High-resolution atmospheric data cubes from the WegenerNet 3D Open-Air Laboratory for Climate Change Research

Kvas, Andreas; Kirchengast, Gottfried; Fuchsberger, Jürgen

doi:10.5194/essd-2025-176

Preprints

https://doi.org/10.5194/essd-2025-176

Preprints

12 Jun 2025

| 12 Jun 2025

Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

High-resolution atmospheric data cubes from the WegenerNet 3D Open-Air Laboratory for Climate Change Research

Andreas Kvas, Gottfried Kirchengast, and Jürgen Fuchsberger

Abstract. This paper describes the first release of Level 1b and Level 2 high-resolution atmospheric data cubes generated in the WegenerNet 3D Open-Air Laboratory for Climate Change Research Feldbach Region (WEGN3D Open-Air Lab). These datasets, based on the continuous WegenerNet 3D observations form a growing multi-year observational data collection at sub-kilometer scale and sub-hourly resolution, capable to support the study of weather extremes in a changing climate, water vapor – cloud – precipitation interactions, and interactions between the surface and the free atmosphere, among other uses.

The data are not assimilated into reanalyses or numerical weather prediction models. Consequently, they can also serve as an independent dataset for evaluation and validation of such models, as well as of climate-oriented modeling, at high spatial and temporal resolution. The instrumentation behind the WEGN3D Open-Air Lab atmospheric data cubes consists of an X-band dual-polarization precipitation radar, a combined microwave/infrared tropospheric sounding radiometer, an infrared cloud structure radiometer, and a six-station water vapor sounding Global Navigation Satellite Systems (GNSS) network with baselines of 5 km to 10 km. These sensors form the WegenerNet 3D Observing System and complement the existing WegenerNet climate station network in the Feldbach region. The site is situated in the Alpine forelands of southeastern Austria and covers an area of approximately 1400 km², with radar volume scans reaching up to an altitude of about 6 km and tropospheric profiles up to an altitude of 10 km. Precipitation radar measurements started in mid-2020, with the current sensor configuration being operational since mid-2021. The dataset will be continuously extended in near real time with the goal of providing a consistent, high-resolution long-term data record for atmospheric and climate sciences.

The temporal resolution of the datasets ranges from 2.5 min for precipitation radar and GNSS-derived datasets to 10 min for radiometer-derived datasets. Precipitation and cloud data cubes are provided on a 200 m by 200 m Cartesian grid, with height level resolution ranging from 20 m near the surface, to 200 m at 10 km altitude. These height levels adequately cover the sensor resolution of the observed tropospheric profiles.

The Level 1b dataset (Kvas et al., 2024a, DOI: https://doi.org/10.25364/WEGC/WPS3D-L1B-10) and the Level 2 dataset (Kvas et al., 2024b, DOI: https://doi.org/10.25364/WEGC/WPS3D-L2-10) are published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license on the WegenerNet Data Portal (https://wegenernet.org/portal/3ddownload/, last accessed 2025-06-10) and are described with standardized metadata formats. The data portal offers users several convenient options for exploring and downloading the individual datasets. These include visualization tools for selected data variables, web interfaces for manual subsetting of datasets, and application programming interfaces (APIs) for automated or scripted downloads.

Received: 28 Mar 2025 – Discussion started: 12 Jun 2025

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Andreas Kvas, Gottfried Kirchengast, and Jürgen Fuchsberger

Status: closed

RC1:
'Comment on essd-2025-176', Francesco Marra, 08 Jul 2025
This manuscript presents the level 1b and level 2 release of the high-resolution atmospheric data cubes generated in the WegenerNet 3D Open-Air Laboratory for Climate Change Research Feldbach Region (WEGN3D Open-Air Lab.
This represents a new important component of the Wegenernet monitoring network. The network monitors a region around Feldbach (Austria) since 2007 with a wide array of atmospheric sensors, and its products are kept independent from the calibration of atmospheric models and other sensors (e.g., satellite precipitation estimates). This makes the network almost unique in the world and extremely useful for a range of applications. The data presented in the manuscript complements this effort with new sensors and monitored variables, for a period since 2021.
I accessed the Wegenernet portal and downloaded some of the described data with no issues. I could acres the data and use it easily. The portal also allows for “bulk download”, which is extremely helpful for research.
I commend the authors for their work, both the hard work on installing and maintaining such a network and for the quality of the submitted manuscript.
To my evaluation, the manuscript is ready for publication as is, aside from two points:
Lines 229-233: “where reasonable” and “too large to be sensibly filled”: these sound a bit generic. Does this mean that each situation is handled ad hoc? It is based on expert opinion? Perhaps some more details would be needed

(Very minor) The second sentence in the abstract (lines 2-5) needs some attention, as it seems a verb is missing

I believe the community should be thankful to the authors for providing such a service and making these data freely accessible to everyone.
Citation: https://doi.org/10.5194/essd-2025-176-RC1
- AC1: 'Reply on RC1', Andreas Kvas, 12 Dec 2025
  
  Dear Francesco Marra,
  thank you for your time and for the constructive review of our manuscript. We amended the manuscript with the missing information, following your comments. Please find our specific responses below.
  
  RC: Lines 229-233: “where reasonable” and “too large to be sensibly filled”: these sound a bit generic. Does this mean that each situation is handled ad hoc? It is based on expert opinion? Perhaps some more details would be needed
  AC: We added the maximum data gap length in hours and rephrased the sentence to “If resulting gaps exceed 24 h, values are replaced by either NaN or an appropriate integer fill value.”
  RC: (Very minor) The second sentence in the abstract (lines 2-5) needs some attention, as it seems a verb is missing
  AC: We split the sentence to make it more readable. It now reads: “These datasets, based on the continuous WegenerNet 3D observations form a growing multi-year observational data collection at sub-kilometer scale and sub-hourly resolution. They are capable to support the study of weather extremes in a changing climate, water vapor – cloud – precipitation interactions and interactions between the surface and the free atmosphere, among other uses.”
  Kind regards,
  
  Andreas Kvas (on behalf of the authors)
  
  Citation: https://doi.org/10.5194/essd-2025-176-AC1
RC2:
'Comment on essd-2025-176', Anonymous Referee #2, 11 Nov 2025

Synthesis
This manuscript presents a long-term dataset of ground-based remote sensing observations of clouds, precipitation and the thermodynamic structure of the lower troposphere. In combination with a unique dense surface based observational network, this dataset is very useful for many studies related to cloud and precipitation processes, as well as for model evaluation or satellite validation purposes.
General comments
The manuscript presents a good overview of the dataset, and gives some examples of uncertainty evaluation. The manuscript is well written and provides all necessary information about the dataset.
I recommend that the manuscript should be published, after some minor comments were have been taken into account (see below).
Specific comments
Figure 1: It would be good to show geographical coordinates (lon/lat) on the x- and y axis
Section 2, TSR radiometer: What dataset are the retrievals for IWV and LWP based on? Can you add some information on this? Could a biased retrieval training dataset potentially explain the offsets in Fig. 5?
Figure 5: I do not have a good explanation for this trend in IWV offset neither, but I find it a bit puzzling. As none of these observations are assimilated into NWP models: Did you check a comparison to model IWV (e.g. from ERA5) to see whether such a trend can be also seen there?
Figure 9: Why do you have large interpolated time periods for LWP after about 14:00? Were there no clouds? Or was the instrument not measuring?

Citation: https://doi.org/10.5194/essd-2025-176-RC2
- AC2: 'Reply on RC2', Andreas Kvas, 12 Dec 2025
  
  Dear Anonymous Referee #2,
  thank you for taking the time to review our manuscript and for your helpful and constructive comments. Please find our responses to the specific comments below.
  
  RC: Figure 1: It would be good to show geographical coordinates (lon/lat) on the x- and y axis.
  AC: We added lon/lat labels following your suggestion.
  RC: Section 2, TSR radiometer: What dataset are the retrievals for IWV and LWP based on? Can you add some information on this? Could a biased retrieval training dataset potentially explain the offsets in Fig. 5?
  AC: The IWV and LWP retrievals are based on neural networks which are trained on reanalysis data. The numerical values of the network coefficients, input/output scaling functions are provided by the manufacturer and are tuned for the instrument location. The theory behind this approach is given in (Jung et al., 1998, Del Frate et al, 1999; Solheim et al, 1999; Churnside et al; 1994) and the implementation details are presented in the instrument software manual (https://www.radiometer-physics.de/download/PDF/Radiometers/HATPRO/RPG_MWR_STD_Software_Manual%20G5_2023.pdf).
  We amended the paragraph describing the TSR in section 2 with the following sentence and corresponding references to incorporate this information:
  
  “These statistical retrievals are implemented using feed-forward neural networks (e.g., Jung et al. 1998) trained on reanalysis data for the specific sensor location. The numerical values of the network coefficients, offset, and scaling factors are provided by the instrument manufacturer.”
  Churnside, J. H., T. A. Stermitz, and J. A. Schroeder, Temperature profiling with neural network inversion of microwave radiometer data, J. Atmos. Oceanic Technol.., 11, 105-109, 1994
  Jung, T., E. Ruprecht, and F. Wagner, Determination of cloud liquid water path over the oceans from SSM/I data using neural networks, Journal of Applied Meteorology, 37, 832844, 1997
  Del Frate, and F., G. Schiavon, A combined natural orthogonal functions/neural network technique for the radiometric estimation of atmospheric profiles, Radio Science, 33, 405410, 1998
  Solheim, F., and J.Godwin, Passive ground-based remote sensing of atmospheric temperature, water vapor, and cloud liquid water profiles by a frequency synthesized microwave radiometer, Meteorol. Zeitschrift, N.F.7, 370-376, 1998
  RC: Figure 5: I do not have a good explanation for this trend in IWV offset neither, but I find it a bit puzzling. As none of these observations are assimilated into NWP models: Did you check a comparison to model IWV (e.g. from ERA5) to see whether such a trend can be also seen there?
  Thank you for this suggestion. We computed the IWV trend from ERA5 for the center of the network. The reanalysis shows a trend similar to that of the radiometer (0.69 +- 0.06) kg / m2 for the same time period with instrument data gaps considered. This means that all measurement systems and ERA5 show a IWV increase for the region, with slightly lower values from GNSS.
  RC: Figure 9: Why do you have large interpolated time periods for LWP after about 14:00? Were there no clouds? Or was the instrument not measuring?
  AC: There was indeed a data gap following the precipitation event.
  
  Kind regards,
  
  Andreas Kvas (on behalf of the authors)
  
  Citation: https://doi.org/10.5194/essd-2025-176-AC2

Status: closed

RC1:
'Comment on essd-2025-176', Francesco Marra, 08 Jul 2025
This manuscript presents the level 1b and level 2 release of the high-resolution atmospheric data cubes generated in the WegenerNet 3D Open-Air Laboratory for Climate Change Research Feldbach Region (WEGN3D Open-Air Lab.
This represents a new important component of the Wegenernet monitoring network. The network monitors a region around Feldbach (Austria) since 2007 with a wide array of atmospheric sensors, and its products are kept independent from the calibration of atmospheric models and other sensors (e.g., satellite precipitation estimates). This makes the network almost unique in the world and extremely useful for a range of applications. The data presented in the manuscript complements this effort with new sensors and monitored variables, for a period since 2021.
I accessed the Wegenernet portal and downloaded some of the described data with no issues. I could acres the data and use it easily. The portal also allows for “bulk download”, which is extremely helpful for research.
I commend the authors for their work, both the hard work on installing and maintaining such a network and for the quality of the submitted manuscript.
To my evaluation, the manuscript is ready for publication as is, aside from two points:
Lines 229-233: “where reasonable” and “too large to be sensibly filled”: these sound a bit generic. Does this mean that each situation is handled ad hoc? It is based on expert opinion? Perhaps some more details would be needed

(Very minor) The second sentence in the abstract (lines 2-5) needs some attention, as it seems a verb is missing

I believe the community should be thankful to the authors for providing such a service and making these data freely accessible to everyone.
Citation: https://doi.org/10.5194/essd-2025-176-RC1
- AC1: 'Reply on RC1', Andreas Kvas, 12 Dec 2025
  
  Dear Francesco Marra,
  thank you for your time and for the constructive review of our manuscript. We amended the manuscript with the missing information, following your comments. Please find our specific responses below.
  
  RC: Lines 229-233: “where reasonable” and “too large to be sensibly filled”: these sound a bit generic. Does this mean that each situation is handled ad hoc? It is based on expert opinion? Perhaps some more details would be needed
  AC: We added the maximum data gap length in hours and rephrased the sentence to “If resulting gaps exceed 24 h, values are replaced by either NaN or an appropriate integer fill value.”
  RC: (Very minor) The second sentence in the abstract (lines 2-5) needs some attention, as it seems a verb is missing
  AC: We split the sentence to make it more readable. It now reads: “These datasets, based on the continuous WegenerNet 3D observations form a growing multi-year observational data collection at sub-kilometer scale and sub-hourly resolution. They are capable to support the study of weather extremes in a changing climate, water vapor – cloud – precipitation interactions and interactions between the surface and the free atmosphere, among other uses.”
  Kind regards,
  
  Andreas Kvas (on behalf of the authors)
  
  Citation: https://doi.org/10.5194/essd-2025-176-AC1
RC2:
'Comment on essd-2025-176', Anonymous Referee #2, 11 Nov 2025

Synthesis
This manuscript presents a long-term dataset of ground-based remote sensing observations of clouds, precipitation and the thermodynamic structure of the lower troposphere. In combination with a unique dense surface based observational network, this dataset is very useful for many studies related to cloud and precipitation processes, as well as for model evaluation or satellite validation purposes.
General comments
The manuscript presents a good overview of the dataset, and gives some examples of uncertainty evaluation. The manuscript is well written and provides all necessary information about the dataset.
I recommend that the manuscript should be published, after some minor comments were have been taken into account (see below).
Specific comments
Figure 1: It would be good to show geographical coordinates (lon/lat) on the x- and y axis
Section 2, TSR radiometer: What dataset are the retrievals for IWV and LWP based on? Can you add some information on this? Could a biased retrieval training dataset potentially explain the offsets in Fig. 5?
Figure 5: I do not have a good explanation for this trend in IWV offset neither, but I find it a bit puzzling. As none of these observations are assimilated into NWP models: Did you check a comparison to model IWV (e.g. from ERA5) to see whether such a trend can be also seen there?
Figure 9: Why do you have large interpolated time periods for LWP after about 14:00? Were there no clouds? Or was the instrument not measuring?

Citation: https://doi.org/10.5194/essd-2025-176-RC2
- AC2: 'Reply on RC2', Andreas Kvas, 12 Dec 2025
  
  Dear Anonymous Referee #2,
  thank you for taking the time to review our manuscript and for your helpful and constructive comments. Please find our responses to the specific comments below.
  
  RC: Figure 1: It would be good to show geographical coordinates (lon/lat) on the x- and y axis.
  AC: We added lon/lat labels following your suggestion.
  RC: Section 2, TSR radiometer: What dataset are the retrievals for IWV and LWP based on? Can you add some information on this? Could a biased retrieval training dataset potentially explain the offsets in Fig. 5?
  AC: The IWV and LWP retrievals are based on neural networks which are trained on reanalysis data. The numerical values of the network coefficients, input/output scaling functions are provided by the manufacturer and are tuned for the instrument location. The theory behind this approach is given in (Jung et al., 1998, Del Frate et al, 1999; Solheim et al, 1999; Churnside et al; 1994) and the implementation details are presented in the instrument software manual (https://www.radiometer-physics.de/download/PDF/Radiometers/HATPRO/RPG_MWR_STD_Software_Manual%20G5_2023.pdf).
  We amended the paragraph describing the TSR in section 2 with the following sentence and corresponding references to incorporate this information:
  
  “These statistical retrievals are implemented using feed-forward neural networks (e.g., Jung et al. 1998) trained on reanalysis data for the specific sensor location. The numerical values of the network coefficients, offset, and scaling factors are provided by the instrument manufacturer.”
  Churnside, J. H., T. A. Stermitz, and J. A. Schroeder, Temperature profiling with neural network inversion of microwave radiometer data, J. Atmos. Oceanic Technol.., 11, 105-109, 1994
  Jung, T., E. Ruprecht, and F. Wagner, Determination of cloud liquid water path over the oceans from SSM/I data using neural networks, Journal of Applied Meteorology, 37, 832844, 1997
  Del Frate, and F., G. Schiavon, A combined natural orthogonal functions/neural network technique for the radiometric estimation of atmospheric profiles, Radio Science, 33, 405410, 1998
  Solheim, F., and J.Godwin, Passive ground-based remote sensing of atmospheric temperature, water vapor, and cloud liquid water profiles by a frequency synthesized microwave radiometer, Meteorol. Zeitschrift, N.F.7, 370-376, 1998
  RC: Figure 5: I do not have a good explanation for this trend in IWV offset neither, but I find it a bit puzzling. As none of these observations are assimilated into NWP models: Did you check a comparison to model IWV (e.g. from ERA5) to see whether such a trend can be also seen there?
  Thank you for this suggestion. We computed the IWV trend from ERA5 for the center of the network. The reanalysis shows a trend similar to that of the radiometer (0.69 +- 0.06) kg / m2 for the same time period with instrument data gaps considered. This means that all measurement systems and ERA5 show a IWV increase for the region, with slightly lower values from GNSS.
  RC: Figure 9: Why do you have large interpolated time periods for LWP after about 14:00? Were there no clouds? Or was the instrument not measuring?
  AC: There was indeed a data gap following the precipitation event.
  
  Kind regards,
  
  Andreas Kvas (on behalf of the authors)
  
  Citation: https://doi.org/10.5194/essd-2025-176-AC2

Andreas Kvas, Gottfried Kirchengast, and Jürgen Fuchsberger

Data sets

WegenerNet 3D Open-Air Laboratory Level 1b Data, Version 1.0 A. Kvas, J. Fuchsberger, G. Kirchengast, D. Scheidl https://wegenernet.org/portal/wps3d/L1b/v1.0/

WegenerNet 3D Open-Air Laboratory Level 2 Data, Version 1.0 A. Kvas, J. Fuchsberger, G. Kirchengast, D. Scheidl https://wegenernet.org/portal/wps3d/L2/v1.0/

Andreas Kvas, Gottfried Kirchengast, and Jürgen Fuchsberger

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Cumulative views and downloads (calculated since 12 Jun 2025)

Month	HTML	PDF	XML	Total
Jun 2025	97	13	6	116
Jul 2025	45	6	15	66
Aug 2025	163	6	3	172
Sep 2025	811	8	2	821
Oct 2025	54	12	2	68
Nov 2025	52	17	5	74
Dec 2025	49	26	11	86
Jan 2026	67	33	4	104
Feb 2026	9	5	2	16

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Short summary

The WegenerNet 3D Open-Air Laboratory for Climate Change Research in southeastern Austria observes the atmosphere from the surface up to an altitude of 10 kilometers. A variety of different sensors measure precipitation, water vapor content, humidity, temperature, and cloud properties in high spatial and temporal resolution. This enables detailed analyses of weather phenomena in a changing climate, such as heavy rainfall events and thunderstorms.


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