the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Mar 2026
SMHIGridClim, 2.5 km resolution gridded climatology of Fennoscandia
Abstract. SMHIGridClim, the Swedish Meteorological and Hydrological Institute Gridded Climatology, covers Fennoscandia at 2.5 km horizontal resolution for the period 1961–2018. It includes two-meter temperature and two-meter relative humidity at 1-, 3-, or 6-hour temporal resolution (which varies over the covered period), as well as daily minimum and maximum temperatures, daily precipitation, and daily snow depth. The gridding is performed using optimal interpolation with the open-source software gridpp from the Norwegian Meteorological Institute. Observations used in the analysis are provided by the Swedish, Finnish, and Norwegian meteorological institutes, as well as the European Centre for Medium-Range Weather Forecasts (ECMWF). Quality control of the observations is conducted using the open-source software TITAN, also developed at the Norwegian Meteorological Institute. The first guess for the optimal interpolation is obtained from the UERRA-HARMONIE reanalysis at 11 km horizontal resolution, which is statistically downscaled to fit a subset of the operational MEPS numerical prediction system at 2.5 km horizontal resolution, with daily and yearly variations in the downscaling parameters. The quality of the analysis varies over time and depends on both the accuracy of forecasts and the quality and density of available observations. In terms of annual mean root mean square error (RMSE), the quality of SMHIGridClim is comparable to similar gridded datasets covering the Nordic countries. SMHIGridClim is available at https://doi.org/10.7910/DVN/ZFZL6K (Andersson et al., 2025).
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Status: final response (author comments only)
- RC1: 'Comment on essd-2025-804', Anonymous Referee #1, 15 Apr 2026
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RC2: 'Comment on essd-2025-804', Anonymous Referee #2, 20 Apr 2026
This paper presents a nice and valuable application of downscaling of the UERRA-HARMONIE regional reanalysis that also incorporates observations by optimum interpolation to derive a 2.5x2.5km gridded dataset.
The paper has a good structure, is well written, and is mostly clearly presented. Even though the paper in general keeps a high standard I have a few concerns that could need some more consideration by the authors.
My main concern is the way the authors define their structure function for OI. In my philosophy this function should describe the physical process and be independent of the station density. I miss an analysis of the effect of various station densities on the structure function, e.g. by estimating it from random subsamples from the most observation rich periods. It is briefly discussed in the discussion, but I think it needs more attention. However, from another viewpoint can the approach taken by authors might be valid (but with the wrong motivation) The climate is non-stationary, and the structure function might vary over time due to climate variability. Can you comment upon that?
I also would have liked to see the real effects of different structure functions in the final grid and how sensitive the estimates are to the structure function.
In ch. 2.2, line 161-3 you state you are using fixed lapse rates for tas and T2dm. Isn’t there a risk that under certain weather situations many observations incorrectly will be excluded because the lapse rate is non-representative? Please comment/discuss.
In the introduction I would have appreciated a more complete introduction to the methodological framework (lines 76-80) to also introduce the downscaling. Now this section appears incomplete.
Minor comments/issues.
L.39: What do you mean by microscale? Consider to use local scale instead.
L.40: You might delete “often”.
L.56: Change Bazile et al.,2017a to Bazile et al., 2017.
L.101: I think frost.met.no is an API, not the archive. Check!
L107: Eq(1). Reformulate to solve Td2m (Td2m =…)
Ch. 2.1.1: How do you treat redundant data from national vs. ECMWF archives?
Fig 2. Add national borders, that would make the connection to the text better.
Fig 3. Increase the font size for readability.
L.251: Panofsky and Brie, 1968 is not in the reference list.
Ch3.2 Consider a different title than “Analysis with gridpp”. gridpp is the tool, SMHIGridClim is your target.
Fig 7. Add axis titles and units.
L.322: You mean south-west part of Norway? In Figure 8 you should consider presenting the difference in precipitation as a ratio instead of difference. Consider also to add legend title and units.
L.328-329: Can these differences also be a result of changes in the observation network over time?
L.373,374: rms? You mean RMSE? Correct or explain.
Figure 9. Sorry, but this figure is completely unreadable in the version I have. Increase the size and fonts to make it readable.
In conclusion; a very nice paper. A more in-depth analysis and discussion concerning the OI structure function would earn the paper a lot. Figures need to be improved. The dataset is a useful supplement to existing gridded datasets for Fennoscandia, especially since it provides more variables and higher temporal resolution than many of the existing datasets. It needs to be updated and made available until present to keep its relevance.
Citation: https://doi.org/10.5194/essd-2025-804-RC2
Data sets
SMHIGridClim Sandra Andersson et al. https://doi.org/10.7910/DVN/ZFZL6K
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- 1
The authors describe SMHIGridClim, a 2.5 km resolution gridded climatology dataset of hydrometeorological (near-)surface parameters of Fennoscandia.
It is DOI-index and hosted on a popular data repository.
The dataset is stored as CF-1.7-compliant NetCDF files in different temporal aggregation periods, which makes it easy to use and to integrate into existing workflows and data analyses.
The authors perform cross validation between background fields and the analyzed grid data, an impact analysis of various processing steps on the resulting grids, and a very concise comparison with other existing gridded datasets for the region.
This manuscript gives an overview of the input data, background fields, interpolation methodology, validation/evaluation, and a discussion about strengths and limitations of the described data.
While I feel that the dataset itself is certainly valuable, much work needs to go into improving the corresponding data description paper.
My major concerns with the manuscript are: a terse description of the methodology behind the dataset which limits the ability to gauge the data characteristics; limited independent evaluation of the data quality; numerous inconsistencies in naming, which make it hard to follow the content; not very well designed figures, both in terms of legibility and information content; and non-consideration of the ESSD author guidelines for English, figures, tables, and mathematical notation which puts a lot of effort on the reader to take in the presented information.
Next to these major points, I have the following detailed comments on the manuscript:
L12: gridpp is sometimes written in full uppercase (GRIDPP, e.g., Fig. 1). I suggest chosing one notation (ideally following the developers) throughout the paper.
L17: Please explain MEPS acronym
L51: It is important to stress the difference between grid resolution and resolution of the input data, i.e. the information content in the data. The authors rightly note the low station density for the NGCD dataset and this should be done for each of the discussed data products.
I suggest to name the grid resolution of the discussed datasets "horizontal grid resolution" to make it clear that even though the grids are produced at these scales, the information content may vary.
L78: two-meter temperature is sometimes referred to as tas and sometimes as T2m. I suggest to adopt a consistent naming scheme (also for the other variables). Also two spellings (2m temperature, Fig. 1; 2 m temperature) exist, this should be consistent.
L88: Sometimes AE is used, sometimes BE. This should be consistent throughout the article (https://www.earth-system-science-data.net/submission.html)
L104: Please provide access date
L106: Is there a source for this equation?
L107: Please see the guidelines for mathematical notation: https://www.earth-system-science-data.net/submission.html#math
L108: Td2m should be in math font face to be consistent with the equation
L109: Same for tas and hurs
L109: On +5 degC: Remove whitespace before magnitude, add whitespace between magnitude and unit.
L112: Please move the BUFR acronym explaination to previous sentence.
Figure 2:
The resolution of the figure is relatively poor which, together with the small font size, makes it difficult to parse the text and distinguish the marker symbols.
I also suggest checking the Figure using a color vision deficiency simulator (see https://www.earth-system-science-data.net/submission.html) to verify that the color scheme used is inclusive.
Concerning the legend, please increase the font size, align the numbers and add a unit directly to the magnitudes.
The variable naming is not consistent with the text.
Figure 3:
The font size is too small. The y-axis should be aligned between the panels (if the range varies too much, please note this in the capition).
The panels can be arranged in a 2-by-2 grid to save space. The y-axis label is not correct. It should be "Number of observations per month per square km" as stated in the capition.
L148/L150: Please provide access date
L162: Is this standard deviation computed per time step or station-wise over time? Please provide more information as this make a difference. Computing the standard deviation per time step over all station differences results in a too optimistic bound for stations with large noise level.
L178: I do not think that MEPS was explained so far.
L179: horizontal grid resolution
Figure 4:
See comment on Fig. 2 concerning color vision deficiency.
Please move the explanations to a proper legend rather verbal descriptions in the title.
UERRA should be named UERRA-HARMONIE as in the main text.
L204: "surrounding 4x4 neighbouring points" could be explained more clearly, maybe by adding "(the 16 closest points/nearest neighbours)". This is done in the next paragraph anyway, but would be better here.
Figure 5:
Typo "Exampel", UERRA should be name UERRA-HARMONIE
Titles: please provide date/time in a proper format (e.g., ISO 8601). Please, also label each panel properly (https://www.earth-system-science-data.net/submission.html).
Colorbar: colorbar label missing or cut off
Grid point panel: please add a legend with the explanations of each point/grid. Also consider using different markers (circles and squares) for UERRA-HARMONIE and SMHIGridClim for better readability.
Figure 6:
Suggestion for renaming x-label: Time -> Time of Day, day number -> Day of Year
Please properly label panels.
Chose either "weights" or "weighting functions", not "weight functions".
L231 - L234: Please see the author guidenlies for math notation.
L238: Please see the author guidenlies for math notation.
L255: Please provide access date; python -> Python
L257: optimal way -> statistically optimal way
L258: bi-linear -> bilinear
L261:
It might not be clear to everyone how the background and observation covariances are handled in gridpp. While this sentence hints at how this is performed, a more precise explanation would be helpful, maybe even with an equation.
Since the parameter determination and -interpolation of the covariances is discussed in the following paragraphs, it should be clear to the reader that this refers to a combined covariance information in the form of a single structure object and covariance ratios.
A sentence about the assumptions of the observation error covariance is also helpful (are they spatially correlated or not?) to understand the following explanations.
L263 - L270: Please consistently highlight software methods and parameters in the text. What is the justification for using the default setting for decorrelation length? Did you conduct a sensitivity study?
L273: It would be interesting to see the results of the leave-on-out cross-validation. I feel like this should be a core validation metric of the dataset to really give users confidence in the data quality.
L275: spatial scale -> spatial structure
L277: It may be helfpul here to reiterate about which structure function you are talking about here.
L279: Please provide more information on the chosen splines (degree, nodal points, ...) as this determines the smoothness of the interpolated parameter time series.
L283: Please see the author guidenlies for math notation.
L284: yr_k= ... should be properly formatted.
L287: "the performance of the analysis at different times": How was this determined?
L288: Is a homogeneous (or more precisely stationary in time) background error covariance justified? The argument about varying observation density and -quality can be made for both the background field and the observations thus requiring temporally-varying covariance information anyway.
L292: "yielded relatively constant parameter values": How large was the variation?
L296: "gridpp.relative_humidity": See comment on highlighting software functions and parameters.
L297/L298: Please note here, that using dewpoint temperature implies a non-linear mixture of relative humidity and 2m temperature errors is diagnosed.
Figure 7:
Please properly label panels according to the author guidelines
Provide y-axis labels for each parameter with units.
Parameter descriptions can then be removed from the caption.
Table 3:
Column headings should be horizontal and vertical correlation distance.
Please right-align numbers and provide consistent number of (significant) digits.
"snowd" should be snd or better: "Snow depth (snd)", similar for precip.
L333: Section numbers are out of order.
Figure 8:
Please add appropriate colorbar labels and panel titles.
The variable naming is not consistent with the text (Tn, Tx have not been introduced, RR should be pr maybe?)
rh2m should be rhurs (maybe?)
Figure 9:
This figure is very hard to parse and generally not well designed. Some suggestions to improve it:
- Color scheme: perform color vision deficiency simulation and adopt accordingly
- font sizes: far too small, figure may be split into two individual figures with less panels to increase the size of each subfigure a bit
- labels: everything is labelled as Error, but different metrics are shown. Please provide proper labels
- abbrevations: avoid "nbr obs" and the other overly terse abbrevations in the legend, the reader should not have to guess
- UERRA-HARMONIE is called UERRA-HARMONIE throughout the paper and should be here as well
L391: 95th
L398: bi-linear -> bilinear
Figure 10: Same as Fig. 8
Figure 11: Same as Fig. 8
L445: Is this the wrong link? It leads to seNorge2
L447 - L453: This paragraph should be expanded and moved to section 4. Validation is a key component of the dataset description and should be given more weight, especially when independent datasets are used.
L456 - L457: This is about the grid resolution and should be communicated as such. As you state in the next paragraphs the effective resolution of SMHIGridClim is far larger than 2.5 km, so the spatial information content of these datasets might not be too far apart. Please indicate this properly.
Table 4: This table is not is not referenced. Also there is a mix in unit notations, e.g., [K] in figures (K) here.
L473: UERRA -> UERRA-HARMONIE
L486: UERRA -> UERRA-HARMONIE