Basic RCMES Workflow (example)

Typical Steps:
- Load Datasets
- Temporal Regrid
- Spatial Regrid
- Configure Metrics
- Evaluate Metrics
- Plot Results

Make sure that OCW is available.



In [1]:

    
%matplotlib inline
import ocw
from functions import commonLatLonGrid

Load a Gridded Satellite Dataset



In [2]:

    
import ocw.data_source.local as local

path = "/Users/bdwilson/Documents/code/RCMES/Workshop/AFRICA_UC-WRF311_CTL_ERAINT_MM_50km-rg_1989-2008_tasmax.nc"
variable = "tasmax"
satellite_ds = local.load_file(path, variable)
print satellite_ds









    



<Dataset - name: None, lat-range: (-45.76, -24.64), lon-range: (-24.64, -24.64), time_range: (1989-01-15 00:00:00, 2008-12-15 00:00:00), var: tasmax, units: K>

Load a Model Dataset



In [3]:

    
path = "/Users/bdwilson/Documents/code/RCMES/Workshop/AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_tasmax.nc"
variable = "tasmax"
model_ds = local.load_file(path, variable)
print model_ds









    



<Dataset - name: None, lat-range: (-45.7599983215, -24.6399993896), lon-range: (-24.6399993896, -24.6399993896), time_range: (1989-01-01 00:00:00, 2008-12-01 00:00:00), var: tasmax, units: K>

Temporal Regrid



In [4]:

    
import ocw.dataset_processor as dsp
from datetime import timedelta

timeRes=timedelta(days=365)
satellite_ds = dsp.temporal_rebin(satellite_ds, timeRes)
model_ds = dsp.temporal_rebin(model_ds, timeRes)
print satellite_ds
print model_ds









    



<Dataset - name: None, lat-range: (-45.76, -24.64), lon-range: (-24.64, -24.64), time_range: (1989-01-01 00:00:00, 2008-01-01 00:00:00), var: tasmax, units: K>
<Dataset - name: None, lat-range: (-45.7599983215, -24.6399993896), lon-range: (-24.6399993896, -24.6399993896), time_range: (1989-01-01 00:00:00, 2008-01-01 00:00:00), var: tasmax, units: K>

Spatial Regrid



In [5]:

    
latRes = 1.0     # degrees
lonRes = 1.0     # degrees
lats, lons = commonLatLonGrid([satellite_ds, model_ds], latRes, lonRes)
# Find common spatial bounds, return desired grid with specified resolution

satellite_ds = dsp.spatial_regrid(satellite_ds, lats, lons)
model_ds = dsp.spatial_regrid(model_ds, lats, lons)









    



commonSpatialBounds: Dataset 0 has boundaries: lat (-45.76, 42.24), lon (-24.64, 60.28).
commonSpatialBounds: Dataset 1 has boundaries: lat (-45.7599983215, 42.2400016785), lon (-24.6399993896, 60.2799987793).
commonSpatialBounds: Common boundaries are: lat (-45.7599983215, 42.24), lon (-24.6399993896, 60.2799987793).

Configure Metric(s) for Evaluation Object



In [6]:

    
import ocw.metrics as metrics
import ocw.evaluation as evaluation

metric = metrics.Bias()
eval = evaluation.Evaluation(satellite_ds, [model_ds], [metric])

Evaluate Metrics



In [7]:

    
eval.run()
results = eval.results

Plot Bias between two models as time-series of contour maps



In [8]:

    
import ocw.plotter as plotter

outputName = "wrf_bias_compared_to_knmi"
config = {'gridshape': (4, 5),
          'ptitle': 'TASMAX Bias of WRF Compared to KNMI (1989 - 2008)',
          'subtitles': range(1989, 2009, 1)
          }

plotter.draw_contour_map(results[0][0], lats, lons, outputName, 'png', **config)
plotFile = outputName + '.png'
print plotFile









    



wrf_bias_compared_to_knmi.png






    



/Users/bdwilson/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors == str('face'):






    





<matplotlib.figure.Figure at 0x10b8af8d0>

Display Plot



In [9]:

    
from IPython.display import Image, display

display(Image(plotFile))



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