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Testing the Feature Alignment Technique (FAT) in an Ensemble-Based Data Assimilation and Forecast System with Multiple-Storm Scenarios

Testing the Feature Alignment Technique (FAT) in an Ensemble-Based Data Assimilation and Forecast... AbstractStorm displacement errors can arise from a number of potential sources of error within a data assimilation (DA) and forecast system. Conversely, storm displacement errors can cause issues for storm-scale, ensemble-based systems using an ensemble Kalman filter (EnKF), such as NSSL’s Warn-on-Forecast System (WoFS). A previous study developed a fully grid-based feature alignment technique (FAT) to mitigate these phase errors and their impacts. However, that study developed and tested the FAT for single-storm cases. This study advances that work by implementing an object-based merging and matching technique into the FAT and tests the updated FAT in more complex scenarios of multiple storms. Ensemble-based experiments are conducted with and without the FAT for each of the scenarios. The experiments’ analyses and forecasts of storm-related fields are then evaluated using subjective and objective methods. Results from these idealized multiple-storm experiments continue to reveal the potential benefits of correcting storm displacement errors. For example, running the FAT even once can mitigate the “spinup” period experienced by the no-FAT experiments. The new results also show that running the FAT prior to every DA cycling step generally leads to more skillful forecasts at the smaller scales, especially in earlier-initialized forecasts. However, repeatedly running the FAT prior to every DA step can eventually lead to deterioration in analyses and forecasts. Potential solutions to this problem include using longer cycling intervals and running the FAT prior to DA less often. Additional ways to improve the FAT along with other results are presented and discussed.Significance StatementThe purpose of this work is to explore the impact of correcting storm displacements on analyses and forecasts of storms using an ensemble-based data assimilation and forecast system in an idealized framework. Storm displacement errors are a common problem in current operational and experimental storm-scale forecast systems, so understanding their impact on these systems and providing a method to help mitigate them is important. Results from this study indicate that correcting storm displacement errors with the feature alignment technique can greatly improve analyses and forecasts in multiple-storm scenarios. Future work will focus on exploring the impact of correcting storm displacement errors in a real-data, storm-scale data assimilation and forecast system. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Monthly Weather Review American Meteorological Society

Testing the Feature Alignment Technique (FAT) in an Ensemble-Based Data Assimilation and Forecast System with Multiple-Storm Scenarios

Stratman, Derek R.; Potvin, Corey K.
Monthly Weather Review , Volume 150 (8): 22 – Aug 5, 2022
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References (38)

Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0493
eISSN
1520-0493
DOI
10.1175/mwr-d-21-0289.1
Publisher site
See Article on Publisher Site

Abstract

AbstractStorm displacement errors can arise from a number of potential sources of error within a data assimilation (DA) and forecast system. Conversely, storm displacement errors can cause issues for storm-scale, ensemble-based systems using an ensemble Kalman filter (EnKF), such as NSSL’s Warn-on-Forecast System (WoFS). A previous study developed a fully grid-based feature alignment technique (FAT) to mitigate these phase errors and their impacts. However, that study developed and tested the FAT for single-storm cases. This study advances that work by implementing an object-based merging and matching technique into the FAT and tests the updated FAT in more complex scenarios of multiple storms. Ensemble-based experiments are conducted with and without the FAT for each of the scenarios. The experiments’ analyses and forecasts of storm-related fields are then evaluated using subjective and objective methods. Results from these idealized multiple-storm experiments continue to reveal the potential benefits of correcting storm displacement errors. For example, running the FAT even once can mitigate the “spinup” period experienced by the no-FAT experiments. The new results also show that running the FAT prior to every DA cycling step generally leads to more skillful forecasts at the smaller scales, especially in earlier-initialized forecasts. However, repeatedly running the FAT prior to every DA step can eventually lead to deterioration in analyses and forecasts. Potential solutions to this problem include using longer cycling intervals and running the FAT prior to DA less often. Additional ways to improve the FAT along with other results are presented and discussed.Significance StatementThe purpose of this work is to explore the impact of correcting storm displacements on analyses and forecasts of storms using an ensemble-based data assimilation and forecast system in an idealized framework. Storm displacement errors are a common problem in current operational and experimental storm-scale forecast systems, so understanding their impact on these systems and providing a method to help mitigate them is important. Results from this study indicate that correcting storm displacement errors with the feature alignment technique can greatly improve analyses and forecasts in multiple-storm scenarios. Future work will focus on exploring the impact of correcting storm displacement errors in a real-data, storm-scale data assimilation and forecast system.

Journal

Monthly Weather Review – American Meteorological Society

Published: Aug 5, 2022

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