monet package

Subpackages

• monet.plots package
  • Submodules
  • monet.plots.colorbars module
  • monet.plots.mapgen module
  • monet.plots.plots module
  • monet.plots.taylordiagram module
  • Module contents
• monet.util package
  • Submodules
  • monet.util.combinetool module
  • monet.util.interp_util module
  • monet.util.resample module
  • monet.util.stats module
  • monet.util.tools module
  • Module contents

Submodules

monet.met_funcs module

DESCRIPTION

This package contains the main routines for estimating variables related to the

Monin-Obukhov (MO) Similarity Theory, such as MO length, adiabatic correctors

for heat and momentum transport. It requires the following package.

monet.met_funcs.calc_L(ustar, T_A_K, rho, c_p, H, LE)

Calculates the Monin-Obukhov length.

Parameters:

• ustar (float) – friction velocity (m s-1).
• T_A_K (float) – air temperature (Kelvin).
• rho (float) – air density (kg m-3).
• c_p (float) – Heat capacity of air at constant pressure (J kg-1 K-1).
• H (float) – sensible heat flux (W m-2).
• LE (float) – latent heat flux (W m-2).

Returns:

L – Obukhov stability length (m).

Return type:

float

References

[Brutsaert2005]

Brutsaert, W. (2005). Hydrology: an introduction (Vol. 61, No. 8). Cambridge: Cambridge University Press.

monet.met_funcs.calc_Psi_H(zoL)

Calculates the adiabatic correction factor for heat transport.

Parameters:zoL (float) – stability coefficient (unitless). Returns:Psi_H – adiabatic corrector factor fof heat transport (unitless). Return type:float

References

[Brutsaert2005]

Brutsaert, W. (2005). Hydrology: an introduction (Vol. 61, No. 8). Cambridge: Cambridge University Press.

monet.met_funcs.calc_Psi_M(zoL)

Adiabatic correction factor for momentum transport.

Parameters:zoL (float) – stability coefficient (unitless). Returns:Psi_M – adiabatic corrector factor fof momentum transport (unitless). Return type:float

References

[Brutsaert2005]

Brutsaert, W. (2005). Hydrology: an introduction (Vol. 61, No. 8). Cambridge: Cambridge University Press.

monet.met_funcs.calc_c_p(p, ea)

Calculates the heat capacity of air at constant pressure.

Parameters:

• p (float) – total air pressure (dry air + water vapour) (mb).
• ea (float) – water vapor pressure at reference height above canopy (mb).

Returns:

c_p

Return type:

heat capacity of (moist) air at constant pressure (J kg-1 K-1)

References

based on equation (6.1) from Maarten Ambaum (2010): Thermal Physics of the Atmosphere (pp 109).

monet.met_funcs.calc_delta_vapor_pressure(T_K)

Calculate the slope of saturation water vapour pressure.

Parameters:T_K (float) – temperature (K). Returns:s – slope of the saturation water vapour pressure (kPa K-1) Return type:float

monet.met_funcs.calc_lambda(T_A_K)

Calculates the latent heat of vaporization.

Parameters:T_A_K (float) – Air temperature (Kelvin). Returns:Lambda – Latent heat of vaporisation (J kg-1). Return type:float

References

based on Eq. 3-1 Allen FAO98

monet.met_funcs.calc_lapse_rate_moist(T_A_K, ea, p)

Calculate moist-adiabatic lapse rate (K/m)

Parameters:

• T_A_K (float or numpy array) – air temperature at reference height (K).
• ea (float or numpy array) – water vapor pressure at reference height (mb).
• p (float or numpy array) – total air pressure (dry air + water vapour) at reference height (mb).

Returns:

Gamma_w – moist-adiabatic lapse rate (K/m)

Return type:

float or numpy array

References

http://glossary.ametsoc.org/wiki/Saturation-adiabatic_lapse_rate

monet.met_funcs.calc_mixing_ratio(ea, p)

Calculate ratio of mass of water vapour to the mass of dry air (-)

Parameters:

• ea (float or numpy array) – water vapor pressure at reference height (mb).
• p (float or numpy array) – total air pressure (dry air + water vapour) at reference height (mb).

Returns:

r – mixing ratio (-)

Return type:

float or numpy array

References

http://glossary.ametsoc.org/wiki/Mixing_ratio

monet.met_funcs.calc_pressure(z)

Calculates the barometric pressure above sea level.

Parameters:z (float) – height above sea level (m). Returns:p – air pressure (mb). Return type:float

monet.met_funcs.calc_psicr(c_p, p, Lambda)

Calculates the psicrometric constant.

Parameters:

• c_p (float) – heat capacity of (moist) air at constant pressure (J kg-1 K-1).
• p (float) – atmopheric pressure (mb).
• Lambda (float) – latent heat of vaporzation (J kg-1).

Returns:

psicr – Psicrometric constant (mb C-1).

Return type:

float

monet.met_funcs.calc_rho(p, ea, T_A_K)

Calculates the density of air.

Parameters:

• p (float) – total air pressure (dry air + water vapour) (mb).
• ea (float) – water vapor pressure at reference height above canopy (mb).
• T_A_K (float) – air temperature at reference height (Kelvin).

Returns:

rho – density of air (kg m-3).

Return type:

float

References

based on equation (2.6) from Brutsaert (2005): Hydrology - An Introduction (pp 25).

monet.met_funcs.calc_richardson(u, z_u, d_0, T_R0, T_R1, T_A0, T_A1)

Richardson number.

Estimates the Bulk Richardson number for turbulence using time difference temperatures.

Parameters:

• u (float) – Wind speed (m s-1).
• z_u (float) – Wind speed measurement height (m).
• d_0 (float) – Zero-plane displacement height (m).
• T_R0 (float) – radiometric surface temperature at time 0 (K).
• T_R1 (float) – radiometric surface temperature at time 1 (K).
• T_A0 (float) – air temperature at time 0 (K).
• T_A1 (float) – air temperature at time 1 (K).

Returns:

Ri – Richardson number.

Return type:

float

References

[Norman2000]

Norman, J. M., W. P. Kustas, J. H. Prueger, and G. R. Diak (2000), Surface flux estimation using radiometric temperature: A dual-temperature-difference method to minimize measurement errors, Water Resour. Res., 36(8), 2263-2274, http://dx.doi.org/10.1029/2000WR900033.

monet.met_funcs.calc_stephan_boltzmann(T_K)

Calculates the total energy radiated by a blackbody.

Parameters:T_K (float) – body temperature (Kelvin) Returns:M – Emitted radiance (W m-2) Return type:float

monet.met_funcs.calc_sun_angles(lat, lon, stdlon, doy, ftime)

Calculates the Sun Zenith and Azimuth Angles (SZA & SAA).

Parameters:

• lat (float) – latitude of the site (degrees).
• long (float) – longitude of the site (degrees).
• stdlng (float) – central longitude of the time zone of the site (degrees).
• doy (float) – day of year of measurement (1-366).
• ftime (float) – time of measurement (decimal hours).

Returns:

• sza (float) – Sun Zenith Angle (degrees).
• saa (float) – Sun Azimuth Angle (degrees).

monet.met_funcs.calc_theta_s(xlat, xlong, stdlng, doy, year, ftime)

Calculates the Sun Zenith Angle (SZA).

Parameters:

• xlat (float) – latitude of the site (degrees).
• xlong (float) – longitude of the site (degrees).
• stdlng (float) – central longitude of the time zone of the site (degrees).
• doy (float) – day of year of measurement (1-366).
• year (float) – year of measurement .
• ftime (float) – time of measurement (decimal hours).

Returns:

theta_s – Sun Zenith Angle (degrees).

Return type:

float

References

Adopted from Martha Anderson’s fortran code for ALEXI which in turn was based on Cupid.

monet.met_funcs.calc_u_star(u, z_u, L, d_0, z_0M)

Friction velocity.

Parameters:

• u (float) – wind speed above the surface (m s-1).
• z_u (float) – wind speed measurement height (m).
• L (float) – Monin Obukhov stability length (m).
• d_0 (float) – zero-plane displacement height (m).
• z_0M (float) – aerodynamic roughness length for momentum transport (m).

References

[Brutsaert2005]

Brutsaert, W. (2005). Hydrology: an introduction (Vol. 61, No. 8). Cambridge: Cambridge University Press.

monet.met_funcs.calc_vapor_pressure(T_K)

Calculate the saturation water vapour pressure.

Parameters:T_K (float) – temperature (K). Returns:ea – saturation water vapour pressure (mb). Return type:float

monet.met_funcs.flux_2_evaporation(flux, T_K=293.15, time_domain=1)

Converts heat flux units (W m-2) to evaporation rates (mm time-1) to a given temporal window

Parameters:

• flux (float or numpy array) – heat flux value to be converted, usually refers to latent heat flux LE to be converted to ET
• T_K (float or numpy array) – environmental temperature in Kelvin. Default=20 Celsius
• time_domain (float) – Temporal window in hours. Default 1 hour (mm h-1)

Returns:

ET – evaporation rate at the time_domain. Default mm h-1

Return type:

float or numpy array

monet.monet_accessor module

MONET Accessor

class monet.monet_accessor.MONETAccessor(xray_obj)

Bases: object

Short summary.

Parameters:xray_obj (type) – Description of parameter xray_obj.

obj

Description of attribute obj.

Type:type

cftime_to_datetime64(name=None)

Short summary.

Parameters:name (type) – Description of parameter name. Returns:Description of returned object. Return type:type

combine_point(data, suffix=None, pyresample=True, **kwargs)

Short summary.

Parameters:

• data (type) – Description of parameter data.
• col (type) – Description of parameter col.
• radius (type) – Description of parameter radius.

Returns:

Description of returned object.

Return type:

type

interp_constant_lat(lat=None, lat_name='latitude', lon_name='longitude', **kwargs)

Interpolates the data array to constant longitude.

Parameters:lon (float) – Latitude on which to interpolate to Returns:DataArray of at constant longitude Return type:DataArray

interp_constant_lon(lon=None, **kwargs)

Interpolates the data array to constant longitude.

Parameters:lon (float) – Latitude on which to interpolate to Returns:DataArray of at constant longitude Return type:DataArray

is_land(return_xarray=False)

checks the mask of land and ocean.

Parameters:return_xarray (bool) – If True, return the data array with the ocean values set to NaN. If False, return a numpy boolean array of the land (True). Returns: Return type:xarray.DataArray or numpy.array

is_ocean(return_xarray=False)

checks the mask of land and ocean.

Parameters:return_xarray (bool) – If True, return the data array with the land values set to NaN. If False, return a numpy boolean array of the ocean (True). Returns: Return type:xarray.DataArray or numpy.array

nearest_ij(lat=None, lon=None, **kwargs)

Uses pyresample to intepolate to find the i, j index of grid with respect to the given lat lon.

Parameters:

• lat (float) – latitude in question
• lon (float) – longitude in question
• **kwargs (dict) – pyresample kwargs for nearest neighbor interpolation

Returns:

Returns the i (x index) and j (y index) of the given latitude longitude value

Return type:

i,j

nearest_latlon(lat=None, lon=None, cleanup=True, esmf=False, **kwargs)

Uses xesmf to intepolate to a given latitude and longitude. Note that the conservative method is not available.

Parameters:

• lat (type) – Description of parameter lat.
• lon (type) – Description of parameter lon.
• **kwargs (type) – Description of parameter **kwargs.

Returns:

Description of returned object.

Return type:

type

quick_imshow(map_kws={}, center=True, **kwargs)

Creates a quick map view of a given data array.

Parameters:

• map_kws (dict) – kwargs for monet.plots.mapgen.draw_map.
• **kwargs (dict) – kwargs for xarray plotting.

Returns:

return of the axes handle for matplotlib.

Return type:

matplotlib.axes

quick_map(map_kws={}, center=True, **kwargs)

Creates a quick map view of a given data array.

Parameters:

• map_kws (dict) – kwargs for monet.plots.mapgen.draw_map.
• **kwargs (dict) – kwargs for xarray plotting.

Returns:

return of the axes handle for matplotlib.

Return type:

matplotlib.axes

remap_nearest(data, **kwargs)

Interpolates from another grid (data) to the current grid of self using pyresample.

it assumes that the dimensions are ordered in y,x,z per

pyresample docs

Parameters:

• da (xarray DataArray or xarray DataSet) – Object to be interpolated
• radius_of_influence (float or integer) – radius of influcence for pyresample in meters.

Returns:

resampled object on current grid.

Return type:

xarray.DataArray

remap_xesmf(dataarray, method='bilinear', **kwargs)

remaps from another grid to the current grid of self using xesmf

Parameters:

• daaarray (ndarray or xarray DataArray) – Description of parameter dset.
• radius_of_influence (float or integer) – radius of influcence for pyresample in meters.

Returns:

resampled object on current grid.

Return type:

xarray.DataArray

stratify(levels, vertical, axis=1)

Short summary.

Parameters:

• levels (type) – Description of parameter levels.
• vertical (type) – Description of parameter vertical.
• axis (type) – Description of parameter axis.

Returns:

Description of returned object.

Return type:

type

structure_for_monet(lat_name='lat', lon_name='lon', return_obj=True)

This will attempt to restucture a given DataArray for use within MONET.

Parameters:

• lat_name (type) – Description of parameter lat_name.
• lon_name (type) – Description of parameter lon_name.
• return (type) – Description of parameter return.

Returns:

Description of returned object.

Return type:

type

tidy(lon_name='longitude')

Tidy’s DataArray–wraps longitudes and sorts lats and lons

Returns:The tidy object Return type:xr.DataArray

window(lat_min=None, lon_min=None, lat_max=None, lon_max=None, rectilinear=False)

Function to window, ie select a specific region, given the lower left latitude and longitude and the upper right latitude and longitude

Parameters:

• lat_min (float) – lower left latitude .
• lon_min (float) – lower left longitude.
• lat_max (float) – upper right latitude.
• lon_max (float) – upper right longitude.
• rectilinear (bool) – flag if this is a rectilinear lat lon grid

Returns:

returns the windowed object.

Return type:

xr.DataArray

wrap_longitudes(lon_name='longitude')

Ensures longitudes are from -180 -> 180

Returns:Description of returned object. Return type:type

class monet.monet_accessor.MONETAccessorDataset(xray_obj)

Bases: object

Monet accessor to the xarray.Dataset.

Parameters:xray_obj (type) – Description of parameter xray_obj.

obj

Description of attribute obj.

Type:type

cftime_to_datetime64(name=None)

Convert cftime o numpy datetime64 objects.

Parameters:name (str) – time variable name. Returns:Description of returned object. Return type:xarray.DataArray

combine_point(data, suffix=None, pyresample=True, **kwargs)

Short summary.

Parameters:

• data (type) – Description of parameter data.
• col (type) – Description of parameter col.
• radius (type) – Description of parameter radius.

Returns:

Description of returned object.

Return type:

type

interp_constant_lat(lat=None, lat_name='latitude', lon_name='longitude', **kwargs)

Interpolates the data array to constant longitude.

Parameters:lon (float) – Latitude on which to interpolate to Returns:DataArray of at constant longitude Return type:DataArray

interp_constant_lon(lon=None, **kwargs)

Interpolates the data array to constant longitude.

Parameters:lon (float) – Latitude on which to interpolate to Returns:DataArray of at constant longitude Return type:xr.Dataset or xr.DataArray

is_land(return_xarray=False)

checks the mask of land and ocean if the global_land_mask libra.

Parameters:return_xarray (bool) – If True, return the data array with the ocean values set to NaN. If False, return a numpy boolean array of the land (True). Returns: Return type:xarray.DataArray or numpy.array

is_ocean(return_xarray=False)

checks the mask of land and ocean.

Parameters:return_xarray (bool) – If True, return the data array with the land values set to NaN. If False, return a numpy boolean array of the ocean (True). Returns: Return type:xarray.DataArray or numpy.array

nearest_ij(lat=None, lon=None, **kwargs)

Uses pyresample to intepolate to find the i, j index of grid with respect to the given lat lon.

Parameters:

• lat (float) – latitude in question
• lon (float) – longitude in question
• **kwargs (dict) – pyresample kwargs for nearest neighbor interpolation

Returns:

Returns the i (x index) and j (y index) of the given latitude longitude value

Return type:

i,j

nearest_latlon(lat=None, lon=None, cleanup=True, esmf=False, **kwargs)

Uses xesmf to intepolate to a given latitude and longitude. Note that the conservative method is not available.

Parameters:

• lat (type) – Description of parameter lat.
• lon (type) – Description of parameter lon.
• **kwargs (type) – Description of parameter **kwargs.

Returns:

Description of returned object.

Return type:

type

remap_nearest(data, radius_of_influence=1000000.0)

Will remap data to the current dataset using the pyresample.kd_tree nearest neighbor interpolation.

Parameters:

• data (xarray.DataArray or xarray.Dataset) – geospatial dataset that includes the latitude and longtide coordinates
• radius_of_influence (float) – radius_of_influence kwarg for pyresample.kd_tree. Default (1e6)

Returns:

The interpolated xarray object

Return type:

xarray.Dataset or xarray.DataArray

remap_xesmf(data, **kwargs)

Resample the xesmf

Parameters:

• data (type) – Description of parameter data.
• **kwargs (type) – Description of parameter **kwargs.

Returns:

Description of returned object.

Return type:

type

stratify(levels, vertical, axis=1)

Short summary.

Parameters:

• levels (type) – Description of parameter levels.
• vertical (type) – Description of parameter vertical.
• axis (type) – Description of parameter axis.

Returns:

Description of returned object.

Return type:

type

tidy(lon_name='longitude')

Tidy’s DataArray–wraps longitudes and sorts lats and lons

Returns:The tidy object Return type:xr.DataArray

window(lat_min, lon_min, lat_max, lon_max)

Function to window, ie select a specific region, given the lower left latitude and longitude and the upper right latitude and longitude

Parameters:

• lat_min (float) – lower left latitude .
• lon_min (float) – lower left longitude.
• lat_max (float) – upper right latitude.
• lon_max (float) – upper right longitude.

Returns:

returns the windowed object.

Return type:

xr.DataSet

wrap_longitudes(lon_name='longitude')

Ensures longitudes are from -180 -> 180

Returns:Description of returned object. Return type:type

class monet.monet_accessor.MONETAccessorPandas(pandas_obj)

Bases: object

center

Short summary.

Returns:Description of returned object. Return type:type

cftime_to_datetime64(col=None)

Short summary.

Parameters:col (type) – Description of parameter col. Returns:Description of returned object. Return type:type

get_sparse_SwathDefinition()

Creates a pyreample.geometry.SwathDefinition for a single point.

Returns: Return type:pyreample.geometry.SwathDefinition

remap_nearest(df, radius_of_influence=100000.0, combine=False, lat_name=None, lon_name=None)

Remap df to find nearest sites

Parameters:

• df (pandas.DataFrame) – dataframe to be interpolated
• radius_of_influence (float) – kwarg for pyresample.kd_tree

Returns:

Returns the interpolated dataframe

Return type:

pandas.dataframe

rename_for_monet(df=None)

Rename latitude and longitude columns in the DataFrame.

Parameters:df (type) – Description of parameter df. Returns:Description of returned object. Return type:type

to_ascii2nc_df(grib_code=126, height_msl=0.0, column='aod_550nm', message_type='ADPUPA', pressure=1000.0, qc=None, height_agl=None)

to_ascii2nc_list(**kwargs)

monet.monet_accessor.wrap_longitudes(lons)

Short summary.

Parameters:lons (type) – Description of parameter lons. Returns:Description of returned object. Return type:type

Module contents