A Study of Multiscale Initial Condition Perturbation Methods for Convection-Permitting Ensemble Forecasts

Aaron Johnson Cooperative Institute for Mesoscale Meteorological Studies, Norman, Oklahoma

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Xuguang Wang School of Meteorology, University of Oklahoma, Norman, Oklahoma

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Abstract

The impacts of multiscale flow-dependent initial condition (IC) perturbations for storm-scale ensemble forecasts of midlatitude convection are investigated using perfect-model observing system simulation experiments. Several diverse cases are used to quantitatively and qualitatively understand the impacts of different IC perturbations on ensemble forecast skill. Scale dependence of the results is assessed by evaluating 2-h storm-scale reflectivity forecasts separately from hourly accumulated mesoscale precipitation forecasts.

Forecasts are initialized with different IC ensembles, including an ensemble of multiscale perturbations produced by a multiscale data assimilation system, mesoscale perturbations produced at a coarser resolution, and filtered multiscale perturbations. Mesoscale precipitation forecasts initialized with the multiscale perturbations are more skillful than the forecasts initialized with the mesoscale perturbations at several lead times. This multiscale advantage is due to greater consistency between the IC perturbations and IC uncertainty. This advantage also affects the short-term, smaller-scale forecasts. Reflectivity forecasts on very small scales and very short lead times are more skillful with the multiscale perturbations as a direct result of the smaller-scale IC perturbation energy. The small-scale IC perturbations also contribute to some improvements to the mesoscale precipitation forecasts after the ~5-h lead time. Altogether, these results suggest that the multiscale IC perturbations provided by ensemble data assimilation on the convection-permitting grid can improve storm-scale ensemble forecasts by improving the sampling of IC uncertainty, compared to downscaling of IC perturbations from a coarser-resolution ensemble.

Corresponding author address: Dr. Aaron Johnson, Cooperative Institute for Mesoscale Meteorological Studies, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: ajohns14@ou.edu

Abstract

The impacts of multiscale flow-dependent initial condition (IC) perturbations for storm-scale ensemble forecasts of midlatitude convection are investigated using perfect-model observing system simulation experiments. Several diverse cases are used to quantitatively and qualitatively understand the impacts of different IC perturbations on ensemble forecast skill. Scale dependence of the results is assessed by evaluating 2-h storm-scale reflectivity forecasts separately from hourly accumulated mesoscale precipitation forecasts.

Forecasts are initialized with different IC ensembles, including an ensemble of multiscale perturbations produced by a multiscale data assimilation system, mesoscale perturbations produced at a coarser resolution, and filtered multiscale perturbations. Mesoscale precipitation forecasts initialized with the multiscale perturbations are more skillful than the forecasts initialized with the mesoscale perturbations at several lead times. This multiscale advantage is due to greater consistency between the IC perturbations and IC uncertainty. This advantage also affects the short-term, smaller-scale forecasts. Reflectivity forecasts on very small scales and very short lead times are more skillful with the multiscale perturbations as a direct result of the smaller-scale IC perturbation energy. The small-scale IC perturbations also contribute to some improvements to the mesoscale precipitation forecasts after the ~5-h lead time. Altogether, these results suggest that the multiscale IC perturbations provided by ensemble data assimilation on the convection-permitting grid can improve storm-scale ensemble forecasts by improving the sampling of IC uncertainty, compared to downscaling of IC perturbations from a coarser-resolution ensemble.

Corresponding author address: Dr. Aaron Johnson, Cooperative Institute for Mesoscale Meteorological Studies, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: ajohns14@ou.edu
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