Winter Precipitation Measurements in New England: Results from the Global Precipitation Measurement Ground Validation Campaign in Connecticut

Filipiak, Brian C.; Wolff, David B.; Spaulding, Aaron; Tokay, Ali; Helms, Charles N.; Loftus, Adrian M.; Chibisov, Alexey V.; Schirtzinger, Carl; Boulanger, Mick J.; Pabla, Charanjit S.; Bliven, Larry; Kim, Eun Yeol; Junyent, Francesc; Chandrasekar, V.; Thant, Hein; Notaros, Branislav M.; Britto Hupsel de Azevedo, Gustavo; Cerrai, Diego

doi:https://doi.org/10.5194/essd-2025-162

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https://doi.org/10.5194/essd-2025-162

Preprints

03 Jun 2025

| 03 Jun 2025

Status: this preprint is currently under review for the journal ESSD.

Winter Precipitation Measurements in New England: Results from the Global Precipitation Measurement Ground Validation Campaign in Connecticut

Brian C. Filipiak, David B. Wolff, Aaron Spaulding, Ali Tokay, Charles N. Helms, Adrian M. Loftus, Alexey V. Chibisov, Carl Schirtzinger, Mick J. Boulanger, Charanjit S. Pabla, Larry Bliven, Eun Yeol Kim, Francesc Junyent, V. Chandrasekar, Hein Thant, Branislav M. Notaros, Gustavo Britto Hupsel de Azevedo, and Diego Cerrai

Abstract. Winter precipitation forecasts of phase and amount are challenging, especially in Northeast United States where mixed precipitation events from various synoptic systems frequently occur. Yet, there are not enough quality observations of winter precipitation, particularly microphysical properties from falling snow or mixed phase precipitation. During the winters of 2021–2022, 2022–2023, and 2023–2024, the NASA Global Precipitation Measurement (GPM) Ground Validation (GV) program conducted a field campaign at the University of Connecticut (UConn). The goal of this campaign was to observe various phases of winter precipitation and winter storm types to validate the GPM satellite precipitation products. Over the three winters at UConn, a total of 40 instruments were deployed across two observing sites that captured 117 precipitation events, including 19 phase transition events as indicated by the PARSIVEL². These instruments included scanning and vertically pointing radars, along with suites of in-situ sensors. In addition, an unmanned aircraft system has been deployed in 2023–2024. Here, an overview of the different field deployments, instrumentation, and the datasets collected are presented. To showcase the observations, this article features a wide-ranging set of measurements collected from the instrument suite for the 28 February 2023 storm, during which six to eight inches of snow accumulated at the two different observing sites. Also included is a discussion on how these observations can be combined with other datasets to validate ground-based and remote sensing measurements and highlight important atmospheric processes that impact winter precipitation phase and amount.

How to cite. Filipiak, B. C., Wolff, D. B., Spaulding, A., Tokay, A., Helms, C. N., Loftus, A. M., Chibisov, A. V., Schirtzinger, C., Boulanger, M. J., Pabla, C. S., Bliven, L., Kim, E. Y., Junyent, F., Chandrasekar, V., Thant, H., Notaros, B. M., Britto Hupsel de Azevedo, G., and Cerrai, D.: Winter Precipitation Measurements in New England: Results from the Global Precipitation Measurement Ground Validation Campaign in Connecticut, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2025-162, in review, 2025.

Received: 20 Mar 2025 – Discussion started: 03 Jun 2025

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RC1:
'Comment on essd-2025-162', Anonymous Referee #1, 02 Jul 2025
Winter Precipitation Measurements in New England: Results from the Global Precipitation Measurement Ground Validation Campaign in Connecticut
essd-2025-162

Overall Comments:
The paper by Filipiak et al., provides a detailed overview of winter precipitation instruments and derived data products from the multi-year observation sites at the University of Connecticut (UConn). Spanning 3 years, there were 117 precipitation events observed across a collection of 40 instruments, providing a detailed suite of surface and atmospheric variables for tracking the evolving state of the falling particles and meteorological conditions across multiple seasons. The multiple instrument redundancies, data QA, and extensive observational sample results in a high quality dataset that can enhance spaceborne retrievals and model parameterizations in future studies. I find many papers often forget to focus on the importance of good, robust datasets, and am therefore excited to see more data papers like this for solid and mixed-phase precipitation being released. I feel that after the authors address a few minor comments and questions below this paper will be in an acceptable state for publication in ESSD, and will be of great interest to its general readership.

General Comments:

Application Comparison: I really liked the final sections of the paper demonstrating the consistency across multiple instruments (e.g., Section 4, Figs. 16 and 17), and how this combination of observations provides a level of certainty in individual product accuracy, along with a more holistic view of the meteorologic conditions at the time of observation. However, as is touted early on in the paper and in the conclusions section, one of the major motivators for this work is to help enhance the GPM algorithm for mixed-phase and solid precipitation. I feel it would be of interest to the reader to also include a very brief comparison between these surface measurements/upward pointing radar in this section to DPR measurements from nearby GPM overpasses (e.g., 20230228-S065400-E082628 in the morning and 20230228-S221849-E235117 in the afternoon). This would go a long way in demonstrating the connection between surface and spaceborne and help motivate the applications of this work even more clearly to the reader if GPM was able to provide some spaceborne insights into the same storm.

Figure Improvements: While I feel that many of the photos early on were excellent, for a paper that is visually comparing the results of multiple instruments, I think some of the other figures could be improved for visual clarity. For instance:
Figure 1: Increasing the size of the site markers and labels would make this easier to read (also missing space after ‘Elevation’ and ‘Distance’ in panel b axis labels).

Figure 14: The font is quite small on both panels here making it challenging to read. I would recommend increasing this by quite a bit and making the plot lines thicker.

Figure 15: I like this figure, but I would make panel (b) use the same font size as the others (larger). Additionally, it might be easier to compare differences in the panel (a) colorbar if a perceptually uniform cmap is used instead of jet.

Figure 16: This is a great figure that shows quite a bit of relevant information, however the labels are too small to read and have different font sizes. I would recommend getting everything to the same scale and larger in general. For subpanel (j) I would also use a non-discrete perceptually uniform cmap and in panel (k) I’d recommend using bwr centered at zero. For (n) and (o), I would make the pie chart labels white so they are more visible. Out of curiosity, in panel (g) the snow rate is ~10 times that of the non-rain precipitation rate reported in panel (h), is this correct?

Figure 17: Similar to my comments for Figure 16, but also I would add the variable and units to the colorbar for panels (h-m) instead of squeezing everything into the title.

Specific Comments:
Lines 83-85: While GPM is certainly better equipped to capture lighter precipitation and falling snow to TRMM, the way this is worded doesn’t make it clear that GPM isn’t great at snowfall and really underestimates light snowfall in particular (e.g., Casella et al., 2017). I would maybe rework this sentence to make this clearer to the reader.

Lines 90-91: Keep an eye out for the S2noCLIME (Snow Sensitivity to Clouds in a Mountain Environment) data in the coming months which uses many of these same instruments.

Lines 162-164: You may want to mention that the two near surface bins from the MRR-2 are typically not used due to surface clutter issues (unless this is addressed here in some form?).

Line 189: It might be nice to include some general statistics around undercatch even with shields as described in Smith 2007, or Pierre et al., 2019, since I am assuming no additional transfer functions were used?

Lines 219-220: It is great to see the multiple redundant instruments being used for additional quality control!

Lines 288-289: What exactly is meant by “artificial intelligence and neural networks” here? I mean technically NNs are a type of machine learning which is itself a subset of AI (AI gets thrown along as a buzz word quite a bit). I would rephrase this and add a bit more detail.

Line 313: This is neat, are there any more details you could provide about the mmWave radar? How sensitive is it and what is its resolution?

Line 381: This introductory sentence is a bit awkward, you may want to rework it to flow better.

Lines 445-446: Do you know what causes this temperature measurement issue?

Figure 15.a: I find it a bit challenging to interpret this panel with the inner and outer colored scatter. What are the key takeaways from this as we move into a cloudy regime and the PSD widens?

Section 4: It wasn’t clear to me at first that this was a new Section, I would rework the subsection title here to make that more obvious.

Line 572: Should this be “first half” and not “second half”?

Section 5: As a data paper, it would be nice if you could also include a few details/statistics about the dataset itself here (e.g., the data format, layout, size, CF-conventions used for metadata). This doesn’t need to be super comprehensive, but might help orient the reader as to how they can interact with the products you’ve put together.

Conclusions Section: I appreciated that you have some space here aimed towards applications. Sometimes by the end of these data papers you are left wondering, alright, what can we actually use this for? I wonder if including a few additional references for followup applications using similar datasets in previous literature might help provide more concrete ideas for readers to follow from here? Some recent papers that came to mind for this include works like: King et al., 2024 for Lines 635-636, and Billault-Roux et al., 2023 and Shates et al., 2025 for lines 637-638. However, I leave this choice up to the authors.

Final Comment: It would also be nice if you could link all the disparate instruments and products together in a single location/repository. It is a pain to have to cycle between different platforms and logins to download things (this also makes it more challenging to find relevant data).

References
Billault-Roux, A.-C., Grazioli, J., Delanoë, J., Jorquera, S., Pauwels, N., Viltard, N., Martini, A., Mariage, V., Gac, C. L., Caudoux, C., Aubry, C., Bertrand, F., Schwarzenboeck, A., Jaffeux, L., Coutris, P., Febvre, G., Pichon, J. M., Dezitter, F., Gehring, J., … Berne, A. (2023). ICE GENESIS: Synergetic Aircraft and Ground-Based Remote Sensing and In Situ Measurements of Snowfall Microphysical Properties. Bulletin of the American Meteorological Society, 104(2), E367–E388. https://doi.org/10.1175/BAMS-D-21-0184.1
Casella, D., Panegrossi, G., Sanò, P., Marra, A. C., Dietrich, S., Johnson, B. T., & Kulie, M. S. (2017). Evaluation of the GPM-DPR snowfall detection capability: Comparison with CloudSat-CPR. Atmospheric Research, 197, 64–75. https://doi.org/10.1016/j.atmosres.2017.06.018
King, F., Pettersen, C., Dolan, B., Shates, J., & Posselt, D. (2024). Primary Modes of Northern Hemisphere Snowfall Particle Size Distributions. Journal of the Atmospheric Sciences, 81(12), 2093–2113. https://doi.org/10.1175/JAS-D-24-0076.1
Pierre, A., Jutras, S., Smith, C., Kochendorfer, J., Fortin, V., & Anctil, F. (2019). Evaluation of Catch Efficiency Transfer Functions for Unshielded and Single-Alter-Shielded Solid Precipitation Measurements. Journal of Atmospheric and Oceanic Technology, 36(5), 865–881. https://doi.org/10.1175/JTECH-D-18-0112.1
Shates, J. A., Pettersen, C., L’Ecuyer, T. S., & Kulie, M. S. (2025). KAZR-CloudSat Analysis of Snowing Profiles at the North Slope of Alaska: Implications of the Satellite Radar Blind Zone. Journal of Geophysical Research: Atmospheres, 130(6), e2024JD042700. https://doi.org/10.1029/2024JD042700
Smith, C. D. 2007. “Correcting the Wind Bias in Snowfall Measurements Made with the Geonor T-200B Precipitation Gauge and Alter Wind Shield.” Proceedings 14th Symposium on Observations and Instrumentation, American Meteorological Society (AMS) Annual Meeting, San Antonio, Texas.
Citation: https://doi.org/10.5194/essd-2025-162-RC1
- AC1: 'Reply on RC1', Brian Filipiak, 18 Aug 2025
  
  Thank you very much for taking the time to provide comments and feedback on our manuscript and dataset. Please see our point-by-point response in the attached file.
  
  Citation: https://doi.org/10.5194/essd-2025-162-AC1
RC2:
'Comment on essd-2025-162', Anonymous Referee #2, 31 Jul 2025

Review of “Winter Precipitation Measurements in New England: Results from the Global
Precipitation Measurement Ground Validation Campaign in Connecticut”
General comments:
This submission from B. Filipiak, D. B. Wolff, A. Spaulding, et al. summarizes a field campaign over 3 winter seasons at the University of Connecticut. This project took place from 2021-2024 and deployed several instruments to two sites. This campaign is motivated by the need for validation of remotely-sensed measurements as part of the Global Precipitation Measurement Ground Validation program. The campaign collected data during 117 distinct precipitation events over the 3 winter seasons and this manuscript illustrates how a Nor’easter on February 28, 2023 can be analyzed using this large dataset. Explanations of decision-making for instrument location and caveats with the data quality are made clear.

All DOIs in the Data Availability statement lead to associated links on Earthdata with clear user guides. However, I’m unable to download any datasets which seems likely to be an issue with my login and/or the website and not within the authors’ control. I’ve contacted Earthdata but haven’t heard back after several days, so I can’t offer a review of the dataset quality and thus rate it "fair."
Overall, the manuscript and field campaign are scientifically interesting and novel. I recommend this manuscript for acceptance to ESS-D with minor revisions, so long as the editor and other reviewers are able to access the datasets.
Specific comments:
The text in multiple figures (Figures 14 onward) could be enlarged.
L222: “first two size bin” is this wording correct? Maybe I’m misunderstanding what’s being stated here.
Figure 7: misspelling in caption, “ACHIVE”
Table 1: In caption, “the superscript D in the 2022-2023 column indicates the instrument was only at the GAIL site” should be the D3R site.
L583: For consistency, use “Between 0-9 UTC” instead of “Z”
L 589: Delete “)” at the end of this line

Citation: https://doi.org/10.5194/essd-2025-162-RC2
- AC2: 'Reply on RC2', Brian Filipiak, 18 Aug 2025
  
  Thank you very much for taking the time to provide comments on our manuscript and dataset. Please see our point-by-point response in the attached file.
  
  Citation: https://doi.org/10.5194/essd-2025-162-AC2

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Short summary

A GPM Ground Validation field campaign in Connecticut collected high-resolution microphysical and radar observations of winter precipitation. This field campaign was unique because there was a wide-ranging suite of instruments capable of observing all phases of precipitation co-located with comparable measurements. The observations provide an opportunity to verify and understand complex winter precipitation events through satellite data, microphysical processes, and numerical model simulations.


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