def add_variables(dataset, width, height):
tu.add_geolocation_variables(dataset,
width,
height,
chunksizes=CHUNKING)
tu.add_quality_flags(dataset, width, height, chunksizes=CHUNKING)
default_array = DefaultData.create_default_vector(
height, np.int32, fill_value=4294967295)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, 4294967295)
variable.attrs["standard_name"] = "time"
variable.attrs[
"long_name"] = "Acquisition time in seconds since 1970-01-01 00:00:00"
tu.add_units(variable, "s")
dataset["time"] = variable
default_array = DefaultData.create_default_array(width,
height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["coordinates"] = "longitude latitude"
dataset["aot"] = variable
dataset["u_independent_aot"] = tu.create_CDR_uncertainty(
width, height, "Uncertainty of aot due to independent effects")
dataset["u_structured_aot"] = tu.create_CDR_uncertainty(
width, height, "Uncertainty of aot due to structured effects")
dataset["u_common_aot"] = tu.create_CDR_uncertainty(
width, height, "Uncertainty of aot due to common effects")
def add_variables(dataset, width, height):
# @todo 1 tb/tb add geolocation 2018-06-25
tu.add_quality_flags(dataset, width, height, chunksizes=CHUNKING)
default_array = DefaultData.create_default_vector(height,
np.int32,
fill_value=-1)
variable = Variable(["y"], default_array)
tu.add_fill_value(variable, -1)
variable.attrs["standard_name"] = "time"
variable.attrs[
"long_name"] = "Acquisition time in seconds since 1970-01-01 00:00:00"
tu.add_units(variable, "s")
dataset["time"] = variable
default_array = DefaultData.create_default_array(width,
height,
np.float32,
fill_value=np.NaN)
variable = Variable(["y", "x"], default_array)
tu.add_fill_value(variable, np.NaN)
variable.attrs["standard_name"] = "surface_albedo"
variable.attrs["coordinates"] = "longitude latitude"
tu.add_chunking(variable, CHUNKING)
dataset["surface_albedo"] = variable
dataset["u_independent_surface_albedo"] = tu.create_CDR_uncertainty(
width, height,
"Uncertainty of surface_albedo due to independent effects")
dataset["u_structured_surface_albedo"] = tu.create_CDR_uncertainty(
width, height,
"Uncertainty of surface_albedo due to structured effects")
dataset["u_common_surface_albedo"] = tu.create_CDR_uncertainty(
width, height,
"Uncertainty of surface_albedo due to common effects")
def add_variables(dataset, width, height):
WriterUtils.add_gridded_global_attributes(dataset)
tu.add_gridded_geolocation_variables(dataset, width, height)
tu.add_quality_flags(dataset, width, height)
dataset["time_ranges_ascend"] = UTH._create_time_ranges_variable(
height, width,
"Minimum and maximum seconds of day pixel contribution time, ascending nodes"
)
dataset["time_ranges_descend"] = UTH._create_time_ranges_variable(
height, width,
"Minimum and maximum seconds of day pixel contribution time, descending nodes"
)
dataset[
"observation_count_ascend"] = UTH._create_observation_counts_variable(
height, width,
"Number of UTH/brightness temperature observations in a grid box for ascending passes"
)
dataset[
"observation_count_descend"] = UTH._create_observation_counts_variable(
height, width,
"Number of UTH/brightness temperature observations in a grid box for descending passes"
)
dataset["overpass_count_ascend"] = UTH._create_overpass_counts_variable(
height, width,
"Number of satellite overpasses in a grid box for ascending passes"
)
dataset["overpass_count_descend"] = UTH._create_overpass_counts_variable(
height, width,
"Number of satellite overpasses in a grid box for descending passes"
)
dataset["uth_ascend"] = UTH._create_uth_variable(
width,
height,
description=
"Monthly average of all UTH retrievals in a grid box for ascending passes (calculated from daily averages)",
)
dataset["uth_descend"] = UTH._create_uth_variable(
width,
height,
description=
"Monthly average of all UTH retrievals in a grid box for descending passes (calculated from daily averages)"
)
dataset["u_independent_uth_ascend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of UTH due to independent effects for ascending passes",
coordinates="lon lat")
dataset["u_independent_uth_descend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of UTH due to independent effects for descending passes",
coordinates="lon lat")
dataset["u_structured_uth_ascend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of UTH due to structured effects for ascending passes",
coordinates="lon lat")
dataset["u_structured_uth_descend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of UTH due to structured effects for descending passes",
coordinates="lon lat")
dataset["u_common_uth_ascend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of UTH due to common effects for ascending passes",
coordinates="lon lat")
dataset["u_common_uth_descend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of UTH due to common effects for descending passes",
coordinates="lon lat")
dataset["uth_inhomogeneity_ascend"] = tu.create_CDR_uncertainty(
width,
height,
"Standard deviation of all daily UTH averages which were used to calculate the monthly UTH average in a grid box for ascending passes",
coordinates="lon lat")
dataset["uth_inhomogeneity_descend"] = tu.create_CDR_uncertainty(
width,
height,
"Standard deviation of all daily UTH averages which were used to calculate the monthly UTH average in a grid box for descending passes",
coordinates="lon lat")
dataset["BT_ascend"] = UTH._create_bt_variable(
width,
height,
description=
"Monthly average of all brightness temperatures which were used to retrieve UTH in a grid box for ascending passes (calculated from daily averages)"
)
dataset["BT_descend"] = UTH._create_bt_variable(
width,
height,
description=
"Monthly average of all brightness temperatures which were used to retrieve UTH in a grid box for descending passes (calculated from daily averages)"
)
dataset["u_independent_BT_ascend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of brightness temperature due to independent effects for ascending passes",
coordinates="lon lat",
units="K")
dataset["u_independent_BT_descend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of brightness temperature due to independent effects for descending passes",
coordinates="lon lat",
units="K")
dataset["u_structured_BT_ascend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of brightness temperature due to structured effects for ascending passes",
coordinates="lon lat",
units="K")
dataset["u_structured_BT_descend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of brightness temperature due to structured effects for descending passes",
coordinates="lon lat",
units="K")
dataset["u_common_BT_ascend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of brightness temperature due to common effects for ascending passes",
coordinates="lon lat",
units="K")
dataset["u_common_BT_descend"] = tu.create_CDR_uncertainty(
width,
height,
"Uncertainty of brightness temperature due to common effects for descending passes",
coordinates="lon lat",
units="K")
dataset["BT_inhomogeneity_ascend"] = tu.create_CDR_uncertainty(
width,
height,
"Standard deviation of all daily brightness temperature averages which were used to calculate the monthly brightness temperature average for ascending passes",
coordinates="lon lat",
units="K")
dataset["BT_inhomogeneity_descend"] = tu.create_CDR_uncertainty(
width,
height,
"Standard deviation of all daily brightness temperature averages which were used to calculate the monthly brightness temperature average for descending passes",
coordinates="lon lat",
units="K")
dataset[
"observation_count_all_ascend"] = UTH._create_observation_counts_variable(
height, width,
"Number of all observations in a grid box for ascending passes - no filtering done"
)
dataset[
"observation_count_all_descend"] = UTH._create_observation_counts_variable(
height, width,
"Number of all observations in a grid box for descending passes - no filtering done"
)
