Importer for AVAPS dropsondes (operated e.g. on HALO). #33
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Measurement gaps will be filled via linear interpolation and missing measurements between the aircraft altitude and the first recorded dropsonde measurement are filled with extrapolation. The extrapolation is based on BAHAMAS measurements (if available) or otherwise guessed from the existing measurements. After having measurement gaps filled, outliers are marked and, if a certain threshold is exceeded, smoothed out. Finally, the dropsonde measurements are interpolated on a 10 m grid.
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Is it okay to leave the dropsonde processing routines inside the importer.py module or shall it be moved elsewhere to keep the importer "clean"? |
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It looks quite nice. Thank you for this work!
It's a lot of code and I just had a quick look. At the first glace, it looks quite verbose and useful. One should check the code e.g. with an polar dropsonde. Question remains whether such a long importer is ok in importer.py or if some code should be shifted to a separate module.
Attached to this comment are two minor code-comments. A third would be that there are some not-needed empty lines for my taste.
| if ceiling > 15000: | ||
| raise ValueError("Dropsonde launch altitude appears to be > 15000 m. Extrapolation is aborted because the " + | ||
| "tropopause may intervene.\n") | ||
| return new_dict, new_ipflag_dict |
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This return will never happen as the ValueError breaks the code flow.
python/pyPamtra/importer.py
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| # 4d variables: hydrometeors: | ||
| shape4d = [Nx, Nx, Nh-1, 5] # potentially 5 hydrometeor classes with this setting | ||
| pamData['hydro_q'] = np.zeros(shape4d) | ||
| pamData['hydro_q'][...,0] = 0# CLOUD | ||
| pamData['hydro_q'][...,1] = 0# ICE | ||
| pamData['hydro_q'][...,2] = 0# RAIN | ||
| pamData['hydro_q'][...,3] = 0# SNOW | ||
| pamData['hydro_q'][...,4] = 0# GRAUPEL | ||
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| # Descriptorfile must be included. otherwise, pam.p.nhydro would be 0 which is not permitted. | ||
| descriptorFile = np.array([ | ||
| #['hydro_name' 'as_ratio' 'liq_ice' 'rho_ms' 'a_ms' 'b_ms' 'alpha_as' 'beta_as' 'moment_in' 'nbin' 'dist_name' 'p_1' 'p_2' 'p_3' 'p_4' 'd_1' 'd_2' 'scat_name' 'vel_size_mod' 'canting'] | ||
| ('cwc_q', -99.0, 1, -99.0, -99.0, -99.0, -99.0, -99.0, 3, 1, 'mono', -99.0, -99.0, -99.0, -99.0, 2e-05, -99.0, 'mie-sphere', 'khvorostyanov01_drops', -99.0), | ||
| ('iwc_q', -99.0, -1, -99.0, 130.0, 3.0, 0.684, 2.0, 3, 1, 'mono_cosmo_ice', -99.0, -99.0, -99.0, -99.0, -99.0, -99.0, 'mie-sphere', 'heymsfield10_particles', -99.0), | ||
| ('rwc_q', -99.0, 1, -99.0, -99.0, -99.0, -99.0, -99.0, 3, 50, 'exp', -99.0, -99.0, 8000000.0, -99.0, 0.00012, 0.006, 'mie-sphere', 'khvorostyanov01_drops', -99.0), | ||
| ('swc_q', -99.0, -1, -99.0, 0.038, 2.0, 0.3971, 1.88, 3, 50, 'exp_cosmo_snow', -99.0, -99.0, -99.0, -99.0, 5.1e-11, 0.02, 'mie-sphere', 'heymsfield10_particles', -99.0), | ||
| ('gwc_q', -99.0, -1, -99.0, 169.6, 3.1, -99.0, -99.0, 3, 50, 'exp', -99.0, -99.0, 4000000.0, -99.0, 1e-10, 0.01, 'mie-sphere', 'khvorostyanov01_spheres', -99.0)], | ||
| dtype=[('hydro_name', 'S15'), ('as_ratio', '<f8'), ('liq_ice', '<i8'), ('rho_ms', '<f8'), ('a_ms', '<f8'), ('b_ms', '<f8'), ('alpha_as', '<f8'), ('beta_as', '<f8'), | ||
| ('moment_in', '<i8'), ('nbin', '<i8'), ('dist_name', 'S15'), ('p_1', '<f8'), ('p_2', '<f8'), ('p_3', '<f8'), | ||
| ('p_4', '<f8'), ('d_1', '<f8'), ('d_2', '<f8'), ('scat_name', 'S15'), ('vel_size_mod', 'S30'), ('canting', '<f8')] | ||
| ) | ||
| for hyd in descriptorFile: pam.df.addHydrometeor(hyd) |
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I think you could skip this for a clear sky simulation
python/pyPamtra/importer.py
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| pamData['lat'] = np.broadcast_to(sonde_dict['lat'][~np.isnan(sonde_dict['lat'])][-1], shape2d) | ||
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| pamData['timestamp'] = np.broadcast_to(sonde_dict['launch_time'], shape2d) | ||
| pamData['groundtemp'] = np.broadcast_to(sonde_dict['T'][0], shape2d) |
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One more comment: I would prefer the groundtemp to be optionally passed as argument. You assume here, that air temperature at 15 or 30 m above the surface is in equilibrium with the sea. That is not always the case. (e.g. in the Arctic). I think more flexibility here would be great.
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sonde_dict['T'][0] is not at 15 or 30 m but a linearly extrapolated value from the lowest dropsonde measurements. Anyway, it certainly does not equal the sea surface temperature.
This would require additional input information to the importer function. I'll add a sst=np.nan argument to the function read_AVAPS_dropsonde(...).
… the AVAPS dropsonde importer. Additionally, some spacing lines have been removed.
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I think we lost track of this pull request. Now it has some conflicts, but I guess it is just a matter of moving the importer to some different lines. @AnWalbroel can you do that? |
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Hi Davide, I'll certainly look into it... but if it isn't urgent, I would rather do it when I return from the ship. |
Measurement gaps will be filled via linear interpolation and missing measurements between the
aircraft altitude and the first recorded dropsonde measurement are filled with extrapolation. The
extrapolation is based on BAHAMAS measurements (if available) or otherwise guessed from the
existing measurements. After having measurement gaps filled, outliers are marked and, if a certain
threshold is exceeded, smoothed out. Finally, the dropsonde measurements are interpolated on a 10
m grid.