How to Retain Cloud-Affected but Informative Data in Quality Control of GNSS Radio Occultation Bending Angle Observation: A New Localized Method

Quality control (QC) is essential before assimilating Global Navigation Satellite System (GNSS) radio occultation (RO) data. Conventional QC methods, applied globally or by latitudinal bands, can inadvertently remove valid data that contain significant meteorological signals. Using Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) bending angle data—a primary RO observable—from the summer of 2023, we demonstrate that this latitude-based QC (LatQC) flags an excessive number of outliers near an altitude of 8 km, a region typically considered highly reliable. These outliers are not random but are strongly clustered near the Solomon Islands, coinciding with frequent summer altostratus clouds with tops exceeding 6 km, which systematically perturb bending angles. Another area with high outlier concentration is the moist lower troposphere within the Intertropical Convergence Zone (ITCZ). Although GNSS RO signals are less affected by clouds due to their long wavelength, the aggressive LatQC in cloudy areas often misclassifies real weather-induced signals as noise. To address this, we developed a local QC method (LocQC), establishing statistical benchmarks within 2.5° × 2.5° longitude–latitude grids instead of broad latitudinal bands. The LocQC significantly improves the spatial distribution of identified outliers: it mitigates over-removal in the 6–8 km layer over the Solomon Islands and aligns outlier detection better with liquid water path and humidity patterns in the 2–4 km layer of the ITCZ. The quality of RO bending angle data is influenced not merely by latitude but significantly by local weather systems with distinct longitudinal signatures, such as clouds and moisture. Consequently, the proposed LocQC, accounting for two-dimensional atmospheric structures, is more appropriate than LatQC. It ensures robust filtering of erroneous data while better preserving valuable local weather signals crucial for numerical weather prediction and climate studies.