Three-Dimensional Multiscale Variational Analysis of Near-Space Temperature Using an MSIS Empirical Background and TIMED/SABER Profiles

The near-space region is of strategic importance for aerospace activities. However, accurate temperature analyses remain challenging due to sparse observations and multiscale spatial structure. In this study, a multiscale variational analysis (MSVA) configuration is applied and evaluated for near-space temperature analyses using the Mass Spectrometer and Incoherent Scatter (MSIS) empirical model as the background field and TIMED/SABER temperature observations as observational constraints. The multiscale strategy is implemented via a prescribed sum of three-dimensional Gaussian background error covariance components with scale-dependent horizontal length scales and a common vertical coupling structure. A set of observing system simulation experiments (OSSEs) and experiments using real observations are conducted to assess MSVA in comparison with a single-scale variational analysis (SSVA). Results from the multistage OSSEs show that the multiscale covariance design improves the recovery of observation-supported structures. In the three-dimensional OSSE, MSVA reduces the global RMSE from 3.93 K for 1200 km SSVA to 3.78 K, and reduces the RMSE over withheld profiles from 3.56 K to 3.16 K. Using real SABER temperature observations, both three-dimensional analyses reduce MSIS–SABER observational space departures; the mean all-profile RMSE decreases from 11.43 K for MSIS to 5.97 K for 3D SSVA and 5.35 K for 3D MSVA. Comparison with unassimilated Aura/MLS observations gives corresponding RMSEs of 8.74, 7.60, and 7.39 K, providing an independent external evaluation of the analyses using real observations. These results demonstrate that representing multiple horizontal scales improves near-space temperature analysis relative to a single-scale formulation, as evidenced by controlled OSSEs and independent MLS evaluation.