Radio occultation (RO) refractivity observations provide information about tropospheric water vapor and temperature in all weather conditions. The impact of using RO refractivity observations on analyses and forecasts of Hurricane Ernesto’s genesis (2006) using an ensemble Kaman filter data assimilation system is investigated. Assimilating RO refractivity profiles in the vicinity of the storm locally moistens the analysis of the lower troposphere and also adjusts the wind analysis in both the lower and upper troposphere through forecast multivariate correlations of RO refractivity and wind. The model forecasts propagate and enhance the added water vapor and the wind adjustments leading to more accurate analyses of the later stages of the genesis of the storm. The root-mean-square errors of water vapor and wind forecasts compared to dropsonde and radiosonde observations are reduced consistently. As a result, assimilating RO refractivity data in addition to traditional observations leads to a stronger initial vortex of the storm and improved forecasts of the storm’s intensification. The benefits of the RO data are much reduced when the RO data in the lower troposphere (below 6 km) are ignored.

<p>Position errors in coherent features have been a challenging problem for data assimilation (DA) due to their high nonlinearity. To effectively reduce position errors, a multiscale alignment (MSA) method was introduced to compute ensemble Kalman filter (EnKF) updates on a sequence of model states at low to high resolutions (large to small scales). Large-scale state has less nonlinearity due to position errors, therefore linear EnKF updates are optimal. The large-scale analysis increments are then utilized to compute the displacement vectors that warp the model grid, reduce position errors and precondition the state at smaller scales before the EnKF update is computed again. This study further tests the performance of the MSA method in an idealized vortex model. The asymptotic behavior is documented for a multiscale solution as number of scales (<em>N</em><sub>s</sub>) increases. We show that the optimal <em>N</em><sub>s</sub> depends on the degree of nonlinearity caused by the position errors. When feature-based observations (such as the vortex position) are used, the MSA performs well with <em>N</em><sub>s</sub>  3 no matter how large the position errors are. A challenging scenario is identified for the MSA method, when the large-scale background flow is incoherent with the small-scale vortex position error (deviation from coherence assumption). In cycling DA experiments, the MSA performs better than the traditional EnKF at equal cost (using decreased ensemble size for MSA to compensate for its increased cost when <em>N</em><sub>s</sub> >1), showing good scalability for real application and potential for improving prediction skill in many multiscale Earth systems.</p>

Abstract. The Arctic is warming at a faster rate compared to the globe on average, commonly referred to as Arctic amplification. Sea ice has been linked to Arctic amplification and gathered attention recently due to the decline in summer sea ice extent. Data assimilation (DA) is the act of combining observations with prior forecasts to obtain a more accurate model state. Sea ice poses a unique challenge for DA because sea ice variables have bounded distributions, leading to non-Gaussian distributions. The non-Gaussian nature violates Gaussian assumptions built into DA algorithms. This study configures different observing system simulated experiments (OSSEs) to find the optimal sea ice and snow observation subset for assimilation to produce the most accurate analyses and forecasts. Findings indicate that not assimilating sea ice concentration observations while assimilating snow depth observation produced the best sea ice and snow forecasts. A simplified DA experiment helped demonstrate that the DA solution is biased when assimilating sea ice concentration observations. The biased DA solution is related to the observation error distribution being a truncated normal distribution and the assumed observation likelihood is normal for the DA method. Additional OSSEs show that using a non-parametric DA method does not alleviate the non-Gaussian effects of the sea ice concentration observations, and assimilating sea ice surface temperatures have a positive impact on snow updates. Lastly, it is shown that perturbed sea ice model parameters, used to create additional ensemble spread in the free forecasts, lead to a year-long negative snow volume bias.