## 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. ### Imports ```python #|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 ``` ### 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: ```python 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** 1. Prepare *pyrnet_config_calibration_prep.json* with contributors metadata and the dummy calibration config file. 1. ```$ pyrnet process l1a -c pyrnet_config.json raw_data/*.bin l1a/``` 1. ```$ pyrnet process l1b_network -c pyrnet_config.json l1a/*.nc l1b_network/``` ### Configuration Set local data paths and lookup metadata. ```python 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 ) ``` #### Load reference MORDOR data ```python #|dropcode #|dropout for i,date in enumerate(dates): fname = pf_mordor.format(date=date) df = pd.read_csv( fname, header=0, skiprows=[0,2,3], date_format="ISO8601", parse_dates=[0], index_col=0 ) dst = df.to_xarray().rename({"TIMESTAMP":"time"}) # drop not needed variables keep_vars = ['TP2_Wm2'] # global shortwave irradiance drop_vars = [v for v in dst if v not in keep_vars] dst = dst.drop_vars(drop_vars) # merge if i == 0: ds = dst.copy() else: ds = xr.concat((ds,dst),dim='time', data_vars='minimal', coords='minimal', compat='override') mordor = ds.copy() mordor = mordor.drop_duplicates("time", keep="last") mordor ```

<xarray.Dataset>
Dimensions:  (time: 5183535)
Coordinates:
  * time     (time) datetime64[ns] 2015-05-06 ... 2015-05-11T23:59:59.900000
Data variables:
    TP2_Wm2  (time) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0

#### Load PyrNet Data ```python #|dropcode #|dropout for i,date in enumerate(dates): # read from thredds server dst = xr.open_dataset(pf_pyrnet.format(date=date)) # drop not needed variables keep_vars = ['ghi','szen'] drop_vars = [v for v in dst if v not in keep_vars] dst = dst.drop_vars(drop_vars) # unify time and station dimension to speed up merging date = dst.time.values[0].astype("datetime64[D]") timeidx = pd.date_range(date, date + np.timedelta64(1, 'D'), freq='1s', inclusive='left') dst = dst.interp(time=timeidx) dst = dst.reindex({"station": stations}) # merge if i == 0: ds = dst.copy() else: ds = xr.concat((ds,dst),dim='time', data_vars='minimal', coords='minimal', compat='override') pyr = ds.copy() pyr ```

<xarray.Dataset>
Dimensions:          (station: 100, maintenancetime: 50, time: 518400)
Coordinates:
  * station          (station) int64 1 2 3 4 5 6 7 8 ... 94 95 96 97 98 99 100
  * maintenancetime  (maintenancetime) datetime64[ns] 2015-05-12T07:55:50 ......
  * time             (time) datetime64[ns] 2015-05-06 ... 2015-05-11T23:59:59
Data variables:
    ghi              (time, station) float64 0.0 nan nan 0.0 ... nan 0.0 0.0 nan
    szen             (time, station) float64 111.0 nan nan ... 109.4 109.4 nan
Attributes: (12/31)
    Conventions:               CF-1.10, ACDD-1.3
    title:                     TROPOS pyranometer network (PyrNet) observatio...
    history:                   2024-11-04T23:59:36: Merged level l1b by pyrne...
    institution:               Leibniz Institute for Tropospheric Research (T...
    source:                    TROPOS pyranometer network (PyrNet)
    references:                https://doi.org/10.5194/amt-9-1153-2016
    ...                        ...
    geospatial_lon_units:      degE
    time_coverage_start:       2015-05-06T00:00:00
    time_coverage_end:         2015-05-06T23:59:59
    time_coverage_duration:    P0DT23H59M59S
    time_coverage_resolution:  P0DT0H0M1S
    site:                      ['', '', '', '', '', '', '', '', '', '', '', '...

### Calibration The calibration follows the [ISO 9847:1992 - Solar energy — Calibration of field pyranometers by comparison to a reference pyranometer](https://archive.org/details/gov.in.is.iso.9847.1992). > TODO: Revise versus 2023 EU version. Cloudy sky treatment is applied. #### 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 = 7*1e-6* F_U $ ```python # 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 ``` #### Step 2 Interpolate reference to PyrNet samples and combine to a single Dataset ```python # 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 } ) ``` #### 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. ```python 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) ``` #### 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. ```python C_main = 1e6*Cds_main['pyrnet_V'] / Cds_main['reference_Wm2'] ``` #### 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. ```python C_main_mean = C_main.mean(dim='time',skipna=True) C_main_std = C_main.std(dim='time',skipna=True) ``` ### Results ```python #|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](calibration_melcol_output_0.png)

![png](calibration_melcol_output_2.png) ```python 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 1: 7.44 +- 0.204 , None 4: 7.41 +- 0.210 , None 5: 7.40 +- 0.182 , None 6: 6.54 +- 0.088 , None 15: 7.34 +- 0.181 , None 16: 7.59 +- 0.190 , None 19: 7.46 +- 0.262 , None 21: 7.40 +- 0.200 , None 22: 7.40 +- 0.205 , None 26: 7.49 +- 0.150 , None 28: 7.47 +- 0.244 , None 29: 7.25 +- 0.165 , None 30: 7.55 +- 0.176 , None 34: 6.93 +- 0.162 , None 35: 7.43 +- 0.182 , None 37: 7.52 +- 0.148 , None 40: 7.37 +- 0.199 , None 42: 7.53 +- 0.213 , None 43: 7.26 +- 0.170 , None 46: 7.65 +- 0.179 , None 49: 7.63 +- 0.241 , None 50: 7.59 +- 0.176 , None 51: 7.35 +- 0.087 , None 53: 7.36 +- 0.256 , None 54: 7.35 +- 0.197 , None 55: 7.18 +- 0.153 , None 57: 6.63 +- 0.170 , None 62: 7.24 +- 0.167 , None 63: 7.38 +- 0.118 , None 64: 7.19 +- 0.111 , None 68: 6.68 +- 0.082 , None 71: 7.32 +- 0.186 , None 72: 7.26 +- 0.184 , None 74: 7.41 +- 0.096 , None 75: 6.52 +- 0.194 , None 77: 7.36 +- 0.089 , None 78: 6.63 +- 0.154 , None 80: 6.85 +- 0.102 , None 81: 7.34 +- 0.088 , None 84: 7.07 +- 0.198 , None 87: 7.26 +- 0.136 , None 88: 6.91 +- 0.102 , None 89: 7.31 +- 0.132 , None 90: 7.17 +- 0.152 , None 91: 7.37 +- 0.182 , None 92: 7.17 +- 0.169 , None 94: 6.57 +- 0.136 , None 95: 7.32 +- 0.203 , None 96: 7.39 +- 0.149 , None 98: 7.20 +- 0.210 , None 99: 7.34 +- 0.126 , None