3. 2015 MelCol¶

processed with pyrnet-0.2.16

The PyrNet was setup for calibration in a dense array on the Melpitz measurement field from 2015-05-06 to 2015-05-11. Cross-calibration is done versus reference observations from the TROPOS MObile RaDiation ObseRvatory (MORDOR) station.

3.1. Imports¶

#|dropcode
import os
import xarray as xr
import pandas as pd
import numpy as np
import datetime as dt
import matplotlib.pyplot as plt
import jstyleson as json

from pyrnet import pyrnet

3.2. Prepare PyrNet data¶

For calibration preparation the PyrNet data is processed to level l1b using a calibration factor of 7 (uV W-1 m2) for all pyranometers with the pyrnet process l1b tool. This is done to unify the conversion to sensor voltage during calibration and not run into valid_range limits for netcdf encoding. Here we generate the calibration.json file for the processing to l1b:

box_numbers = np.arange(1,101)
calibrations = {f"{bn:03d}":[7,7] for bn in box_numbers}
calibjson = {"2000-01-01": calibrations}
with open("pyrnet_calib_prep.json","w") as txt:
    json.dump(calibjson, txt)

Within pyrnet_config.json:

{"file_calibration" : "pyrnet_calib_prep.json"}

Workflow for preparation

Prepare pyrnet_config_calibration_prep.json with contributors metadata and the dummy calibration config file.
$ pyrnet process l1a -c pyrnet_config.json raw_data/*.bin l1a/
$ pyrnet process l1b_network -c pyrnet_config.json l1a/*.nc l1b_network/

3.3. Configuration¶

Set local data paths and lookup metadata.

pf_mordor = "mordor/{date:%Y-%m-%d}_Radiation.dat"
pf_pyrnet = "l1b_network/{date:%Y-%m-%d}_P1D_pyrnet_melcol_n000l1bf1s.c01.nc"
dates = pd.date_range("2015-05-06","2015-05-11")
stations = np.arange(1,101)

# lookup which box contains actually a pyranometer/ extra pyranometer
mainmask = [] 
for box in stations:
    _, serials, _, _ = pyrnet.meta_lookup(dates[0],box=box)
    mainmask.append( True if len(serials[0])>0 else False )

3.4. Calibration¶

The calibration follows the ISO 9847:1992 - Solar energy — Calibration of field pyranometers by comparison to a reference pyranometer.

TODO: Revise versus 2023 EU version.

Cloudy sky treatment is applied.

3.4.1. Step 1¶

Drop Night measures and low signal measures from pyranometer data. Since calibration without incoming radiation doesnt work.

This data is kept for calibration:

solar zenith angle < 80° ( as recommended in ISO 9847)
Measured Voltage > 0.033 V, e.g. ADC count is 0 or 1 of 1023 (drop the lowest ~1%)

Voltage measured ($V_m$) at the logger is the amplified Senor voltage ($V_S$) by a gain of 300.

$ V_m = 300 V_S$

As the uncalibrated flux measurements ($F_U$) are calibrated with a fixed factor of 7 uV W-1 m2:

$ V_s = 71e-6 F_U $

# Set flux values to nan if no pyranometer is installed.
pyr.ghi.values = pyr.ghi.where(mainmask).values

# convert to measured voltage
pyr.ghi.values = pyr.ghi.values * 7 * 1e-6

# Step 1, select data
pyr = pyr.where(pyr.szen<80, drop=True)
pyr.ghi.values = pyr.ghi.where(pyr.ghi>0.033/300.).values

3.4.2. Step 2¶

Interpolate reference to PyrNet samples and combine to a single Dataset

# interpolate reference to PyrNet
mordor = mordor.interp(time=pyr.time)

# Calibration datasets for main and extra pyranometer
Cds_main = xr.Dataset(
    data_vars={
        'reference_Wm2': ('time', mordor['TP2_Wm2'].data),
        'pyrnet_V': (('time','station'), pyr['ghi'].data)
    },
    coords= {
        "time": pyr.time,
        "station": pyr.station
    }
)

3.4.3. Step 3¶

Remove outliers from series using xarray grouping and apply function. The following functions removes outliers (deviation more than 2% according to ISO 9847) from a selected group. This step involves calculating calibration series and the integration of one hour intervals to smooth out high variable situation, which would break the calibration even when time synchronization is slightly off. Also this gets rid of some random shading events like birds / chimney / rods in line of sigth, which would affect calibration otherwise. We following ISO 9847 5.4.1.1 equation (2) here.

def remove_outliers(x):
    """
    x is an xarray dataset containing these variables:
    coords: 'time' - datetime64
    'pyrnet_V' - array - voltage measures of pyranometer
    'reference_Wm2' - array - measured irradiance of reference
    """

    # calculate calibration series for single samples
    C = x['pyrnet_V'] / x['reference_Wm2']
    # integrated series 
    ix = x.integrate('time')
    M = ix['pyrnet_V'] / ix['reference_Wm2']
    
    while np.any(np.abs(C-M) > 0.02*M):
        #calculate as long there are outliers deviating more than 2 percent
        x = x.where(np.abs(C-M) < 0.02*M)
        C = x['pyrnet_V'] / x['reference_Wm2']
        #integrated series 
        ix = x.integrate('time')
        M = ix['pyrnet_V'] / ix['reference_Wm2']
        
    #return the reduced dataset x
    return x

# remove outliers
Cds_main = Cds_main.groupby('time.hour').apply(remove_outliers)

# hourly mean
Cds_main = Cds_main.coarsen(time=60*60,boundary='trim').mean(skipna=True)

3.4.4. Step 4¶

The series of measured voltage and irradiance is now without outliers. So we use equation 1 again to calculate from this reduced series the calibration factor for the instant samples.

C_main = 1e6*Cds_main['pyrnet_V'] / Cds_main['reference_Wm2']

3.4.5. Step 5¶

We just found the Calibration factor to be the mean of the reduced calibration factor series and the uncertainty to be the standard deviation of this reduced series. Steo 3, 4 and 5 are done for every pyranometer seperate.

C_main_mean = C_main.mean(dim='time',skipna=True)
C_main_std = C_main.std(dim='time',skipna=True)

3.5. Results¶

#|dropcode
fig, ax = plt.subplots(1,1, figsize=(10,5))
ax.set_title("Main Pyranometer")
ax.plot(C_main.time, C_main, ls ="", marker='.')
ax.set_xlabel("Date")
ax.set_ylabel("Calibration factor (uV / Wm-2)")
ax.grid(True)
fig.show()

plt.figure()
fig, ax = plt.subplots(1,1, figsize=(10,5))
ax.set_title("Main Pyranometer")
ax.plot(pyr.szen.interp_like(C_main), C_main, ls ="", marker='.')
ax.set_xlabel("solar zenith angle (deg)")
ax.set_ylabel("Calibration factor (uV / Wm-2)")
ax.grid(True)
fig.show()

png

<Figure size 640x480 with 0 Axes>

png

calibration_new = {}
print(f"Box:    Main       ,     Extra  ")
for box in C_main_mean.station:
    Cm = float(C_main_mean.sel(station=box).values)
    Um = float(C_main_std.sel(station=box).values)
    
    calibration_new.update({
        f"{box:03d}": [np.round(Cm,2), None]
    })
    print(f"{box:3d}: {Cm:.2f} +- {Um:.3f} , {None}")

calibjson = {"2015-05-06": calibration_new}
with open("pyrnet_calib_new.json","w") as txt:
    json.dump(calibjson, txt)

Box:    Main       ,     Extra  
7.44 +- 0.204 , None
7.41 +- 0.210 , None
7.40 +- 0.182 , None
6.54 +- 0.088 , None
7.34 +- 0.181 , None
7.59 +- 0.190 , None
7.46 +- 0.262 , None
7.40 +- 0.200 , None
7.40 +- 0.205 , None
7.49 +- 0.150 , None
7.47 +- 0.244 , None
7.25 +- 0.165 , None
7.55 +- 0.176 , None
6.93 +- 0.162 , None
7.43 +- 0.182 , None
7.52 +- 0.148 , None
7.37 +- 0.199 , None
7.53 +- 0.213 , None
7.26 +- 0.170 , None
7.65 +- 0.179 , None
7.63 +- 0.241 , None
7.59 +- 0.176 , None
7.35 +- 0.087 , None
7.36 +- 0.256 , None
7.35 +- 0.197 , None
7.18 +- 0.153 , None
6.63 +- 0.170 , None
7.24 +- 0.167 , None
7.38 +- 0.118 , None
7.19 +- 0.111 , None
6.68 +- 0.082 , None
7.32 +- 0.186 , None
7.26 +- 0.184 , None
7.41 +- 0.096 , None
6.52 +- 0.194 , None
7.36 +- 0.089 , None
6.63 +- 0.154 , None
6.85 +- 0.102 , None
7.34 +- 0.088 , None
7.07 +- 0.198 , None
7.26 +- 0.136 , None
6.91 +- 0.102 , None
7.31 +- 0.132 , None
7.17 +- 0.152 , None
7.37 +- 0.182 , None
7.17 +- 0.169 , None
6.57 +- 0.136 , None
7.32 +- 0.203 , None
7.39 +- 0.149 , None
7.20 +- 0.210 , None
7.34 +- 0.126 , None

3. 2015 MelCol¶

3.1. Imports¶

3.2. Prepare PyrNet data¶

3.3. Configuration¶

3.3.1. Load reference MORDOR data¶

3.3.2. Load PyrNet Data¶