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The Utility of Ensemble – Sensitivity Analysis for Targeted Observing, Ensemble Sub setting , and Diagnosing Environmental Controls on Storm Characteristics

Presented by: Aaron J. Hill - Texas Tech University
Date: September 25, 2018 11:00 am
Location: CIRA Directors Conference Room



At Texas Tech University, we are interested in developing and using novel ensemble tools to improve our understanding of severe storm predictability.


Ensemble sensitivity is one such tool that when applied within an ensemble framework reveals atmospheric flow features (e.g. position of a jet streak, or

magnitude of a low-level moisture plume) at early forecast times that are related to a chosen forecast response later in the forecast window. Typically, responses are

chosen that diagnose forecast features of interest, e.g., accumulated rainfall, maximum updraft helicity, or low-level vertical vorticity. The ESA relationships

between forecast responses and earlier-time variables has the potential to inform where additional sampling should occur in order to improve the response forecast.

Intelligent methods to target additional observations have been available for decades, which take into account fast growing errors (i.e., singular vectors) and

gradients of tangent linear models (i.e., adjoint sensitivity). Unfortunately, less than- ideal results have been realized when ESA has been utilized for targeted

observing of mesoscale convection/precipitation forecasts.


Recent literature has noted near-neutral average impacts of additional observations when targeted with

ESA. Given the desire to exploit advantages of ESA over other targeting methodologies (e.g., execution time), it is imperative to understand factors, which

may include forecast nonlinearity, data assimilation procedures, and model error, that influence the prediction of observation impacts and the impacts after

assimilation. A 50-member ensemble is generated over ten cases of severe storms along the dryline with the Advanced Research core of the Weather Research and

Forecasting model (WRF) and Data Assimilation Research Testbed (DART) software. The observing system simulation experiment (OSSE) methodology is

employed to control for degrees of freedom, including model error. A number of experiment permutations will be discussed, which aim to diagnose relative impacts

of target observations on mesoscale convection forecasts.


This presentation will also discuss the utility of ESA to subset ensembles for

improved probability forecasts as well as evaluate environmental controls on storm-scale processes. During the Hazardous Weather Testbed Spring Forecast

Experiment, we demonstrated the use of ESA to subset ensembles to improve probabilistic forecasts of severe convection. Moreover, the application of ESA for

storm-scale simulations (i.e., < 3-km grid spacing), despite linear constraints in the algorithm, reveals environmental heterogeneities and relevant storm-scale features

that may influence low-level rotation in organized deep convection.