Flexible time steps

[pulkkins]

This pull request aims to address issue #176. The main changes are:

• Previously the output of the methods in the nowcasts module were constrained to have the same time step with the inputs. Now the user can supply a list of arbitrary output time steps.
• Allow using extrapolation.semilagrangian.extrapolate with precip=None to integrate along the advection field without interpolating the precipitation field.

[codecov]

Codecov Report

Merging #191 (ac8148a) into master (cba5f08) will increase coverage by 0.19%.
The diff coverage is 88.08%.

@@            Coverage Diff             @@
##           master     #191      +/-   ##
==========================================
+ Coverage   75.67%   75.86%   +0.19%     
==========================================
  Files         123      123              
  Lines        9273     9450     +177     
==========================================
+ Hits         7017     7169     +152     
- Misses       2256     2281      +25     

Flag	Coverage Δ
unit_tests	75.86% <88.08%> (+0.19%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
pysteps/extrapolation/interface.py	86.66% <ø> (ø)
pysteps/nowcasts/interface.py	94.44% <ø> (ø)
pysteps/nowcasts/extrapolation.py	14.28% <14.28%> (-1.10%)	⬇️
pysteps/extrapolation/semilagrangian.py	70.65% <72.72%> (+1.24%)	⬆️
pysteps/nowcasts/anvil.py	69.37% <83.87%> (-1.27%)	⬇️
pysteps/nowcasts/utils.py	97.22% <84.61%> (-2.78%)	⬇️
pysteps/nowcasts/sseps.py	87.79% <90.00%> (+0.26%)	⬆️
pysteps/nowcasts/steps.py	85.79% <94.28%> (+1.63%)	⬆️
pysteps/nowcasts/sprog.py	90.30% <94.73%> (+0.67%)	⬆️
pysteps/tests/test_nowcasts_anvil.py	87.50% <100.00%> (+0.54%)	⬆️
... and 7 more

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[dnerini]

Great work, @pulkkins !

So, how did you implement the computation of the AR process with irregular timesteps? Did you use the time-weighted interpolation that you proposed in #176?

Did you face any particular challenge to implement this to all the pysteps nowcasting systems?

[pulkkins]

Thanks @dnerini!

Yes, I used linear interpolation between the AR time steps, including the perturbation fields in STEPS. Apart from some technical work, I didn't face any major difficulties.

[dnerini]

@pulkkins I went through the code, and I only found very minor suggestions (many of them simply repeat across all nowcasting methods, which explains the number of comments somewhat large…).

I only have one major concern: I included for all nowcast methods (anvil, sseps, steps) an additional test for cases when a list of timesteps is passed (bbd8aeb). My expectation was that the result (in terms of skill) would be equivalent to passing the corresponding number of timesteps (i.e., timesteps=3 vs timesteps=[3]), but for sseps and steps, this doesn't seem to be the case (the skill is lower, see failed test results). Is this to be expected because of the stochastic nature of the two method?

[pulkkins]

I only have one major concern: I included for all nowcast methods (anvil, sseps, steps) an additional test for cases when a list of timesteps is passed (bbd8aeb). My expectation was that the result (in terms of skill) would be equivalent to passing the corresponding number of timesteps (i.e., timesteps=3 vs timesteps=[3]), but for sseps and steps, this doesn't seem to be the case (the skill is lower, see failed test results). Is this to be expected because of the stochastic nature of the two method?

Good that you raised that concern. I need to do more testing to make sure that the new time step scheme works as intended in all cases. By the way, are you sure that the tests you are referring to are correct?

Line 85 of test_nowcasts_steps.py:
crps = verification.probscores.CRPS(precip_forecast[-1], precip_obs[-1])

The first index of precip_forecast refers to the ensemble member, not time step. Should that be precip_forecast[:,-1]?

[dnerini]

I only have one major concern: I included for all nowcast methods (anvil, sseps, steps) an additional test for cases when a list of timesteps is passed (bbd8aeb). My expectation was that the result (in terms of skill) would be equivalent to passing the corresponding number of timesteps (i.e., timesteps=3 vs timesteps=[3]), but for sseps and steps, this doesn't seem to be the case (the skill is lower, see failed test results). Is this to be expected because of the stochastic nature of the two method?

By the way, are you sure that the tests you are referring to are correct?

Line 85 of test_nowcasts_steps.py:
crps = verification.probscores.CRPS(precip_forecast[-1], precip_obs[-1])

The first index of precip_forecast refers to the ensemble member, not time step. Should that be precip_forecast[:,-1]?

You're absolutely right! This could actually explain the failing tests: with timesteps=[3]the "ensemble" is smaller (essentially one single field, instead of the three fields with timesteps=3) and so produce the higher CRPS!
I'll fix it right now and see if the results align this way...

edit: That worked (see 79ac28d)! Nowcasting using timesteps=3 or timesteps=[3] produces the same skill! Brilliant!

[dnerini]

Thanks for the last changes, @pulkkins , this now looks ready to be merged, well done!