With the TGRS receiver, COSMIC-2 has a larger mean SNR than that of Spire and KOMPSAT-5. The mean COSMIC-2 L1 SNR ranges from 250 to 2500 \(v/v\)[31; 32], while the L1 SNR for Spire ranges from 200 \(v/v\) to 1500 \(v/v\), lower than that from COSMIC-2 (Figure 1). The mean L1 SNR for KOMPSAT-5 is 570 \(v\). With a higher SNR than that of other RO missions, COSMIC-2 is expected to penetrate deeper into the lower troposphere [30; 31; 32; 33]. 2. Does lower-SNR Spire RO data lead to less accurate retrieval results? Whether the RO data products derived from lower SNR signals obtained from commercial CubeSats are as accurate as those from high SNR signals is a significant concern for the RO community and climate and atmospheric scientists. The causes of retrieval uncertainty may include receiver quality, antenna geometry, the accuracy of the Precise Orbit Determination (POD) estimation, L0 to L1a processing, L1a-L1b (excess phase) processing, and L1b-L2 (bending angle and refractivity profiles) processing [33]. How do we optimize the Spire RO data in the NWP data assimilation (DA) system? As mentioned above, because the RO bending angle and refractivity uncertainty, especially in the lower troposphere, are highly related to the atmospheric condition,  
Figure 1: The distribution of the normalized SNR frequency sample numbers (defined as the sample numbers for each SNR bin normalized to the maximum number of the SNR bin) for GPS (in red line), GLONASS (in orange line), and GALILEO (in blue line) signals on (**a**) Spire, (**b**) COSMIC-2, and (**c**) KOMPSAT-5 over the CWDP Delivery-Order 3 (DO3, from 8 September 2021 to 15 March 2022). The total number of observations from each GNSS satellite is listed in the figures. we must carefully examine the observation uncertainty for each RO mission to use the RO data optimally in NWP DA algorithms. The objective of this study is to precisely characterize the quality of the Spire CubeSats data in the neutral atmosphere in terms of their (i) precision, (ii) stability, and (iii) retrieval accuracy and uncertainty [2]. Many studies [34; 35; 36; 37; 38; 39; 40; 41; 42] have reported on the quality of Spire-retrieved data products using collocated in situ radiosondes, satellite observations, or reanalysis data interpolated onto RO locations. For example, Ref. [34] compared Spire-simulated brightness temperatures (BTs) with collocated measurements from NOAA-20 Advanced Technology Microwave Sounder (ATMS). However, because the quality of ATMS BTs (and in situ radiosonde, see Section 4.3) may be affected by the solar viewing angle [2; 26], the quality of ATMS BTs may vary between day and night. Refs. [23; 24; 25] suggested that the best approach to quantifying the retrieval accuracy and uncertainty of one RO mission is to compare them with independent retrievals from another RO mission and compare the RO-RO pairs with other references. In addition, because RO data quality and retrieval uncertainty may also be affected by atmospheric conditions and viewing geometry, especially in the lower troposphere, to eliminate representative errors, the best approach is to conduct coplanar RO-RO comparisons. The NOAA Center for Satellite Applications and Research (STAR) has developed capabilities as a GNSS RO science and data center (STAR RO DSC, see [[https://www.star.nesdis.noaa.gov/smcd/GNSSRO/RO/index.php](https://www.star.nesdis.noaa.gov/smcd/GNSSRO/RO/index.php)]([https://www.star.nesdis.noaa.gov/smcd/GNSSRO/RO/index.php](https://www.star.nesdis.noaa.gov/smcd/GNSSRO/RO/index.php)), access on 1 June 2023), also see [31; 33; 43]). STAR RO DSC aims to develop enterprise RO-processing algorithms for all RO missions, like other NOAA infrared and microwave satellite missions. STAR has developed the RO inversion package for COSMIC-2 [33; 44; 45; 46; 47; 48; 49]. In addition to the Spire RO data processed by UCAR, STAR has also processed Spire data using an independently developed inversion package (see Appendix A).