Pair Modelled and Observed Data

One common analytical need is to obtain a combined data set of modelled and observed observations at the same times and locations. This can be done using the collect_inst_model_pairs routine in utils.match. This routine takes a date range as input, and then extracts modelled observations at the specified instrument location. However, it does not interpolate in time. Details about the interpolation of modelled data onto the instrument location can be found in the utils.extract routine: extract_modelled_observations.

In the example below, we load a DINEOFs file created for testing purposes and pair it with C/NOFS IVM data. This is a global example for a single time, since in this instance DINEOFs was used to create a day-specific emperical moodel. Comparisons with output from a global circulation model would look different, as one would be more likely to desire the the closest observations to the model time rather than all observations within the model time.

This example uses the external modules:

• pysat
• pysatNASA

import datetime as dt
from os import path
import pysat
import pysatNASA
import pysatModels as ps_mod

stime = dt.datetime(2009, 1, 1)
tinc = dt.timedelta(days=1)  # Required input is not used in this example
input_kwargs = dict()

# Initialize the model input information, including the keyword arguements
# needed to load the model Instrument into an xarray Dataset using
# the match.load_model_xarray routine
dineofs = pysat.Instrument(inst_module=ps_mod.models.pydineof_dineof,
                           tag='test')
if len(dineofs.files.files) == 0:
    dineofs.download(stime, stime)
filename = dineofs.files.files[stime]
input_kwargs["model_load_kwargs"] = {'model_inst': dineofs,
                                     'filename': filename}

# Ended HERE
# Initialize the DMSP IVM input, and ensure the best model interpolation
# by extracting the clean model data after matching.
dmsp_f18 = pysat.Instrument(inst_module=pysatMadrigal.instruments.dmsp_ivm, sat_id='f18', clean_level='none')
dmsp_f18.download(stime, stime) # Skip this if you already have the data
dmsp_f18.custom.attach(sami2_meridian, kind='modify', at_pos='end', args=['glon', lon_min, lon_max])
input_kwargs["inst_clean_rout"] = pysatMadrigal.instruments.dmsp_ivm.clean
input_kwargs["inst_download_kwargs"] = {"skip_download": True}

# Many keyword arguements are required, as they provide information on
# the names of coordinates needed to extract data
input_kwargs["inst_lon_name"] = "glon"
input_kwargs["mod_lon_name"] = "glon"
input_kwargs["inst_name"] = ["gdlat", "gdalt"] # Not considering longitude
input_kwargs["mod_name"] = ["glat", "zalt"]    # in the data matching
input_kwargs["mod_units"] = ["deg", "km"]
input_kwargs["mod_datetime_name"] = "time"
input_kwargs["mod_time_name"] = "time"

# Now we are ready to run using the defaults!
matched_inst = ps_mod.utils.match.collect_inst_model_pairs(stime, stime+tinc, tinc, dmsp_f18, **input_kwargs)

This returns a pysat Instrument object with the DMSP IVM data and SAMI2 data at the same times, latitudes, and altitudes along the SAMI2 meridian. The DMSP IVM data has the same names as the normal Instrument, and the SAMI2 data has the same name as the SAMI2 data, but with model_ as a prefix to prevent confusion. You can change this prefix using the model_label keyword argument, allowing multiple models to be matched to the same observational data set.

# Using the results from the prior example
print([cc for cc in matched_inst.data.variables.keys() if cc.find("model_") == 0])

This produces the output line: ['model_deni', 'model_vsi', 'model_ti', 'model_te', 'model_slt']

You can also match model and data results by location alone. This is done by setting the time_method keyword arguement to 'max'.