A Multivariate Additive Inflation Approach to Improve Storm‐Scale Ensemble‐Based Data Assimilation and Forecasts: Methodology and Experiment With a Tornadic Supercell
Abstract Ensemble‐based convective‐scale radar data assimilation commonly suffers from an underdispersive background ensemble. This study introduces a multivariate additive‐inflation method to address such deficiency. The multivariate additive inflation (AI) approach generates coherent random pertur...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2023-01-01
|
| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2022MS003307 |
| _version_ | 1828033606709149696 |
|---|---|
| author | Yongming Wang Xuguang Wang |
| author_facet | Yongming Wang Xuguang Wang |
| author_sort | Yongming Wang |
| collection | DOAJ |
| description | Abstract Ensemble‐based convective‐scale radar data assimilation commonly suffers from an underdispersive background ensemble. This study introduces a multivariate additive‐inflation method to address such deficiency. The multivariate additive inflation (AI) approach generates coherent random perturbations drawn from a newly constructed convective‐scale static background error covariance matrix for all state variables including hydrometeors and vertical velocity. This method is compared with a previously proposed univariate AI approach, which perturbs each variable individually without cross‐variable coherency. Comparisons are performed on the analyses and forecasts of the 8 May 2003 Oklahoma City tornadic supercell. Within assimilation cycles, the multivariate approach is more efficient in increasing reflectivity spread and thus has a reduced spinup time than the univariate approach; the additional inclusion of hydrometeors and vertical velocity results in more background spread for both reflectivity and radial velocity. Significant differences among AI experiments also exist in the subsequent forecasts and are more pronounced for the forecasts initialized from the earlier assimilation cycles. The multivariate approach yields better forecasts of low‐level rotation, reflectivity distributions, and storm maintenance for most lead times. The additional inclusion of hydrometeor and vertical velocity in the multivariate method is beneficial in forecasts. Conversely, the additional inclusion of hydrometeor and vertical velocity in the univariate method poses negative impacts for the majority of forecast lead times. |
| first_indexed | 2024-04-10T15:22:14Z |
| format | Article |
| id | doaj.art-0c085a7900144de2ba3d68823b8f5bc3 |
| institution | Directory Open Access Journal |
| issn | 1942-2466 |
| language | English |
| last_indexed | 2024-04-10T15:22:14Z |
| publishDate | 2023-01-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj.art-0c085a7900144de2ba3d68823b8f5bc32023-02-14T13:45:31ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662023-01-01151n/an/a10.1029/2022MS003307A Multivariate Additive Inflation Approach to Improve Storm‐Scale Ensemble‐Based Data Assimilation and Forecasts: Methodology and Experiment With a Tornadic SupercellYongming Wang0Xuguang Wang1School of Meteorology University of Oklahoma Norman OK USASchool of Meteorology University of Oklahoma Norman OK USAAbstract Ensemble‐based convective‐scale radar data assimilation commonly suffers from an underdispersive background ensemble. This study introduces a multivariate additive‐inflation method to address such deficiency. The multivariate additive inflation (AI) approach generates coherent random perturbations drawn from a newly constructed convective‐scale static background error covariance matrix for all state variables including hydrometeors and vertical velocity. This method is compared with a previously proposed univariate AI approach, which perturbs each variable individually without cross‐variable coherency. Comparisons are performed on the analyses and forecasts of the 8 May 2003 Oklahoma City tornadic supercell. Within assimilation cycles, the multivariate approach is more efficient in increasing reflectivity spread and thus has a reduced spinup time than the univariate approach; the additional inclusion of hydrometeors and vertical velocity results in more background spread for both reflectivity and radial velocity. Significant differences among AI experiments also exist in the subsequent forecasts and are more pronounced for the forecasts initialized from the earlier assimilation cycles. The multivariate approach yields better forecasts of low‐level rotation, reflectivity distributions, and storm maintenance for most lead times. The additional inclusion of hydrometeor and vertical velocity in the multivariate method is beneficial in forecasts. Conversely, the additional inclusion of hydrometeor and vertical velocity in the univariate method poses negative impacts for the majority of forecast lead times.https://doi.org/10.1029/2022MS003307multivariate additive inflationensemble spreadensemble‐based data assimilationconvective‐scale numerical weather prediction |
| spellingShingle | Yongming Wang Xuguang Wang A Multivariate Additive Inflation Approach to Improve Storm‐Scale Ensemble‐Based Data Assimilation and Forecasts: Methodology and Experiment With a Tornadic Supercell Journal of Advances in Modeling Earth Systems multivariate additive inflation ensemble spread ensemble‐based data assimilation convective‐scale numerical weather prediction |
| title | A Multivariate Additive Inflation Approach to Improve Storm‐Scale Ensemble‐Based Data Assimilation and Forecasts: Methodology and Experiment With a Tornadic Supercell |
| title_full | A Multivariate Additive Inflation Approach to Improve Storm‐Scale Ensemble‐Based Data Assimilation and Forecasts: Methodology and Experiment With a Tornadic Supercell |
| title_fullStr | A Multivariate Additive Inflation Approach to Improve Storm‐Scale Ensemble‐Based Data Assimilation and Forecasts: Methodology and Experiment With a Tornadic Supercell |
| title_full_unstemmed | A Multivariate Additive Inflation Approach to Improve Storm‐Scale Ensemble‐Based Data Assimilation and Forecasts: Methodology and Experiment With a Tornadic Supercell |
| title_short | A Multivariate Additive Inflation Approach to Improve Storm‐Scale Ensemble‐Based Data Assimilation and Forecasts: Methodology and Experiment With a Tornadic Supercell |
| title_sort | multivariate additive inflation approach to improve storm scale ensemble based data assimilation and forecasts methodology and experiment with a tornadic supercell |
| topic | multivariate additive inflation ensemble spread ensemble‐based data assimilation convective‐scale numerical weather prediction |
| url | https://doi.org/10.1029/2022MS003307 |
| work_keys_str_mv | AT yongmingwang amultivariateadditiveinflationapproachtoimprovestormscaleensemblebaseddataassimilationandforecastsmethodologyandexperimentwithatornadicsupercell AT xuguangwang amultivariateadditiveinflationapproachtoimprovestormscaleensemblebaseddataassimilationandforecastsmethodologyandexperimentwithatornadicsupercell AT yongmingwang multivariateadditiveinflationapproachtoimprovestormscaleensemblebaseddataassimilationandforecastsmethodologyandexperimentwithatornadicsupercell AT xuguangwang multivariateadditiveinflationapproachtoimprovestormscaleensemblebaseddataassimilationandforecastsmethodologyandexperimentwithatornadicsupercell |