16])
/daily/by_station/station8178D frame (shape->[10862,16])
/daily/by_station/station8368U frame (shape->[9680,16])
/daily/by_station/station8414A frame (shape->[17437,16])
/daily/by_station/station8416 frame (shape->[27363,16])
/daily/by_station/station8500A frame (shape->[13423,16])
/daily/by_station/station8501 frame (shape->[13115,16])
/daily/by_station/station9087 frame (shape->[14211,16])
/daily/by_station/station9091O frame (shape->[13135,16])
/daily/by_station/station9170 frame (shape->[23345,16])
/daily/by_station/station9262 frame (shape->[21427,16])
/daily/by_station/station9263D frame (shape->[13788,16])
/daily/by_station/station9381 frame (shape->[15796,16])
/daily/by_station/station9381I frame (shape->[6538,16])
/daily/by_station/station9390 frame (shape->[33754,16])
/daily/by_station/station9434 frame (shape->[26206,16])
/daily/by_station/station9771 frame (shape->[8646,16])
/daily/by_station/station9771C frame (shape->[10835,16])
/daily/by_station/station9898 frame (shape->[24899,16])
/daily/by_station/station9981A frame (shape->[33482,16])
/daily/by_station/stationB228 frame (shape->[12692,16])
/daily/by_station/stationB278 frame (shape->[14671,16])
/daily/by_station/stationB893 frame (shape->[16619,16])
/daily/by_station/stationB954 frame (shape->[22484,16])
/daily/by_station/stationC029O frame (shape->[14579,16])
/daily/by_station/stationC139E frame (shape->[15509,16])
/daily/by_station/stationC249I frame (shape->[15826,16])
/daily/by_station/stationC429I frame (shape->[11780,16])
/daily/by_station/stationC430E frame (shape->[32881,16])
/daily/by_station/stationC447A frame (shape->[22920,16])
/daily/by_station/stationC449C frame (shape->[29839,16])
/daily/by_station/stationC649I frame (shape->[22554,16])
/daily/by_station/stationC929I frame (shape->[14184,16])
/monthly/by_station/station0016A frame (shape->[789,33])
/monthly/by_station/station0076 frame (shape->[998,33])
/monthly/by_station/station0200E frame (shape->[1112,33])
/monthly/by_station/station0367 frame (shape->[472,33])
/monthly/by_station/station0370B frame (shape->[601,33])
/monthly/by_station/station1014 frame (shape->[685,33])
/monthly/by_station/station1024E frame (shape->[1017,33])
/monthly/by_station/station1082 frame (shape->[785,33])
/monthly/by_station/station1109 frame (shape->[681,33])
/monthly/by_station/station1110 frame (shape->[887,33])
/monthly/by_station/station1111 frame (shape->[383,33])
/monthly/by_station/station1208 frame (shape->[297,33])
/monthly/by_station/station1208A frame (shape->[452,33])
/monthly/by_station/station1208H frame (shape->[139,33])
/monthly/by_station/station1212E frame (shape->[526,33])
/monthly/by_station/station1249I frame (shape->[489,33])
/monthly/by_station/station1387 frame (shape->[984,33])
/monthly/by_station/station1387E frame (shape->[490,33])
/monthly/by_station/station1428 frame (shape->[825,33])
/monthly/by_station/station1484 frame (shape->[618,33])
/monthly/by_station/station1484C frame (shape->[324,33])
/monthly/by_station/station1495 frame (shape->[741,33])
/monthly/by_station/station1505 frame (shape->[404,33])
/monthly/by_station/station1549 frame (shape->[741,33])
/monthly/by_station/station1690A frame (shape->[601,33])
/monthly/by_station/station1690B frame (shape->[381,33])
/monthly/by_station/station2030 frame (shape->[826,33])
/monthly/by_station/station2331 frame (shape->[830,33])
/monthly/by_station/station2401 frame (shape->[801,33])
/monthly/by_station/station2422 frame (shape->[468,33])
/monthly/by_station/station2444 frame (shape->[357,33])
/monthly/by_station/station2444C frame (shape->[350,33])
/monthly/by_station/station2462 frame (shape->[801,33])
/monthly/by_station/station2465 frame (shape->[288,33])
/monthly/by_station/station2465A frame (shape->[772,33])
/monthly/by_station/station2539 frame (shape->[909,33])
/monthly/by_station/station2614 frame (shape->[1112,33])
/monthly/by_station/station2661 frame (shape->[897,33])
/monthly/by_station/station2867 frame (shape->[813,33])
/monthly/by_station/station2870 frame (shape->[444,33])
/monthly/by_station/station3013 frame (shape->[745,33])
/monthly/by_station/station3129 frame (shape->[741,33])
/monthly/by_station/station3168A frame (shape->[652,33])
/monthly/by_station/station3168C frame (shape->[279,33])
/monthly/by_station/station3175 frame (shape->[736,33])
/monthly/by_station/station3191E frame (shape->[396,33])
/monthly/by_station/station3195 frame (shape->[1112,33])
/monthly/by_station/station3196 frame (shape->[809,33])
/monthly/by_station/station3200 frame (shape->[741,33])
/monthly/by_station/station3259 frame (shape->[743,33])
/monthly/by_station/station3260B frame (shape->[368,33])
/monthly/by_station/station3469 frame (shape->[804,33])
/monthly/by_station/station3469A frame (shape->[358,33])
/monthly/by_station/station4121 frame (shape->[503,33])
/monthly/by_station/station4121C frame (shape->[597,33])
/monthly/by_station/station4452 frame (shape->[694,33])
/monthly/by_station/station4605 frame (shape->[772,33])
/monthly/by_station/station4642E frame (shape->[340,33])
/monthly/by_station/station5000A frame (shape->[625,33])
/monthly/by_station/station5000C frame (shape->[112,33])
/monthly/by_station/station5270 frame (shape->[767,33])
/monthly/by_station/station5270B frame (shape->[325,33])
/monthly/by_station/station5402 frame (shape->[594,33])
/monthly/by_station/station5514 frame (shape->[917,33])
/monthly/by_station/station5530E frame (shape->[483,33])
/monthly/by_station/station5783 frame (shape->[741,33])
/monthly/by_station/station5796 frame (shape->[747,33])
/monthly/by_station/station5910 frame (shape->[395,33])
/monthly/by_station/station5960 frame (shape->[800,33])
/monthly/by_station/station5973 frame (shape->[657,33])
/monthly/by_station/station6000A frame (shape->[723,33])
/monthly/by_station/station6001 frame (shape->[1104,33])
/monthly/by_station/station6155A frame (shape->[845,33])
/monthly/by_station/station6297 frame (shape->[579,33])
/monthly/by_station/station6325O frame (shape->[537,33])
/monthly/by_station/station7031 frame (shape->[818,33])
/monthly/by_station/station7178I frame (shape->[342,33])
/monthly/by_station/station7228 frame (shape->[865,33])
/monthly/by_station/station8019 frame (shape->[549,33])
/monthly/by_station/station8025 frame (shape->[888,33])
/monthly/by_station/station8096 frame (shape->[740,33])
/monthly/by_station/station8175 frame (shape->[874,33])
/monthly/by_station/station8178D frame (shape->[357,33])
/monthly/by_station/station8368U frame (shape->[318,33])
/monthly/by_station/station8414A frame (shape->[573,33])
/monthly/by_station/station8416 frame (shape->[899,33])
/monthly/by_station/station8500A frame (shape->[441,33])
/monthly/by_station/station8501 frame (shape->[655,33])
/monthly/by_station/station9087 frame (shape->[466,33])
/monthly/by_station/station9091O frame (shape->[471,33])
/monthly/by_station/station9170 frame (shape->[777,33])
/monthly/by_station/station9262 frame (shape->[704,33])
/monthly/by_station/station9263D frame (shape->[453,33])
/monthly/by_station/station9381 frame (shape->[519,33])
/monthly/by_station/station9381I frame (shape->[215,33])
/monthly/by_station/station9390 frame (shape->[1109,33])
/monthly/by_station/station9434 frame (shape->[861,33])
/monthly/by_station/station9771 frame (shape->[284,33])
/monthly/by_station/station9771C frame (shape->[356,33])
/monthly/by_station/station9898 frame (shape->[821,33])
/monthly/by_station/station9981A frame (shape->[1113,33])
/monthly/by_station/stationB228 frame (shape->[417,33])
/monthly/by_station/stationB278 frame (shape->[649,33])
/monthly/by_station/stationB893 frame (shape->[546,33])
/monthly/by_station/stationB954 frame (shape->[741,33])
/monthly/by_station/stationC029O frame (shape->[479,33])
/monthly/by_station/stationC139E frame (shape->[510,33])
/monthly/by_station/stationC249I frame (shape->[520,33])
/monthly/by_station/stationC429I frame (shape->[387,33])
/monthly/by_station/stationC430E frame (shape->[1105,33])
/monthly/by_station/stationC447A frame (shape->[824,33])
/monthly/by_station/stationC449C frame (shape->[1089,33])
/monthly/by_station/stationC649I frame (shape->[741,33])
/monthly/by_station/stationC929I frame (shape->[466,33])
To access the values of the daily data of a station:
from pprint import pprint
import pandas as pd
store = pd.HDFStore('path/to/aemet.h5', mode='r')
# To access, e.g., the daily values of station 0076:
df = store['/daily/by_station/station0076']
The metadata for that dataset could be accessed using:
key = 'aemet_station_metadata'
st_meta = store.root.daily.by_station.station0076._v_attrs[key]
key = 'aemet_variables_metadata'
cols_desc = store.root.daily.by_station.station0076._v_attrs[key]
pprint(st_meta) # station metadata
pprint(cols_desc) # Long description of columnsThe result of the previous code will print:
{'altitude': 4,
'code': '0076',
'latitude': 41.292777777777779,
'longitude': 2.0700000000000003,
'name': 'BARCELONA AEROPUERTO',
'province': 'BARCELONA',
'synop_code': '08181'}
{'date': 'Day when the records were measured',
'pressure_max[hPa]': 'Maximum air pressure recorded during the day in '
'hectopascals.',
'pressure_max_timestamp': 'Timestamp (UTC) when the maximum air pressure was '
'recorded.',
'pressure_min[hPa]': 'Minimum air pressure recorded during the day in '
'hectopascals.',
'pressure_min_timestamp': 'Timestamp (UTC) when the maximum air pressure was '
'recorded.',
'rainfall[mm]': 'Accumulated rainfall during the day in mm.',
'sun[h]': 'Accumulated hours of sun during the day.',
'temperature_avg[C]': 'Average temperature recorded during the day in Celsius '
'degrees.',
'temperature_max[C]': 'Maximum temperature recorded during the day in Celsius '
'degrees.',
'temperature_max_timestamp': 'Timestamp (UTC) when the maximum temperature '
'was recorded.',
'temperature_min[C]': 'Minimum temperature recorded during the day in Celsius '
'degrees.',
'temperature_min_timestamp': 'Timestamp (UTC) when the minimum temperature '
'was recorded.',
'wind_gust_timestamp': 'Timestamp (UTC) when the wind gust was recorded.',
'winddirection_gust[degrees]': 'Direction of the wind gust recorded during '
'the day in degrees clockwise from the North.',
'windspeed_avg[m/s]': 'Average wind speed recorded during the day in meters '
'per second.',
'windspeed_gust[m/s]': 'Wind speed gust recorded during the day in meters per '
'second.'}
This file is not compressed to keep things simple.
It contains the python script used to create csv, parquet and hdf5 folders from data contained in aemet_original_data folder.
You can read about it here (in spanish).
A summary for the usage of the data could be interpreted as:
People can use freely this data. You should mention AEMET as the collector of the original data in every situation except if you are using this data privately and individually. AEMET makes no warranty as to the accuracy or completeness of the data. All data are provided on an "as is" basis. AEMET is not responsible for any damage or loss derived from the interpretation or use of this data.
The license for the derived work from the original data is MIT.
If you work with the data and find an issue, please, tell me to correct it.
If you think the data could be improved, please, tell me with a detailed explanation to think about it and apply new changes if necessary or, even better, send a pull request with your ideas.
- A sqlite version but I have to think about the best scheme to keep things simple.
A front-end showing the location of the stations with links to each file.See https://kikocorreoso.github.io/datos_aemet/
It is very interesting to know what you are doing with the data, how useful are the provided formats, if you have any need regarding these datasets,...
I have found the excellent work done by @chucheria available on data.world.