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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.

Journal article(s) based on this preprint

29 May 2024
WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024,https://doi.org/10.5194/gmd-17-4433-2024, 2024
Short summary
Changliang Shao and Lars Nerger

Interactive discussion

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

Interactive discussion

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

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Changliang Shao on behalf of the Authors (14 Mar 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (24 Mar 2024) by Yuefei Zeng
RR by Anonymous Referee #1 (28 Mar 2024)
RR by Anonymous Referee #2 (03 Apr 2024)
ED: Publish as is (07 Apr 2024) by Yuefei Zeng
AR by Changliang Shao on behalf of the Authors (10 Apr 2024)  Author's response   Manuscript 

Journal article(s) based on this preprint

29 May 2024
WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024,https://doi.org/10.5194/gmd-17-4433-2024, 2024
Short summary
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

Viewed

Total article views: 278 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
192 63 23 278 14 12
  • HTML: 192
  • PDF: 63
  • XML: 23
  • Total: 278
  • BibTeX: 14
  • EndNote: 12
Views and downloads (calculated since 13 Nov 2023)
Cumulative views and downloads (calculated since 13 Nov 2023)

Viewed (geographical distribution)

Total article views: 276 (including HTML, PDF, and XML) Thereof 276 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 03 Sep 2024
Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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.