A neural network-based observation operator for weather radar data assimilation

Stefanelli, Marco; Zaplotnik, Žiga; Skok, Gregor

doi:10.48550/arXiv.2512.18289

Preprints

https://doi.org/10.48550/arXiv.2512.18289

Preprints

20 Jan 2026

| 20 Jan 2026

Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

A neural network-based observation operator for weather radar data assimilation

Marco Stefanelli, Žiga Zaplotnik, and Gregor Skok

Abstract. In three-dimensional variational data assimilation (3DVar) for numerical weather prediction (NWP), the observation operator H plays a central role by mapping model state variables to an observation equivalent. For weather radar, however, specifying H is particularly challenging: reflectivity is a nonlinear, microphysics-dependent diagnostic quantity that only indirectly relates to the model’s prognostic variables, making traditional parameterised radar operators complex, regime-dependent and difficult to tune.

In this study, we propose a neural-network (NN)-based observation operator for radar reflectivity and apply it within a 3DVar data assimilation (DA) framework. Using five years (2019–2023) of radar reflectivity data from the Lisca radar and 4.4 km-resolution short-range forecasts from ALADIN model over Slovenia, we train a convolutional encoder–decoder neural network to map model temperature, humidity, horizontal wind components and surface pressure fields to radar reflectivity. Across independent test cases spanning clear-sky, stratiform and convective regimes, the NN-based operator accurately reproduces the spatial structure and intensity of observed reflectivity, relying primarily on the model state in the vicinity of the observation point. In the extreme precipitation case, which caused widespread floods in Slovenia on August 4, 2023, assimilating the full radar disc reduces the domain-averaged reflectivity root-mean-square error (RMSE) from 5.99 dBZ to 3.47 dBZ and improves the alignment between the analysed and observed convective bands.

Embedded within 3DVar, the Jacobian of the NN observation operator allows radar reflectivity observations to inform model state variables, producing corresponding analysis increments. The proposed NN radar observation operator offers a flexible alternative to traditional parameterised radar operators for improving convective-storm forecasts.

Received: 06 Jan 2026 – Discussion started: 20 Jan 2026

Marco Stefanelli, Žiga Zaplotnik, and Gregor Skok

Status: open (until 17 Mar 2026)

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RC1:
'Comment on egusphere-2026-77', Anonymous Referee #1, 24 Feb 2026 reply
Review: A neural network-based observation operator for weather radar data assimilation by Stefanelli et al.
In this paper, the observation operator for radar data assimilation is replaced by a neural network that maps state variables such as temperature, wind, and relative humidity at different vertical levels with the reflectivity. The machine-learning-based observation operator is coupled with a 3DVar data assimilation system, and observation impact experiments are performed in order to provide a preliminary evaluation of this observation operator.
The topic is novel and is aligned with the current trend of merging data assimilation with machine learning. However, there are aspects of the obtained results that are not very clear and that should be discussed in more detail. There are also some decisions taken in the design of the experiments and the methodology that deserve further discussion. My recommendation is that the paper should undergo major revisions before being considered for publication in GMD.

Major comments:

The authors selected 4 vertical levels from different state variables to be linked with the reflectivity. These levels correspond to low levels (i.e., approximately below 2 km). What is the motivation for considering only low levels? For example, deep convection can be strongly linked with upper-level winds and perturbations in temperature and relative humidity.

The authors decided not to include hydrometeors as control variables, and because of this, these are not part of the observation operator. Since these are the variables more directly linked with radar reflectivity, some motivation should be provided. My impression is that choosing variables such as temperature and winds is an interesting approach, since this can better represent the links between radar reflectivity and the dynamic and thermodynamic fields (which are otherwise difficult to correct in a 3DVar framework). This can be an advantage of the selected approach and not a limitation, as the authors discuss at the end of the paper.

Since there are missing radar volumes (and maybe missing model outputs), it would be nice to provide more information about the actual sizes of the training, validation, and testing datasets before or after data augmentation.

Section 3.2. When the results in Figure 7 are described, there is no clear indication of the innovation that produced these analysis increments. This is said later in the discussion of Figure 8.

The authors should discuss in more detail the increments presented in Figure 6. There are some aspects that do not seem to be physically consistent. For example, the increment in V changes signs in vertical levels that are very close to each other. This also happens in r, where some levels show a dominant moistening effect while others (850 hPa) show a dominant drying effect. Also, the increment is not restricted or bounded to the circle, particularly for low-level temperature and relative humidity. In the wind field, it is difficult to see a dipole consistent with low-level convergence (something that would be expected if convection intensity is enhanced within the circle). Figure A.13 also shows some of these aspects, with changing signs in the wind increment at different vertical levels and no dipole structure in the wind increments.

Following from the previous comment, Figure 9 includes the effect of spatial covariances. However, the vertical structure is still unclear. For r, some levels indicate that the observations produce a moistening (where the main precipitation band is located), while a drying effect is observed at other levels (975 and 800 hPa). Similar changes in signs are observed in the increments in V, so although the increments are aligned with the rainband, there is no consistent increase in low-level convergence (which would be the expected behavior).

I could not follow figure 8. Panel d shows the innovation. But if the innovation is defined only within the small circle, why are there shades outside the circle? And why are the values outside the circle so different from the values inside the circle?

Minor points

The quality-controlled radar observations are summed into 1-hour intervals to match the resolution of the model output. However, the model output consists of instantaneous fields, not time-averaged or aggregated quantities (at least for the variables used in the observation operator). Using a time-averaged reflectivity, on the other hand, can help to smooth out small-scale details in the radar data that are not well resolved by the model.

Radar data is interpolated to the model grid using nearest neighbor interpolation. This may not be the best approach if the resolution of the radar data is larger than that of the model. In that case, a box-averaging approach can be better.

Figure 10 is mentioned before Figure 9.

RMSE instead of RM in the caption of Figure 8.

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Citation: https://doi.org/10.5194/egusphere-2026-77-RC1

Marco Stefanelli, Žiga Zaplotnik, and Gregor Skok

Data sets

LISCA-ALADIN HNN Marco Stefanelli https://zenodo.org/records/17880623

Model code and software

3DVar Neural Network-Based Observation Operator Marco Stefanelli https://zenodo.org/records/17898084

3DVar for Neural Network-Based Observation Operator Marco Stefanelli https://zenodo.org/records/17899025

Marco Stefanelli, Žiga Zaplotnik, and Gregor Skok

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Short summary

Weather radars provide storm intensity and location, but weather forecasting systems do not readily use them. We trained a neural network on 5 years of reflectivity radar and model output data to map model fields into radar reflectivity space, allowing forecasts to be corrected with radar data. In a major flood case, this cut errors in storm position and strength. Broadly speaking, the methodology provides a simplified solution for assimilating observations with no direct model-equivalent field.


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