Simultaneous Multiscale Data Assimilation Using Scale‐ and Variable‐Dependent Localization in EnVar for Convection Allowing Analyses and Forecasts: Methodology and Experiments for a Tornadic Supercell

• Published in: Journal of advances in modeling earth systems 2023-05, Vol.15 (5), p.n/a
• Main Authors: Wang, Yongming, Wang, Xuguang
• Format: Article
• Language: English
• Subjects: Convection; convective‐scale numerical weather prediction; Data assimilation; Data collection; Decomposition; Localization; Methods; Moisture; Radar; Radar data; Reflectance; scale‐dependent localization; simultaneous multiscale data assimilation; Storm forecasting; Storms; Turbulence models; Variables; variable‐dependent localization; Weather forecasting
• ISSN: 1942-2466; 1942-2466
• DOI: 10.1029/2022MS003430

This study introduces a simultaneous multiscale data assimilation method by implementing model space spatial scale‐dependent localization (SDL) and variable‐dependent localization (VDL) within an ensemble variational system. This method updates all resolved scales by assimilating all observations at once. The impact of such an approach is examined by a series of radar data assimilation experiments. Single‐observation experiments show that SDL concurrently and more properly updates the storm and its ambient environments compared to a traditional single scale localization (SSL) for radar data assimilation. Including VDL on top of SDL (SDLVDL) realistically decreases the spatial coverage and intensity of moisture increments compared to SDL. Comparisons are then performed on the analyses and forecasts of the 8 May 2003 Oklahoma City supercell storm. Results show that SDL improves the analyses and forecasts during the data assimilation cycling by producing more realistic enhanced low‐level convergences than SSL. SDLVDL obtains more accurate analyses and subsequent forecasts for moisture than SDL. SDLVDL yields the best performance in reflectivity forecasts and storm maintenance. Compared to SSL, SDL has higher forecast skills before 2230 UTC and produces degraded forecasts in the later lead time. Plain Language Summary Convection‐allowing numerical weather prediction models resolve atmospheric flows at a wide range of scales. Therefore an effective data assimilation method is required to properly update all resolved scales. This study introduces and describes an ensemble based simultaneous multiscale data assimilation method by utilizing a scale‐dependent and variable‐dependent localization (VDL) method in the model state space. This approach corrects storms and corresponding larger scale environments concurrently. Results and diagnostics from a tornadic supercell case study assimilating radar observations show that the proposed multiscale approach improves the analyses and the earlier forecasts compared to the traditional single‐scale method. Further applying a VDL method in the multiscale approach yields the best forecast performance during the entire forecast period. Key Points This study introduces a simultaneous multiscale data assimilation method using spatial scale‐dependent and variable‐dependent localization (VDL) The scale‐dependent method concurrently updates storm and its ambient environments compared to a traditional single scale method Further app