In Ensemble Kalman Filter data assimilation, localization modifies the error covariance matrices to suppress the influence of distant observations, removing spurious long distance correlations. In addition to allowing efficient parallel implementation, this takes advantage of the atmosphere's lower dimensionality in local regions. There are two primary methods for localization. In B-localization, the background error covariance matrix elements are reduced by a Schur product so that correlations between grid points that are far apart are removed. In R-localization, the observation error covariance matrix is multiplied by a distance-dependent function, so that far away observations are considered to have infinite error. Successful numerical weather prediction depends upon well-balanced initial conditions to avoid spurious propagation of inertial-gravity waves.Previous studies note that B-localization can disrupt the relationship between the height gradient and the wind speed of the analysis increments, resulting in an analysis that can be significantly ageostrophic.This study begins with a comparison of the accuracy and geostrophic balance of EnKF analyses using no localization, B-localization, and R-localization with simple onedimensional balanced waves derived from the shallow water equations, indicating that the optimal length scale for R-localization is shorter than for B-localization, and that for the same length scale R-localization is more balanced. The comparison of localization techniques is then expanded to the SPEEDY global atmospheric model. Here, natural imbalance of the slow manifold must be contrasted with undesired imbalance introduced by data assimilation. Performance of the two techniques is comparable, also with a shorter optimal localization distance for R-localization than for B-localization.

[1] Thermal Emission Spectrometer (TES) retrieved temperature profiles are assimilated into the GFDL Mars Global Climate Model (MGCM) using the Local Ensemble Transform Kalman Filter (LETKF) to produce synoptic maps of temperature, winds, and surface pressure and their uncertainties over the course of a Martian year. Short-term (0.25 sol) forecasts compared to independent observations show reduced root mean square error (to 3-4 K global RMSE for a 30-sol evaluation period during the northern hemisphere autumn) and bias compared to a free running model. Several enhanced techniques result in further performance gains. A 4D-LETKF considers observations at their correct hour of occurrence rather than every 6 h. Spatially varying adaptive inflation and varying the dust distribution among ensemble members refine estimates of analysis uncertainty through the ensemble spread. Enhancing dust and water ice aerosol schemes and the application of empirical bias correction using time mean analysis increments help account for model biases. Full-year experiments using prescribed dust opacities and observed TES dust opacities show that while realistic dust distributions are essential to match observed temperatures with a free run simulation, analyses from data assimilation are more robust with respect to imperfections in aerosol distribution. The data assimilation system described here is being used to generate a new reanalysis of Mars weather and climate, which will have many scientific and engineering applications.

The structure and evolution of the Martian polar vortices is examined using two recently available reanalysis systems: version 1.0 of the Mars Analysis Correction Data Assimilation (MACDA) and a preliminary version of the Ensemble Mars Atmosphere Reanalysis System (EMARS). There is quantitative agreement between the reanalyses in the lower atmosphere, where Mars Global Surveyor (MGS) Thermal Emission Spectrometer (TES) data are assimilated, but there are differences at higher altitudes reflecting differences in the free‐running general circulation model simulations used in the two reanalyses. The reanalyses show similar potential vorticity (PV) structure of the vortices: There is near‐uniform small PV equatorward of the core of the westerly jet, steep meridional PV gradients on the polar side of the jet core, and a maximum of PV located off of the pole. In maps of 30 sol mean PV, there is a near‐continuous elliptical ring of high PV with roughly constant shape and longitudinal orientation from fall to spring. However, the shape and orientation of the vortex varies on daily time scales, and there is not a continuous ring of PV but rather a series of smaller scale coherent regions of high PV. The PV structure of the Martian polar vortices is, as has been reported before, very different from that of Earth's stratospheric polar vortices, but there are similarities with Earth's tropospheric vortices which also occur at the edge of the Hadley Cell, and have near‐uniform small PV equatorward of the jet, and a large increase of PV poleward of the jet due to increased stratification.

The Ensemble Mars Atmosphere Reanalysis System (EMARS) dataset version 1.0 contains hourly gridded atmospheric variables for the planet Mars, spanning Mars Year (MY) 24 through 33 (1999 through 2017). A reanalysis represents the best estimate of the state of the atmosphere by combining observations that are sparse in space and time with a dynamical model and weighting them by their uncertainties. EMARS uses the Local Ensemble Transform Kalman Filter (LETKF) for data assimilation with the GFDL/NASA Mars Global Climate Model (MGCM). Observations that are assimilated include the Thermal Emission Spectrometer (TES) and Mars Climate Sounder (MCS) temperature retrievals. The dataset includes gridded fields of temperature, wind, surface pressure, as well as dust, water ice, CO2 surface ice and other atmospheric quantities. Reanalyses are useful for both science and engineering studies, including investigations of transient eddies, the polar vortex, thermal tides and dust storms, and during spacecraft operations.