Preprints
https://doi.org/10.5194/egusphere-2023-2311
https://doi.org/10.5194/egusphere-2023-2311
13 Nov 2023
 | 13 Nov 2023

WRF-PDAF v1.0: Implementation and Application of an Online Localized Ensemble Data Assimilation Framework

Changliang Shao and Lars Nerger

Abstract. Data assimilation is a common technique employed to estimate the state and its associated uncertainties in numerical models. Ensemble-based methods are a prevalent choice, although they can be computationally expensive due to the required ensemble integrations. In this study, we enhance the capabilities of Weather Research and Forecasting–Advanced Research WRF (WRF-ARW) model by coupling it with the Parallel Data Assimilation Framework (PDAF) in a fully online mode. Through minimal modifications to the WRF-ARW model code, we have developed an efficient data assimilation system. This system leverages parallelization and in-memory data transfers between the model and data assimilation processes, greatly reducing the need for file I/O and model restarts during assimilation. We detail the necessary program modifications in this study. One advantage of the resulting assimilation system is a clear separation of concerns between data assimilation method development and model application resulting from PDAF’s model-agnostic structure. To evaluate the assimilation system, we conduct a twin experiment simulating an idealized tropical cyclone. Cycled data assimilation experiments focus on the impact of temperature profiles. The assimilation not only significantly enhances temperature field accuracy but also improves the initial U and V fields. The assimilation process introduces only minimal overhead in run time when compared to the model without data assimilation and exhibits excellent parallel performance. Consequently, the online WRF-PDAF system emerges as an efficient framework for implementing high-resolution mesoscale forecasting and reanalysis.

Changliang Shao and Lars Nerger

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2311', Anonymous Referee #1, 10 Dec 2023
    • AC1: 'Reply on RC1', Changliang Shao, 13 Dec 2023
  • RC2: 'Comment on egusphere-2023-2311', Anonymous Referee #2, 10 Mar 2024
    • AC2: 'Reply on RC2', Changliang Shao, 14 Mar 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2311', Anonymous Referee #1, 10 Dec 2023
    • AC1: 'Reply on RC1', Changliang Shao, 13 Dec 2023
  • RC2: 'Comment on egusphere-2023-2311', Anonymous Referee #2, 10 Mar 2024
    • AC2: 'Reply on RC2', Changliang Shao, 14 Mar 2024
Changliang Shao and Lars Nerger

Data sets

Observation assimilated by WRF-PDAF Changliang Shao https://doi.org/10.5281/zenodo.10083810

Model code and software

WRF-PDAF v1.0 Changliang Shao https://doi.org/10.5281/zenodo.8367112

Changliang Shao and Lars Nerger

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Latest update: 13 Apr 2024
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Short summary
This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiments results underscore the effectiveness of the WRF-PDAF system.