the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Nov 2025
DANRA: The Kilometer-Scale Danish Regional Atmospheric Reanalysis
Abstract. The DANish regional atmospheric ReAnalysis (DANRA) is a novel high-resolution (2.5 km) reanalysis dataset covering Denmark and its surrounding regions over a 34-year period (1990–2023). Denmark’s complex coastline, with over 400 islands and an extensive 7,400 km coastline, means that most municipalities experience mixed land-sea variability. This complexity requires a regional climate reanalysis that can resolve fine-scale coastal and inland features, as well as their impact on climate variability. DANRA is based on the HARMONIE-AROME Numerical Weather Prediction (NWP) model and assimilates a comprehensive set of observations, with a particular focus on Denmark. Compared to global reanalyses such as the ECMWF Reanalysis v5 (ERA5), DANRA demonstrates superior performance in representing essential climate variables, including near-surface weather parameters during both extreme and ordinary conditions. We illustrate these improvements in the representation of several extreme weather cases over Denmark, such as the December 1999 hurricane-force storm, the July 2022 national temperature record, and the August 2007 cloudburst in South Jutland. DANRA is made to support climate adaptation, impact modelling, and the training of next-generation data-driven atmospheric forecasting models. DANRA is distributed as Zarr dataset freely accessible from an object store (doi.org/10.5281/zenodo.17294179), maximizing its usability for climate adaptation, impact modelling, and data-driven research.
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Status: open (until 05 Jan 2026)
- RC1: 'Comment on essd-2025-610', Anonymous Referee #1, 18 Dec 2025 reply
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DANRA X. Yang et al. https://doi.org/10.5281/zenodo.17294179
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- 1
Review comments to the paper ”DANRA: The Kilometer-Scale Danish Regional Atmospheric Reanalysis”, manuscript ID essd-2025-610.
General comments:
The paper describes the DANRA high resolution regional reanalysis and compares the output from this to the global ERA5 reanalysis. The latter is considered to be the current state of the art reanalysis so any added value is a measure of good quality. It is very nice to read a well written paper that discuss and describe a well made and good quality reanalysis since there is an added value in the DANRA data set compared to ERA5. Even though the manuscript is well written and mostly clear, there are a few questions that arise and also some editorial issues. I would recommend the paper to be published after these questions and comments have been answered and updated in the manuscript.
1. First a somewhat provocative question: What is the purpose of creating this reanalysis? And who are the intended users? The authors claim that it is an important data set that is asked for by both public and private bodies and the high resolution is important for local applications. It is also touched upon at the very end of the paper but not very much. It would be beneficial for the paper and for understanding the need for this reanalysis if a few real use cases were presented.
2. Another major thing is the observation usage. Since observations and the use of observations, is a very crucial part of a reanalysis, as the authors also point out, it would be nice with a bit more information on what observations are used and how. The background error statistics for example, how is that derived, for what period and is it the same during the entire reanalysis? A figure of how the number of observations changes during the period or how the coverage changes would also be nice to see.
3. Several acronyms are not defined in the paper (HARMONIE-AROME, ACCORD...). Please look through and define the acronyms the first time they appear.
4. In the manuscript, only three parameters are verified, temperature at 2 meters, wind at 10 meters and precipitation. This is of course interesting for the extreme weather events presented but from a model evaluation perspective it would be interesting to also see verification of other model parameters. I am confident that the authors have looked at this so what is the motivation for not including more verification in the paper?
5. The authors mainly refer to the higher resolution being the reason for the improvements compared to ERA5. Are the improvements only attributed to the higher resolution or are there other factors contributing to this, like additional observations, better parametrisations or similar?
Specific comments:
Lines 42-43: It says that HARMONIE-AROME Cycle 40h1 is the operational model in Denmark and Norway. That might have been true at the time but that information is old by now. I think that this should be stated by writing e.g. “the operational model at the time of the project start” or something similar. Besides HARMONIE-AROME Cycle 40h1 was used by several more countries than Denmark and Norway.
Line 45: It says “...improved modelling for the ‘cold’ climate condition…” but it is never mentioned what those improvements contain.
Line 195: “It would be faster if index files were pre-computed and GRIB files were uncompressed”. How much faster and what would this mean for storage space?
Lines 199-200: “users should not have to fetch and read too many time steps” and “too many grid points”. How many are too many? Please be more specific or reformulate.
Figure 7: DANRA always shows lower wind speed than ERA5. In the end of the period, when the bias is positive, this is good but during the first half of the period it is not good. Any explanation to why?
In addition, the transition from negative to positive bias occurs when the number of verification observations is constant. How do you explain this transition? Are there any other observations introduced in the data assimilation during this time?
Line 256: There is a ? after North Jutland.
Line 272: “...resulting in a markedly higher wind speed than CERRA.” It should most likely be ERA5 instead of CERRA.
Line 283: Another ?. Missing reference?
Line 284: I don’t understand what the authors are trying to say with this sentence? Please be a bit more specific.
Line 320 and Figure 9: The small dots marking the synop stations are really hard do see, especially in areas with high temperature (dark colours)
Line 321: “these variability” should probably be “this variability”.
Line 346: Summary and outlook. I can mostly see a summary and not really any outlook. Am I missing something or could this be extended e.g with some use cases as mentioned in the general comments or more specific ideas and plans for what this data set could be used for?