# Atmospheric GNSS RO 1D-Var in Use at UCAR: Description and Validation
## 4 Discussion  
It is worth touching on the similarities and differences between 1D-Var and variational assimilation of RO data into NWP models (hereafter referred to as data assimilation). The relationship with data assimilation is a question often raised by data users but which has not been discussed so far. We find that the comparison to data assimilation offers a different new angle that helps us understand 1D-Var better. Data assimilation and 1D-Var are based on the same principle. Both methods attempt to combine the complementary information from observations and a priori data into an optimal analysis (estimate) of the atmospheric states of relevance. Nonetheless, they have diverging purposes and practices, apart from the difference in the problem's dimensionality. The difference in purpose between 1D-Var and data assimilation can be said that 1D-Var utilizes a priori information to enhance and interpret observations; in comparison, data assimilation uses the observations to improve weather forecasts by bettering the model's initial condition. This difference may sound subtle but can lead to profoundly different perspectives and outcomes. A few practical differences between 1D-Var and data assimilation include: (1) Data assimilation produces global analyses valid at certain fixed times (i.e., snapshots at synoptic times) using observations irregularly distributed in time and space. Hence, data assimilation uses observations to obtain an analysis that is continuous and consistent across scales by propagating observational information to data-sparse regions and times. 1D-Var seeks its solution in a very narrow subspace: along the trajectory of rays' tangent points at the exact time of the observation, which is somewhat similar to the ascending path of a radiosonde balloon. (2) In data assimilation, the analysis at a RO profile location is affected by other nearby observations and is thus less strongly constrained by the RO observation. Using all platforms' observations is desirable in data assimilation but not necessarily from the standpoint of RO retrieval, especially for RO-based climate studies. In 1D-Var, the atmospheric profiles are retrieved under the direct influence of RO observation and are constrained well by the observation. (3) The optimal analysis in data assimilation is the  
Figure 15: Standard deviations of NEW minus RS92 for different collocation thresholds (see legend): (**a**) temperature (\({}^{\circ}\)C), (**b**) pressure (hPa), (**c**) specific humidity (g kg\({}^{-1}\)), and (**d**) data counts. Results on near-surface heights are not shown when the data counts are less than 50.  
analysis that leads to the best forecast, not the one achieving local optimality at the location of observations. The optimality of 1D-Var relates to the quality of retrieved profiles, which is definable on its own and assessable in the observation's space. (4) The low cost of 1D-Var permits the specification of precise error covariances, including detailed spatiotemporal variations. Doing so is impractical in data assimilation, which uses a state vector of enormous size and numerous observing platforms. (5) Unlike data assimilation, 1D-Var is not restricted by the model geometry. The 1D-Var grid can be of much higher resolution and is extendable to the thermosphere. The flexibility in setting the computational grid is advantageous to recovering small-scale atmospheric structures and reconstructing the upper atmosphere. (6) The 1D-Var does not apply any internal quality control or bias correction to RO observations, except for the data rejection below the top of the highest a priori super-refraction layer and the slight data thinning at stratospheric heights. The intent is to retain as much observational information as possible, enable retrieval in extreme weather conditions (or events), and embody the aspects poorly represented or undetected by data assimilation systems, e.g., model bias and climate change signals.