Sensitivity analysis of a 3D convective storm: Implications for variational data assimilation and forecast error

Seon Ki Park, Kelvin K. Droegemeier

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


In this study a nonhydrostatic 3D cloud model, along with an automatic differentiation tool, is used to investigate the sensitivity of a supercell storm to prescribed errors (perturbations) in the water vapor field. The evolution of individual storms is strongly influenced by these perturbations, though the specific impact depends upon their location in time and space. Generally, perturbations in the rain region above cloud base have the largest impact on storm dynamics, especially for subsequent storms, while perturbations in the ambient environment above cloud base influence mostly the main storm. Although perturbations in the subcloud layer have a relatively small impact on upper-level storm structure, they do impact the low-level structure, especially during the period immediately following insertion. Sensitivities are also examined in the context of variational data assimilation and forecast error. For perturbations added inside the active storm, the cost function, which is prescribed to measure the discrepancy between forecast and observations for all variables over time and space, is found to be most sensitive to temperature, followed by pressure and water vapor. This implies that the quality of variational data assimilation can be affected by the inaccuracy of observing or retrieving those quantities. It is also noted that, at least for the case studied here, the pressure field has the largest influence on forecast error immediately after the errors are inserted, while the temperature field does so over a longer time period.

Original languageEnglish
Pages (from-to)140-159
Number of pages20
JournalMonthly Weather Review
Issue number1
StatePublished - Jan 2000


Dive into the research topics of 'Sensitivity analysis of a 3D convective storm: Implications for variational data assimilation and forecast error'. Together they form a unique fingerprint.

Cite this