the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Apr 2025
Hourly precipitation fields at 1 km resolution over Belgium from 1940 to 2016 based on the analog technique
Abstract. High-resolution gridded precipitation data is scarce, especially at time intervals shorter than daily. However hydrological applications for example benefit from a finer temporal resolution of rainfall information. In this context, we introduce an hourly precipitation dataset for Belgium, featuring a resolution of 1 km. An hourly high-resolution gridded precipitation product over Belgium can provide valuable insights into the dynamics of both short-term and long-term rainfall events, which can be used for wide-ranging applications.
A high resolution precipitation grid of hourly precipitation data for Belgium covering the period from 1940 to 2016 using the analog technique, is created. The analogs are sampled from the period 2017–2022 for which high resolution radar data precipitation fields are available. The initial step involves identifying the criteria, i.e. atmospheric parameters such as atmospheric pressure, temperature and humidity, that can be used to determine analogous days. These atmospheric parameters are obtained from the ERA5 observational data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). In a second step, hourly precipitation data for suitable analog days are extracted from the radar database, and then used to create the high resolution grid of hourly precipitation for Belgium from 1940 to 2016. Data from rain gauges on the terrain were used for validation of the candidate precipitation analogs.
The dataset compiled for this project provides a top 25 analog days for 1940–2016 based on similarities in weather patterns. The analogs are ranked based on how closely they match to their target day.
The database is relying on the Zarr archiving format and is composed of two archives. A first archive contains all target days together with the 25 best analogs. The second one provides a precipitation field for each hour of every day in the past, representing the hourly median of the analog ensemble. The Zarr format of the database allows slicing through the database. For example, it allows one to easily delimit a specific area of interest and a specific time frame for which the high resolution gridded median hourly precipitation fields are needed. The median field dataset is available on Zenodo (https://doi.org/10.5281/zenodo.14965710) (Debrie et al., 2025).
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Status: final response (author comments only)
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RC1: 'Comment on essd-2025-149', Anonymous Referee #1, 07 Jun 2025
- AC1: 'Reply on RC1', Elke Debrie, 13 Aug 2025
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RC2: 'Comment on essd-2025-149', Anonymous Referee #2, 06 Jul 2025
Review of manuscript “Hourly precipitation fields at 1 km resolution over Belgium from 1940 to 2016 based on the analog technique”
General comments
This manuscript presents a long-term precipitation data set for Belgium based on the analog method sampling from a radar data set of six years. A precipitation data set with an hourly temporal and 1 km spatial resolution for a time period of 77 years is impressive. In general, such a data set has potential and fits the scope of ESSD well. However, I agree with the concerns of the referee #1 that the sampling from a five-year radar data set is too short and not well representative. Additionally, the description methodology could be improved and the description of the archive data set is sparse.
- I agree with the concerns of the referee #1. The sampling from a six-year radar data set, which is at the end of the period 1940 to 2016 seems to be too short and misrepresenting taking the climate change into account. Please discuss this in your paper.
- Why was 2006 chosen for verification, and how do other years compare? Are the hourly precipitation fields representative or only the daily sums (as used in the verification)?
- Please describe the methodology (in section 3) more precisely. Regarding the predictors: Why were these time steps selected? Why were these predictors selected? TEMP12 (one predictor), PRES012 (two predictors), RELHUM (three predictors at the same heights), TWS3 (three predictors at different heights), and TWS4 (four predictors) were compared with each other, and it was concluded that geopotential height provides the best results. It seems that the selection is very centered on the geopotential height right from the start.
- It is not clear to me how many analogs (10 or 25) were used to compute the median for the published data set.
- The data set can easily be read in with xarray, and I appreciate the sample code. However, the dataset, which must be downloaded and unpacked, is quite large at approx. 40 GB in zip format. Is there a reason zarr files with shorter time spans (e.g., one or ten years) were not created?
- An example precipitation field from the published data set would be good to include in the paper to give the reader an insight of the quality/variability of the precipitation field.
Specific comments
- Section 1 Discussions and references to existing rainfall climatologies, such as EURADCLIM (https://doi.org/10.5194/essd-15-1441-2023) and RADKLIM (10.5676/DWD/RADKLIM_RW_V2017.002), are missing and would provide additional context for this data set.
- Section 2.1.2 Please provide more information about the radar data set. The link to the Radclim user guide is insufficient. In a few years, this link may no longer be accessible, leaving the paper lacking further information on the radar data.
- Section 2.1.3 Although the title indicates that this is daily rain gauge data, these data are not described in section 2.1.3. Are only 2006 and 2017 to 2022 really available for verification?
- Section 3.1 Do I understand correctly that during the validation of the data set, a point measurement was compared with the average of the four nearest grid points of the radar data set? Please describe more clearly.
- Section 3.2 Why was the evaluation made for the location Uccle? What are the results for other locations?
- Section 4: The description of the actual data set is in general very short, the structure of the published data set could be described a little more. For example, I was not aware at the beginning that the first zarr data set only contains timestamps of the 25 analog days.
- L4-5 “wide-ranging” applications is very general, please give specific examples here.
- L90-92 This sentence is part of the methodology and not data and may be confusing at first reading.
- L199-200 Why were these threshold values chosen?
- L213 How many precipitation events exceeded 15 mm/24 hours in 2016?
- L245ff Figure 14 and the two locations without monitoring points do not provide much additional information. I suggest removing Figure 14 and this sentence.
- Figure 15b Is the hourly precipitation data from a radar or a rain gauge? Section 2 only introduced daily rain gauge data.
- L269-270 Please describe/justify this statement
Technical comments
- Figure 1 There is no reference to this figure within the text.
- Figure 2 Please add the x and y axes to the left figure.
- Figure 3 Please add units to the y-axis.
- Figure 15 Please add the unit to the x-axis.
- Line 201 The reference Wil (2019) is missing in the reference list.
Citation: https://doi.org/10.5194/essd-2025-149-RC2 - AC2: 'Reply on RC2', Elke Debrie, 13 Aug 2025
Data sets
RADCLIM-Analogs: High-Resolution Gridded Hourly Median Precipitation dataset for Belgium (1940-2016) Using the Analogue Technique Elke Debrie et al. https://doi.org/10.5281/zenodo.14965711
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