the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Jun 2026
A new method for updating snow fields in the NWP models using satellite snow extent based ’Snow Barrel’ pseudo-observations as applied to HARMONIE-AROME cycle 43
Abstract. This paper introduces the "snow barrel" method, a new approach to integrate satellite-derived snow observations into NWP models. Snow is a key component of the environment, helping to regulate surface temperature, atmospheric and soil conditions, and playing a key role in the water cycle. However, assimilating satellite snow data into NWP models remains challenging due to resolution mismatches and the complexity of handling snow extent observations. The snow barrel method addresses these challenges by aggregating satellite pixel observations from the EUMETSAT H-SAF H32 intermediate product into 10x10 pixel areas and creating pseudo-observations that align with NWP model scales. Implemented within the HARMONIE-AROME model in the MetCoOp operational domain covering northern Europe, this approach selectively applies snow barrel observations where they conflict with the model's background field, particularly in regions with thin or patchy snow cover. The results demonstrate improved representation of snow cover during the transitional seasons without disrupting areas of solid cover or imposing a significant computational burden. The method effectively combines the spatial coverage advantages of satellite data with the precision of in situ measurements, particularly benefiting areas with sparse ground observations. Although constrained by cloud cover and lighting conditions inherent to optical satellite products, the snow barrel methodology offers a flexible framework that could be expanded to other satellite platforms and potentially adapted for additional surface parameters beyond snow cover.
- Preprint
(7922 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 18 Sep 2026)
- RC1: 'Comment on egusphere-2026-1408', Anonymous Referee #1, 07 Sep 2026 reply
-
RC2: 'Comment on egusphere-2026-1408', Anonymous Referee #2, 10 Sep 2026
reply
Thank you very much for the chance to review, “A new method for updating snow fields in the NWP models using satellite snow extent based ’Snow Barrel’ pseudo-observations as applied to HARMONIE-AROME cycle 43.” The paper presents a novel technique to update snow field in NWP models using a “snow barrel” approach, whereby the method identifies snow cover and non-covered pixels from optical satellites and compares those pixels to the model background fields and the H32 product. If there is a difference, the method proceeds to update the model.
I found the technique to be very interesting, and the figures well done. I believe there are a couple sections that could be better organized and framed for the reader, which I elaborate more on below. Additionally, I understand that the authors found most discrepancy between days of thin snow cover, but this seems inherent on the threshold for snow from the initial SWE prediction (which is currently unclear). More description and justification on this would be helpful.
Introduction
- I found the introduction could be overall a bit tighter to the question at hand and could better highlight the gap that researchers are accomplishing. That is to say, why snow is currently tough for NWP models, how/why current methods are insufficient, and why new methods are needed. I found the current structure drifted a bit and it was hard to follow the research gaps they were addressing.
- Line 30: I found the introduction to be lacking one critical paragraph that focused on what is a NWP, why accurate SWE is important for NWP, and why it is currently challenging. Paragraph 3 of the introduction does this somewhat, but I think this could be strengthen to justify why snow is important for NWP and why new methods are needed.
- Line 37-55: I am not really sure what paragraph 4 is adding. I think Paragraphs 4 and 5 could be better framed around how these observations are or are not currently used by NWP models. This is touched on in Line 61, but I think these sections could be tighter and more direct to say why data assimilation of these observations is insufficient, hence why a barrel method is needed. The current method made it hard to follow some of the acronyms for the reader, so I feel this could be better organized.
Data
- I found some of the information here redundant to the introduction (Line 66 and 109 are very similar) and a lot of the information in the data section also belonged into the introduction to strengthen the research gap (for example lines 79 – 89). I recommend the authors streamline the introduction to present the research gap and current challenges and use the data section to focus on the datasets and processing steps used for this analysis.
- I noticed that the study period later appeared to be April 2024 to April 2025. Was there a reason for this study period? And the same thing for this study region. Both sets of information would be stronger in the data section.
Model and Methods
- I appreciated figures 1 and 2 to guide the description of the analysis. I think some subsection headings would also benefit the reader as it was at times hard to follow the various steps to develop the model runs. I also found that some decisions appeared to be less justified, and it would benefit the reader to know the impacts of those decisions.
- Line 161: typo for “similar”
- Line 171: what does all members mean?
- Line 179: Where did the 20% for snow or no snow come from? Other papers have cited 40% as a threshold for snow or no snow (Hall et al. 1995; 10.1016/0034-4257(95)00137-P). This is an example of a decision that was challenging to know how it was made and what impacts it might have on the analysis.
- Line 184-188: I am bit confused. I understand that snow barrel values are only applied when the model prognostic variable disagrees with the snow barrel for snow or no snow, and the model variable is SWE, but this implies that there is a cut-off for SWE that the authors deem “snow.” Was this threshold anything above >0? Could really small SWE values (e.g., <1) also be considered no snow, in which case barrel values that indicate no snow for these respective values get assimilated into the model?
- Line 190: It would be helpful to understand the breakdown of how the authors got to 0-10 cm snow depth. Is there any prior literature to back up this claim?
- Line 214-215: Where do these standard deviations come from? Were they calculated or assigned or something else?
Results
- Overall comment: do forested areas impacted utility of the barrel method? Given that optical cannot penetrate through the canopy these values would indicate no snow when the model would produce SWE. Could incorporating these values actually make the snow fields worse?
- There are some “Cloud Gap Filled” optical products. Do the authors think these could provide utility?
- For Figure 7 (Lines 259-265): Can you provide the RMSE and bias differences between Barrels and Reference across all months and then separated by season? It seems like the barrel method is particularly useful in the spring when clouds are scarce and snow is thin and patchy, but I didn’t see these explicit numbers, and I found it very hard to figure out the differences between barrel and reference in the plot because the points were very small.
- I think some subsection headings for the Results would help here too.
Discussion
- Line 278: The authors bring up “computationally efficient” multiple times, but I couldn’t find this comparison. Can the authors provide a metric for this efficiency compared to other methods?
- Some of the topics in the Discussion belonged in the methods or the results. For example, Lines 296-300, for example. Lines 311-318 was also unclear how it related to interpretation of the study as it was mostly descriptive. I’d recommend the authors tighten the Discussion and work on reorganizing for the key takeaways, strengths, and limitations.
Citation: https://doi.org/10.5194/egusphere-2026-1408-RC2
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 166 | 87 | 22 | 275 | 15 | 14 |
- HTML: 166
- PDF: 87
- XML: 22
- Total: 275
- BibTeX: 15
- EndNote: 14
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
General comments
This manuscript describes the use of a new “Snow Barrel” methodology to process satellite snow cover observations from the EUMETSAT H SAF H32 product for assimilation within the HARMONIE-AROME regional NWP system. The manuscript is well written and clearly presented, and the assimilation experiments demonstrate clear benefits to the analysed snow field relative to the use of in situ observations alone. Although it represents a potentially valuable contribution to the NWP and snow DA community, I remain unconvinced that the manuscript fully establishes (1) the novelty of the Snow Barrel concept relative to existing observation-processing approaches for satellite snow cover, (2) the added value of the Snow Barrel methodology itself relative to the underlying satellite observations, and (3) the operational benefit of the approach for NWP forecasting. These issues are discussed in the general comments below:
-----------------------------------------------
Specific comments
Line 59-60: “However, current snow data assimilation techniques in NWP remain limited, restricted to using in situ measurements” – this statement is overly restrictive as current snow DA approaches in NWP are not limited to use of in situ observations. Several operational centres also assimilate satellite-derived snow cover products, including the Met Office, ECMWF, NOAA, and potentially others. Please revise this statement and provide a more balanced overview of current operational practice.
Line 82: The manuscript would benefit from a brief explanation of why ground-based observations of snow depth need to be integrated with satellite retrievals in satellite-based snow water equivalent (SWE) products. A short addition would be useful highlighting the limitations of passive microwave retrievals alone, with respect to snowpack sensitivity and retrieval uncertainty under certain conditions, such as wet snow and forest cover.
Line 93: The mention of H43 alongside H31 and H34 is a little unclear. H43 is based on FCI observations from MTG, whereas the current wording could be interpreted as implying that it originates from MSG. Please clarify. In addition, it would be useful to comment on whether the higher spatial resolution of H43 (compared to its MSG-based predecessors) is actually sufficient for many NWP applications in northern Europe.
Line 154: “Additionally, snow cover enhances the surface emissivity.” Please specify the relevant wavelength range or part of the electromagnetic spectrum when discussing changes in emissivity. This statement is currently too general. For example, snow tends to increase emissivity in the thermal infrared, reduce emissivity in the visible spectrum, and can have varying effects in the microwave depending on frequency.
Line 179-180: Please clarify the meaning of “if more than 20% of pixels are classified as either ‘snow’ or ‘no snow’”. Does this mean that the combined total of snow and no-snow classifications must exceed 20% of pixels, or that a single category must individually exceed the 20% threshold (i.e. >20% snow or >20% no-snow)?
Line 183-184: The statement “They are only applied where they conflict with the model's background field...” does not appear to be fully consistent with the decision tree shown in Figure 3. If I have understood the flowchart correctly, observations indicating snow can be accepted when the background also contains snow, provided the background SWE is below 25 kg m⁻². Please clarify or rephrase.
Line 195 and Table 2: It is unclear whether these climatological snow-density values derived from a regional or global climatology? I also wonder whether use of the model background snow density might provide a more physically consistent approach, since it would be location-specific, meteorologically dependent, and evolve with the modelled snowpack state. I note that the manuscript discusses this as possible future work, but some additional justification of the current approach would be helpful.
Line 199-216 and Figure 3: The rationale for several of the observation selection criteria described in lines 199-216 (and illustrated in Figure 3) is not sufficiently convincing to me, and I would appreciate further justification for the chosen thresholds:
Line 228: As noted above, I am not fully convinced that restricting the allowable background SWE range maximises the beneficial value of the observations in the melting zone. It seems possible that potentially informative observations are being excluded in situations where they could provide useful corrections to the model state.
Line 239: The statement “The magnitude of the difference….is approximately 30 kg m⁻²” requires clarification. Does this refer to the cumulative SWE difference across the entire domain, including both positive and negative increments, or some other measure?
Line 274-275: “A key strength of the snow barrels is…” This appears to be a general advantage of satellite snow-cover observations rather than a feature unique to the Snow Barrel methodology. The same benefit would presumably be obtained from the underlying H32 product. The wording should be revised accordingly.
Line 338-339: The specified observation errors appear surprisingly small. Given that the observations provide information primarily on snow presence or absence rather than snow quantity, I would have expected the observation error to be substantially larger relative to the background error. Additional justification of the chosen error values, or evidence supporting their calibration, would be helpful.
-----------------------------------------------
Technical corrections
Line 129: “developed in frames of ACCORD” should be revised to “developed in the framework of ACCORD”.
Line 241-243: The feature being discussed is difficult to identify in Figure 4. Please consider providing a zoomed panel or enlarged view of the relevant region.
Figure 7: Please improve the distinction between RMSE and bias curves. Different colours or line styles, or annotation of the lines, would make the figure easier to interpret.