metadata

license: eupl-1.1
task_categories:
  - time-series-forecasting
tags:
  - climate
size_categories:
  - 100M<n<1B

Dataset Summary

Contains hourly 2 meters of land (on-shore) air temperature data within grid areas of Thailand country.
Data is retrieved from Corpernicus Climate Data Store on ERA5-Land hourly data from 1950 to present
Thailand areas in this context is Latitude = [5.77434, 20.43353] and Longitude = [97.96852, 105.22908]
For more details of data, you can refer to ERA5-Land hourly data from 1950 to present)

Data Granularity: Hourly per Latitude/ Longitude
Period: 01/Jan/2010 - 13/May/2023
Temperature Unit: Kelvin

Source Data

Organization of the producer: ECMWF

Data Creation

Below is an example of how to make data query using Python via CDS API in monthly requests.

import cdsapi
c = cdsapi.Client()

month_list = [str(num).zfill(2) for num in range(1, 13)]
day_list = [str(num).zfill(2) for num in range(1, 32)]
time_list = [str(num).zfill(2) + ":00" for num in range(0, 24)]
year_list = [str(num) for num in range(2000, 2022)]

for year in year_list:
    for month in month_list:
        c.retrieve('reanalysis-era5-land',
        {
            'variable': [
                '2m_temperature']
        ,
        'year': year,
        'month' : month,
        'day': day_list,
        'time': time_list,
        'format': 'grib',
        'area': [
                    20.43, 97.96, 5.77,
                    105.22,
                ],
                },
        f'{year}_{month}_hourly_2m_temp_TH.grib')

Direct file output from API is in .grib format, to make it easy for futher analysis work, I have converted it to .parquet format. To convert GRIB format to pandas datafram, you can use xrray and cfgrib library to help as below example snippet of code.

import xarray as xr
import cfgrib

ds = xr.open_dataset('2022_12_31_hourly_2m_temp_TH.grib', engine='cfgrib')
df = ds.to_dataframe().reset_index()

Licensing

Climate Data Store Product Licensing

Citation

This data was generated using Copernicus Climate Change Service information and
contains modified Copernicus Climate Change Service information on 2020/Jan/01 - 2023/May/13 data period
Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present.
Copernicus Climate Change Service (C3S) Climate Data Store (CDS).
DOI: 10.24381/cds.e2161bac (Accessed on 13-May-2023)
Copernicus Climate Change Service (C3S) (2022): ERA5-Land hourly data from 1950 to present.
Copernicus Climate Change Service (C3S) Climate Data Store (CDS).
DOI: 10.24381/cds.e2161bac (Accessed on 13-May-2023)