## 5 Conclusions  
In this study, tropical and subtropical water vapor distribution and variability was explored from September-December 2019 using SH profiles from the recently launched COSMIC-2 satellite constellation and compared with profiles from the ERA5 and MERRA-2 reanalyses to determine the strength of the relationship between the datasets, quantify the magnitude of any differences, and provide possible physical explanations as to why these differences occur. Good agreement is generally seen between C2 and each reanalysis, although larger differences are observed for the MERRA-2 time-means. On a zonal scale, C2 displayed a negative moisture bias within the boundary layer likely due to a combination of various receiver tracking errors and the presence of super-refraction. In the mid-troposphere, C2 displayed slightly more moisture than ERA5 (6-12%) and much less moisture than MERRA-2 (15-30%), which was attributed to the underlying MERRA-2 AGCM that produces too much evaporation of tropical middle and upper tropospheric cloud ice crystals [35]. In the latitude/longitude analysis, excellent agreement was seen between C2 and ERA5 at 3 km. At 7 km, a positive bias was observed in the percentage differences, especially within the tropics (9-15%). For C2 and MERRA-2 at 3 km, large difference hotspots (\(\pm\)20%) were observed which had a dipole structure over the Southern Hemisphere oceans near the stratocumulus cloud regions of the Atlantic and Pacific oceans. At 7 km, a large negative bias was observed within the tropics, with the largest differences (20-30%) observed in regions that experience frequent deep convection. Strong relationships between C2 and reanalysis SHs were observed. Pearson correlation coefficients were typically over 0.9 in the subtropics. Slightly lower coefficients were observed throughout the deep tropics, with stronger relationships observed for C2 relative to ERA5 (0.7-0.8) than MERRA-2 (0.5-0.7). A closer look at the deep tropics revealed excellent agreement between C2 and ERA5/MERRA-2 at 3 km, although larger variability was observed for C2/MERRA-2. At 7 km, C2 started to exhibit larger SH values than ERA5 when moisture amounts became higher whereas, interestingly, MERRA-2 displayed positive moisture biases in both wetter and drier conditions. The relationship between OLR and the standard deviation of the C2/reanalysis SH differences was also explored. The standard deviation of the differences was generally larger for C2 and MERRA-2. Locations of high/low OLR matched up extremely well with small/large SH difference standard deviations at 7 km and only moderately well at 3 km. While this suggests that deep convection can impact moisture differences between C2 and reanalysis, a strong relationship was not observed between transient OLR and profile pair SH differences (not shown). Larger C2 and reanalysis moisture differences were observed in locations with strong moisture gradients. A late December Pacific Warm Pool case study was shown with a very strong moisture gradient and a C2 profile located within the gradient. This resulted in large C2 and reanalysis SH differences which highlighted the importance of resolution differences between each dataset. After examining the relationship between C2 and reanalysis profile pair SH differences and the maximum local reanalysis SH gradient, larger profile pair SH differences were generally observed as local humidity gradients became larger at both altitudes. Larger SH differences were also more commonly observed when the C2 ray path crossed through (rather than paralleled) sharp moisture gradients (not shown). Finally, we examined two regions (central Africa and northern South America) that had weaker correlations and experience frequent deep convection. The sampling distributions of profile pairs showed that ERA5 has a negative SH bias at 3 km in higher moisture environments. However, at 7 km, MERRA-2 displayed large positive bias whereas C2 agreed well with ERA5.