def gpt2w(dset):
"""Calculates meteorological data and mapping function coefficients based on GPT2w model
The GPT2w model is described in Boehm et al. :cite:`boehm2015`.
Args:
dset (Dataset): Model data.
Returns:
tuple of Numpy Arrays: Includes the following elements, each with entries for each observation
============ =========== =======================================================
Element Unit Description
============ =========== =======================================================
pressure hPa Pressure value
temperature Celsius Temperature values
dt degree/km Temperature lapse rate
tm K Mean temperature of the water vapor
e hPa Water vapor pressure
mh Hydrostatic mapping function coefficient ah
mw Wet mapping function coefficient aw
la Water vapor decrease factor
geoid_undu m Geoid undulation (based on 9x9 EGM model)
============ =========== =======================================================
"""
press = np.empty(dset.num_obs)
temp = np.empty(dset.num_obs)
dt = np.empty(dset.num_obs)
tm = np.empty(dset.num_obs)
e = np.empty(dset.num_obs)
ah = np.empty(dset.num_obs)
aw = np.empty(dset.num_obs)
mh = np.empty(dset.num_obs)
mw = np.empty(dset.num_obs)
la = np.empty(dset.num_obs)
undu = np.empty(dset.num_obs)
mjd = dset.time.utc.mjd
lat, lon, height = dset.site_pos.pos.llh.T
zd = dset.site_pos.zenith_distance
# Determine GPT2W values for each observation by interpolating between two unique
# daily solutions
for obs in range(dset.num_obs):
# Start 'gpt2.f' day-by-day in folder where 'gpt2_5.grd' is placed and carry out
# linear interpolation
(press[obs], temp[obs], dt[obs], tm[obs], e[obs], ah[obs], aw[obs],
la[obs], undu[obs]) = gpt2w_wrapper(mjd[obs], [lat[obs]], [lon[obs]],
[height[obs]])
# Determine mapping function values based on coefficients 'ah' and 'aw'
mh[obs], mw[obs] = iers.vmf1_ht(ah[obs], aw[obs], mjd[obs], lat[obs],
height[obs], zd[obs])
return press, temp, dt, tm, e, mh, mw, la, undu
def vmf1_mapping_function(dset):
"""Calculates VMF1 hydrostatic and wet mapping functions
This routine determines the VMF1 (Vienna Mapping Functions 1) described in Boehm et al. :cite:`boehm2006a` and
Kouba :cite:`kouba2007` and uses the 'vmf1_ht.f' from the IERS software library :cite:`iers2010`. The mapping
function coefficients 'ah' and 'aw' are taken from VMF1 gridded files.
TODO: Multiplication with 1.e-8 should maybe be handled when reading gridded VMF1-files and not here.
Args:
dset (Dataset): Model data.
Returns:
tuple of Numpy Arrays: Includes the following elements, each with entries for each observation
============ =========== =======================================================
Element Unit Description
============ =========== =======================================================
mh Hydrostatic mapping function coefficient ah
mw Wet mapping function coefficient aw
============ =========== =======================================================
"""
# Get gridded VMF1 data
vmf1 = apriori.get("vmf1", time=dset.time)
mh = np.empty(dset.num_obs)
mw = np.empty(dset.num_obs)
lat, lon, height = dset.site_pos.pos.llh.T
zd = dset.site_pos.zenith_distance
for obs in range(dset.num_obs):
# Interpolation in time and space in VMF1 grid
# t = dset.time[obs]
# TODO vlbi t_2
# if dset._default_field_suffix == '_2':
# baseline_gcrs = dset.site_pos_2.gcrs - dset.site_pos_1.gcrs
# delta_t = (dset.src_dir.unit_vector[:, None, :] @ baseline_gcrs[:, :, None])[:, 0, 0] / constant.c
# t -= timedelta(seconds=delta_t[obs])
mh[obs], mw[obs] = iers.vmf1_ht(
vmf1["ah"](dset.time[obs], lon[obs], lat[obs]) * 1e-8,
vmf1["aw"](dset.time[obs], lon[obs], lat[obs]) * 1e-8,
dset.time.utc.mjd_int[obs],
lat[obs],
height[obs],
zd[obs],
)
return mh, mw
def gpt2(dset):
"""Calculates meteorological data and mapping function coefficients based on GPT2 model
The GPT2 model is described in Lagler et al. :cite:`lagler2013`.
Args:
dset (Dataset): Model data.
Returns:
tuple of Numpy Arrays: Includes the following elements, each with entries for each observation
============ =========== =======================================================
Element Unit Description
============ =========== =======================================================
pressure hPa Pressure value
temperature Celsius Temperature values
dt degree/km Temperature lapse rate
e hPa Water vapor pressure
mh Hydrostatic mapping function coefficient ah
mw Wet mapping function coefficient aw
geoid_undu m Geoid undulation (based on 9x9 EGM model)
============ =========== =======================================================
"""
press = np.empty(dset.num_obs)
temp = np.empty(dset.num_obs)
dt = np.empty(dset.num_obs)
e = np.empty(dset.num_obs)
ah = np.empty(dset.num_obs)
aw = np.empty(dset.num_obs)
mh = np.empty(dset.num_obs)
mw = np.empty(dset.num_obs)
undu = np.empty(dset.num_obs)
# Determine GPT2 values for each observation by interpolating between two unique
# daily solutions
for obs, _ in enumerate(dset.values()):
mjd = dset.time.utc.mjd[obs]
lat, lon, height = dset.site_pos.llh[obs]
zd = dset.site_pos.zenith_distance[obs]
# Start 'gpt2.f' day-by-day in folder where 'gpt2_5.grd' is placed and carry out
# linear interpolation
press[obs], temp[obs], dt[obs], e[obs], ah[obs], aw[obs], undu[
obs] = gpt2_wrapper(mjd, [lat], [lon], [height])
# Determine mapping function values based on coefficients 'ah' and 'aw'
mh[obs], mw[obs] = iers.vmf1_ht(ah[obs], aw[obs], mjd, lat, height, zd)
return press, temp, dt, e, mh, mw, undu