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Quick Start

Basic usage

  1. Setup your Python environment
  2. Create your project directory
  3. Configure the file config.yml to fit your problem (see config.yml for an example)
  4. Run TopoPyScale
import pandas as pd
from TopoPyScale import topoclass as tc
from matplotlib import pyplot as plt

# ========= STEP 1 ==========
# Load Configuration
config_file = './config.yml'
mp = tc.Topoclass(config_file)
mp.get_era5()

# ======== STEP 2 ===========
# Compute parameters of the DEM (slope, aspect, sky view factor)
mp.compute_dem_param()
mp.extract_topo_param()

# ========= STEP 3 ==========
# compute solar geometry and horizon angles
mp.compute_solar_geometry()
mp.compute_horizon()

# ========= STEP 4 ==========
# Perform the downscaling
mp.downscale_climate()

# ========= STEP 5 ==========
# explore the downscaled dataset. For instance the temperature difference between each point and the first one
(mp.downscaled_pts.t-mp.downscaled_pts.t.isel(point_id=0)).plot()
plt.show()

# ========= STEP 6 ==========
# Export output to desired format
mp.to_netcdf()

TopoClass will create a file structure in the project folder (see below). TopoPyScale assumes you have a DEM in GeoTiFF, and a set of climate data in netcdf (following ERA5 variable conventions). TopoPyScale can either segment the DEM using clustering (e.g. K-means), or use a list of predefined point coordinates in pts_list.csv. Make sure all parameters in config.yml are correct. 

my_project/
    ├── inputs/
        ├── dem/ 
            ├── my_dem.tif
            └── pts_list.csv  (OPTIONAL: to downscale to specific points)
        └── climate/
            ├── PLEV*.nc
            └── SURF*.nc
    ├── outputs/
            ├── tmp/
    ├── pipeline.py (OPTIONAL: script for the downscaling instructions)
    └── config.yml

Plotting

TopoPyScale incudes a number of plotting tools. Some are wrapped in the topoclass object: 

# To plot cluster map in the case clustering was used
mp.toposub.plot_clusters_map()

# To plot sky view factor or any other variable
mp.toposub.plot_clusters_map(var='svf', cmap=plt.cm.viridis)

# to plot a map version of a toposub downscaled output
mp.plot.map_variable()

However, the basic plotting functions are defined within the file topo_plot.py. So after having ran the downscaling in spatial (toposub) mode, run the following: 

from TopoPyScale import topo_plot as plot
import matplotlib.pyplot as plt

# to plot one single timetep
plot.map_variable(mp.downscaled_pts, mp.toposub.ds_param, time_step=100, var='t_surface')
plt.show()

# to plot the mean air temperature across the entire timeseries
plot.map_variable(mp.downscaled_pts.t.mean(dim='time').to_dataset(), mp.toposub.ds_param)
plt.title('Mean Air Temperature')
plt.show()

Comparison to Observations

TopoPyScale includes tools to fetch weather station observations from public databases:

These functions are available into topo_obs.py. topo_compare.py includes functions to estimate and also correct bias between downscaled and a reference timeseries.