| 09 Sep 2026
RADALIA: A new tool for dealiasing Doppler cloud radar observations
Abstract. Doppler cloud radars provide valuable insights into cloud dynamics and microphysical processes. However, their measurements are often affected by velocity aliasing when hydrometeor motions exceed the radar Nyquist velocity. Aliasing affects Doppler velocity spectra (DVS) and the derived spectral moments, introducing uncertainties in cloud classification and microphysical retrievals. Despite its importance, a dedicated dealiasing algorithm for cloud radar observations is not yet available. We present RADALIA (RAdar De-ALIsing Algorithm), a new open-source tool designed to detect and correct aliasing in Doppler cloud radar spectra. RADALIA includes two complementary approaches: an interpolation-based method (IntB) that can operate with either single-frequency (i.e., standalone) or dual-frequency observations, and an iterative method based on vertical continuity (IterBU) that operated on single-frequency observations. The algorithms were assessed using dual-frequency cloud radar observations at two ACTRIS CCRES stations: AGORA in Granada (Spain) and JOYCE in Jülich (Germany), representing contrasting atmospheric conditions and levels of dealiasing complexity. The dual-frequency configuration was used as a reference, since it provides the most robust performance in complex situations involving strong attenuation and multiple hydrometeor populations. Under straightforward spectral conditions, all standalone approaches successfully dealiased the DVS, achieving 98.6 %–99.6 % agreement with the dual-frequency reference. In the most challenging spectral scenario, standalone approaches achieved success rates above 70 %. RADALIA addresses a longstanding gap in cloud radar processing and serves as the basis for the dealiasing algorithm being implemented within the Cloudnet processing chain. It has the potential to improve cloud radar products and their application to microphysical retrievals, model evaluation, and satellite validation. Future work will focus on broader validation and extension to additional cloud radar systems and scanning observations.
General comments
This manuscript presents a set of two algorithms for automated dealiasing of Doppler cloud radar velocity spectra, which use collocated dual-frequency observations or previously dealiased spectra as a first guess and employ temporal continuity (IntB) or vertical continuity (IterBU) constraints for dealiasing. The algorithms and evaluations are scientifically sound, and the technical design is explained in enough detail for replicability.
The manuscript is generally well-written and well-organized, and I feel the work is novel and clearly within the scope of AMT. However, I believe this work would benefit from some added clarifications and broader discussion before publication. Specifically, a clearer explanation of IntB-SF and IntB-SFT is needed, and more discussion of algorithm performance with respect to observational conditions and improving dealiasing quality would be beneficial. Additionally, a large number of references are incomplete. I recommend this manuscript be considered for acceptance after major revisions following the comments below.
Specific comments
Major comments:
Additionally, in lines 210-215, you state that IntB-SF uses VM from the previous profile as a first guess for VG of the current profile. Later in lines 236-247, the same process is given for the description of IntB-SFT. I am confused on how IntB-SF functions and how it differs from IntB-SFT, since their explanations both seem to involve temporal continuity constraints. Can you clarify the IntB-SF description and how IntB-SFT is different?
Minor comments:
Technical corrections
-FMCW (line 66)
-RPG (line 96)
-VM with tilde (line 243)
-MIRA (line 506)