Andy Wilkins - July 23

Convective Meteorology (Mesoscale Dynamics) Seminar   Multi-scale Object-based Probabilistic Forecast Evaluation of WRF-based CAM Ensemble Configurations   Andy Wilkins  Thursday, July 23rd 10:00 AM Central Time (US and Canada)   Join Zoom Meeting https://oklahoma.zoom.us/j/91311593353?pwd=TW1UVFVkVUNhbFRScnB3alNPaXU0UT09   Meeting ID: 913 1159 3353 Password: 0=piZDbF   Recent developments in numerical weather prediction have

Start

July 23, 2020 - 10:00 am

End

July 23, 2020 - 11:00 am

Convective Meteorology (Mesoscale Dynamics) Seminar

 

Multi-scale Object-based Probabilistic Forecast Evaluation of WRF-based CAM Ensemble Configurations

 

Andy Wilkins 

Thursday, July 23rd

10:00 AM Central Time (US and Canada)

 

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https://oklahoma.zoom.us/j/91311593353?pwd=TW1UVFVkVUNhbFRScnB3alNPaXU0UT09

 

Meeting ID: 913 1159 3353

Password: 0=piZDbF

 

Recent developments in numerical weather prediction have included increased usage of ensemble forecasts in contrast to single, deterministic forecasts.  In particular, convection-allowing model (CAM) ensembles have been utilized as they contain a distinctive ability to predict convective initiation location, mode and morphology.  Such information can be extremely useful to forecasters predicting severe weather threats associated with particular storm modes and their morphological transitions spatially and temporally.  Numerous studies verifying CAM ensemble forecast performance been conducted, however, have used neighborhood-based methods and therefore, have primarily focused on spatial coverage rather than convective mode and morphology.  The problem with neighborhood based verification methods lie in their inability to adequately represent storm scale features of greatest subjective interest to forecasters.  In contrast, as an alternative to neighborhood-based methods, recent object-based probabilistic framework has been introduced to assess forecasts, providing a unique setting to re-evaluate aspects of optimal CAM ensemble design with a focus on mode and morphology prediction.

Here we adopt an optimized object-based probabilistic (OBPROB) forecasting method in conjunction with a traditional neighborhood based method to evaluate forecasts of four separately designed 10-member ensembles.  The ensemble configurations evaluated include single model, single physics (SMSP) configurations, single vs. multi-model, single vs. multi-physics, and multi-model vs multi-physics.  Due to the implementation of multiple verification techniques and separate verification of objects on different spatial scales, comparisons of forecast performance in terms of mesoscale precipitation locations and forecasted storm mode and morphology can be made explicitly, leading to insight on optimal CAM ensemble design for specific storm modes and morphologies.  Results show for mesoscale organized convection, multi-model and multi-physics ensemble designs are preferred to single-model single-physics designs.

 

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