# Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation
## Appendix C 4 qc  
This four QCs processing is used in [30]. In this method, reliability was calculated using critical parameter Z. (usually about Z > 2-4, the smaller Z is stricter) excludes outliers [30]. In this study, mean value is used to discuss all the results. If the sample is obey Gaussian distribution, the mean value has representativeness. The 4 QCs discard outliers, making the sample similar to Gaussian distribution. Each level will be checked by QC. If a level cannot pass the QC, the whole profile will be discarded.  
(1) Ensure soundings were between \(-60\)\({}^{\circ}\)S and \(60\)\({}^{\circ}\)N horizontally, and between 5-80 km vertically; The RO data at high altitude may easily be affected by the ionosphere, and some sounding may lack observation in the lower level below 5 km. After this QC1, 2824 (out of 5914) were left.  
(2) Eliminate outliers horizontally on each level: use the bi-weight method proposed by [45] to FY3D's refractivity on each level, and set the Z > 3, which is the crucial parameter for the bi-weight method. After this QC2, 1655 (out of 2824) were left.  
(3) Ensure the FY3D's BA is closer to EC4 Dvar's BA: use bi-weight method to the difference of the refractivity between FY3D and EC4 Dvar with Z as 3. It is noticed that this QC solved the drift tangent point phenomenon partly because we only made comparisons horizontally at every level, the slant profile will be discarded. Drift tangent pointphenomenon is that the actual RO profile is slanted, which results in a bias of the difference between the slanted FY3D and EC4 Dvar. As long as FY3D and EC4 Dvar are closer in each level horizontally, the drift tangent point phenomenon will be ignored, and collocation spatially will prove to be reasonable. Furthermore, experiment one used statistics, the average bending angle, which can effectively eliminate the anomalies. Accordingly, the bias question left after spatial collocation is fixed too. After this QC3, 1436 (out of 1665) were left.  
(4) Eliminate the anomaly interval via the Bi-weight method. A similar interval among each profile effectively can reduce the bias introduced by interpolation. After QC4, 368 (out of 1436) were left.  
The 4 QC help eliminate bias from the spatial collocation and drift tangent point phenomenon, ensuring the forward modelled bias mainly resulted from integral and interpolation methods. These soundings are evenly distributed spatially and temporally (Figure A7), making the mean (statistical values) feasible in the following analysis. Figure A7 shows FY3D soundings in experiment one are generally distributed evenly spatially and temporally, which makes the mean (statistical values) feasible in the following analysis.  
To test the quality of refractivity profiles after 4 QC, we used statistics, the mean relative difference of refractivity between FY3D profiles and EC4 Dvar in each level. Their original profiles are interpolated into fixed levels with an interval of 100 m. As shown in Figure A8, although the standard deviation of those profiles is large than 2%, the mean hovers around zero. Unfortunately, there is a large difference at the heights of 18 (errors about 0.3%), 48 (errors about 1.3%), 58 (errors about 3.1%), 78 (errors about 10%) km, which passed our 4 QC. This error will be propagated to the following experiments.\"