QC Flag Definitions

PyFluxPro QC Flag Definitions

PyFluxPro assigns a quality control (QC) flag to every record for every variable. Each QC flag value has a unique definition that allows the user to trace the provenance of a record all of the way through the PyFluxPro processing path. In particular, the QC flag value will tell the user why a particular data point has been rejected and what method has been used to gap fill the missing data at a later stage.

The QC flags are stored in the netCDF files as the variable name with the suffix "_QCFlag" appended e.g. Fsd_QCFlag is the quality control flag for downwelling shortwave radiation.

There are 2 rules that govern the QC flag value and the analysis that may be supported by the data point, see below:

1. A QC flag value of 0 indicates an original measurement that has passed all of the quality control checks. These data are recommended for process-based studies.
2. A QC flag value that ends in 0 (e.g. 10, 20, ... or mod(flag, 10)==0) indicates a measurement that has been changed (e.g. linear correction) or gap filled. Data that has been corrected is recommended for process-based studies. Data that has been gap filled is not recommended for process-based studies but may be used for budget-style studies.

The QC flag definitions are given in the table below.

Flag	Definition
0	Data has passed all QC checks
1	Data missing from the L1 input file
2	Failed range check
3	Failed SONIC check, Diag_SONIC flag != 0
4	Failed IRGA check, Diag_IRGA flag != 0 plus dependencies on AGC_IRGA, AH_IRGA_Sd, CO2_IRGA_Sd, AH_IRGA_Vr, CO2_IRGA_Vr
5	Failed diurnal check
6	Date/time range excluded
7	Hour range excluded
8	Value of -9999 (missing data) found with QC flag value of 0
9	Fre set to missing when (Fsd>threshold) or (ustar<threshold)
10	Linear correction or linear drift correction applied to data
11	Dependent data rejected during 2D coordinate rotation
12	Data rejected due to dependencies when calculating Massman frequency corrections (MassmanStandard)
13	Fh rejected during conversion of Fhv to Fh (FhvtoFh)
14	Fco2 rejected during WPL correction due to dependencies Fe rejected during WPL correction due to dependencies
15	Ta calculated from SONIC Tv rejected due to dependencies
16	Data rejected at L3 due to failed range check
17	Data rejected at L3 due to failed diurnal check
18	Ustar below threshold (FilterUstar)
19	Data rejected during coordination of gaps in flux series

Table 1: Definition of QC flag values from 0 to 19.

Flag	Definition
20	GapFilling: Driver gap filled using alternate data CONFLICT: same code used by ReplaceRotatedCovariance
21	Missing rotated covariance replaced with non-rotated value CONFLICT: same code used in gfalternate_main
22	Soil moisture set to default value in CorrectFgForStorage
23
24
25
26
27
28
29
30	GapFilling: Flux Gap Filled by ANN (SOLO)
31	GapFilling: Flux Gap not Filled by ANN
32
33
34
35	Data replaced by alternate value when difference between data and alternate value exceeds threshold (ReplaceWhenDiffExceedsRange)
36
37
38	Data rejected at L4 due to failed range check (do_qcchecks)
39	Data rejected at L4 due to failed diurnal check (do_qcchecks)
40	Gap filled from climatology
41	No value available from GapFillUsingClimatology
42
43
44
45
46
47
48
49

Table 2: Definition of QC flag values from 20 to 49.

Flag	Definition
50	Gap filled by interpolation
51	Fsd below threshold when calculating albedo
52	Hour outside range 1000 to 1400 when calculating albedo
53
54
55
56
57
58
59
60	Flux data generated by GapFillFluxFromDayRatio
61	Stomatal resistance less than 0 (get_stomatalresistance)
62	Fe less than threshold when calculating stomatal resistance (get_stomatalresistance)
63	Fsd less than threshold when calculating stomatal resistance (get_stomatalresistance)
64	Wind speed = 0 when calculating stomatal resistance (get_stomatalresistance)
65
66
67
68
69
70	Partitioning Night: Re computed from exponential temperature response curves
71
72
73
74
75
76
77
78
79

Table 3: Definition of QC flag values from 50 to 79.

Flag	Definition
80	Partitioning Day: GPP/Re computed from light-response curves, GPP = Re - Fc
81	Partitioning Day: GPP night mask
82	Partitioning Day: Fc > Re, GPP = 0, Re = Fc
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100

Table 4: Definition of QC flag values from 80 to 100.

Flag	Definition
400	Reserved
410	Gap filled using automatic weather station (AWS) data.
420	Gap filled using ACCESS-G numerical weather prediction (NWP) data.
430	Gap filled using ECMWF Re-Analysis Interim (ERAI) data.
440	Gap filled using ECMWF Re-Analysis 5 (ERA5) data.
450	Gap filled using climatology, interpolated daily (default)
460	Gap filled using climatology, monthly (not implemented)
470	Gap filled using Marginal Distribution Sampling (MDS)

Table 5: Definition of QC flag values for Level 4.

Flag	Definition
500	Reserved
510	Flux gap filled using SOLO neural network (windowed)
520	Flux gap filled using SOLO neural network (long gaps)
530
540
550
560
570	Flux gap filled using Marginal Distribution Sampling (MDS)

Table 6: Definition of QC flag values for Level 5.

Flag	Definition
600	Reserved
610	ER estimated by SOLO neural network from nocturnal, u*-filtered NEE
620	ER estimated using Lloyd & Taylor (1994) from nocturnal, u*-filtered NEE
630	ER estimated using Lasslop et al (2010) from nocturnal, u*-filtered NEE (E0) and both daytime and nighttime NEE (alpha, beta0, VPD0, k, rb)

Table 7: Definition of QC flag values for Level 6.