Update README.md

README.md

@@ -9,17 +9,21 @@ size_categories:
 ---
 
 # Dataset Summary
-Contains hourly 2 meters air temperature data within grid areas of Thailand country. <br/>
-Data is retrived 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)
+Contains hourly 2 meters of land (on-shore) air temperature data within grid areas of Thailand country. <br/>
+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)
  <br/>
 Thailand areas in this context is **Latitude** = **[5.77434, 20.43353]** and **Longitude** = **[97.96852, 105.22908]** <br/>
 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))
+- Data Granularity: Hourly per Latitude/ Longitude
+- Period: 01/Jan/2010 - 13/May/2023
+- Temperature Unit: Kelvin
+
 
 # Source Data
-- Organization of the producer : ECMWF
+- Organization of the producer: ECMWF
 
 # Data Creation
-Below is how to make data query using python via [CDS API](https://cds.climate.copernicus.eu/api-how-to). <br/>
+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/>
 
 ``` python
 import cdsapi
@@ -49,16 +53,29 @@ for year in year_list:
                 },
         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](https://github.com/pydata/xarray) and [cfgrib](https://github.com/ecmwf/cfgrib) library to help as below example snippet of code.
+
+``` python
+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](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf)
 
 ## Citation
-- This data was generated using **Copernicus Climate Change Service** information and contains modified **Copernicus Climate Change Service** information <br/>
-on 2020/Jan/01 - 2023/May/13 data period
+- This data was generated using **Copernicus Climate Change Service** information and <br/>
+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. <br/>
-Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: [10.24381/cds.e2161bac](https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.e2161bac?tab=overview) (Accessed on 13-May-2023)
+- Copernicus Climate Change Service (C3S) Climate Data Store (CDS). <br/>
+DOI: [10.24381/cds.e2161bac](https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.e2161bac?tab=overview) (Accessed on 13-May-2023)
 
 - Copernicus Climate Change Service (C3S) (2022): ERA5-Land hourly data from 1950 to present. <br/>
-Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: [10.24381/cds.e2161bac](https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.e2161bac?tab=overview) (Accessed on 13-May-2023)
+Copernicus Climate Change Service (C3S) Climate Data Store (CDS). <br/>
+DOI: [10.24381/cds.e2161bac](https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.e2161bac?tab=overview) (Accessed on 13-May-2023)