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--- |
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license: eupl-1.1 |
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task_categories: |
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- time-series-forecasting |
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tags: |
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- climate |
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size_categories: |
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- 100M<n<1B |
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--- |
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# Dataset Summary |
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Contains hourly 2 meters of land (on-shore) air temperature data within grid areas of Thailand country. <br/> |
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Data is retrieved from [Corpernicus Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/home) on [ERA5-Land hourly data from 1950 to present](https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.e2161bac?tab=overview) |
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<br/> |
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Thailand areas in this context is **Latitude** = **[5.77434, 20.43353]** and **Longitude** = **[97.96852, 105.22908]** <br/> |
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For more details of data, you can refer to [ERA5-Land hourly data from 1950 to present](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview) |
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- Data Granularity: Hourly per Latitude/ Longitude |
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- Period: 01/Jan/2010 - 13/May/2023 |
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- Temperature Unit: Kelvin |
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# Source Data |
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- Organization of the producer: ECMWF |
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# Data Creation |
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Below is an example of how to make data query using Python via [CDS API](https://cds.climate.copernicus.eu/api-how-to) in monthly requests. <br/> |
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``` python |
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import cdsapi |
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c = cdsapi.Client() |
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month_list = [str(num).zfill(2) for num in range(1, 13)] |
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day_list = [str(num).zfill(2) for num in range(1, 32)] |
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time_list = [str(num).zfill(2) + ":00" for num in range(0, 24)] |
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year_list = [str(num) for num in range(2000, 2022)] |
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for year in year_list: |
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for month in month_list: |
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c.retrieve('reanalysis-era5-land', |
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{ |
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'variable': [ |
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'2m_temperature'] |
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, |
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'year': year, |
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'month' : month, |
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'day': day_list, |
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'time': time_list, |
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'format': 'grib', |
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'area': [ |
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20.43, 97.96, 5.77, |
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105.22, |
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], |
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}, |
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f'{year}_{month}_hourly_2m_temp_TH.grib') |
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``` |
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Direct file output from API is in ```.grib``` format, to make it easy for futher analysis work, I have converted it to ```.parquet``` format. |
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To convert GRIB format to pandas datafram, you can use [xrray](https://github.com/pydata/xarray) and [cfgrib](https://github.com/ecmwf/cfgrib) library to help as below example snippet of code. |
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``` python |
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import xarray as xr |
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import cfgrib |
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ds = xr.open_dataset('2022_12_31_hourly_2m_temp_TH.grib', engine='cfgrib') |
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df = ds.to_dataframe().reset_index() |
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``` |
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## Licensing |
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[Climate Data Store Product Licensing](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf) |
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## Citation |
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- This data was generated using **Copernicus Climate Change Service** information and <br/> |
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contains modified **Copernicus Climate Change Service** information on 2020/Jan/01 - 2023/May/13 data period |
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- Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. <br/> |
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- Copernicus Climate Change Service (C3S) Climate Data Store (CDS). <br/> |
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DOI: [10.24381/cds.e2161bac](https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.e2161bac?tab=overview) (Accessed on 13-May-2023) |
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- Copernicus Climate Change Service (C3S) (2022): ERA5-Land hourly data from 1950 to present. <br/> |
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Copernicus Climate Change Service (C3S) Climate Data Store (CDS). <br/> |
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DOI: [10.24381/cds.e2161bac](https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.e2161bac?tab=overview) (Accessed on 13-May-2023) |
