Improving CONUS Convective-Scale Forecasting With Simultaneous Multiscale Data Assimilation

Accurate initialization of CONUS convective-scale forecasting requires a proper estimate of all resolved scales. This study further develops and examines a simultaneous multiscale data assimilation (MDA) approach in EnVar with modulated cross-scale and cross-variable covariances. The method is examined using 10 retrospective cases with the assimilation of both in situ and radar reflectivity observations (hereafter, SimMDA). The necessity of the modulated and therefore weakened cross-covariances in simultaneous MDA for CONUS convective-scale forecasting is first demonstrated. The relative benefits of increasing the decomposed-scale number with increased computational cost in SimMDA are also discussed. The impact of the further developed simultaneous MDA method is revealed by comparing it with a commonly adopted DA approach (Baseline), which separately assimilates in situ and reflectivity observations using individual single-scale localization. During DA cycling, SimMDA improves analysis accuracy for temperature and reflectivity and reduces biases in all variables compared to Baseline. SimMDA yields significantly better forecasts than Baseline for most lead times. Additional experiments are conducted to attribute such improvements in a case study. Specifically, an experiment the same as Baseline except using simultaneous MDA for reflectivity assimilation enhances cold pools and inflows and thus improves storms by making larger-scale increments. An experiment the same as Baseline except using simultaneous MDA for in situ assimilation more properly constrains small-scale covariances, leading to more reasonable correlations along the front and more accurate moisture near the dryline and consequently improved analyses and forecasts. Both effects together largely contribute to the overall improvements of SimMDA compared to Baseline. The CONUS domain contains atmospheric flows ranging from synoptic to convective scales and multiple in situ and remote sensing observations sampling a variety of scales. An accurate forecast of CONUS severe weather requires a proper update of all resolved scales. To address this issue, previous studies commonly adopt an approach to separately update a certain range of scales with the assimilation of the corresponding scale-representative observations. This study further develops and examines a simultaneous multiscale approach to simultaneously update all resolved atmospheric scales with the assimilation of all observations at once. Given the unique challenges associated with a large range of scales in CONUS, this simultaneous approach is further developed by including additional modulation on the cross-scale and cross-variable covariances. A total of 10 high-impact convective events are used to demonstrate the benefits of the further developed simultaneous multiscale DA approach and make diagnostics. Results show that the simultaneous multiscale approach improves the analyses and forecasts compared to the baseline approach. The simultaneous multiscale DA approach yields these improvements by more properly updating different scales. This study further develops simultaneous multiscale data assimilation (DA) with adjustable cross-scale and cross-variable covariances The benefits of simultaneous multiscale DA are demonstrated for CONUS convective-scale forecasting using 10 retrospective cases Simultaneous multiscale DA of in situ and reflectivity observations separately contribute to different aspects of the benefits