Station Bias Correction Applied to Structured Grid #153

Activity

1. msw09090

   added

   FeatureNew feature or requestNew feature or request

   on Jan 23, 2025
2. btschwertfeger commented on Jan 24, 2025

   

   btschwertfeger

   on Jan 24, 2025

   Owner

   More actions

   Hello @msw09090, great question.

   python-cmethods is designed to be used with NetCDF-based data sets, so you'll need to convert your CSV data into NetCDF. For this you could use pandas and xarray for loading the CSV file, assigning the lats and lons (and time dimension) + saving it to a dataset.

   Simplified and not tested example, you may need to perform further steps, depending on the data:

   # Python script
   import pandas as pd
   import numpy as np
   
   scenario = xr.open_dataset("scenario.nc")
   
   # Extract the grid
   lats = scenario['latitude']
   lons = scenario['longitude']
   
   # Load the CSV data
   csv_data = pd.read_csv("temperature_data.csv")
   
   temperature_values = csv_data['temperature'].values # assuming there is a head with this value
   
   # Assume the temperature data is static and spans the grid only once
   temperature_grid = temperature_values.reshape(len(lats), len(lons))
   
   # Create an xarray Dataset
   ds = xr.Dataset(
       {
           "temperature": (["latitude", "longitude"], temperature_grid)
       },
       coords={
           "latitude": lats,
           "longitude": lons,
       }
   )
   
   # Save as NetCDF
   ds.to_netcdf("temperature_data.nc")
   
   # ... after that you can use the `ds`/dataset for applying bias correction methods.

   The bias adjustment techniques covered in this package are designed to be applied to thee data sets, two of them covering a "historical" time range, the so-called reference period, where one data set is the reference data/observations/reanalysis data and the other is modeled data. These two data sets are then used to bias-adjust a scenario/future/predicted time series.

   Using only two datasets is possible, but the results may lack strong statistical significance.
3. Repository owner locked and limited conversation to collaborators on Jan 24, 2025
4. btschwertfeger

   converted this issue into a discussion #154 on Jan 24, 2025