WasuratS/ECMWF_Thailand_Land_Air_Temperatures

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: 31/Dec/1999 - 08/May/2023
• Temperature Unit: Celsius (°C) (Original data from ERA5-Land hourly data from 1950 to present is 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.
Script can be found here

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 further analysis work, I have converted it to .parquet format.
To convert GRIB format to pandas dataframe, 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 1999/Dec/31 - 2023/May/08 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)