the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation of Dust Emission and Land Surface Schemes in Predicting a Mega Asian Dust Storm over South Korea Using WRF-Chem (v4.3.3)
Abstract. This study evaluates the performance of the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) in forecasting a mega Asian Dust Storm (ADS) event that occurred over South Korea on March 28–29, 2021. We specifically evaluated a combination of five dust emission schemes and four land surface schemes, which are crucial for predicting ADSs. Using in-situ and remote sensing data, we assessed surface meteorological and air quality variables, including 2 m temperature, 2 m relative humidity, 10 m wind speed, particulate matter 10 (PM10), and aerosol optical depth (AOD) over South Korea. Our results indicate that prediction of surface meteorological variables is more influenced by the land surface scheme than by the dust emission scheme—generally showing good performance when dust emission schemes are combined with the Noah land surface model with Multiple Parameterization options (Noah-MP). In contrast, prediction of air quality variables, including PM10 and AOD, is strongly affected by the dust emission schemes, which is directly related to the generation and amount of dust through interaction with surface properties. Among the total of 20 available scheme combinations, the University of Cologne 2004 combined with the Community Land Model version 4.0 (UoC04-CLM4) showed the best performance, closely followed by the University of Cologne 2001 combined with CLM4 (UoC01-CLM4). UoC04-CLM4 outperformed the other scheme combinations by reducing the root mean square errors of PM10 up to 29.6 %. However, both UoC04-CLM4 and UoC01-CLM4 simulated values closest to the MODIS AOD but tended to overestimate the AOD in some regions during the origination and transportation processes. In contrast, other scheme combinations significantly underestimated the AOD throughout the entire simulation process of ADSs.
- Preprint
(2715 KB) - Metadata XML
-
Supplement
(1464 KB) - BibTeX
- EndNote
Status: open (until 31 Oct 2024)
-
CEC1: 'Comment on gmd-2024-114', Juan Antonio Añel, 14 Aug 2024
reply
Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
The WRF-Chem 4.3.3 code is archived on GitHub. However, GitHub is not a suitable repository for scientific publication. GitHub itself instructs authors to use other alternatives for long-term archival and publishing. Therefore, please, publish the WRF-Chem 4.3.3 code in one of the appropriate repositories, and reply to this comment with the relevant information (link and and permanent identifier for it (e.g. DOI)) as soon as possible, as we can not accept manuscripts in Discussions that do not comply with our policy.Also, you must include in a potentially reviewed manuscript the modified 'Code Availability' section, with the new link and DOI for the code.
Please, note that if you do not fix this problem, we will have to reject your manuscript for publication in our journal.
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/gmd-2024-114-CEC1 -
AC1: 'Reply on CEC1', Ji Won Yoon, 15 Aug 2024
reply
Dear Executive Editor,
We appreciate your pointing this out. We have addressed the issue regarding the repository of WRF-Chem (v4.3.3) code.
The code is now accessible via the following link: https://doi.org/10.5281/zenodo.13324490.
Additionally, we have migrated all existing data to the same repository.
We have also revised the 'Code Availability' section to reflect this update, and we will incorporate it into the potentially reviewed manuscript.
Best,
Ji Won Yoon
Citation: https://doi.org/10.5194/gmd-2024-114-AC1
-
AC1: 'Reply on CEC1', Ji Won Yoon, 15 Aug 2024
reply
-
RC1: 'Comment on gmd-2024-114', Paul Miller, 06 Sep 2024
reply
Title: Evaluation of Dust Emission and Land Surface Schemes in Predicting a Mega Asian Dust Storm over South Korea Using WRF-Chem (v4.3.3)
Reviewer: Paul Miller, Louisiana State University
This manuscript examines the performance of 20 combinations of WRF-Chem dust aerosol parameterizations and land-surface schemes in reproducing a mega Asian Dust Storm (ADS) from March 2021. The validation study is highly specific to a single numerical modeling system and individual event, so the novelty of this study and its broader impact on the wider atmospheric sciences is limited. However, I recognize that verification studies like this are sometimes important incremental steps within larger projects, and the results may nonetheless help guide the selection of appropriate dust physics settings among other researchers and practitioners in East Asia. The manuscript, though modest in its scope and potential scientific impact, is nonetheless well written and soundly conducted. I believe it could be accepted for publication pending the revisions suggested below.
Overall Comments:
- The manuscript computes both POD and FAR for several ACWS-relevant PM10 thresholds, and it emphasizes that these two scores need to be interpreted jointly. However, performance metrics such as the Critical Success Index (CSI) do exactly that. The manuscript would be strengthened by the addition of CSI, or a similar metric, that merges these two ideas into a single score. The CSI is easy to compute with the information already provided in the manuscript.
- The study references PM10 PCC values (Figure 6) and scatterplot relationships as “good” for some scheme combinations. However, visually, the observed-vs-simulated PM10 relationships appear quite weak. The PCCs for even the most skillful LSM-dust scheme combinations still only explain a relatively small fraction of the variance (~30% at most) if thought of as R2 rather than R. The manuscript should clarify how the PCCs, even low ones, are indicating “good” performance.
Line 166: Does this mean you wrote the output at 1-hr intervals? The integration timestep had to be much shorter than this.
Line 281: MAE is referenced as MBE throughout the rest of the manuscript. Please revise for consistency.
Line 290: 1.0 does not necessarily indicate a “perfect forecast.” It just indicates that all true events were successfully identified. The manuscript clarifies this in the following sentence, but “skillful” is more appropriate phrasing than “perfect.”
Table 4 caption: What is the basis of using 0.4 as the threshold for a “weak” correlation?
Line 346, 351, and elsewhere: By my understanding, no forecasts were produced in this study (i.e., there was no attempt to predict the future). So, “forecasted” values is really referring to “modeled” or “simulated” values.
Citation: https://doi.org/10.5194/gmd-2024-114-RC1 -
AC2: 'Reply on RC1', Ji Won Yoon, 11 Sep 2024
reply
Dear Professor Miller
We sincerely appreciate the reviewer’s valuable comments.
We have attached the file titled 'Reply to Reviewer-1'
Best,
Ji Won Yoon
Data sets
Various Datasets for Evaluation Ji Won Yoon https://zenodo.org/records/11649488
Model code and software
Model Code (WRF v4.3.3) Ji Won Yoon https://zenodo.org/records/11649488
Viewed
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
236 | 69 | 50 | 355 | 25 | 10 | 10 |
- HTML: 236
- PDF: 69
- XML: 50
- Total: 355
- Supplement: 25
- BibTeX: 10
- EndNote: 10
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1