Improving CONUS Convective‐Scale Forecasting With Simultaneous Multiscale Data Assimilation

Abstract

AbstractAccurate 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.