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#!/usr/bin/env python |
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# vim:fileencoding=utf-8 |
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# |
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# Copyright (c) 2018 Stefan Bender |
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# |
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# This file is part of sciapy. |
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# sciapy is free software: you can redistribute it or modify it |
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# under the terms of the GNU General Public License as published by |
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# the Free Software Foundation, version 2. |
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# See accompanying LICENSE file or http://www.gnu.org/licenses/gpl-2.0.html. |
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"""SCIAMACHY level 2 data post processing |
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Main script for SCIAMACHY orbital retrieval post processing |
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and data combining (to netcdf). |
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""" |
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from __future__ import absolute_import, division, print_function |
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import glob |
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import os |
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import argparse as ap |
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import datetime as dt |
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import logging |
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from pkg_resources import resource_filename |
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import numpy as np |
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import pandas as pd |
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import xarray as xr |
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from scipy.interpolate import interp1d |
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#import aacgmv2 |
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#import apexpy |
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from astropy import units |
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from astropy.time import Time |
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import astropy.coordinates as coord |
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import sciapy.level1c as sl |
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from . import scia_akm as sa |
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from .igrf import gmag_igrf |
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from .aacgm2005 import gmag_aacgm2005 |
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try: |
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from nrlmsise00 import msise_flat as msise |
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except ImportError: |
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msise = None |
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try: |
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from .noem import noem_cpp |
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except ImportError: |
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noem_cpp = None |
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F107_FILE = resource_filename("sciapy", "data/indices/f107_noontime_flux_obs.txt") |
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F107A_FILE = resource_filename("sciapy", "data/indices/f107a_noontime_flux_obs.txt") |
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AP_FILE = resource_filename("sciapy", "data/indices/spidr_ap_2000-2012.dat") |
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F107_ADJ_FILE = resource_filename("sciapy", "data/indices/spidr_f107_2000-2012.dat") |
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KP_FILE = resource_filename("sciapy", "data/indices/spidr_kp_2000-2012.dat") |
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PHI_FAC = 11.91 |
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LST_FAC = -0.62 |
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View Code Duplication |
def solar_zenith_angle(alt, lat, lon, time): |
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atime = Time(time) |
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loc = coord.EarthLocation.from_geodetic( |
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height=alt * units.km, |
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lat=lat * units.deg, |
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lon=lon * units.deg, |
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) |
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altaz = coord.AltAz(location=loc, obstime=atime) |
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sun = coord.get_sun(atime) |
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return sun.transform_to(altaz).zen.value |
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View Code Duplication |
def read_spectra(year, orbit, spec_base=None, skip_upleg=True): |
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"""Read and examine SCIAMACHY orbit spectra |
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Reads the limb spactra and extracts the dates, times, latitudes, |
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longitudes to be used to re-assess the retrieved geolocations. |
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Parameters |
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---------- |
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year: int |
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The measurement year to select the corresponding subdir |
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below `spec_base` (see below). |
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orbit: int |
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SCIAMACHY/Envisat orbit number of the spectra. |
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spec_base: str, optional |
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The root path to the level 1c spectra. Uses the current |
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dir if not set or set to `None` (default). |
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skip_upleg: bool, optional |
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Skip upleg limb scans, i.e. night time scans. For NO retrievals, |
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those are not used and should be not used here. |
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Default: True |
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Returns |
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------- |
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(dts, times, lats, lons, mlsts, alsts, eotcorr) |
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""" |
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fail = (None,) * 7 |
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if spec_base is None: |
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spec_base = os.curdir |
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spec_path = os.path.join(spec_base, "{0}".format(year)) |
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spec_path2 = os.path.join(spec_base, "{0}".format(int(year) + 1)) |
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logging.debug("spec_path: %s", spec_path) |
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logging.debug("spec_path2: %s", spec_path) |
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if not (os.path.isdir(spec_path) or os.path.isdir(spec_path2)): |
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return fail |
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# the star stands for the (optional) date subdir |
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# to find all spectra for the orbit |
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spfiles = glob.glob( |
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'{0}/*/SCIA_limb_*_1_0_{1:05d}.dat.l_mpl_binary' |
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.format(spec_path, orbit)) |
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# sometimes for whatever reason the orbit ends up in the wrong year subdir |
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# looks in the subdir for the following year as well. |
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spfiles += glob.glob( |
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'{0}/*/SCIA_limb_*_1_0_{1:05d}.dat.l_mpl_binary' |
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.format(spec_path2, orbit)) |
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if len(spfiles) < 2: |
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return fail |
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dts = [] |
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times = [] |
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lats = [] |
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lons = [] |
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mlsts = [] |
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alsts = [] |
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sls = sl.scia_limb_scan() |
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for f in sorted(spfiles): |
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sls.read_from_file(f) |
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# copy the values from the l1c file |
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lat, lon = sls.cent_lat_lon[:2] |
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mlst, alst, eotcorr = sls.local_solar_time(False) |
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tp_lats = sls.limb_data.tp_lat |
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date = sls.date |
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# debug output if requested |
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logging.debug("file: %s", f) |
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logging.debug("lat: %s, lon: %s", lat, lon) |
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logging.debug("mlst: %s, alst: %s, eotcorr: %s", mlst, alst, eotcorr) |
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logging.debug("tp_lats: %s", tp_lats) |
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logging.debug("date: %s", date) |
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if skip_upleg and ((tp_lats[1] - tp_lats[-2]) < 0.5): |
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# Exclude non-downleg measurements where the latitude |
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# of the last real tangent point (the last is dark sky) |
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# is larger than or too close to the first latitude. |
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# Requires an (empirical) separation of +0.5 degree. |
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logging.debug("excluding upleg point at: %s, %s", lat, lon) |
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continue |
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dtdate = pd.to_datetime(dt.datetime(*date), utc=True) |
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time_hour = dtdate.hour + dtdate.minute / 60.0 + dtdate.second / 3600.0 |
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logging.debug("mean lst: %s, apparent lst: %s, EoT: %s", mlst, alst, eotcorr) |
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dts.append(dtdate) |
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times.append(time_hour) |
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lats.append(lat) |
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lons.append(lon) |
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mlsts.append(mlst) |
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alsts.append(alst) |
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if len(lats) < 2: |
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# interpolation will fail with less than 2 points |
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return fail |
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return (np.asarray(dts), |
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np.asarray(times), |
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np.asarray(lats), |
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np.asarray(lons), |
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np.asarray(mlsts), |
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np.asarray(alsts), eotcorr) |
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View Code Duplication |
def _get_orbit_ds(filename): |
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# >= 1.5 (NO-v1.5) |
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columns = [ |
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"id", "alt_max", "alt", "alt_min", |
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"lat_max", "lat", "lat_min", "lons", "densities", |
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"dens_err_meas", "dens_err_tot", "dens_tot", "apriori", "akdiag", |
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] |
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# peek at the first line to extract the number of columns |
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with open(filename, 'rb') as _f: |
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ncols = len(_f.readline().split()) |
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# reduce the columns depending on the retrieval version |
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# default is >= 1.5 (NO-v1.5) |
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if ncols < 16: # < 1.5 (NO_emiss-183-gcaa9349) |
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columns.remove("akdiag") |
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if ncols < 15: # < 1.0 (NO_emiss-178-g729efb0) |
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columns.remove("apriori") |
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if ncols < 14: # initial output << v1.0 |
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columns.remove("lons") |
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sdd_pd = pd.read_table(filename, header=None, names=columns, skiprows=1, sep='\s+') |
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sdd_pd = sdd_pd.set_index("id") |
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logging.debug("orbit ds: %s", sdd_pd.to_xarray()) |
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ind = pd.MultiIndex.from_arrays( |
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[sdd_pd.lat, sdd_pd.alt], |
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names=["lats", "alts"], |
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) |
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sdd_ds = xr.Dataset.from_dataframe(sdd_pd).assign(id=ind).unstack("id") |
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logging.debug("orbit dataset: %s", sdd_ds) |
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sdd_ds["lons"] = sdd_ds.lons.mean("alts") |
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sdd_ds.load() |
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logging.debug("orbit ds 2: %s", sdd_ds.stack(id=["lats", "alts"]).reset_index("id")) |
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return sdd_ds |
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class _circ_interp(object): |
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"""Interpolation on a circle""" |
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def __init__(self, x, y, **kw): |
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self.c_intpf = interp1d(x, np.cos(y), **kw) |
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self.s_intpf = interp1d(x, np.sin(y), **kw) |
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def __call__(self, x): |
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return np.arctan2(self.s_intpf(x), self.c_intpf(x)) |
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View Code Duplication |
def process_orbit( |
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orbit, |
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ref_date="2000-01-01", |
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dens_path=None, |
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spec_base=None, |
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use_msis=True, |
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): |
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"""Post process retrieved SCIAMACHY orbit |
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Parameters |
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---------- |
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orbit: int |
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SCIAMACHY/Envisat orbit number of the results to process. |
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ref_date: str, optional |
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Base date to calculate the relative days from, |
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of the format "%Y-%m-%d". Default: 2000-01-01 |
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dens_path: str, optional |
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The path to the level 2 data. Uses the current |
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dir if not set or set to `None` (default). |
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spec_base: str, optional |
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The root path to the level 1c spectra. Uses the current |
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dir if not set or set to `None` (default). |
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Returns |
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------- |
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(dts0, time0, lst0, lon0, sdd): tuple |
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dts0 - days since ref_date at equator crossing (float) |
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time0 - utc hour into the day at equator crossing (float) |
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lst0 - apparent local solar time at the equator (float) |
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lon0 - longitude of the equator crossing (float) |
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sdd - `scia_density_pp` instance of the post-processed data |
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""" |
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def _read_gm(fname): |
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return dict(np.genfromtxt(fname, usecols=[0, 2], dtype=None)) |
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fail = (None,) * 5 |
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logging.debug("processing orbit: %s", orbit) |
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dtrefdate = pd.to_datetime(ref_date, format="%Y-%m-%d", utc=True) |
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logging.debug("ref date: %s", dtrefdate) |
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dfiles = glob.glob( |
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"{0}/000NO_orbit_{1:05d}_*_Dichten.txt" |
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.format(dens_path, orbit)) |
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if len(dfiles) < 1: |
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return fail |
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logging.debug("dfiles: %s", dfiles) |
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logging.debug("splits: %s", [fn.split('/') for fn in dfiles]) |
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ddict = dict([ |
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(fn, (fn.split('/')[-3:-1] + fn.split('/')[-1].split('_')[3:4])) |
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for fn in dfiles |
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]) |
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logging.debug("ddict: %s", ddict) |
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year = ddict[sorted(ddict.keys())[0]][-1][:4] |
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logging.debug("year: %s", year) |
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dts, times, lats, lons, mlsts, alsts, eotcorr = \ |
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read_spectra(year, orbit, spec_base) |
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if dts is None: |
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# return early if reading the spectra failed |
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return fail |
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dts = pd.to_datetime(dts, utc=True) - dtrefdate |
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dts = np.array([dtd.days + dtd.seconds / 86400. for dtd in dts]) |
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logging.debug("lats: %s, lons: %s, times: %s", lats, lons, times) |
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sdd = _get_orbit_ds(dfiles[0]) |
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logging.debug("density lats: %s, lons: %s", sdd.lats, sdd.lons) |
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# Re-interpolates the location (longitude) and times from the |
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# limb scan spectra files along the orbit to determine the values |
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# at the Equator and to fill in possibly missing data. |
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# |
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# y values are unit circle angles in radians (0 < φ < 2π or -π < φ < π) |
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# longitudes |
|
287
|
|
|
lons_intpf = _circ_interp( |
|
288
|
|
|
lats[::-1], np.radians(lons[::-1]), |
|
289
|
|
|
fill_value="extrapolate", |
|
290
|
|
|
) |
|
291
|
|
|
# apparent local solar time (EoT corrected) |
|
292
|
|
|
lst_intpf = _circ_interp( |
|
293
|
|
|
lats[::-1], np.pi / 12. * alsts[::-1], |
|
294
|
|
|
fill_value="extrapolate", |
|
295
|
|
|
) |
|
296
|
|
|
# mean local solar time |
|
297
|
|
|
mst_intpf = _circ_interp( |
|
298
|
|
|
lats[::-1], np.pi / 12. * mlsts[::-1], |
|
299
|
|
|
fill_value="extrapolate", |
|
300
|
|
|
) |
|
301
|
|
|
# utc time (day) |
|
302
|
|
|
time_intpf = _circ_interp( |
|
303
|
|
|
lats[::-1], np.pi / 12. * times[::-1], |
|
304
|
|
|
fill_value="extrapolate", |
|
305
|
|
|
) |
|
306
|
|
|
# datetime |
|
307
|
|
|
dts_retr_interpf = interp1d(lats[::-1], dts[::-1], fill_value="extrapolate") |
|
308
|
|
|
|
|
309
|
|
|
# equator values |
|
310
|
|
|
lon0 = np.degrees(lons_intpf(0.)) % 360. |
|
311
|
|
|
lst0 = (lst_intpf(0.) * 12. / np.pi) % 24. |
|
312
|
|
|
mst0 = (mst_intpf(0.) * 12. / np.pi) % 24. |
|
313
|
|
|
time0 = (time_intpf(0.) * 12. / np.pi) % 24. |
|
314
|
|
|
dts_retr_interp0 = dts_retr_interpf(0.) |
|
315
|
|
|
logging.debug("utc day at equator: %s", dts_retr_interp0) |
|
316
|
|
|
logging.debug("mean LST at equator: %s, apparent LST at equator: %s", mst0, lst0) |
|
317
|
|
|
|
|
318
|
|
|
sdd["utc_hour"] = ("lats", (time_intpf(sdd.lats) * 12. / np.pi) % 24.) |
|
319
|
|
|
sdd["utc_days"] = ("lats", dts_retr_interpf(sdd.lats)) |
|
320
|
|
|
|
|
321
|
|
|
if "lons" not in sdd.data_vars: |
|
322
|
|
|
# recalculate the longitudes |
|
323
|
|
|
# estimate the equatorial longitude from the |
|
324
|
|
|
# limb scan latitudes and longitudes |
|
325
|
|
|
lon0s_tp = lons - PHI_FAC * np.tan(np.radians(lats)) |
|
326
|
|
|
clon0s_tp = np.cos(np.radians(lon0s_tp)) |
|
327
|
|
|
slon0s_tp = np.sin(np.radians(lon0s_tp)) |
|
328
|
|
|
lon0_tp = np.arctan2(np.sum(slon0s_tp[1:-1]), np.sum(clon0s_tp[1:-1])) |
|
329
|
|
|
lon0_tp = np.degrees((lon0_tp + 2. * np.pi) % (2. * np.pi)) |
|
330
|
|
|
logging.info("lon0: %s", lon0) |
|
331
|
|
|
logging.info("lon0 tp: %s", lon0_tp) |
|
332
|
|
|
# interpolate to the retrieval latitudes |
|
333
|
|
|
tg_retr_lats = np.tan(np.radians(sdd.lats)) |
|
334
|
|
|
calc_lons = (tg_retr_lats * PHI_FAC + lon0) % 360. |
|
335
|
|
|
calc_lons_tp = (tg_retr_lats * PHI_FAC + lon0_tp) % 360. |
|
336
|
|
|
sdd["lons"] = calc_lons_tp |
|
337
|
|
|
logging.debug("(calculated) retrieval lons: %s, %s", |
|
338
|
|
|
calc_lons, calc_lons_tp) |
|
339
|
|
|
else: |
|
340
|
|
|
# sdd.lons = sdd.lons % 360. |
|
341
|
|
|
logging.debug("(original) retrieval lons: %s", sdd.lons) |
|
342
|
|
|
|
|
343
|
|
|
sdd["mst"] = (sdd.utc_hour + sdd.lons / 15.) % 24. |
|
344
|
|
|
sdd["lst"] = sdd.mst + eotcorr / 60. |
|
345
|
|
|
mean_alt_km = sdd.alts.mean() |
|
346
|
|
|
|
|
347
|
|
|
dt_date_this = dt.timedelta(np.asscalar(dts_retr_interp0)) + dtrefdate |
|
348
|
|
|
logging.info("date: %s", dt_date_this) |
|
349
|
|
|
|
|
350
|
|
|
gmlats, gmlons = gmag_igrf(dt_date_this, sdd.lats, sdd.lons, alt=mean_alt_km) |
|
351
|
|
|
# gmlats, gmlons = apexpy.Apex(dt_date_this).geo2qd(sdd.lats, sdd.lons, mean_alt_km) |
|
352
|
|
|
sdd["gm_lats"] = ("lats", gmlats) |
|
353
|
|
|
sdd["gm_lons"] = ("lats", gmlons) |
|
354
|
|
|
logging.debug("geomag. lats: %s, lons: %s", sdd.gm_lats, sdd.gm_lons) |
|
355
|
|
|
aacgmgmlats, aacgmgmlons = gmag_aacgm2005(sdd.lats, sdd.lons) |
|
356
|
|
|
# aacgmgmlats, aacgmgmlons = aacgmv2.convert(sdd.lats, sdd.lons, mean_alt_km, dt_date_this) |
|
357
|
|
|
sdd["aacgm_gm_lats"] = ("lats", aacgmgmlats) |
|
358
|
|
|
sdd["aacgm_gm_lons"] = ("lats", aacgmgmlons) |
|
359
|
|
|
logging.debug("aacgm geomag. lats: %s, lons: %s", |
|
360
|
|
|
sdd.aacgm_gm_lats, sdd.aacgm_gm_lons) |
|
361
|
|
|
|
|
362
|
|
|
# current day for MSIS input |
|
363
|
|
|
f107_data = _read_gm(F107_FILE) |
|
364
|
|
|
f107a_data = _read_gm(F107A_FILE) |
|
365
|
|
|
ap_data = _read_gm(AP_FILE) |
|
366
|
|
|
msis_dtdate = dt.timedelta(np.asscalar(dts_retr_interp0)) + dtrefdate |
|
367
|
|
|
msis_dtdate1 = msis_dtdate - dt.timedelta(days=1) |
|
368
|
|
|
msis_date = msis_dtdate.strftime("%Y-%m-%d").encode() |
|
369
|
|
|
msis_date1 = msis_dtdate1.strftime("%Y-%m-%d").encode() |
|
370
|
|
|
msis_f107 = f107_data[msis_date1] |
|
371
|
|
|
msis_f107a = f107a_data[msis_date] |
|
372
|
|
|
msis_ap = ap_data[msis_date] |
|
373
|
|
|
logging.debug("MSIS date: %s, f10.7a: %s, f10.7: %s, ap: %s", |
|
374
|
|
|
msis_date, msis_f107a, msis_f107, msis_ap) |
|
375
|
|
|
|
|
376
|
|
|
# previous day for NOEM input |
|
377
|
|
|
f107_adj = _read_gm(F107_ADJ_FILE) |
|
378
|
|
|
kp_data = _read_gm(KP_FILE) |
|
379
|
|
|
noem_dtdate = dt.timedelta(np.asscalar(dts_retr_interp0) - 1) + dtrefdate |
|
380
|
|
|
noem_date = noem_dtdate.strftime("%Y-%m-%d").encode() |
|
381
|
|
|
noem_f107 = f107_adj[noem_date] |
|
382
|
|
|
noem_kp = kp_data[noem_date] |
|
383
|
|
|
logging.debug("NOEM date: %s, f10.7: %s, kp: %s", |
|
384
|
|
|
noem_date, noem_f107, noem_kp) |
|
385
|
|
|
|
|
386
|
|
|
for var in ["noem_no"]: |
|
387
|
|
|
if var not in sdd.data_vars: |
|
388
|
|
|
sdd[var] = xr.zeros_like(sdd.densities) |
|
389
|
|
|
if "sza" not in sdd.data_vars: |
|
390
|
|
|
sdd["sza"] = xr.zeros_like(sdd.lats) |
|
391
|
|
|
if "akdiag" not in sdd.data_vars: |
|
392
|
|
|
sdd["akdiag"] = xr.full_like(sdd.densities, np.nan) |
|
393
|
|
|
#akm_filename = glob.glob('{0}_orbit_{1:05d}_*_AKM*'.format(species, orb))[0] |
|
394
|
|
|
akm_filename = glob.glob( |
|
395
|
|
|
"{0}/000NO_orbit_{1:05d}_*_AKM*" |
|
396
|
|
|
.format(dens_path, orbit))[0] |
|
397
|
|
|
logging.debug("ak file: %s", akm_filename) |
|
398
|
|
|
ak = sa.read_akm(akm_filename, sdd.nalt, sdd.nlat) |
|
399
|
|
|
logging.debug("ak data: %s", ak) |
|
400
|
|
|
#ak1a = ak.sum(axis = 3) |
|
401
|
|
|
#dak1a = np.diagonal(ak1a, axis1=0, axis2=2) |
|
402
|
|
|
sdd["akdiag"] = ak.diagonal(axis1=1, axis2=3).diagonal(axis1=0, axis2=1) |
|
403
|
|
|
|
|
404
|
|
|
if msise is not None: |
|
405
|
|
|
_msis_d_t = msise( |
|
406
|
|
|
msis_dtdate, |
|
407
|
|
|
sdd.alts.values[None, :], |
|
408
|
|
|
sdd.lats.values[:, None], |
|
409
|
|
|
sdd.lons.values[:, None] % 360., |
|
410
|
|
|
msis_f107a, msis_f107, msis_ap, |
|
411
|
|
|
lst=sdd.lst.values[:, None], |
|
412
|
|
|
) |
|
413
|
|
|
if "temperature" not in sdd.data_vars or use_msis: |
|
414
|
|
|
sdd["temperature"] = xr.zeros_like(sdd.densities) |
|
415
|
|
|
sdd.temperature[:] = _msis_d_t[:, :, -1] |
|
416
|
|
|
if "dens_tot" not in sdd.data_vars or use_msis: |
|
417
|
|
|
sdd["dens_tot"] = xr.zeros_like(sdd.densities) |
|
418
|
|
|
sdd.dens_tot[:] = np.sum(_msis_d_t[:, :, np.r_[:5, 6:9]], axis=2) |
|
419
|
|
|
for i, (lat, lon) in enumerate( |
|
420
|
|
|
zip(sdd.lats.values, sdd.lons.values)): |
|
421
|
|
|
if noem_cpp is not None: |
|
422
|
|
|
sdd.noem_no[i] = noem_cpp(noem_date.decode(), sdd.alts, |
|
423
|
|
|
[lat], [lon], noem_f107, noem_kp)[:] |
|
424
|
|
|
else: |
|
425
|
|
|
sdd.noem_no[i][:] = np.nan |
|
426
|
|
|
sdd.sza[:] = solar_zenith_angle( |
|
427
|
|
|
mean_alt_km, |
|
428
|
|
|
sdd.lats, sdd.lons, |
|
429
|
|
|
(pd.to_timedelta(sdd.utc_days.values, unit="days") + dtrefdate).to_pydatetime(), |
|
430
|
|
|
) |
|
431
|
|
|
sdd["vmr"] = sdd.densities / sdd.dens_tot * 1.e9 # ppb |
|
432
|
|
|
# drop unused variables |
|
433
|
|
|
sdd = sdd.drop(["alt_min", "alt", "alt_max", "lat_min", "lat", "lat_max"]) |
|
434
|
|
|
# time and orbit |
|
435
|
|
|
sdd = sdd.expand_dims("time") |
|
436
|
|
|
sdd["time"] = ("time", [dts_retr_interp0]) |
|
437
|
|
|
sdd["orbit"] = ("time", [orbit]) |
|
438
|
|
|
return dts_retr_interp0, time0, lst0, lon0, sdd |
|
439
|
|
|
|
|
440
|
|
|
|
|
441
|
|
View Code Duplication |
def get_orbits_from_date(date, mlt=False, path=None, L2_version="v6.2"): |
|
|
|
|
|
|
442
|
|
|
"""Find SCIAMACHY orbits with retrieved data at a date |
|
443
|
|
|
|
|
444
|
|
|
Parameters |
|
445
|
|
|
---------- |
|
446
|
|
|
date: str |
|
447
|
|
|
The date in the format "%Y-%m-%d". |
|
448
|
|
|
mlt: bool, optional |
|
449
|
|
|
Look for MLT mode data instead of nominal mode data. |
|
450
|
|
|
Increases the heuristics to find all MLT orbits. |
|
451
|
|
|
path: str, optional |
|
452
|
|
|
The path to the level 2 data. If `None` tries to infer |
|
453
|
|
|
the data directory from the L2 version using |
|
454
|
|
|
'./*<L2_version>'. Default: None |
|
455
|
|
|
|
|
456
|
|
|
Returns |
|
457
|
|
|
------- |
|
458
|
|
|
orbits: list |
|
459
|
|
|
List of found orbits with retrieved data files |
|
460
|
|
|
""" |
|
461
|
|
|
logging.debug("pre-processing: %s", date) |
|
462
|
|
|
if path is None: |
|
463
|
|
|
density_base = os.curdir |
|
464
|
|
|
path = "{0}/*{1}".format(density_base, L2_version) |
|
465
|
|
|
logging.debug("path: %s", path) |
|
466
|
|
|
|
|
467
|
|
|
dfiles = glob.glob("{0}/000NO_orbit_*_{1}_Dichten.txt".format( |
|
468
|
|
|
path, date.replace("-", ""))) |
|
469
|
|
|
orbits = sorted([int(os.path.basename(df).split('_')[2]) for df in dfiles]) |
|
470
|
|
|
if mlt: |
|
471
|
|
|
orbits.append(orbits[-1] + 1) |
|
472
|
|
|
return orbits |
|
473
|
|
|
|
|
474
|
|
|
|
|
475
|
|
View Code Duplication |
def combine_orbit_data(orbits, |
|
|
|
|
|
|
476
|
|
|
ref_date="2000-01-01", |
|
477
|
|
|
L2_version="v6.2", |
|
478
|
|
|
dens_path=None, spec_base=None, |
|
479
|
|
|
save_nc=False): |
|
480
|
|
|
"""Combine post-processed SCIAMACHY retrieved orbit data |
|
481
|
|
|
|
|
482
|
|
|
Parameters |
|
483
|
|
|
---------- |
|
484
|
|
|
orbits: list |
|
485
|
|
|
List of SCIAMACHY/Envisat orbit numbers to process. |
|
486
|
|
|
ref_date: str, optional |
|
487
|
|
|
Base date to calculate the relative days from, |
|
488
|
|
|
of the format "%Y-%m-%d". Default: 2000-01-01 |
|
489
|
|
|
L2_version: str, optional |
|
490
|
|
|
SCIAMACHY level 2 data version to process |
|
491
|
|
|
dens_path: str, optional |
|
492
|
|
|
The path to the level 2 data. If `None` tries to infer |
|
493
|
|
|
the data directory from the L2 version looking for anything |
|
494
|
|
|
in the current directory that ends in <L2_version>: './*<L2_version>'. |
|
495
|
|
|
Default: None |
|
496
|
|
|
spec_base: str, optional |
|
497
|
|
|
The root path to the level 1c spectra. Uses the current |
|
498
|
|
|
dir if not set or set to `None` (default). |
|
499
|
|
|
save_nc: bool, optional |
|
500
|
|
|
Save the intermediate orbit data sets to netcdf files |
|
501
|
|
|
for debugging. |
|
502
|
|
|
|
|
503
|
|
|
Returns |
|
504
|
|
|
------- |
|
505
|
|
|
(sdday, sdday_ds): tuple |
|
506
|
|
|
`sdday` contains the combined data as a `scia_density_day` instance, |
|
507
|
|
|
`sdday_ds` contains the same data as a `xarray.Dataset`. |
|
508
|
|
|
""" |
|
509
|
|
|
if dens_path is None: |
|
510
|
|
|
# try some heuristics |
|
511
|
|
|
density_base = os.curdir |
|
512
|
|
|
dens_path = "{0}/*{1}".format(density_base, L2_version) |
|
513
|
|
|
|
|
514
|
|
|
sddayl = [] |
|
515
|
|
|
for orbit in sorted(orbits): |
|
516
|
|
|
dateo, timeo, lsto, lono, sdens = process_orbit(orbit, |
|
517
|
|
|
ref_date=ref_date, dens_path=dens_path, spec_base=spec_base) |
|
518
|
|
|
logging.info( |
|
519
|
|
|
"orbit: %s, eq. date: %s, eq. hour: %s, eq. app. lst: %s, eq. lon: %s", |
|
520
|
|
|
orbit, dateo, timeo, lsto, lono |
|
521
|
|
|
) |
|
522
|
|
|
if sdens is not None: |
|
523
|
|
|
sddayl.append(sdens) |
|
524
|
|
|
if save_nc: |
|
525
|
|
|
sdens.to_netcdf(sdens.filename[:-3] + "nc") |
|
526
|
|
|
if not sddayl: |
|
527
|
|
|
return None |
|
528
|
|
|
return xr.concat(sddayl, dim="time") |
|
529
|
|
|
|
|
530
|
|
|
|
|
531
|
|
|
VAR_ATTRS = { |
|
532
|
|
|
"2.1": { |
|
533
|
|
|
"MSIS_Dens": dict( |
|
534
|
|
|
units='cm^{-3}', |
|
535
|
|
|
long_name='total number density (NRLMSIS-00)', |
|
536
|
|
|
), |
|
537
|
|
|
"MSIS_Temp": dict( |
|
538
|
|
|
units='K', |
|
539
|
|
|
long_name='temperature', |
|
540
|
|
|
model="NRLMSIS-00", |
|
541
|
|
|
), |
|
542
|
|
|
}, |
|
543
|
|
|
"2.2": { |
|
544
|
|
|
}, |
|
545
|
|
|
"2.3": { |
|
546
|
|
|
"aacgm_gm_lats": dict( |
|
547
|
|
|
long_name='geomagnetic_latitude', |
|
548
|
|
|
model='AACGM2005 at 80 km', |
|
549
|
|
|
units='degrees_north', |
|
550
|
|
|
), |
|
551
|
|
|
"aacgm_gm_lons": dict( |
|
552
|
|
|
long_name='geomagnetic_longitude', |
|
553
|
|
|
model='AACGM2005 at 80 km', |
|
554
|
|
|
units='degrees_east', |
|
555
|
|
|
), |
|
556
|
|
|
"orbit": dict( |
|
557
|
|
|
axis='T', calendar='standard', |
|
558
|
|
|
long_name='SCIAMACHY/Envisat orbit number', |
|
559
|
|
|
standard_name="orbit", |
|
560
|
|
|
units='1', |
|
561
|
|
|
), |
|
562
|
|
|
}, |
|
563
|
|
|
} |
|
564
|
|
|
VAR_RENAME = { |
|
565
|
|
|
"2.1": { |
|
566
|
|
|
# Rename to v2.1 variable names |
|
567
|
|
|
"MSIS_Dens": "TOT_DENS", |
|
568
|
|
|
"MSIS_Temp": "temperature", |
|
569
|
|
|
}, |
|
570
|
|
|
"2.2": { |
|
571
|
|
|
}, |
|
572
|
|
|
"2.3": { |
|
573
|
|
|
}, |
|
574
|
|
|
} |
|
575
|
|
|
FLOAT_VARS = [ |
|
576
|
|
|
"altitude", "latitude", "longitude", |
|
577
|
|
|
"app_LST", "mean_LST", "mean_SZA", |
|
578
|
|
|
"aacgm_gm_lats", "aacgm_gm_lons", |
|
579
|
|
|
"gm_lats", "gm_lons", |
|
580
|
|
|
] |
|
581
|
|
|
|
|
582
|
|
|
|
|
583
|
|
View Code Duplication |
def sddata_set_attrs( |
|
|
|
|
|
|
584
|
|
|
sdday_ds, |
|
585
|
|
|
file_version="2.2", |
|
586
|
|
|
ref_date="2000-01-01", |
|
587
|
|
|
rename=True, |
|
588
|
|
|
species="NO", |
|
589
|
|
|
): |
|
590
|
|
|
"""Customize xarray Dataset variables and attributes |
|
591
|
|
|
|
|
592
|
|
|
Changes the variable names to match those exported from the |
|
593
|
|
|
`scia_density_day` class. |
|
594
|
|
|
|
|
595
|
|
|
Parameters |
|
596
|
|
|
---------- |
|
597
|
|
|
sdday_ds: `xarray.Dataset` instance |
|
598
|
|
|
The combined dataset. |
|
599
|
|
|
file_version: string "major.minor", optional |
|
600
|
|
|
The netcdf file datase version, determines some variable |
|
601
|
|
|
names and attributes. |
|
602
|
|
|
ref_date: str, optional |
|
603
|
|
|
Base date to calculate the relative days from, |
|
604
|
|
|
of the format "%Y-%m-%d". Default: 2000-01-01 |
|
605
|
|
|
rename: bool, optional |
|
606
|
|
|
Rename the dataset variables to match the |
|
607
|
|
|
`scia_density_day` exported ones. |
|
608
|
|
|
Default: True |
|
609
|
|
|
species: str, optional |
|
610
|
|
|
The name of the level 2 species, used to prefix the |
|
611
|
|
|
dataset variables to be named <species>_<variable>. |
|
612
|
|
|
Default: "NO". |
|
613
|
|
|
""" |
|
614
|
|
|
if rename: |
|
615
|
|
|
sdday_ds = sdday_ds.rename({ |
|
616
|
|
|
# 2d vars |
|
617
|
|
|
"akdiag": "{0}_AKDIAG".format(species), |
|
618
|
|
|
"apriori": "{0}_APRIORI".format(species), |
|
619
|
|
|
"densities": "{0}_DENS".format(species), |
|
620
|
|
|
"dens_err_meas": "{0}_ERR".format(species), |
|
621
|
|
|
"dens_err_tot": "{0}_ETOT".format(species), |
|
622
|
|
|
"dens_tot": "MSIS_Dens", |
|
623
|
|
|
"noem_no": "{0}_NOEM".format(species), |
|
624
|
|
|
"temperature": "MSIS_Temp", |
|
625
|
|
|
"vmr": "{0}_VMR".format(species), |
|
626
|
|
|
# 1d vars and dimensions |
|
627
|
|
|
"alts": "altitude", |
|
628
|
|
|
"lats": "latitude", |
|
629
|
|
|
"lons": "longitude", |
|
630
|
|
|
"lst": "app_LST", |
|
631
|
|
|
"mst": "mean_LST", |
|
632
|
|
|
"sza": "mean_SZA", |
|
633
|
|
|
"utc_hour": "UTC", |
|
634
|
|
|
}) |
|
635
|
|
|
# relative standard deviation |
|
636
|
|
|
sdday_ds["{0}_RSTD".format(species)] = 100.0 * np.abs( |
|
637
|
|
|
sdday_ds["{0}_ERR".format(species)] / sdday_ds["{0}_DENS".format(species)]) |
|
638
|
|
|
# fix coordinate attributes |
|
639
|
|
|
sdday_ds["time"].attrs = dict(axis='T', standard_name='time', |
|
640
|
|
|
calendar='standard', long_name='equatorial crossing time', |
|
641
|
|
|
units="days since {0}".format( |
|
642
|
|
|
pd.to_datetime(ref_date, utc=True).isoformat(sep=" "))) |
|
643
|
|
|
sdday_ds["altitude"].attrs = dict(axis='Z', positive='up', |
|
644
|
|
|
long_name='altitude', standard_name='altitude', units='km') |
|
645
|
|
|
sdday_ds["latitude"].attrs = dict(axis='Y', long_name='latitude', |
|
646
|
|
|
standard_name='latitude', units='degrees_north') |
|
647
|
|
|
# Default variable attributes |
|
648
|
|
|
sdday_ds["{0}_DENS".format(species)].attrs = { |
|
649
|
|
|
"units": "cm^{-3}", |
|
650
|
|
|
"long_name": "{0} number density".format(species)} |
|
651
|
|
|
sdday_ds["{0}_ERR".format(species)].attrs = { |
|
652
|
|
|
"units": "cm^{-3}", |
|
653
|
|
|
"long_name": "{0} density measurement error".format(species)} |
|
654
|
|
|
sdday_ds["{0}_ETOT".format(species)].attrs = { |
|
655
|
|
|
"units": "cm^{-3}", |
|
656
|
|
|
"long_name": "{0} density total error".format(species)} |
|
657
|
|
|
sdday_ds["{0}_RSTD".format(species)].attrs = dict( |
|
658
|
|
|
units='%', |
|
659
|
|
|
long_name='{0} relative standard deviation'.format(species)) |
|
660
|
|
|
sdday_ds["{0}_AKDIAG".format(species)].attrs = dict( |
|
661
|
|
|
units='1', |
|
662
|
|
|
long_name='{0} averaging kernel diagonal element'.format(species)) |
|
663
|
|
|
sdday_ds["{0}_APRIORI".format(species)].attrs = dict( |
|
664
|
|
|
units='cm^{-3}', long_name='{0} apriori density'.format(species)) |
|
665
|
|
|
sdday_ds["{0}_NOEM".format(species)].attrs = dict( |
|
666
|
|
|
units='cm^{-3}', long_name='NOEM {0} number density'.format(species)) |
|
667
|
|
|
sdday_ds["{0}_VMR".format(species)].attrs = dict( |
|
668
|
|
|
units='ppb', long_name='{0} volume mixing ratio'.format(species)) |
|
669
|
|
|
sdday_ds["MSIS_Dens"].attrs = dict(units='cm^{-3}', |
|
670
|
|
|
long_name='MSIS total number density', |
|
671
|
|
|
model="NRLMSIS-00") |
|
672
|
|
|
sdday_ds["MSIS_Temp"].attrs = dict(units='K', |
|
673
|
|
|
long_name='MSIS temperature', |
|
674
|
|
|
model="NRLMSIS-00") |
|
675
|
|
|
sdday_ds["longitude"].attrs = dict(long_name='longitude', |
|
676
|
|
|
standard_name='longitude', units='degrees_east') |
|
677
|
|
|
sdday_ds["app_LST"].attrs = dict(units='hours', |
|
678
|
|
|
long_name='apparent local solar time') |
|
679
|
|
|
sdday_ds["mean_LST"].attrs = dict(units='hours', |
|
680
|
|
|
long_name='mean local solar time') |
|
681
|
|
|
sdday_ds["mean_SZA"].attrs = dict(units='degrees', |
|
682
|
|
|
long_name='solar zenith angle at mean altitude') |
|
683
|
|
|
sdday_ds["UTC"].attrs = dict(units='hours', |
|
684
|
|
|
long_name='measurement utc time') |
|
685
|
|
|
sdday_ds["utc_days"].attrs = dict( |
|
686
|
|
|
units='days since {0}'.format( |
|
687
|
|
|
pd.to_datetime(ref_date, utc=True).isoformat(sep=" ")), |
|
688
|
|
|
long_name='measurement utc day') |
|
689
|
|
|
sdday_ds["gm_lats"].attrs = dict(long_name='geomagnetic_latitude', |
|
690
|
|
|
model='IGRF', units='degrees_north') |
|
691
|
|
|
sdday_ds["gm_lons"].attrs = dict(long_name='geomagnetic_longitude', |
|
692
|
|
|
model='IGRF', units='degrees_east') |
|
693
|
|
|
sdday_ds["aacgm_gm_lats"].attrs = dict(long_name='geomagnetic_latitude', |
|
694
|
|
|
# model='AACGM2005 80 km', # v2.3 |
|
695
|
|
|
model='AACGM', # v2.1, v2.2 |
|
696
|
|
|
units='degrees_north') |
|
697
|
|
|
sdday_ds["aacgm_gm_lons"].attrs = dict(long_name='geomagnetic_longitude', |
|
698
|
|
|
# model='AACGM2005 80 km', # v2.3 |
|
699
|
|
|
model='AACGM', # v2.1, v2.2 |
|
700
|
|
|
units='degrees_east') |
|
701
|
|
|
sdday_ds["orbit"].attrs = dict( |
|
702
|
|
|
axis='T', calendar='standard', |
|
703
|
|
|
# long_name='SCIAMACHY/Envisat orbit number', # v2.3 |
|
704
|
|
|
long_name='orbit', # v2.1, v2.2 |
|
705
|
|
|
standard_name="orbit", |
|
706
|
|
|
# units='1', # v2.3 |
|
707
|
|
|
units='orbit number', # v2.1, v2.2 |
|
708
|
|
|
) |
|
709
|
|
|
# Overwrite version-specific variable attributes |
|
710
|
|
|
for _v, _a in VAR_ATTRS[file_version].items(): |
|
711
|
|
|
sdday_ds[_v].attrs = _a |
|
712
|
|
|
if rename: |
|
713
|
|
|
# version specific renaming |
|
714
|
|
|
sdday_ds = sdday_ds.rename(VAR_RENAME[file_version]) |
|
715
|
|
|
if int(file_version.split(".")[0]) < 3: |
|
716
|
|
|
# invert latitudes for backwards-compatitbility |
|
717
|
|
|
sdday_ds = sdday_ds.sortby("latitude", ascending=False) |
|
718
|
|
|
else: |
|
719
|
|
|
sdday_ds = sdday_ds.sortby("latitude", ascending=True) |
|
720
|
|
|
|
|
721
|
|
|
# for var in FLOAT_VARS: |
|
722
|
|
|
# _attrs = sdday_ds[var].attrs |
|
723
|
|
|
# sdday_ds[var] = sdday_ds[var].astype('float32') |
|
724
|
|
|
# sdday_ds[var].attrs = _attrs |
|
725
|
|
|
|
|
726
|
|
|
dateo = pd.to_datetime( |
|
727
|
|
|
xr.conventions.decode_cf_variable("date", sdday_ds.time).data[0], |
|
728
|
|
|
utc=True, |
|
729
|
|
|
).strftime("%Y-%m-%d") |
|
730
|
|
|
logging.debug("date %s dataset: %s", dateo, sdday_ds) |
|
731
|
|
|
return sdday_ds |
|
732
|
|
|
|
|
733
|
|
|
|
|
734
|
|
View Code Duplication |
def main(): |
|
|
|
|
|
|
735
|
|
|
"""SCIAMACHY level 2 post processing |
|
736
|
|
|
""" |
|
737
|
|
|
logging.basicConfig(level=logging.WARNING, |
|
738
|
|
|
format="[%(levelname)-8s] (%(asctime)s) " |
|
739
|
|
|
"%(filename)s:%(lineno)d %(message)s", |
|
740
|
|
|
datefmt="%Y-%m-%d %H:%M:%S %z") |
|
741
|
|
|
|
|
742
|
|
|
parser = ap.ArgumentParser() |
|
743
|
|
|
parser.add_argument("file", default="SCIA_NO.nc", |
|
744
|
|
|
help="the filename of the output netcdf file") |
|
745
|
|
|
parser.add_argument("-M", "--month", metavar="YEAR-MM", |
|
746
|
|
|
help="infer start and end dates for month") |
|
747
|
|
|
parser.add_argument("-D", "--date_range", metavar="START_DATE:END_DATE", |
|
748
|
|
|
help="colon-separated start and end dates") |
|
749
|
|
|
parser.add_argument("-d", "--dates", help="comma-separated list of dates") |
|
750
|
|
|
parser.add_argument("-B", "--base_date", |
|
751
|
|
|
metavar="YEAR-MM-DD", default="2000-01-01", |
|
752
|
|
|
help="Reference date to base the time values (days) on " |
|
753
|
|
|
"(default: %(default)s).") |
|
754
|
|
|
parser.add_argument("-f", "--orbit_file", |
|
755
|
|
|
help="the file containing the input orbits") |
|
756
|
|
|
parser.add_argument("-r", "--retrieval_version", default="v6.2", |
|
757
|
|
|
help="SCIAMACHY level 2 data version to process") |
|
758
|
|
|
parser.add_argument("-R", "--file_version", default="2.2", |
|
759
|
|
|
help="Postprocessing format version of the output file") |
|
760
|
|
|
parser.add_argument("-A", "--author", default="unknown", |
|
761
|
|
|
help="Author of the post-processed data set " |
|
762
|
|
|
"(default: %(default)s)") |
|
763
|
|
|
parser.add_argument("-p", "--path", default=None, |
|
764
|
|
|
help="path containing the L2 data") |
|
765
|
|
|
parser.add_argument("-s", "--spectra", default=None, metavar="PATH", |
|
766
|
|
|
help="path containing the L1c spectra") |
|
767
|
|
|
parser.add_argument("-m", "--mlt", action="store_true", default=False, |
|
768
|
|
|
help="indicate nominal (False, default) or MLT data (True)") |
|
769
|
|
|
parser.add_argument("-X", "--xarray", action="store_true", default=False, |
|
770
|
|
|
help="DEPRECATED, kept for compatibility reasons, does nothing.") |
|
771
|
|
|
loglevels = parser.add_mutually_exclusive_group() |
|
772
|
|
|
loglevels.add_argument("-l", "--loglevel", default="WARNING", |
|
773
|
|
|
choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], |
|
774
|
|
|
help="change the loglevel (default: 'WARNING')") |
|
775
|
|
|
loglevels.add_argument("-q", "--quiet", action="store_true", default=False, |
|
776
|
|
|
help="less output, same as --loglevel=ERROR (default: False)") |
|
777
|
|
|
loglevels.add_argument("-v", "--verbose", action="store_true", default=False, |
|
778
|
|
|
help="verbose output, same as --loglevel=INFO (default: False)") |
|
779
|
|
|
args = parser.parse_args() |
|
780
|
|
|
if args.quiet: |
|
781
|
|
|
logging.getLogger().setLevel(logging.ERROR) |
|
782
|
|
|
elif args.verbose: |
|
783
|
|
|
logging.getLogger().setLevel(logging.INFO) |
|
784
|
|
|
else: |
|
785
|
|
|
logging.getLogger().setLevel(args.loglevel) |
|
786
|
|
|
|
|
787
|
|
|
logging.info("processing L2 version: %s", args.retrieval_version) |
|
788
|
|
|
logging.info("writing data file version: %s", args.file_version) |
|
789
|
|
|
|
|
790
|
|
|
pddrange = [] |
|
791
|
|
|
if args.month is not None: |
|
792
|
|
|
d0 = pd.to_datetime(args.month + "-01", utc=True) |
|
793
|
|
|
pddrange += pd.date_range(d0, d0 + pd.tseries.offsets.MonthEnd()) |
|
794
|
|
|
if args.date_range is not None: |
|
795
|
|
|
pddrange += pd.date_range(*args.date_range.split(':')) |
|
796
|
|
|
if args.dates is not None: |
|
797
|
|
|
pddrange += pd.to_datetime(args.dates.split(','), utc=True) |
|
798
|
|
|
logging.debug("pddrange: %s", pddrange) |
|
799
|
|
|
|
|
800
|
|
|
olist = [] |
|
801
|
|
|
for date in pddrange: |
|
802
|
|
|
try: |
|
803
|
|
|
olist += get_orbits_from_date(date.strftime("%Y-%m-%d"), |
|
804
|
|
|
mlt=args.mlt, path=args.path, L2_version=args.retrieval_version) |
|
805
|
|
|
except: # handle NaT |
|
806
|
|
|
pass |
|
807
|
|
|
if args.orbit_file is not None: |
|
808
|
|
|
olist += np.genfromtxt(args.orbit_file, dtype=np.int32).tolist() |
|
809
|
|
|
logging.debug("olist: %s", olist) |
|
810
|
|
|
|
|
811
|
|
|
if not olist: |
|
812
|
|
|
logging.warn("No orbits to process.") |
|
813
|
|
|
return |
|
814
|
|
|
|
|
815
|
|
|
sd_xr = combine_orbit_data(olist, |
|
816
|
|
|
ref_date=args.base_date, |
|
817
|
|
|
L2_version=args.retrieval_version, |
|
818
|
|
|
dens_path=args.path, spec_base=args.spectra, save_nc=False) |
|
819
|
|
|
|
|
820
|
|
|
if sd_xr is None: |
|
821
|
|
|
logging.warn("Processed data is empty.") |
|
822
|
|
|
return |
|
823
|
|
|
|
|
824
|
|
|
sd_xr = sddata_set_attrs(sd_xr, ref_date=args.base_date, file_version=args.file_version) |
|
825
|
|
|
sd_xr = sd_xr[sorted(sd_xr.variables)] |
|
826
|
|
|
sd_xr.attrs["author"] = args.author |
|
827
|
|
|
sd_xr.attrs["creation_time"] = dt.datetime.utcnow().strftime( |
|
828
|
|
|
"%a %b %d %Y %H:%M:%S +00:00 (UTC)") |
|
829
|
|
|
sd_xr.attrs["software"] = "sciapy {0}".format(__version__) |
|
|
|
|
|
|
830
|
|
|
sd_xr.attrs["L2_data_version"] = args.retrieval_version |
|
831
|
|
|
sd_xr.attrs["version"] = args.file_version |
|
832
|
|
|
print(sd_xr) |
|
833
|
|
|
sd_xr.to_netcdf(args.file, unlimited_dims=["time"]) |
|
834
|
|
|
|
|
835
|
|
|
|
|
836
|
|
|
if __name__ == "__main__": |
|
837
|
|
|
main() |
|
838
|
|
|
|
st-bender /
sciapy
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